Development Of System Research Articles

Malaysian Rainwater Harvesting System for In-House Power Generation

Energy harvesting monitoring systems have become more important as the Internet of Things (IoT) have grown. An intelligent system to monitor rainwater harvesting at UNITEN COE BN is being designed and developed in this study. Rainwater harvesting operations will be improved by developing an intelligent system. Monitoring techniques are studied, and sensors are designed for simulation. Smart rainwater harvesting systems are designed and implemented in this study, contributing to the field of smart monitoring systems. Rainwater collection, storage and usage are monitored and analyzed with smart sensors and data acquisition systems. Water turbine speed, voltage, and rainfall intensity are monitored by sensors in the developed system. Data from sensors is processed in Python GUIs. Visual displays allow users to monitor the rainwater harvesting system remotely. Durability and infrastructure compatibility are considered when selecting materials. It is found that smart rainwater harvesting system performance and reliability can be improved through simulation testing and validation. The study concluded that, storm water resources can be optimized by accessing real-time information.

Terrain detection and segmentation for autonomous vehicle navigation: A state-of-the-art systematic review

This review comprehensively investigates the current state and emerging trends of autonomous vehicle terrain detection and segmentation. By systematically reviewing literature from various databases, this study outlines the evolution of detection and segmentation techniques from traditional computer vision methods to advanced machine learning and deep learning approaches. It identifies critical technological advancements, evaluates their performance, and discusses the challenges faced under various environmental conditions, data acquisition, and integration with vehicle systems. This study also highlights the need for standardized benchmarks and datasets to facilitate the development and testing of robust terrain detection systems. This review encompasses terrain detection and segmentation in structured environments, such as urban roads and highways, and unstructured environments, including rural paths and off-road terrains, to comprehensively analyze autonomous vehicle navigation challenges. By analyzing recent research findings, this review provides insights into future directions for overcoming these limitations and fostering innovation in the autonomous driving domain.

The Consolidation of Risk Pools in the National Health Insurance Program of the Republic of Korea: Analysis of the Political Processes

ABSTRACT South Korea is one of the few countries that has successfully consolidated its national risk pools into a single-payer health insurance system. This study analyzes national health insurance (NHI) consolidation in South Korea between 1980 and 2003, drawing lessons for countries seeking to consolidate their risk pools. The paper contextualizes the development of the South Korean national health insurance system and the consolidation of its risk pools in the changes in the political environment, particularly the processes of political democratization. It examines how these processes have affected the societal meanings and roles of the health insurance system. The paper focuses on two policy stages. During the agenda-setting stage, the government and employers emphasized the efficiency of the health insurance system. This emphasis contributed to the failure of a consolidation plan that emphasized equity over efficiency. However, as democratization expanded, the power of civil society movements and the popular demand for equity grew stronger. During the policy adoption phase, consolidation of health insurance trusts in South Korea was achieved through social and political processes that influenced public opinion, expanded civil society participation in decision-making, and worked with the government to integrate health insurance organizations and funds.

This (AI)n’t fair? Employee reactions to artificial intelligence (AI) in career development systems

AbstractOrganizations increasingly implement AI for career development to enhance efficiency. However, there are concerns about employees’ acceptance of AI and the literature on employee acceptance of AI is still in its infancy. To address this research gap, integrating justice theory, we investigate the effects of the deciding entity (human, human and AI, and AI) and the impact of the data source (internal data, external data), on employees’ reactions. Using a scenario-based between-subject design, displaying a common situation in organizations (N = 280) and an additional causal-chain-approach (N = 157), we examined whether a decrease of human involvement in decision making diminishes employees’ perceived fairness and satisfaction with the career development process and increases their perceived privacy intrusion. Although we also considered other data sources to moderate the proposed relationships, we found no support for interaction effects. Finally, fairness and privacy intrusion mediated the influence of the deciding entity and data source on turnover intention and employer attractiveness, while satisfaction with the process did not. By addressing how the employees react to AI in career development–showing the negative reactions, our study holds considerable relevance for research and practice.

Next Steps for Medical Specialist Enterprises in the Netherlands: Building Strong Clinical Governance and Leadership; Comment on "Alignment in the HospitalPhysician Relationship: A Qualitative Multiple Case Study of Medical Specialist Enterprises in the Netherlands"

This commentary article responds to the research into development of Medical Specialist Enterprises (MSEs) in the Netherlands conducted by Ubels and van Raaij. The MSEs are a relatively new phenomenon in the Netherlands and similar conceptually to medically-led developments in other health systems. With the foundation for medical specialist organisation in place this provides several opportunities for further development. This commentary considers these opportunities, drawing from the example of New Zealand. This is because New Zealand has had considerable experience with clinically-led organisation which provides useful lessons for the MSEs. The lessons include building strong clinical governance with a focus on collaboration with other health professionals and management, working with primary care to support community service delivery, building integrated care, developing whole of system planning and service delivery approaches and population health management.

Interior-point policy optimization based multi-agent deep reinforcement learning method for secure home energy management under various uncertainties

Improving the efficiency of home energy management (HEM) is of great significance in reducing resource waste and prompting renewable energy consumption. With the development of advanced HEM systems and internet-of-things technology, more residents are willing to participate in the electricity market through a demand response mechanism, which can not only reduce the electricity bill but also stabilize the operation of the main grid through peak shaving and valley filling. However, the uncertainties from household appliance parameters, user behaviors, penetrated renewable energy, and electricity prices render the HEM problem a non-trivial task. Although previous deep reinforcement learning (DRL) methods have no requirement for system dynamics due to their model-free and data-driven merits, the unrestricted action space may violate the physical constraints of various devices, and few works have concentrated on this area before. To solve the above challenges, this paper proposes a novel interior-point policy optimization-based multi-agent DRL algorithm to optimize the home energy scheduling procedure while guaranteeing safety during its operation. Besides, a time-series prediction model based on a non-stationary Transformer neural network is proposed to predict the future trend of solar generation and electricity price, which can provide the agents with abundant information to make better decisions. The superior performance of our proposed predictive-control coupled method is demonstrated through extensive numerical experiments, which achieves more precise prediction results, near-zero constraint violations, and better trade-offs between cost and safety than several benchmark methods.

Multi-Energy Load Prediction Method for Integrated Energy System Based on Fennec Fox Optimization Algorithm and Hybrid Kernel Extreme Learning Machine.

To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively.

Microencapsulation of Zn-10 mass% Al alloy phase change material via dry synthesis method

Thermal energy storage (TES) via latent heat storage (LHS) using metal alloy phase change material (PCM) offers advantages in medium-to-high temperature operation around 400 °C and high heat storage capacity. Microencapsulation of alloys as PCMs can mitigate issues of reactivity and improve handling properties of PCM. This research presents the novel development of microencapsulated PCM (MEPCM) with a core of Zn–10 mass% Al that operates around 400 °C. The MEPCMs were synthesized using high-speed impact blending (HIB), where fine AlOOH particles mechanically coat the surface of Zn–10 mass% Al alloy microspheres, resulting in robust surface modification. Subsequent heat-oxidation treatment at 800 °C was performed to obtain stable oxide coating. The HIB treatment resulted in an AlOOH coating containing Zn nanoparticles, which the heat-oxidation process transformed into α-Al2O3 and ZnO nanoparticles. The MEPCMs exhibited heat storage densities, including latent and sensible heat (ΔT=200 °C), that were 1.9 times higher than the capacity of solar salt, which is commonly used as a sensible heat storage material. Furthermore, the MEPCMs maintained their shape and latent heat capacity after 300 and 1000 cyclic tests. These superior MEPCM characteristics hold the potential to significantly advance the development of LHS-based TES systems operating around 400 °C, with applications in energy generation and industrial processes.

Sensor for Real-Time Glucose Measurement in Aqueous Media based on Nanomaterials Incorporating an Artificial Neural Network Algorithm on a System-On-Chip

The aim of this paper is to present the development of a real-time measurement system for glucose in aqueous media. The proposed system incorporates two lines of research: i) design, synthesis, and implementation of a non-enzymatic electrochemical sensor of Multi-Walled Carbon Nanotubes with Copper nanoparticles (MWCNT-Cu) and ii) design and implementation of a machine learning algorithm based on an Artificial Neural Network Multilayer Perceptron (ANN-MLP), which is embedded in an ESP32 SoC (System on Chip). From the current data that is extracted in real-time during the oxidation-reduction process to which an aqueous medium is subjected, it feeds the algorithm embedded in the ESP32 SoC to estimate the glucose value. The experimental results show that the nanostructured sensor improves the resolution in the amperometric response by identifying an ideal place for data collection. For its part, the incorporation of the algorithm based on an ANN embedded in a SoC provides a level of 97.8 % accuracy in the measurements. It is concluded that incorporating machine learning algorithms embedded in low-cost SoC in complex experimental processes improves data manipulation, increases the reliability of results, and adds portability.

Model predictive control of multilevel inverter used in a wind energy conversion system application

The rapid growth of renewable energy, particularly wind energy, has significantly contributed to global electricity generation, with global renewable energy capacity reaching an all-time high of 3870 gigawatts (GW) in 2023. However, ensuring high power quality for grid integration remains crucial for wind energy's role as a leading source in the energy transition. This necessitates the development of advanced Wind Energy Conversion Systems (WECS) capable of regulating current, voltage, and frequency to ensure power quality. This work proposes a novel predictive control strategy for multilevel inverters in WECS to achieve a total harmonic distortion (THD) below 5% in the generated electrical power. The proposed predictive control algorithm is presented and implemented in a WECS simulation with a multilevel inverter. The performance of the proposed system is evaluated through simulations using MATLAB/SIMULINK. Our results demonstrate that the predictive control approach outperforms traditional controllers for multilevel inverters in terms of output voltage, harmonic reduction, execution time, and overall WECS control.

A new direction for neighbourhood governance and community construction in China: the case of Zhejiang province’s ‘Future Communities’

ABSTRACT In March 2019, Zhejiang province introduced a new approach to China’s neighbourhood governance, placing digital technology at the centre of communities. Utilizing a case study design, we examine this approach and the role of technology in cultivating community autonomy, public participation, and local social interaction. The findings show how sociospatial dynamics guide the orchestration and reception of smart innovations for community engagement and empowerment, indicating what works and for whom. The empirical work provides a basis for future recommendations, signalling the limitations of digital innovation within communal life and reinforcing a human-centred approach to developing smart systems of community development.

Designing a digital-twin based dashboard system for a flexible assembly line

The current dashboard system is limited of coordination, flexibility and scalability. This study proposed a digital-twin based dashboard system. The digital twin components are divided into four, namely the operator, screw machine, industrial robot and automatic optical inspection in a flexible GPU card assembly line. This study designs a protocol enabling digital twins to interact and operate the system collaboratively and flexibly. In addition, this study presents a landscape of digital twin dashboards to demonstrate the relationship between the digital twins and the physical modules, providing a use case for the future digital-twin based dashboard design. The proposed system is implemented in a flexible GPU card assembly line and constructed by using JMobile to build a real-time dashboard system capable of scalability. The proposed dashboard framework for the GPU card assembly line offers several advantages over existing dashboards in terms of productivity, quality, and accident reduction. This study contributes to a scalable real-time dashboard system framework by incorporating human–machine collaboration, laying the foundation for advanced dashboard system development.

Bioelectronics for bitterness-based phytocompound detection using human bitter taste receptor nanodiscs

Various organisms produce several products to defend themselves from the environment and enemies. These natural products have pharmacological and biological activities and are used for therapeutic purposes, retaining bitter taste because of chemical defense mechanisms. Cnicin is a plant-derived bitter sesquiterpene lactone with pharmacological characteristics such as anti-bacterial, anti-myeloma, anti-cancer, anti-tumor, anti-oxidant, anti-inflammatory, allelopathic, and cytotoxic properties. Although many studies have focused on cnicin detection, they have limitations and novel cnicin-detecting strategies are required. In this study, we developed the bioelectronics for screening cnicin using its distinct taste. hTAS2R46 was produced using an Escherichia coli expression system and reconstituted into nanodiscs (NDs). The binding sites and energy between hTAS2R46 and cnicin were investigated using biosimulations. hTAS2R46–NDs were combined with a side-gated graphene micropatterned field-effect transistor (SGMFET) to construct hTAS2R46–NDs bioelectronics. The construction was examined by chemical and electrical characterization. The developed system exhibited unprecedented performance, 10 fM limit of detection, rapid response time (within 10 s), 0.1354 pM−1 equilibrium constant, and high selectivity. Furthermore, the system was stable as the sensing performance was maintained for 15 days. Therefore, the hTAS2R46-NDs bioelectronics can be utilized to screen cnicin from natural products and applied in the food and drug industries.

Hyperspectral imaging system for pre- and post-harvest defect detection in paprika fruit

External defects in paprika fruit (Capsicum annuum, L. var. angulosum) directly impact their storage and marketability, potentially leading to internal decay and altering fruit appearance. However, automatically detecting various paprika defects, including cracks, scars, diseases, and bruises, poses a significant challenge due to variations in defect size and visual similarities among different types of defects. This study proposed a comprehensive, accurate, and rapid defect detection method utilizing hyperspectral imaging (HSI) alongside multivariate analysis and imaging technologies. The reflective surface of fruit poses a primary limitation to efficient defect detection using imaging technologies. Therefore, a polarized HSI visible–near-infrared (VIS-NIR, 397–1000 nm) system was developed, and over 500 paprika fruit with various types of defects were imaged using the developed system. Additionally, fruit were manually bruised in different sizes to ensure spectral data variability and measured every 12 hours for two days. Spectral data (over 4580 spectral data) from the regions of interest (ROIs) of the paprika fruit samples, including manually bruised fruit, were extracted. Subsequently, a partial least squares discrimination analysis (PLS-DA) model was developed to distinguish between normal and defective fruit. Remarkably, the developed model achieved an accuracy of over 99 % in predicting normal and defective fruit. Moreover, the chemical images generated enhance the inspection process. The successive projection algorithm (SPA) and variable importance in projection (VIP) were further utilized for critical band selection, yielding an accuracy of 93 % and 98 % in the prediction set, respectively. Furthermore, a classification algorithm based on analysis of variance (ANOVA) was employed to determine the optimal wavelength band ratios (F-values). This approach yielded an impressive accuracy rate of over 88.8 % and further enhanced the performance of the band ratio by an improved watershed segmentation algorithm (IWSA) to reach an accuracy of 91.3 % in multi-defect detection. These findings offer a practical and efficient solution for the external quality inspection of paprika fruit, particularly in cases with naturally occurring defects. By integrating hyperspectral imaging, PLS-DA, band selection methods, ANOVA-based classification, and IWSA, this comprehensive analysis provides a reliable and practical means for identifying and categorizing defects in paprika fruit, ultimately enhancing product quality and consumer satisfaction.

Lowered GnT-I Activity Decreases Complex-Type N-Glycan Amounts and Results in an Aberrant Primary Motor Neuron Structure in the Spinal Cord.

The attachment of sugar to proteins and lipids is a basic modification needed for organismal survival, and perturbations in glycosylation cause severe developmental and neurological difficulties. Here, we investigated the neurological consequences of N-glycan populations in the spinal cord of Wt AB and mgat1b mutant zebrafish. Mutant fish have reduced N-acetylglucosaminyltransferase-I (GnT-I) activity as mgat1a remains intact. GnT-I converts oligomannose N-glycans to hybrid N-glycans, which is needed for complex N-glycan production. MALDI-TOF MS profiles identified N-glycans in the spinal cord for the first time and revealed reduced amounts of complex N-glycans in mutant fish, supporting a lesion in mgat1b. Further lectin blotting showed that oligomannose N-glycans were more prevalent in the spinal cord, skeletal muscle, heart, swim bladder, skin, and testis in mutant fish relative to WT AB, supporting lowered GnT- I activity in a global manner. Developmental delays were noted in hatching and in the swim bladder. Microscopic images of caudal primary (CaP) motor neurons of the spinal cord transiently expressing EGFP in mutant fish were abnormal with significant reductions in collateral branches. Further motor coordination skills were impaired in mutant fish. We conclude that identifying the neurological consequences of aberrant N-glycan processing will enhance our understanding of the role of complex N-glycans in development and nervous system health.

A PM2.5 pollution-level adaptive air filtration system based on elastic filters for reducing energy consumption

Exacerbated by human activities and natural events, air pollution poses severe health risks, requiring effective control measures to ensure healthy living environments. Traditional filtration systems that employ high-efficiency particulate air (HEPA) filters are capable of effectively removing particulate matter (PM) in indoor environments. However, these systems often work without considering the fluctuations in air pollution levels, leading to high energy consumption. This study proposed a novel PM2.5 pollution-level adaptive air filtration system that combined elastic thermoplastic polyurethane (TPU) filters and an Internet of Things (IoT) system. The developed system can effectively adjust its filtration performance (i.e., pressure drop and PM2.5 filtration efficiency) in response to real-time air quality conditions by mechanically altering the structures of TPU filters. Furthermore, while operating in varied pollution conditions, the proposed system demonstrated remarkable reductions in pressure drop without notably compromising the pollution control capability. Finally, the energy consumption of the pollution-level adaptive air filtration system was estimated when applied in mechanical ventilation systems in different cities (Hong Kong, Beijing, and Xi’an) with various pollution conditions. The results revealed that, compared to a traditional fixed system, the annual energy consumption could be reduced by up to ∼26.4 % in Hong Kong.

A Study on the Development of the Stainless Steel Tube Bundle Structure Detecting System Using Ultrasonic Guided Wave.

In this study, an ultrasonic guided wave system that can be used to detect broken tubes in stainless steel tube bundle structures (e.g., heat exchangers) with fairly narrow spacing between the tubes was designed. The interval between the tubes was 1.5 mm, and the thickness of the strip with a transducer that can be inspected by passing between the tubes was designed to be 1 mm. The damaged specimen was filled with water, and it was confirmed that the signal amplitude was smaller than that of the normal specimen filled with air. The ultrasonic properties of stainless steel were analyzed using the developed system, and it is expected that this will contribute to breakage inspection for tube bundles with narrow spacing.

Preferential territories in the spatial development of regional economic systems

Preferential territories in the spatial development of regional economic systems

Technology development and applications of high precision extreme ultraviolet and X-ray thin film optical systems

Technology development and applications of high precision extreme ultraviolet and X-ray thin film optical systems

River Communities, Disaster Science, and the Politics of Water Safety: Understanding Water Quality Debates in the Aftermath of the Gold King Mine Spill

ABSTRACT After environmental disasters, scientific claims about environmental risk face a paradox. Impacted communities turn to scientific bodies for safety information, yet scientific findings are often challenged publicly in post-disaster debates. This study explores this paradox after the 2015 Gold King Mine spill in the Animas River, USA. We ask how a river is declared “safe” after such a disaster, by whom, and based on what metrics. We examine how scientific claims about water safety were debated in newspapers in three locations: Silverton and Durango, Colorado, and the Navajo Nation. Drawing on Cash et al.’s [Knowledge systems for sustainable development. Proceedings of the National Academy of Sciences, 100(14), 8086–8091. https://do.org/10.1073/pnas.1231332100] key features of science for public decision-making, we find that local debates hinged on issues of salience, credibility, and legitimacy, but these criteria varied in importance across locations. We also argue that these criteria do not sufficiently explain river safety debates. Attention to regulatory and scientific standards used to interpret river safety is necessary for understanding spill responses. Clean Water Act (CWA) standards sidelined public concerns about waterways that fell outside CWA benchmarks. This study highlights the importance of scrutinizing the standards used to deem environments “safe” when producing and communicating disaster science. Key policy highlights Scientific communication after an environmental disaster should be salient, credible, and legitimate. In addition, the scientific and regulatory standards used to deem post-disaster environments “safe” must be scrutinized and their limitations acknowledged. Acknowledging and addressing community-specific concerns and histories – especially those of indigenous communities – is vital in disaster-recovery communication.