Search Results (8,466)

The operational reliability of Insulated Gate Bipolar Transistor (IGBT) modules in demanding transportation applications, such as traction systems, is critically challenged by solder layer and bond wire failures under cyclic thermal stress. To address this, this paper proposes a novel health monitoring framework that innovatively synergizes micro-scale spatial thermal analysis with microsecond electrical dynamics inversion. The method requires only non-invasive temperature measurements on the module baseplate and utilizes standard electrical signals (load current, duty cycle, switching frequency, DC-link voltage) readily available from the converter’s controller, enabling simultaneous diagnosis without dedicated voltage or high-bandwidth current sensors. First, a non-invasive assessment of solder layer fatigue is achieved by correlating the normalized thermal gradient ($\nabla T_{\mathrm{P}}$) on the baseplate with the underlying thermal impedance ($Z_{\mathrm{JC}}$). Second, for bond wire aging, a cost-effective inversion algorithm estimates the on-state voltage ($V_{\mathrm{ce},\mathrm{on}}$) by calculating the total power loss from temperature, isolating the conduction loss ($P_{\mathrm{cond}}$) with the aid of a Foster-model-based junction temperature ($T_{\mathrm{J}}$) estimate, and finally computing $V_{\mathrm{ce},\mathrm{on}}$ at a unique current inflection point ($I_{\mathrm{C},\inf }$) to nullify $T_{\mathrm{J}}$ dependency. Third, the health states from both failure modes are fused for comprehensive condition evaluation. Experimental validation confirms the method’s accuracy in tracking both degradation modes. This work provides a practical and economical solution for online IGBT condition monitoring, enhancing the predictive maintenance and operational safety of transportation electrification systems. Full article

Operational expenditure in wind farms is heavily influenced by unplanned maintenance, much of which stems from undetected rotor system faults. Although many fault-detection methods have been proposed, most remain confined to laboratory test. Blade-root bending-moment measurements are among the few techniques applied in the field, yet their reliability is limited by strong sensitivity to varying operational and environmental conditions. This study presents a data-driven rotor health-monitoring framework that enhances the diagnostic value of blade bending-moments. Assuming that the wind speed profile remains approximately stationary over short intervals (e.g., 20 s), a machine-learning model is trained on bending-moment data from healthy blades to predict the incident wind-speed profile under a wide range of conditions. During operation, real-time bending-moment signals from each blade are independently processed by the trained model. A healthy rotor yields consistent wind-speed profile predictions across all three blades, whereas deviations for an individual blade indicate rotor asymmetry. In this study, the methodology is verified using high-fidelity OpenFAST simulations with controlled blade pitch misalignment as a representative fault case, providing simulation-based verification of the proposed framework. Results demonstrate that the proposed inverse-modeling and cross-blade consistency framework enables sensitive and robust detection and localization of pitch-related rotor faults. While only pitch misalignment is explicitly investigated here, the approach is inherently applicable to other rotor asymmetry mechanisms such as mass imbalance or aerodynamic degradation, supporting reliable condition monitoring and earlier maintenance interventions. Using OpenFAST simulations, the proposed framework reconstructs height-resolved wind profiles with RMSE below 0.15 m/s (R² > 0.997) under healthy conditions, and achieves up to 100% detection accuracy for moderate-to-severe pitch misalignment faults. Full article

As a cornerstone of China’s energy infrastructure, the coal mining industry relies heavily on the stability of its underground roadways, where the support of soft rock formations presents a critical and persistent technological challenge. This challenge arises primarily from the high content of expansive clay minerals and well-developed micro-fractures within soft rock, which collectively undermine the effectiveness of conventional support methods. To address the soft rock control problem in China’s Longdong Mining Area, an integrated material–structure control approach is developed and validated in this study. Based on the engineering context of the 3205 material gateway in Xin’an Coal Mine, the research employs a combined methodology of micro-mesoscopic characterization (SEM, XRD), theoretical analysis, and field testing. The results identify the intrinsic instability mechanism, which stems from micron-scale fractures (0.89–20.41 μm) and a high clay mineral content (kaolinite and illite totaling 58.1%) that promote water infiltration, swelling, and strength degradation. In response, a novel synergistic technology was developed, featuring a high-performance grouting material modified with redispersible latex powder and a tiered thick anchoring system. This technology achieves microscale fracture sealing and self-stress cementation while constructing a continuous macroscopic load-bearing structure. Field verification confirms its superior performance: roof subsidence and rib convergence in the test section were reduced to approximately 10 mm and 52 mm, respectively, with grouting effectively sealing fractures to depths of 1.71–3.92 m, as validated by multi-parameter monitoring. By integrating microscale material modification with macroscale structural optimization, this study provides a systematic and replicable solution for enhancing the stability of soft rock roadways under demanding geo-environmental conditions. Soft rock roadways, due to their characteristics of being rich in expansive clay minerals and having well-developed microfractures, make traditional support difficult to ensure roadway stability, so there is an urgent need to develop new active control technologies. This paper takes the 3205 Material Drift in Xin’an Coal Mine as the engineering background and adopts an integrated method combining micro-mesoscopic experiments, theoretical analysis, and field tests. The soft rock instability mechanism is revealed through micro-mesoscopic experiments; a high-performance grouting material added with redispersible latex powder is developed, and a “material–structure” synergistic tiered thick anchoring reinforced load-bearing technology is proposed; the technical effectiveness is verified through roadway surface displacement monitoring, anchor cable axial force monitoring, and borehole televiewer. The study found that micron-scale fractures of 0.89–20.41 μm develop inside the soft rock, and the total content of kaolinite and illite reaches 58.1%, which is the intrinsic root cause of macroscopic instability. In the test area of the new support scheme, the roof subsidence is about 10 mm and the rib convergence is about 52 mm, which are significantly reduced compared with traditional support; grouting effectively seals rock mass fractures in the range of 1.71–3.92 m. This synergistic control technology achieves systematic control from micro-mesoscopic improvement to macroscopic stability by actively modifying the surrounding rock and optimizing the support structure, significantly improving the stability of soft rock roadways. Full article

This article introduces the design and implementation of an Unmanned Surface Vehicle (USV), named “AquaMIB”, which introduces a novel and integrated approach for real-time and autonomous water quality monitoring in aquatic environments. The system integrates modular hardware and software, combining sensors for temperature, pH, conductivity, dissolved oxygen, and oxidation reduction potential with GPS, LiDAR, a digital compass, communication modules, and a dedicated power unit. Software components include Python on a Raspberry Pi for navigation and control, C on an Atmega 324P for sensing, C++ on an Arduino Uno for remote control, and C#/JavaScript for the web-based control center. Users assign task points, and the USV autonomously navigates, collects data, and transmits it via RESTful API. Field trials showed 96.5% navigation accuracy over 2.2 km, with 66% of task points reached within 3 m. A total of 120 measurements were processed in real time and visualized as GIS-based spatial maps. The system demonstrates a cost-effective, modular solution for aquatic monitoring. The system’s ability to generate real-time GIS maps enables immediate identification of environmental anomalies, transforming raw sensor data into an actionable decision-support tool for aquatic management. Full article

The present study describes the ten-year (2014–2024) validation of a Class 100,000ISO 8 qualified air system used in the manufacture of non-sterile pharmaceutical dosage forms in a GMP-certified facility. The lifecycle evaluation included design, installation, qualification, continuous operation, environmental monitoring, cleaning and disinfection verification, and annual third-party validation. The system was assessed for critical parameters, including air renewal rates, airflow directionality, the integrity of high-efficiency particulate air (HEPA) filters and ultra-low penetration air (ULPA) filters, environmental recovery times, and non-viable particle counts. Particle monitoring focused on 0.5 $\mathsf{\mu }$m and 1.0 $\mathsf{\mu }$m channels within the 0.5–5 $\mathsf{\mu }$m range specified by ISO 14644-1 for ISO 8 areas. The 0.5–1.0 $\mathsf{\mu }$m range was prioritized because it provides higher statistical representativeness for evaluating filter performance and controlling fine particulate dispersion, which is particularly relevant in non-sterile pharmaceutical production, while larger particles (>5 $\mathsf{\mu }$m) are more critical in aseptic processes. The influence of personnel and air exchange rates on cleanliness was also assessed during the final years of the study. Results demonstrate that continuous, systematic validation ensures the controlled environmental conditions required for pharmaceutical production and supports the sustained quality and safety of the finished products. This study provides a technical reference for engineers, pharmacists, and quality professionals involved in cleanroom design, qualification, and regulatory compliance. Full article

High-precision non-contact online voltage monitoring has attracted considerable attention due to its improved safety. Based upon existing research works and validation of non-contact voltage measurement techniques, an enhanced approach for online voltage monitoring is proposed in this paper. By analyzing the influence of the relationship between coupling capacitance and input capacitance on monitoring results, an RC-type signal input circuit with enhanced adaptability has been designed for practical engineering scenarios that may involve large input capacitance. Furthermore, a mixed-signal measurement method based on phase dithering is proposed to eliminate detection errors caused by relative phase drift during synchronous sampling in existing signal injection approaches. This improvement enhances measurement accuracy and offers a more robust theoretical basis for selecting injection signal frequencies. The hardware circuit architecture and data processing scheme presented in this work are straightforward and have been validated using an experimental prototype tested at 50 Hz/500 V and 2000 Hz/300 V. Long-term energized testing demonstrates that the system operates stably at room temperature with a relative measurement error below 0.5%. This study provides a high-precision, easily implementable non-contact measurement solution for online monitoring of low-frequency, low-voltage signals in complex electromagnetic environments such as industrial control signals, low-voltage power signals, and rail transit signals. Full article

The past decade has witnessed an unprecedented transformation in breast cancer management, driven by parallel advances in targeted therapies, immunomodulation, drug-delivery technologies, and molecular diagnostic tools. This review summarizes the key achievements of 2015–2025, encompassing all major biological subtypes of breast cancer as well as technological innovations with substantial clinical relevance. In hormone receptor-positive (HR+)/HER2− disease, the integration of CDK4/6 inhibitors, modulators of the PI3K/AKT/mTOR pathway, oral Selective Estrogen Receptor Degraders (SERDs), and real-time monitoring of Estrogen Receptor 1 (ESR1) mutations has enabled clinicians to overcome endocrine resistance and dynamically tailor treatment based on evolving molecular alterations detected in circulating biomarkers. In HER2-positive breast cancer, treatment paradigms have been revolutionized by next-generation antibody–drug conjugates, advanced antibody formats, and technologies facilitating drug penetration across the blood–brain barrier, collectively improving systemic and central nervous system disease control. The most rapid progress has occurred in triple-negative breast cancer (TNBC), where synergistic strategies combining selective cytotoxicity via Antibody-Drug Conjugates (ADCs), DNA damage response inhibitors, immunotherapy, epigenetic modulation, and therapies targeting immunometabolic pathways have markedly expanded therapeutic opportunities for this historically challenging subtype. In parallel, photodynamic therapy has emerged as an investigational and predominantly local phototheranostic approach, incorporating nanocarriers, next-generation photosensitizers, and photoimmunotherapy capable of inducing immunogenic cell death and modulating antitumor immune responses. A defining feature of the past decade has been the surge in patent-driven innovation, encompassing multispecific antibodies, optimized ADC architectures, novel linker–payload designs, and advanced nanotechnological and photoactive delivery systems. By integrating data from clinical trials, molecular analyses, and patent landscapes, this review illustrates how multimechanistic, biomarker-guided therapies supported by advanced drug-delivery technologies are redefining contemporary precision oncology in breast cancer. The emerging therapeutic paradigm underscores the convergence of targeted therapy, immunomodulation, synthetic lethality, and localized immune-activating approaches, charting a path toward further personalization of treatment in the years ahead. Full article

Background: Cellulite is a highly prevalent condition with dermal and subcutaneous alterations poorly captured by visual grading systems. Ultrasound has emerged as a non-invasive imaging modality capable of objectively quantifying morphological features relevant to cellulite. This systematic review evaluated the evidence on ultrasound for the diagnosis, structural characterization, and treatment monitoring of cellulite, identifying methodological limitations and research gaps. Methods: This systematic review (PROSPERO:CRD420251185486) followed the PRISMA statement. Searches were conducted in PubMed, Scopus, and CENTRAL up to November 2025. Risk of bias was evaluated using ROBINS-I and the Newcastle–Ottawa Scale. Results: Nine studies involving 785 participants were included. Ultrasound frequencies ranged from 12 to 35 MHz, with some scanners operating across broader bandwidths. Despite variability in devices, acquisition protocols, and clinical comparators, all studies consistently demonstrated that ultrasound quantifies key structural characteristics of cellulite. Diagnostic investigations reported moderate-to-strong correlations (r ≈ 0.31–0.64) between ultrasound-derived measures and clinical severity scores. Interventional studies showed measurable reductions in dermal and subcutaneous thickness, decreased adipose protrusion height, and improved dermal echogenicity across multiple treatment modalities. Ultrasound frequently detected microstructural remodeling not readily visible on clinical examination. Conclusions: Ultrasound is a valuable imaging modality for objectively characterizing cellulite and monitoring treatment-induced tissue remodeling. Standardized acquisition protocols, validated analytic criteria, and larger controlled studies are needed to support integration into routine dermatologic and esthetic practice. The quantitative and reproducible nature of ultrasound-derived parameters also provides a suitable foundation for future integration with data-driven and artificial intelligence–based image analysis frameworks. Full article

Background: Sleep is a key determinant of recovery and performance in elite athletes, yet its optimization extends beyond sleep duration alone and encompasses multiple subjective and physiological dimensions. Environmental factors, including the sleep surface, represent modifiable components of sleep that may influence perceived sleep quality. This study aimed to examine whether an individually adjustable modular sleep system improves subjective sleep quality in elite athletes and whether alterations in objective sleep metrics, circadian timing, or nocturnal autonomic physiology accompany such changes. Methods: Forty-three elite athletes participated in this pre–post-intervention study (without a control group). Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while objective sleep and physiological parameters were recorded using a wearable device (Oura Ring, 3rd generation). Outcomes were averaged across three consecutive nights at baseline (T0) and post-intervention (T1). Baseline values were derived from the final three nights of a standardized pre-intervention monitoring period (minimum 7 nights), and post-intervention values from the final three nights following a standardized intervention exposure period (minimum 14 nights). Statistical analyses included paired frequentist tests and complementary Bayesian paired-sample analyses. Results: Subjective sleep quality improved significantly following the intervention, with a mean reduction in PSQI score of 0.67 points (p < 0.001). In contrast, no meaningful changes were observed in total sleep time (p = 0.28), REM duration (p = 0.26), circadian timing (p = 0.47), or nocturnal minimum heart rate (p = 0.42), as supported by the absence of physiological changes in these parameters. Conclusions: It seems that an individually adjustable sleep system can be able to improve perceived sleep quality in elite athletes without disrupting sleep architecture, circadian regulation, or nocturnal autonomic function. In athletes whose sleep duration and physiological sleep metrics are already near optimal, such micro-environmental interventions may offer a feasible, low-risk means of enhancing recovery by targeting subjective sleep quality. This dimension dissociates from objective sleep measures. Optimizing the sleep surface may therefore represent a practical adjunct to existing recovery strategies in high-performance sport. Full article

To investigate the complex interaction in multi-structure systems, this study establishes a refined 3D numerical model based on a transportation hub project in Tianjin to analyze the asymmetric coupling deformation mechanism of a deep excavation adjacent to a shared-wall metro station and elevated bridge piles. This study highlights the transition from soil-mediated interaction mechanisms to those dominated by structures under shared-wall constraints. Results show that the existing station acts as a high-stiffness boundary, effectively suppressing lateral-wall deflection and basal heave on the proximal side. A critical finding is the reversal of the station’s deformation mode: while stations with a soil buffer typically tilt toward the excavation, the shared-wall station exhibits a clockwise rotation away from the excavation; this phenomenon is driven by excavation-induced basal rebound directly transferred through the common diaphragm wall. Furthermore, the station exerts a significant “shielding effect” on adjacent bridge piles, shifting their maximum lateral displacement from the pile head to the toe and reducing overall deformation. Parametric analyses reveal that optimizing shared-wall thickness is more effective for controlling lateral deformation, whereas increasing wall depth primarily mediates vertical heave. This study concludes that, for shared-wall systems, design priorities must shift from settlement control to anti-heave measures, and pile monitoring should extend to the deeper critical zones identified in this study. Full article

Accurate spatiotemporal forecasting underpins high-stakes decision making in smart urban systems, from traffic control and energy scheduling to environment monitoring. Yet two persistent gaps limit current models: (i) spatial modules are often biased toward low-pass smoothing and struggle to reconcile slow global trends with sharp local dynamics; and (ii) the graph structure required for forecasting is frequently latent, while learned graphs can be unstable when built from temporally derived node features alone. We propose SpeQNet, a query-enhanced spectral graph filtering framework that jointly strengthens node representations and graph construction while enabling frequency-selective spatial reasoning. SpeQNet injects global spatial context into temporal embeddings via lightweight learnable spatiotemporal queries, learns a task-oriented adaptive adjacency matrix, and refines node features with an enhanced ChebNetII-based spectral filtering block equipped with channel-wise recalibration and nonlinear refinement. Across twelve real-world benchmarks spanning traffic, electricity, solar power, and weather, SpeQNet achieves state-of-the-art performance and delivers consistent gains on large-scale graphs. Beyond accuracy, SpeQNet is interpretable and robust: the learned spectral operators exhibit a consistent band-stop-like frequency shaping behavior, and performance remains stable across a wide range of Chebyshev polynomial orders. These results suggest that query-enhanced spatiotemporal representation learning and adaptive spectral filtering form a complementary and effective foundation for effective spatiotemporal forecasting. Full article

The predominant approach in the realm of industrial process monitoring and control involves the utilization of HMI (Human–Machine Interface) interfaces and conventional SCADA (Supervisory Control and Data Acquisition) systems. This limitation restricts user mobility, interaction with industrial equipment, and process status assessment. In the context of Industry 4.0, the ability to monitor and control industrial processes in real time is paramount. The present paper designs and implements a system for monitoring and controlling an industrial assembly line based on mixed reality. The technology employed to facilitate communication between the system and the industrial line is S7.Net. These elements facilitate direct communication with the industrial process equipment. The system facilitates the visualization of operating parameters and the status of the equipment utilized in the industrial process and its control. All data is superimposed on the physical environment through virtual operational panels. The system functions independently, negating the necessity for intermediate servers or other complex structures. The system’s operation is predicted on a series of algorithms. These instruments facilitate the automated analysis of industrial process parameters. These devices are utilized to ascertain the operational dynamics of the industrial line. The experimental results were obtained using a real industrial line. These models are employed to demonstrate the performance of data transmission, the identification of the system’s operating states, and the system’s ability to shut down in the event of operating errors. The proposed system is designed to function in a variety of industrial environments within the paradigm of Industry 4.0, facilitating the utilization of multiple virtual interfaces that enable user interaction with various elements through which the assembly process is monitored and controlled. Full article

This article presents a gamified digital twin in Minecraft designed to support practical exercises in digital logic in the Computer Engineering I course at the University of Salamanca. Implemented as a Spigot/Paper server plugin based on the Platform for Automatic coNstruction of orGanizations of intElligent Agents (PANGEA) multi-agent architecture, the system orchestrates four virtual organizations and employs a world cloning strategy (via Multiverse and WorldGuard) to ensure individual and isolated workspaces, while also enabling collaborative work. The central contribution is a multi-agent system with an integrated ‘black box’ verification engine that mitigates redstone asynchrony and latency through controlled signal injection and software clock synchronization, enabling real-time deterministic validation of both basic logic gates and more complex sequential circuits. Additionally, the ecosystem includes a specialized suite of logic scenarios and a web-based dashboard for real-time teacher monitoring. In a pilot study ($N=30$), the system achieved an average task completion rate of 89.1%, and an adapted Unified Theory of Acceptance and Use of Technology (UTAUT) analysis indicated that technical stability is positively associated with student performance. Full article

Phosphorus is a key nutrient regulating algal growth and eutrophication in aquatic systems, yet its isolated effect on microalgae-based wastewater treatment remains underexplored. This study evaluated how varying phosphorus loads drive microalgal community structure and purification performance in controlled photobioreactors fed synthetic wastewater. The synthetic wastewater was formulated with constant carbon and nitrogen but graded phosphorus at C/N/P ratios of 100/5/1, 100/5/10, and 100/5/20 under 6000 lux, a 14 h photoperiod, and 24 ± 2 °C with a 15-day hydraulic retention time. Monitoring of chlorophyll a, pH, total and volatile suspended solids, and algal composition showed that phosphorus enrichment significantly increased chlorophyll a (up to 43.9 µg/L at 20 mg P/L) and particulate biomass (TSS and VSS), while pH remained near neutral to slightly alkaline, with no significant differences among the three bioreactors. Although the same core taxa—Chlorella spp., Scenedesmus spp., Navicula spp., and filamentous algae were present across all bioreactors, their relative abundances shifted significantly with phosphorus concentration. A two-way ANOVA confirmed a highly significant interaction between bioreactor (P level) and genus (p < 0.001), demonstrating phosphorus-driven changes in the microalgal community. Notably, filamentous cyanobacteria (Anabaena spp.) were undetectable in the low- and medium-phosphorus treatments but emerged prominently only at the highest phosphorus level (20 mg/L). Nutrient removal efficiencies peaked in this high-phosphorus bioreactor (C), achieving 85% for bCOD, 78% for nitrogen, and >70% for phosphorus. These results show that phosphorus loading drives predictable shifts in microalgal community composition toward fast-growing algae and cyanobacteria and that these shifts likely contribute to enhanced nutrient removal. The findings support optimization of phosphorus supply and hydraulic residence time in low-cost, sunlight-driven systems to improve polishing performance for small settlements in arid regions. Full article

This study investigated the effects of calcium (Ca²⁺) and iron (II) Fe²⁺ concentrations (0–500 mg/L) on the heterogeneous nucleation and crystallization behavior of struvite (MgNH₄PO₄·6H₂O) through controlled batch precipitation experiments. Struvite formed under different Ca²⁺ and Fe²⁺ concentrations were systematically characterized using XRD, SEM, FTIR, and XPS, while real-time pH and redox potential (Eh) monitoring was employed to elucidate reaction dynamics and thermodynamic speciation and saturation indices were calculated, and classical nucleation theory (CNT) was applied to interpret nucleation behavior. The results show that Ca²⁺ primarily suppresses struvite formation through bulk-phase competition with Mg²⁺ for phosphate, diverting phosphate into Ca–P phases and progressively reducing struvite supersaturation, which leads to decreased crystallinity and distorted Crystal habit. In contrast, Fe²⁺ does not form detectable crystalline Fe-P phases but inhibits struvite crystallization mainly through surface-mediated processes. Surface analyses indicate that Fe-bearing species adsorb onto struvite surfaces and promote amorphous Fe-P deposition, increasing interfacial resistance to nucleation and growth. CNT analysis further reveals that Ca²⁺ inhibition is governed by reduced thermodynamic driving force, whereas Fe²⁺ inhibition is dominated by surface-related kinetic barriers. This study provides mechanistic insight into ion-specific interference during struvite crystallization and offers guidance for optimizing phosphorus recovery in ion-rich wastewater systems. Full article