Search Results (17,238)

Sustainable building research increasingly incorporates AI technologies to enhance efficiency and decision-making, yet little is known about how algorithmic mediation shapes the cultural identity processes essential for heritage sustainability. This study proposes and validates a Content-Driven Dual-Track (CDDT) Model examining the relationships among AI content quality (AIQ), technology acceptance, and Architectural Cultural Identity (ACI). Based on a survey of 631 architecture and design students, structural equation modeling identified three patterns. First, AIQ strongly predicts perceived usefulness, perceived ease of use (PEOU), and perceived enjoyment, supporting a content-driven formation of system evaluations. Second, an “ease-of-use paradox” is observed: PEOU negatively relates to ACI (β = −0.18, p = 0.005), suggesting that frictionless browsing may hinder the cognitive effort required for deeper heritage value internalization. Third, ACI independently predicts continuous engagement intention (β = 0.110, p < 0.05) and correlates strongly with perceived content quality (r = 0.719, p < 0.001). Together, these findings suggest that while operational convenience serves as an essential entry point, sustainable digital heritage engagement requires moving beyond interface usability to prioritize the cultural depth of content assets, a principle applicable to BIM-driven cultural heritage systems and AI-based educational platforms. Full article

The discharge of untreated paper mill effluent poses significant ecological risks due to its high organic and nutrient loads. This study aimed to assess the phytoremediation potential of Azolla pinnata for treating paper mill effluent. Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling approaches were applied and optimization was used for pollutant removal and plant biomass production. Experiments were designed using a Central Composite Design with two independent variables: effluent concentration (0, 50, and 100%) and plant density (10, 20, and 30 g per container). The responses measured were biochemical oxygen demand (BOD), chemical oxygen demand (COD) removal efficiencies, and final biomass yield after 16 days of exposure. RSM produced statistically significant (p < 0.05) second-order regression models for all three responses (coefficient of determination; R² > 0.98), while ANN showed slightly lower prediction errors within the experimental range studied. Maximum observed removal efficiencies were 91.74% for BOD, 80.91% for COD, and 92.66 g biomass yield under 50% effluent concentration and 30 g plant density. Optimization via both models suggested closely comparable operating conditions (79% effluent concentration and 29 g biomass) for optimal performance. The results indicate that A. pinnata demonstrates potential as a low-cost, nature-based treatment system for industrial effluent remediation under controlled conditions. The integration of data-driven optimization with biological treatment contributes to sustainable effluent management strategies by reducing chemical inputs, minimizing energy demand, and enabling biomass generation with potential downstream valorization. Full article

This study develops and validates a two-stage comparative framework for predicting Photovoltaic (PV) cleaning schedules by integrating high-resolution operational data with regression-based simulated datasets generated from statistical models trained on real measurements. The work directly addresses the growing need to assess whether model-based regression-based simulated data can reliably substitute real measurements in predictive PV maintenance. These models are employed to generate clean-condition power baselines and to estimate daily energy losses attributable to soiling under two distinct paradigms: (i) using real historical PV performance and environmental measurements, and (ii) using regression-derived, regression-based simulated data representing idealized clean operating conditions. Model performance is rigorously quantified using correlation coefficients (R), coefficients of determination (R²), mean absolute deviations, and binary classification metrics including accuracy, precision, recall, and F1-score. The comprehensive results demonstrate that regression-based simulated datasets exhibit high fidelity with real measurements across key electrical variables. This is evident for datasets generated using PLSR, Ridge Regression, and Robust Regression. Strong correlations are observed for DC power (R² = 0.9545) and DC current (R² = 0.9520), with mean deviations consistently below 2.2%. When a threshold-based binary decision rule (“clean” versus “do not clean”) is applied, cleaning decisions derived from simulated and real datasets show near-perfect concordance, achieving a mean F1-score of 0.9792. These results indicate that for a fixed performance-loss threshold, models using regression-based simulated data reproduce real-data-based cleaning triggers with an accuracy exceeding 97%. Furthermore, the findings confirm that regression-based simulation frameworks constitute a reliable and scalable foundation for data-driven PV maintenance optimization. By enabling efficient cleaning scheduling, these frameworks can significantly reduce operational expenditure and maximize energy yield, particularly in regions where continuous, high-quality PV monitoring data are limited or difficult to obtain. Full article

This study offers a descriptive analysis of complex thinking as a form of cognitive competency among a group of 52 public servants holding local leadership positions in the Philippines. By extending the empirical examination of complex thinking beyond educational contexts and into local public leadership, the study contributes to an emerging line of research on the cognitive competencies associated with decision making in decentralized governance environments. Drawing on complexity theory applied to public decision making, it assumes that local governance requires the capacity to integrate heterogeneous information, anticipate interdependencies, and act under conditions of uncertainty. The assessment employed the eComplexity instrument using an adapted 21-item version structured into four dimensions: systemic, scientific, critical, and innovative thinking. Scores were rescaled to a 0–100 metric and, after confirming non-normality (Shapiro–Wilk), non-parametric tests were applied (Mann–Whitney, Kruskal–Wallis, and Dunn’s post hoc test with Bonferroni correction), along with Spearman’s rho correlations to examine dimensional coherence. No significant differences were observed by gender or income. Age showed overall variation across several dimensions, but robust pairwise differences were concentrated between the 31–40 and 41–50 age groups in systemic thinking and in the global score. Employment status differentiated only scientific thinking, with higher medians among permanent staff than contractual/project personnel. Correlations among dimensions were positive and significant, with particularly strong associations between systemic, critical, and innovative thinking, supporting the interpretation of complex thinking as an integrated competency in local public leadership. The findings should be interpreted considering the study’s descriptive design, localized convenience sample, and reliance on self-reported measures, which limit statistical generalizability beyond the analyzed context. Beyond its descriptive findings, the study offers initial empirical evidence relevant to governance research on the cognitive competencies associated with decision making among grassroots public leaders operating in decentralized institutional contexts. Examining complex thinking at this level helps illuminate how public actors interpret interdependencies, evaluate information, and navigate uncertainty in everyday governance practice. Full article

Infrastructure as Code (IaC) underpins automated cloud provisioning in modern DevOps environments; however, controlled comparative evaluations of leading IaC tools under identical conditions remain limited. This study presents a controlled comparative evaluation of Terraform, Pulumi, and AWS CloudFormation within a standardized Amazon Web Services environment. An identical multi-tier architecture was implemented using each tool, and repeated deployment cycles were conducted to observe differences in provisioning duration, removal time, structural maintainability, and operational characteristics. Descriptive statistical analysis across 30 controlled repetitions indicates that Terraform and Pulumi achieve comparable deployment performance, whereas CloudFormation requires more than twice the average provisioning time under the conditions evaluated. Removal durations were similar across tools but remained longest for CloudFormation. Structural analysis reveals trade-offs between declarative modular design, programmatic flexibility, and native cloud integration. The study provides a controlled, comparative framework to support evidence-based selection of IaC tools in production-oriented cloud environments. Full article

Non-Intrusive Load Monitoring (NILM) systems are increasingly applied in residential and commercial environments to disaggregate energy consumption without requiring additional hardware sensors. The integration of Machine Learning (ML) techniques has enhanced the accuracy and efficiency of load identification and classification in smart meter-based systems. This study presents a systematic review and meta-analysis aimed at identifying, classifying, and quantitatively evaluating ML models applied to NILM. Searches were conducted in the IEEE Xplore and Scopus databases, restricted to peer-reviewed publications from 2017 to 2024. Thirty studies met the eligibility criteria and were included in the quantitative synthesis using a random-effects meta-analysis model (DerSimonian–Laird estimator). The primary effect measure was the F1-score. Statistical analyses were performed using R (version 4.5.0) and Python (version 3.10.0), including heterogeneity assessment and subgroup analyses according to model type. Hybrid models, such as SVDT-KNN-MLP, LE-CRNN, and RBFNN-MOGA, achieved the highest pooled F1-scores, although supported by a limited number of studies. Traditional approaches, including CNN, KNN, and Random Forest, demonstrated consistently strong performance and broader validation, whereas Boosted Trees and RNN-based models showed lower or more variable results. Substantial heterogeneity was observed across studies, highlighting the need for dataset standardization, reproducible evaluation frameworks, and further validation of emerging hybrid architectures in diverse operational scenarios. This study contributes by providing a quantitative synthesis of machine learning models applied to NILM using a structured PRISMA-based methodology and subgroup analysis by model architecture. Unlike previous narrative reviews, this work integrates scientometric analysis with meta-analytic performance aggregation, offering a consolidated and comparative evidence base for future NILM research. Full article

Background/Objectives: An effective inferior alveolar nerve block (IANB) is critical for mandibular third molar surgery, especially for novice student operators who face steep learning curves. This study compared the clinical efficacy and safety of 4% articaine, 2% lidocaine, and 2% mepivacaine in an undergraduate setting. Methods: A prospective observational study was conducted with 189 patients undergoing third molar surgery performed by dental students. Patients received either 4% articaine (n = 69), 2% lidocaine (n = 61), or 2% mepivacaine (n = 59). Anesthetic efficacy was evaluated using a two-stage assessment comprising Vincent’s sign (Stage 1) and profound surgical anesthesia (Stage 2). Intra- and postoperative pain, anesthetic volume, surgical duration, and postoperative complications were recorded and compared among anesthetic groups. Results: Baseline demographics, impaction patterns, and difficulty indices did not differ significantly among groups. Stage 2 profound success rate was significantly higher with articaine (76.8%) than with lidocaine (55.7%) and mepivacaine (61.0%) (p = 0.031). Articaine was also associated with a longer duration of anesthesia (261.7 vs. 164.6 and 192.6 min; p < 0.001), a lower total anesthetic volume (2.1 vs. 2.4 and 2.3 mL; p = 0.007), and significantly lower intraoperative pain scores (14.3 vs. 31.0 and 29.8 mm on the Heft–Parker VAS pain scale (HPS); p < 0.001). Postoperative pain through Day 7 and complication rates were comparable among anesthetics, with no serious adverse events reported. Conclusions: Within the limitations of this observational study, four percent articaine was associated with higher profound IANB success rates and lower intraoperative pain observed in this cohort. These observed associations with higher success and tissue diffusion properties may mitigate the impact of novice technical variability within this cohort. Full article

Background/Objectives: Metabolic dysfunction-associated steatotic liver disease (MASLD) is characterized by excessive triglyceride accumulation in the liver, the presence of one or more cardiometabolic risk factors, and an absence of harmful alcohol intake. Oxidative stress plays a crucial role in the development and severity of this disease, contributing to an increased cardiovascular risk (CVR). Malondialdehyde (MDA), an oxidative biomarker resulting from lipid peroxidation, is closely associated with metabolic dysfunction. This study aimed to evaluate the role of MDA as a predictor of steatosis severity and CVR. Methodology: An observational cross-sectional study was conducted in a population with MASLD with hepatic steatosis confirmed by ultrasonography and computed tomography. Subjects were classified according to severity of the hepatic steatosis as grade I or grade II-III. Nutritional, anthropometric, and serum biochemical parameters were measured. MDA levels were determined using a spectrophotometric method. The CVR was assessed using waist-to-hip ratio (WHR), triglycerides-glucose (TyG) index, lipid accumulation product (LAP), and atherogenic index of plasma (AIP). Receiver operating characteristic (ROC) curve analysis was performed to identify MDA cut-off value, followed by multivariable logistic regression to assess its association with severity of steatosis adjusted for body fat percentage. Results: A total of 50 patients were included (21 men and 29 women). An MDA cut-off value ≥ 0.13 nmol/mL was associated with higher severity (grade II–III vs. grade I) (OR = 5.0; 95% CI: 1.20–20.0; p = 0.022). Higher WHR values were found in subjects with grade I (p = 0.049), and elevated TyG index values were observed in patients with grade I-III (p = 0.042) both indicating increased CVR. Conclusions: Elevated MDA levels and higher body fat percentage were associated with higher degree of hepatic steatosis and increased CVR in the population from southeastern Mexico. Full article

Mierspenaeopsis hardwickii (Miers, 1878) represents an important economic resource for coastal artisanal fishers and small-scale fisheries operations. However, very little is known about the distribution patterns related to environmental factors and migration routes of M. hardwickii. In this study, we employed research vessels to obtain CPUE_w (weight in catch per unit effort) and CPUE_n (abundance in catch per unit effort) data in 2018–2019. Our results showed that the largest number was found at 20–40 m in spring and summer, extending in autumn (40–90 m) and shrinking in winter (40–60 m). The scattered distribution pattern of M. hardwickii was observed in spring with sea bottom temperature (SBT) 11–18 °C and sea bottom salinity (SBS) 32–34 and winter (SBT 9–19 °C, SBS 32–35); most individuals were observed in summer (SBT 26–28 °C, SBS 30–31) and autumn (SBT 19–22 °C, SBS 32–35). The annual mean CPUE_w and CPUE_n were 3624 g·h^–1 and 799.4 ind·h^–1, respectively. We hypothesize that in spring, most parent cohorts aggregate in Dasha in the southern Yellow Sea, while many cohorts gather in the coastal waters of the East China Sea, with sharply reduced abundance in the offshore deeper waters. In summer, the parent cohorts produced offspring in Lvsi in the southern Yellow Sea, the Yangtze River estuary, and coastal water areas of the East China Sea. In autumn, juveniles in the coastal waters migrated to the offshore water area. In winter, a few individuals were sparsely distributed in the offshore water areas of the southern Yellow and East China Seas, and part of the recruitment in the Taiwan Strait might migrate northward to Yushan and Wentai fishing grounds for the nursery. The present investigations provide baseline data that will enable fishers and policymakers to better manage and conserve this resource for future use. Full article

Wind energy harvesting based on triboelectric nanogenerators (TENGs) is a promising solution for powering distributed Internet of Things (IoT) nodes, yet its practical efficiency and stability are often hindered by the fluctuating and unpredictable nature of wind. Here, we propose a self-regulating TENG (SR-TENG) that leverages the synergistic effects of centrifugal, elastic, and frictional forces to automatically switch between non-contact and contact modes based on wind speed. This configuration achieves an ultra-low start-up wind speed of 0.86 m/s, ensures sustainable high-performance output across a broad wind speed range, and exhibits excellent durability with no observable performance degradation during 23,000 s of continuous operation at 375 rpm. Systematic structural optimization enables the SR-TENG to reach a peak open-circuit voltage of 140 V, a short-circuit current of 12.5 μA, and a transferred charge of 300 nC at 375 rpm. When integrated with a customized power management circuit, the system delivers a 30.39-fold increase in effective output power at a 1 MΩ load and a 4-fold faster charging rate for a 10 μF capacitor. For practical validation, the harvested ambient wind energy successfully powers a wireless temperature-humidity sensor for real-time cloud data transmission. These results highlight that the SR-TENG holds great potential for advanced wind energy harvesting and self-powered sensing applications in distributed IoT systems. Full article

Precise track measurement of the geometric state of high-speed railways is a prerequisite for their smooth and safe operation. Current track inspection trolleys, which integrate only an inertial navigation system (INS) and a total station (TS), rely entirely on the track control network (CPIII) deployed along the track when calibrating their absolute location to avoid INS errors. Due to the high dependency on the surrounding CPIII points, this method faces severe challenges in terms of operational efficiency and cost control. To address this issue, this study utilizes the fast and precise positioning capability of the Chinese Beidou System (BDS) and proposes a novel adaptive location calibration approach using tightly integrated BDS/TS data. Using the Kalman filtering framework, this approach integrates BDS observations with the TS distance measurements in the observation domain, and the number of CPIII points to be observed is adaptively reduced according to the surrounding environments. Thus, the absolute location of track inspection trolleys can be quickly and accurately calibrated without INS data, greatly reducing dependency on CPIII points. Experiments were conducted under two typical scenarios: open-sky and blocked BDS signals. The results demonstrate that, under open-sky scenarios, the adopted BDS-only solution achieves positioning errors of less than 1.0 cm in the north, east, and up directions within 5 min, completely getting rid of the reliance on the control network, while in obstructed scenarios, where the BDS-only solution fails to converge at the 1 cm level within 5 min, the tightly integrated BDS/TS approach, combined with CPIII data, enables fast convergence in the northward and eastward, with positioning errors of less than 1 cm. The proposed approach provides a novel location calibration scheme in the track geometric states measurement under different environments, effectively reducing the dependence of track measurement operations on CPIII points and significantly enhancing measurement efficiency and flexibility. Full article

Background and Objectives: Predictive performance of the modified myocardial performance index (Mod-MPI) for emergency cesarean delivery secondary to intrapartum fetal compromise (IFC) was examined in late-onset fetal growth restriction (FGR) or small-for-gestational-age (SGA) pregnancies. Materials and Methods: This prospective observational cohort comprised 120 singleton term pregnancies affected by late-onset FGR or SGA, classified in line with the Delphi consensus criteria, for whom a trial of vaginal delivery was planned. The primary endpoint was emergency cesarean delivery indicated by IFC, while the secondary endpoint was the development of composite adverse perinatal outcomes (CAPO). Measurements of Mod-MPI, the umbilical artery, the middle cerebral artery, and the cerebroplacental ratio (CPR) were performed within the final 72 h before delivery and were blinded to the clinicians managing the intrapartum period. Results: IFC constituted 28.3% (n = 34) of the study cohort. The IFC group exhibited significantly higher Mod-MPI values and lower CPR values (p < 0.001). In multivariable analysis, elevated Mod-MPI (≥0.61) and reduced CPR (<5th percentile) were identified as independent predictors of IFC. On receiver operating characteristic analysis, Mod-MPI demonstrated superior discriminative performance compared with CPR for predicting IFC (area under the curve [AUC]: 0.835 vs. 0.759). In contrast, CPR showed the highest diagnostic performance for predicting CAPO (AUC: 0.779). Conclusions: Mod-MPI reflects subclinical cardiac dysfunction in fetuses with late-onset FGR and SGA and represents a valuable parameter for predicting tolerance to acute intrapartum stress. Rather than routine implementation, it appears most appropriate as a complementary tool contributing to intrapartum risk assessment in selected high-risk cases. Full article

Emergency Departments (EDs) are the primary hospital interface during disasters and mass-casualty incidents, yet surge capacity assessments predominantly emphasize workforce and logistics while overlooking measurable spatial determinants. Observations from five tertiary hospitals in India indicate that circulation bottlenecks, incompatible functional adjacencies, and contamination risks can compromise safety and operational performance. This study develops and validates the Spatial Surge Capacity Assessment Framework (SSCAF) to operationalize spatial resilience as a quantifiable dimension of healthcare infrastructure preparedness. A sequential mixed-methods design was applied across five tertiary hospitals using structured spatial walkthroughs; architectural and disaster-planning document review; and focus group discussions involving 81 clinicians, administrators, and facility planners. The outcome of this thematic analysis produced 42 spatial indicators, refined through three Delphi rounds with a multidisciplinary expert panel. Consensus retained 30 key performance indicators (median ≥ 4/5; IQR ≤ 1; Kendall’s W = 0.855; χ² = 297.42; p < 0.001). Content validity was strong (I-CVI 0.75–1.00; S-CVI/Ave = 0.93), and reliability was high (ICC 0.82–0.91), structured into eight operational domains. The resulting weighted scoring matrix standardizes the measurement of spatial surge preparedness. The SSCAF provides an evidence-based audit and planning tool supporting resilient hospital infrastructure. It aligns with the Sendai Framework, enabling governance audits, guiding ED retrofitting, and supporting performance-based evaluation for planners and architects. Full article

Smart energy networks, including electricity distribution and district heating, are increasingly operated as sensor-enabled infrastructures where maintenance decisions must be made under heterogeneous and time-varying operating conditions. In these settings, time-to-event data are rarely complete; preventive actions and limited observation horizons routinely introduce censoring and truncation, so models and validation procedures must account for partially observed lifetimes to avoid biased inference and misleading performance estimates. This review surveys survival models for predictive maintenance (PdM) and remaining useful life (RUL) estimation, spanning non-parametric, semi-parametric, parametric, and learning-based approaches, with emphasis on censoring-aware formulations and the use of static and time-varying covariates derived from sensor, inspection, and contextual information. A structured taxonomy and a systematic mapping of model families to data types, core assumptions (proportional hazards versus parametric distributional structure), and decision-oriented outputs such as risk ranking, horizon failure probabilities, and RUL distributions are presented. Evaluation practice is also synthesized by covering discrimination metrics, censoring-aware RUL accuracy measures, and probabilistic assessment via proper scoring rules, including the time-dependent Brier score and Integrated Brier Score (IBS). The review provides researchers and practitioners with a practical guide to selecting, fitting, and evaluating survival models for risk-informed maintenance planning in smart energy networks. Full article

To systematically investigate the influence of metro train types on the operational noise of elevated rail transit, this study conducted field measurements on elevated sections of the Wuhan Metro Yangluo Line, Wuhan Metro Line 2, and Guangzhou Metro Line 4, comparing the noise characteristics of 4-car A-type, 6-car B-type, and 4-car L-type trains operating at 70 ± 2 km/h. Analysis of sound pressure levels and frequency spectra at multiple points revealed that wheel-rail noise peaks occurred at 630 Hz and 2500 Hz for A-type trains, around 800 Hz for B-type trains, and within 800–1250 Hz for L-type trains, while bridge structure-borne noise was consistently concentrated in the 63–100 Hz low-frequency range. Distinct emission patterns were observed: at on-girder points, noise levels were highest for A-type trains, followed by B-type and then L-type trains, a trend potentially linked to axle loads; conversely, at under-girder points, the order reversed with L-type trains producing the highest noise. At points 7.5 m and 25 m from the track centerline, A-type and B-type trains exhibited similar noise levels, whereas L-type trains were slightly quieter. Furthermore, all three train types showed a consistent noise attenuation rate of approximately 6 dB(A) per doubling of distance from the track centerline. The findings will serve as a reference and basis for rail transit noise prediction and control. Full article