Search Results (8,181)

In casting processes, mechanical properties such as hardness are highly sensitive to both chemical composition and process parameters, making parameter design a complex and uncertain task during the embodiment stage of engineering design. Conventional trial-and-error-based approaches are often costly, time-consuming, and impractical in industrial environments. To address these challenges, this study proposes an optimized fuzzy artificial neural network (FANN)-based decision-support approach for hardness-oriented parameter design in a casting process. The developed model uses chemical composition variables, including carbon, silicon, manganese, phosphorus, sulfur, chromium, copper, and tin, together with process parameters such as casting temperature and casting time as inputs, while Brinell hardness is considered as the output. A dataset consisting of 170 experimental casting samples was employed; 128 samples were used for model development and hyperparameter selection, and 42 samples were reserved as an independent final test set. The proposed model was implemented as a scaled direct FANN weighted ensemble, in which fuzzified input variables were used to predict standardized continuous hardness values. A total of 300 FANN configurations were evaluated using five-fold cross-validation, and the five best-performing configurations were combined through RMSE-based weighted ensemble averaging. The final model was compared with Random Forest, Linear Regression, Ridge Regression, and SVR-RBF models using MSE, RMSE, MAE, R², MAPE, normalized RMSE, and ±5% prediction success rate. The results showed that the optimized FANN ensemble achieved the lowest mean RMSE in the full-data five-fold cross-validation analysis, slightly outperforming the Random Forest benchmark. In the independent final test set, Random Forest produced the lowest prediction error, whereas the proposed FANN ensemble remained competitive and achieved the same ±5% prediction success rate as Random Forest, Linear Regression, and Ridge Regression. Furthermore, a target-hardness case study demonstrated that the proposed approach could identify candidate casting conditions very close to a desired hardness level, with the nearest prediction reaching 202.985 HB for a target value of 203 HB. These findings indicate that the proposed FANN-based framework can serve not only as a hardness prediction model but also as a practical fuzzy decision-support tool for target-hardness-oriented parameter design in casting processes. Full article

Centered on lean production, this study integrates operational technologies (OT), communication technologies (CT), and information technologies (IT) within an open-system software architecture. Under stochastic customer demand, reliance on static data and experience-based decision-making constrains firms’ responsiveness to market. The integration of lean management with a data-driven database enhances operational flexibility and decision quality, enabling small and medium-sized enterprises (SMEs) in the bicycle industry to develop responsive digital factory environments with real-time monitoring and improved operational transparency. The proposed platform is applicable to both manufacturing processes and operational management, improving overall equipment effectiveness (OEE), production efficiency, process optimization, and reducing quality losses, inventory levels, and workforce misallocation. This study investigates the application of the Analytic Hierarchy Process (AHP) and multi-criteria decision-making (MCDM) within a performance framework integrating ESG indicators and a balanced scorecard to identify key success factors for digital lean improvement in the bicycle industry. A case study of a bicycle manufacturer was conducted using questionnaire surveys and expert interviews with exporters. The results indicate that the five most critical success factors are: enhancing return on invested capital, strengthening digital capabilities, improving product quality, minimizing inventory waste, and reducing lead time. These findings provide practical guidance for decision-makers in designing more effective lean management strategies in highly competitive digital markets. Furthermore, by facilitating the adoption of appropriate digital technologies under a reasonable return on investment, this approach supports the systematic implementation of Industry 4.0 initiatives and transforms traditional lean practices into more efficient and sustainable digital lean operations. Full article

Background/Objectives: Endoscopic ultrasound-guided gallbladder drainage (EUS-GBD) with Lumen-Apposing Metal Stent (LAMS) is an established option for high-surgical-risk patients, with high technical and clinical success. Indications include acute cholecystitis and palliation of jaundice in malignant distal biliary obstruction (MDBO). Both trans-gastric and trans-duodenal approaches are used, but the optimal route remains debated. The aim of the study was to compare trans-gastric and trans-duodenal access in terms of technical success, adverse events, readmissions, and reinterventions. Methods: We implemented a single-centre retrospective study of consecutive EUS-GBD procedures with LAMS at a tertiary endoscopy unit (January 2020–January 2026). Demographic, clinical, and procedural data were analyzed using appropriate statistical tests. Results: Seventy patients were included (51.4% male; mean age 77 ± 12 years). Indications were acute cholecystitis (64.3%) and MDBO (35.7%). Trans-gastric access was used in 48.5% of cases. A Hot-Axios LAMS was deployed in 77.2% of cases, mostly >10 mm. Technical success was achieved in 98.5% of cases. Naso-cystic drainage (NCD) was used through the LAMS in 47.1% of patients, while a double pig-tail plastic stent was used in 7.2% of patients. Adverse events were rare (1.4% misdeployment). LAMS obstruction occurred in 10% of patients, with reintervention required in 12.8% of patients. No differences were found between access routes in indication, technical success, LAMS type/size, or adjunctive drainage. However, trans-gastric access was associated with a higher reintervention rate (p = 0.01). Conclusions: EUS-GBD is a safe and effective procedure. While both approaches are comparable in most outcomes, the trans-gastric route may carry a higher risk of reintervention and should be avoided when alternative access is feasible. Full article

Background/Objectives: Artificial Intelligence (AI) integration in healthcare promises improved diagnostic accuracy, patient safety, and operational efficiency. However, AI acceptance among healthcare workers remains limited due to knowledge gaps, risk concerns, and governance challenges, particularly in developing countries like Saudi Arabia, where rapid healthcare modernization faces unique infrastructure, organizational, and cultural challenges. This research investigates the factors influencing AI acceptance among medical practitioners, nurses, administrators, and students in Saudi Arabian hospitals to identify key determinants and barriers to adoption. Methods: This cross-sectional study employed an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework integrated with ethical considerations from the Model for Ethical Assessment and Analysis of AI in Medicine (MEAAM). A structured bilingual questionnaire was administered to 119 healthcare professionals and students across Saudi Arabia, measuring constructs including Awareness and Knowledge, Performance Expectancy, Effort Expectancy, Facilitating Conditions, Social Influence, Trust, Perceived Risk, Ethical Governance, and Price Value. Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed for quantitative analysis, supplemented by thematic analysis of open-ended qualitative responses. Results: The PLS-SEM analysis explained 59.8% of variance in behavioral intention to adopt AI (R² = 0.598). Awareness and Knowledge emerged as the strongest predictor (β = +0.505, p < 0.001), followed by Performance Expectancy (β = +0.229, p < 0.05) and Social Influence (β = +0.123). Perceived Risk functioned as the primary barrier (β = −0.185, p < 0.05). Qualitative findings identified infrastructure gaps, regulatory ambiguities, and training deficiencies as major implementation barriers, while emphasizing opportunities in diagnostic accuracy and remote monitoring. Conclusions: AI acceptance in Saudi healthcare is primarily driven by knowledge, with perceived usefulness and peer support as secondary facilitators, while safety and accountability concerns remain substantial obstacles. Successful AI integration requires coordinated efforts in education, transparent governance frameworks, and institutional support. This study contributes theoretically by validating extended UTAUT in a non-Western healthcare context and practically by providing evidence-based strategies for sustainable AI adoption that enhance healthcare quality while respecting professional roles and ethical principles. Full article

Rising cancer incidence and survival rates have led to an unprecedented demand for psychosocial care. Yet, limited financial and practical resources present a barrier to the provision of evidence-based care. Clinical practice guidelines (CPGs) are well-positioned to enhance the quality and efficiency of psychosocial oncology care; however, little is known about their use and perceptions in the field. The present study explored the use and perceptions of CPGs among 172 Canadian psychosocial oncology clinicians via a cross-sectional, online survey. Findings revealed substantial variation in awareness, with over 20% of participants reporting no familiarity with CPGs, and low to moderate use of CPGs (M = 2.97, SD = 2.96) among users. Key barriers included a lack of formal training, limited applicability to local contexts, and systemic constraints such as high workloads. Conversely, participants highly endorsed facilitators, including accessible training programs, relevant tools/interventions, and greater institutional and community engagement. Clinician perspectives are paramount to the dissemination and implementation of psychosocial oncology CPGs. Our findings suggest that successful implementation requires broader accessibility, widespread adaptation, and greater community engagement. By addressing these systemic constraints, CPGs may be better positioned to bridge the gap between evidence and real-world service provision. Full article

Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Hydrogen production and storage constitute a promising technology in the path towards a global energy scenario featured by renewable energy penetration, decarbonization, sustainable development and resilience. In particular, so-called green hydrogen is generated from renewable energy sources, generally produced in an electrolyzer by means of Proton Exchange Membrane (PEM) water electrolysis. To make these expectations reality, experimental and real-world facilities are required, dealing with challenging aspects such as new technologies and integration of equipment. Thus, this paper presents the implementation and operation of a pilot plant for green hydrogen generation and storage based on a commercial 30 kW PEM electrolyzer. The renewable source is a photovoltaic generator of 60.6 kW which supplies the hydrogen generator through an inverter. Furthermore, the deployment of a supervisory system entirely based on open-source technologies is reported. The equipment employed and the supervisory system developed in this work exhibit a level of complexity and scale that is uncommon in the literature. Therefore, this article is a novelty in the literature and aims to contribute to the advancement of green hydrogen production and storage by providing experimental data and descriptions of a fully functional plant operating under real-world conditions. The achieved results under real operation conditions prove the successful implementation of the pilot plant as well as the suitability of the supervisory system to effectively track the most relevant variables. Full article

Industrial water reuse and resource recovery are essential for advancing circular economy principles in water-intensive industries. Despite technological maturity, large-scale implementation continues to lag due to high costs, effluent variability, integration challenges, and weak economic returns. Going beyond technology, this paper critically examines what truly works at scale, why most systems fail, and how to build resilient multi-stream recovery solutions. Drawing on major European demonstration projects (INCOVER, RESURGENCE, MEloDIZER) and recent literature, the paper demonstrates that multi-stream systems significantly outperform single-resource approaches. Success depends less on individual technologies and more on modular design, digital integration, sector-specific adaptation, and supportive governance. The study introduces the Industrial Circular Performance Framework (ICPF) and provides clear, actionable pathways to move from promising pilots to bankable, resilient circular industrial water systems. Full article

This article presents a simple, original data augmentation algorithm for Received Signal Strength Indicator (RSSI), dedicated to indoor localization systems. The aim of the research was to develop a synthetic data generation method to serve as a regularization technique, making models more robust against measurement noise. The proposed approach combines propagation modeling using polynomial regression with the individual statistical characteristics of each Access Point (AP), accounting for signal fluctuations and a probabilistic signal outage mechanism. The effectiveness of the proposed solution was experimentally verified by evaluating K-NN and MLP neural network models in both classification and regression variants. The study was conducted on datasets with different measurement grid granularities, demonstrating the algorithm’s ability to improve the generalization properties of estimators, even with a limited number of samples in the training set. The results showed that the use of augmentation reduced the Mean Absolute Error (MAE) by an average of approximately 20% for the dense training set and about 17% for the sparse set. Within the evaluated test environment, models trained on the augmented sparse measurement grid, which contained 67% fewer physical calibration points (30 points compared to the dense grid’s 92), reached a precision comparable to models trained on the dense real-world dataset. Analysis of histograms and Cumulative Distribution Functions (CDF) of the error confirmed the preservation of the signal’s statistical integrity and the effective mitigation of gross errors. The proposed solution constitutes an efficient and easy-to-implement alternative to complex generative models (e.g., GANs). These findings serve as a successful proof-of-concept and pilot study, laying the foundation for further development and validation in larger, more complex spatial environments. Full article

Electron configurations are known to provide valuable insights into the electronic structure and behavior of atoms. They specify which and how the electronic (sub-) shells are occupied, and are thus an essential ingredient for most atomic observables. When combined with the shell model and the successive filling of shells, these configurations help explain the Periodic Table and much of chemical binding. They also establish a qualitative framework for analyzing excitation, ionization and relaxation processes and may facilitate a wide range of astrophysical and plasma simulations. Here, we review the role of electron configurations for understanding atomic behavior in interactions with particles and radiation. In particular, we identify several central requirements for an efficient treatment of configuration lists and define a domain-specific language in order to generate, manipulate and analyze such lists as well as to extract physically relevant information. We also demonstrate the implementation of this language in Jac, the Jena Atomic Calculator. An efficient handling of configurations will refine the coupling of structure codes with the spectral synthesis of plasma radiation, the setup of ionic cascades or even non-LTE plasma simulations. This common framework for dealing with electron configurations therefore improves consistency, reproducibility and scalability of atomic modeling. Full article

To enhance drive performance, this paper introduces an advanced speed controller architecture intended for a brushless DC motor (BLDCM) operating under field-oriented control (FOC). This newly developed controller integrates decision tree theory (DTT) with sliding mode theory (SMT). Initially, the regression algorithm from the classification and regression tree (CART) framework is applied to partition the deviation between the actual motor speed and the target command into 10 distinct error zones. These intervals serve as the basis for configuring three critical parameters of a standard exponential reaching law sliding mode controller (ERLSMC): namely, the sliding mode dynamic trajectory control gain, the exponential reaching gain, and the constant speed reaching gain. Following each split, the mean squared error (MSE) of the respective nodes is evaluated to determine the root node. The dataset is recursively bifurcated into dual subsets using the chosen split variables and thresholds, establishing a structured decision pathway through each successive child node. As a result, the sliding mode speed controller receives dynamically optimized modifications for its three key gains in real time during BLDCM operation. In addition, the controller continuously computes an updated sliding mode dynamic trajectory control gain by tracking the derivative of the speed error. Tuning these three operational gains effectively mitigates the transient overshoot typically induced by the conventional exponential reaching law (ERL) across diverse running states. This mechanism ensures that the speed response of the BLDCM drive system dynamically and accurately follows target commands under fluctuating conditions. Advantageously, the introduced control strategy avoids intensive computational routines and eliminates the need for extensive training datasets, ensuring straightforward implementation. To validate this approach, the proposed methodology is applied to the BLDCM drive system using the Matlab/Simulink environment. Its execution is benchmarked against conventional sliding mode controllers (SMCs) configured with three distinct control strategies: the constant speed reaching law (CSRL), the standard ERL, and the extension theory combined with exponential reaching law (ETERL). The resulting simulation data confirms that the proposed adaptive controller delivers superior performance over the alternative three reaching laws regarding both transient command tracking and robustness in load regulation. Full article

The deployment of offshore wind farms (OWFs) has increased impressively over the last decade. While a group of frontrunner countries has led early deployment, the offshore wind sector is expanding to new regions; the Thracian Sea represents a promising area for OWFs deployment due to its favorable wind and wave climate. The successful implementation of OWFs projects depends on a comprehensive understanding of local environmental conditions, with particular emphasis on complex wind–wave interactions quantification, as well as on robust and representative power performance evaluation. In the present paper, hourly environmental data spanning 29 years (1993–2021), including wind and wave parameters, are utilized to quantify joint probability distributions at selected four locations in the Thracian Sea. Corresponding environmental contours are derived and presented using a probabilistic model for given return period. The joint probability distributions of wind and wave conditions are estimated and the environmental contour surfaces for 50- and 100-year return periods are calculated and presented for generic use. Furthermore, the power production of an OWF comprising nine IEA 15 MW turbine units arranged in an orthogonal grid layout is assessed through a numerical model developed in an open access computational tool. The model accounts for key physical processes influencing OWF capacity performance, including wake interactions, atmospheric conditions, turbine control strategies, and layout effects. The results indicate a substantial value of annual energy production and capacity factor for different zones within Thracian Sea achieving a value of 526 GWh and 44%, respectively. The presented results provide practical guidance for OWFs development in the Thracian Sea and contributes to reducing uncertainty in early-stage project planning and future engineering studies. Full article

This work deals with questions of enhancing the scalability and security of linear chain Bitcoin by introducing a D-BTC (Domino Bitcoin) protocol, supported by a simply connected two-dimensional structure. The paper seeks to answer the question: can the linear topology of Bitcoin be replaced by a richer geometric structure that simultaneously (i) enlarges the number of valid positions where parallel mining can occur, and (ii) strengthens the asymptotic decay of the double-spend reversal probability? In the D-BTC protocol, the blocks, called B-dominoes (Bitcoin dominoes) are organized as a finite connected region subset of ${\mathbb{Z}}^2$ without holes, also called a lattice. Simple connectivity plays a central role in D-BTC and to mine a (valid) B-domino, a miner has to compute four PoW (Proof of Work), corresponding to cardinal directions, allowing them to add it to the frontier of the lattice, under the constraint that the new lattice is simply connected. We introduce a new deterministic consensus based on maximization of the lattice surface. By using a simple version of the isoperimetric inequality, we see that the frontier size grows as $\Omega \left(\sqrt{n}\right)$, where n is the lattice size. Following the Nakamoto’s heuristic, and under the honest majority assumption, a double-spending attack is successful with probability decaying exponentially in $k^2$, where k is the minimum Manhattan distance of the concerned B-domino from the lattice frontier. Additionally, we set up implementations and experiments to demonstrate the practical viability of the protocol with authentic gossip-based message propagation and complete Merkle tree verification. Full article

The Tua River is a tributary of the Douro River in the North of Portugal used as a spawning ground for potamodromous fish, namely the Iberian barbel (Luciobarbus bocagei, Steindachner, 1864). Although access to this tributary became severely constrained after the construction of the Foz Tua Hydroelectric Facility (AHFT), fish continued to use the remaining accessible 1.1 km stretch of the Tua River below the dam, especially during their spawning season. Therefore, this study presents the monitoring of migratory fish influx downstream of the AHFT and associated mitigation measures. Fixed and mobile surveys, using an ARIS 1800 sonar, and focused on Iberian barbel were conducted between March and July, from 2023 to 2025. In 2023, fixed sonar monitoring recorded 100,289 individuals, showing a progressive increase over the sampling period, while mobile surveys confirmed high local concentrations (2083 individuals) and temporal fluctuations. In 2024, total counts rose substantially to 182,216 individuals (fixed surveys) and 2656 individuals (mobile surveys), with a peak in early May followed by a gradual reduction in these numbers. In 2025, the highest abundance was observed, with 196,935 individuals (fixed surveys) and 5441 individuals (mobile surveys), alongside higher variability between monitoring campaigns. Overall, these results suggest an intensifying pattern of fish accumulation downstream of the AHFT during the sampled periods, with recurring seasonal peaks. As a method to mitigate massive accumulation of fish downstream of this dam, in 2024 and 2025, a near real-time detection and mitigation protocol was implemented. This protocol identifies an initial “trigger” and a sequential methodology that recognizes possible massive accumulation scenarios, followed by the application of an adaptive operational management measure (e.g., ecological flow regulation) by the AHFT. The application of these measures effectively contributed to reducing fish accumulation during the critical periods. In conclusion, the results highlight a consistent increase in migratory fish accumulation, over the study period, downstream of the AHFT. The successful application of adaptive measures demonstrates that the implemented strategy seems to be effective so far and provides a strong basis for future management actions. Full article

Reintroductions of extirpated species are an important tool in wildlife conservation. Understanding how reintroduced populations acclimatize to novel environments can lend insight into social learning that in turn is valuable for assessing reintroduction success and maximizing efficacy of subsequent efforts. During 2012, 2013, and 2014, the Virginia Department of Wildlife Resources implemented soft releases of elk (Cervus canadensis) translocated to southwestern Virginia from eastern Kentucky. We investigated home range establishment and post-release movements of these reintroduced elk (n = 60). We found adults moved farther from the release site than either yearlings or calves (F = 6.93, p = 0.001). Elk released in 2012 and 2013 took similar amounts of time to establish home ranges (median 181 days, range 108–214 days; and median 189 days, range 147–209 days, respectively), but individuals released in 2013 remained closer to the release site ($\overline{x}$ = 605.5 m, SD = 335.7 m, closer) presumably by joining established social groups. However, the 2014 cohort generally took longer to establish home ranges (median: 231 days; range: 56–258 days) and moved farthest from the release site ($\overline{x}$ = 1360.2 m, SD = 293.9 m, farther than 2012 individuals) possibly due to the larger cohort size and resulting intraspecific competition, or the earlier release date that year. Our findings suggest the number of consecutively released cohorts, the timing of the release, and the composition of age classes for released individuals are important considerations for reintroductions. Full article