Search Results (7,533)

Levodopa is the gold standard symptomatic treatment for Parkinson’s disease. Disease progression due to alpha-synuclein accumulation, brain inflammation, and the loss of dopamine neurons, as well as motor fluctuations, due to variations in levodopa plasma levels, remain a significant problem for Parkinson’s patients. Developing a therapeutic option that can simultaneously reduce the neuropathology associated with alpha-synuclein aggregation, attenuate oxidative stress and inflammation, and overcome variations in levodopa plasma levels is an unmet need to treat Parkinson’s disease. We determined the pharmacokinetics and pharmacodynamics of a small molecule, dubbed Pegasus, that conjugates dopamine with a nonantibiotic doxycycline derivative via a molecular linker. Mice harboring the human A53T mutation of alpha-synuclein or treated with MPTP were injected once daily with 50 mg/kg Pegasus for 2 weeks and assessed for motor, behavioral, and cognitive effects, followed by biochemical and histochemical analysis. Pegasus is a poor brain penetrant but it was metabolized to stable dopamine and tetracycline derivatives, and abundant plasma and brain levels of these metabolites were detected. Pegasus reduced soluble and insoluble alpha-synuclein levels, protected dopamine-producing neurons, and reduced astrocytic activation in A53T mice. Mice treated with Pegasus exhibited motor improvement (6.5 h) and reduction in anxiety-like behavior. Rotarod and grip strength improved in MPTP-treated mice when mice were treated with Pegasus or levodopa. Pegasus may be a multi-modal therapeutic option that can deliver stable dopamine into the CNS and reduce misfolded alpha-synuclein, activate dopamine receptors, and attenuate variations in dopamine levels. Full article

Ship type (e.g., Cargo, Tanker and Fishing) classification is crucial for marine management, environmental protection, and maritime safety, as it enhances navigation safety and aids regulatory agencies in combating illegal activities. Traditional ship type classification methods with AIS data are often plagued by problems such as data imbalance, insufficient feature extraction, reliance on single-model approaches, or unscientific model combination methods, which reduce the accuracy of classification. In this paper, we propose an ensemble classification method based on a stacking strategy to overcome these challenges. We apply the SMOTE technique to balance the dataset by generating minority class samples. Then, a more comprehensive ship behavior model is developed by combining static and dynamic features. A stacking strategy is adopted for the classification, integrating multiple tree structure-based classifiers to improve classification performance. The experimental results show that the ensemble classification method based on the stacking strategy outperforms traditional classifiers such as CatBoost, Random Forest, Decision Tree, LightGBM, and the ensemble classification method, especially in terms of improving classification precision, recall, F1 score, ROC curve, and AUC. This method improves the accuracy of ship type recognition, and it is suitable to real-time online classification, which is helpful for applications in marine safety monitoring, law enforcement, and illegal fishing detection. Full article

The accurate recognition of pig behaviors in intensive farming is crucial for health monitoring and growth assessment. To address multi-scale recognition challenges caused by perspective distortion (non-frontal camera angles), this study proposes MACA-Net, a YOLOv8n-based model capable of detecting four key behaviors: eating, lying on the belly, lying on the side, and standing. The model incorporates a Mamba Global–Local Extractor (MGLE) Module, which leverages Mamba to capture global dependencies while preserving local details through convolutional operations and channel shuffle, overcoming Mamba’s limitation in retaining fine-grained visual information. Additionally, an Adaptive Multi-Path Attention (AMPA) mechanism integrates spatial-channel attention to enhance feature focus, ensuring robust performance in complex environments and low-light conditions. To further improve detection, a Cross-Layer Feature Pyramid Transformer (CFPT) neck employs non-upsampled feature fusion, mitigating semantic gap issues where small target features are overshadowed by large target features during feature transmission. Experimental results demonstrate that MACA-Net achieves a precision of 83.1% and mAP of 85.1%, surpassing YOLOv8n by 8.9% and 4.4%, respectively. Furthermore, MACA-Net significantly reduces parameters by 48.4% and FLOPs by 39.5%. When evaluated in comparison to leading detectors such as RT-DETR, Faster R-CNN, and YOLOv11n, MACA-Net demonstrates a consistent level of both computational efficiency and accuracy. These findings provide a robust validation of the efficacy of MACA-Net for intelligent livestock management and welfare-driven breeding, offering a practical and efficient solution for modern pig farming. Full article

Background/Objectives: Triamcinolone acetonide (TA), a poorly water-soluble corticosteroid, presents formulation challenges due to limited membrane permeability. This study aimed to identify suitable drug–polymer–plasticizer systems for TA using combined theoretical and experimental methods. Methods: Using Hansen solubility parameters, seven hot-melt extrusion (HME)-grade polymers and four plasticizers were initially screened for miscibility with TA. Based on Δδt values, four polymers—Eudragit^® L100 (EUD), Parteck^® MXP (PVA), Plasdone^® S-630 (PVPVA), and Aquasolve™ AS-MG (HPMCAS)—along with triethyl citrate (TEC), were selected for experimental evaluation. Differential scanning calorimetry, thermogravimetric analysis, and Fourier transform infrared spectroscopy assessed thermal behavior, miscibility, and chemical compatibility. Results: Amorphous TA content was highest with EUD (81.1%), followed by PVA (67.5%), PVPVA (45.6%), and HPMCAS (8.5%). Thermal incompatibility and TEC evaporation were observed in PVA, PVPVA, and HPMCAS systems. FTIR suggested TEC should be avoided in melt-based formulations with PVA and PVPVA due to PVA degradation and partial TA oxidation. No significant interactions were detected in HPMCAS samples heated to 220 °C, aligning with theoretical predictions. In contrast, the EUD–TEC system showed limited chemical reactivity and maintained TA’s structural integrity. Infrared bands at 1758 and 1802 cm⁻¹ indicated minor anhydride formation above 160 °C with partial TEC evaporation. Conclusions: EUD/TEC were identified as a promising combination for the HME processing of TA. This work supports the rational formulation of stable amorphous systems for thermolabile drugs with poor solubility. Full article

Significant vulnerabilities in traditional authentication systems have been demonstrated due to the high dependence on smartphone hardware devices to execute many different and complicated tasks. PINs, passwords, and static biometric techniques have been shown to be subjected to various serious attacks, such as environmental limitations, spoofing, and brute force attacks, and this in turn mitigates the security level of the entire system. In this study, a robust framework for smartphone authentication is presented. Touch dynamic pattern recognitions, including trajectory curvature, touch pressure, acceleration, two-dimensional spatial coordinates, and velocity, have been extracted and assessed as behavioral biometric features. The TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methodology has also been incorporated to obtain the most affected and valuable features, which are then fed as input to three different Machine Learning (ML) algorithms: Random Forest (RF), Gradient Boosting Machines (GBM), and K-Nearest Neighbors (KNN). Our analysis, supported by experimental results, ensure that the RF model outperforms the two other ML algorithms by getting F1-Score, accuracy, recall, and precision of 95.1%, 95.2%, 95.5%, and 94.8%, respectively. In order to further increase the resiliency of the proposed technique, the data perturbation approach, including temporal scaling and noise insertion, has been augmented. Also, the proposal has been shown to be resilient against both environmental variation-based attacks by achieving accuracy above 93% and spoofing attacks by obtaining a detection rate of 96%. This emphasizes that the proposed technique provides a promising solution to many authentication issues and offers a user-friendly and scalable method to improve the security of the smartphone against cybersecurity attacks. Full article

Brain stimulation therapies may be used to correct motor, social, emotional, and cognitive consequences of traumatic brain injury (TBI). Neuromodulation applied with anatomical specificity can ameliorate desired symptoms while leaving functional circuits intact. Before applying precision medicine approaches, preclinical animal studies are needed to explore potential neurophysiological signatures that could be modulated with neurostimulation. This review discusses potential neural signatures of cognition, particularly reward processing, which is chronically impaired after brain injury. Electrophysiology, compared to other types of biomarkers, can detect deficits missed by structural measures, holds translational potential between humans and animals, and directly informs neuromodulatory treatments. Disturbances in oscillatory activity underscore structural, molecular, and behavioral impairments seen following TBI. For instance, cortico-striatal beta frequency activity (15–30 Hz) during reward processing represents subjective value and is chronically disturbed after frontal TBI in rodents. We use the example of evoked beta oscillations in the cortico-striatal network as a putative marker of reward processing that could be targeted with electrical stimulation to improve decision making after TBI. This review highlights the necessity of collecting electrophysiological data in preclinical models to understand the underlying mechanisms of cognitive behavioral deficits after TBI and to develop targeted stimulation treatments in humans. Full article

Digital twin is a virtual replica of a physical system that updates in real time using sensor data to enable simulations and predictions. For bridges constructed using rotation construction methods, the rotation phase demands continuous monitoring of structural behavior and coordination with surrounding traffic infrastructure. Therefore, a digital twin system for monitoring rotation construction is vital to ensure safety and schedule compliance. This paper explores the application of model-based systems engineering (MBSE), a modern approach that replaces text-based documentation with visual system models, to design a digital twin system for monitoring the rotation construction of a 90 m + 90 m single-tower cable-stayed bridge. A V-model architecture for the digital twin system, based on requirements analysis, functional analysis, logical design, and physical design analysis (RFLP), is proposed. Based on SysML language, the system’s requirements, functions, behaviors, and other aspects are modeled and analyzed using the MBSE approach, converting all textual specifications into the unified visual models. Compared to the traditional document-driven method, MBSE improves design efficiency by reducing ambiguities in system specifications and enabling early detection of design flaws through simulations. The digital twin system allows engineers to predict potential risks during bridge rotation and optimize construction plans before implementation. These advancements demonstrate how MBSE supports proactive problem-solving (forward design) and provides a robust foundation for future model validation and engineering applications. Full article

With the rapid advancement of intelligent transportation systems (ITSs), behavior detection within the Internet of Vehicles (IoVs) has become increasingly critical for maintaining system security and operational stability. However, existing detection approaches face significant challenges related to data privacy, node trustworthiness, and system transparency. To address these limitations, this study proposes a blockchain-driven federated learning framework for anomaly detection in IoV environments. A reputation evaluation mechanism is introduced to quantitatively assess the credibility and contribution of connected and autonomous vehicles (CAVs), thereby enabling more effective node management and incentive regulation. In addition, a multi-level model aggregation strategy based on dynamic vehicle selection is developed to integrate local models efficiently, with the optimal global model securely recorded on the blockchain to ensure immutability and traceability. Furthermore, a reputation-based prepaid reward mechanism is designed to improve resource utilization, enhance participant loyalty, and strengthen overall system resilience. Experimental results confirm that the proposed framework achieves high anomaly detection accuracy and selects participating nodes with up to 99% reliability, thereby validating its effectiveness and practicality for deployment in real-world IoV scenarios. Full article

Blockchain technology has transformed modern digital ecosystems by enabling secure, transparent, and automated transactions through smart contracts. However, the increasing complexity of these contracts introduces significant challenges, including high computational costs, scalability limitations, and difficulties in detecting anomalous behavior. In this study, we propose an AI-based optimization framework that enhances the efficiency and security of blockchain smart contracts. The framework integrates Neural Architecture Search (NAS) to automatically design optimal Convolutional Neural Network (CNN) architectures tailored to blockchain data, enabling effective anomaly detection. To address the challenge of limited labeled data, transfer learning is employed to adapt pre-trained CNN models to smart contract patterns, improving model generalization and reducing training time. Furthermore, Model Compression techniques, including filter pruning and quantization, are applied to minimize the computational load, making the framework suitable for deployment in resource-constrained blockchain environments. Experimental results on Ethereum transaction datasets demonstrate that the proposed method achieves significant improvements in anomaly detection accuracy and computational efficiency compared to conventional approaches, offering a practical and scalable solution for smart contract monitoring and optimization. Full article

Background/Objectives: Mild behavioral impairment (MBI) is an early marker of Alzheimer’s disease (AD) and other neurodegenerative diseases, often preceding cognitive decline. The MBI Checklist (MBI-C) is a 34-item tool designed to detect MBI. This study aimed to assess the psychometric properties of the Greek version of the MBI-C and its ability to differentiate patients with mild cognitive impairment due to AD (MCI-AD) from cognitively unimpaired older adults (healthy participants, HPs). Methods: A total of 181 participants (104 MCI-AD, 77 HPs) were recruited from the Third Age Day Care Center IASIS (2019–2023), accompanied by a close informant. Participants underwent neuropsychological assessment [Mini-Mental State Examination (MMSE), Addenbrooke’s Cognitive Examination-Revised (ACE-R)], and informants completed the MBI-C. Internal consistency was evaluated using Cronbach’s α and known-group validity was assessed via comparing MBI-C between the MCI-AD and HPs groups. Diagnostic accuracy was determined via receiver operating characteristic (ROC) analysis. Results: The Greek MBI-C showed excellent internal consistency (Cronbach’s α = 0.899). Among its domains, impulse dyscontrol demonstrated the highest reliability (α = 0.901), whereas decreased motivation (α = 0.564) and abnormal perception/thought content (α = 0.617) exhibited lower reliability. MBI-C total and domain scores were significantly higher in patients with MCI-AD than HPs (p < 0.001). The area under the curve (AUC) was 0.871 (optimal cutoff = 9.5), indicating excellent diagnostic performance. Conclusions: Overall, the Greek MBI-C has strong psychometric properties for MCI-AD. Sociocultural factors might influence symptom identification and reporting, particularly in the domains of decreased motivation and abnormal perception/thought content. Future research should investigate its predictive value for dementia conversion and its applicability to other populations, including individuals with subjective cognitive decline and non-AD causes of MCI. Full article

Previous efforts to retrieve thermospheric density using satellite payloads have been limited to a small number of satellites equipped with GNSS (Global Navigation Satellite System) receivers and accelerometers. These satellites are confined to a few orbital planes, and analysis can only be conducted after the data are processed and updated, resulting in sparse and delayed thermospheric density datasets. In recent years, the Starlink constellation, developed and deployed by SpaceX, has emerged as the world’s largest low Earth orbit (LEO) satellite constellation, with over 6000 satellites in operations as of October 2024. Through the strategic use of multiple orbital shells featuring various inclinations and altitudes, Starlink ensures continuous near-global coverage. Due to extensive coverage and frequent maneuvers, SpaceX has publicly released predicted ephemeris data for all Starlink satellites since May 2021, with updates approximately every 8 h. With the ephemeris data of Starlink satellites, we first apply a maneuver detection algorithm based on mean orbital elements to analyze their maneuvering behavior. The results indicate that Starlink satellites exhibit more frequent maneuvers during thermospheric disturbances. Then, we calculate the mechanical energy loss caused by non-conservative forces (primarily atmospheric drag) through precise dynamical models. The results demonstrate that, despite certain limitations in Starlink ephemeris data, the calculated mechanical energy loss still effectively captures thermospheric density variations during both quiet and disturbed geomagnetic periods. This finding is supported by comparisons with Swarm-B data, revealing that SpaceX incorporates the latest space environment conditions into its orbit extrapolation models during each ephemeris update. With a maximum lag of only 8 h, this approach enables near-real-time monitoring of thermospheric density variations using Starlink ephemeris. Full article

The escalating global incidence of malware presents critical cybersecurity threats to manufacturing, automation, and industrial process control systems. Given the fast-developing web applications and IoT devices in use by industry operations, securing a transparent and effective malware detection mechanism has become imperative to operational resilience and data integrity. Classical methods of malware detection are conventionally opaque “black boxes” with limited transparency, thus eroding trust and hindering deployment in security-sensitive contexts. In this respect, this research proposes MAL-XSEL—a malware detection framework using an explainable stacking ensemble learning approach for performing high-accuracy classification and interpretable decision-making. MAL-XSEL explicates the model predictions through Shapley additive explanations (SHAP) and local interpretable model-agnostic explanations (LIME), which enable security analysts to validate how the detection logic works and prioritize the features contributing to the most critical threats. Evaluated on two benchmark datasets, MAL-XSEL outperformed conventional machine learning models, achieving top accuracies of 99.62% (ClaMP dataset) and 99.16% (MalwareDataSet). Notably, it surpassed state-of-the-art algorithms such as LightGBM (99.52%), random forest (99.33%), and decision trees (98.89%) across both datasets while maintaining computational efficiency. A unique interaction of ensemble learning and XAI is employed for detection, not only with improved accuracy but also with interpretable insight into the behavior of malware, thereby allowing trust to be substantiated in an automated system. By closing the divide between performance and interpretability, MAL-XSEL enables cybersecurity practitioners to deploy transparent and auditable defenses against an ever-growing resource of threats. This work demonstrates how there can be no compromise on explainability in security-critical applications and, as such, establishes a roadmap for future research on industrial malware analysis tools. Full article

(1) Background and objective: Mobility is crucial for healthy aging, and its loss significantly impacts the quality of life, healthcare costs, and mortality among older adults. Clinical mobility assessment methods, though precise, are resource-intensive and economically impractical, and most of the existing solutions for automatic detection of mobility anomalies are either obtrusive or improper for long time monitoring. This study explores the feasibility of using non-intrusive, low-cost binary sensors for continuous, remote detection of mobility anomalies in older adults, aiming to identify both sudden mobility events and gradual mobility loss. (2) Method: The study utilized publicly available datasets (CASAS Aruba and HH120) containing annotated activity data recorded from binary sensors installed in residential environments. After data preprocessing—including filtering irrelevant sensor events and aggregation into behaviorally meaningful places (BMPs)—a time series forecasting model (Prophet) was used to predict normal mobility patterns. A fuzzy inference module analyzed deviations between observed and predicted sensor data to determine the probability of mobility anomalies. (3) Results: The system effectively identified periods of prolonged inactivity indicative of potential falls or other mobility disruptions. Preliminary evaluation indicated a detection rate of approximately 77–81% for point mobility anomalies, with a false positive rate ranging from 12 to 16%. Additionally, the approach successfully detected simulated gradual declines in mobility (1% per day reduction), evidenced by statistically significant regression trends in activity levels over time. (4) Conclusions: The study argues that non-intrusive binary sensors, combined with lightweight forecasting models and fuzzy inference, may provide a practical and scalable solution for detecting mobility anomalies in older adults. Although performance can be further enhanced through improved data preprocessing, predictive modeling, and anomaly threshold tuning, the proposed system effectively addresses key limitations of existing mobility assessment approaches. Full article

Detecting behavioral routines is an important research area with many implications in various practical applications. One such application involves studying the behavior of older adults residing in care homes. This paper proposes a comprehensive methodology for extracting and analyzing the daily routines of older adults in care homes. The methodology utilizes raw data comprising signal strength measurements obtained from smartwatches worn by six volunteers over five months. To establish the basis for estimating daily activities, fingerprint-based localization techniques are employed to track the minute-by-minute location of each volunteer. Subsequently, the activity performed by each volunteer is estimated for each day. Finally, the study estimates the probability of a user undertaking each one of the studied activities on a given weekday. Full article

The deployment of machine learning models on mobile platforms has ushered in a new era of innovation across diverse sectors, including agriculture, where such applications hold immense promise for empowering farmers with cutting-edge technologies. In this context, the threat posed by insects to crop yields during harvest has escalated, fueled by factors such as evolution and climate change-induced shifts in insect behavior. To address this challenge, smart insect monitoring systems and detection models have emerged as crucial tools for farmers and IoT-based systems, enabling interventions to safeguard crops. The primary contribution of this study lies in its systematic investigation of model optimization techniques for edge deployment, including Post-Training Quantization, Quantization-Aware Training, and Data Representative Quantization. As such, we address the crucial need for efficient, on-site pest detection tools in agricultural settings. We provide a detailed analysis of the trade-offs between model size, inference speed, and accuracy across different optimization approaches, ensuring practical applicability in resource-constrained farming environments. Our study explores various methodologies for model development, including the utilization of Mobile-ViT and EfficientNet architectures, coupled with transfer learning and fine-tuning techniques. Using the Dangerous Farm Insects Dataset, we achieve an accuracy of 82.6% and 77.8% on validation and test datasets, respectively, showcasing the efficacy of our approach. Furthermore, we investigate quantization techniques to optimize model performance for on-device inference, ensuring seamless deployment on mobile devices and other edge devices without compromising accuracy. The best quantized model, produced through Post-Training Quantization, was able to maintain a classification accuracy of 77.8% while significantly reducing the model size from 33 MB to 9.6 MB. To validate the generalizability of our solution, we extended our experiments to the larger IP102 dataset. The quantized model produced using Post-Training Quantization was able to maintain a classification accuracy of 59.6% while also reducing the model size from 33 MB to 9.6 MB, thus demonstrating that our solution maintains a competitive performance across a broader range of insect classes. Full article