Search Results (7,778)

Accurate prediction of the RUL of electric drive bearings over the entire service life cycle for new energy vehicles optimizes maintenance strategies and reduces costs, addressing clear application needs. Full life data of electric drive bearings exhibit long time spans and abrupt degradation, complicating the modeling of time dependent relationships and degradation states; therefore, a piecewise linear degradation model is appropriate. An RUL prediction method is proposed based on degradation assessment and spatiotemporal feature fusion, which extracts strongly time correlated features from bearing vibration data, evaluates sensitive indicators, constructs weighted fused degradation features, and identifies abrupt degradation points. On this basis, a piecewise linear degradation model is constructed that uses a path graph structure to represent temporal dependencies and a temporal observation window to embed temporal features. By incorporating GAT-LSTM, RUL prediction for bearings is performed. The method is validated on the XJTU-SY dataset and on a loaded ball bearing test rig for electric vehicle drive motors, yielding comprehensive vibration measurements for life prediction. The results show that the method captures deep degradation information across the full bearing life cycle and delivers accurate, robust predictions, providing guidance for the health assessment of electric drive bearings in new energy vehicles. Full article

The concentration of intracellular adenosine triphosphate (ATP) is one of the most important characteristics of the metabolic state of the cells of microorganisms and their viability. This indicator, monitored by bioluminescent ATP-metry, and accumulation of the suspension biomass in the medium were used to assess the effect of particles of different synthetic microplastics (MPs) (non-biodegradable and biodegradable) on the cells of yeast, filamentous fungi, bacteria and phototrophic microorganisms (microalgae and cyanobacteria) co-exposed with polymer samples in different environments and concentrations. It was found that the effect of MPs on microorganisms depends on the concentration of MPs (1–5 g/L), as well as on the initial concentration of cells (10⁴ or 10⁷ cells/mL) in the exposure medium with polymers. It was shown that the lack of a sufficient number of nutrition sources in the medium with MPs is not fatal for the cells. The study of the effect of MPs on the photobacteria Photobacterium phosphoreum, widely used as a bioindicator for assessing the ecotoxicity of various environments, demonstrated a correlation between the residual bioluminescence of these cells and the level of their intracellular ATP in media with biodegradable polycaprolactone and polylactide, which had an inhibitory effect on these cells. Marine representatives of phototrophic microorganisms showed the greatest sensitivity to the presence of MPs, which was confirmed by both a decrease in the level of intracellular ATP and the concentration of their biomass. Among the eight microorganisms studied, bacteria of the genus Pseudomonas turned out to be not only the most tolerant to the presence of the seven MP samples used in the work, but also actively growing in their presence. Full article

Faced with complex, ever-changing battlefield environments and diverse attacks, enabling warship combat systems to recover rapidly and effectively after damage is key to enhancing resilience and sustained combat capability. We construct a representative naval battle scenario and propose an integrated Attack-Defense-Recovery Strategy (ADRS) grounded in warship system models for different attack types. To address high parameter sensitivity, weak initial pheromone feedback, suboptimal solution quality, and premature convergence in traditional ant colony optimization (ACO), we introduce three improvements: (i) grid-search calibration of key ACO parameters to enhance global exploration, (ii) a non-uniform initial pheromone mechanism based on the wartime importance of equipment to guide early solutions, and (iii) an ADRS-consistent state-transition rule with group-based starting points to prioritize high-value equipment during the search. Simulation results show that the improved ACO (IACO) outperforms classical ACO in convergence speed and solution optimality. Across torpedo, aircraft/missile, and UAV scenarios, ADRS-ACO improves over GRS-ACO by 7.2%, 0.3%, and 5.5%, while ADRS-IACO achieves gains of 34.9%, 17.1%, and 16.7% over GRS-ACO and 25.9%, 16.7%, and 10.6% over ADRS-ACO. Overall, ADRS-IACO consistently delivers the best solutions. In high-intensity, high-damage torpedo conditions, ADRS-IACO demonstrates superior path planning and repair scheduling, more effectively identifying critical equipment and allocating resources. Moreover, under multi-wave combat, coupling with ADRS effectively reduces cumulative damage and substantially improves overall warship-system resilience. Full article

Understanding how solvents influence the luminescence behavior of Cu(I) complexes is crucial for designing advanced optical sensors. This study reports the synthesis, structures and photophysical investigation of an 8-hydroxyquinoline-functionalized 1H-imidazo[4,5-f][1,10]phenanthroline ligand, ipqH₂, and its four Cu(I) complexes with diphosphine co-ligands. Photoluminescence studies demonstrated distinct solvent-dependent excited-state mechanisms. In DMSO/alcohol mixtures, free ipqH₂ exhibited excited-state proton transfer (ESPT) and enol-keto tautomerization, producing dual emission at about 447 and 560 nm, while the complexes resisted ESPT due to hydrogen bond blocking by PF₆⁻ anions and Cu(I) coordination. In DMSO/H₂O, aggregation-caused quenching (ACQ) and high-energy O–H vibrational quenching dominated, but complexes 1 and 2 showed a significant red-shifted emission (569–574 nm) with high water content due to solvent-stabilized intra-ligand charge transfer and metal-to-ligand charge transfer ((IL+ML)CT) states. In DMSO/DMF, hydrogen bond competition and solvation-shell reorganization led to distinct responses: complexes 1 and 3, with flexible bis[(2-diphenylphosphino)phenyl]ether (POP) ligands, displayed peak splitting and (IL + ML)CT redshift emission (501 ⟶ 530 nm), whereas complexes 2 and 4, with rigid 9,9-dimethyl-4,5-bis(diphenylphosphino)-9H-xanthene (xantphos), showed weaker responses. The flexibility of the diphosphine ligand dictated DMF sensitivity, while the coordination, the hydrogen bonds between PF₆⁻ anions and ipqH₂, and water solubility governed the alcohol/water responses. This work elucidates the multifaceted solvent-responsive mechanisms in Cu(I) complexes, facilitating the design of solvent-discriminative luminescent sensors. Full article

Against the backdrop of the growing prevalence of green trade barriers, these unilateral measures are continually eroding the industrial chain resilience of developing countries. Taking China’s steel industry as a case study, this research employs the Pressure–State–Response (PSR) framework and a system dynamics model to explore the role of green finance in this process. Scenario-based simulation results indicate that: (1) green trade barriers exert shocks on the industrial chain resilience of China’s steel industry, yet the degree of variation in resistance, recovery, and adaptive capacity differs across dimensions; (2) green finance and its accompanying policies can still play an effective role in responding to green trade barriers, though they are neither a panacea nor the sole solution; (3) the sensitivity of different regulatory measures varies with respect to the distinct dimensions of industrial chain resilience. Drawing on the simulation analysis and subsequent discussion, this study puts forward a set of conditional policy recommendations, providing a reference for governmental decision-making under comparable circumstances. Full article

Maintaining the integrity of cryogenically preserved biological materials is critical, as even brief, undetected temperature excursions in storage can compromise sample viability. Existing monitoring systems may miss transient thaw–refreeze events, posing serious quality risks. To address this, we developed and validated frozen indicator tubes that visually signal deviations from the frozen state, serving as a cost-effective backup to electronic monitors. Our first method uses an aqueous dye solution that immobilizes the dye when frozen; any thawing causes the dye to disperse, providing a clear, external visual cue of a partial or complete thaw. For ultra-low-temperature storage (−80 °C), we introduced a second method using an ethanol-based solution calibrated to indicate thaw events. This system detects temperature rises of 10 °C or more sustained for at least fifteen minutes—conditions that may jeopardize sample stability. When paired with standard monitoring systems, these indicator tubes offer an added layer of protection by providing simple, reliable, and immediate visual confirmation of critical temperature breaches. This innovation enhances confidence in cryogenic storage protocols and supports the long-term preservation of sensitive biological materials. Full article

Prior work established the presence of six crosscutting patterns of clinically significant family maltreatment (FM) and psychological health (PH) problems among active-duty service members. Here, we develop a brief screener for these patterns via Classification and Regression Trees (CART) analyses using a sample of active-duty members of the United States Air Force. CART is a predictive algorithm used in machine learning. It balances prediction accuracy and model parsimony to identify an optimal set of predictors and identifies the thresholds on those predictors in relation to a discrete condition of interest (e.g., diagnosis of pathology). A 22-item screener predicted membership in five of the six classes (sensitivities and specificities > 0.96; positive and negative predictive values > 0.90). However, for service members at extremely high risk of clinically significant externalizing behavior, sensitivity and positive predictive values were much lower. The resulting 22-item brief screener can facilitate feasible, cost-effective detection of five of the six identified FM and PH problem patterns with a small number of items. The sixth pattern can be predicted far better than chance. Researchers and policymakers can use this tool to guide prevention efforts for FM and PH problems in service members. Full article

The increasing use of AI systems for face, object, action, scene, and emotion recognition raises significant privacy risks, particularly when processing Personally Identifiable Information (PII). Current privacy-preserving methods lack adaptability to users’ preferences and contextual requirements, and obfuscate user faces uniformly. This research proposes a user-centric, context-aware, and ontology-driven privacy protection framework that dynamically adjusts privacy decisions based on user-defined preferences, entity sensitivity, and contextual information. The framework integrates state-of-the-art recognition models for recognising faces, objects, scenes, actions, and emotions in real time on data acquired from vision sensors (e.g., cameras). Privacy decisions are directed by a contextual ontology based in Contextual Integrity theory, which classifies entities into private, semi-private, or public categories. Adaptive privacy levels are enforced through obfuscation techniques and a multi-level privacy model that supports user-defined red lines (e.g., “always hide logos”). The framework also proposes a Re-Identifiability Index (RII) using soft biometric features such as gait, hairstyle, clothing, skin tone, age, and gender, to mitigate identity leakage and to support fallback protection when face recognition fails. The experimental evaluation relied on sensor-captured datasets, which replicate real-world image sensors such as surveillance cameras. User studies confirmed that the framework was effective, with over 85.2% of participants rating the obfuscation operations as highly effective, and the other 14.8% stating that obfuscation was adequately effective. Amongst these, 71.4% considered the balance between privacy protection and usability very satisfactory and 28% found it satisfactory. GPU acceleration was deployed to enable real-time performance of these models by reducing frame processing time from 1200 ms (CPU) to 198 ms. This ontology-driven framework employs user-defined red lines, contextual reasoning, and dual metrics (RII/IVI) to dynamically balance privacy protection with scene intelligibility. Unlike current anonymisation methods, the framework provides a real-time, user-centric, and GDPR-compliant method that operationalises privacy-by-design while preserving scene intelligibility. These features make the framework appropriate to a variety of real-world applications including healthcare, surveillance, and social media. Full article

Background: Insulin resistance (IR) is common in overweight or obese individuals. Dysregulation of trace elements such as cobalt (Co) and manganese (Mn) has been associated with obesity and IR markers in individuals with diabetes. However, their role in non-diabetic states is less understood. Objective: This study aimed to analyze the association between serum Co and Mn levels and IR in overweight and obese women without diabetes. Methods: A total of 112 overweight or obese women were evaluated for their anthropometric, metabolic, and biochemical characteristics. To estimate IR, the homeostatic model assessment of insulin resistance (HOMA-IR), quantitative insulin sensitivity check index (QUICKI), triglyceride–glucose index (TyG), and triglyceride–glucose–body mass index (TyG-BMI) were calculated. Serum Co and Mn concentrations were quantified by inductively coupled plasma mass spectrometry (ICP-MS). Results: Our results show that 77% of participants exhibited central fat accumulation and a high prevalence of IR. Fasting insulin (FINS), HOMA-IR, and TyG-BMI were significantly higher in obese women, while adiponectin (Adpn) was lower. Moreover, Co was inversely associated with FINS (p = 0.003) and HOMA-IR (p = 0.011), and positively associated with QUICKI (p = 0.011) in obese women. In contrast, serum Mn levels showed negative correlations with fasting glucose (FG) (p = 0.021) and the TyG index (p = 0.048) in overweight women. Conclusions: Co serum levels were positively associated with FG and QUICKI and negatively associated with FINS and HOMA-IR in the obese group. Mn showed negative associations with FG and the TyG index, suggesting that these trace elements may play a role in the IR in people with obesity. Full article

As more machine learning models are used in sensitive fields like healthcare, finance, and smart infrastructure, protecting structured tabular data from privacy attacks is a key research challenge. Although several privacy-preserving methods have been proposed for tabular data, a comprehensive comparison of their performance and trade-offs has yet to be conducted. We introduce and empirically assess a combined defense system that integrates differential privacy, federated learning, adaptive noise injection, hybrid cryptographic encryption, and ensemble-based obfuscation. The given strategies are analyzed on the benchmark tabular datasets (ADULT, GSS, FTE), showing that the suggested methods can mitigate up to 50 percent of model inversion attacks in relation to baseline models without decreasing the model utility (F1 scores are higher than 0.85). Moreover, on these datasets, our results match or exceed the latest state-of-the-art (SOTA) in terms of privacy. We also transform each defense into essential data privacy laws worldwide (GDPR and HIPAA), suggesting the best applicable guidelines for the ethical and regulation-sensitive deployment of privacy-preserving machine learning models in sensitive spaces. Full article

This study examines the longitudinal relationship and interactions among comprehensive water management, human development, and fragility. The seventeen Middle Eastern countries were examined for the period from 1996 to 2023. The Human Development Index (HDI) and Fragile States Index (FSI) were considered as a proxy for human development and fragility. In addition, the Water Poverty Index (WPI) was thoroughly assessed using classical and improved methods to measure multidisciplinary water management. Findings highlight that “Resources” and “Environment” are the most critical components of WPI. Iran performed the most consistently across WPI versions, whereas Palestine performed the worst. “Capacity,” “Environment,” and “Access” are the most influential components of HDI. FSI was found to be the most sensitive to “Capacity” and “Environment”, which contribute to both human development and stability. This study provides empirical evidence to inform SDG 6 implementation by demonstrating the linkage between WPI components and progress in human development. Full article

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

Desertification threatens the sustainability of dryland ecosystems, yet many existing monitoring frameworks rely on static maps, coarse spatial resolution, or lack temporal forecasting capacity. To address these limitations, this study introduces PyGEE-ST-MEDALUS, a novel spatiotemporal framework combining the full MEDALUS desertification model with deep learning (CNN, LSTM, DeepMLP) and machine learning (RF, XGBoost, SVM) techniques on the Google Earth Engine (GEE) platform. Applied across Tebessa Province, Algeria (2001–2028), the framework integrates MODIS and Sentinel-1/-2 data to compute four core indices—climatic, soil, vegetation, and land management quality—and create the Desertification Sensitivity Index (DSI). Unlike prior studies that focus on static or spatial-only MEDALUS implementations, PyGEE-ST-MEDALUS introduces scalable, time-series forecasting, yielding superior predictive performance (R² ≈ 0.96; RMSE < 0.03). Over 71% of the region was classified as having high to very high sensitivity, driven by declining vegetation and thermal stress. Comparative analysis confirms that this study advances the state-of-the-art by integrating interpretable AI, near-real-time satellite analytics, and full MEDALUS indicators into one cloud-based pipeline. These contributions make PyGEE-ST-MEDALUS a transferable, efficient decision-support tool for identifying degradation hotspots, supporting early warning systems, and enabling evidence-based land management in dryland regions. Full article

Bullying is a global issue that is associated with negative life outcomes. Anti-bullying programs have been shown to be effective, but with heterogeneity across studies. Thus, we examined how sensory processing sensitivity (SPS)—a biologically based trait associated with Differential Susceptibility to environmental factors—moderates the effects of a school-based, anti-bullying program. Students (301 middle-school students, M age = 12 years) in the United States underwent a 4-week anti-bullying and competency-enhancing program. They also completed competency (e.g., social skills) and bullying prevention skills measures prior to (T1) and after the intervention (T2); and the Highly Sensitive Child Scale (measure of SPS). Results of multivariate analyses revealed that youth with higher SPS showed greater increases in decision-making, media resistance, social, and bullying prevention skills at T2. Consistent with theories of Differential Susceptibility and Environmental Sensitivity, results revealed that high SPS was associated with stronger responsivity to a psychoeducational intervention, as shown by increased cognitive, social, and behavioral domain scores. Findings from the present study underscore the moderating role of SPS on factors that impact human health and development. Full article

In recent years, convolutional neural network-based object detectors have achieved extensive applications in remote sensing (RS) image interpretation. While multi-scale feature modeling optimization remains a persistent research focus, existing methods frequently overlook the symmetrical balance between feature granularity and morphological diversity, particularly when handling high-aspect-ratio RS targets with anisotropic geometries. This oversight leads to suboptimal feature representations characterized by spatial sparsity and directional bias. To address this challenge, we propose the Parallel Interleaved Convolutional Kernel Network (PICK-Net), a rotation-aware detection framework that embodies symmetry principles through dual-path feature modulation and geometrically balanced operator design. The core innovation lies in the synergistic integration of cascaded dynamic sparse sampling and symmetrically decoupled feature modulation, enabling adaptive morphological modeling of RS targets. Specifically, the Parallel Interleaved Convolution (PIC) module establishes symmetric computation patterns through mirrored kernel arrangements, effectively reducing computational redundancy while preserving directional completeness through rotational symmetry-enhanced receptive field optimization. Complementing this, the Global Complementary Attention Mechanism (GCAM) introduces bidirectional symmetry in feature recalibration, decoupling channel-wise and spatial-wise adaptations through orthogonal attention pathways that maintain equilibrium in gradient propagation. Extensive experiments on RSOD and NWPU-VHR-10 datasets demonstrate our superior performance, achieving 92.2% and 84.90% mAP, respectively, outperforming state-of-the-art methods including EfficientNet and YOLOv8. With only 12.5 M parameters, the framework achieves symmetrical optimization of accuracy-efficiency trade-offs. Ablation studies confirm that the symmetric interaction between PIC and GCAM enhances detection performance by 2.75%, particularly excelling in scenarios requiring geometric symmetry preservation, such as dense target clusters and extreme scale variations. Cross-domain validation on agricultural pest datasets further verifies its rotational symmetry generalization capability, demonstrating 84.90% accuracy in fine-grained orientation-sensitive detection tasks. Full article