The impact of nanomaterials on energy-centric form-finding of educational buildings in semi-arid climate

In the modern world, the use of novel technologies in architecture has become highly significant and transformative for human-environment interactions. One of the most critical concerns in architecture is achieving optimal forms and selecting suitable materials for effective design across diverse climatic zones. Also, adopting innovative climate design methods in public spaces, such as educational buildings, is essential due to their strategic urban locations and diverse user populations. Therefore, the research conducted in this study focuses on two main aspects: optimizing building form based on energy consumption and solar radiation received by vertical surfaces, and, selecting appropriate nanomaterials for building surface to reduce energy usage and maintenance costs. This study begins with theoretical foundations, defining the key terms through a comprehensive review of relevant literature. Then, four identical classroom modules with consistent height and floor levels are proposed, and Energy Plus software is used to evaluate the energy consumption based on a module simulation in initial forms of square and rectangle with varying proportions. The best module and orientation is determined using specified climatic data of the coldest and the hottest days of the year. Further, investigations involve combining these modules in various layouts, emphasizing those that align with the functional requirements of educational spaces. Finally, two parameters of energy consumption and solar radiation on vertical surfaces are measured during specified time interval between sunrise and sunset. The results indicate that among the four proposed modules, the 18 × 18 module with a north-south orientation is the most optimal in the semi-arid climate of Tehran, and therefore, Type 3 layout demonstrates the best performance for energy consumption. This is while by incorporating selected nanotechnology (self-cleaning nanomaterial paint), energy usage decreases in all layouts, regardless of the season.

Development model based on visual image big data applied to art management

This paper aims to explore the application of visual image big data (BD) in art management, and proposes and develops a new art management model. First of all, this study conducted extensive research on the overview and application of big data, focusing on analyzing the characteristics of big data and its characteristics and application methods in art management. By introducing image processing (IP) technology, this paper expounds on the application of visual image technology in art management in detail and discusses the classification of computer vision images to determine its application direction. On this basis, this paper proposes the application of visual images and big data in art management from three aspects: the accurate acquisition of visual images, the development model of art management, and the development of visual image technology in art resource management and teaching, and strengthens the development model of art management based on IP algorithm. Experiments and surveys show that the art management model development system built by the newly introduced visual image technology, big data technology, and IP algorithm can increase user satisfaction by 24 %. This result shows that the new model has a significant effect in improving the efficiency and quality of art management, providing strong technical support for the field of art management, while also providing designers with a more accurate tool for assessing market trends, helping to adhere to and promote good design concepts.

From Data to Diagnosis: How Machine Learning Is Changing Heart Health Monitoring

The rapid advances in science and technology in the field of artificial neural networks have led to noticeable interest in the application of this technology in medicine. Given the need to develop medical sensors that monitor vital signs to meet both people's needs in real life and in clinical research, the use of computer-based techniques should be considered. This paper describes the latest progress in heart rate sensors empowered by machine learning methods. The paper is based on a review of the literature and patents from recent years, and is reported according to the PRISMA 2020 statement. The most important challenges and prospects in this field are presented. Key applications of machine learning are discussed in medical sensors used for medical diagnostics in the area of data collection, processing, and interpretation of results. Although current solutions are not yet able to operate independently, especially in the diagnostic context, it is likely that medical sensors will be further developed using advanced artificial intelligence methods.

CYP1B1 Gene Polymorphism Based on Health Monitoring and Nursing Methods after Minimally Invasive Surgery for Lung Cancer

The rapid development of science and technology has become an indispensable part of human life. Minimally invasive lung cancer surgery, that is, thoracoscopic surgery and da Vinci robotic surgery, has many advantages over previous surgeries, there is no need to make a large incision in the chest, the patient after such surgery, and recovery is also better and can also reduce the incision of the operation. Therefore, with the rapid development of science and technology today, how to detect changes in patients' health and establish an intelligent health monitoring system has become a development trend. This paper proposes to apply health monitoring in CYP1B1 gene polymorphism and nursing after clinical treatment of minimally invasive lung cancer surgery, after analyzing the society's demand for real-time health monitoring in this paper. It also studies the health monitoring system based on the advantages of smart phones. The system is suitable for the Android operating system and can monitor the temperature, weight, and other data of the human body. The experimental results show that the data value of the information displayed by the android software has a high degree of matching with the measured value, which basically keeps floating around 80, and the data consistency is strong.

Status Quo Analysis of Physical Fitness Test Data Based on Health Monitoring

One of the important symbols of a country's level of social progress and the continuous spread of civilization throughout the world includes the level of national physique and health. People's living standards have been significantly improved, and a moderately prosperous society has been preliminarily realized. The national physique should be improved, especially the teenagers who are in the rapid and golden period of physical and psychological development. But not everything develops according to wishes. For the past 20 years, the physical health of Chinese students has been in a downward trend. Therefore, it is urgent to analyze and study the data of adolescent health monitoring and physical fitness test. Through the analysis of D-S evidence theory composition rules, SVM network protocol, and other technologies, the accuracy of adolescent physique monitoring data has been improved by 38.4%, enhanced students' willingness to exercise, 65% of students have enhanced physical health awareness, and a network data platform has been established, which can clearly reflect the physical health of students and summarize all monitoring data information. Teenagers are the future builders and successors of the country, and they play a pivotal role in the entire country. The analysis of the status quo of adolescent physical fitness test data is related to the strength of the country, the rise and fall of the nation, the happiness of the family, and the future of the individual.

A Flexible, Wearable, and Wireless Biosensor Patch with Internet of Medical Things Applications

Monitoring the vital signs and physiological responses of the human body in daily activities is particularly useful for the early diagnosis and prevention of cardiovascular diseases. Here, we proposed a wireless and flexible biosensor patch for continuous and longitudinal monitoring of different physiological signals, including body temperature, blood pressure (BP), and electrocardiography. Moreover, these modalities for tracking body movement and GPS locations for emergency rescue have been included in biosensor devices. We optimized the flexible patch design with high mechanical stretchability and compatibility that can provide reliable and long-term attachment to the curved skin surface. Regarding smart healthcare applications, this research presents an Internet of Things-connected healthcare platform consisting of a smartphone application, website service, database server, and mobile gateway. The IoT platform has the potential to reduce the demand for medical resources and enhance the quality of healthcare services. To further address the advances in non-invasive continuous BP monitoring, an optimized deep learning architecture with one-channel electrocardiogram signals is introduced. The performance of the BP estimation model was verified using an independent dataset; this experimental result satisfied the Association for the Advancement of Medical Instrumentation, and the British Hypertension Society standards for BP monitoring devices. The experimental results demonstrated the practical application of the wireless and flexible biosensor patch for continuous physiological signal monitoring with Internet of Medical Things-connected healthcare applications.

ApproxBioWear: Approximating Additions for Efficient Biomedical Wearable Computing at the Edge

Wearables in the biomedical domain have been of extensive use in the current era. Given the importance of wearable computing, it has become necessary to innovate on enhancing hardware efficiency. The domain of approximate computing offers a conclusive method to lower area, power and delay in hardware in addition to a marginal loss in accuracy. In this paper, we investigate ApproxBioWear, a technique which enables the use of approximate computing for efficient biomedical wearable computing at the edge. The methodology involves approximating additions during the functional stages of an error-resilient biomedical signal processing algorithm and determining the application accuracy. Upon evaluating the Pan-Tompkins algorithm, which is used to detect QRS peaks in ECG signals, it is observed that the ApproxBioWear approach reduces the power consumption and chip area by 19.27% and 19.71% respectively on an average with a marginal loss in accuracy.

Anytime ECG Monitoring through the Use of a Low-Cost, User-Friendly, Wearable Device

Every year cardiovascular diseases kill the highest number of people worldwide. Among these, pathologies characterized by sporadic symptoms, such as atrial fibrillation, are difficult to be detected as state-of-the-art solutions, e.g., 12-leads electrocardiogram (ECG) or Holter devices, often fail to tackle these kinds of pathologies. Many portable devices have already been proposed, both in literature and in the market. Unfortunately, they all miss relevant features: they are either not wearable or wireless and their usage over a long-term period is often unsuitable. In addition, the quality of recordings is another key factor to perform reliable diagnosis. The ECG WATCH is a device designed for targeting all these issues. It is inexpensive, wearable (size of a watch), and can be used without the need for any medical expertise about positioning or usage. It is non-invasive, it records single-lead ECG in just 10 s, anytime, anywhere, without the need to physically travel to hospitals or cardiologists. It can acquire any of the three peripheral leads; results can be shared with physicians by simply tapping a smartphone app. The ECG WATCH quality has been tested on 30 people and has successfully compared with an electrocardiograph and an ECG simulator, both certified. The app embeds an algorithm for automatically detecting atrial fibrillation, which has been successfully tested with an official ECG simulator on different severity of atrial fibrillation. In this sense, the ECG WATCH is a promising device for anytime cardiac health monitoring.

Area efficient folded undecimator based ECG detector

This paper presents an area-efficient folded wavelet filter-based Electrocardiogram (ECG) detector for cardiac pacemakers. The modified folded undecimator based detector consists of Wavelet Filter Bank, QRS complex detector with Generalized Likelihood Ratio Test (GLRT) block and noise detector. A high-level transformation technique such as folding transformation and Cutset retiming are applied to the GLRT block in order to reduce the silicon area. Folding is a high-level transformation applied at the architectural level to enhance the performance of DSP architectures. It reduces the number of adders, multipliers and delay elements in the architecture. The Cutset retiming reduces clock period of the architecture by changing position of delay elements in the critical path. The folding transformation and cutset retiming implement the functional blocks of the GLRT circuit with minimum hardware. The modified folded ECG detector is tested for short term and long-term MIT-BIH databases. The results show that the modified folded undecimator detector has hardware savings and achieves sensitivity of 99.95%, positive prediction of 99.97% and Detection Error Rate (DER) of 0.061. The folded GLRT block architecture is synthesized with FPGA Zed board XC7Z010CLG484-1. Results show that the device utilization and power consumption are lesser than the conventional GLRT structure.

El Kassabi H, Ismail H, Nujum Navaz A. ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges

Health monitoring and its related technologies is an attractive research area. The electrocardiogram (ECG) has always been a popular measurement scheme to assess and diagnose cardiovascular diseases (CVDs). The number of ECG monitoring systems in the literature is expanding exponentially. Hence, it is very hard for researchers and healthcare experts to choose, compare, and evaluate systems that serve their needs and fulfill the monitoring requirements. This accentuates the need for a verified reference guiding the design, classification, and analysis of ECG monitoring systems, serving both researchers and professionals in the field. In this paper, we propose a comprehensive, expert-verified taxonomy of ECG monitoring systems and conduct an extensive, systematic review of the literature. This provides evidence-based support for critically understanding ECG monitoring systems' components, contexts, features, and challenges. Hence, a generic architectural model for ECG monitoring systems is proposed, an extensive analysis of ECG monitoring systems' value chain is conducted, and a thorough review of the relevant literature, classified against the experts' taxonomy, is presented, highlighting challenges and current trends. Finally, we identify key challenges and emphasize the importance of smart monitoring systems that leverage new technologies, including deep learning, artificial intelligence (AI), Big Data and Internet of Things (IoT), to provide efficient, cost-aware, and fully connected monitoring systems.

Local field potential dynamics in the primate cortex in relation to parkinsonism reveled by machine learning: A comparison between the primary motor cortex and the supplementary area

The present study compares the cortical local field potentials (LFPs) in the primary motor cortex (M1) and the supplementary motor area (SMA) of non-human primates rendered Parkinsonian with administration of dopaminergic neurotoxin, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine. The dynamic of the LFPs was investigated under several mathematical frameworks and machine learning was used to discriminate the recordings based on these features between healthy, parkinsonian with off-medication and parkinsonian with on-medication states. The importance of each feature in the discrimination process was further investigated. The dynamic of the LFPs in M1 and SMA was affected regarding its variability (time domain analysis), oscillatory activities (frequency domain analysis) and complex patterns (non-linear domain analysis). Machine learning algorithms achieved accuracy near 0.90 for comparisons between conditions. The TreeBagger algorithm provided best accuracy. The relative importance of these features differed with the cortical location, condition and treatment. Overall, the most important features included beta oscillation, fractal dimension, gamma oscillation, entropy and asymmetry of amplitude fluctuation. The importance of features in discriminating between normal and pathological states, and on- or off-medication states depends on the pair-comparison and it is region-specific. These findings are discussed regarding the refinement of current models for movement disorders and the development of on-demand therapies.

Flexible Electrode Based on MWCNT Embedded in a Cross-Linked Acrylamide/Alginate Blend: Conductivity vs. Stretching

A polyacrylamide-alginate hydrogel electrolyte, blended with Multi-Walled Carbon Nanotubes (MWCNT) as an electronically conductive fraction, allows for the creation of a flexible, durable, and resilient electrode. The MWCNT content is correlated with mechanical characteristics such as stretch modulus, tensile resistance, and electrical conductivity. The mechanical analysis demonstrates tensile strength that is comparable to similar hydrogels reported in the literature, with increasing strength for MWCNT-embedded hydrogels. The impedance spectroscopy reveals that the total resistance of electrodes decreases with increasing MWCNT content upon elongation and that bending and twisting do not obstruct their conductivity. The MWCNT-inserted hydrogels show mixed ionic and electronic conductivities, both within a range of 1–4 × 10⁻² S cm⁻¹ in a steady state. In addition, the thermal stability of these materials increases with incrementing MWCNT content. This observation agrees with long-term charge-discharge cycling that shows enhanced electrochemical durability of the MWCNT-hydrogel hybrid when compared to pure hydrogel electrolyte. The hydrogel-carbon films demonstrate an increased interfacial double-layer current at a high MWCNT content (giving an area-specific capacitance of ~30 mF cm⁻² at 2.79 wt.% of MWCNT), which makes them promising candidates as printable and flexible electrodes for lightweight energy storage applications. The maximum content of MWCNT within the polymer electrolyte was estimated at 2.79 wt.%, giving a very elastic polymer electrode with good electrical characteristics.

Implicit Irregularity Detection Using Unsupervised Learning on Daily Behaviors

The irregularity detection of daily behaviors for the elderly is an important issue in homecare. Plenty of mechanisms have been developed to detect the health condition of the elderly based on the explicit irregularity of several biomedical parameters or some specific behaviors. However, few research works focus on detecting the implicit irregularity involving the combination of diverse behaviors, which can assess the cognitive and physical wellbeing of elders but cannot be directly identified based on sensor data. This paper proposes an Implicit IRregularity Detection (IIRD) mechanism that aims to detect the implicit irregularity by developing the unsupervised learning algorithm based on daily behaviors. The proposed IIRD mechanism identifies the distance and similarity between daily behaviors, which are important features to distinguish the regular and irregular daily behaviors and detect the implicit irregularity of elderly health condition. Performance results show that the proposed IIRD outperforms the existing unsupervised machine-learning mechanisms in terms of the detection accuracy and irregularity recall.

Physical Wellbeing Monitoring Employing Non-Invasive Low-Cost and Low-Energy Sensor Socks

Determining and improving the wellbeing of people is one of the priorities of the OECD countries. Nowadays many sensors allow monitoring different parameters in regard to the wellbeing of people. These sensors can be deployed in smartphones, clothes or accessories like watches. Many studies have been performed on wearable devices that monitor certain aspects of the health of people, especially for specific diseases. In this paper, we propose a non-invasive low-cost and low-energy physical wellbeing monitoring system that provides a wellness score based on the obtained data. We present the architecture of the system and the disposition of the sensors on the sock. The algorithm of the system is presented as well. The wellness threshold evaluation module allows determining if the monitored parameter is within healthy ranges. The message forwarding module allows decreasing the energy consumption of the system by detecting the presence of alerts or changes in the data. Finally, a simulation was performed in order to determine the energy consumption of the system. Results show that our algorithm allows saving 44.9% of the initial energy in 10,000 min for healthy people.

Evaluation of the IngVaL Pedobarography System for Monitoring of Walking Speed

Objectives

Walking speed is an important component of movement and is a predictor of health in the elderly. Pedobarography, the study of forces acting between the plantar surface of the foot and a supporting surface, is an approach to estimating walking speed even when no global positioning system signal is available. The developed portable system, Identifying Velocity and Load (IngVaL), is a cost effective alternative to commercially available pedobarography systems because it only uses three force sensing resistors. In this study, the IngVaL system was evaluated. The three variables investigated in this study were the sensor durability, the proportion of analyzable steps, and the linearity between the system output and the walking speed.

Methods

Data was collected from 40 participants, each of whom performed five walks at five different self-paced walking speeds. The linearity between the walking speed and step frequency measured with R² values was compared for the walking speed obtained ‘A’ only using amplitude data from the force sensors, ‘B’ that obtained only using the step frequency, and ‘C’ that obtained by combining amplitude data for each of the 40 test participants.

Results

Improvement of the wireless data transmission increased the percentage of analyzable steps from 83.1% measured with a prototype to 96.6% for IngVaL. The linearity comparison showed that the methods A, B, and C were accurate for 2, 15, and 23 participants, respectively.

Conclusions

Increased sensor durability and a higher percentage of analyzed steps indicates that IngVaL is an improvement over the prototype system. The combined strategy of amplitude and step frequency was confirmed as the most accurate method.