The Plegma dataset: Domestic appliance-level and aggregate electricity demand with metadata from Greece

The growing availability of smart meter data has facilitated the development of energy-saving services like demand response, personalized energy feedback, and non-intrusive-load-monitoring applications, all of which heavily rely on advanced machine learning algorithms trained on energy consumption datasets. To ensure the accuracy and reliability of these services, real-world smart meter data collection is crucial. The Plegma dataset described in this paper addresses this need bfy providing whole- house aggregate loads and appliance-level consumption measurements at 10-second intervals from 13 different households over a period of one year. It also includes environmental data such as humidity and temperature, building characteristics, demographic information, and user practice routines to enable quantitative as well as qualitative analysis. Plegma is the first high-frequency electricity measurements dataset in Greece, capturing the consumption behavior of people in the Mediterranean area who use devices not commonly included in other datasets, such as AC and electric-water boilers. The dataset comprises 218 million readings from 88 installed meters and sensors. The collected data are available in CSV format.

Do Trade and Financial Cooperation Improve Environmentally Sustainable Development: A Distinction Between de facto and de jure Globalization

The adoption of growth strategies based on foreign trade, especially in the previous century when liberal policies began to dominate, is one of the main reasons for the increase in output and indirectly for environmental concerns. On the other hand, there are complex claims about the environmental effects of liberal policies and thus of globalization. This study intends to analyze the effects of global collaborations involving 11 transition economies that have completed the transition process on the environmentally sustainable development of these nations. In this direction, the effects of financial and commercial globalization indices on carbon emissions are investigated. The distinctions of globalization are used to distinguish the consequences of the two types of globalization. In doing so, the de facto and de jure indicator distinctions of globalization are used to differentiate the consequences of two types of globalization. In addition, the effects of real GDP, energy efficiency, and use of renewable energy on environmental pollution are dissected. For the main purpose of the study, the CS-ARDL estimation technique that allows cross-sectional dependency among observed countries is used to separate the short and long-run influences of explanatory variables. In addition, CCE-MG estimator is used for robustness check. According to the empirical findings, the economic growth and increasing energy intensity increases carbon emissions, but the increase in renewable energy consumption improves environmental quality. Furthermore, trade globalization does not have a significant impact on the environment in the context of globalization. On the other hand, the increase in de facto and de jure financial globalization indices results in an increase in carbon emissions, but de jure financial globalization causes more environmental damage. The harmful impact of de jure financial globalization on environmental quality suggests that the decreasing investment restrictions and international investment agreements of transition countries have been implemented in a manner that facilitates the relocation of investments from pollution-intensive industries to these countries.

An energy efficiency assessment of Yttrium-aluminum-garnet laser in vitro

To study the effect range of the Nd:YAG laser through various levels of cloudy medium for targets with varying grayscale values in vitro. The coated paper cards with grayscale values of 0, 50, 100, and 150 were used as the laser's targets, which were struck straightly with varying energies using three burst modes (single pulse, double pulse, and triple pulse). Six filters (transmittances of 40, 50, 60, 70, 80, and 90) were applied to simulate various levels of cloudy refractive medium. Image J software was used to measure the diameters and regions of the laser spots. The ranges of the Nd:YAG laser spots increased with energy in the same burst mode (P < 0.05). Under the same amount of energy, the ranges of the Nd:YAG laser spot increased with the grayscale value of the targets (P < 0.05). The greater the transmittance of the filters employed, the larger the range of the Nd: YAG laser spots produced. Assuming that the total pulse energy is identical, the effect ranges of multi-pulse burst modes were significantly larger than those of single-pulse burst mode (P < 0.05). The effect range of a Nd:YAG laser grows with increasing energy and the target's grayscale value. A cloudy refractive medium has a negative impact on the effect range of the Nd: YAG laser. The single pulse mode has the narrowest and safest efficiency range.

Asymmetric Electrode Design for High-Area Capacity and High-Energy Efficiency Hybrid Zn Batteries

Compared to Zn-air batteries, by integrating Zn-transition metal compound reactions and oxygen redox reactions at the cell level, hybrid Zn batteries are proposed to achieve higher energy density and energy efficiency. However, attaining relatively higher energy efficiency relies on controlling the discharge capacity. At high area capacities, the proportion of the high voltage section can be neglected, resulting in a lower energy efficiency similar to that of Zn-air batteries. Here, a high-loading integrated electrode with an asymmetric structure and asymmetric wettability is fabricated, which consists of a thick nickel hydroxide (Ni(OH)2) electrode layer with vertical array channels achieving high capacity and high utilization, and a thin NiCo2O4 nanopartical-decorated N-doped graphene nanosheets (NiCo2O4/N-G) catalyst layer with superior oxygen catalytic activity. The asymmetric wettability satisfies the wettability requirements for both Zn-Ni and Zn-air reactions. The hybrid Zn battery with the integrated electrode exhibits a remarkable peak power density of 141.9 mW cm-2, superior rate performance with an energy efficiency of 71.4% even at 20 mA cm-2, and exceptional cycling stability maintaining a stable energy efficiency of ≈84% at 2 mA cm-2 over 100 cycles (400 h).

Secure Data Aggregation Using Authentication and Authorization for Privacy Preservation in Wireless Sensor Networks

Existing secure data aggregation protocols are weaker to eliminate data redundancy and protect wireless sensor networks (WSNs). Only some existing approaches have solved this singular issue when aggregating data. However, there is a need for a multi-featured protocol to handle the multiple problems of data aggregation, such as energy efficiency, authentication, authorization, and maintaining the security of the network. Looking at the significant demand for multi-featured data aggregation protocol, we propose secure data aggregation using authentication and authorization (SDAAA) protocol to detect malicious attacks, particularly cyberattacks such as sybil and sinkhole, to extend network performance. These attacks are more complex to address through existing cryptographic protocols. The proposed SDAAA protocol comprises a node authorization algorithm that permits legitimate nodes to communicate within the network. This SDAAA protocol's methods help improve the quality of service (QoS) parameters. Furthermore, we introduce a mathematical model to improve accuracy, energy efficiency, data freshness, authorization, and authentication. Finally, our protocol is tested in an intelligent healthcare WSN patient-monitoring application scenario and verified using an OMNET++ simulator. Based upon the results, we confirm that our proposed SDAAA protocol attains a throughput of 444 kbs, representing a 98% of data/network channel capacity rate; an energy consumption of 2.6 joules, representing 99% network energy efficiency; an effected network of 2.45, representing 99.5% achieved overall performance of the network; and time complexity of 0.08 s, representing 98.5% efficiency of the proposed SDAAA approach. By contrast, contending protocols such as SD, EEHA, HAS, IIF, and RHC have throughput ranges between 415-443, representing 85-90% of the data rate/channel capacity of the network; energy consumption in the range of 3.0-3.6 joules, representing 88-95% energy efficiency of the network; effected network range of 2.98, representing 72-89% improved overall performance of the network; and time complexity in the range of 0.20 s, representing 72-89% efficiency of the proposed SDAAA approach. Therefore, our proposed SDAAA protocol outperforms other known approaches, such as SD, EEHA, HAS, IIF, and RHC, designed for secure data aggregation in a similar environment.

Ultra-Thin Ion Exchange Membranes by Low Ionomer Blending for Energy Harvesting

Exploring the utilization of ion exchange membranes (IEMs) in salinity gradient energy harvesting, a technique that capitalizes on the salinity difference between seawater and freshwater to generate electricity, this study focuses on optimizing PVDF to Nafion ratios to create ultra-thin membranes. Specifically, our investigation aligns with applications such as reverse electrodialysis (RED), where IEMs facilitate selective ion transport across salinity gradients. We demonstrate that membranes with reduced Nafion content, particularly the 50:50 PVDF:Nafion blend, retain high permselectivity comparable to those with higher Nafion content. This challenges traditional understandings of membrane design, highlighting a balance between thinness and durability for energy efficiency. Voltage-current analyses reveal that, despite lower conductivity, the 50:50 blend shows superior short-circuit current density under salinity gradient conditions. This is attributed to effective ion diffusion facilitated by the blend's unique microstructure. These findings suggest that blended membranes are not only cost-effective but also exhibit enhanced performance for energy harvesting, making them promising candidates for sustainable energy solutions. Furthermore, these findings will pave the way for advances in membrane technology, offering new insights into the design and application of ion exchange membranes in renewable energy.

A Mathematical Programming Approach for IoT-Enabled, Energy-Efficient Heterogeneous Wireless Sensor Network Design and Implementation

The Internet of Things (IoT) is playing a pivotal role in transforming various industries, and Wireless Sensor Networks (WSNs) are emerging as the key drivers of this innovation. This research explores the utilization of a heterogeneous network model to optimize the deployment of sensors in agricultural settings. The primary objective is to strategically position sensor nodes for efficient energy consumption, prolonged network lifetime, and dependable data transmission. The proposed strategy incorporates an offline model for placing sensor nodes within the target region, taking into account the coverage requirements and network connectivity. We propose a two-stage centralized control model that ensures cohesive decision making, grouping sensor nodes into protective boxes. This grouping facilitates shared resource utilization, including batteries and bandwidth, while minimizing box number for cost-effectiveness. Noteworthy contributions of this research encompass addressing connectivity and coverage challenges through an offline deployment model in the first stage, and resolving real-time adaptability concerns using an online energy optimization model in the second stage. Emphasis is placed on the energy efficiency, achieved through the sensor consolidation within boxes, minimizing data transmission hops, and considering energy expenditures in sensing, transmitting, and active/sleep modes. Our simulations on an agricultural farmland highlights its practicality, particularly focusing on the sensor placement for measuring soil temperature and humidity. Hardware tests validate the proposed model, incorporating parameters from the real-world implementation to enhance calculation accuracy. This study provides not only theoretical insights but also extends its relevance to smart farming practices, illustrating the potential of WSNs in revolutionizing sustainable agriculture.

Enhancing Occupant Comfort and Building Sustainability: Lessons from an Internet of Things-Based Study on Centrally Controlled Indoor Shared Spaces in Hot Climatic Conditions

It is well known that buildings have a sizeable energy and environmental footprint. In particular, in environments like university campuses, the occupants as well as occupancy in shared spaces varies over time. Systems for cooling in such environments that are centrally controlled are typically threshold driven and do not account for occupant feedback and thus are often relying on a reactive approach (fix after identifying problems). Therefore, having a fixed thermal operating set point may not be optimal in such cases-both from an occupant comfort and well-being as well as an energy efficiency perspective. To address this issue, a study was conducted which involved development and deployment of an experimental Internet of Things (IoT) prototype system and an Android application that facilitated people engagement on a university campus located in the UAE which typically exhibits hot climatic conditions. This paper showcases data driven insights obtained from this study, and in particular, how to achieve a balance between the conflicting goals of improving occupant comfort and energy efficiency. Findings from this study underscore the need for regular reassessments and adaptation. The proposed solution is low cost and easy to deploy and has the potential to reap significant savings through a reduction in energy consumption with estimates indicating around 50-100 kWh/day of savings per building and the resulting environmental impact. These findings would appeal to stakeholders who are keen to improve energy efficiency and reduce their operating expenses and environmental footprint in such climatic conditions. Furthermore, collective action from a large number of entities could result in significant impact through this cumulative effect.

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

Energy-Efficient BWP Configuration for Multi-Slice Users

Fifth Generation (5G) mobile networks introduce the concept of slicing to ensure isolation among the various supported heterogeneous services. The User Equipment (UE) can be connected to multiple slices simultaneously. Additionally, the notion of a Bandwidth Part (BWP) was also instigated to reduce power consumption. A BWP is a small chunk of the bandwidth scanned by the UE to retrieve its service data. Therefore, a UE connected to multiple services can be configured with multiple BWPs each associated with a given service. Such UEs find themselves scanning multiple BWPs, which can be time consuming and highly energy intensive. Hence, it is paramount to study the appropriate choice of the BWP configuration from an energy-efficiency perspective for multi-slice users depending on their battery level. In this paper, two energy-efficient BWP selection solutions are proposed for users connected to multiple slices. The first solution is based on a centralized approach where UEs are stirred optimally to the best BWP configuration, while the second solution relies on a user-centric distributed approach using non-cooperative game theory. The proposed schemes take into account the users' battery level and their sojourn time in the network as well as the scanned BWP size. Both solutions are compared with one another and against the legacy solution. Intensive simulation results demonstrate the efficiency of our proposition in terms of users' energy efficiency and quality of service.

A Survey on Heterogeneity Taxonomy, Security and Privacy Preservation in the Integration of IoT, Wireless Sensor Networks and Federated Learning

Federated learning (FL) is a machine learning (ML) technique that enables collaborative model training without sharing raw data, making it ideal for Internet of Things (IoT) applications where data are distributed across devices and privacy is a concern. Wireless Sensor Networks (WSNs) play a crucial role in IoT systems by collecting data from the physical environment. This paper presents a comprehensive survey of the integration of FL, IoT, and WSNs. It covers FL basics, strategies, and types and discusses the integration of FL, IoT, and WSNs in various domains. The paper addresses challenges related to heterogeneity in FL and summarizes state-of-the-art research in this area. It also explores security and privacy considerations and performance evaluation methodologies. The paper outlines the latest achievements and potential research directions in FL, IoT, and WSNs and emphasizes the significance of the surveyed topics within the context of current technological advancements.

Regeneration and Long-Term Stability of a Low-Power Eco-Friendly Temperature Sensor Based on a Hydrogel Nanocomposite

A water-processable and low-cost nanocomposite material, based on gelatin and graphene, has been used to fabricate an environmentally friendly temperature sensor. Demonstrating a temperature-dependent open-circuit voltage between 260 and 310 K, the sensor effectively detects subzero ice formation. Notably, it maintains a constant temperature sensitivity of approximately -19 mV/K over two years, showcasing long-term stability. Experimental evidence demonstrates the efficient regeneration of aged sensors by injecting a few drops of water at a temperature higher than the gelation point of the hydrogel nanocomposite. The real-time monitoring of the electrical characteristics during the hydration reveals the initiation of the regeneration process at the gelation point (~306 K), resulting in a more conductive nanocomposite. These findings, together with a fast response and low power consumption in the range of microwatts, underscore the potential of the eco-friendly sensor for diverse practical applications in temperature monitoring and environmental sensing. Furthermore, the successful regeneration process significantly enhances its sustainability and reusability, making a valuable contribution to environmentally conscious technologies.

Enhanced Thermoelectric Performance of Mg-Sn Thin Films: Role of Mg9Sn5 Phase and One-Dimensional Electronic Structure

Mg-Sn alloy thin films have garnered significant attention for their outstanding thermoelectric (TE) properties and cost-effective elemental composition, making them potential candidates for wearable energy harvesting devices. While previous studies have explored the properties of these thin films, limited research has been conducted to identify physical factors that can further enhance their performance. In this study, we present a novel approach utilizing a convenient electron beam coevaporation technique to fabricate Mg-Sn alloy thin films. Experimental results revealed that controlling the tin content in the Mg-Sn thin films at 38.9% led to the formation of a mixed-phase structure, comprising Mg2Sn and Mg9Sn5. This dual-phase structure exhibited a notable advantage in enhancing the TE performance. The presence of the Mg9Sn5 phase significantly increased the carrier concentration, while maintaining the original Seebeck coefficient and mobility, thereby improving the conductivity of Mg2Sn. Theoretical calculations indicated that the Mg9Sn5 phase displayed 1D-like characteristics, leading to a highly effective valley degeneracy and consequently a high power factor. Overall, this work introduces a promising approach to fabricate high-performance Mg-Sn alloy thin films through electron beam coevaporation, opening up possibilities for their application in wearable energy harvesting devices.

A Hybrid Energy-Efficient, Area-Efficient, Low-Complexity Switching Scheme in SAR ADC for Biosensor Applications

A hybrid energy-efficient, area-efficient, low-complexity switching scheme in SAR ADC for biosensor applications is proposed. This scheme is a combination of the monotonic technique, the MSB capacitor-splitting technique, and a new switching method. The MSB capacitor-splitting technique, as well as the reference voltage Vaq allow for more options for reference voltage conversion, resulting in higher area savings and higher energy efficiency. In a capacitor array, the circuit performs unilateral switching during all comparisons except for the second and last two comparisons, reducing the difficulty in designing the drive circuit. The proposed switching scheme saves 98.4% of the switching energy and reduces the number of unit capacitors by 87.5% compared to a conventional scheme. Furthermore, the SAR ADC employs low-noise and low-power dynamic comparators utilizing multi-clock control, low-sampling error-sampling switches based on the bootstrap technique, and dynamic SAR logic. The simulation results demonstrated that the proposed SAR ADC achieves 61.51 dB SNDR, 79.21 dB SFDR and consumes 0.278 μW of power in a 180 nm process with a 1 V power supply, a full swing input signal frequency of 23.33 kHz, and a sampling rate of 100 kS/s.

Self-Powered Hydrogen Production with Improved Energy Efficiency via Polysulfides Redox

In the pursuit of efficient solar-driven electrocatalytic water splitting for hydrogen production, the intrinsic challenges posed by the sluggish kinetics of anodic oxygen evolution and intermittent sunlight have prompted the need for innovative energy systems. Here, we introduce an approach by coupling the polysulfides oxidation reaction with the hydrogen evolution reaction for energy-saving H2 production, which could be powered by an aqueous zinc-polysulfides battery to construct a self-powered energy system. This unusual hybrid water electrolyzer achieves 300 mA cm-2 at a low cell voltage of 1.14 V, saving electricity consumption by 100.4% from 5.47 to 2.73 kWh per m3 H2 compared to traditional overall water splitting. Benefiting from the favorable reaction kinetics of polysulfides oxidation/reduction, the aqueous zinc-polysulfides battery exhibits an energy efficiency of approximately 89% at 1.0 mA cm-2. Specially, the zinc-polysulfide battery effectively stores intermittent solar energy as chemical energy during light reaction by solar cells. Under an unassisted light reaction, the batteries could release energy to drive H2 production through a hybrid water electrolyzer for uninterrupted hydrogen production. Therefore, the aim of simultaneously generating H2 and eliminating the restrictions of intermittent sunlight is realized by combining the merits of polysulfides redox, an aqueous metal-polysulfide battery, and solar cells. We believe that this concept and utilization of polysulfides redox will inspire further fascinating attempts for the development of sustainable energy via electrocatalytic reactions.

Potentiality of Sustainable Maize Production under Rainfed Conditions in the Tropics by Triggering Agro-Physio-Biochemical Traits Ascertained from a Greenhouse

A major portion of maize is produced under rainfed conditions in the tropics with relatively poor yield because of the unpredictable and irregular distribution of seasonal rainfall, as well as a decline in pre-rainy season rainfall due to climate change, so identification of sustainable production options is utmost needed. Thus, the present studies were conducted in a greenhouse (GH) to ascertain the water stress-tolerant traits of maize and at the field level in the tropical environment of Thailand to see the stimulating possibility of the ascertained traits in a locally popular cultivar using ethephon. Depending on tolerance level, three maize genotypes (Suwan 2301 > Suwan 4452 > S 7328) were tested under different water conditions-well-watered, short-term, and long-term water stress-in the GH. At the field level, the locally popular maize cultivar Suwan 5819 was examined with six ethephon levels (doses in g a.i. ha-1 of ethephon, i.e., T1, 281 at V6 stage; T2, 281 at V6 + 281 at V10 stage; T3, 281 at V10 stage; T4, 562 at V6 stage; T5, 562 at V6 + 562 at V10 stage; T6, 562 at V10 stage) against no ethephon application (T0) under rainfed conditions. Maize suffered from the scarcity of sufficient rainfall during 26-39 days after planting (DAP) and 43-63 DAP in the field. The yield index (YI) was identified from biplot analysis as one of the suitable standards for drought tolerance checks for maize at GH as well as at field level in the tropics. The YI value of observed agro-physio-biochemical traits of maize in GH showed that relative water content (RWC, 1.23), stem base diameter (SBD, 1.21), total soluble sugar (TSS, 1.15), proline (Pr, 1.13), aboveground plant biomass (APB, 1.13), root weight (RW, 1.13), relative growth rate (RGR, 1.15), specific leaf weight (SLW, 1.12), and net assimilation rate (NAR, 1.08) were the most desirable. Efforts were made to stimulate these traits under water stress at the field level. Ethephon application as T1 helped to gain higher kernel yield (KY) (5.26 t ha-1) with the support of higher RWC (90.38%), proline (24.79 µmol g-1 FW), TSS (1629 mg g-1 FW), SBD (24.49 mm), APB (271.34 g plant-1), SLW (51.71 g m-2), RGR (25.26 mg plant-1 day-1), and NAR (0.91 mg cm-2 day-1) compared to others, especially no ethephon application. Furthermore, the attributes SLW, SBD, Pr, heat utilization efficiency (HUE), 100-kernel weight, TSS, electrolyte leakage, and lodging percentage showed a substantial direct effect and significant correlation with KY. Aside from higher KY, ethephon application as T1 tactics resulted in higher values of energy efficiency (1.66), HUE (2.99 kg ha-1 °C days-1), gross margin (682.02 USD ha-1), MBCR (3.32), and C absorption (6.19 t C ha-1), indicating that this practice may be a good option for maize sustainable production under rainfed conditions.

Effect of Size-Distribution Environment on Breakage Parameters Using Closed-Cycle Grinding Tests

The so-called population balance model (PBM) is the most widely used approach to describe the grinding process. The analysis of the grinding data is carried out using-among others-the one-size fraction BII method. According to the BII method, the breakage parameters can be determined when a narrow particle size fraction is used as feed material to the mill. However, it is commonly accepted that these parameters are influenced by changing the particle size distribution in the mill. Thus, this study examines the breakage parameters through kinetic testing in different natural-size distribution environments generated by closed-cycle grinding tests that simulate industrial milling conditions. The differentiation of the milling environments was accomplished using various reference sieves in the closed-cycle tests. The experimentally determined breakage parameters were back-calculated and then used to simulate the closed-cycle tests using the MODSIMTM software. Additionally, the energy efficiency was evaluated based on the specific surface area of the grinding products and the energy consumption. The results of the kinetic tests showed that the breakage rate of the coarse particles increases as the aperture size of the reference sieve decreases, and consequently, the content of fines in the mill increases. The back-calculated breakage parameters can be reliably used to simulate closed-cycle circuits, thus helping control industrial milling operations.

Green synthesis of silver nanoparticles by using Allium sativum extract and evaluation of their electrical activities in bio-electrochemical cell

An electrical application of green synthesized silver nanoparticles (Ag NPs) by developing a unique bio-electrochemical cell (BEC) has been addressed in the report. Here, garlic extract (GE) has been used as a reducing agent to synthesize Ag NPs, and as a bio-electrolyte solution of BEC. Ag NPs successfully formed into face-centered cubic structures with average crystallite and particle sizes of 8.49 nm and 20.85 nm, respectively, according to characterization techniques such as the UV-vis spectrophotometer, XRD, FTIR, and FESEM. A broad absorption peak at 410 nm in the UV-visible spectra indicated that GE played a vital role as a reducing agent in the transformation of Ag+ions to Ag NPs. After that four types of BEC were developed by varying the concentration of GE, CuSO4. 5H2O, and Ag NPs electrolyte solution. The open circuit voltage and short circuit current of all cells were examined with the time duration. Moreover, different external loads (1 Ω, 2 Ω, 5 Ω, and 6 Ω) were used to investigate the load voltage and load current of BEC. The results demonstrated that the use of Ag NPs on BEC played a significant role in increasing the electrical performance of BEC. The use of GE-mediated Ag NPs integrated the power, capacity, voltage efficiency, and energy efficiency of BEC by decreasing the internal resistance and voltage regulation. These noteworthy results can take a frontier forward to the development of nanotechnology for renewable and low-cost power production applications.

Electrification-Enhanced Low-Temperature NOx Storage-Reduction on Pt and K Co-Supported Antimony-Doped Tin Oxides

NOx storage-reduction (NSR), a promising approach for removing NOx pollutants from diesel vehicles, remains elusive to cope with the increasingly lower exhaust temperatures (especially below 250 °C). Here, we develop a conceptual electrified NSR strategy, where electricity with a low input power (0.5-4 W) is applied to conductive Pt and K co-supported antimony-doped tin oxides (Pt-K/ATO), with C3H6 as a reductant. The ignition temperature for 10% NOx conversion is nearly 100 °C lower than that of the traditional thermal counterpart. Furthermore, reducing the power in the fuel-lean period relative to that in the fuel-rich period increases the maximum energy efficiency by 23%. Electrically driven release of lattice oxygen is revealed to play vital roles in multiple steps in NSR, including NO adsorption, desorption, and reduction, for improved NSR activity. This work provides an electrification strategy for developing high-activity NSR catalysis utilizing electricity onboard hybrid vehicles.

Optimization of energy recovery efficiency from sweet sorghum stems by ethanol and methane fermentation processes coupling

Taken separately, a single sweet sorghum stem bioconversion process for bioethanol and biomethane production only leads to a partial conversion of organic matter. The direct fermentation of crushed whole stem coupled with the methanization of the subsequent solid residues in a two-stage process was experimented to improve energy bioconversion yield, efficiency, and profitability. The raw stalk calorific value was 17,144.17 kJ/kg DM. Fermentation step performed using Saccharomyces cerevisiae resulted in a bioconversion yield of 261.18 g Eth/kg DM, i.e. an energy recovery efficiency of 6921.27 kJ/kg DM. The methanogenic potentials were 279 and 256 LCH₄/kg DM, respectively, for raw stem and fermentation residues, i.e. energy yields of 10,013.31 and 9187.84 kJ/kg DM, respectively. Coupling processes have significantly increased yield and made it possible to reach 13,309.57 kJ/kg DM, i.e. 77.63% of raw stem energy recovery yield, compared to 40.37% and 58.40%, respectively, for single fermentation and methanization processes.

Energy management in the water sector: A policy statement review

Water and wastewater utilities consume significant energy. While they are regulated on water quality and other environmental issues, energy management is largely optional. In between enforceable public policy and voluntary internal action, professional associations can influence their industries to act. In this perspective article, we discuss 10 policy statements from three professional associations in the water sector and discuss how they support energy management. While few of the statements directly address energy management, we conclude that they justify it in terms of ethics, sustainability, affordability, and asset management. Still, their disparate nature exposes a policy gap. We recommend that the associations develop definitive policy statements on the subject to clarify their positions and set expectations. PRACTITIONER POINTS: Water/wastewater utilities use energy but are not regulated for energy management. In the absence of public policy, professional associations can influence action. We discuss 10 policy statements from ASCE, AWWA, and WEF in the water industry. The statements support energy management indirectly but leave a policy gap. We recommend developing policy statements specifically on this topic.

Feasible supply of steel and cement within a carbon budget is likely to fall short of expected global demand

The current decarbonization strategy for the steel and cement industries is inherently dependent on the build-out of infrastructure, including for CO2 transport and storage, renewable electricity, and green hydrogen. However, the deployment of this infrastructure entails considerable uncertainty. Here we explore the global feasible supply of steel and cement within Paris-compliant carbon budgets, explicitly considering uncertainties in the deployment of infrastructure. Our scenario analysis reveals that despite substantial growth in recycling- and hydrogen-based production, the feasible steel supply will only meet 58-65% (interquartile range) of the expected baseline demand in 2050. Cement supply is even more uncertain due to limited mitigation options, meeting only 22-56% (interquartile range) of the expected baseline demand in 2050. These findings pose a two-fold challenge for decarbonizing the steel and cement industries: on the one hand, governments need to expand essential infrastructure rapidly; on the other hand, industries need to prepare for the risk of deployment failures, rather than solely waiting for large-scale infrastructure to emerge. Our feasible supply scenarios provide compelling evidence of the urgency of demand-side actions and establish benchmarks for the required level of resource efficiency.

Assessment of green technology innovation on energy-environmental efficiency in China under the influence of environmental regulation considering spatial effects

Enhancing energy-environmental efficiency (EEE) is crucial for achieving energy conservation and emission reduction goals. Investigating the mechanism through which green technology innovation (GTI) affects EEE and understanding the role of environmental regulation (ER) in this process provides a theoretical basis for efficient utilization of GTI and ER. This study employs a Dynamic Spatial Durbin Model and utilizes panel data from 2003 to 2017 for 30 Chinese provinces to examine the impact of GTI on EEE in the presence of ER. The empirical results reveal: (1) GTI has a U-shaped impact on EEE, primarily driven by SubGI. (2) GTI's influence on EEE is predominantly reflected in PTE, also stemming from SubGI. (3) The interaction term between ER and GTI is 0.0022, while the GTI coefficient is - 0.0741, and the GTI quadratic term coefficient is 0.0007, all statistically significant. This implies that ER mitigates the negative impact of GTI on EEE while strengthening its positive effect. These findings provide empirical evidence and policy insights for more effectively utilizing GTI and ER to enhance EEE and achieve energy conservation and emissions reduction goals.

Performance Optimization and Exergy Analysis of Thermoelectric Heat Recovery System for Gas Turbine Power Plants

Thermoelectric (TE) waste heat recovery has attracted significant attention over the past decades, owing to its direct heat-to-electricity conversion capability and reliable operation. However, methods for application-specific, system-level TE design have not been thoroughly investigated. This work provides detailed design optimization strategies and exergy analysis for TE waste heat recovery systems. To this end, we propose the use of TE system equipped on the exhaust of a gas turbine power plant for exhaust waste heat recovery and use it as a case study. A numerical tool has been developed to solve the coupled charge and heat current equations with temperature-dependent material properties and convective heat transfer at the interfaces with the exhaust gases at the hot side and with the ambient air at the heat sink side. Our calculations show that at the optimum design with 50% fill factor and 6 mm leg thickness made of state-of-the-art Bi2Te3 alloys, the proposed system can reach power output of 10.5 kW for the TE system attached on a 2 m-long, 0.5 × 0.5 m2-area exhaust duct with system efficiency of 5% and material cost per power of 0.23 $/W. Our extensive exergy analysis reveals that only 1% of the exergy content of the exhaust gas is exploited in this heat recovery process and the exergy efficiency of the TE system can reach 8% with improvement potential of 85%.

Systematic Performance Evaluation of Reinforcement Learning Algorithms Applied to Wastewater Treatment Control Optimization

Treatment of wastewater using activated sludge relies on several complex, nonlinear processes. While activated sludge systems can provide high levels of treatment, including nutrient removal, operating these systems is often challenging and energy intensive. Significant research investment has been made in recent years into improving control optimization of such systems, through both domain knowledge and, more recently, machine learning. This study leverages a novel interface between a common process modeling software and a Python reinforcement learning environment to evaluate four common reinforcement learning algorithms for their ability to minimize treatment energy use while maintaining effluent compliance within the Benchmark Simulation Model No. 1 (BSM1) simulation. Three of the algorithms tested, deep Q-learning, proximal policy optimization, and synchronous advantage actor critic, generally performed poorly over the scenarios tested in this study. In contrast, the twin delayed deep deterministic policy gradient (TD3) algorithm consistently produced a high level of control optimization while maintaining the treatment requirements. Under the best selection of state observation features, TD3 control optimization reduced aeration and pumping energy requirements by 14.3% compared to the BSM1 benchmark control, outperforming the advanced domain-based control strategy of ammonia-based aeration control, although future work is necessary to improve robustness of RL implementation.

Technological and Modelling Progress in Green Engineering and Sustainable Development: Advancements in Energy and Materials Engineering

Due to a growing number of environmental issues, including global warming, water scarcity, and fossil fuel depletion, the topic of modern materials in energy is becoming crucial for our civilization. The technological advancements that have been observed bring many innovations that significantly impact how energy can be generated, stored, and distributed. Moreover, new opportunities have emerged in energy and materials engineering due to the increasing computational capability of current data processing systems. Methods that are highly demanding, time-consuming, and difficult to apply may now be considered when developing complete and sophisticated models in many areas of science and technology. Combining computational methods and AI algorithms allows for multi-threaded analyses solving advanced and interdisciplinary problems. Therefore, knowledge and experience in this subject, as well as the investigation of new, more efficient, and environmentally friendly solutions, currently represent one of the main directions of scientific research. The Special Issue "Advances in Materials: Modelling Challenges and Technological Progress for Green Engineering and Sustainable Development" aims to bring together research on material advances, focusing on modelling challenges and technological progress (mainly for green engineering and sustainable development). Original research studies, review articles, and short communications are welcome, especially those focusing on (but not limited to) artificial intelligence, other computational methods, and state-of-the-art technological concepts related to the listed keywords within energy and materials engineering.

Government intervention, industrial structure, and energy eco-efficiency: an empirical research on new energy demonstration in cities

This study investigates the relationships among government intervention, industrial structure, and energy eco-efficiency (EE). Energy eco-efficiency was measured based on a non-radial directional distance function for 236 cities in China from 2005 to 2019. Additionally, the difference-in-difference model (DID) method and spatial econometric models were used to analyse the impact of government intervention and industrial structure on energy eco-efficiency and their spatial spill-over effects. Government intervention includes fiscal expenditures and policy orientation for new energy demonstration construction. Our results indicate that: China's EE has a fluctuating upward trend and increased 17.85% in the period, and its spatial distribution imbalance gradually developed into a regional distribution balance. Moreover, government intervention and adjustment of the industrial structure improved urban energy eco-efficiency by 7.43% and 0.92%, respectively, which also has spatial spill-over effects in neighbouring regions. Furthermore, economic development, technological innovation, and foreign direct investment enable EE. However, urbanisation hinders the improvement of energy eco-efficiency. Finally, heterogeneity analysis showed that the policy of the new energy demonstration city has better effects on eastern and western cities in promoting EE.

An Energy-Efficient Multi-Level Sleep Strategy for Periodic Uplink Transmission in Industrial Private 5G Networks

This paper proposes an energy-efficient multi-level sleep mode control for periodic transmission (MSC-PUT) in private fifth-generation (5G) networks. In general, private 5G networks meet IIoT requirements but face rising energy consumption due to dense base station (BS) deployment, particularly impacting operating expenses (OPEX). An approach of BS sleep mode has been studied to reduce energy consumption, but there has been insufficient consideration for the periodic uplink transmission of industrial Internet of Things (IIoT) devices. Additionally, 5G New Reno's synchronization signal interval limits the effectiveness of the deepest sleep mode in reducing BS energy consumption. By addressing this issue, the aim of this paper is to propose an energy-efficient multi-level sleep mode control for periodic uplink transmission to improve the energy efficiency of BSs. In advance, we develop an energy-efficient model that considers the trade-off between throughput impairment caused by increased latency and energy saving by sleep mode operation for IIoT's periodic uplink transmission. Then, we propose an approach based on proximal policy optimization (PPO) to determine the deep sleep mode of BSs, considering throughput impairment and energy efficiency. Our simulation results verify the proposed MSC-PUT algorithm's effectiveness in terms of throughput, energy saving, and energy efficiency. Specifically, we verify that our proposed MSC-PUT enhances energy efficiency by nearly 27.5% when compared to conventional multi-level sleep operation and consumes less energy at 75.21% of the energy consumed by the conventional method while incurring a throughput impairment of nearly 4.2%. Numerical results show that the proposed algorithm can significantly reduce the energy consumption of BSs accounting for periodic uplink transmission of IIoT devices.

Dual-Anion Regulation for Reversible and Energetic Aqueous Zn-CO2 Batteries

Aqueous Zn-CO2 batteries can not only convert CO2 into high-value chemicals but also store/output electric energy for external use. However, their performance is limited by sluggish and complicated CO2 electroreduction at the cathode. Herein, a dual-anion regulated Bi electrocatalyst is developed to selectively reduce CO2 to formate with a Faradaic efficiency of up to 97% at a large current density of 250 mA cm-2 . With O and/or F, the rate-determine step of CO2 electroreduction has been manipulated (from the first hydrogenation to *HCOOH desorption step) with a reduced energy barrier. Significantly, the fabricated Zn-CO2 battery exhibits a high discharge voltage of 1.2 V, optimal power density of 4.51 mW cm-2 , remarkable energy density of 802 Wh kg-1 , and energy-conversion efficiency of 70.74%, stability up to 200 cycles and 68 h. This study provides possible strategies to fabricate reversible and energetic aqueous Zn-CO2 batteries by addressing cathodic problems.

EEDC: An Energy Efficient Data Communication Scheme Based on New Routing Approach in Wireless Sensor Networks for Future IoT Applications

Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing "Region based Hierarchical Clustering for Efficient Routing (RHCER)"-a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

Self-Adjustment Energy Efficient Redeployment Protocol for Underwater Sensor Networks

The diversity of applications supported by Underwater Sensor Networks (UWSNs) explains the success of this type of network and the increasing interest in exploiting and monitoring seas and oceans. One of the most important research fields is network deployment, since this deployment will affect all other research aspects in the UWSNs. Moreover, the initial random deployment resulting from scattering underwater sensor nodes on the network area's surface does not ensure this area's coverage and network connectivity. In this research, we propose a self-adjustment redeployment protocol that enhances network coverage and connectivity while reducing the energy consumed during network deployment. This protocol takes into account the peculiar dynamism of the underwater environment due to the water currents. First, we study the impact of these water currents on network deployment. Then, we exploit these water currents to adjust the nodes' positions to achieve total area coverage and reduce the energy consumed during the deployment by reducing the total distance traveled by the underwater sensor nodes. Simulation results show that the proposed protocol achieves a very high coverage rate (97%) and reduces the distance traveled by nodes during the deployment by 41%.

Current Trends in Aerogel Use in Heritage Buildings: Case Studies from the Aerogel Architecture Award 2021

Silica aerogels are high-performance thermal insulation materials that can be used to provide unique solutions in the envelopes of buildings when space is limited. They are most often applied in historic buildings due to thin insulation thicknesses and since they are compatible with historic structures. In 2021, the first Aerogel Architecture Award was held at Empa in Switzerland in order to collect, evaluate and award outstanding uses of this relatively new building material. From the submitted projects, three were selected for an award by an expert jury. They showcased applications in which heritage protection and the conservation of a building's character and expression were reconciled with significant improvements in the energy efficiency of the building. The submissions also showed that a broader communication of these types of solutions is important in order to provide more information and security to planners and heritage offices and to facilitate the application of these materials in the future so that they can contribute to the protection of cultural heritage and reductions in the operational and embodied emissions of our building stock by extending the life expectancy and energy efficiency of existing buildings.

Cluster Content Caching: A Deep Reinforcement Learning Approach to Improve Energy Efficiency in Cell-Free Massive Multiple-Input Multiple-Output Networks

With the explosive growth of micro-video applications, the transmission burden of fronthaul and backhaul links is increasing, and meanwhile, a lot of energy consumption is also generated. For reducing energy consumption and transmission delay burden, we propose a cell-free massive multiple-input multiple-output (CF-mMIMO) system in which the cache on the access point (AP) is used to reduce the load on the link. In this paper, a total energy efficiency (EE) model of a cache-assisted CF-mMIMO system is established. When optimizing EE, forming the co-operation cluster is critical. Therefore, we propose an energy-efficient joint design of content caching, AP clustering, and low-resolution digital-to-analog converter (DAC) in a cache-assisted CF-mMIMO network based on deep reinforcement learning. This scheme can effectively cache content in APs and select the appropriate DAC resolution. Then, taking into account the channel state information and user equipment (UE)'s content request preference, a deep deterministic policy gradient algorithm is used to jointly optimize the cache strategy, AP clustering, and DAC resolution decisions. Simulation results show that the energy efficiency of the proposed scheme is 4% higher than that of other schemes without the resolution optimization and is much higher than that of the only AP clustering without the joint design of content caching and channel quality.

Analysis of the Influence of External Wall Material Type on the Thermal Bridge at the Window-to-Wall Interface

BACKGROUND

Although many works focus on increasing the energy efficiency of buildings, there are still a number of problems that need to be solved, such as reducing heat losses at the window-to-wall interface, especially since the requirements for saving energy used for heating/cooling rooms are constantly increasing. This paper analyses the impact of the material parameters of the external wall and the window installation in the insulation layer on the size of thermal bridges around the window.

PURPOSE

The aim of the work is to demonstrate the benefits resulting from the correct installation of the window, the appropriate location of the window in relation to the face of the external wall, as well as the correct selection of construction materials.

METHODOLOGY

In order to show the improvement in the energy efficiency of buildings, an analysis of the heating/cooling energy consumption was carried out for the selected buildings. The thermal and humidity analyses were carried out using TRISCO program, while the economic analysis was performed using the Audytor OZC program.

RESULTS

It was found that the proposed system of window installation in the thermal insulation layer reduced the annual heating demand by at least 10% on average.

CONCLUSIONS

It has been shown that the method of window installation and the type of the wall structural materials are interrelated and therefore should be considered simultaneously. Their proper selection allows for a reduction in the amount of energy needed for heating and cooling buildings, and thus a reduction of heating/cooling costs, as well as limiting greenhouse gas emissions.

Ecological effect life cycle assessment of house buildings based on emergy footprint model

Construction is an important sector for climate action. The construction, operation and maintenance, demolition and disposal stages of house buildings consume many resources and have a significant impact on society, the economy and the environment. To assess such efforts, we propose the emergy footprint model of house buildings, which can quantitatively analyse the ecological effect in the house buildings life cycle. The research shows the following. China's ecological efficiency of the housing sector is characterized by improvement. In the house building fifty-year life cycle, the emergy footprint of the operation and maintenance stage is the largest (75.92%), followed by the construction stage (21.95%), but the emergy footprint intensity of the latter is 4.82 times that of the former. Reducing energy consumption and carbon dioxide emissions in the operation and maintenance stage is the key to reducing the life cycle emergy footprint of house buildings. The ecological impact coefficient of house buildings is negatively exponentially correlated with their service life. It reaches ecological break-even when the service period of the house building is equal to 36.73 years. If the house building is demolished after less than nine years of service, the impact is extremely unfavourable.

An Energy-Efficient Routing Algorithm for WSNs Using Fuzzy Logic

Battery replacement or recharging is essential for sensor nodes because they are typically powered by batteries in wireless sensor network (WSN) applications. Therefore, creating an energy-efficient data transfer technique is required. The base station (BS) receives data from one sensor node and routes the data to another sensor node. As a result, an energy-efficient routing algorithm using fuzzy logic (EERF) represents a novel approach that is suggested in this study. One of the reasoning techniques utilized in scenarios where there is a lot of ambiguity is fuzzy logic. The remaining energy, the distance between the sensor node and the base station, and the total number of connected sensor nodes are all inputs given to the fuzzy system of the proposed EERF algorithm. The proposed EERF is contrasted with the current systems, like the energy-aware unequal clustering using fuzzy logic (EAUCF) and distributed unequal clustering using fuzzy logic (DUCF) algorithms, in terms of evaluation criteria, including energy consumption, the number of active sensor nodes for each round in the network, and network stability. EAUCF and DUCF were outperformed by EERF.

Points for energy renovation (PointER): A point cloud dataset of a million buildings linked to energy features

Rapid renovation of Europe's inefficient buildings is required to reduce climate change. However, evaluating buildings at scale is challenging because every building is unique. In current practice, the energy performance of buildings is assessed during on-site visits, which are slow, costly, and local. This paper presents a building point cloud dataset that promotes a data-driven, large-scale understanding of the 3D representation of buildings and their energy characteristics. We generate building point clouds by intersecting building footprints with geo-referenced LiDAR data and link them with attributes from UK's energy performance database via the Unique Property Reference Number (UPRN). To mimic England's building stock's features well, we select one million buildings from a range of rural and urban regions, of which half a million are linked to energy characteristics. Building point clouds in new regions can be generated with our published open-source code. The dataset enables novel research in building energy modeling and can be easily expanded to other research fields by adding building features via the UPRN or geo-location.

Bistability of AlGaAs/GaAs Resonant-Tunneling Diodes Heterostructural Channel

This paper presents an effective compact model of current transfer for the estimation of hysteresis parameters on the volt-ampere characteristics of resonant-tunneling diodes. In the framework of the compact model, the appearance of hysteresis is explained as a manifestation of internal bistability due to interelectronic interaction in the channel of the resonant-tunneling structure. Unlike the models based on the method of equivalent circuits, the interelectronic interaction in the compact model is taken into account using the concentration parameter. Model validation allowed us to confirm the high accuracy of the model not only at the initial section of the volt-ampere characteristics, but also at the hysteresis parameters traditionally predicted with low accuracy, namely the loop width (∆ < 0.5%) and contrast (∆ < 7%). Thus, it is concluded that the models are promising for integration into systems for synthesizing the electrical characteristics of resonant-tunneling diodes.

Constrained DRL for Energy Efficiency Optimization in RSMA-Based Integrated Satellite Terrestrial Network

To accommodate the requirements of extensive coverage and ubiquitous connectivity in 6G communications, satellite plays a more significant role in it. As users and devices explosively grow, new multiple access technologies are called for. Among the new candidates, rate splitting multiple access (RSMA) shows great potential. Since satellites are power-limited, we investigate the energy-efficient resource allocation in the integrated satellite terrestrial network (ISTN)-adopting RSMA scheme in this paper. However, this non-convex problem is challenging to solve using conventional model-based methods. Because this optimization task has a quality of service (QoS) requirement and continuous action/state space, we propose to use constrained soft actor-critic (SAC) to tackle it. This policy-gradient algorithm incorporates the Lagrangian relaxation technique to convert the original constrained problem into a penalized unconstrained one. The reward is maximized while the requirements are satisfied. Moreover, the learning process is time-consuming and unnecessary when little changes in the network. So, an on-off mechanism is introduced to avoid this situation. By calculating the difference between the current state and the last one, the system will decide to learn a new action or take the last one. The simulation results show that the proposed algorithm can outperform other benchmark algorithms in terms of energy efficiency while satisfying the QoS constraint. In addition, the time consumption is lowered because of the on-off design.

Dead-zone-compensated design as general method of flow field optimization for redox flow batteries

One essential element of redox flow batteries (RFBs) is the flow field. Certain dead zones that cause local overpotentials and side effects are present in all conventional designs. To lessen the detrimental effects, a dead-zone-compensated design of flow field optimization is proposed. The proposed architecture allows for the detection of dead zones and their compensation on existing flow fields. Higher reactant concentrations and uniformity factors can be revealed in the 3D multiphysical simulation. The experiments also demonstrate that at an energy efficiency (EE) of 80%, the maximum current density of the novel flow field is 205 mA cm-2, which is much higher than the values for the previous ones (165 mA cm-2) and typical serpentine flow field (153 mA cm-2). Extensions of the design have successfully increased system EE (2.7 to 4.3%) for a variety of flow patterns. As a result, the proposed design is demonstrated to be a general method to support the functionality and application of RFBs.

An Intelligent Bio-Inspired Autonomous Surveillance System Using Underwater Sensor Networks

Energy efficiency is important for underwater sensor networks. Designing such networks is challenging due to underwater environmental traits that hinder network lifespan extension. Unlike terrestrial protocols, underwater settings require novel protocols due to slower signal propagation. To enhance energy efficiency in underwater sensor networks, ongoing research concentrates on developing innovative solutions. Thus, in this paper, an intelligent bio-inspired autonomous surveillance system using underwater sensor networks is proposed as an efficient method for data communication. The tunicate swarm algorithm is used for the election of the cluster heads by considering different parameters such as energy, distance, and density. Each layer has several clusters, each of which is led by a cluster head that continuously rotates in response to the fitness values of the SNs using the tunicate swarm algorithm. The performance of the proposed protocol is compared with existing methods such as EE-LHCR, EE-DBR, and DBR, and results show the network's lifespan is improved by the proposed work. Due to the effective fitness parameters during cluster head elections, our suggested protocol may more effectively achieve energy balance, resulting in a longer network lifespan.

An Approach to Improve Energy Efficiency during Antimicrobial Blue Light Inactivation: Application of Pulse-Width Modulation Dimming to Balance Irradiance and Irradiation Time

Antimicrobial blue light (aBL) is an effective non-destructive inactivation technique and has received increasing attention. Despite its significance, the existing research has not thoroughly delved into the impacts of irradiance and irradiation time on enhancing energy efficiency during aBL inactivation and the explanation of the enhancement effect of pulse exposure. In this paper, a series of Escherichia coli inactivation experiments with different duty cycles, pulse frequencies, and irradiation times were conducted, and the relative concentrations of reactive oxygen species (ROS) were measured under corresponding conditions. A two-dimensional (2-D) Hom model was proposed to evaluate the effect of irradiance and irradiation time. The results show that, compared to continuous exposure, pulsed aBL (duty cycle = 25%) can save ~37% of the energy to achieve the same inactivation effect and generate a 1.95 times higher ROS concentration. The 2-D Hom model obtains the optimal combination of average irradiance and time according to the desired reduction and shows that the irradiation time has a higher weight than the irradiance (1.677 and 1.083, respectively). Therefore, using pulse exposure with a lower average irradiance for a longer period of time can achieve a better inactivation effect when consuming equivalent energy. The proposed pulse-width modulation dimming approach helps promote the application of the aBL technique.

Modified Membranes for Redox Flow Batteries-A Review

In this review, the state of the art of modified membranes developed and applied for the improved performance of redox flow batteries (RFBs) is presented and critically discussed. The review begins with an introduction to the energy-storing chemical principles and the potential of using RFBs in the energy transition in industrial and transport-related sectors. Commonly used membrane modification techniques are briefly presented and compared next. The recent progress in applying modified membranes in different RFB chemistries is then critically discussed. The relationship between a given membrane modification strategy, corresponding ex situ properties and their impact on battery performance are outlined. It has been demonstrated that further dedicated studies are necessary in order to develop an optimal modification technique, since a modification generally reduces the crossover of redox-active species but, at the same time, leads to an increase in membrane electrical resistance. The feasibility of using alternative advanced modification methods, similar to those employed in water purification applications, needs yet to be evaluated. Additionally, the long-term stability and durability of the modified membranes during cycling in RFBs still must be investigated. The remaining challenges and potential solutions, as well as promising future perspectives, are finally highlighted.

HedgeRank: Heterogeneity-Aware, Energy-Efficient Partitioning of Personalized PageRank at the Edge

Personalized PageRank (PPR) is a widely used graph processing algorithm used to calculate the importance of source nodes in a graph. Generally, PPR is executed by using a high-performance microprocessor of a server, but it needs to be executed on edge devices to guarantee data privacy and network latency. However, since PPR has a variety of computation/memory characteristics that vary depending on the graph datasets, it causes performance/energy inefficiency when it is executed on edge devices with limited hardware resources. In this paper, we propose HedgeRank, a heterogeneity-aware, energy-efficient, partitioning technique of personalized PageRank at the edge. HedgeRank partitions the PPR subprocesses and allocates them to appropriate edge devices by considering their computation capability and energy efficiency. When combining low-power and high-performance edge devices, HedgeRank improves the execution time and energy consumption of PPR execution by up to 26.7% and 15.2% compared to the state-of-the-art PPR technique.

Prototype of an Innovative Vacuum Dryer with an Ejector System: Comparative Drying Analysis with a Vacuum Dryer with a Vacuum Pump on Selected Fruits

The following article describes new research about the design, construction and installation of the new prototype of a vacuum dryer with an ejector system. Moreover, the testing of this new prototype involved comparing the qualities of fruit dried in a vacuum drier with an ejector system to fruit dried in a convectional vacuum drier. The data obtained were then analyzed and presented. Due to their economic relevance and highly valuable nutritional value and sensory properties, sour cherries and apricots have been chosen to be the subjects for the testing. The most appropriate quality indicators for analyzing were moisture content, aw value, share and penetration force, total phenol, flavonoid and anthocyanin content and antioxidant activity (FRAP, DPPH and ABTS test). The main results of this study were achieved by designing, constructing, installing and testing the usage of the innovative prototype of a vacuum dryer with an ejector system in the laboratory of the Technology of fruit and vegetable products of the Faculty of Technology Novi Sad, University of Novi Sad. Based on our analyses of the obtained data, it was concluded that vacuum dryer with an ejector system are similar to vacuum dryer with a vacuum pump in terms of all tested physical, chemical and biological properties of dried samples. We observed similarities in some of the most important parameters, including product safety and quality, such as the aw value and the total phenol content, respectively. For example, in dried sour cherry, the aw values ranged from 0.250 to 0.521 with the vacuum pump and from 0.232 to 0.417 with the ejector system; the total phenol content ranged from 2322 to 2765 mg GAE/100 g DW with the vacuum pump and from 2327 to 2617 mg GAE/100 g DW with the ejector system. In dried apricot, the aw ranged from 0.176 to 0.405 with the vacuum pump and from 0.166 to 0.313 with the ejector system; total phenol content ranged from 392 to 439 mg GAE/100 g DW with the vacuum pump and from 378 to 428 mg GAE/100 g DW with the ejector system.

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.

A Multi-Classifiers Based Algorithm for Energy Efficient Tasks Offloading in Fog Computing

The IoT has connected a vast number of devices on a massive internet scale. With the rapid increase in devices and data, offloading tasks from IoT devices to remote Cloud data centers becomes unproductive and costly. Optimizing energy consumption in IoT devices while meeting deadlines and data constraints is challenging. Fog Computing aids efficient IoT task processing with proximity to nodes and lower service delay. Cloud task offloading occurs frequently due to Fog Computing's limited resources compared to remote Cloud, necessitating improved techniques for accurate categorization and distribution of IoT device task offloading in a hybrid IoT, Fog, and Cloud paradigm. This article explores relevant offloading strategies in Fog Computing and proposes MCEETO, an intelligent energy-aware allocation strategy, utilizing a multi-classifier-based algorithm for efficient task offloading by selecting optimal Fog Devices (FDs) for module placement. MCEETO decision parameters include task attributes, Fog node characteristics, network latency, and bandwidth. The method is evaluated using the iFogSim simulator and compared with edge-ward and Cloud-only strategies. The proposed solution is more energy-efficient, saving around 11.36% compared to Cloud-only and approximately 9.30% compared to the edge-ward strategy. Additionally, the MCEETO algorithm achieved a 67% and 96% reduction in network usage compared to both strategies.

Truck platooning reshapes greenhouse gas emissions of the integrated vehicle-road infrastructure system

Reducing greenhouse gas emissions has turned into a pillar of climate change mitigation. Truck platooning is proposed as a strategy to lower emissions from vehicles on roads. However, the potential interactive impacts of this technology on road infrastructure emissions remain unclear. Here, we evaluate the decarbonization effects of truck platooning on the integrated vehicle-road system at a large-scale road network level, spanning 1457 road sections across North America. We show that truck platooning decreases emissions induced by truck operations, but it degrades faster the durability of road infrastructure and leads to a 27.9% rise in road emissions due to more frequent maintenance work. Overall, truck platooning results in a 5.1% emission reduction of the integrated vehicle-road system. In contrast to the benefits of emission reduction, truck platooning leads to additional financial burdens on car users and transportation agencies, calling for the consideration of tradeoffs between emissions and costs and between agencies and users. Our research provides insights into the potential applications of truck platooning to mitigate climate change.

Autonomous driving controllers with neuromorphic spiking neural networks

Autonomous driving is one of the hallmarks of artificial intelligence. Neuromorphic (brain-inspired) control is posed to significantly contribute to autonomous behavior by leveraging spiking neural networks-based energy-efficient computational frameworks. In this work, we have explored neuromorphic implementations of four prominent controllers for autonomous driving: pure-pursuit, Stanley, PID, and MPC, using a physics-aware simulation framework. We extensively evaluated these models with various intrinsic parameters and compared their performance with conventional CPU-based implementations. While being neural approximations, we show that neuromorphic models can perform competitively with their conventional counterparts. We provide guidelines for building neuromorphic architectures for control and describe the importance of their underlying tuning parameters and neuronal resources. Our results show that most models would converge to their optimal performances with merely 100-1,000 neurons. They also highlight the importance of hybrid conventional and neuromorphic designs, as was suggested here with the MPC controller. This study also highlights the limitations of neuromorphic implementations, particularly at higher (> 15 m/s) speeds where they tend to degrade faster than in conventional designs.

Clustering-Based Energy-Efficient Self-Healing Strategy for WSNs Under Jamming Attacks

The Internet of Things (IoT) is a key technology to interconnect the real and digital worlds, enabling the development of smart cities and services. The timely collection of data is essential for IoT services. In scenarios such as agriculture, industry, transportation, public safety, and health, wireless sensor networks (WSNs) play a fundamental role in fulfilling this task. However, WSNs are commonly deployed in sensitive and remote environments, thus facing the challenge of jamming attacks. Therefore, these networks need to have the ability to detect such attacks and adopt countermeasures to guarantee connectivity and operation. In this work, we propose a novel clustering-based self-healing strategy to overcome jamming attacks, in which we denominate fairness cooperation with power allocation (FCPA). The proposed strategy, aware of the presence of the jammer, clusters the network and designates a cluster head that acts as a sink node to collect information from its cluster. Then, the most convenient routes to overcome the jamming are identified and the transmit power is adjusted to the minimum value required to guarantee the reliability of each link. Finally, through the weighted use of the relays, the lifetime of each subnetwork is extended. To show the impact of each capability of FCPA, we compare it with multiple benchmarks that only partially possess these capabilities. In the proposal evaluation, we consider a WSN composed of 64 static nodes distributed in a square area. Meanwhile, to assess the impact of the jamming attack, we consider seven different locations of the attacker. All experiments started with each node's battery full and stopped after one of these batteries was depleted. In these scenarios, FCPA outperforms all other strategies by more than 50% of the information transmitted, due to the efficient use of relay power, through the weighted balance of cooperative routes. On average, FCPA permits 967,961 kb of information transmitted and 63% of residual energy, as energy efficiency, from all the analyzed scenarios. Additionally, the proposed clustering-based self-healing strategy adapts to the change of jammer location, outperforming the rest of the strategies in terms of information transmitted and energy efficiency in all evaluated scenarios.