Search Results (1,872,965)

To address the challenges of difficult feature extraction for rolling bearing vibration signals, low efficiency in optimizing diagnostic model parameters, and the tendency to get trapped in local optima, this paper proposes an improved ICEEMDAN feature extraction method based on Bayesian optimization and adaptive noise signal ratio enhancement (BAYES-ICEEMDAN-SNR) and combines it with the improved Coriolis force optimization algorithm (ITOC) to optimize the least squares support vector machine (LSSVM) fault diagnosis model. Firstly, Bayesian optimization is used to adaptively determine the noise parameters and introduce a dynamic signal-to-noise ratio adjustment mechanism to enhance the robustness of feature extraction; secondly, Chebyshev chaotic mapping, Cauchy mutation, and dynamic reverse learning strategies are applied to enhance the global search and local escape capabilities of ITOC, thereby optimizing the hyperparameters of LSSVM; and finally, the Keesey-Chestnut University bearing dataset and Huazhong University of Science and Technology gear dataset are used for verification. The experimental results show that the average fault identification accuracy of the proposed method reaches over 97%, which is superior to that of the comparison models, and the effectiveness of each core improvement module of the proposed model is verified through ablation experiments, providing an effective solution for intelligent fault diagnosis of rolling bearings and gears. Full article

The long-term safety and durability of concrete building structures depend largely on their internal quality. Wall voids, as typical hidden defects, directly affect the structural bearing capacity and service life. Such defects may not only lead to surface hollowing and peeling but also develop into serious safety accidents such as local collapse. At present, acoustic detection methods are widely used in engineering practice due to their convenience and efficiency. Among them, the strike method and the slide method, as two basic excitation methods, are commonly used on-site detection methods. However, existing studies still lack the systematic comparative analysis of these two methods, especially in terms of objective evaluation based on quantitative characteristics. To fill this research gap, this study designed a strict controlled experimental scheme. By collecting acoustic signals under these two excitation methods, the time-domain waveform characteristics, frequency-domain response characteristics, and time-frequency distribution patterns were systematically analyzed. The results show that, compared with the traditional strike excitation, the slide excitation method shows significant advantages in concrete wall void detection. It not only has higher detection accuracy but also exhibits better stability and repeatability. Further analysis found that the slide signal is superior to the strike signal in terms of feature distinguish ability and anti-interference ability. Its voltage distribution curve shows more obvious separation characteristics, which significantly reduces the risk of misjudgment. Through systematic quantitative comparison, this study provides a reliable experimental basis for the acoustic detection of concrete wall voids and has important reference value for promoting the standardization and intelligent development of non-destructive testing technology. Full article

Background/Objectives: The rates of adverse maternal and neonatal outcomes—including preterm birth < 37 weeks’ gestation (PTB), hypertensive disorders of pregnancy (HDP), gestational diabetes mellitus (GDM), small for gestational age (SGA), and large for gestational age (LGA)—remain elevated in the United States. Preventive strategies beyond the current standard of care (SOC) may be needed, particularly in diverse and socioeconomically vulnerable populations. The study evaluated a targeted diet and lifestyle intervention incorporating selected nutrient and gene variant analysis with personalized trimester-based counseling and supplementation (Standard of Care Plus, PLUS). Methods: The prospective observational study compared outcomes among participants receiving PLUS in addition to SOC with regional SOC data. A Nevada PLUS cohort (n = 15), consisting of high-risk participants with 100% Medicaid coverage, received the intervention virtually. An Oregon PLUS cohort (n = 387), consisting of moderate-risk participants with approximately 50% Medicaid coverage, received PLUS through in-person group sessions. Outcomes were compared with regional SOC rates and between PLUS cohorts. Cochran–Mantel–Haenszel (CMH) analyses were performed to account for site-level differences in pooled analyses. Primary outcome was PTB < 37 weeks’ gestation; secondary outcomes included HDP, GDM, SGA, and LGA. Results: The Nevada PLUS application was associated with lower adverse outcome rates compared with regional SOC; however, statistical significance was not observed, likely reflecting limited sample size. The Oregon PLUS cohort experienced statistically significant association with reductions across all five outcomes (all p < 0.001) compared to regional SOC. No statistically significant differences were observed between the Nevada (virtual) and Oregon (in-person) PLUS cohorts. In pooled analyses (n = 402), significant reductions compared with SOC were observed for PTB (RR = 0.23), HDP (RR = 0.11), GDM (RR = 0.06), SGA (RR = 0.25), and LGA (RR = 0.35) (all p < 0.001). Conclusions: The implementation of selected nutrient and gene variant analysis combined with targeted nutritional and lifestyle interventions, delivered in collaboration with standard obstetric care, was associated with reduced adverse maternal and neonatal outcomes. Interpretation of virtual delivery remains limited by small sample size. Full article

In recent years, sustainable biopolymers have attracted increasing attention in environmental engineering as alternatives to conventional synthetic materials due to their renewable origins, biodegradability, and functional versatility. However, their performance and technological viability are strongly influenced by structural design, modification strategies, and behavior under realistic environmental conditions. This review critically analyzes recent advances in biopolymers for environmental remediation, covering their main application formats such as hydrogels, membranes, beads, aerogels, and composites, their interaction mechanisms with contaminants, and their performance relative to conventional adsorbents. Particular emphasis is placed on emerging approaches, including advanced functionalization, integration with inorganic phases, and green synthesis technologies, which have significantly improved efficiency, selectivity, and operational stability. Despite these advances, key limitations persist, particularly regarding mechanical robustness, regenerability, reproducibility, and scalability, underscoring the need for standardized evaluation protocols in complex matrices. The role of biopolymers within circular economy frameworks is also examined, emphasizing their capacity to integrate material sustainability, resource recovery, and multifunctional environmental applications. Overall, sustainable biopolymers are positioned not only as substitutes for traditional materials but also as strategic platforms for the development of next-generation regenerative environmental technologies. Full article

Tubulin is a heterodimeric protein composed of α- and β-subunits, which polymerize to form the cell’s microtubules. The latter are key components in mitotic spindle formation and essential targets in anticancer therapy. Compounds such as paclitaxel, tubulysins, dolastatins and synthetic analogues of these latter compounds, including cemadotin, exert their cytotoxic effects by disrupting microtubule dynamics. Previously, we reported the production and anticancer activity of a library of cemadotin analogues featuring a C-terminal tertiary amide functionalized with a variety of N-substituents, thus resulting in compounds occurring as a mixture of amide rotamers. Here we describe a comprehensive NMR and conformational study that provides new insights into the effect of the conformational equilibrium on the binding mode of the novel cemadotin analogues to the tubulin target. The conformational behavior of the isomer equilibrium of cemadotin’s terminal amide bond was investigated by TOCSY and ROESY NMR experiments, which allowed the identification and quantification of individual rotamer populations. A slow interconversion between the s-cis and s-trans amide rotamers was observed under standard NMR conditions (25 °C), indicating a significant energy barrier and conformational rigidity. Molecular docking and saturation transfer difference (STD) NMR experiments were performed with a representative analogue and tubulin to assess the binding mode. The results revealed that the s-trans rotamer is the predominant conformer in solution and exhibits a more favorable interaction with tubulin compared to the s-cis isomer, thus helping to understand the conformational requirements for an improved tubulin binding and the inhibition of the polymerization process. Full article

In the context of advancing two pivotal national commitments, namely the “Dual Carbon” goals and the common prosperity strategy, energy policy formulation must move beyond purely economic or environmental considerations and adopt integrated social welfare assessments. This study develops an optimal dispatch model for a multi-microgrid system that incorporates dynamic cross subsidy and tiered carbon trading. From the perspective of welfare economics, the socioeconomic impacts of the proposed model are then systematically evaluated. First, a unified operational framework is established, combining dynamic electricity tariff cross subsidy with a tiered carbon trading mechanism. Next, a quantitative model for electricity tariff cross subsidy is proposed, and a dynamic subsidy rate linked to renewable energy output is designed to guide electricity consumption behavior. Finally, a comparative simulation is conducted across three scenarios: no subsidy, traditional cross subsidy, and the proposed dynamic cross subsidy. The results demonstrate that the proposed dynamic mechanism reduces system carbon emissions by 17.05% compared to the non-subsidy baseline while significantly optimizing total costs. Full article

This paper examines the use of Machine Learning (ML) approaches in maintaining academic integrity using the information provided in the Moodle system logs. The paper focuses on data set construction, handling the issue of class imbalance, and the assessment of the performance of different ML models in uncovering academic fraud. Twelve different data sets were created by using the concept of temporal windows (e.g., one-day and three-day windows) during the feature extraction stage from the Moodle system logs. The manual labeling of the data sets was done based on a predefined set of rules that outline the fraudulent activities. The issue of class imbalance was treated using eleven different resampling approaches, such as SMOTE, ADASYN, Tomek Links, and NearMiss. We evaluated six classification algorithms, thus resulting in a total of 792 experiments based on the interactions between the data sets, resampling methods, and classification algorithms. The results from the experiment show that the Random Forest and AdaBoost models performed the best in the experiment. Furthermore, we observed a trade-off between fraud detection rates and model precision based on the temporal windows and resampling methods. The shortest temporal windows and hybrid undersampling approaches resulted in the maximum recall value in this study and could identify the greatest number of at-risk students. On the other hand, the longest temporal windows and hybrid oversampling approaches with data cleaning resulted in the best results in terms of F1-Score and Cohen’s Kappa statistics. The results provide conclusive evidence that the models can identify fraud; however, they should be used as predictive models for the improvement of proctoring approaches, such as random selection for verification or seating arrangement strategies, instead of judgment models. Full article

In recent years, Latin America has experienced a growing incorporation of Artificial Intelligence (AI) into business and organizational environments, driven by digital transformation, data availability, and competitive pressures. Across multiple sectors, AI-based tools are increasingly used to support complex decision-making processes, raising both opportunities and challenges related to efficiency, ethics, and organizational readiness. Within this context, this systematic review examines the scientific evidence on the implementation of AI in business decision-making in Latin America. Following PRISMA 2020 guidelines, a systematic search was conducted in the Scopus database for articles published between 2021 and 2025. The search strategy combined Boolean operators related to AI and decision-making. Inclusion criteria comprised original, open-access research articles conducted in Latin American countries and published in Spanish or Portuguese. After screening for temporality, geographic focus, language, document type, accessibility, duplication, and relevance, 27 studies were selected from an initial pool of 276,302 records. The studies originated mainly from Peru, Colombia, Chile, and Ecuador. The findings show that AI is applied across sectors such as industry, agriculture, finance, education, and public services, primarily to enhance predictive capacity, automate processes, and support data-driven decisions. While AI adoption improves efficiency, cost reduction, and strategic innovation, its effectiveness depends on staff training, ethical governance, and strategic alignment. Persistent challenges include resistance to change, data quality limitations, and concerns regarding transparency and algorithmic bias. Overall, AI emerges as a transformative but context-dependent tool for business decision-making in Latin America. Full article

In public safety scenarios, the precise detection and positioning of prohibited weapons such as firearms and knives along with the involved personnel are the core pre-requisite technologies for violent risk warning and emergency response. However, in security surveillance scenarios, there are common problems such as object occlusion, difficulty in capturing small-sized weapons, and complex background interference, which lead to the shortcomings of existing general object detection models in the tasks of detecting and locating security-related objects, including poor adaptability, low detection accuracy, and insufficient robustness in complex scenarios. Therefore, this paper proposes a threat object detection framework for security scenarios (PDGV-DETR) based on adaptive dynamic convolution and cross-scale semantic fusion, specifically optimized for the detection and positioning tasks of weapons and personnel objects in static security surveillance images. This research focuses on category recognition at the object level and pixel-level spatial positioning, and does not involve the classification and identification of violent behaviors based on temporal information. There are clear technical boundaries and scene limitations between the two. This framework is optimized through three core modules: designing a dynamic hierarchical channel interaction convolution module to reduce computational complexity while enhancing the ability to detect occluded and incomplete objects; constructing an improved bidirectional hybrid feature pyramid network, combining the cross-scale fusion module to strengthen multi-scale feature expression, and adapting to the simultaneous detection requirements of small weapon objects and large personnel objects; and introducing a global semantic weaving and elastic feature alignment network to solve the problem of low discrimination between objects and complex backgrounds. Under the same experimental configuration, the proposed model is verified against current mainstream models on typical datasets: on a dataset of 2421 conflict scene personnel violent images, the peak average precision mAP50 of PDGV-DETR reached 85.9%. Through statistical verification, compared with the baseline model RT-DETR with an average value ± standard deviation of 0.840 ± 0.007, the average value ± standard deviation of PDGV-DETR reached 0.858 ± 0.004, demonstrating statistically significant performance improvement, with a p-value less than 0.01. This model can accurately complete the task of locating the object area of personnel, and compared with the deformable DETR, the accuracy improvement rate reached 15.1%.; on the weapon-specific dataset OD-WeaponDetection, the mAP for gun and knife detection reached 93.0%, improving by 2.2% compared to RT-DETR. Compared to the performance fluctuations of other general object detection models in complex security scenarios, PDGV-DETR not only has better detection and positioning accuracy for security-related objects, but also significantly improves the generalization and stability of the model. The results show that PDGV-DETR effectively balances the accuracy of positioning, detection, and computational efficiency, accurately completing end-to-end detection and positioning of weapon and personnel objects in static security surveillance images, demonstrating highly competitive performance in the detection and positioning of security-related objects in security scenes, providing core object-level pre-processing technology support for scenarios such as public area monitoring, intelligent video monitoring, and early warning of violent risks, and providing basic data for subsequent violent behavior recognition based on temporal data. Full article

Leadership plays a central role in the long-term sustainability of family enterprises, yet existing evidence is fragmented across contexts and methodologies. This systematic review synthesizes empirical findings on leadership practices that support sustainable family entrepreneurship. The objectives are to identify available evidence on leadership in sustainable family enterprises, describe the methodologies employed, and examine how leadership is perceived and enacted across global contexts. The review followed PRISMA 2020 guidelines. Searches were conducted on 28 January 2025 on Scopus, Web of Science, EBSCOHost, and ProQuest. Eligibility criteria included empirical studies, full accessibility, publication in English, non-duplicated records, and relevance to leadership in sustainable family enterprises. Twenty-six studies met the inclusion criteria. Data extraction focused on context, methodology, leadership evidence, and key findings. Studies spanning Asia, Europe, Latin America, Africa, and the Middle East indicate that leadership strongly shapes sustainability outcomes in family firms. Three core leadership dimensions emerged: transformational leadership, which promotes innovation, engagement, affective commitment, and continuity; transactional leadership, which supports governance, succession planning, operational control, and performance; and ethical leadership, which fosters trust, shared values, and social responsibility. Cross-cutting themes include gendered leadership contributions, succession risk management, and cultural influences. Sustainable family enterprises rely on multidimensional leadership integrating these approaches, reinforced by structured succession processes, value alignment, and human capital investment. Full article

Locus coeruleus (LC) noradrenergic neurons project their axons to the cerebellar cortex and modulate cerebellar circuit function via distinct adrenergic receptor (AR) subtypes. The present study investigated the mechanism by which optogenetic activation of LC noradrenergic neurons modulates facial stimulation-evoked long-term synaptic plasticity at cerebellar mossy fiber-granule cell (MF-GrC) synapses in urethane-anesthetized DBH-Cre mice. Blockade of GABA_A receptors, 20 Hz facial stimulation induced MF-GrC long-term potentiation (LTP) in the control group, and this LTP was impaired by optogenetic activation of LC noradrenergic neurons via α2-ARs. Meanwhile, facial stimulation induced LTP of glutamate sensor fluorescence in the granular layer, which was abolished by chemogenetic activation of LC noradrenergic neurons. Following NMDA receptor blockade, optogenetic activation of LC noradrenergic neurons triggered facial stimulation-induced MF-GrC long-term depression (LTD) via α2A-ARs. Optogenetically activated LC noradrenergic neuron-induced MF-GrC LTD was abolished by protein kinase A (PKA) inhibition but not by protein kinase C inhibition. Immunofluorescence results revealed abundant α2A-AR expression in the granular layer, with particularly high levels in glomeruli, and no colocalization with the glutamate sensor. These results indicate that optogenetic activation of LC noradrenergic neurons impairs facial stimulation-induced MF-GrC LTP by triggering presynaptic LTD via the α2A-AR/PKA signaling cascade. Full article

The recent advancements in technologies such as artificial intelligence (AI), computer vision (CV), and Internet of Things (IoT) have significantly extended various fields, particularly in surveillance systems. These innovations enable real-time facial recognition processing, enhancing security and ensuring safety. However, mobile robots are commonly employed in surveillance systems to handle risky tasks that are beyond human capability. In this paper, we present a prototype of a cost-effective mobile surveillance robot built on the Raspberry PI 4, designed for integration into various industrial environments. This smart robot detects intruders using IoT and face recognition technology. The proposed system is equipped with a passive infrared (PIR) sensor and a camera for capturing live-streaming video and photos, which are sent to the control room through IoT technology. Additionally, the system uses face recognition algorithms to differentiate between company staff and potential intruders. The face recognition method combines high-order statistical features and evidence theory to improve facial recognition accuracy and robustness. High-order statistical features are used to capture complex patterns in facial images, enhancing discrimination between individuals. Evidence theory is employed to integrate multiple information sources, allowing for better decision-making under uncertainty. This approach effectively addresses challenges such as variations in lighting, facial expressions, and occlusions, resulting in a more reliable and accurate face recognition system. When the system detects an unfamiliar individual, it sends out alert notifications and emails to the control room with the captured picture using IoT. A web interface has also been set up to control the robot from a distance through Wi-Fi connection. The proposed face recognition method is evaluated, and a comparative analysis with existing techniques is conducted. Experimental results with 400 test images of 40 individuals demonstrate the effectiveness of combining various attribute images in improving human face recognition performance. Experimental results indicate that the algorithm can identify human faces with an accuracy of 98.63%. Full article