Search Results (52,783)

Climate change and urbanization are intensifying the urban heat island effect and negatively impacting outdoor thermal comfort in cities. Innovative planning strategies are required to design more livable and resilient urban spaces. Building on a state of the art of current Urban Digital Twins (UDTs) for outdoor thermal comfort analysis, this paper presents the design and implementation of a functional UDT prototype. Developed for a pilot area in Huelva, Spain, the system integrates real-time environmental data, spatial modeling, and simulation tools within an open-source architecture. The literature reveals that while UDTs are increasingly used in urban management, their application to outdoor thermal comfort remains limited and technically challenging, especially in terms of real-time data, modeling accuracy, and user interaction. The case study demonstrates the feasibility of a modular, open-source UDT capable of simulating mean radiant temperature and outdoor thermal comfort indexes at high resolution and visualizing the results in a 3D interactive environment. UDTs have strong potential for supporting microclimate-sensitive planning and improving outdoor thermal comfort. However, important challenges remain, particularly in simulation efficiency, model detail, and stakeholder accessibility. The proposed prototype addresses several of these gaps and provides a basis for future improvements. Full article

Background/Objectives: The COVID-19 (Coronavirus Disease, 2019) pandemic affected all countries in a variety of ways, and forced policymakers to adapt national health infrastructure. In this context, the strategic adaptation and policy evolution of small island states are understudied. Therefore, the objective of this study was to quantitatively analyse the relationship between confirmed COVID-19 cases and health policy decisions in Northern Cyprus. We also examined the shifting management strategies employed during the pandemic using a replicable statistical analysis framework. Methods: In this mixed-methods study, we used systematic thematic analysis to categorise official policy decisions from March 2020 to December 2022. Yearly linear regression models using SPSS and Python correlated the monthly number of decisions with the number of confirmed COVID-19 cases. The analyses included R² values, p-values, and visualisations with 95% confidence intervals. Results: The findings of this study highlight a three-phase strategic period. In 2020, the results (R² = 0.03, p = 0.63) showed no significant relationship, indicating initial uncertainty. The results (R² = 0.60, p = 0.003) indicate a strong negative correlation in 2021, which reflects the consistency of the proactive suppression strategies adopted. Conversely, for 2022, the results (R² = 0.79, p < 0.001) show a strong positive correlation representing the shift to a reactive mitigation strategy, in which the government responded based on case peaks. Conclusions: This study’s primary finding is that strategic agility was key to managing the pandemic. For small island states in particular, the effectiveness of geographic advantages like border control depends on a coherent strategy that transcends initial uncertainty. Our data-driven framework provides a tool for analysing this strategic evolution and guiding responses to future pandemics. Full article

The present study investigated factors influencing cybersecurity in railway critical infrastructure by identifying relevant factors and criteria and then prioritizing them in order of importance. To address the lack of multi-criteria analysis in previous studies on this topic, the present study applied the analytical hierarchy process to identify factors and criteria influencing cybersecurity and then selected the top 70% of influencing criteria to serve as a reference for railway cybersecurity project management. A total of 25 valid expert questionnaires were collected for weight vector analysis, revealing that the influencing criteria in the top 70% were inability to monitor train occupancy in track sections (locations); inability of controllers to issue commands to safety control systems; inability to provide drivers with information on upcoming signals, block status, and train occupancy; failure to automatically apply brakes when the train exceeds the speed limit; increased risk of catastrophic accidents due to power system security vulnerabilities; and inability of the dispatching system to automatically track train numbers. Full article

By applying the Non-Perturbative Approach (NPA), the corresponding linear differential equation is obtained. Aimed at organizational investigation, the resulting linear equation is used. Strong agreement between numerical calculations and the precise frequency is demonstrated, and the reliability of the results acquired is established by the correlation with the numerical solution. Additionally, this study explores a new control process to affect the stability and behavior of dynamic motorcycle systems that vibrate nonlinearly. A multiple time-scale method (MTSM) is applied to examine the analytical solution of the nonlinear differential equations describing the aforementioned system. Every instance of resonance was taken out of the second-order approximations. The simultaneous primary and 1:1 internal resonance case ($\mathsf{\Omega }\approx {\omega }_{eq},$${\omega }_2\approx {\omega }_{eq}$) is recorded as the worst resonance case caused while working on the model. We investigated stability with frequency–response equations and bifurcation. Numerical solutions for the system are covered. The effects of the majority of the system parameters were examined. In order to mitigate harmful vibrations, the controller under investigation uses (PD) proportional derivatives with (PPF) positive position feedback as a new control technique. This creates a new active control technique called PDPPF. A comparison between the PD, PPF, and PDPPF controllers demonstrates the effectiveness of the PDPPF controller in reducing amplitude and suppressing vibrations. Unwanted consequences like chaotic dynamics, limit cycles, or loss of stability can result from bifurcation, which is the abrupt qualitative change in a system’s behavior as a parameter. The outcomes showed how effective the suggested controller is at reducing vibrations. According to the findings, bifurcation analysis and a control are crucial for designing vibrating dynamic motorcycle systems for a range of engineering applications. The MATLAB software is utilized to match the analytical and numerical solutions at time–history and frequency–response curves (FRCs) to confirm their comparability. Additionally, case studies and numerical simulations are presented to show how well these strategies work to control bifurcations and guarantee the desired system behaviors. An analytical and numerical solution comparison was prepared. Full article

Background/Objectives: The global incidence of thyroid cancer has been increasing, necessitating improved diagnostic strategies for detecting lymph node metastases. Fine-Needle Aspiration Biopsy (FNA) is a widely used diagnostic tool; however, its accuracy is sometimes limited, particularly in cases with non-diagnostic results. Washout Thyroglobulin (Washout Tg) measurement has emerged as an important adjunctive tool in refining thyroid cancer diagnosis. Methods: This retrospective study analyzed 723 patients who underwent thyroid cancer surgery at Samsung Medical Center from 2013 to 2023. The patients were categorized based on their thyroid status into three groups: 1. total thyroidectomy with modified radical neck dissection (mRND); 2. completion thyroidectomy with mRND; and 3. mRND or selective neck dissection (SND) without thyroidectomy. The Washout Tg levels and their diagnostic performance were evaluated using Receiver Operating Characteristic (ROC) analysis, determining the optimal cutoff values for predicting lymph node metastasis. Results: Washout Tg demonstrated high sensitivity for detecting metastases, with the optimal cutoff values varying based on thyroid status. For the patients who had total thyroidectomy, the cutoff was 23.3 ng/mL (AUC = 0.85, sensitivity = 82.6%, and specificity = 75.0%). In completion thyroidectomy cases, a threshold of 7.2 ng/mL (AUC = 0.879) achieved 98.4% sensitivity and 80.0% specificity. For patients without thyroidectomy (mRND/SND group), a cutoff of 0.1 ng/mL (AUC = 0.766) yielded 98.9% sensitivity but lower specificity (60.0%). Additionally, the Washout Tg/serum Tg ratio demonstrated high diagnostic accuracy with a cutoff of >1 (sensitivity = 97.09% and specificity = 63.64%). Conclusions: The Washout Tg measurement and the Washout Tg/serum Tg ratio play a crucial role in detecting lymph node metastases, particularly in patients post-thyroidectomy. The findings emphasize the necessity of adjusting the Washout Tg cutoff values based on thyroid status to enhance diagnostic accuracy. Further prospective studies are required to validate these thresholds and optimize their clinical application. Full article

Tight gas reservoirs present unique forecasting challenges due to steep decline rates, nonlinear production dynamics, and sensitivity to operational conditions. Conventional decline-curve methods and reservoir simulations are limited either by oversimplifying assumptions or by the need for extensive input data, although univariate deep learning models fail to fully capture external influences on well performance. To address these limitations, this study develops a transfer learning–enhanced N-BEATSx (Neural Basis Expansion Analysis Time Series with exogenous variables) framework for multivariate forecasting of tight gas well production. The model integrates exogenous variables, particularly casing pressure, with production histories to jointly represent reservoir behavior and operational effects. A pretraining dataset, comprising more than 100,000-day records from Block S of the Sulige Gas Field, was used to initialize the model, which was subsequently applied in a zero-shot setting to wells A1 and A2. Comparative analysis with the transfer learning-enhanced N-BEATS model demonstrates that N-BEATSx achieves consistently higher accuracy, with RMSE reductions of 23.9%, 39.1%, and 33.1% for Well A1 in short-, medium-, and long-term forecasts, respectively. These advances establish N-BEATSx as a robust tool for multivariate production forecasting, with direct industrial value in optimizing resource allocation, guiding development strategies, and enhancing operational decision-making in unconventional gas fields. Full article

In the context of global sustainable development, the construction of green mining facilities has emerged as a pivotal strategy for advancing sustainable mining practices. As a substantial mineral resource base in China, the Qinghai-Xizang Plateau (QXP) is of significant concern due to its importance for mineral exploitation. However, the natural conditions of the region, such as freezing temperatures, low oxygen levels, frequent freeze–thaw cycles, and fragile ecology, pose substantial challenges to mining activities, making green mine construction an inevitable choice for mining development on the QXP. This study uses SWOT analysis to macroscopically evaluate the strengths, weaknesses, opportunities, and threats of green mine construction on the QXP. This study adopts SWOT analysis to sort out, from a macro and systematic perspective, the internal resource endowments, technical reserves, external policy and market opportunities, as well as multiple challenges such as ecological vulnerability, harsh climate, regulation, and public opinion in the construction of green mining on the QXP. Furthermore, four typical cases, namely the Julong Copper Mine, Zhaxikang Lead–Zinc Mine, Zaozigou Gold Mine, and Duolong Copper Mine, are selected for analysis, and their differentiated paths in ecological restoration, digital mines, tailings disposal, and community-benefit sharing are summarized. International comparisons reveal the similarities and differences in policies, technologies, and other aspects between the QXP and other high-altitude regions. The study holds that it is necessary to promote the coordinated development of resource exploitation and ecological protection in green mining on the QXP through technological innovation, policy optimization, community collaboration, and the construction of a full-life-cycle environmental-monitoring system. At the same time, it points out the limitations of the current research in quantitative analysis and future research directions. Full article

This paper presents a comprehensive sensitivity analysis of the pioneering real-world deployment of computer vision-enabled construction waste sorting in Finland, implemented by a leading provider of robotic recycling solutions. Building upon and extending the findings of prior field research, the study analyzes an industry flagship case to examine the financial feasibility of computer vision-enabled robotic sorting compared to conventional sorting. The sensitivity analysis covers cost parameters related to labor, wages, personnel training, machinery (including AI software, hardware, and associated components), and maintenance operations, as well as capital expenses. We further expand the existing cost model by integrating the net present value (NPV) of investments. The results indicate that the computer vision-enabled automated system (CVAS) achieves cost competitiveness over conventional sorting (CS) under conditions of higher labor-related costs, such as increased headcount, wages, and training expenses. For instance, when annual wages exceed EUR 20,980, CVAS becomes more cost-effective. Conversely, CS retains cost advantages in scenarios dominated by higher machinery and maintenance costs or extremely elevated discount rates. For example, when the average machinery cost surpasses EUR 512,000 per unit, CS demonstrates greater economic viability. The novelty of this work arises from the use of a pioneering real-world case study and the improvements offered to a comprehensive comparative cost model for CVAS and CS, and furthermore from clarification of the impact of key cost variables on solution (CVAS or CS) selection. Full article

Auxiliary frequency modulation (FM) for coal-fired units has been recognized as a promising approach through multiple batteries, which is due to their rapid charging and discharging characteristics. However, long-period engineering application needs continuous optimization of operational strategies to resist the decay characteristics of the battery, which greatly increases the difficulty of promotion. Hence, two replacement strategies of the battery were first proposed in this work, and they are characterized by simple operation. To test their feasibility, a lead–acid battery was selected as one study example, and the corresponding relationship between the duration day and the replacement scheme was emphatically analyzed, according to the AGC instruction and the self-adjustment capacity of coal-fired units. Results showed that the replacement capacity of the battery is nearly linear in the duration day, while the difference from the discharge depth is negligible in this study. In addition, the capacity ratio of 1.3 to 5 is considered to have the best application potential because of the same duration days between old and new batteries. The commutative replacement can immortally extend the duration day, and obviously, the replacement process of old and new batteries always maintains that two battery groups work. Conclusively, the case analysis for two replacement strategies showed that they deeply lowered the initial capacity of the battery, which can reduce the investment costs. In a word, two replacement strategies for the battery proposed in this study provide a reference for the economic evaluation and optimization of battery use for auxiliary FM. Full article

Background: Large Language Models (LLMs) have driven significant advances in artificial intelligence (AI), with transformative applications across numerous scientific fields, including biomedical research and drug development. However, despite growing interest in adjacent domains, their adoption in pharmacometrics, a discipline central to model-informed drug development (MIDD), remains limited. This study aims to systematically explore the potential role of LLMs across the pharmacometrics workflow, from data processing to model development and reporting. Methods: We conducted a comprehensive literature review to identify documented applications of LLMs in pharmacometrics. We also analyzed relevant use cases from related scientific domains and structured these insights into a conceptual framework outlining potential pharmacometrics tasks that could benefit from LLMs. Results: Our analysis revealed that studies reporting LLMs in pharmacometrics are few and mainly limited to code generation in general-purpose programming languages. Nonetheless, broader applications are theoretically plausible and technically feasible, including information retrieval and synthesis, data collection and formatting, model coding, PK/PD model development, support to PBPK and QSP modeling, report writing and pharmacometrics education. We also discussed visionary applications such as LLM-enabled predictive modeling and digital twins. However, challenges such as hallucinations, lack of reproducibility, and the underrepresentation of pharmacometrics data in training corpora limit the actual applicability. Conclusions: LLMs are unlikely to replace mechanistic pharmacometrics models but hold great potential as assistive tools. Realizing this potential will require domain-specific fine-tuning, retrieval-augmented strategies, and rigorous validation. A hybrid future, integrating human expertise, traditional modeling, and AI, could define the next frontier for innovation in MIDD. Full article

Meeting the demands for advanced technology and superior quality in the automotive industry has become essential. Continuous evolution requires a rigorous analysis of every step taken. Customers demand high performance in the technology, design, and digitalization, as well as, of course, quality at a competitive price. To meet these expectations, engineers ensure transparency and trust at every stage of the project, guaranteeing flawless execution. This paper aims to highlight a clear and transparent approach to the 8D analysis method, demonstrating its effectiveness in identifying and solving engineering problems. Furthermore, quality planning and 8D analysis are fundamental pillars of quality management in the automotive industry. To ensure a comprehensive and well-founded approach, this paper combines several research methods: a review of the specialized literature, a hypothetical case study approach, and comparative analysis. The proposed methodology allows for a deep understanding of the concepts addressed, facilitating their applicability in real situations. The main conclusions drawn from this research are that quality planning in an automotive buckle development project has proven to be an essential and complex process, directly influencing the success of the project, the safety of end users, and their satisfaction. The analysis of the implementation of the quality planning process, as previously described, has highlighted several fundamental aspects that must be considered to ensure the success and performance of such a project. Full article

Background/Objectives: Synovial sarcoma (SS) is an aggressive soft-tissue tumor characterized by the chromosomal translocation t(X;18) (p11.2;q11.2), most commonly involving the fusion of the SYT gene on chromosome 18 with the SSX1 or SSX2 genes on chromosome X. This study aims to explore the clinicopathological and molecular characteristics of synovial sarcoma in a cohort of Moroccan patients. Methods: We analyzed 48 cases of synovial sarcoma using formalin-fixed, paraffin-embedded (FFPE) tissue samples. Histological grading was performed according to the FNCLCC system. Immunohistochemical staining was employed to detect cytokeratin (CK) and epithelial membrane antigen (EMA). Molecular analysis included fluorescence in situ hybridization (FISH) to identify SS18 gene rearrangements and reverse transcription–polymerase chain reaction (RT-PCR) to detect SYT-SSX fusion transcripts. Results: Among the cohort, 56% of cases showed SS18 gene rearrangements via FISH, while RT-PCR confirmed the presence of SS18-SSX1 and SS18-SSX2 transcripts in 60% and 32% of cases, respectively. The remainder was classified as undifferentiated sarcoma. Notably, no significant associations were observed between SYT-SSX fusion type and clinicopathological features. Conclusions: These findings underscore the importance of integrating molecular techniques for precise diagnosis in synovial sarcoma. The results align with global patterns, emphasizing the necessity for molecular testing to enhance diagnostic accuracy and informing potential therapeutic advancements. Full article

This study investigates the interpretation of hand gestures in nonverbal communication, with particular attention paid to cases where gesture form does not reliably convey the intended meaning. Hand gestures are a key medium for expressing impressions, complementing or substituting verbal communication. For example, the “Thumbs Up” gesture is generally associated with approval, yet its interpretation can vary across contexts and individuals. Using participant-generated descriptive words, sentiment analysis with the VADER method, and fuzzy membership modeling, this research examines the variability and ambiguity in gesture–intention mappings. Our results show that Negative gestures, such as “Thumbs Down,” consistently align with Negative sentiment, while Positive and Neutral gestures, including “Thumbs Sideways” and “So-so,” exhibit greater interpretive flexibility, often spanning adjacent sentiment categories. These findings demonstrate that rigid, category-based classification systems risk oversimplifying nonverbal communication, particularly for gestures with higher interpretive uncertainty. The proposed fuzzy logic-based framework offers a more context-sensitive and human-aligned approach to modeling gesture intention, with implications for affective computing, behavioral analysis, and human–computer interaction. Full article

Introduction: Tumors of the lateral skull base, particularly those arising from the external auditory canal and peri-retroauricular regions, present considerable surgical and reconstructive difficulties due to their intricate anatomy and aggressive nature. The anterolateral thigh (ALT) free flap offers a viable reconstruction option, enhancing oncological radicality while minimizing morbidity. Materials and Methods: A retrospective analysis was conducted on five cases of malignant tumors involving the external auditory canal and adjacent soft tissues, treated at a tertiary center from March 2023 to March 2025. All patients underwent radical resection, which included lateral or subtotal temporal bone resection, along with parotidectomy and neck dissection when necessary. Reconstruction was performed using myocutaneous anterolateral thigh free flaps. The study evaluated clinical outcomes, complications, and esthetic results. Results: The ALT flap was used in all cases without major postoperative complications. Two patients received adjuvant radiotherapy; one did not start treatment due to early recurrence. At follow-up, three patients were disease-free, while two had died from unrelated causes. Esthetic and functional outcomes were satisfactory in all patients. Conclusions: The ALT free flap is a universal and reliable option for the reconstruction of complex lateral skull base defects, especially in patients following the radical removal of tumors of the external auditory canal and peri-retroauricular areas. It offers excellent tissue coverage, facilitates adjuvant treatment, and has a low complication rate. While our experience supports its use in aggressive oncological resection, larger studies are needed to confirm these findings. Level of evidence: 4. Full article

This study presents a holistic framework for adaptive thermal energy storage (A-TES) in solar-assisted systems. This framework aims to support a reliable industrial energy supply, particularly during periods of limited sunlight, while also facilitating industrial decarbonization. In previous studies, the focus was not on addressing the framework of the entire problem, but rather on specific parts of it. Therefore, the innovation in this study lies in bringing these aspects together within a unified framework through a data-driven approach that combines the analysis of efficiency, technology, environmental impact, sectoral applications, operational challenges, and policy into a comprehensive system. Sensible thermal energy storage with an adaptive approach can be utilized in numerous industries, particularly concentrated solar power plants, to optimize power dispatch, enhance energy efficiency, and reduce gas emissions. Simulation results indicate that stable regulations and flexible incentives have led to a 60% increase in solar installations, highlighting their significance in investment expansion within the renewable energy sector. Integrated measures among sectors have increased energy availability by 50% in rural regions, illustrating the need for partnerships in renewable energy projects. The full implementation of novel advanced energy management systems (AEMSs) in industrial heat processes has resulted in a 20% decrease in energy consumption and a 15% improvement in efficiency. Making the switch to open-source software has reduced software expenditure by 50% and increased productivity by 20%, demonstrating the strategic advantages of open-source solutions. The findings provide a foundation for future research by offering a framework to analyze a specific real-world industrial case. Full article