Search Results (22,948)

In response to the rising global energy dilemma and associated environmental concerns, research into creating less hazardous solar technology has exploded. Due to their cost-effective fabrication process and exceptional optoelectronic properties, perovskite-based solar cells have emerged as promising candidates. However, their commercialization faces obstacles, including lead contamination, interface recombination, and instability. This study examines CaV_0.5Fe_0.5O₃ (CVFO) as an alternative to lead-based perovskites, highlighting its improved stability and high efficiency through a series of simulation and modeling results. A record power conversion efficiency (PCE) of 23.28% was achieved (V_oc = 1.38 V, J_sc = 19.8 mA/cm², FF = 85.2%) using a 550 nm thick CaV_0.5Fe_0.5O₃ as an absorber. This was accomplished by optimizing the electron transport layer (ETL: TiO₂, 40 nm, 10²⁰ cm⁻³ doping) and the hole transport layer (HTL: Cu₂O, 50 nm, 10²⁰ cm⁻³ doping). Subsequently, it was established that defects at the ETL/perovskite interface significantly diminish performance relative to defects on the HTL side, and thermal stability assessments verified proper operation up to 350 K. To maintain efficiency, it is necessary to reduce series resistance (R_s < 1 Ω·cm²) and increase shunt resistance (R_sh > 10⁴ Ω·cm²). The findings indicate that CaV_0.5Fe_0.5O₃ serves as a feasible alternative to perovskites and has the potential to enhance the performance of scalable solar cells. Full article

The consequent-pole interior permanent-magnet (CPM) motor is a promising alternative for minimizing rare-earth magnet usage while supporting high-speed operation. However, rotor flux asymmetry often leads to distorted back-electromotive force waveforms and increased torque ripple. This study investigated a flared-type CPM motor that employs ferrite magnets arranged in a flared configuration to enhance flux concentration within a compact rotor. To address waveform distortion, structural modifications such as bridge removal and an asymmetric air-gap design were implemented. Three rotor parameters—polar angle, asymmetric air-gap length, and rotor opening length—were optimized using Latin hypercube sampling combined with an evolutionary algorithm. Finite element method analyses conducted under no-load and rated-load conditions showed that the optimized model achieved a 77.8% reduction in torque ripple, a 43.4% decrease in cogging torque, and a 0.5% improvement in efficiency compared with the basic model. Stress analyses were performed to examine the structural bonding strength and rotor deformation of the optimized model under high-speed operation. The results revealed a 5.5× safety margin at four times the rated speed. The proposed approach offers a cost-effective and sustainable alternative to rare-earth magnet machines for high-efficiency household appliances, where vibration reduction, cost stability, and energy efficiency are critical. Full article

A sensor network based on 10 stationary nodes distributed in Bari (Southern Italy) has been deployed for urban air quality (AQ) monitoring. The low-cost sensor systems have been installed in specific sites (e.g., buildings, offices, schools, streets, ports, and airports) to enhance environmental awareness of the citizens and to supplement the expensive official air-monitoring stations with cost-effective sensor nodes at high spatial and temporal resolution. Continuous measurements were performed by low-cost electrochemical gas sensors (CO, NO₂, O₃), optical particle counter (PM₁₀), and NDIR infrared sensor (CO₂), including micro-sensors for temperature and relative humidity. The sensors are operated to assess the performance during a campaign (July 2015–December 2017) of several months for citizen science in sustainable smart cities. Typical values of CO₂, measured by distributed nodes, varied from 312 to 494 ppm (2016), and from 371 to 527 ppm (2017), depending on seasonal micro-climate change and site-specific conditions. The results of the AQ-monitoring long-term campaign for selected sensor nodes are presented with a relative error of 26.2% (PM₁₀), 21.7% (O₃), 25.5% (NO₂), and 79.4% (CO). These interesting results suggest a partial compliance, excluding CO, with Data Quality Objectives (DQO) by the European Air Quality Directive (2008/50/EC) for Indicative (Informative) Measurements. Full article

Hybrid energy systems (HESs) have garnered significant interest in recent years because they combine many energy sources to enhance efficiency and dependability. This review article thoroughly examines the most effective design approaches and tactics for improving performance in hybrid energy systems through efficient energy management. The problem encompasses multiple aspects of HES design optimization, such as identifying the most efficient component sizes, choosing the most appropriate technology, and setting up the system. Furthermore, it involves implementing an energy management system (EMS) to optimize the system’s overall efficiency. Moreover, this article examines difficulties, current progress, and potential research prospects. A hybrid system, which integrates renewable sources with backup units, provides a cost-efficient, eco-friendly, and dependable energy supply and outperforms single-source systems in satisfying diverse load requirements. An essential factor in these hybrid systems is the precise evaluation of the ideal dimensions of the components to ensure that they sufficiently meet all the load requirements while minimizing both the initial investment and ongoing operating expenses. This study extensively examines suitable methods for determining the proper sizes, as the current body of literature describes. These methods can significantly enhance renewable energy systems’ economic feasibility and practicality, promoting their wider adoption. Full article

This study aims to help researchers quickly understand the latest research status of kiwifruit picking robots to expand their research ideas. The centralized picking of kiwifruit is confronted with challenges such as high labor intensity and labor shortage. A series of social issues including the decline in agricultural population and population aging have further increased the cost of its harvest. Therefore, intelligent picking robots replacing manual operations is an effective solution. This paper, through literature review and organization, analyzes and evaluates the performance characteristics of various current kiwifruit picking robots. It summarizes the key technologies of kiwifruit picking robots, from the aspects of robot vision systems, mechanical arms, and the end effector. At the same time, it conducts an in-depth analysis of the problems existing in automatic kiwifruit harvesting technology in modern agriculture. Finally, it is concluded that in the future, research should be carried out in aspects such as kiwifruit cluster recognition algorithms, picking efficiency, and damage cost and universality to enhance the operational performance and market promotion potential of kiwifruit picking robots. The significance of this review lies in addressing the imminent labor crisis in agricultural production and steering agriculture toward intelligent and precise transformation. Its contributions are reflected in greatly advancing robotic technology in complex agricultural settings, generating substantial technical achievements, injecting new vitality into related industries and academic fields, and ultimately delivering sustainable economic benefits and stable agricultural supply to society. Full article

Monitoring nitrate levels in water is critical to protect public health and ensure compliance with regulatory standards. This study provides a comprehensive evaluation of four analytical techniques—test strips, ion-selective electrodes (ISE), colorimetric methods, and titration—to assess nitrate levels in a variety of water sources, including standard solutions, rainwater, bottled water, and groundwater from both shallow and deep wells located in semi-urban regions of Saudi Arabia. Each method was assessed for sensitivity, accuracy, detection limits, reproducibility, and operational practicality. Test strips offer rapid, low-cost screening but consistently underestimate nitrate concentrations, particularly at low levels. The ISE demonstrated broad applicability and reliable performance across a wide concentration range when properly calibrated, making it suitable for both field and laboratory applications. Colorimetric methods provide excellent sensitivity for trace-level detection, whereas titration delivers the highest accuracy for high-nitrate samples despite its time-intensive nature. By calibrating and validating the methods against certified standards, we quantitatively demonstrated their reliability through statistical measures such as precision and accuracy rates. Moreover, the application of Geographic Information System (GIS) techniques in spatial analysis has revealed significant differences in the distribution of nitrates. Notably, shallow wells located in the northern regions surpass the 50 mg/L limit set by the World Health Organization (WHO), thereby indicating the presence of localized contamination hotspots. This study is among the first to systematically compare nitrate detection methods across a wide range of water types in a semi-urban area of Saudi Arabia. Building on a detailed analysis of each method, we underline the crucial need for the strategic selection of nitrate analysis techniques. This selection should be tailored to specific operational contexts, accuracy requirements, and concentration ranges to guide stakeholders towards more informed decision-making. These findings provide actionable guidance for public health officials and water managers to prioritize monitoring, safeguard drinking-water sources, and mitigate nitrate-related health risks in semi-urban communities. Full article

To address the challenges of inefficient path planning, discontinuous trajectories, and inadequate safety margins in autonomous spraying vehicles for greenhouse environments, this paper proposes a hierarchical motion control architecture. At the global path planning level, the heuristic function of the A* algorithm was redesigned to integrate channel width constraints, thereby optimizing node expansion efficiency. A continuous reference path is subsequently generated using a third-order Bézier curve. For local path planning, a state-space sampling method was adopted, incorporating a multi-objective cost function that accounts for collision distance, curvature change rate, and path deviation, enabling the real-time computation of optimal obstacle-avoidance trajectories. At the control level, an adaptive look-ahead distance pure pursuit algorithm was designed for trajectory tracking. The proposed framework was validated through a Simulink-ROS co-simulation environment and deployed on a Huawei MDC300F computing platform for real-world vehicle tests under various operating conditions. Experimental results demonstrated that compared with the baseline methods, the proposed approach improved the planning efficiency by 38.7%, reduced node expansion by 16.93%, shortened the average path length by 6.3%, and decreased the path curvature variation by 65.3%. The algorithm effectively supports dynamic obstacle avoidance, multi-vehicle coordination, and following behaviors in diverse scenarios, offering a robust solution for automation in facility agriculture. Full article

The emergence of mobile private networks (MPNs) has enabled tailored communication solutions for industries, enterprises, and specialized applications, fostering improved control, security, and flexibility. With the rapid advancements in software-defined radio (SDR) platforms, implementing MPNs using cost-effective and versatile hardware has become increasingly feasible. Analyzing the radio coverage of such networks is critical for optimizing performance, ensuring reliable connectivity, and addressing site-specific challenges in deployment. This paper investigates the radio coverage of a 4G MPN implemented using as radio front-end an SDR platform from the Universal Software Radio Peripheral (USRP) family and the srsRAN-4G open-source software suite. Using the HTZ Communication software as simulation tool and field-test measurements performed using an off-the-shelf mobile phone as user equipment (UE), an analysis is made to evaluate the accuracy of various propagation models in predicting network coverage, in several different frequency bands. The results provide valuable insights into the design and deployment of MPNs, highlighting the importance of accurate coverage estimation in achieving robust and efficient network operation. Full article

The development of compact, energy-efficient integrated lasers operating at 1.3 µm re-mains a critical focus in silicon photonics, essential for advancing data communications and optical interconnect technologies. This paper presents a numerical study of distributed Bragg reflector (DBR) hybrid III-V-on-silicon lasers, analyzing design trade-offs and optimization strategies based on supermode theory. The III-V section of the design incorporates InAs/(Al)GaAs quantum dots (QDs), which offer improved temperature insensitivity at the cost of more complex III-V/Si optical coupling, due to the high refractive index of (Al)GaAs. Consequently, many current laser designs rely on silicon waveguides with a thickness exceeding 220 nm, which helps coupling but limits their compatibility with standard CMOS technologies. To address this challenge, we perform detailed simulations focusing on 220-nm-thick silicon waveguides. We first examine how the mode profiles jointly depend on the silicon waveguide dimensions and the geometry and composition of the III-V stack. Based on this analysis, we propose a novel epitaxial design that enables effective III-V/Si coupling, with the optical mode strongly confined within the III-V waveguide in the gain section and efficiently transferred to the silicon waveguide in the passive sections. Moreover, the final design is shown to be robust to fabrication-induced deviations from nominal parameters. Full article

Over the last couple of decades, significant efforts have been made to develop structural health monitoring solutions. The growing need for the dynamic characterization of structures supports the implementation of condition assessments, maintenance, and monitoring strategies for existing and new civil engineering structures, and to provide increased safety for the public. Wireless monitoring systems are still being improved as the technology is finding a wider use for the monitoring of civil engineering structures, thanks to their easier installation and reduced costs when compared to the wired counterparts. In this context, this paper presents a new wireless network system for the dynamic characterization of civil engineering structures, whose distinguishing features comprise combining cutting-edge accelerometers, excellent signal synchronization, low battery consumption nodes, and a cloud-based framework to support the monitoring operations. The performance characteristics are validated through laboratory tests and are demonstrated on a newly constructed 211 m tall building. Full article

Accurate estimation of the state of health (SOH) of lithium-ion batteries is essential for the safe and efficient operation of electric vehicles (EVs). Conventional approaches, including Coulomb counting, electrochemical impedance spectroscopy, and equivalent circuit models, provide useful insights but face practical limitations such as error accumulation, high equipment requirements, and limited applicability across different conditions. These challenges have encouraged the use of machine learning (ML) methods, which can model nonlinear relationships and temporal degradation patterns directly from cycling data. This paper reviews four machine learning algorithms that are widely applied in SOH estimation: support vector regression (SVR), random forest (RF), convolutional neural networks (CNNs), and long short-term memory networks (LSTMs). Their methodologies, advantages, limitations, and recent extensions are discussed with reference to the existing literature. To complement the review, MATLAB-based simulations were carried out using the NASA Prognostics Center of Excellence (PCoE) dataset. Training was performed on three cells (B0006, B0007, B0018), and testing was conducted on an unseen cell (B0005) to evaluate cross-battery generalisation. The results show that the LSTM model achieved the highest accuracy (RMSE = 0.0146, MAE = 0.0118, R² = 0.980), followed by CNN and RF, both of which provided acceptable accuracy with errors below 2% SOH. SVR performed less effectively (RMSE = 0.0457, MAPE = 4.80%), reflecting its difficulty in capturing sequential dependencies. These outcomes are consistent with findings in the literature, indicating that deep learning models are better suited for modelling long-term battery degradation, while ensemble approaches such as RF remain competitive when supported by carefully engineered features. This review also identifies ongoing and future research directions, including the use of optimisation algorithms for hyperparameter tuning, transfer learning for adaptation across battery chemistries, and explainable AI to improve interpretability. Overall, LSTM and hybrid models that combine complementary methods (e.g., CNN-LSTM) show strong potential for deployment in battery management systems, where reliable SOH prediction is important for safety, cost reduction, and extending battery lifetime. Full article

The global reliance on fossil fuels has caused severe environmental challenges, emphasizing the urgent need for sustainable and renewable energy sources. Bioethanol production from lignocellulosic biomass has emerged as a promising alternative due to its abundance, renewability, and carbon-neutral footprint. However, its economic feasibility remains a major obstacle owing to high production costs, particularly those associated with low ethanol titers and the energy-intensive distillation process costs for low titers. High-solid loading processes (≥15% w/w or w/v) have demonstrated potential to overcome these limitations by minimizing water and solvent consumption, enhancing sugar concentrations, increasing ethanol titers, and lowering downstream processing cost. Nevertheless, high-solid loading also introduces operational bottlenecks, such as elevated viscosity, poor mixing, and limited mass and heat transfer, which hinder enzymatic hydrolysis efficiency. This review critically examines emerging pretreatment and enzymatic hydrolysis strategies tailored for high-solid loading conditions. It also explores techniques that improve sugar yields and conversion efficiency while addressing key technical barriers, including enzyme engineering, process integration, and optimization. By evaluating these challenges and potential mitigation strategies, this review provides actionable insights to intensify lignocellulosic ethanol production and advance the development of scalable, cost-effective biorefinery platforms. Full article

The automation of recycling processes for electric vehicle lithium-ion battery packs is crucial for the advancement of green energy transportation. Testing disassembly strategies on real equipment is time consuming, expensive, and poses significant safety risks. This paper presents a novel simulation-based framework that leverages the integration of a high-fidelity virtual environment with a Robot Operating System (ROS) to visualize and accurately calculate the time required for complex robotic disassembly operations. The calculated operation times are then used as input for genetic algorithm optimization to improve process efficiency. The results demonstrate that automation significantly improves the total speed of the disassembly process compared to manual methods. By utilizing this novel simulation and optimization approach, a 25% improvement in performance was achieved for the pack-to-module disassembly stage. This method provides a safe and cost-effective approach for process design, contributing directly to the development of a circular economy and supporting the transition towards sustainable transportation. Full article

Context: Population segmentation is a critical health system planning activity that enables more integrated, needs-responsive, and sustainable care. This paper describes the development and evaluation of a Systemic Health System Population Segmentation Model based on the person-centred and needs-based Unified Care Model by Yishun Health, a regional population health system in Singapore. We highlight three implications to enhance health systems operational relevance: (i) psychosocial factors as key determinants of outcomes, (ii) accountability and resource allocation across differentiated segments, and (iii) integration of lifelong and episodic care needs. Methods: Three interdependent models were developed, a Lifelong Care Segmentation Model, a Needs-Based Sub-Segmentation Model, and an Episodic Care Segmentation Model, all underpinned by the Unified Care Model. These models systematically stratify residents into mutually exclusive and collectively exhaustive population groups based on biopsychosocial needs across different health system levels. An expert-driven design process was used, supported by integrated administrative and clinical data. Model evaluation examined the ability to stratify patients into distinct risk groups using healthcare utilisation, costs, and readmission outcomes. Findings: In 2022, 78,810 residents were segmented into seven lifelong care segments, with 43,473 residents with chronic conditions further stratified into sub-segments reflecting varying complexity and psychosocial needs. Additionally, 14,335 emergency admissions were categorised into six episodic care segments. Healthcare utilisation and annual healthcare costs differed significantly across needs-based sub-segments (p < 0.001). Higher episodic care needs were associated with longer hospital stays, higher rates of emergency readmissions, and admission costs (p < 0.001). Psychosocial issues consistently emerged as a key determinant of poorer outcomes, underscoring implications for more systemic and systematic accountability assignment and more deliberate resource planning, especially for care integration horizontally. The integration of lifelong and episodic care needs further enabled operational redesign for vertically integrated health systems. Conclusions: By incorporating psychosocial drivers, focusing on clarifying accountability and resource allocation, and lifelong-episodic care integration, our Systemic Health System Population Segmentation Model strengthens the operational utility of segmentation as a foundation for population health system transformation and provided a robust framework for health systems governance and leadership system redesign globally. Full article

Additive manufacturing (AM) is rapidly transforming clinical workflows in dentistry by enabling the customized, efficient, and digitally integrated production of dental devices. However, the existing literature lacks a process-oriented perspective on its technical and operational impact. This study aims to address this gap through a dual-phase analysis using the Input–Transformation–Output (ITO) framework, providing practical insights into the operational reconfiguration enabled by AM. The first phase examined materials, image acquisition methods, design and lamination software, printing technologies, and key parameters across each stage of the AM workflow. The second phase analyzed four clinical applications (dental models, crowns and bridges, occlusal splints, and surgical guides) supported by a structured fabrication protocol and scanning electron microscopy (SEM) of 18 resin samples to assess surface quality and process-related defects. In addition, for each application, a comparative process analysis with traditional workflows was conducted using ASME diagramming. The findings indicate that AM reduces cycle times, manual intervention, and supply chain reliance while enabling production models such as Make-to-Order (MTO) and Engineer-to-Order (ETO). Its integration also fosters decentralized, in-clinic manufacturing with enhanced autonomy, flexibility, and reduced lead times. Nonetheless, this study highlights persisting challenges, including post-processing quality control, training requirements, and cost-efficiency concerns in low-volume settings. A hybrid model combining AM with conventional methods emerges as a pragmatic strategy for clinical adoption. Full article