Search Results (96,048)

This study presents a novel technological framework that integrates web-based 3D modeling and digital prototyping into interdisciplinary design education. Addressing the gap between traditional theoretical assessment and modern industry demands, the research investigates the implementation of interactive micro-websites and high-fidelity 3D product models as standard deliverables. Using a quasi-experimental design, the proposed digital workflow was tested on 53 final-year graphic design students at the University of Zagreb, divided into three groups based on the end users of their digital prototypes: real industry clients, peers, or academic mentors. The systemic reliability of the technological framework was measured through the technical quality of the final output (grades) analyzed via ANOVA, while user engagement with the digital process was tracked longitudinally. Results indicate that the implemented technological pipeline produced consistently high-quality outputs across all cohorts, with the client-facing group achieving the highest technical scores (M = 4.37; SD = 0.57). The lack of statistically significant variance between groups highlights a “ceiling effect,” demonstrating that the structured digital workflow itself is operationally stable and ensuring top-tier technical performance and prepress accuracy regardless of the evaluator. The study concludes that integrating advanced 3D web technologies and interactive public deliverables into the curriculum provides a scalable, industry-aligned technological model that successfully prepares design engineers for complex professional environments. Full article

Artificial intelligence has greatly accelerated the design and screening of metal–organic materials, particularly for crystalline systems with well-defined topologies and increasingly standardized structural databases. However, this success has also created a structure-centric design paradigm that is less suitable for metal–organic systems whose functions are governed by process history, interfacial assembly, and dynamic coordination rather than by a single idealized lattice. This Perspective proposes that artificial intelligence (AI)-guided design of metal–organic materials should expand beyond crystalline metal–organic frameworks (MOFs) to encompass a broader structural continuum, ranging from long-range ordered frameworks to dynamic, non-periodic coordination networks. Metal–polyphenol networks (MPNs) are used here as an experimentally tractable example within a broader family of structurally dynamic metal–organic materials, as they arise from coordination interactions between metal ions and polyphenolic ligands, generally lack long-range crystallographic periodicity, and exhibit functions that are governed by interfacial assembly, environmental responsiveness, and pathway-dependent structural evolution. These features challenge conventional descriptor design and database-driven prediction, but also create opportunities for AI approaches that are process-aware, interface-sensitive, and function-oriented. By placing MOFs and MPNs within a unified framework of structural order, this Perspective outlines how machine learning, multimodal characterization, active learning, and closed-loop experimentation could expand metal–organic materials design from topology prediction toward dynamic network optimization. Full article

Lactic acid bacteria exhibit high adaptability to their environment due to their wide variety of enzymes. Despite extensive knowledge of bacterial cell walls, the subcellular localization of β-galactosidase in many probiotic lactic acid bacteria, including Limosilactobacillus fermentum, remains unclear. Determining the cellular localization of such enzymes may improve insight into bacterial metabolic mechanisms and support the development of efficient downstream processes, as well as applications. In this study, three cell disruption strategies (mechanical homogenization and chemical disruption with different agents) were applied to assess the subcellular localization of β-galactosidase from the Ll. fermentum LF08 strain. Enzyme activity was measured in a ferment broth, a supernatant and cell-associated fractions. No and very low β-galactosidase activity was detected in the ferment broth and the supernatant, respectively, when either chemical or mechanical treatment was applied, whereas the main enzyme activity was assayed in the cell suspension fraction. Combined lysozyme and CTAB treatment resulted in a 21.4-fold increase in β-galactosidase activity in the supernatant fraction (2.14 U/mL), compared with CTAB treatment alone (0.10 U/mL). Bioinformatic analyses provided additional significant information to propose the potential cell wall association (maybe the outer side of the cell wall) of the subcellular localization of β-galactosidase. This feature may support understanding of the interactions between probiotic bacteria and host tissues, as well as the development of probiotic immobilized cell systems for applications such as the elimination of lactose, designing novel functional foods. Full article

This study aims to identify and prioritize digital economy factors affecting inflation and to determine effective policy strategies for managing digitally driven inflationary pressures in the context of financial systems and risk dynamics. The analysis considers twelve key digital economy indicators, including e-commerce penetration, digital payment systems, internet infrastructure, price transparency, digital advertising, Industry 4.0 technologies, data-driven inventory and demand systems, fintech adoption, cryptocurrency usage, and digital financial access. In parallel, eight policy strategies are evaluated, covering digital price transparency, expansion of digital payments, digital logistics optimization, digital public services, smart manufacturing, intelligent-based demand forecasting, fintech integration, and digital workforce development. The study employs a novel intelligent-supported decision-making framework integrating an attention-based expert weighting approach, generalized fractal fuzzy sets, the MEREC method, and the ARLON technique. The empirical design is based on expert evaluations obtained from ten specialists with at least 12 years of experience in digital economy, finance, and policymaking. Rather than relying on country-specific or time-series inflation datasets, the study examines the structural relationship between digitalization and inflation through a multi-criteria expert-based approach, with data collected in 2025. The findings indicate that e-commerce penetration and the prevalence of digital payment systems are the most influential factors affecting inflation. In addition, digital price transparency and the expansion of digital payment systems emerge as the most effective strategies for mitigating inflationary pressures. These results provide important insights into how digital transformation reshapes inflation dynamics, monetary transmission mechanisms, and inflation-related financial risks. The proposed model offers a robust and systematic framework for analyzing inflation in digitalized economies and supports policymakers and financial decision-makers in managing emerging risks in intelligent-driven economic environments. Full article

Retrieval grounding is crucial for high-stakes administrative applications, since large language models remain prone to hallucinations when addressing legal questions. This problem is particularly relevant in Moroccan territorial governance, where official legislative PDFs have highly heterogeneous digital quality, user interactions often occur in Moroccan Darija, and the legal corpus is bilingual Arabic–French. This paper presents CollectivIA, a multilingual Retrieval-Augmented Generation system implemented for Moroccan territorial governance law. The system supports queries in French, Arabic, and Moroccan Darija and indexes 2272 article-level segments from sixteen official legislative documents. We compare two end-to-end retrieval pipelines: an LLM-assisted semantic chunking architecture using Gemini and ChromaDB and a regex-based chunking architecture using FAISS. Based on an expanded multilingual benchmark of 150 legal queries, with 50 queries per language group, the LLM-assisted pipeline achieved higher RAGAS scores than the regex-based pipeline, particularly improving Context Precision from 0.315 to 0.818. The multimodal Vision fallback successfully recovered 456 articles, which remained inaccessible under the regex-based pipeline. Overall, the LLM-assisted pipeline yielded legal boundaries with greater coherence and retrieved contexts with higher focus, while the regex-based design maintained a broader source diversity. These results suggest that LLM-assisted semantic chunking with multimodal fallback is a promising approach to enhance multilingual legal RAG over heterogeneous Moroccan legal corpora. Full article

Accurate thermal management is crucial for ensuring the safety, longevity, and performance of lithium-ion batteries, especially in compact embedded systems like USB chargers, power banks, and IoT nodes. Despite extensive research on predictive thermal models and intelligent control frameworks, their implementation in resource-constrained microcontroller-class devices has been limited. Existing strategies in the literature, such as threshold-based or PID logic, cloud-enabled analytics, machine learning models, and observer-based estimators, are often reactive, computationally intensive, or dependent on external infrastructure, making them unsuitable for low-power, standalone applications. This study introduces a novel Scalable Embedded Thermal Intelligence architecture designed for real-time battery thermal regulation in locally executable, without cloud dependency, low-cost platforms. Unlike conventional methods, the proposed system operates entirely on-device using closed-form models implemented on an ESP32 microcontroller. It combines two synergistic algorithms: a static preemptive model that calculates a safe C-rate at startup based solely on ambient and initial battery temperature, and a dynamic disturbance-aware model that monitors temperature rise per SOC step and adjusts airflow or current adaptively without requiring high memory, floating-point units, or supervisory control. The architecture achieves sub-second response times, <7% RAM, and <25% Flash usage, and does not need cloud connectivity, simulation backend, or complex thermal-management infrastructures such as liquid cooling circuits, phase-change systems, or cloud-supervised architectures. The significant contribution of this work is not the introduction of a new electrochemical–thermal formulation, but the effective integration and application of previously validated closed-form thermal predictors on low-cost microcontroller-class hardware, designed for anticipatory battery thermal regulation while adhering to strict computational limitations. Compared to traditional battery thermal management systems using PCM, liquid-cooling circuits, or cloud-based predictive estimators, the proposed approach eliminates the need for complex thermal hardware, fluidic systems, external computing infrastructure and resource-efficient edge operation. This makes the system suitable for deployment in real-world embedded applications like USB-C smart charging cables, compact IoT power banks, and portable medical devices, where form factors, energy efficiency, and cost are critical. The proposed SETI framework offers a firmware-integrated architecture and a firmware-integrated solution that provides a lightweight embedded alternative for predictive thermal regulation for distributed energy systems and miniaturized electronics. Full article

This paper presents a five-coil array integrated wireless power transfer (WPT) system designed for implantable medical devices. The proposed structure features a collaborative design of driving and radiating coils, where each driving coil excites its corresponding radiating coil to emit power. All coil units are precisely tuned to operate at the 13.56 MHz ISM band. The unique array configuration generates a highly uniform magnetic field distribution within the target area, enabling excellent tolerance to lateral misalignment. System analysis based on scattering parameters (S-parameters) confirms the design’s outstanding power transfer efficiency at the operating frequency. By integrating the five-coil array onto a double-layer printed circuit board, the system achieves the miniaturization and high integration level required for implantable applications. The experimental results demonstrate that the system reaches a maximum transfer efficiency of 55% under ideal alignment conditions (with a transfer distance of 10–20 mm). Notably, even with a lateral displacement of 5–10 mm at a 10 mm transfer distance, the system maintains stable performance, with efficiency consistently exceeding 50%. These results validate the system’s capability for reliable and efficient wireless power delivery in clinical settings. Full article

Silicone structural sealant is a key material in glass curtain wall systems, for which an appropriate hyperelastic constitutive model is essential for enhancing the accuracy of finite element analysis (FEA). Firstly, an improved comprehensive fixture of silicone structural sealants for tensile and shear tests was conducted. Secondly, the hyperelastic constitutive model was selected to fit data through experiments, which was comprehensively evaluated using three indicators, such as goodness of fit, root mean square error, and relative deviation. Thirdly, this constitutive model was utilized for FEA validation under uniaxial tensile and shear conditions, the Mooney–Rivlin 3 (M-R3) model demonstrated optimal fitting performance. Finally, the mechanical response of the glass curtain wall under wind load was systematically analyzed under various debonding scenarios. This study reveals the failure evolution law of silicone structural sealant under actual service conditions, providing practical guidance for enhancing the safety design of glass curtain wall structures. Full article

This study aimed to develop a monocular vision-based tennis serve analysis system and evaluate its effectiveness in beginner training. The system uses MediaPipe Pose to extract 33 body landmarks from monocular video, calculates joint angles using three-dimensional vector operations, identifies serve phases through threshold-based rules, constructs an approximate 3D pose representation using anthropometric constraints, and generates corrective feedback through a rule-based expert system. In a quasi-experimental study, 90 beginner tennis players (final n = 82) completed an 8-week intervention and were allocated to a high-frequency feedback group, a moderate-frequency feedback group, or a conventional training group. All groups showed significant improvements in Serve Quality Mastery (SQM) scores (p < 0.001). The high-frequency feedback group showed the greatest SQM improvement (SQM: +30.5 points), followed by the moderate-frequency feedback group (+24.2 points) and the conventional training group (+15.5 points). Between-group differences were significant F(2, 79) = 74.30, p < 0.001, η² = 0.65. These findings indicate a graded pattern across the feedback-frequency groups, with more frequent system-generated feedback being associated with greater improvements in training performance. The findings support the potential use of the monocular pose-based, rule-driven feedback system as a supplementary tool for beginner tennis serve instruction. Full article

This research aims to understand Chinese international students’ perceptions of the academic support provided by Spanish universities. It explores students’ feedback on their participation in such support, its perceived effectiveness, and their expectations and needs. Drawing from a hybrid sociocultural framework, this study employed a qualitative research design, conducting semi-structured interviews to collect data from 14 Chinese postgraduate students at the Autonomous University of Barcelona. Data were analysed using thematic analysis to uncover key themes related to the students’ experiences with the academic support system. The findings reveal that, although UAB offers various forms of academic support, both participation in, and the perceived effectiveness of, these provisions remain limited. Chinese students encounter challenges such as language barriers, unfamiliarity with the academic support system, and varying attitudes from faculty. The findings highlight a need for more practical and systematic academic writing and speaking courses, as well as culturally sensitive and internationalised support mechanisms. As a practical implication, the study suggests that universities should prioritise “situational” oral communication training that prepares students for active classroom participation and implement proactive outreach strategies, such as engaging departmental coordinators to directly promote available library and digital resources, thereby overcoming the current lack of student awareness. This study contributes to addressing a gap in the literature by providing empirical insights into the learning experiences of Chinese postgraduate students regarding academic support in Spain. It offers recommendations for UAB and other Spanish institutions to enhance their academic support systems, promoting a more inclusive and international environment for international students. Full article

Immersive solutions are becoming a trending technology for decision support across fields such as transportation, healthcare, and urban planning. Despite their role, the mechanism by which they affect decision-making is unclear. Our study examines the design primitives in immersive technology that are manipulated to influence decision-making and synthesizes how they operate to shape decision outcomes. We follow PRISMA guidelines to search. A total of 198 studies were included. Eight primitive families were identified, including perceptual realism, environmental structure, interactivity, temporal simulation, embodiment, social presence, multisensory integration, and other contextual manipulations. Mechanisms through which they impacted decision-making were classified into cognitive, perceptual, affective, motivational, social-influence, and behavioral-heuristic mechanisms. Perceptual realism, environmental structure, and interactivity emerged as the most frequently investigated primitives, while presence, risk perception, spatial cognition, engagement, and social influence were among the most reported mechanisms. Our results suggest that immersive technologies function as decision-shaping systems that alter how users perceive uncertainty, risks, consequences, and alternatives, highlighting the need for theory-driven research and evaluation in high-stakes decision contexts. Full article

To address the challenges of difficult feature extraction and insufficient recognition accuracy caused by the small size of chili flowers, occlusion by branches and leaves, and illumination variations in complex field environments, a dual-backbone-based chili flower pose estimation algorithm, termed DCFP-YOLO, is proposed. Built upon the YOLO11n framework, the proposed method performs classification and recognition of five typical upward-oriented chili flower poses. To alleviate the loss of local detail features of small chili flowers under complex backgrounds, a dual-backbone feature extraction network composed of StarNet and ShuffleNetV2 is constructed. Specifically, the StarNet backbone enhances the extraction of fine-grained local features from key floral regions, while the ShuffleNetV2 backbone improves the perception of global spatial structural information. The complementary fusion of dual-backbone features strengthens the representation capability of chili flower pose features in complex environments. To mitigate the attenuation of shallow detail information during multi-scale feature transmission, a Bidirectional Multi-branch Auxiliary Feature Pyramid Network (BiMAFPN) is designed to enhance feature propagation through cross-scale feature interaction, thereby improving pose recognition performance under occlusion and overlapping conditions. Furthermore, a Programmable Gradient Information (PGI)-assisted training mechanism is introduced to optimize gradient propagation paths and alleviate information bottlenecks in deep networks, thereby enhancing the robustness of multi-pose feature extraction under occlusion, blur, and complex illumination conditions. Experimental results demonstrate that DCFP-YOLO achieves recall, mAP50, and mAP50 values of 87.4%, 92.0%, and 66.9%, respectively, representing improvements of 1.7, 1.3, and 3.5 percentage points over the baseline model. Overall performance surpasses that of current mainstream object detection algorithms. After deployment on the NVIDIA Jetson AGX Orin platform, the model achieves an inference speed of 20.9 frames/s, which can basically satisfy the real-time perception requirements of chili flower pose recognition in complex agricultural environments. The proposed method provides an effective visual perception framework for chili flower pose recognition in complex agricultural environments. Rather than constituting a complete robotic pollination solution, the developed model serves as a potential perception component for future intelligent pollination robotic systems, providing reliable flower pose information for subsequent research on target localization, end-effector alignment, and robotic pollination in unstructured greenhouse environments. Full article

Donor–acceptor (D–A) molecular systems are gaining increasing attention in cancer imaging and phototherapy due to their tunable optical properties and high photosensitizing efficiency. Encapsulation of such D–A molecules in nano-sized polymeric carriers can enhance the efficiency of antitumor therapy by passive tumor accumulation and controlled drug release. Here, we synthesized two D–A molecules—TTDCV and TTInd—based on triphenylamine with thiophene π-spacers and electron-withdrawing dicyanovinyl or indene-1,3-dione moieties. These molecules were used to preparate nanoparticles (NPs) via nanoprecipitation with amphiphilic polymers—poly(ethylene glycol)-block polylactide methyl ether (PEG-b-PLA) and polyethylene oxide-polypropylene oxide (PEO-PPO-PEO, Pluronic^® F-127). The resulting NPs had spherical morphology, core–shell structure and a tunable mean size (66–139 nm), depending on the polymer type used. Photothermal and photodynamic properties of the NPs were confirmed by intracellular reactive oxygen species generation and efficient heating even under 530 nm low dose irradiation (1 J/cm²), leading to substantial in vitro cytotoxicity against Sk-Br-3 and MCF-7 human breast cancer cells. Pluronic-encapsulated systems showed the strongest effect, reducing IC₅₀ values down to 0.99 µg/mL and achieving phototoxicity indices up to 22, accompanied by increased intracellular accumulation studied by confocal microscopy and flow cytometry. This study establishes relationships between molecular design, encapsulation approaches, and the biological performance of nanoparticles, enabling the rational engineering of D–A-derived nanotherapeutics for precision cancer treatment. Full article

Background: Renal cell carcinoma accounts for nearly 15,000 deaths annually in the US, and approximately 30–40% of patients present with metastatic disease (mRCC). The advent of immune checkpoint inhibitors (IO) and tyrosine kinase inhibitors (TKI) has revolutionized the treatment paradigm of patients with mRCC. However, the role of cytoreductive nephrectomy (CN) in the IO-TKI era, particularly for rare and understudied histologies such as non-clear-cell RCC, remains poorly understood. Methods: We conducted a retrospective cohort study of patients with metastatic non-clear-cell RCC. Patients were stratified by receipt of CN. Baseline demographic, clinical, histologic, and metastatic site variables were collected. Overall survival was analyzed using Kaplan–Meier methods and compared with the log-rank test. Cox proportional hazards regression was performed to identify independent predictors of survival, including CN, systemic therapy, year of diagnosis, histology, and metastatic sites. Results: Among 2753 patients with metastatic nccRCC, 1654 (60.1%) underwent CN and 1099 (39.9%) did not undergo CN. The 2-year and 5-year OS rates were 35.52% and 19.22% in the CN group versus 18.53% and 7.47% in the non-CN group (p < 0.001). In the doubly robust IPTW-weighted multivariable Cox regression analysis, CN was associated with improved overall survival, corresponding to a 40% lower risk of death compared with no CN (HR 0.60, 95% CI 0.54–0.66; p < 0.001). Additionally, more recent treatment eras were associated with progressively improved overall survival, with patients diagnosed between 2015 and 2017 and 2018 onward demonstrating significantly improved OS compared with those diagnosed between 2004 and 2014. Conclusions: Our study demonstrates that CN was associated with improved OS in patients with non-clear-cell mRCC by reducing the risk of death by 40% after adjusting for baseline characteristics. These findings emphasize the role of CN even in the IO-TKI era for the management of patients with non-clear-cell mRCC. However, these findings should be interpreted in the context of the retrospective study design, potential selection bias, and lack of granular systemic therapy data within the NCDB. Full article

Multifunctional membranes capable of simultaneously separating oil–water emulsions and removing organic dyes from complex aqueous systems have garnered considerable attention in recent years. However, the facile fabrication of high-performance membranes that integrate both separation and adsorption functions remains a significant challenge. Herein, we report the fabrication of a polyacrylonitrile/polyvinyl alcohol–chitosan (PAN/PVA-CS) bilayer membrane with asymmetric wettability via electrospinning. The micro/nanostructures and surface wettability of the as-prepared membranes were precisely tailored by modulating the chitosan (CS) concentration. The resultant PAN/PVA-CS membrane exhibited an overall separation efficiency exceeding 97.5% for mechanically emulsified samples. Notably, the PVA-CS layer demonstrated superhydrophilicity and excellent underwater oleophobicity, enabling the gravity-driven simultaneous separation of oil-in-water emulsions and adsorption of water-soluble Congo red dye without requiring external pressure. The maximum adsorption capacity for Congo red reached 61.3 mg g⁻¹, surpassing that of numerous reported membrane-based and adsorbent materials. Concurrently, the hydrophobic PAN layer in the bilayer structure enabled the separation of water-in-oil emulsions. Overall, this work provides a promising strategy for the rational design of asymmetrically wettable multifunctional membranes with great potential for practical application in the purification of complex industrial wastewater containing both emulsified oils and soluble organic dyes. Full article