Search Results (6,021)

Recent advancements in large language models (LLMs) have significantly enhanced the effectiveness of Retrieval-Augmented Generation (RAG) systems. This study focuses on the development and evaluation of a domain-specific AI chatbot designed to support international student admissions by leveraging LLM-based RAG pipelines. We implement and compare multiple pipeline configurations, combining retrieval methods (e.g., Dense, MMR, Hybrid), chunking strategies (e.g., Semantic, Recursive), and both open-source and commercial LLMs. Dual evaluation datasets of LLM-generated and human-tagged QA sets are used to measure answer relevancy, faithfulness, context precision, and recall, alongside heuristic NLP metrics. Furthermore, latency analysis across different RAG stages is conducted to assess deployment feasibility in real-world educational environments. Results show that well-optimized open-source RAG pipelines can offer comparable performance to GPT-4o while maintaining scalability and cost-efficiency. These findings suggest that the proposed chatbot system can provide a practical and technically sound solution for international student services in resource-constrained academic institutions. Full article

The incorporation of waste polyethylene terephthalate (PET) as a modifier for asphalt presents a promising approach to addressing the environmental pollution associated with waste plastics while simultaneously extending the service life of road surfaces. This study investigates the fundamental physical properties and rheological properties of asphalt modified with waste PET at both high and low temperatures. Utilizing the theory of fractional derivatives, performance evaluation indicators, such as the deformation factor and viscoelasticity factor, have been developed for the assessment of waste PET-modified asphalt. The underlying mechanism of this modification was examined through scanning electron microscopy and Fourier transform infrared spectroscopy. The results indicate that the addition of waste PET enhances the high-temperature stability of the base asphalt but reduces its resistance to cracking at low temperatures. The fractional derivative model effectively describes the dynamic shear rheological properties of waste PET-modified asphalt, achieving a maximum correlation coefficient of 0.99991. Considering the performance of modified asphalt at both high and low temperatures, the optimal concentration of waste PET was determined to be 6%. At this concentration, the minimum creep stiffness of the PET-modified asphalt was approximately 155 MPa at −6 °C. Additionally, the rutting factor of the waste PET-modified asphalt achieved a maximum value of 527.12 KPa at 52 °C. The interaction between waste PET and base asphalt was primarily physical, with mutual adsorption leading to the formation of a spatial network structure that enhanced the deformation resistance of the asphalt. This study provides a theoretical foundation and technical support for the engineering application of waste PET as a modifier in asphalt. Full article

A systematic sensitivity analysis using three-dimensional discrete element models with discrete fracture networks (DEM-DFN) was conducted to evaluate underground excavation support in jointed rock masses at the CLAB2 site in Southeastern Sweden. The site features a joint network comprising six distinct joint sets, each with unique geometrical properties. The study examined 10 DFNs and 19 rock bolt patterns, both conventional and unconventional. It covered 200 scenarios, including 10 unsupported and 190 supported cases. Technical and economic criteria for stability were assessed for each support system. The results indicated that increasing rock bolt length enhances stability up to a certain point. However, multi-length rock bolt patterns with similar consumption can yield significantly different stability outcomes. Notably, the arrangement and properties of rock bolts are crucial for stability, particularly in blocks between bolting sections. These blocks remain interlocked in unsupported areas due to the induced pressure from supported sections. Although equal-length rock bolt patterns are commonly used, the analysis revealed that triple-length rock bolts (3, 6, and 9 m) provided the most effective support across all ten DFN scenarios. Full article

Background: Computed tomography urography (CTU) is routinely used to evaluate the upper urinary tract in patients with hematuria. CTU may detect incidental findings outside the urinary tract, but it remains unclear if this adds value. This study aimed to develop a deep learning algorithm that automatically segments and selectively visualizes the urinary tract on CTU. Methods: The urinary tract (kidneys, ureters, and urinary bladder) was manually segmented on 2 mm dual-phase CTU slices of 111 subjects. With this dataset, a deep learning-based AI was trained to automatically segment and selectively visualize the urinary tract on CTU scans (including accompanying unenhanced CT scans), which we dub “focused view CTU”. Focused view CTU was technically optimized and tested in 39 subjects with hematuria. Results: The technically optimized focused view CTU algorithm provided complete visualization of 97.4% of kidneys, 80.8% of ureters, and 94.9% of urinary bladders. All urinary tract organs were completely visualized in 66.6% of cases. In these cases (excluding 33.3% of cases with incomplete visualization), focused view CTU intrinsically achieved a sensitivity, specificity, positive predictive value, and negative predictive value of 100.0%, 92.3%, 92.9%, and 100.0% for lesions in the urinary tract compared to unmodified CT, although interrater agreement was moderate (κ = 0.528). All incidental findings were successfully hidden by focused view CTU. Conclusions: Focused view CTU provides adequate urinary tract segmentation in most cases, but further research is needed to optimize the technique (segmentation does not succeed in about one-third of cases). It offers selective urinary tract visualization, potentially aiding in assessing relevance and cost-effectiveness of detecting incidental findings in hematuria patients through a prospective randomized trial. Full article

This study evaluates the replacement of petroleum-based naphthenic oil with four types of soybean-derived alternatives—virgin soybean oil (SBO), epoxidized SBO (ESBO), expired SBO, and recycled SBO—in styrene–butadiene rubber (SBR) composites. The materials were tested in both staining rubber (SR) and non-staining rubber (NSR) systems to assess processing characteristics, mechanical performance, and environmental durability. Among the alternatives, SBO demonstrated the best overall performance, improving processability and tensile strength by over 10%, while ESBO enhanced ozone resistance by 35% due to its epoxide functionality. Expired and recycled SBOs maintained essential mechanical properties within 90% of virgin SBO values. The full replacement of CH450 with SBO in tire prototypes resulted in burst strength exceeding 1000 kPa and stable appearance after 5000 km of road testing. To validate industrial relevance, the developed green tire was exhibited at the 2025 Taipei International Cycle Show, attracting interest from international buyers and stakeholders for its eco-friendly composition and carbon footprint reduction potential, thereby demonstrating both technical feasibility and commercial viability. Full article

In power distribution networks, the growing integration of renewable energy sources (RESs) presents a challenge for the electricity system and its operators, who need to make the energy sector more flexible and resilient. In this context, this paper proposes a novel flexibilization service for the distribution system leveraging the role of renewable energy communities (RECs), an emerging entity with the potential to facilitate the sustainable energy transition through Virtual Islanding operation. The concept of Virtual Islanding is investigated in the paper and a methodology for its validation is developed. Its effectiveness is then assessed using an IEEE-standard 33-node network with significant penetration of RESs, considering the presence of multiple RECs to prove its benefits on electrical distribution networks. The results showcase the advantages of the VI paradigm both from technical and sustainability viewpoint. Full article

Artificial intelligence has taken a seat at the executive table and is threatening the fact that human beings are the only ones who should be in a position of power. This article gives conjectures on the future of leadership in which managers will collaborate with learning algorithms in the Neural Adaptive Artificial Intelligence Leadership Model, which is informed by the transformational literature on leadership and socio-technical systems, as well as the literature on algorithmic governance. We assessed the model with thirty in-depth interviews, system-level traces of behavior, and a verified survey, and we explored six hypotheses that relate to algorithmic delegation and ethical oversight, as well as human judgment versus machine insight in terms of agility and performance. We discovered that decisions are made quicker, change is more effective, and interaction is more vivid where agile practices and good digital understanding exist, and statistical tests propose that human flexibility and definite governance augment those benefits as well. It is single-industry research that contains self-reported measures, which causes research to be limited to other industries that contain more objective measures. Practitioners are provided with a practical playbook on how to make algorithmic jobs meaningful, introduce moral fail-safes, and build learning feedback to ensure people and machines are kept in line. Socially, the practice is capable of minimizing bias and establishing inclusion by visualizing accountability in the code and practice. Filling the gap between the theory of leadership and the reality of algorithms, the study provides a model of intelligent systems leading in organizations that can be reproduced. Full article

Background/Objectives: This study aimed to assess the long-term clinical performance of full-arch fixed restorations made of fiber-reinforced composite (FRC) supported by four ultra-short implants (4.0 × 5.0 mm) in patients with edentulous, atrophic mandibles. Methods: Ten patients were treated at Sapienza University of Rome and monitored over a 10-year period. Each case involved the placement of four plateau-design implants with a pure conometric connection and a calcium phosphate-treated surface. The final prostheses were fabricated using CAD/CAM-milled Trinia^® fiber-reinforced composite frameworks. Clinical parameters included implant and prosthesis survival, marginal bone level (MBL), peri-implant probing depth (PPD), and patient-reported outcome measures (PROMs). Results: Implant and prosthesis survival reached 100% over the 10-year follow-up. MBL data showed a minor bone gain of approximately 0.11 mm per 5 years (p < 0.0001). PPD remained stable under 3 mm, with a minimal increase of 0.16 mm over the same period (p < 0.0001). PROMs reflected sustained high patient satisfaction. No technical complications, such as chipping or framework fracture, were observed. Conclusions: Rehabilitation of the edentulous mandible with ultra-short implants and metal-free FRC prostheses proved to be a minimally invasive and long-lasting treatment option. The 10-year follow-up confirmed excellent implant and prosthetic outcomes, favorable peri-implant tissue health, and strong patient satisfaction. Nonetheless, further studies with larger sample sizes are needed to confirm these encouraging results and strengthen the clinical evidence. Full article

Increased food production demands, labor shortages, and environmental concerns are driving the need for innovative agricultural technologies. However, effective adoption depends critically on aligning robot innovations with the needs of farmers. This paper examines the alignment between the needs of farmers and the robotic systems used in row crop farming. We review current commercial agricultural robots and research, and map these to the needs of farmers, as expressed in the literature, to identify the key issues holding back large-scale adoption. From initial pool of 184 research articles, 19 survey articles, and 82 commercial robotic solutions, we selected 38 peer-reviewed academic studies, 12 survey articles, and 18 commercially available robots for in-depth review and analysis for this study. We identify the key challenges faced by farmers and map them directly to the current and emerging capabilities of agricultural robots. We supplement the data gathered from the literature review of surveys and case studies with in-depth interviews with nine farmers to obtain deeper insights into the needs and day-to-day operations. Farmers reported mixed reactions to current technologies, acknowledging efficiency improvements but highlighting barriers such as capital costs, technical complexity, and inadequate support systems. There is a notable demand for technologies for improved plant health monitoring, soil condition assessment, and enhanced climate resilience. We then review state-of-the-art robotic solutions for row crop farming and map these technological capabilities to the farmers’ needs. Only technologies with field validation or operational deployment are included, to ensure practical relevance. These mappings generate insights that underscore the need for lightweight and modular robot technologies that can be adapted to diverse farming practices, as well as the need for farmers’ education and simpler interfaces to robotic operations and data analysis that are actionable for farmers. We conclude with recommendations for future research, emphasizing the importance of co-creation with the farming community to ensure the adoption and sustained use of agricultural robotic solutions. Full article

Direct electrification and hydrogen utilization represent two key pathways for decarbonizing the glass industry, with their effectiveness subject to adequate furnace design and renewable energy availability. This study presents a techno-economic assessment for optimal solar energy integration in a representative 300 t/d oxyfuel container glass furnace with a specific energy consumption of 4.35 GJ/t. A mixed-integer linear programming formulation is developed to evaluate specific melting costs, carbon emissions, and renewable energy self-consumption and self-production rates across three scenarios: direct solar coupling, battery storage, and a hydrogen-based infrastructure. Battery storage achieves the greatest reductions in specific melting costs and emissions, whereas hydrogen integration minimizes electricity export to the grid. By incorporating capital investment considerations, the study quantifies the cost premiums and capacity requirements under varying decarbonization targets. A combination of 30 MW of solar plant and 9 MW of electric boosting enables the realization of around 30% carbon reduction while increasing total costs by 25%. Deeper decarbonization targets require more advanced systems, with batteries emerging as a cost-effective solution. These findings offer critical insights into the economic and environmental trade-offs, as well as the technical constraints associated with renewable energy adoption in the glass industry, providing a foundation for strategic energy and decarbonization planning. Full article

Objective: To determine reference values of liver stiffness during the first week of extrauterine life in healthy newborns, according to gestational age, sex, and birth weight, using three elastography techniques: point shear wave elastography (pSWE) and two-dimensional shear wave elastography (2D-SWE) with convex and linear probes. Materials and Methods: This was a cross-sectional observational study conducted at a single center on a hospital-based cohort of 287 newborns between 24 and 42 weeks of gestation, admitted between January 2023 and May 2024. Cases with liver disease, significant neonatal morbidity, or technically invalid studies were excluded. Hepatic elastography was performed during the first week of life using pSWE and 2D-SWE with both convex and linear probes. Clinical and technical neonatal variables were recorded. Liver stiffness values were analyzed in relation to gestational age, birth weight, and sex. Linear regression models were applied to assess associations, considering p-values < 0.05 as statistically significant. Results: After applying exclusion criteria, valid liver stiffness measurements were obtained in 208 cases with pSWE, 224 with 2D-SWE (convex probe), and 222 with 2D-SWE (linear probe). A statistically significant inverse association between liver stiffness and gestational age (p < 0.03) was observed across all techniques except for 2D-SWE with the linear probe. Only 2D-SWE with the convex probe showed a significant association with birth weight. No significant differences were observed based on neonatal sex. The 2D-SWE technique with the convex probe demonstrated significantly shorter examination times compared to pSWE (p < 0.001). Conclusions: Neonatal liver stiffness measured by pSWE and 2D-SWE with a convex probe shows an inverse correlation with gestational age, potentially reflecting the structural and functional maturation of the liver. These techniques are safe, reliable, and provide useful information for distinguishing normal findings in preterm neonates from early hepatic pathology. The values obtained represent a valuable reference for clinical hepatic assessment in the neonatal period. Full article

Background/Objective: The most effective strategy for addressing users’ prosthetic needs is a comprehensive clinical assessment that provides a holistic understanding of the individual’s symptoms, health, function, and environmental barriers and facilitators. A standardized evaluation form would provide guidance for a structured approach to comprehensive clinical assessments of people with LLA. The objective of this study was to determine a list of relevant elements to be included in prosthetic evaluation for adults with lower limb amputation. Methods: Three independent focus group discussions were conducted with prosthetists (n = 15), prosthesis users (n = 11), and decision makers (n = 4) to identify all relevant elements that should be included in the clinical assessment of prosthetic services. The final content was then determined using the Delphi technique, with 35 panelists (18 prosthetists and decision makers, and 17 prosthesis users) voting in each round. Results: A total of 91 elements were identified through the focus group, of which 78 were included through the Delphi process. The identified elements are mostly related to the physical health of the prosthesis user (e.g., mobility, pain, and medical information), while others address personal or psychosocial aspects (e.g., activities of daily living, goals, and motivation) or technical aspects (prosthesis-related). Conclusions: Through a Delphi consensus, a list of relevant elements to be included in a prosthetic evaluation was generated. These results will inform the development of a standardized clinical prosthetic assessment form. This form has the potential to improve the quality of clinical evaluations, guide interventions, and enhance the well-being of prosthetic users. Full article

Background: Peripancreatic collections (PPCs) are a frequent and severe complication of acute and chronic pancreatitis, as well as pancreatic surgery, often requiring interventions to treat and prevent infection, gastric obstruction, and other complications. Endoscopic ultrasound (EUS)-guided drainage has emerged as a minimally invasive alternative to surgical and percutaneous approaches, offering reduced morbidity and shorter recovery times. However, the effectiveness of EUS-guided drainage in post-surgical PPCs remains underexplored. Methods: This retrospective, single-center study evaluated the technical and clinical outcomes of EUS-guided drainage in patients with PPCs between October 2021 and December 2024. Patients were categorized as having post-pancreatitis or post-surgical PPCs. Technical success, clinical success, complications, recurrence rates, and the need for reintervention were assessed. Results: A total of 50 patients underwent EUS-guided drainage, including 42 (84%) with post-pancreatitis PPCs and 8 (16%) with post-surgical PPCs. The overall technical success rate was 100%, with clinical success achieved in 96% of cases. Lumen-apposing metal stents (LAMSs) were used in 84% of patients, including 7.1% as a dual-gate salvage strategy after the failure of double-pigtail drainage. The complication rate was 24%, with infection being the most common (16%). The recurrence rate was 25%, with no significant difference between post-pancreatitis and post-surgical cases. Patients with walled-off necrosis had a significantly higher reintervention rate (35%) than those with pseudocysts (18%; p = 0.042). Conclusions: EUS-guided drainage is a highly effective and safe intervention for PPCs, including complex post-surgical cases. The 100% technical success rate reinforces its reliability, even in anatomically altered post-surgical collections. While recurrence rates remain a consideration, EUS-guided drainage offers a minimally invasive alternative to surgery, with comparable outcomes in both post-pancreatitis and post-surgical patients. Future multi-center studies should focus on optimizing treatment strategies and reducing recurrence in high-risk populations. Full article

Background/Objectives: Accurate and accessible perioperative health information empowers patients and enhances recovery outcomes. Artificial intelligence tools, such as ChatGPT, have garnered attention for their potential in health communication. This study evaluates the accuracy and readability of responses generated by ChatGPT to questions commonly asked about breast cancer. Methods: Fifteen simulated patient queries about breast cancer surgery preparation and recovery were prepared. Responses generated by ChatGPT (4o version) were evaluated for accuracy by a pool of breast surgeons using a 4-point Likert scale. Readability was assessed with the Flesch–Kincaid Grade Level (FKGL). Descriptive statistics were used to summarize the findings. Results: Of the 15 responses evaluated, 11 were rated as “accurate and comprehensive”, while 4 out of 15 were deemed “correct but incomplete”. No responses were classified as “partially incorrect” or “completely incorrect”. The median FKGL score was 11.2, indicating a high school reading level. While most responses were technically accurate, the complexity of language exceeded the recommended readability levels for patient-directed materials. Conclusions: The model shows potential as a complementary resource for patient education in breast cancer surgery, but should not replace direct interaction with healthcare providers. Future research should focus on enhancing language models’ ability to generate accessible and patient-friendly content. Full article

Inspired by the Toyota Mirai, this study presents a high-fidelity data-driven approach for modelling and simulation of a fuel cell hybrid electric powertrain. This study utilises technical assessment data sourced from Argonne National Laboratory’s publicly available report, faithfully modelling most of the vehicle subsystems as data-driven entities. The simulation framework is developed in the MATLAB/Simulink environment and is based on a power dynamics approach, capturing nonlinear interactions and performance intricacies between different powertrain elements. This study investigates subsystem synergies and performance boundaries under a combined driving cycle composed of the NEDC, WLTP Class 3 and US06 profiles, representing urban, extra-urban and aggressive highway conditions. To emulate the real-world load-following strategy, a state transition power management and allocation method is synthesised. The proposed method dynamically governs the power flow between the fuel cell stack and the traction battery across three operational states, allowing the battery to stay within its allocated bounds. This simulation framework offers a near-accurate and computationally efficient digital counterpart to a commercial hybrid powertrain, serving as a valuable tool for educational and research purposes. Full article