Search Results (161,620)

Precise control over nanostructure evolution is critical for optimizing the electrochemical performance of pseudocapacitive materials. In this work, Nb₂O₅ nanostructures were synthesized via a time-engineered hydrothermal route by systematically varying the reaction duration (6, 12, and 18 h) to elucidate its influence on structural development, charge storage kinetics, and supercapacitor performance. Structural and surface analyses confirm the formation of phase-pure monoclinic Nb₂O₅ with a stable Nb⁵⁺ oxidation state. Morphological investigations reveal that a 12 h reaction time produces hierarchically organized Nb₂O₅ architectures composed of nanograin-assembled spherical aggregates with interconnected porosity, providing optimized ion diffusion pathways and enhanced electroactive surface exposure. Electrochemical evaluation demonstrates that the NbO-12 electrode delivers superior pseudocapacitive behavior dominated by diffusion-controlled Nb⁵⁺/Nb⁴⁺ redox reactions, exhibiting high areal capacitance (5.504 F cm⁻² at 8 mA cm⁻²), fast ion diffusion kinetics, low internal resistance, and excellent cycling stability with 85.73% capacitance retention over 12,000 cycles. Furthermore, an asymmetric pouch-type supercapacitor assembled using NbO-12 as the positive electrode and activated carbon as the negative electrode operates stably over a wide voltage window of 1.5 V, delivering an energy density of 0.101 mWh cm⁻² with outstanding durability. This study establishes hydrothermal reaction-time engineering as an effective strategy for tailoring Nb₂O₅ nanostructures and provides valuable insights for the rational design of high-performance pseudocapacitive electrodes for advanced energy storage systems. Full article

Progressive climate change and building morphology influence the specific microclimate of built-up areas. This has a fundamental role in research on energy use and thermal comfort inside buildings. Most studies using data for dynamic energy simulation are based on information collected at meteorological stations in rural areas. This can lead to erroneous predictions. The main goal of the study was to combine two simulation tools—ENVI-met for microclimate predictions around historical building layouts, and DesignBuilder for assessing indoor comfort. Illustrating the impact of input data on simulation results was conducted using three types of weather data: (1) from a field campaign, (2) from a suburban station, and (3) from the typical meteorological year. The obtained results confirm that the highest precision was achieved in analyses where information obtained at a real scale in the city centre was used as boundary conditions (field measurements: MAPE = 0.6 °C, RMSE = 0.7 °C). The next step was to estimate the thermal sensations inside the living room of the existing residential building. Thermal comfort was determined using the operative temperature as an indicator. Incorporating realistic urban weather inputs enhanced the reliability of indoor comfort modelling and provided a more accurate basis for planning thermal resilience in historic residential buildings. Full article

Ensuring safety in energy storage systems increasingly relies on advanced diagnostic tools, among which magnetic field mapping plays a critical role. This work aims to develop and validate a high-sensitivity magnetic field sensor array for accurate field mapping and preliminary battery diagnostics. We present a 4 × 4 array of magnetic sensors integrated with a calibration procedure to ensure accurate output. The system was experimentally tested by characterizing the magnetic field generated by two planar copper conductors. Finite element simulations were performed for comparison and validation. Experimental measurements exhibited strong agreement with the simulation results, confirming the reliability of the sensor array. Next, the system was employed to map the magnetic field distribution of a pouch-type lithium-ion battery, demonstrating its capability for noninvasive diagnostics. Although this study focuses on magnetic field measurement rather than direct battery diagnosis, the results suggest that the proposed system—capable of measuring magnetic fields in batteries operating under normal conditions—could also perform these measurements under abusive conditions, thereby enabling diagnostic assessments. The proposed sensor array provides a scalable and precise solution for low-intensity magnetic field mapping, with potential applications in battery health monitoring and safety assessment. Full article

The integration of extended reality (XR) into industrial robotics requires robust middleware solutions capable of bridging heterogeneous systems, protocols, and user interactions. This paper presents a novel middleware framework designed to connect industrial robots with XR devices such as the HoloLens. The architecture employs a hybrid communication layer that combines MQTT (Message Queuing Telemetry Transport) and ØMQ (Zero Message Queue), leveraging the Sparkplug Robotics API model for robot data and publisher–subscriber streaming for XR camera feeds. A Redis cache database is introduced to ensure efficient data handling and prevent data corruption. On the robot side, the system is built on ROS 2 (Robot Operating System) and connects to proprietary industrial protocols through dedicated bridges, enabling seamless interoperability. Spatial alignment between physical robots and XR overlays is achieved using ArUco marker-based synchronization, while real-time kinematic and process data are visualized directly in XR. The middleware further supports bidirectional interaction, allowing users to adjust parameters and issue commands through XR devices. Beyond functionality, safety considerations are incorporated by integrating human–robot interaction safeguards and ensuring compliance with industrial communication standards. The proposed solution demonstrates how middleware-driven XR integration enhances transparency, control, and safety in robotic manufacturing processes, laying the foundation for greater efficiency and adaptability in Industry 4.0 environments. Full article

Background/Objectives: Minimally invasive repair of pectus excavatum (MIRPE) is an established surgical treatment for adult pectus excavatum (PE). Compared with pediatric patients, adults generally have a more rigid chest wall and greater costal cartilage ossification, often resulting in more severe postoperative pain. However, most previous studies have focused on pediatric patients with PE and on evaluating effective analgesic methods. This study aimed to investigate perioperative factors associated with postoperative opioid requirements and pain intensity in adult patients undergoing MIRPE. Methods: This study was a single-center retrospective study of adult PE patients who underwent MIRPE between March 2011 and January 2023. The primary outcome was total opioid consumption during the first 24 postoperative hours. Secondary outcomes included opioid and rescue analgesic use within 0–6, 6–24, and 24–48 h, as well as pain intensity during each interval. Multivariable linear regression analysis was performed to identify factors associated with postoperative opioid consumption. Results: A total of 382 patients were analyzed. Pain intensity peaked within the first 6 postoperative hours, decreased during the 6–24 h and increased during 24–48 h period. Higher BMI and placement of more than three bars were independently associated with greater opioid consumption during the first 6 h (p < 0.001). Within 24 and 48 h, male sex, longer operation time and higher BMI were independently associated with opioid consumption (p < 0.001). During 6–24 and 24–48 h period, VAS severity was significantly higher in male patients and those with longer operation times. Conclusions: Male sex, higher BMI, prolonged operation time, and multiple bar insertion may contribute to greater postoperative opioid requirements during the early postoperative phase in adult patients undergoing MIRPE. Full article

High-penetration integration of distributed photovoltaics (PV) into distribution networks introduces significant challenges regarding voltage limit violations and fluctuations. To address these issues, this manuscript proposes a hierarchical coordinated voltage control strategy for medium- and low-voltage distribution networks utilizing a cloud-edge collaboration architecture. The research methodology involves constructing a multi-objective optimization model at the cloud layer to minimize network losses and voltage deviations, solved via an improved Honey Badger Algorithm (HBA). Simultaneously, at the edge layer, a multi-mode coordinated control strategy incorporating Virtual Synchronous Generator (VSG) technology is developed to provide fast reactive power support and inertial response. Through simulation analysis on an IEEE 33-node test system, the findings demonstrate that the proposed strategy significantly mitigates voltage fluctuations and enhances the hosting capacity of distributed energy resources. The study concludes that the cloud-edge framework effectively decouples control time-scales, ensuring both global economic operation and local transient stability. These results are significant for advancing the resilient operation of active distribution networks with high renewable penetration. Full article

Vibration is a critical issue in aerospace structures, where lightweight design, high flexibility, and complex operational environments often lead to pronounced nonlinear dynamic responses. Excessive vibrations induced by harmonic excitations, aerodynamic loads, or onboard equipment can significantly degrade structural integrity, control accuracy, and service life. Consequently, advanced passive vibration suppression techniques with strong robustness and broadband effectiveness are of great importance in aerospace engineering applications. The bifurcation boundary and vibration suppression performance of Piezoelectric–Monostable Nonlinear Energy Sink (PMNES) are crucial for evaluating its effectiveness on the main structure. To simplify the analysis of flexible aerospace structures, a reduced-order model is derived by modal truncation in the low-frequency range, which is then treated as a two-degree-of-freedom main structure. To focus on the underlying nonlinear dynamic mechanisms, an equivalent two-degree-of-freedom lumped-parameter system is adopted as a generic representation of the dominant low-frequency dynamics of flexible aerospace structures. In this work, the electromechanical coupling control equations of the system of a two-degree-of-freedom main structure coupled with PNES are derived through the application of Newton’s second law and Kirchhoff’s voltage law. The methods of complexification-averaging (CX-A) and Runge–Kutta (RK) are employed to assess the vibration suppression performance and stability characteristics of the system under harmonic excitation. The approximate solution is validated through numerical solutions. The approximate solutions of the system are employed to derive the Saddle Node (SN) bifurcation and codimension-two cusp bifurcation points, while the enhanced algorithm is employed to ascertain the most unfavorable amplitude at each external excitation circular frequency and to determine whether the mark represents a Hopf Bifurcation (HB) point. The generalized transmissibility is utilized to assess the efficacy of vibration suppression. The various vibration suppression efficiency regions are created by superimposing the vibration suppression efficiency maps and bifurcation maps. The influence of PNES parameters on the vibration suppression region is investigated. The results indicate that this method can effectively evaluate the bifurcation boundary and vibration suppression performance of PMNES. Full article

Background and Objectives: Incidental gallbladder carcinoma (IGBC) is an uncommon but clinically significant finding after elective cholecystectomy, as failure to recognize malignancy preoperatively may lead to inadequate initial surgical management. Inflammation-based hematological indices have been explored in various gastrointestinal malignancies; however, data focusing on preoperative discrimination of IGBC are limited. This study aimed to evaluate the diagnostic performance of the Advanced Lung Cancer Inflammation Index (ALI) and other inflammatory and immunonutritional indices in distinguishing IGBC from benign gallbladder disease. Materials and Methods: This retrospective matched case–control study included patients who underwent elective cholecystectomy between 2020 and 2025. Nineteen patients with histopathologically confirmed IGBC were matched 1:4 by age and sex with 76 patients with benign gallbladder disease. Preoperative laboratory parameters obtained within 72 h before surgery were used to calculate inflammatory indices, including NLR, PLR, LMR, SII, CAR, ALI, and the CALLY index. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis. Independent associations with malignancy were evaluated using matched conditional logistic regression. Results: No significant differences were observed between benign and malignant groups in baseline demographic or routine laboratory parameters. Among derived indices, ALI and LMR demonstrated borderline differences, representing the closest discriminatory markers. In ROC analysis, ALI showed the highest diagnostic performance (AUC = 0.69), followed by LMR (AUC = 0.639). In matched conditional logistic regression, ALI was independently and inversely associated with malignancy (adjusted OR = 0.997 per unit, 95% CI: 0.9937–1.0000; p = 0.04); when rescaled per 100-unit increase, the adjusted OR was 0.93, whereas LMR did not reach statistical significance. Conclusions: Among evaluated preoperative inflammatory and immunonutritional indices, ALI demonstrated a more consistent association with incidental gallbladder carcinoma. Although its discriminative ability was moderate, ALI may serve as a complementary biomarker for preoperative risk stratification when integrated with clinical and radiological assessment. Full article

This paper develops a fractional six-compartment model to describe malware spread in wireless sensor networks. To represent actual network activity, the model is constructed using generalized proportional-Caputo operators that incorporate memory and tempering effects. The existence and uniqueness of solutions are proved by applying fixed-point theorems. The stability of the system is then studied using the Ulam–Hyers approach and its extended form. A fractional Adams predictor–corrector method is employed to illustrate the dynamics. The results suggest that memory and tempering play an important role in shaping infection patterns, and they indicate that fractional calculus can provide a useful framework for studying and managing malware in distributed sensor networks. Full article

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article

Nature-based Solutions (NbS) such as rolled cover crops are increasingly adopted in rainfed vineyards to reduce soil degradation and drought risk, but their effectiveness depends on local soil physical conditions. We compared spontaneous inter-row vegetation managed by mowing (Control) with a cereal-based rolled cover crop (C-R) in two vineyards of the Oltrepò Pavese (Northern Italy) with contrasting texture, structure, and slope: Canevino (CNV) and Santa Maria della Versa (SMV). From 2021 to 2025, continuous soil moisture monitoring was combined with field measurements of saturated hydraulic conductivity (K_s) and bulk density, interpreted using temporal indicators (MRD, ITS) and a drought index (SWDI) calibrated to field moisture thresholds. During wet phases, average saturation at 50 cm was consistently higher at SMV (about 78 to 84 percent) than at CNV (about 68 to 75 percent). Under water-limited conditions, management contrasts were most evident at SMV: at 50 cm during the post-termination dry phase, saturation remained around 70 percent under C-R versus about 64 percent under the Control, and K_s was higher under C-R (8.32 × 10⁻⁶ m/s in topsoil) than under the Control (7.39 × 10⁻⁶ m/s). At CNV, SWDI at 50 cm indicated a moderate improvement in one agronomic year (median −1.2 under C-R versus −5.3 under the Control in 2021 to 2022), while a full tillage operation in 2024 defined a disturbed phase that was interpreted separately. SWDI occasionally suggested severe drought levels not fully matching field evidence, highlighting the need for site-calibrated reference thresholds in structured fine-textured soils. Overall, soil physical properties set the hydrological envelope, while rolled cover management can enhance buffering and preserve conductive pathways during dry phases; therefore, NbS performance should be evaluated with site-adapted monitoring and cautious inference from temporally autocorrelated time series. Full article

The conventional treatment of high-concentration volatile organic compounds (VOCs) relies on energy-intensive dilution to avoid explosion risks. This study proposes an efficient catalytic combustion process treating VOCs directly within the explosive range while recovering reaction heat using Pt/γ-Al₂O₃-based catalysts promoted with La and Ce. Catalysts (0.05–0.5 wt% Pt) were synthesized via impregnation and characterized using FE-SEM, BET, and XRD. Catalytic combustion experiments at VOC concentrations up to 13,000 ppm showed combustion initiation below 200 °C, achieving 83–99% conversions at 300 °C with complete oxidation to CO₂. Although 5 vol.% moisture significantly inhibited low-temperature activity through competitive adsorption, La and Ce promoters (10 wt%) effectively overcame this limitation by increasing surface area (up to 194.93 m²/g) and oxygen mobility. The Ce-promoted catalyst demonstrated superior water tolerance, achieving complete conversion at 200–210 °C due to its high Oxygen Storage Capacity (OSC). Bench-scale validation using a 1 Nm³/h system confirmed industrial feasibility. Operating at 220 °C with 13,000 ppm toluene for 100 h, the catalyst maintained >99.98% conversion with negligible deactivation and THC emissions below 2 ppm. The double-jacket heat exchanger effectively managed reaction heat (limiting temperature rise to ~20 °C) and recovered it as steam. Compared to Regenerative Thermal Oxidation, this Regenerative Catalytic Oxidation approach reduced emissions and energy consumption. This work demonstrates a robust “combustion-with-recovery” strategy for high-concentration VOC treatment, offering a sustainable alternative with high efficiency, stability, and safe energy-integrated operation. Full article

Background and Objective: Postoperative hospital length of stay (LOS) reflects surgical recovery and resource demand but remains difficult to predict due to heterogeneous perioperative trajectories. We aimed to develop and validate an interpretable machine learning framework that integrates multimodal perioperative data to accurately predict LOS and uncover clinically meaningful drivers of prolonged hospitalization. Methods: We studied 97,937 adult surgical cases from a large perioperative registry. Routinely collected perioperative data included patient demographics, comorbid conditions, preoperative laboratory values, intraoperative physiologic summaries, and procedural characteristics. Length of stay was modeled using a supervised regression approach with internal cross-validation and independent holdout evaluation. Model performance was assessed at both the cohort and individual levels, and explanatory analyses were performed to quantify the contribution of clinically defined perioperative domains. Results: The model achieved R² = 0.61 and MAE ≈ 1.34 days on the holdout set, with nearly identical cross-validation performance (R² = 0.60, MAE ≈ 1.34 days). Operative duration, diagnostic complexity, intraoperative hemodynamic variability, and preoperative laboratory indices—particularly albumin and hematocrit—emerged as the strongest determinants of postoperative stay. Patients with shorter recoveries typically had brief operations, stable physiology, and normal laboratory profiles, whereas prolonged hospitalization was linked to complex procedures, malignant or respiratory diagnoses, and lower albumin levels. Conclusions: Interpretable machine learning enables accurate and generalizable estimation of postoperative LOS while revealing clinically actionable perioperative domains. Such frameworks may facilitate more efficient perioperative planning, improved allocation of hospital resources, and personalized recovery strategies. Full article

Conventional transformer protection schemes are limited by the difficulty in distinguishing inrush currents from internal and external faults, which restricts operational accuracy to below 70%. Existing solutions are constrained by a trade-off: sensitivity is compromised when setting values are increased, while speed is sacrificed when time delays are introduced. To address these limitations, a novel deep learning-based method for transformer fault identification is proposed. First, a feature model is constructed utilizing the time-domain sum of voltage and current fault components alongside current polarity characteristics. Subsequently, a channel attention-based Capsule Network (SE-CapsuleNet) is employed to automatically extract deep features across normal operation, inrush currents, and fault types. Simulation results demonstrate that inrush conditions are accurately differentiated from fault states. Robustness is maintained under high fault resistance (400 Ω) and 20 dB noise interference, while immunity to current transformer (CT) saturation and core residual magnetism is exhibited. The proposed protection relay simultaneously meets the requirements of rapid response, high sensitivity, and safety stability. Full article

Accurate analysis of commutation phenomena in permanent magnet DC (PMDC) motors requires simultaneous consideration of electromagnetic field distribution and armature circuit dynamics. Classical circuit-based models are unable to properly capture transient effects occurring in short-circuited coils during commutation, while purely field-based models neglect the influence of the supply circuit. In this paper, a coupled field–circuit model of a PMDC motor with an innovative magnetic circuit based on rectangular NdFeB permanent magnets is presented. The model combines a two-dimensional finite element electromagnetic analysis with a segmented armature circuit and dynamic commutator switching, allowing the electromotive force to be computed individually for each coil based on the actual magnetic field distribution. The novelty of the proposed approach lies in the integration of a non-standard rectangular permanent magnet topology with a coil-resolved field–circuit commutation model, validated on a physical motor prototype. Simulation results are compared with experimental measurements obtained from a laboratory prototype at rotational speeds of 850 and 1000 r/min. The predicted electromagnetic torque shows good agreement with measurements, with deviations below 5%, while the armature current is estimated with an error of up to approximately 20%, primarily due to model simplifications. The developed model provides direct access to transient commutation waveforms and constitutes a practical tool for the analysis and design optimization of PMDC motors operating under dynamic conditions, particularly in cost-sensitive and reliability-oriented applications. Full article