Search Results (2,416)

Data-driven neural networks (NNs) have gained significant attention across engineering disciplines, particularly in design optimization and experimental settings, where they are widely used to construct surrogate models for high-dimensional regression problems. Despite their power as global approximators, neural networks often struggle to accurately capture local features without relying on a large number of trainable parameters and training data points, resulting in increased training time. To address these limitations, in this paper we propose domain decomposition methods (DDMs), which divide the input feature space into multiple local subdomains, each modeled by a simpler NN, trained in parallel. Interface constraints are introduced in the local loss functions to enforce continuity between subdomains. They are enforced with two different approaches: by utilizing Lagrange multipliers or augmented Lagrange multiplier methods. Compared to unconstrained approximations, both methods significantly improve continuity across subdomain interfaces. For a 2D and a 3D problem, computational time and accuracy are investigated across varying numbers of subdomains to identify optimal partitioning strategies. The use of DDMs improves approximation accuracy in local regions with smaller number of parameters when compared to standard global NN training. In terms of convergence, the augmented Lagrange method outperforms the standard Lagrange formulation by converging faster due to lower convergence requirements, albeit with a slightly lower accuracy. Overall, these results highlight the augmented Lagrange method as a promising DDM approach for training efficient and scalable NN surrogate models. Full article

The 2021–2024 global energy crisis intensified the energy transition, with mainstream media coverage playing a pivotal role in shaping public perceptions. Guided by Burke’s and Lippmann’s theories, and supported by corpus-based critical and rhetorical discourse analyses, this interdisciplinary study aimed to analyze the role of lexical patterns and rhetorical strategies in framing the transition within a corpus of 1341 news articles retrieved from the websites of five English-language mainstream media outlets. Corpus-based analysis identified generic frames, including economic consequences, responsibility, conflict, technological, emotion, and moral duty frames. Rhetorical discourse analysis revealed specific frames, including economic opportunities, technological progress and challenges, energy security and independence, global leadership, energy partnerships, partisan divide, global disparities, corporate greenwashing, necessity, hope, and uncertainty frames, that indicated an ambivalence in the framing of the transition, thereby contributing to the polarization and manipulation of public opinion. The findings indicated a discrepancy: while British, American, and Brazilian media focused more on political divides, Indian and Chinese media emphasized energy partnerships and patriotism. Appeals to experts were less frequent, whereas appeals to emotions were often employed to shape public perceptions. The findings illustrate how lexical patterns and rhetorical strategies function as powerful framing tools within journalism, applied linguistics, and media rhetoric. Full article

The study investigates the application of machine learning techniques for classifying rockbursts among non-destructive tremors recorded in the Rydułtowy part of the ROW hard coal mine in the Upper Silesian Coal Basin, Poland. The mining environment is dominated by ultra-thick, high-strength sandstone strata, which significantly increase the likelihood of high-energy tremors. The interaction of geological/geomechanical, mining, technical/technological, and seismic factors is highly nonlinear, rendering deterministic analytical approaches insufficient for reliable rockburst identification. A dataset comprising 99 records, including 16 dynamic phenomena, was divided into training and testing subsets, with 75% of the data used to evaluate the discriminative power of the input variables and to train the machine learning models. Three parameters consistently exhibit the highest predictive relevance: peak particle velocity, seismic energy, and the rock mass bursting tendency index. Ten machine learning classifiers were evaluated using stratified 10-fold cross-validation. Ensemble-based models—particularly XGBoost, AdaBoost and Random Forest—demonstrated the most stable and accurate performance. The results indicate that machine learning models provide an effective computational framework for supporting rockburst hazard assessment in geologically complex mining conditions associated with ultra-thick sandstone strata. Full article

Background/Objectives: Type 1 diabetes (T1D) and multiple sclerosis (MS) are autoimmune, multifactorial, organ-specific disorders mediated by immune cells. Their co-occurrence has been partially attributed to shared genetics and environmental factors. We aimed to dissect the shared genetic architecture between T1D and MS using large-scale genome-wide association studies (GWASs) and colocalization analyses. Methods: We applied a Bayesian colocalization framework to two large-scale GWAS data sets: a T1D study comprising 18,942 cases and 501,638 controls, and an MS GWAS including 14,802 cases and 26,703 controls. Results: We identified 26 shared colocalizing association signals between T1D and MS. Among them, seven loci (EOMES, RGS14, DLL1, ZNF438/ZEB1, SESN3, WARS1/SLC25A47, and IRF8) were novel for T1D and two (UBAC2 and LAT) for MS. Several signals showed supportive evidence in additional datasets and demonstrated functional annotation characteristics consistent with disease involvement. Conclusions: Colocalization can be a powerful discovery tool for disorders with co-divided genetic architecture, as prioritizing shared rather than individual causal variants may enhance the detection of novel loci. Our findings indicate that T1D and MS predominantly share general autoimmune susceptibility signals (17/26), rather than disease-specific (private), often with opposite direction of effect (9/26), underscoring their immunological heterogeneity. Full article

Introduction: Propofol is one of the most commonly used intravenous anesthetics worldwide and is considered safe for all age groups. However, there have been reports that propofol can induce severe atrioventricular block in humans, and several studies have shown that propofol hinders or prevents the inducibility of arrhythmias during electrophysiological studies (EPS) and radiofrequency (RF) ablation. Objectives: To compare arrhythmia inducibility during electrophysiological study and radiofrequency ablation in pediatric patients with Wolff–Parkinson–White syndrome undergoing propofol-based sedation versus sevoflurane-based general anesthesia. Methods: We conducted a retrospective observational cohort study including 45 pediatric patients aged 0–18 years. Patients were identified through a review and analysis of a database of individuals with Wolff–Parkinson–White syndrome who were referred for electrophysiological study and/or radiofrequency ablation at the Electrophysiology Laboratory of the Institute of Cardiology (IC/FUC) in Porto Alegre over the past five years (2019–2024). Patients with prior ablation, structural heart disease, or ongoing antiarrhythmic therapy were excluded. The patients were divided into two groups and designated as group S (who received sedation) or group G (who received general anesthesia). Sedation (group S) was performed with midazolam (0.08–0.2 mg/kg), fentanyl (0.1–0.2 μcg/kg), and propofol 50–60 µg/kg/min in continuous infusion. General anesthesia (group G), in turn, was performed with sevoflurane at an average dose of 2% (1 MAC according to age). Results: From 4874 invasive electrophysiology procedures performed during the study period, 45 involved pediatric patients with WPW. The sedation group (n = 29) had significantly older patients (14.6 ± 2.5 vs. 10.3 ± 2.8 years, p < 0.001) with higher weight (65.9 ± 16.3 vs. 41.2 ± 7.8 kg, p < 0.001) compared to the general anesthesia group (n = 16). Arrhythmia was successfully induced in 15/29 (51.7%) patients in the sedation group compared to 13/16 (81.2%) in the general anesthesia group (p = 0.062, Fisher’s exact test). Although this difference did not reach statistical significance, it represents a clinically relevant 29.5% lower induction rate in the sedation group. Post hoc power analysis revealed the study was underpowered (49.8%), suggesting a possible Type II error. Analysis of the “procedure room time” revealed a longer duration in the general anesthesia group (97.8 ± 36.7 vs. 67.8 ± 24.4 min), and this difference was statistically significant (p = 0.002). Conclusions: This study compared propofol-based sedation with sevoflurane-based general anesthesia in pediatric WPW patients. While sedation with propofol did not show a statistically significant reduction in arrhythmia inducibility, there was a concerning trend toward lower induction rates (29.5% difference) that may be clinically relevant. The study’s limited statistical power (49.8%) suggests these findings should be interpreted cautiously, and larger prospective studies are needed to definitively establish whether propofol affects arrhythmia inducibility in this population. Propofol remains a viable option for these procedures, but clinicians should be aware of the potential for reduced inducibility, particularly in cases where arrhythmia induction is critical for diagnosis and treatment. Full article

Herein, we present the analysis, design, optimization, and fabrication of a broadband, stepped-impedance Wilkinson power divider. The proposed structure employs stepped-impedance transmission lines and open-circuited stubs, achieving a simple and compact implementation while maintaining a wideband frequency response. Initially, transmission-line-based circuit analysis was performed to extract the design equations, followed by simulation and optimization to enhance impedance matching and output-port isolation over a broad bandwidth. Finally, the proposed divider was fabricated using microstrip-line technology, and experimental measurements were conducted using the Agilent E5071C vector network analyzer. The simulation and measurement results showed efficient wideband operation over the 1–4 GHz frequency range. Specifically, the measured return loss at the input port was <−10 dB; the corresponding return loss at the output ports was <−15 dB. The measured insertion loss was −3.73 ± 0.42 dB. The isolation between the output ports was <−10 dB, reaching approximately −30 dB at 2.1 GHz and −25 dB at the center operating frequency (f₀ = 2.5 GHz). The amplitude and phase imbalances were 0 ± 0.2 dB and 0^o ± 0.8^o, respectively. Furthermore, the overall size of the proposed wideband Wilkinson power divider was 0.35λg × 0.21λg. Compared to previous designs, the divider proposed in this study exhibits an improved and more symmetric frequency response, as well as a substantially reduced size, making it suitable for several modern wireless technologies such as Wi-Fi, Bluetooth, GPS, DCS, WCDMA, and sub-6 GHz 5G communication systems. Full article

“Can ChatGPT become a general purpose technology?” “How does the “privacy paradox” play a role in adopting ubiquitous AI technologies in a humane AI ecosystem?” To answer these research questions, this study examined the roles of AI equality, trust in the large language model (LLM) ChatGPT, the need to belong, perceived benefits of ubiquitous AI, and privacy concerns about potentially ubiquitous generative artificial intelligence (GenAI) in a human-centered AI ecosystem. Drawing from the emerging literature on the AI divide (vs. AI equality) and AI-powered digital transformation, cross-sectional survey data were collected from current ChatGPT users. The results of testing PROCESS macro models with 5000 bootstrap samples showed the relationship between AI equality and purchase intention is mediated by trust in ChatGPT and is moderated by the need to belong. Privacy concerns about ChatGPT moderate the relationship between AI equality and perceived benefits of ubiquitous GenAI, which, in turn, mediates the relationship between AI equality and purchase intention. Ethical dilemmas in developing an equitable AI ecosystem, practical implications of the “privacy paradox” for designing trustworthy and ubiquitous AI interfaces in the dynamically evolving AI-powered digital transformation landscape and electronic marketplaces, and theoretical implications of the ChatGPT epidemic in a humane AI ecosystem for the literature on general purpose technology (GPT) are discussed. Full article

Background/Objectives: Managing postoperative drainage and reducing the length of hospital stays continue to represent significant challenges in thoracic surgery. While systemic antifibrinolytics are effective, concerns persist regarding neurotoxicity and thromboembolic risks. In this study, we evaluated the efficacy and safety of a unique, high-volume topical tranexamic acid (t-TXA) lavage protocol designed to optimize pleuroparenchymal contact and stabilize local hyperfibrinolysis. Methods: A retrospective comparative study was conducted involving 52 patients undergoing major lung resection, divided into a t-TXA group (n = 26) and a control group (n = 26). The t-TXA group received an intrathoracic lavage consisting of 5 g of tranexamic acid (TXA) diluted in 500 mL of saline, while the control group received 500 mL of saline alone. The primary outcomes included postoperative day (POD) 1 drainage volumes and length of stay (LOS). The secondary outcomes were focused on hematological parameters and safety profiles, including a structured one-year follow-up for all patients. Due to the study’s exploratory nature, primary outcomes were assessed using 95% confidence intervals for hypothesis generation rather than a priori sample size calculations. Results: No significant differences were observed between groups regarding sex, surgical approach, or resection type. The t-TXA group demonstrated a significantly shorter LOS (4.20 ± 1.23 days) compared to the control group (5.88 ± 2.23 days; p = 0.001). While POD 1 drainage was numerically lower in the t-TXA group (189.23 ± 235.06 mL) versus the control (284.23 ± 169.40 mL), this difference did not reach statistical significance (p = 0.101). However, exploratory correlation analysis revealed a moderate negative association between t-TXA application and POD 1 drainage (r = −0.412; p = 0.002). Postoperative platelet counts were significantly lower in the t-TXA group (p = 0.009). No thromboembolic events, late complications, or deaths occurred in either group during the one-year follow-up period. Conclusions: High-volume t-TXA lavage is a promising adjuvant associated with significantly shorter hospital stays and a trend toward reduced postoperative drainage. While our 12-month follow-up confirmed a favorable safety profile with no adverse events, these findings should be interpreted as preliminary and hypothesis-generating. The retrospective nature of this study precludes definitive recommendations, underscoring the need for well-powered prospective randomized trials to establish the long-term safety and clinical utility of t-TXA in thoracic surgery. Full article

This paper argues that English translations of the Qur’ān play a pivotal role in shaping interfaith dialogue, either fostering mutual understanding or reinforcing religious division, depending on the translator’s ideological stance. While interreligious relations have historically been marred by conflict, the 1893 Parliament of the World’s Religions marked a turning point toward more inclusive and dialogical engagement. In this context, translating the Qur’ān emerged as a crucial medium through which Islamic teachings could be made accessible to non-Muslim audiences. Several scholars, including Kidwai and Elmarsafy, have explored the Orientalist framing of Qur’ān translation; however, few researchers have examined how modern renderings consciously reposition the text as a site of interfaith ethics. This study critically examines whether George Sale’s influential translation of the Qur’ān—reprinted nearly 200 times—contributes to or hinders interfaith dialogue between Muslims and Christians. It compares Sale’s Qur’ān rendition into English with five contemporary translations. The paper analyzes the translations of five Qur’ānic verses promoting coexistence, with particular attention to key terms such as إِكْرَاهَ ikrah (compulsion), الدِّينِ ad-dīn (religion), تَّقْوَىٰ taqwā (piety), and مُسْلِمُونَ muslimūn (submitters). Guided by Munday’s theory of ideology in translation, the analysis demonstrates that Sale’s rendering adopts a distinctly polemical tone intended to assert Christian superiority. The findings indicate a clear shift from polemical to dialogical translation strategies. Sale’s Orientalist approach—evident in his footnote on Q.4:157, where he characterizes Muslim exegesis as intellectually deficient—ultimately constrains meaningful interfaith engagement. In contrast, Khattab employs an inclusive and ethically grounded approach that actively fosters interreligious dialogue. By positioning Qur’ān translation at the intersection of theology, linguistics, and interfaith relations, this paper demonstrates that translation choices hold significant power: they can either bridge divides or exacerbate tensions between religious communities. Full article

As the population increases, the demand for power continues to rise. As fossil fuel resources reduce, wind energy emerges as a sustainable alternative and helps address adverse effects of global warming and environmental pollution caused by fossil fuels. Thus, this study focuses on increasing the efficiency of wind turbines by improving their energy conversion. In this study, the NREL Phase VI wind turbine blade was modified by adding a Gurney flap at trailing edge along the entire span. Computational fluid dynamics simulations using ANSYS CFX 19.2 were performed on the modified blades to evaluate their aerodynamic performance. Three different flap lengths were investigated with six wind speeds varying from 5 m/s to 20 m/s. The results obtained were compared with those from NREL Phase VI original shape and a blade equipped with a winglet. Computational domain was divided into a rotating cylindrical region and a stationary rectangular part. The aerodynamic parameters calculated include torque, thrust, and normal and tangential forces coefficients. At low velocities, the addition of a Gurney flap had an insignificant impact on torque and thrust, whereas at medium to high wind speeds, significant increases were observed on torque, indicating more power production. Full article

Smart power management practices are needed for a sustainable EV charging infrastructure due to the fast use of renewable energy resources. An innovative power management structure for a small grid-connected solar PV system-based AC and DC charging station, combined with a backup purpose battery energy system (BESS), is demonstrated in this paper’s study. The sustainability transition is associated with integrating renewable energy resources with a battery storage system, providing a helpful solution for managing large power-demanding entities (EV, microgrid, etc.). In this study, a solar PV system takes 500 datasets (based on data availability or to prevent overfitting) of PV voltage, solar irradiance, and air temperature, and the performance of controlling for the maximum power point tracker by training these datasets using Levenberg–Marquardt (LM), which was implemented in the ANN toolbox and created this technique in MATLAB 2016 or Simulink. Also, using this technique for the estimation and forecasting of the datasets of solar PV systems and EVs obtains better results for achieving further targets. To enhance decision-making capability through optimized technique, we have to find it before forecasting PV power generation and EV datasets throughout the day (24 h). The optimized power flows among solar PV power generation, EV charging demand (including AC charging and DC fast charging), the BESS, and the utility/small grid under several priority operating scenarios. A famous technique for optimization, mixed-integer linear programming (MILP), is applied. In this technique, the objective function is used for the solution of problem formation and compliance with system constraints such as the power balancing equation, charging/discharging limits, SOC limits, and grid export/import exchange limits: basically, equality, inequality, and bounds limits. Optimized results show that the coordinated power flow operations are consented to by EV users, by prioritizing some key points, such as solar PV use at the maximum, reducing the grid power dependency, and the first power flow towards EV charging demand. The verified MILP-based solutions boost the maximum utilization of renewable energy resources, feasible EV charging demand, and scaling power flow among these entities. The key contribution of this study is suitable for different powered EV charging stations based on both AC and DC, with different ratings of EVs (including fast and slow charging). Most solar PV-based generation supports the EVCS and backup for ranking-wise BESS, and grid support for the EVCS. Also, the key contribution of hybrid techniques in this article is divided into two stages: in the first stage, an artificial neural network (ANN) is utilized for estimating the PV voltage at the maximum point and forecasting, while in the second stage, mixed-integer linear programming (MILP) employs optimal power management. Full article

Objective: Analyze physical fitness variables and body composition to define patterns or similarities in performance using principal component analysis. Materials and Methods: Sixty-eight players participated in the study, divided into three groups according to their age: Under-13 (n = 23), Under-15 (n = 27) and Under-17 (n = 18). A comparative cross-sectional study was performed. The variables analyzed were squat jump, countermovement jump, countermovement jump with arms, hamstring strength, COD-Timer 5-0-5, COD-Timer 5 + 5, speed (5, 10, 15 m), and running anaerobic sprint test. Body composition variables were skinfold thickness, diameters, and circumferences. Results: For the squat jump, 10.4% of the variability in speed (η² = 0.104) and 12.5% of the variability in jump height (η² = 0.125) are explained by category, both with moderate effect sizes. For the countermovement jump, 10.8% of the variability in speed (η² = 0.108) and 13.2% of the variability in jump height (η² = 0.132) are explained by category, both with moderate effects. For the running anaerobic sprint test power test, a large effect size was determined for each of the six times, indicating that at least 57.4% of the variability in time is explained by category. Conclusions: Two control groups were identified according to category (Under-13, Under-15, Under-17), revealing that principal component 1 and principal component 2 were significant in the performance of anthropometric variables such as residual mass, bi-styloid diameter, arm span, and residual mass, and physical variables, specifically related to COD-Timer 5 + 5, COD-Timer 5-0-5, and speed. Full article

Accurately identifying urban functional zones and revealing their spatial interaction characteristics is crucial for understanding urban operational mechanisms and optimizing spatial layouts. Addressing the limitations of traditional research in simultaneously capturing static functional attributes and dynamic resident travel behaviors, this study takes the central urban area of Lhasa as the research object, integrating ride-hailing trajectory data with Point of Interest (POI) data to conduct research on urban functional zone identification and spatial interaction characteristics. First, Thiessen polygons were used to quantify the spatial influence range of POIs, and an address matching algorithm was employed to associate ride-hailing origins and destinations (ODs) with POIs. A weighted land use intensity index was constructed, and functional zones were precisely identified using information entropy and K-Means clustering. Secondly, with basic research units as nodes and OD flows as edges, a directed weighted spatial interaction network was constructed. Complex-network indicators and the Infomap community detection algorithm were utilized to analyze network characteristics, node importance, and community interaction patterns. The results show that: (1) The functional mixing degree in the study area exhibits a pattern of “highly composite core, relatively differentiated periphery.” Eight functional zone types, including commercial–residential mixed, science–education–culture, and transportation service zones, were ultimately identified. Residential areas form the base, while the core area features multi-functional agglomeration. (2) The spatial interaction network exhibits typical small-world effects, while its degree distribution is better characterized by a lognormal distribution rather than a power law. Node importance is dominated by betweenness centrality, with Lhasa Station, the Potala Palace, and core commercial areas constituting key hubs. (3) The network can be divided into four functionally coupled communities: the core multi-functional area, the western industry–residence integrated area, the eastern science–education-dominated area, and the southern transportation hub area, forming a “core leading, two wings supporting” center–subcenter spatial organization pattern. This study verifies the effectiveness of integrating trajectory and POI data for identifying urban functional zones and provides a new perspective for understanding the spatial structure and planning of plateau cities. Full article

Existing methods for assessing the stability of deep tunnel face rarely account for the weakening effect of rock mass parameters caused by excavation disturbance. This paper employs a vertical discretization method to divide the rigid failure body into vertical strip elements with fixed horizontal widths. By considering the weakening effect of rock mass parameters, a stability analysis model for the tunnel face is established. The equivalent cohesion and internal friction angle of the rock mass are obtained using the Hoek–Brown criterion and the equivalent Mohr–Coulomb transformation. Combined with the disturbance weakening factor, these yield the equivalent rock mass parameters after disturbance. Stability is solved using limit analysis and the principle of virtual power. The accuracy of the established model is verified through numerical simulation, demonstrating that the proposed analytical approach requires only about 90 s per run compared to approximately 7 h for 3D numerical models. The results indicate that the importance of parameters, in descending order under the specified reference conditions for deep-buried tunnels, is $GSI>D_r>h_1>m_i$, where $GSI$ play a dominant role. Excavation disturbance significantly reduces rock mass strength numerically. Assessing$GSI$ and controlling the blasting disturbance are key to ensuring the stability of deep tunnels. Full article

This letter presents a Ku-band 2 × 2 patch array antenna that supports dual-polarization operation using a simple cooperative feed network. Depending on the selected input port of the proposed simple feed network, the 2 × 2 array antenna radiates either vertically or horizontally polarized waves. The proposed feed structure consists of two serially connected power dividers placed on the same geometrical plane, enabling dual-polarization without additional multilayer routing. The microstrip line-based feed network also enables a 180° reversed placement of the radiating elements thereby improving the cross-polarization ratio of the proposed array antenna, achieving better than 30 dB across the operating band. The fabricated antenna, designed for a center frequency of 14.9 GHz with a 6.8% fractional bandwidth, demonstrates a realized gain higher than 10 dB for both polarization modes. Measurement results in terms of the input impedance bandwidth, isolation, gain, and cross-polarization ratio are in good agreement with simulation results. Full article