Search Results (1,261)

Smart lighting is becoming a strategic component of intelligent and low-carbon urban infrastructure because it combines efficient illumination with connectivity, remote management, and continuous operational monitoring. This study proposes a reproducible unit-level methodological framework that transforms field records from a functional smart-lighting installation into traceable indicators of electrical performance, energy efficiency, avoided emissions, preliminary economic benefit, sensitivity, and conditional scalability. The approach treats the luminaire not only as an electrical load, but as a monitored urban energy node whose operation can be validated, characterized, and compared under planning-oriented control scenarios. The methodology integrates data preprocessing, electrical consistency assessment, representative baseline definition, scenario-based energy modeling, explicit environmental conversion, and conditional scaling to homogeneous lighting assets. The results reveal a stable electrical operating regime and show that managed operating conditions can generate sustained reductions in energy use and associated environmental impacts while preserving analytical transparency between measured variables and scenario-derived indicators. Sensitivity and multivariable analyses further support the robustness of the unit-level interpretation and highlight the value of monitored lighting data for comparative decision-making. The framework therefore provides a technically grounded reference for smart-city lighting management, energy planning, and scalable infrastructure assessment, with relevance to the objectives of SDG 7, SDG 11, and SDG 13. Overall, the study contributes an original data-driven perspective for integrating IoT-enabled lighting, remote supervision, and sustainability-oriented urban management within a common analytical structure. Full article

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

Effective weed management is critical to sustaining rice productivity and profitability, particularly under conservation-oriented production systems, where weed dynamics may differ from conventional puddled cultivation. A two-year field experiment was conducted to evaluate weed management practices under puddled transplanted rice (PTR) and zero-till non-puddled transplanted rice (ZT NPTR) production systems during the boro seasons of 2020 and 2021. Six weed management practices, comprising season-long weed-free, season-long weedy, Pretilachlor, Acetachlor (14%) + Bensulfuron methyl (4%), Pyrazosulfuron-ethyl, and Penoxsulam treatments, were evaluated. Weed density and dry biomass were significantly influenced by production system and weed management practice. Compared with 2020, weed density in 2021 decreased by approximately 74% at both 30 and 60 days after transplanting (DAT) under PTR and by 91% and 86%, respectively, under ZT NPTR. Similarly, weed dry biomass decreased by 90% and 86% under PTR and by 81% and 77% under ZT NPTR at 30 and 60 DAT, respectively. Pyrazosulfuron-ethyl and Penoxsulam achieved 100% weed control efficiency, comparable to the season-long weed-free treatment, whereas Pretilachlor, and Acetachlor (14%) + Bensulfuron methyl (4%) provided comparatively lower weed suppression. The ZT NPTR system increased grain yield by approximately 5% in 2020 and 35% in 2021 relative to the PTR system. Among weed management practices, Penoxsulam produced the highest or a statistically comparable grain yield and increased yield by approximately 105% and 80% over the season-long weedy treatment in 2020 and 2021, respectively. Economic analysis showed that Pyrazosulfuron-ethyl was the most profitable treatment under PTR in both years and under ZT NPTR in 2021, whereas Penoxsulam provided the greatest economic return under ZT NPTR in 2020. Overall, effective post-emergence weed management with Pyrazosulfuron-ethyl or Penoxsulam within the ZT NPTR production system provided effective weed suppression, high rice productivity, and favourable economic returns. However, because pre-plant glyphosate application was an integral component of the ZT NPTR system, the differences between the PTR and ZT NPTR systems should be interpreted as responses to the overall production systems rather than to tillage alone. Full article

Background/Objectives: Transcranial magnetic stimulation (TMS) initiates a cascade from intracranial electric-field exposure through neural recruitment and plasticity to distributed network responses. Neuronavigation improves the geometric reproducibility of delivery but does not guarantee equivalent cortical exposure or target engagement. This narrative review integrates these levels within an operational framework for precision TMS. Methods: Six domain-specific PubMed searches covering 1 January 1985 to 31 July 2026 were supplemented by Google Scholar and citation tracking. A documented rerun on 17 August 2026 yielded 6430 records (5617 unique after cross-query deduplication). Evidence was synthesized narratively; no quantitative synthesis or formal risk-of-bias assessment was performed. Results: Neuronavigation improves geometric precision by stabilizing target definition and coil pose, whereas individualized electric-field models estimate intracranial exposure. Neither establishes biological precision, which also depends on neuronal orientation, brain state, circuit architecture, medication, and behavior. Motor-system measures are not validated as universal biomarkers for nonmotor cortex, and no single validated biomarker captures TMS-induced plasticity. Convergent, controlled multimodal evidence may strengthen inference about target engagement; adaptive and closed-loop approaches remain experimental. Conclusions: Geometric delivery, modeled exposure, biological engagement, and durable functional or clinical benefit require separate validation. Spatial accuracy alone does not establish clinical value. Full article

Smart-environment cybersecurity increasingly depends on experimental platforms that can reproduce attacks against buildings, homes, and cities under realistic conditions. However, the literature remains fragmented across testbed design, attack demonstration, and defensive validation. This makes it particularly difficult to judge what kind of security evidence each study actually provides. This review systematically analyses 28 experimentally grounded studies published from 2020 onwards, focusing on how testbed realism, cyber–physical coupling, and evaluation mode shape the strength of the resulting claims. The corpus spans physical, hybrid, emulated, and dataset-driven environments across smart buildings, smart homes, and smart cities. Through our investigation, we discern a clear asymmetry in the field. Detection-oriented studies dominate, especially those based on emulation or public datasets, while live evidence for prevention, response, containment, and recovery is comparatively scarce. Availability and integrity/control attacks are the most frequently exercised, whereas authentication compromise and software exploitation remain rare because they are harder to stage on real hardware. Moreover, an important observation we arrive at is that physical and hardware-in-the-loop platforms support the strongest cyber–physical evidence, but emulated and replayed environments remain valuable for scale and reproducibility. At the same time, public datasets and offline classification results do not by themselves establish operational resilience in a live smart environment. To make these distinctions explicit, we introduce a cross-domain taxonomy of testbed architectures, attack families, and defensive control coverage, and map the evidence strength of reported mitigations using NIST cybersecurity framework-derived operational functions. Last, we identify open challenges, including weak recovery evaluation, limited reuse of reference testbeds, and the need for live, context-aware datasets, outlining promising future directions. Full article

Conventional heavy metal remediation technologies are constrained by low efficiency, secondary pollution risks, and limited scalability. Dissolved organic matter (DOM), with its green, cost-effective complexation properties, has become a promising pathway for pollution control. Existing patent bibliometric studies in this field suffer from single-database bias, limited causal quantification of policy impacts, and incomplete depiction of global–local technological heterogeneity. To address these gaps, this study maps the technological innovation landscape of DOM interactions with four typical heavy metals (Cd, Pb, Cu, Zn) during 2004–2024, using a complementary dual-database framework combining Derwent and IncoPat. We integrate a three-dimensional “time–region–technology” analytical framework with interrupted time series analysis (ITSA), after standardized data processing including family deduplication and citation normalization. Cross-validation confirms that China contributes the largest share of global patent output (46.6% in Derwent, 55.0% in IncoPat). Three milestone environmental policies in China exert sequentially intensifying causal effects on patent growth (all p < 0.05), forming a closed-loop mechanism of policy orientation, funding support, technology transfer, and international diffusion. We identify a pronounced global–local technological divergence: global frontier innovation centers on digital basic research, whereas local innovation in China prioritizes engineering applications. Core patents advance the field through cross-domain technology adaptation, and the representative technical paradigm (exemplified by patent CN101168852A) has been industrially validated. These findings provide empirical support for engineering translation and policy optimization in DOM-based heavy metal remediation. Full article

Grain boundaries (GBs) are important recombination-active defects in polycrystalline and multicrystalline silicon solar cells, but the effects of their electrical activity, geometry, and spatial arrangement are often difficult to separate. In this work, two-dimensional (2D) drift–diffusion simulations are used to investigate how GB trap density, carrier capture cross-section, orientation, length, number, and network geometry affect silicon solar-cell performance. A controlled comparison between rotating GBs whose length changes with angle and fixed-length GBs shows that the strong apparent orientation dependence is dominated by the accompanying variation in active GB length. When the GB length is fixed at 100 μm, the variations in short-circuit current density ($J_{\mathrm{sc}}$), open-circuit voltage ($V_{\mathrm{oc}}$), efficiency, and fill factor are comparatively small. As a second contribution, irregular polycrystalline microstructures are generated by Voronoi tessellation, producing distributions of grain sizes, shapes, boundary lengths, and junctions that are more representative than simplified structures based on isolated or regularly spaced boundaries. These networks are used to connect grain size, total electrically active GB length, recombination, local electric fields, carrier-flow redistribution, and device performance. As the characteristic grain size increases from 5 to 100 μm, $J_{\mathrm{sc}}$ rises from 15 to $34\mathrm{mA}{\mathrm{cm}}^{-2}$, $V_{\mathrm{oc}}$ from $0.54$ to above $0.61$ V, and the power conversion efficiency from $6.5\%$ to $17\%$. GB-induced photovoltaic loss is therefore governed not by GB number or nominal orientation alone, but by the combined effects of electrical activity, total active boundary length, and network geometry. Full article

Renewable energy and energy storage stations require frequent updates of monitoring and control logic across heterogeneous devices and changing operating strategies. This paper proposes a rule graph-based reference architecture that combines low-code logic configuration, graph–model semantic binding, and microservice-oriented functional decomposition. A component status matrix separates the target architecture from the implemented subset. The runnable subset comprises a minimal FastAPI backend, REST/WebSocket telemetry interfaces, an in-process queue, and stateful rule evaluators; gateway, authentication, external message bus, time-series database, visual editor, and industrial protocol services remain design-level elements. Beyond the original single-rule example, a priority-ordered multi-device rule is implemented for cooperative BESS dispatch, communication/topology blocking, low-SOC protection, frequency-based load shedding, backup request, and five-sample recovery release. Existing local network benchmarks are complemented by a 600-step software-in-the-loop trace with scripted telemetry fluctuations and communication quality faults and by 500 in-process ASGI timing samples at each of the four point levels. The trace produced no safety dispatch or protected device violations. P99 application path latency ranged from 1.1962 to 5.5287 ms, but one 75.3065 ms outlier exceeded a 50 ms reference deadline, demonstrating that the Windows/FastAPI path is not deterministic. No industrial controller, hardware-in-the-loop facility, field data, or engineer usability study was used. Accordingly, the paper makes no claim of industrial real-time readiness or measured development effort reduction. Full article

Electric motorcycles are central to Sub-Saharan Africa’s transition to electric mobility, yet manufacturers in the region typically rely on costly and time-consuming physical prototyping to optimise powertrains built from imported components. This paper presents a validated full-powertrain digital twin of the Roam Air, an electric motorcycle assembled in Nairobi, Kenya, developed in MATLAB/Simulink as four interconnected subsystems: the battery, the controller, the motor, and the vehicle dynamics. The battery is modelled as a Thévenin equivalent circuit whose parameters were experimentally derived at the pack level through Hybrid Pulse Power Characterisation tests, and the controller replicates the motorcycle’s field-oriented control with a maximum torque per ampere strategy, including its battery current and voltage limiting behaviour. The motorcycle’s regenerative braking characteristics, drag coefficient, and rolling resistance coefficient were experimentally obtained through braking, coasting, and coast-down tests. The digital twin ingests rider inputs and environmental information, and it predicts the motor’s speed and the battery’s power. Validation against six measured drive cycles in Stellenbosch, South Africa, demonstrates high correlation between predicted and measured profiles, with Pearson’s r values of 0.905–0.981 for battery power and 0.912–0.996 for motor speed, and energy consumption predicted to within 2.71% for five of the six trips. The presented modelling and characterisation framework offers manufacturers a transferable, computationally efficient alternative to iterative physical prototyping for powertrain optimisation. Full article

Diamond has emerged as a promising ultrawide-bandgap semiconductor material for next-generation electronics because of its unique combination of a wide bandgap, high critical electric field, superior carrier transport properties, exceptionally high thermal conductivity, and strong chemical and radiation stability. Over the past two decades, progress in crystal growth, substrate engineering, surface control, dielectric integration, and device fabrication has advanced diamond electronics beyond early proof-of-concept demonstrations. The review connects material properties, growth, doping, defects, and figures of merit with reported performance in hydrogen-terminated field-effect transistors, MOSFETs, Schottky and p–i–n diodes, and related power-device architectures. Emerging opportunities in ultraviolet photodetectors, multifunctional electronics, and memory-oriented diamond devices are also briefly considered. Among the device classes reviewed, diamond diodes currently show the strongest evidence of high-voltage capability, whereas transistor development remains constrained by threshold-voltage control, normally off operation, contact resistance, interface stability, and reliability. Diamond is therefore more likely to complement than replace established SiC and GaN technologies, particularly in specialized high-field, high-temperature, radiation-rich, and chemically demanding applications. Broader deployment will require scalable low-defect wafers, reliable n-type doping, stable interfaces and contacts, and more cost-effective manufacturing. Full article

The anisotropic mechanical behavior of laser powder bed fused (LPBF) IN718 results from the combined effects of crystallographic texture and grain morphology, although their individual contributions remain difficult to quantify. In this work, six representative volume elements (RVEs) are systematically compared using a unified EBSD–Dream3D–DAMASK crystal plasticity framework to separate the effects of texture and morphology. The microstructures include two EBSD-derived RVEs, two discretized columnar RVEs, and two discretized lamellar RVEs generated from identical orientation distributions. Predicted elastic moduli and yield strengths are validated against experiment, while Taylor factor analysis, directional effective grain size, slip compatibility, KAM, and local crystal plasticity fields are used to identify the governing deformation mechanisms. The results show that crystallographic texture predominantly controls the elastic response, whereas grain morphology governs the onset of plastic deformation. Lamellar RVEs provide the closest agreement with the experimental yield-strength anisotropy by reproducing the directional effective grain size, the connectivity of mechanically hard domains, and the resulting redistribution of stress and plastic strain. Furthermore, texture discretization preserves the dominant anisotropic trends while substantially reducing the computational cost of full EBSD reconstructions, establishing an efficient and physically meaningful framework for crystal plasticity simulations of LPBF materials. Full article

Reliable traffic speed forecasting depends on trustworthy historical road-sensor observations, yet deployed sensors may exhibit missing values, outages, noise, calibration drift, and stuck readings. Existing forecasting models are commonly evaluated on cleaned inputs, whereas conventional imputation optimizes historical reconstruction rather than downstream prediction. This study presents the Sensor-Reliability-Aware Framework with Identity-Preserved Design (SRAF-ID), a prediction-oriented speed-channel repair front-end trained end to end using only future forecasting loss. The final model requires no controlled fault-location labels during training or inference. SRAF-ID constructs same-sensor temporal and mask-aware graph-neighborhood candidates, combines them through learned two-way softmax fusion, and preserves node-identity and temporal-context features. On raw-time-disjoint 70%/10%/20% splits of the Metropolitan Los Angeles (METR-LA) and California Performance Measurement System Bay Area (PEMS-BAY) datasets, ten-seed matched stress tests cover six window-level controlled perturbations. SRAF-ID reduces faulty-average mean absolute error from 5.12 to 4.82 on METR-LA and from 1.99 to 1.94 on PEMS-BAY, corresponding to relative reductions of 5.7% and 2.4%, respectively. It achieves a lower mean MAE in all 12 dataset-fault comparisons and a lower faulty-average MAE in all ten seeds on both datasets; the clean-input MAE also decreases. Checkpoint-only tests retain positive all-sensor and affected-sensor mean gains in all eight localized dataset-condition pairs, whereas unseen 0.75-standard-deviation global drift produces small adverse means with paired intervals crossing zero. The evidence therefore supports fault-label-free robustness under the defined stress protocols while leaving field-recorded event continuity and fault frequency for external validation. Full article

Full-field water saturation is central to waterflood surveillance but cannot be observed continuously across a reservoir, whereas well histories provide sparse dynamic evidence. We formulate target-time saturation reconstruction as a mapping from static geology, well locations, scheduled controls, simulated multi-well production responses, and development time to a two-dimensional saturation field. The water-balance-guided saturation network (WB-SatNet) combines a U-Net spatial pathway, a fixed-order gated recurrent unit (GRU) history encoder, explicit time conditioning, and a closed-boundary water-storage consistency term. Experiments used 400 geological realizations, 800 simulation runs, six target times, realization-wise train/validation/test splitting, and three random seeds. On the held-out test set, WB-SatNet achieved a mean absolute error (MAE) of 0.01175, a coefficient of determination (${\mathrm{R}}^2$) of 0.98145, a structural similarity index measure (SSIM) of 0.99177, a flooded-area intersection over union (IoU) of 0.95523, and a global storage-consistency error of 0.00944. Its mean MAE was 6.31% lower than that of TCN-U-Net, the strongest temporal convolutional network baseline. Target-time, component-ablation, flooded-area, storage-consistency, history-window, noise, and operating-regime analyses support a monitoring-oriented interpretation. These results indicate that WB-SatNet provides an effective framework for production-history-conditioned water-saturation reconstruction and waterflood state monitoring in the investigated setting. Full article

Urban parks are essential components of green infrastructure that support residents’ physical health and well-being. However, less attention has been paid to how women can secure such benefits under equitable, safe, and sustainable park-use conditions. Using Xi’an Expo Park as a case study, this study examined the associations among Inclusive Urban Park Design (IUPD), Place Attachment (PA), Environmental Experience (EE), and Women’s Well-Being (WWB), with a focus on whether PA showed an indirect association between IUPD and WWB. This study adopted a quantitative-dominant mixed-methods design, combining 220 valid questionnaires (115 female and 105 male participants) with field observations and semi-structured interviews. Data were analyzed using correlation analysis, gender-comparison tests, mediation models, and robustness checks. Among female participants, the baseline model showed a statistically significant indirect association between IUPD and WWB through PA; however, this association was not retained after controlling for EE, nor in the fully adjusted model including EE, age, and visit frequency. The gender-comparison and qualitative findings suggested that similar levels of PA and WWB did not necessarily reflect equal conditions of park use. Qualitative evidence further indicated that some women’s park use was shaped by childcare, family activities, and other task-oriented responsibilities, which may involve hidden time, physical, and psychological costs. The subdimension-level findings suggested that perceived safety, accessibility and spatial legibility, and facility support and maintenance should be understood as interrelated use conditions rather than distinct independent effects. This study extends urban park equity research beyond spatial provision by emphasizing actual conditions of use and the equitable realization of well-being benefits. The findings provide implications for gender-responsive park design and social sustainability, while their relevance to everyday resilience should be understood as a conceptual extension rather than as direct evidence of community resilience. Full article

Yellow River irrigation districts must maintain food production under variable inflows, rigid diversion quotas, sedimentation, groundwater depletion, and soil salinization. This systematic review synthesized 79 journal articles from Web of Science and CNKI to clarify how resilience can be assessed and managed at the irrigation-district scale. The evidence indicates that resilience is a time-dependent combination of resistance, recovery, adaptability, and transformability within a coupled water source–canal–field–drainage–ecology–institution system. Although composite indices and hydrological–crop models have advanced, three gaps remain: operational thresholds rarely connect indicators to failure and recovery; farmer and institutional feedbacks are weakly represented; and assessments seldom translate into executable schedules. We, therefore, propose an irrigation-district-specific framework that couples water, sediment, salt, crops, ecology, and governance across basin–district–field scales without transferring risk between scales. Management priorities differ spatially: upstream districts require coordinated water–salt control; middle-reach well–canal systems require surface-water substitution and groundwater recovery; and downstream diversion districts require multi-source allocation and adaptive intake. A digital twin-based closed loop—continuous monitoring, forecasting, optimization, operational commands, and feedback correction—can translate diagnosis into canal rotation, recharge, drainage, and emergency actions. This review provides operational indicators and a decision-oriented research agenda for resilient irrigation modernization. Full article