Natural science and engineering instructors' knowledge and practice of brain-based instruction in Ethiopian higher education institutions

Currently, the Brain-Based Instructional approach has become an alternative instructional method in the schooling system of different countries of the world. This study explored the current situation of natural science and engineering instructors' knowledge and practices of brain-based instruction in Ethiopian higher education institutions. A descriptive survey research design with concurrent mixed methods was employed. Data collection tools were developed based on the twelve principles of brain-based learning theory and confirmed their validity and reliability. Survey questions were used to gather quantitative data from 512 randomly selected instructors. Qualitative data were collected through interviews with 14 purposely selected instructors. Classroom observation was also conducted to triangulate data obtained through interviews and survey questions. Quantitative data were analyzed using descriptive statistics, whereas qualitative data were analyzed thematically. The findings of this study depict that most natural science and engineering instructors have good knowledge of brain-based instruction but not transferable knowledge and skills. There is a clear gap between instructors' knowledge and classroom practice of brain-based instruction. Thus, to improve instructional practices, higher education institutions need to work strongly to narrow the existing variation. Implications and further recommendations are also suggested.

An accelerated sine mapping whale optimizer for feature selection

An improved whale optimization algorithm (SWEWOA) is presented for global optimization issues. Firstly, the sine mapping initialization strategy (SS) is used to generate the population. Secondly, the escape energy (EE) is introduced to balance the exploration and exploitation of WOA. Finally, the wormhole search (WS) strengthens the capacity for exploitation. The hybrid design effectively reinforces the optimization capability of SWEWOA. To prove the effectiveness of the design, SWEWOA is performed in two test sets, CEC 2017 and 2022, respectively. The advantage of SWEWOA is demonstrated in 26 superior comparison algorithms. Then a new feature selection method called BSWEWOA-KELM is developed based on the binary SWEWOA and kernel extreme learning machine (KELM). To verify its performance, 8 high-performance algorithms are selected and experimentally studied in 16 public datasets of different difficulty. The test results demonstrate that SWEWOA performs excellently in selecting the most valuable features for classification problems.

Diya - A universal light illumination platform for multiwell plate cultures

Recent progress in protein engineering has established optogenetics as one of the leading external non-invasive stimulation strategies, with many optogenetic tools being designed for in vivo operation. Characterization and optimization of these tools require a high-throughput and versatile light delivery system targeting micro-titer culture volumes. Here, we present a universal light illumination platform - Diya, compatible with a wide range of cell culture plates and dishes. Diya hosts specially designed features ensuring active thermal management, homogeneous illumination, and minimal light bleedthrough. It offers light induction programming via a user-friendly custom-designed GUI. Through extensive characterization experiments with multiple optogenetic tools in diverse model organisms (bacteria, yeast, and human cell lines), we show that Diya maintains viable conditions for cell cultures undergoing light induction. Finally, we demonstrate an optogenetic strategy for in vivo biomolecular controller operation. With a custom-designed antithetic integral feedback circuit, we exhibit robust perfect adaptation and light-controlled set-point variation using Diya.

Advanced slime mould algorithm incorporating differential evolution and Powell mechanism for engineering design

The slime mould algorithm (SMA) is a population-based swarm intelligence optimization algorithm that simulates the oscillatory foraging behavior of slime moulds. To overcome its drawbacks of slow convergence speed and premature convergence, this paper proposes an improved algorithm named PSMADE, which integrates the differential evolution algorithm (DE) and the Powell mechanism. PSMADE utilizes crossover and mutation operations of DE to enhance individual diversity and improve global search capability. Additionally, it incorporates the Powell mechanism with a taboo table to strengthen local search and facilitate convergence toward better solutions. The performance of PSMADE is evaluated by comparing it with 14 metaheuristic algorithms (MA) and 15 improved MAs on the CEC 2014 benchmarks, as well as solving four constrained real-world engineering problems. Experimental results demonstrate that PSMADE effectively compensates for the limitations of SMA and exhibits outstanding performance in solving various complex problems, showing potential as an effective problem-solving tool.

Resource potential mapping of bifacial photovoltaic systems in India

Bifacial photovoltaic is one of the technologies that can spearhead Indian ambitions to achieve the 7th United Nations Sustainable Development Goals and Nationally Determined Contributions of COP 26. But, like all emerging technology, the lack of awareness and unavailability of extensive data, like technology potential maps that could aid people in identifying the advantages of newer technologies, has led to sluggish growth in the Indian market. To expedite the growth of bifacial PV in the Indian market, optimized resource potential maps of bifacial PV were developed from an experimentally validated view factor-based bifacial PV model. The annual average bifacial gain was found to vary between 2.5% and 22% at various locations in India. The effect of ground albedo and height of installation was also studied. Furthermore, the annual and seasonal power out of bifacial PV was higher than monofacial PV at any location in India.

Mechanism that determines the economics of 100% renewable power systems

Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and power generation profiles determine the optimal capacities of generation and storage and their trade-off relationship. Furthermore, this study demonstrates the comprehensive quantification of the corresponding relationships among the factor cost of technologies, their optimal capacities, and total system cost. Based on these findings, the study also shows that hybrid systems comprising multiple renewable energy sources and different types of storage, including long-duration energy storage, are critical to reducing the total system cost by using actual profile data for multiple years and regions in Japan. This suggests that large-scale deployment of current-level power-to-gas technologies, such as water electrolysis, can contribute to the economics of 100% renewable power systems.

Effects of different drop height training on lower limb explosive and change of direction performance in collegiate Sanda athletes

The purpose of the present study was to examine the effects of 6 weeks of 40-, 60-, or 80-cm drop jump (DJ) training on lower limb explosive and change of direction (CoD) performance in collegiate Sanda athletes. Repeated-measure ANOVA revealed that there was a significant group × time interaction for standing long jump test (p = 0.006), counter movement jump test (p = 0.026), Illinois agility test (p = 0.003), square test (p = 0.018), Nebraska test (p = 0.027), t test (p = 0.032), and hexagon test (p = 0.012) due to the best performance observed at post-test compared with pre-test for DJ60 (effect size = 0.89-2.89), and the improvement was higher than that of the other groups. These findings suggest that 6 weeks of DJ training could improve the lower limb explosive and CoD performance in collegiate Sanda athletes and that 60 cm may be the optimal drop height.

Design and validation of depth camera-based static posture assessment system

Postural abnormalities have become a prevalent issue affecting individuals of all ages, resulting in a diminished quality of life. Easy-use and reliable posture assessment tools can aid in screening for and correcting posture deviation at an early stage. In this study, we present a depth camera-based static posture assessment system to screen for common postural anomalies such as uneven shoulders, pelvic tilt, bowlegs and knock-knees, forward head, scoliosis, and shoulder blade inclination. The system consists of an Azure Kinect camera, a laptop, and evaluation software. Our system accurately measures skeleton and posture indexes and shows favorable agreement with a golden standard optical infrared motion capture system. The findings indicate that the system is a low-cost posture assessment tool with high precision and accuracy, suitable for initial screening of postural abnormalities in individuals of all ages.

Research progress and future perspectives on electromagnetic wave absorption of fibrous materials

Electromagnetic waves have caused great harm to military safety, high-frequency electronic components, and precision instruments, and so forth, which urgently requires the development of lightweight, high-efficiency, broadband electromagnetic waves (EMW) absorbing materials for protection. As the basic fibrous materials, carbon fibers (CFs) and SiC fibers (SiCf) have been widely applied in EMW absorption due to their intrinsic characteristics of low density, high mechanical properties, high conductivity, and dielectric loss mechanism. Nevertheless, it has remained a great challenge to develop lightweight EMW-absorbing fibrous materials with strong absorption capability and broad frequency range. In this review, the fundamental electromagnetic attenuation mechanisms are firstly introduced. Furthermore, the preparation, structure, morphology, and absorbing performance of CFs and SiCf-based EMW absorbing composites are summarized. In addition, prospective research opportunities are highlighted toward the development of fibrous absorbing materials with the excellent absorption performance.

Advanced operation of heated fluidic resonators via mechanical and thermal loss reduction in vacuum

For simultaneous and quantitative thermophysical measurements of ultrasmall liquid volumes, we have recently developed and reported heated fluidic resonators (HFRs). In this paper, we improve the precision of HFRs in a vacuum by significantly reducing the thermal loss around the sensing element. A vacuum chamber with optical, electrical, and microfluidic access is custom-built to decrease the convection loss by two orders of magnitude under 10-4 mbar conditions. As a result, the measurement sensitivities for thermal conductivity and specific heat capacity are increased by 4.1 and 1.6 times, respectively. When differentiating between deionized water (H2O) and heavy water (D2O) with similar thermophysical properties and ~10% different mass densities, the signal-to-noise ratio (property differences over standard error) for H2O and D2O is increased by 9 and 5 times for thermal conductivity and specific heat capacity, respectively.

Towards smart scanning probe lithography: a framework accelerating nano-fabrication process with in-situ characterization via machine learning

Scanning probe lithography (SPL) is a promising technology to fabricate high-resolution, customized and cost-effective features at the nanoscale. However, the quality of nano-fabrication, particularly the critical dimension, is significantly influenced by various SPL fabrication techniques and their corresponding process parameters. Meanwhile, the identification and measurement of nano-fabrication features are very time-consuming and subjective. To tackle these challenges, we propose a novel framework for process parameter optimization and feature segmentation of SPL via machine learning (ML). Different from traditional SPL techniques that rely on manual labeling-based experimental methods, the proposed framework intelligently extracts reliable and global information for statistical analysis to fine-tune and optimize process parameters. Based on the proposed framework, we realized the processing of smaller critical dimensions through the optimization of process parameters, and performed direct-write nano-lithography on a large scale. Furthermore, data-driven feature extraction and analysis could potentially provide guidance for other characterization methods and fabrication quality optimization.

Active methods in electricity and magnetism courses: Influence of degree, academic level and gender on student performance

The performance of first-year students in electromagnetism (E&M) courses of different engineering degrees at a Spanish public university was measured using the Brief Electricity and Magnetism Assessment (BEMA), a standard research-based instrument to assess students' understanding after attending introductory courses in electricity and magnetism. In all cases, Flipped classroom (FC) built on information and communications technology was used. The objective of this paper is to analyse if the gain in the BEMA pre and post-test results is influenced by several factors such as the degree, the students' academic grade, and gender. Moreover, as some studies have shown that the students' retention of the concepts was significantly stronger in active learning than in traditional approaches, a third BEMA test was performed by the students to analyse the long-term retention gain dependence on the same factors. Students from different engineering degree programs were asked to complete two BEMA tests during the course and a third one after a few months. ANOVA tests were used to analyse the existence of significant differences in gain between student degree programs, student academic level and student gender. Results have shown no differences in the BEMA performance by degree program, but significant differences were found by academic level and gender. Retention did not depend on the degree course but on the academic level. Mean gain value by academic level, and gender was obtained and concluded that the best students presented the best gain results and that gain depends on the students' gender: males outperformed females in the BEMA tests, although there were no significant differences in the course grades. It is thus necessary to understand these differences and to implement measures in daily teaching work to improve women's performance.

Valorization of lignin-derived compounds into poly(3-hydroxybutyrate-co-3-hydroxyvalerate) by engineered Halomonas sp. Y3

Poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (PHBV) is a biopolyester with great potential, but its high production cost via the propionate-dependent pathway has hindered its development. Herein, we engineer Halomonas sp. Y3 to achieve efficient conversion of various LDCs into PHBV without propionate supplement. Initially, we successfully achieve PHBV production without propionate supplement by overexpressing threonine synthesis. The resulting biopolyester exhibits a 3 HV proportion of up to 7.89 mol%, comparable to commercial PHBV (8 mol%) available from Sigma Aldrich (403105). To further enhance PHBV production, we rationally design the reconstruction of aromatic compound catabolism. The engineered strain Y3_18 efficiently assimilates all LDCs containing syringyl (S), guaiacyl (G), and p-hydroxyphenyl-type (H) units. From 1 g/L of S-, G-, and H-type LDCs, Y3_18 produces PHBV at levels of 449 mg/L, 488 mg/L, and 716 mg/L, respectively, with yields of 44.9 % (g/g), 48.8 % (g/g), and 71.6 % (g/g). Moreover, to improve PHBV yield from lignin, we integrate laccase-secretion and PHBV production modules. This integration leads to the accumulation of 425.84 mg/L of PHBV with a yield of 21.29 % (g/g) and a 3 HV proportion of 6.38 mol%. By harnessing the capabilities of Halomonas sp. Y3, we demonstrate an efficient and sustainable approach for PHBV production from a variety of LDCs.

Economical production of Pichia pastoris single cell protein from methanol at industrial pilot scale

BACKGROUND

Methanol, synthesized from CO2, is a potentially sustainable one-carbon (C1) resource for biomanufacturing. The use of methanol as a feedstock to produce single cell protein (SCP) has been investigated for decades as an alternative to alleviate the high global demand for animal-derived proteins. The methylotrophic yeast Pichia pastoris is an ideal host for methanol-based SCP synthesis due to its natural methanol assimilation ability. However, improving methanol utilization, tolerance to higher temperature, and the protein content of P. pastoris are also current challenges, which are of great significance to the economical industrial application using methanol as a feedstock for SCP production.

RESULTS

In the present work, adaptive laboratory evolution (ALE) has been employed to overcome the low methanol utilization efficiency and intolerance to a higher temperature of 33 °C in P. pastoris, associated with reduced carbon loss due to the lessened detoxification of intracellular formaldehyde through the dissimilation pathway and cell wall rearrangement to temperature stress resistance following long-term evolution as revealed by transcriptomic and phenotypic analysis. By strengthening nitrogen metabolism and impairing cell wall synthesis, metabolic engineering further increased protein content. Finally, the engineered strain via multi-strategy produced high levels of SCP from methanol in a pilot-scale fed-batch culture at 33 °C with a biomass of 63.37 g DCW/L, methanol conversion rate of 0.43 g DCW/g, and protein content of 0.506 g/g DCW. SCP obtained from P. pastoris contains a higher percentage of protein compared to conventional foods like soy, fish, meat, whole milk, and is a source of essential amino acids, including methionine, lysine, and branched-chain amino acids (BCAAs: valine, isoleucine, leucine).

CONCLUSIONS

This study clarified the unique mechanism of P. pastoris for efficient methanol utilization, higher temperature resistance, and high protein synthesis, providing a P. pastoris cell factory for SCP production with environmental, economic, and nutritional benefits.

Direct recycling of spent Li-ion batteries: Challenges and opportunities toward practical applications

With the exponential expansion of electric vehicles (EVs), the disposal of Li-ion batteries (LIBs) is poised to increase significantly in the coming years. Effective recycling of these batteries is essential to address environmental concerns and tap into their economic value. Direct recycling has recently emerged as a promising solution at the laboratory level, offering significant environmental benefits and economic viability compared to pyrometallurgical and hydrometallurgical recycling methods. However, its commercialization has not been realized in the terms of financial feasibility. This perspective provides a comprehensive analysis of the obstacles that impede the practical implementation of direct recycling, ranging from disassembling, sorting, and separation to technological limitations. Furthermore, potential solutions are suggested to tackle these challenges in the short term. The need for long-term, collaborative endeavors among manufacturers, battery producers, and recycling companies is outlined to advance fully automated recycling of spent LIBs. Lastly, a smart direct recycling framework is proposed to achieve the full life cycle sustainability of LIBs.

Combining LCA-MFA models to identify China's plastic value chain environmental impact mitigation pathways

Characterizing material flows and environmental impacts of plastic value chain is crucial for sustainable plastic management. Here, we combine material flow analysis and life cycle assessment methods to map the flows of eight major plastics and investigate the multiple environmental impacts of China's plastic value chain. We find that packaging and textile sectors dominate plastic consumption and are responsible for the value chain environmental burdens, but with low recycling rates. Major environmental impacts are generated in plastic production and product manufacturing stages because of the consumption of coal-based feedstocks and electricity. We therefore set up six scenarios by considering carbon neutrality energy pathway, plastic recycling improvement, and technology updating, finding that the value chain environmental impact can be reduced by 14%-57% in 2060 under combined scenario. Particularly, carbon neutrality renewable energy pathway plays an important role. These findings provide valuable insights to identify key mitigation pathways for plastic value chain.

Techno-economic analysis of residential building heating strategies for cost-effective upgrades in European cities

The energy crisis in Europe requires cost-effective evaluations of residential heating strategies to reduce costs and mitigate greenhouse gas emissions. This research studied different heating systems in China and Europe. Based on heating energy surveys, simulation models were developed and further expanded for European cities. Monte Carlo analyses were conducted to understand the heating demand and utility costs in Rome, Madrid, and Athens. The sensitivity analysis found that electrifying heating systems with heat pumps can reduce household heating costs and mitigate European cities' dependence on natural gas. However, the high upfront investment may hinder the cost-effective deployment of high-performance heat pump systems. Building envelope retrofits can also provide plausible energy savings despite relatively long payback periods. Financial incentive analyses were conducted to quantify how fiscal measures can improve technologies' techno-economic performance. Finally, the paper provided policy recommendations on future building cost-effective retrofits and heating electrification in Europe.

3D free-assembly modular microfluidics inspired by movable type printing

Reconfigurable modular microfluidics presents an opportunity for flexibly constructing prototypes of advanced microfluidic systems. Nevertheless, the strategy of directly integrating modules cannot easily fulfill the requirements of common applications, e.g., the incorporation of materials with biochemical compatibility and optical transparency and the execution of small batch production of disposable chips for laboratory trials and initial tests. Here, we propose a manufacturing scheme inspired by the movable type printing technique to realize 3D free-assembly modular microfluidics. Double-layer 3D microfluidic structures can be produced by replicating the assembled molds. A library of modularized molds is presented for flow control, droplet generation and manipulation and cell trapping and coculture. In addition, a variety of modularized attachments, including valves, light sources and microscopic cameras, have been developed with the capability to be mounted onto chips on demand. Microfluidic systems, including those for concentration gradient generation, droplet-based microfluidics, cell trapping and drug screening, are demonstrated. This scheme enables rapid prototyping of microfluidic systems and construction of on-chip research platforms, with the intent of achieving high efficiency of proof-of-concept tests and small batch manufacturing.