Search Results (59,328)

Plain bearings are widely used in heavy-duty applications, e.g., wind turbine drivetrains or ship propulsion systems. Plain bearings offer high load-carrying capacity and good damping, but abnormal events such as oil supply failure (OSF) can rapidly damage the bearing and cause failure of the entire drivetrain. An adequate oil supply is essential for the operation of the plain bearing. A failure of the oil supply can cause fatal failure of the bearing due to adhesive wear within a matter of seconds. Existing condition monitoring systems (CMS) for plain bearings generally cannot detect OSF in time, or require costly and complex installation, limiting their practical applicability. This paper presents temperature field measurement (TFM) as a simple, low-cost CMS approach for early OSF detection. The results presented within this work demonstrate that TFM detects the onset of OSF within 30 s of oil supply interruption, at least 30 s before a rise in friction torque can be detected in the corresponding test bearing. The findings demonstrate that TFM is a valid, simple, and effective method for the timely detection of OSF, offering a practical alternative to existing CMS approaches for heavy-duty plain bearing applications. Full article

Animal material culture is most often discussed in relation to tool use and foraging, while non-foraging object use remains poorly documented in many taxa. This study reports opportunistic observations of a novel object-directed behavior in the critically endangered pygmy raccoon (Procyon pygmaeus), an island-endemic species restricted to Cozumel, Mexico. During fieldwork on tourism effects between January and February 2026, researchers used direct observation, site assessment, photography, videography, and interviews to document raccoon behavior at several sites. At one beach club, two juvenile female siblings were observed retrieving paper waste, dipping it in water, rolling and compacting it with sand, and using the resulting balls in play. One juvenile first performed the full sequence while the other observed and then attempted the behavior; the second juvenile later completed the sequence independently after her sister had been removed from the site. However, at a comparable tourism site with juvenile raccoons and access to paper and water, this behavior was not observed. These observations provide potential evidence of socially mediated material engagement in pygmy raccoons. The findings expand the known behavioral repertoire of the species and support continued field-based behavioral monitoring, especially where tourism, waste exposure, and juvenile development overlap. Full article

Insulator types and materials vary substantially across power system inspection scenarios, while damage defects occur infrequently; consequently, defect images that match a target insulator type and operating environment are often difficult to obtain. Existing open-source insulator image datasets provide limited coverage of equipment types, scene backgrounds, and defect morphologies. Their direct use for detector training may therefore cause domain mismatch and poor generalization. To address these limitations, this study proposes a scene-domain-adaptive sample expansion method for few-shot damaged-insulator detection. The method adapts a general-purpose pretrained diffusion model to the insulator inspection domain and incorporates three-dimensional (3D) structural constraints to generate targeted samples of damaged insulators. First, low-rank adaptation (LoRA) is used for scene-domain adaptation, enabling the generation model to learn the characteristic geometry, appearance, and material properties of insulators. Second, a 3D model of the target insulator is constructed, and physical damage simulation and edge extraction are applied to obtain geometric guidance maps containing shed boundaries and fracture contours. These maps constrain the locations and shapes of the generated defects. Finally, the geometric guidance is injected into the diffusion process to synthesize damaged-insulator images, which are combined with limited real samples to train detectors for damaged-insulator instances, which were evaluated exclusively on real validation images. Experimental results show that adding a moderate number of generated samples enriches the scarce defect features in the real dataset and improves detector performance. In the mixture-ratio experiment using YOLOv8, mAP@0.5 increased by 8.2 percentage points. Additional experiments with multiple detectors yielded performance gains of varying magnitudes, demonstrating that the generated samples provide an effective supplement to limited, real-world data. The proposed method alleviates the scarcity of insulator defect samples and offers a practical data-augmentation strategy for intelligent inspection of power equipment. Full article

Agricultural technological innovation is essential for sustainable agricultural modernization and rural development. However, agricultural technology enterprises often face persistent financing constraints because research and development (R&D) activities involve long investment cycles, high uncertainty, and delayed returns. Using a firm-level panel dataset of Chinese agricultural technology enterprises, this paper examines the effect of relational patient capital (RPC) on agricultural technological innovation by employing a two-way fixed-effects model. The results show that RPC significantly promotes agricultural innovation output. Mechanism analysis indicates that RPC enhances innovation through two channels. First, it facilitates firms’ digital transformation, thereby reducing R&D uncertainty and organizational costs. Second, it alleviates financing constraints by stabilizing cash flows to support R&D investment. The results remain robust after clustering standard errors, excluding the years affected by the COVID-19 pandemic, and employing lagged specifications. Heterogeneity analyses further reveal that the positive effect is more pronounced among small-scale enterprises and firms located in central and eastern China, where financing frictions and resource constraints are relatively more severe. By linking RPC to firm-level agricultural innovation, this study extends the literature on agricultural finance and innovation financing, highlighting the role of long-term, relationship-based capital in addressing market failures in agricultural R&D. The findings suggest that rural financial policies should encourage stable, long-term investment, strengthen financing support for small agricultural technology enterprises, and integrate patient capital with digital transformation initiatives to promote sustainable agricultural and rural development. Full article

This study presents the development of an Artificial Intelligence (AI)-based legal assistant using the Retrieval-Augmented Generation (RAG) architecture to provide legal assistance to citizens of the Republic of Kazakhstan. The proposed solution is designed to generate accurate, evidence-based responses to user queries using the regulatory legal acts of the Republic of Kazakhstan as the primary source of information. A legal corpus comprising 101,000 legislative documents and court decisions, with approximately 77 million tokens in Kazakh and Russian, was constructed to support the retrieval component of the system. To identify the most effective semantic retrieval method, three multilingual embedding models—Multilingual-E5-Large, BGE-M3, and KazEmbed-V5—were evaluated for vector search. The experimental results showed retrieval accuracies of 87.6%, 76.8%, and 83.3%, respectively. The GPT-5.4 and Llama-4-Scout-17B-16E-Instruct large language models were used to generate legal reasoning and responses based on documents retrieved through semantic search. The quality of the generated responses was evaluated using two complementary approaches. First, legal experts assessed the factual correctness and legal validity of the answers. Second, automatic evaluation was performed using word-level F1, BLEU, ROUGE, and BERTScore-F1 metrics. Among all evaluated configurations, GPT-5.4 combined with Multilingual-E5-Large achieved the highest overall accuracy (88.5%), whereas Llama-4-Scout-17B-16E-Instruct combined with KazEmbed-V5 achieved an accuracy of 83.6%. Based on the proposed architecture and the selected semantic retrieval and language models, an AI legal assistant was developed and integrated into the “Adal Azamat” legal services platform providing users in Kazakhstan with practical access to AI-assisted legal consultation. Full article

The global energy transition requires countries to rapidly expand renewable energy infrastructure, yet the factors associated with this transition remain comparatively underexplored in resource-dependent economies. The Gulf Cooperation Council (GCC) represents a particularly important case: despite their substantial role in global hydrocarbon markets, they are simultaneously pursuing renewable energy expansion and economic diversification. Examining this transition therefore offers insights not only for the GCC but also for other hydrocarbon-dependent economies facing similar diversification challenges. This study examines, for the first time, the association between tourism development and renewable energy capacity share across six GCC countries from 2014 to 2024. Unlike most existing studies that use renewable energy consumption as the dependent variable, this study focuses on renewable energy capacity share, which more directly reflects long-term infrastructure investment and commitment to the energy transition. Using a balanced panel of 66 observations, the empirical analysis tests for cross-sectional dependence, applies the second-generation cross-sectionally augmented IPS (CIPS) unit root test, and estimates alternative panel specifications, with the fixed-effects model selected as the preferred specification based on the Hausman test. The results show that tourism receipts and foreign direct investment are positively and significantly associated with renewable energy capacity share, while GDP per capita shows no robust significant association. These findings highlight the potential relevance of tourism development and foreign investment to renewable energy expansion in hydrocarbon-dependent economies and provide policy-relevant evidence for countries seeking to align economic diversification with the energy transition. Full article

Perilla frutescens is a specialty oilseed crop rich in polyunsaturated fatty acids, particularly α-linolenic acid (ALA), and the molecular regulation of seed lipid metabolism is an active area of research. Reverse-transcription quantitative PCR (RT-qPCR) is essential for investigating the molecular mechanisms underlying seed oil biosynthesis; however, systematically validated reference genes are lacking for this species. Based on P. frutescens transcriptome data, seven commonly used housekeeping genes—PfActin, Pf18S (18S pre-ribosomal assembly protein GAR2-like), PfEF-1α, PfCYP, PfTUA, PfTUB, and PfGAPDH—were initially selected. After PfGAPDH was excluded because of its extremely low abundance and unstable expression, the remaining six candidates were evaluated in vegetative organs (leaves, stems, and roots), seeds at different developmental stages, and leaves subjected to PEG 6000 treatment. Expression stability was independently assessed using geNorm, NormFinder, and BestKeeper, and the resulting stability values were integrated with RefFinder. Pf18S showed the highest overall stability in the complete sample set and developing seeds. Under osmotic stress, PfTUB was the most stable single reference gene, and geNorm identified PfTUB + Pf18S as the optimal two-gene combination. In vegetative organs, PfTUB ranked first and PfTUA second, whereas PfEF-1α was among the least stable genes in most sample sets. The recommended two-gene combinations were PfTUA + PfTUB for vegetative organs, Pf18S + PfTUA for developing seeds, and PfTUB + Pf18S for osmotic stress. Biological validation showed that normalization to Pf18S reproduced spatiotemporal expression patterns of six key lipid-biosynthesis genes that were consistent with the dynamic accumulation of total seed oil and ALA. These results support a tiered normalization strategy using Pf18S for preliminary screening and condition-specific reference-gene pairs for precise quantification. Full article

Pesticides pose significant hazards to aquatic ecosystems and public health; therefore, understanding their fate in aquatic systems is critically important. Photochemical processes driven by direct and indirect photolysis, particularly hydroxyl radical (^•OH) reactions, play a pivotal role in the transformation of these contaminants in natural waters. This study employed an efficient and selective ^•OH production technique using a high-power UV light-emitting diode (UV-LED) combined with nitrite photolysis to determine the second-order reaction rate constants between ^•OH and selected pesticides (k_X,OH). This approach enabled reliable estimation of the indirect photodegradation rate constants (k_IP) for the selected pesticides in aquatic systems. In addition, direct photodegradation rate constants (k_DP) of the selected pesticides were determined under simulated sunlight conditions using a solar simulator equipped with a 500 W xenon lamp. The photochemical half-lives of selected pesticides in river water and seawater were calculated from k_DP and kIP under assumed steady-state HO^• concentrations. The results demonstrated that direct photolysis rate constants of the selected pesticides ranged from 1.62 × 10⁻⁷ to 5.52 × 10⁻⁴ s⁻¹. The second-order reaction rate constants between the investigated pesticides and ^•OH ranged from 0.045 × 10⁹ to 14.8 × 10⁹ M⁻¹ s⁻¹. Estimated half-lives under direct photolysis in seawater ranged from hours to days, whereas half-lives attributed to indirect photolysis in seawater extended to several years. In contrast, half-lives of pesticides in river water ranged from hours to days for indirect photolysis. Under the assumed steady-state ^•OH concentrations, direct photolysis generally produced shorter calculated half-lives than the ^•OH pathway in seawater, whereas the higher assumed ^•OH concentration substantially reduced the calculated indirect-photolysis half-lives in river water. These findings should be interpreted as condition-specific kinetic comparisons rather than direct measurements of environmental persistence. Full article

Background/Objectives: Multiple chronic conditions (MCCs) are highly prevalent among older adults and require effective collaboration between the patient and caregiver. Mutuality, reflecting the quality of the dyadic relationship, is associated with better self-care and health outcomes. However, the Mutuality Scale (MS) has not been validated on patient–caregiver dyads managing MCCs living in a low–middle-income country (LMIC). Aim: This study seeks to evaluate the structural and convergent validity and reliability of the MS among patient–caregiver dyads managing MCCs living in a LMIC. Methods: A cross-sectional study was conducted on MCC patients and their caregiver recruited from community and outpatient settings. The MS, Self-care of Chronic Illness Inventory (SC-CII) and Caregiver Contribution to self-care Inventory (CC-SCCII) were used for measuring mutuality, patient self-care, and Caregiver Contribution (CC) to patient self-care, respectively. Confirmatory factor analysis (CFA) was performed separately for patients and caregivers to evaluate the original four-factor structure of the MS. Convergent validity was examined through correlations with self-care and CC to patient self-care. Reliability was evaluated using composite reliability and the Global Reliability Index for multidimensional scale. Results: A sample of 406 patient–caregiver dyads was examined. Patients had a mean age of 73.9 (±6.2) years. Caregivers had a mean age of 47.8 (±15.5) years. The four-factor structure was supported in both samples, with acceptable model fit (patients: Comparative Fit Index (CFI) = 0.953 and Root Mean Square Error of Approximation (RMSEA) = 0.078; caregiver CFI = 0.945 and RMSEA = 0.085. The second-order CFA supported a hierarchical structure. Patient and caregiver mutuality scores were strongly correlated (r = 0.778, p < 0.01). Higher mutuality was associated with better patient self-care (r = 0.276–0.479) and CC to self-care (r = 0.174–0.556). Reliability indices ranged from 0.70 to 0.91 for patients and 0.66 to 0.89 for caregivers. Conclusions: The findings support the validity and reliability of the MS for assessing mutuality in patients and their caregivers managing MCCs in an LMIC characterized by limited healthcare resources and formal support. Its use provides empirical support for assessing relationship quality and facilitating dyadic care within this vulnerable population. Future longitudinal studies should evaluate its predictive validity, responsiveness, and measurement invariance across different groups and between patient and caregiver. Full article

This case study investigates specific language phenomena and dominance patterns among second-generation speakers of Sri Lankan origin in Naples within the framework of the HELLO Campania Project. While second generations are typically assumed to be dominant in the societal majority language, the Sri Lankan community—characterized by exceptionally high rates of heritage language maintenance—offers a critical testing ground for this assumption. Drawing on a corpus of sociolinguistic interviews with first- and second-generation speakers, the analysis focuses on two morphosyntactic variables that function as indicators of acquisitional development in Italian as L2: verbal morphology and the realization of definite and indefinite articles. Verbal forms were classified along a four-stage developmental scale derived from research on Italian interlanguage, while article use was evaluated through a three-level accuracy scale. Descriptive and inferential statistics reveal a clear asymmetry within the second generation specifically in terms of gender variable. Male speakers display near-native competence consistent with the expected dominance shift toward Italian. In contrast, several female speakers exhibit persistent features—such as infinitive overextension, auxiliary omission in perfective constructions, and residual article omission—patterns more typical of first stages of second-language acquisition than of heritage-speaker dominance. These findings suggest that, in contexts of strong heritage language maintenance and dense ethnic social networks, second-generation speakers may experience incomplete acquisition of the majority language rather than incomplete acquisition of the heritage language. Methodologically, the study demonstrates the importance of integrating perceptual self-reports with production data. Theoretically, it challenges universalist assumptions about second-generation dominance and underscores the need for community-specific, migration-scenario-based analyses in heritage language research. Full article

Background: Age, race, body mass index (BMI), breast density, parity, smoking and alcohol use are associated with increased risk for breast cancer. These factors, as well as tumor characteristics, treatment regimens and comorbidities, are analyzed for associations with five-year event free survival (EFS) in a sample of women with Stage I-III breast cancer who received chemotherapy with curative intent. Methods: EFS was defined in terms of breast cancer recurrence, second primary, metastasis, and overall survival. Analyses were stratified by age (under age 65 vs. over age 65). EFS was estimated using the Kaplan–Meier method and compared using a Cox proportional hazard model. Results: In a sample of 821 women, mean age at diagnosis was 54 years, with 75% White and 22% Black. Younger women had higher proportions of Stage II and III tumors (p = 0.005), larger tumor size (p = 0.0004), and higher breast density (p = 0.003). Five-year EFS was 91% among younger vs. 82% among older women (p = 0.0005). In women aged < 65, there were 48 EFS events, and triple negative patients had significantly worse EFS compared to other subtypes (p = 0.003). Smokers also had worse EFS (p = 0.04). In women aged ≥ 65, there were 26 events, and both tumor size (p = 0.02) and mastectomy (p = 0.03) were significant for EFS. Conclusions: In our sample, triple negative subtype, smoking history, tumor size, and surgery type were significantly associated with shorter EFS. Race, BMI, alcohol use, parity, breast density, radiation treatment, and specific chemotherapy regimen were not significant for EFS in either age group. Full article

Background/Objectives: Double reading with arbitration improves diagnostic reliability in fundus screening but requires repeated human interpretation. Whether artificial intelligence (AI) can serve as a first-pass decision source within this workflow, and whether this applies consistently across retinal diseases with differing inter-reader agreement, remains unclear. Methods: In this retrospective study, 6904 color fundus photographs from 2593 patients at a tertiary screening center were analyzed for age-related macular degeneration (AMD), diabetic retinopathy (DR), and retinal vein occlusion (RVO). Three retina specialists independently labeled each image, and an AI system provided binary classifications at a prespecified operating threshold targeting 0.99-sensitivity. In AI–human double reading, the AI and one reader independently interpreted each image, and a second reader arbitrated discordant cases; this was compared with conventional human–human double reading. Three reader combinations were evaluated per disease. Results: AI–human reading required 1.02–1.11 human reads per image versus 2.00–2.10 for human–human reading. For DR and RVO, human–human reading yielded sensitivities of 0.974 and 0.982 and specificities of 0.999 and 1.000, respectively. Across AI–human combinations, sensitivity and specificity did not differ significantly from human–human reading (all p ≥ 0.05; specificity differences ≤0.001). For AMD (human–human sensitivity 0.845, specificity 1.000), AI–human sensitivity varied: two combinations were higher (0.916 and 0.950; both p < 0.001) and one comparable (0.842; p = 0.742). AMD specificity remained ≥0.978. Conclusions: AI–human reading halved human reading volume without significant loss for DR and RVO; AMD varied by configuration. AI use within double-reading workflows should account for disease-specific inter-reader agreement, reader composition, and operating threshold. This was a single-center, retrospective study, prospective external validation in a multicenter setting is warranted before clinical implementation. Full article

Non-stationarity is increasingly recognized as a defining feature of contemporary hydroclimatic regimes, challenging the statistical assumptions that underpin inter-annual discharge deficits analysis and water-resources design. This study investigates how shifts in first- and second-order statistical moments (mean and variance, respectively) alter the perceived rarity and persistence of inter-annual drought events in low-memory, rapid-response mountain systems. I develop a stochastic Monte Carlo framework to explore changes in inter-annual discharge deficit frequency and multi-year drought clustering across successive climatic regimes, using the Northern Apennines (Italy) as a representative case study. The model is explicitly exploratory: it does not aim to reproduce observed discharge distributions, but to quantify how regime shifts in mean and variability propagate into tail exceedances and drought spells under stationarity-based metrics. Results show a pronounced amplification of annual hydrological drought exceedances and the emergence of persistent multi-year drought spells under contemporary conditions, which are strongly underestimated when historical baselines are assumed stationary. A comparison with long-term regional discharge trends—while acknowledging the distinct hydro-climatic response of high-memory versus low-memory basins—serves to contextualize the systemic nature of the observed drought amplification. The findings highlight the structural vulnerability of low-memory catchments to non-stationary forcing and underscore the limitations of traditional design thresholds for drought-risk assessment under the evolving climate. Full article

An integrated model is presented to estimate the energy consumption of a Formula SAE–type electric vehicle by combining MATLAB/Simulink simulations with real operational data from the Cotopaxi kart circuit. The implemented subsystem incorporates vehicle dynamics, speed, and grade profiles, and it enables the calculation of tractive energy across different competition scenarios. The L3 cycle (maximum speed 90.91 km/h, distance 16.67 km) proved the most demanding, with a tractive energy consumption of 1389.10 Wh, mechanical losses of 630.89 Wh, and a useful net energy of 758.21 Wh. Rolling resistance and inertia accounted for 21.33% and 56.29% of the consumption, respectively, highlighting the influence of acceleration/deceleration dynamics. All cycles were dominated by active phases, with 0% stops and cruising time < 0.21%, validating the model for high-demand conditions. The technical feasibility of second-life Lithium Iron Phosphate (LFP) cells for the battery pack was also confirmed: a 30s2p configuration using 100 Ah cells meets the peak energy demand of 18.30 kWh while maintaining adequate operational margin and a safe discharge C-rate. Structural simulation in ANSYS enabled optimization of mass and stiffness, reducing overall energy demand. This multidisciplinary approach provides a quantitative basis for battery sizing and energy management strategies in competitive electric vehicle applications. Full article

Accurate State-of-Health (SOH) prediction is essential for deploying retired electric vehicle batteries into reliable second-life energy storage systems. However, this task is challenged by sparse and noisy operational data from onboard Battery Management Systems (BMS). This study systematically evaluates a spectrum of deep learning architectures for SOH forecasting under BMS-style data constraints derived from laboratory cycling data: a BiLSTM on aggregated cycle statistics (Model A), preliminary zero-shot transfer to a single unseen cell (Model B), a waveform BiLSTM with full intra-cycle voltage, current, and temperature trajectories (Model C), a baseline TCN (Model D) and a probabilistic TCN-GPR hybrid (Model E). All models are constrained to identical low-fidelity BMS-style variables extracted from the NASA battery aging dataset. Model C achieves the lowest point accuracy error of 0.46% ± 0.18% MAE across five random seeds, demonstrating that high-resolution waveform inputs capture degradation signatures, notably voltage plateau morphology, transient dynamics, and implicit SOC information, that aggregated features irreversibly lose. Model D using the same waveform inputs and evaluation protocol as Model C, achieves a MAE of 2.99% at a single seed, providing direct architectural comparison evidence that the BiLSTM’s position-sensitive temporal summarization outperforms GlobalAveragePooling1D under these conditions. Model E achieves a higher MAE of 2.12% ± 0.33% but uniquely provides calibrated predictive distributions of 99.4% ± 1.2% coverage, NLL = −1.877 ± 0.038, with approximately uniform 95% predictive intervals (mean width 19.83% SOH across 34 test cycles at seed = 42), reflecting the near-constant posterior variance produced by the large optimized GPR length-scale under the frozen two-stage training design. A paired t-test confirms that Model C statistically significantly outperforms Model E on point accuracy (p < 0.01). Isotonic regression recalibration reduces mean calibration error from 0.138 to 0.010, demonstrating that shape-level miscalibration is correctable post hoc. The central implication for second-life battery deployment is a clear accuracy–uncertainty trade-off: Model C is preferred when point estimates suffice, while Model E is essential for risk-aware decisions requiring confidence intervals. Full article