Search Results (5,237)

The large-scale integration of wind power, photovoltaic systems, and energy storage systems (ESSs) into power grids has increasingly influenced the transient stability of power systems due to their dynamic response characteristics. Considering the commercial confidentiality of core control parameters from equipment manufacturers, parameter identification has become a crucial approach for analyzing ESS dynamic behaviors during high-voltage ride-through (HVRT) and low-voltage ride-through (LVRT) and for optimizing control strategies. In this study, we present a multidimensional feature-integrated parameter identification framework for ESSs, combining a multi-scenario voltage disturbance testing environment built on a real-time laboratory platform with field-measured data and enhanced optimization algorithms. Focusing on the control characteristics of energy storage converters, a non-intrusive identification method for grid-connected control parameters is proposed based on dynamic trajectory feature extraction and a hybrid optimization algorithm that integrates an improved particle swarm optimization (PSO) algorithm with gradient-based coordination. The results demonstrate that the proposed approach effectively captures the dynamic coupling mechanisms of ESSs under dual-mode operation (charging and discharging) and voltage fluctuations. By relying on measured data for parameter inversion, the method circumvents the limitations posed by commercial confidentiality, providing a novel technical pathway to enhance the fault ride-through (FRT) performance of energy storage systems (ESSs). In addition, the developed simulation verification framework serves as a valuable tool for security analysis in power systems with high renewable energy penetration. Full article

Background/Objectives: Accurate visualization of the Achilles tendon enthesis is critical for distinguishing mechanical, degenerative, and inflammatory pathologies. Although ultrasonography is the first-line modality for suspected enthesis disease, recent technical advances may expand the role of magnetic resonance imaging (MRI). This study evaluated the utility of ultra-short echo time (UTE) and zero echo time (ZTE) sequences versus proton density-weighted imaging (PD-WI) for depicting the enthesis organ in healthy volunteers. Methods: In this institutional review board (IRB)-approved prospective single-center study, 50 asymptomatic adult volunteers underwent 3-Tesla hindfoot MRI with fat-suppressed PD-WI, UTE, and ZTE between 2018 and 2023. Four radiologists assessed image quality, signal-to-noise ratio, visibility, and abnormal high signal intensities (SIs) of the periost, sesamoid, and enthesis fibrocartilages (PCa, SCa, and ECa, respectively). Statistical tests included Chi-square, McNemar, paired Wilcoxon, and Benjamini–Hochberg adjustments for multiple comparisons. Results: The median age was 36 years (range: 20–51); 58% women were included. PD-WI and ZTE sequences were always available while UTE was unavailable in 24% of patients. PD-WI consistently failed to concomitantly visualize all fibrocartilages. ZTE and UTE visualized all fibrocartilages in 72% and 92.1% of volunteers, respectively, with significant differences favoring ZTE and UTE over PD-WI (p < 0.0001) and UTE over ZTE (p = 0.027). Inter-rater agreement exceeded 80% except for SCa on ZTE (68%, 95%CI: 53.2–80.1). Abnormal SCa findings in asymptomatic patients were more frequent with UTE (23.7%) and ZTE (34%) than with PD-WI (2%) (p = 0.0045). Conclusions: At 3-Tesla, UTE and ZTE sequences reliably depict the enthesis organ of the Achilles tendon, outperforming PD-WI. However, the high sensitivity of these sequences also presents challenges in interpretation. Full article

Glass microspheres are essential components in horizontal road markings due to their retroreflective properties, enhancing visibility and safety under low-light conditions. Traditionally produced from soda-lime glass made with high-purity silica from sand, their manufacturing raises environmental concerns amid growing global sand scarcity. This study explores the viability of rice husk ash (RHA)—a high-silica byproduct of rice processing—as a sustainable raw material for microsphere fabrication. A glass composition containing 70 wt% SiO₂ was formulated using RHA and melted at 1500 °C. Microspheres were produced through flame spheroidization and characterized following the Brazilian standard NBR 16184:2021 for Type IB beads. The RHA-derived microspheres exhibited high sphericity, appropriate size distribution (63–300 μm), density of 2.42 g/cm³, and the required acid resistance. UV-Vis analysis confirmed their optical transparency, and the refractive index was measured as 1.55 ± 0.03. Retroreflectivity tests under standardized conditions revealed performance comparable to commercial counterparts. These results demonstrate the technical feasibility of replacing conventional silica with RHA in glass microsphere production, aligning with circular economy principles and promoting sustainable infrastructure. Given Brazil’s significant rice production and corresponding RHA availability, this approach offers both environmental and socio-economic benefits for road safety and material innovation. Full article

This study investigates whether institutional quality amplifies the emissions-reducing effect of energy efficiency in hydrocarbon-dependent economies. Addressing a gap in the energy–environment literature, it tests how governance conditions shape the effectiveness of technical mitigation strategies. Using panel data from six Gulf Cooperation Council (GCC) countries between 2000 and 2023, we estimate a fixed-effects model with interaction terms between energy intensity (as a proxy for efficiency) and institutional quality (proxied by Control of Corruption). The results show that energy efficiency is associated with lower CO₂ emissions, and this relationship is significantly moderated by institutional quality. We also analyze the emissions impact of natural gas consumption and identify a structural shift following the 2014 energy reforms: while gas use was positively associated with emissions before 2014, the post-reform period shows a weaker or reversed effect. Robustness checks using alternative governance indicators—Regulatory Quality and Government Effectiveness—confirm the moderating role of institutions. The study offers new empirical evidence on the energy–institution–environment nexus and introduces a novel interaction-based methodology suited to resource-rich economies undergoing institutional transition. Full article

In today’s increasingly data-driven educational landscape, teachers are expected to use data to inform instructional decisions. However, effective data use depends not only on statistical competence but also on motivation, attitudes, and academic self-concept. This study examines how these factors influence student teachers’ readiness to engage with standardized assessment data. A survey of 164 Flemish primary education student teachers assessed their motivation, attitudes toward data use, and academic self-concept. Cluster analysis identified four distinct profiles, ranging from highly competent yet disengaged users to low-performing but externally motivated individuals, highlighting significant variability in data engagement. A pre- and post-test study design involving an e-course on basic statistical concepts demonstrated that targeted instruction can enhance perceived competence, particularly in areas such as box plot interpretation. Findings suggest that technical training alone is insufficient to promote sustained data use; fostering intrinsic motivation, positive attitudes, and a strong academic self-concept is essential for long-term engagement with data. Full article

Virtual fencing (VF) offers a promising alternative to conventional or electrified fences for managing livestock grazing distribution. This study evaluated the behavioral responses of 25 Rarámuri Criollo cows fitted with Nofence^® collars in Pine Valley, CA, USA. The VF system was deployed in chaparral rangeland pastures. The study included a 14-day training phase followed by an 18-day testing phase. The collar-recorded variables, including audio warnings and electric pulses, animal movement, and daily typical behavior patterns of cows classified into a High or Low virtual fence response group, were compared using repeated-measure analyses with mixed models. During training, High-response cows (i.e., resistant responders) received more audio warnings and electric pulses, while Low-response cows (i.e., active responders) had fewer audio warnings and electric pulses, explored smaller areas, and exhibited lower mobility. Despite these differences, both groups showed a time-dependent decrease in the pulse-to-warning ratio, indicating increased reliance on audio cues and reduced need for electrical stimulation to achieve similar containment rates. In the testing phase, both groups maintained high containment with minimal reinforcement. The study found that Rarámuri Criollo cows can effectively adapt to virtual fencing technology, achieving over 99% containment rate while displaying typical diurnal patterns for grazing, resting, or traveling behavior. These findings support the technical feasibility of using virtual fencing in chaparral rangelands and underscore the importance of accounting for individual behavioral variability in behavior-based containment systems. Full article

Integrating blockchain into healthcare devices offers the potential for improved data control but faces significant usability and acceptance challenges. This study addresses this gap by evaluating CipherPal, an improved blockchain-enabled Smart Fidget Toy prototype, using a multi-framework approach to understand the interplay between technology, design, and user experience. We synthesized insights from three complementary frameworks: an expert review assessing adherence to Web3 Design Guidelines, a User Acceptance Toolkit assessment with professionals based on UTAUT2, and an extended three-day user testing study. The findings revealed that users valued CipherPal’s satisfying tactile interaction and perceived benefits for well-being, such as stress relief. However, significant usability barriers emerged, primarily related to challenging device–application connectivity and data synchronization. The multi-framework approach proved valuable in revealing these core tensions. While the device was conceptually accepted, the blockchain integration added significant interaction friction that overshadowed its potential benefits during the study. This research underscores the critical need for user-centered design in health-related blockchain applications, emphasizing that seamless usability and abstracting technical complexity are paramount for adoption. Full article

Healthcare supply chain management operates amid fluctuating patient demand, rapidly advancing biotechnologies, and unpredictable supply disruptions pose high risks and create an imperative for sustainable resource optimization. This study investigates the underlying mechanisms through which digital intelligence drives strategic decision optimization in healthcare supply chains. Drawing on the Resource-Based View and Dynamic Capabilities Theory, we develop a chain-mediated model, defined as the multistage indirect path whereby digital intelligence first bolsters innovation capability, which then activates supply chain resilience (absorptive, response, and restorative capability), to improve decision optimization. Data were collected from 360 managerial-level respondents working in healthcare supply chain organizations in China, and the proposed model was tested using structural equation modeling. The results indicate that digital intelligence enhances innovation capability, which in turn activates all three dimensions of resilience, producing a synergistic effect that promotes sustained decision optimization. However, the direct effect of digital intelligence on decision optimization was not statistically significant, suggesting that its impact is primarily mediated through organizational capabilities, particularly supply chain resilience. Practically, the findings suggest that in the process of deploying digital intelligence systems and platforms, healthcare organizations should embed technological advantages into organizational processes, emergency response mechanisms, and collaborative operations, so that digitalization moves beyond the technical system level and is truly internalized as organizational innovation capability and resilience, thereby leading to sustained improvement in decision-making performance. Full article

The global shift towards the use of renewable energy is essential to ensure sustainable development, and geothermal energy stands out as a suitable option that can support various cascading projects. Spent geothermal water (SGW) requires proper treatment to ensure that it does not become an environmental burden. Typically, companies often face the dilemma of choosing between discharging spent geothermal water (SGW) into surface waters or injecting it into rock masses, and the economic and environmental impacts of the decision made determines the feasibility of geothermal plant development. In this study, we aimed to comprehensively assess the technical, economic, and environmental feasibility of SGW injection into rock masses. To this end, we employed a comprehensive analytical approach using the Chochołów GT-1 geothermal injection borehole in Poland as a reference case. We also performed drilling and hydrogeological testing, characterized rock samples in the laboratory, and corrected hydrodynamic parameters for thermal lift effects to ensure accurate aquifer characterization. The results obtained highlight the importance of correcting hydrogeological parameters for thermal effects, which if neglected can lead to a significant overestimation of the calculated hydrogeological parameters. Based on our analysis, we developed a framework for assessing SGW injection feasibility that integrates detailed hydrogeological and geotechnical analyses with environmental risk assessment to ensure sustainable geothermal resource exploitation. This framework should be mandatory for planning new geothermal power plants or complexes worldwide. Our results also emphasize the need for adequate SGW management so as to ensure that the benefits of using a renewable and zero-emission resource, such as geothermal energy, are not compromised by the low absorption capacity of rock masses or adverse environmental effects. Full article

Against the backdrop of global energy transition and strict environmental regulations, supercritical carbon dioxide (scCO₂) fracturing and oil displacement technologies have emerged as pivotal green approaches in shale gas exploitation, offering the dual advantages of zero water consumption and carbon sequestration. However, the inherent low viscosity of scCO₂ severely restricts its sand-carrying capacity, fracture propagation efficiency, and oil recovery rate, necessitating the urgent development of high-performance thickeners. The current research on scCO₂ thickeners faces a critical trade-off: traditional fluorinated polymers exhibit excellent philicity CO₂, but suffer from high costs and environmental hazards, while non-fluorinated systems often struggle to balance solubility and thickening performance. The development of new thickeners primarily involves two directions. On one hand, efforts focus on modifying non-fluorinated polymers, driven by environmental protection needs—traditional fluorinated thickeners may cause environmental pollution, and improving non-fluorinated polymers can maintain good thickening performance while reducing environmental impacts. On the other hand, there is a commitment to developing non-noble metal-catalyzed siloxane modification and synthesis processes, aiming to enhance the technical and economic feasibility of scCO₂ thickeners. Compared with noble metal catalysts like platinum, non-noble metal catalysts can reduce production costs, making the synthesis process more economically viable for large-scale industrial applications. These studies are crucial for promoting the practical application of scCO₂ technology in unconventional oil and gas development, including improving fracturing efficiency and oil displacement efficiency, and providing new technical support for the sustainable development of the energy industry. This study innovatively designed an amphiphilic modified amino silicone oil polymer (MA-co-MPEGA-AS) by combining maleic anhydride (MA), methoxy polyethylene glycol acrylate (MPEGA), and amino silicone oil (AS) through a molecular bridge strategy. The synthesis process involved three key steps: radical polymerization of MA and MPEGA, amidation with AS, and in situ network formation. Fourier transform infrared spectroscopy (FT-IR) confirmed the successful introduction of ether-based CO₂-philic groups. Rheological tests conducted under scCO₂ conditions demonstrated a 114-fold increase in viscosity for MA-co-MPEGA-AS. Mechanistic studies revealed that the ether oxygen atoms (Lewis base) in MPEGA formed dipole–quadrupole interactions with CO₂ (Lewis acid), enhancing solubility by 47%. Simultaneously, the self-assembly of siloxane chains into a three-dimensional network suppressed interlayer sliding in scCO₂ and maintained over 90% viscosity retention at 80 °C. This fluorine-free design eliminates the need for platinum-based catalysts and reduces production costs compared to fluorinated polymers. The hierarchical interactions (coordination bonds and hydrogen bonds) within the system provide a novel synthetic paradigm for scCO₂ thickeners. This research lays the foundation for green CO₂-based energy extraction technologies. Full article

This work presents the development of NOVA, an educational virtual assistant designed for the Parallel Computing course, built using a Retrieval-Augmented Generation (RAG) architecture combined with Large Language Models (LLMs). The assistant operates entirely in Spanish, supporting native-language learning and increasing accessibility for students in Latin American academic settings. It integrates vector and relational databases to provide an interactive, personalized learning experience that supports the understanding of complex technical concepts. Its core functionalities include the automatic generation of questions and answers, quizzes, and practical guides, all tailored to promote autonomous learning. NOVA was deployed in an academic setting at Universidad Politécnica Salesiana. Its modular architecture includes five components: a relational database for logging, a vector database for semantic retrieval, a FastAPI backend for managing logic, a Next.js frontend for user interaction, and an integration server for workflow automation. The system uses the GPT-4o mini model to generate context-aware, pedagogically aligned responses. To evaluate its effectiveness, a test suite of 100 academic tasks was executed—55 question-and-answer prompts, 25 practical guides, and 20 quizzes. NOVA achieved a 92% excellence rating, a 21-second average response time, and 72% retrieval coverage, confirming its potential as a reliable AI-driven tool for enhancing technical education. Full article

This study evaluated the effect of various adhesive systems, particularly silane application, on the repair bond strength of a nanofill resin composite and associated stress distribution using finite element analysis (FEA). A total of 105 composite specimens (4 × 6 mm) were aged by thermal cycling (10,000 cycles), roughened, etched with phosphoric acid, and assigned to seven groups (n = 15): G1. control—no adhesive; G2. Single Bond Universal Adhesive; G3. composite primer; G4. PQ1; G5. Silane + PQ1; G6. Clearfil Universal Bond; G7. All-Bond Universal. Shear bond strength was measured using a universal testing machine (1 mm/min), and failure modes were microscopically classified. FEA was conducted under static and fatigue conditions using 3D models built in Fusion-360. Mechanical properties were obtained from technical data and the literature. A 300 N load was applied and contact detection (0.05 mm) and constraint zones were defined. Statistical analysis was performed using one-way ANOVA and Tukey’s HSD (p = 0.05). Pearson’s correlation was used to assess the relationship between bond strength and von Mises stress. The highest bond strength was found in G2 (21.54 MPa) while G1 showed the lowest (8.86 MPa). Silane-treated groups exhibited favorable stress distribution and a strong correlation between experimental and simulated outcomes. Silane applications significantly enhance composite repair performance. Full article

In order to meet the needs for the rapid detection of crop growth and support variable management in farmland, an intelligent chlorophyll content in cotton leaves (CCC) detection system based on hyperspectral imaging (HSI) technology was designed and developed. The system includes a near-infrared (NIR) hyperspectral image acquisition module, a spectral extraction module, a main control processor module, a model acceleration module, a display module, and a power module, which are used to achieve rapid and non-destructive detection of chlorophyll content. Firstly, spectral images of cotton canopy leaves during the seedling, budding, and flowering-boll stages were collected, and the dataset was optimized using the first-order differential algorithm (1D) and Savitzky–Golay five-term quadratic smoothing (SG) algorithm. The results showed that SG had better processing performance. Secondly, the sparrow search algorithm optimized backpropagation neural network (SSA-BPNN) and one-dimensional convolutional neural network (1DCNN) algorithms were selected to establish a chlorophyll content detection model. The results showed that the determination coefficients $R_p^2$ of the chlorophyll SG-1DCNN detection model during the seedling, budding, and flowering-boll stages were 0.92, 0.97, and 0.95, respectively, and the model performance was superior to SG-SSA-BPNN. Therefore, the SG-1DCNN model was embedded into the detection system. Finally, a CCC intelligent detection system was developed using Python 3.12.3, MATLAB 2020b, and ENVI, and the system was subjected to application testing. The results showed that the average detection accuracy of the CCC intelligent detection system in the three stages was 98.522%, 99.132%, and 97.449%, respectively. Meanwhile, the average detection time for the samples is only 20.12 s. The research results can effectively solve the problem of detecting the nutritional status of cotton in the field environment, meet the real-time detection needs of the field environment, and provide solutions and technical support for the intelligent perception of crop production. Full article

The efficiency of solar water heating systems is strongly influenced by variable weather conditions, making the optimization of control strategies essential for maximizing energy performance. This study presents the development and evaluation of a rule-based adaptive control strategy that dynamically selects one of three predefined control modes—ON–OFF, proportional, or indirect proportional control (IPC)—based on real-time weather classification. The classification algorithm assigns each day to one of four solar irradiance categories, enabling the controller to respond appropriately to current environmental conditions. The proposed adaptive controller was implemented and tested under real operating conditions and compared with a conventional commercial solar controller. Over a 40-day testing period, the adaptive system achieved a 12.7% increase in thermal energy storage efficiency. Specifically, despite receiving 4.8% less solar radiation (719 kWh vs. 755 kWh), the adaptive controller stored 453 kWh of heat in the water tank compared to 416 kWh with the traditional system. This corresponds to an efficiency improvement from 0.55 to 0.63. These results demonstrate the adaptive controller’s superior ability to utilize available solar energy across all weather scenarios. The findings confirm that intelligent control strategies not only enhance technical performance but also improve the economic and environmental value of solar thermal systems. Full article

To address the challenges of low recognition accuracy and substantial model complexity in crop disease identification models operating in complex field environments, this study proposed a novel hybrid model named ConvTransNet-S, which integrates Convolutional Neural Networks (CNNs) and transformers for crop disease identification tasks. Unlike existing hybrid approaches, ConvTransNet-S uniquely introduces three key innovations: First, a Local Perception Unit (LPU) and Lightweight Multi-Head Self-Attention (LMHSA) modules were introduced to synergistically enhance the extraction of fine-grained plant disease details and model global dependency relationships, respectively. Second, an Inverted Residual Feed-Forward Network (IRFFN) was employed to optimize the feature propagation path, thereby enhancing the model’s robustness against interferences such as lighting variations and leaf occlusions. This novel combination of a LPU, LMHSA, and an IRFFN achieves a dynamic equilibrium between local texture perception and global context modeling—effectively resolving the trade-offs inherent in standalone CNNs or transformers. Finally, through a phased architecture design, efficient fusion of multi-scale disease features is achieved, which enhances feature discriminability while reducing model complexity. The experimental results indicated that ConvTransNet-S achieved a recognition accuracy of 98.85% on the PlantVillage public dataset. This model operates with only 25.14 million parameters, a computational load of 3.762 GFLOPs, and an inference time of 7.56 ms. Testing on a self-built in-field complex scene dataset comprising 10,441 images revealed that ConvTransNet-S achieved an accuracy of 88.53%, which represents improvements of 14.22%, 2.75%, and 0.34% over EfficientNetV2, Vision Transformer, and Swin Transformer, respectively. Furthermore, the ConvTransNet-S model achieved up to 14.22% higher disease recognition accuracy under complex background conditions while reducing the parameter count by 46.8%. This confirms that its unique multi-scale feature mechanism can effectively distinguish disease from background features, providing a novel technical approach for disease diagnosis in complex agricultural scenarios and demonstrating significant application value for intelligent agricultural management. Full article