Search Results (581)

The understanding and control of protein crystallization are crucial in structural biology, drug development, and biomaterial design. This study introduces a unified framework for modeling and comparing crystallization kinetics using selected growth functions. Experimental datasets from the literature for four proteins, Lysozyme, Thaumatin, Ribonuclease A, and Proteinase K, under Hanging Drop and Langmuir–Blodgett conditions were analyzed. Five kinetic models, Avrami, Kashchiev, Hill, Logistic, and Generalized Sigmoid (GSM), were fitted to size–time data of the four benchmark proteins. From each fit, four descriptors were extracted: crystallization half-time, time of maximum growth, width at half-maximum, and peak growth rate. These metrics summarize crystallization dynamics and enable cross-comparison of proteins and methods. Langmuir–Blodgett templating accelerated onset and improved synchrony, though the effect varied by protein and model. Logistic, Hill, and GSM models provided consistent fits across most conditions, while Avrami and Kashchiev were more sensitive to early or late deviations. Notably, descriptor extraction remained reliable even with limited or uneven sampling, revealing kinetic regimes such as synchrony, asymmetry, or prolonged nucleation, not evident in raw data. This transferable analytical framework supports quantitative evaluation of crystallization behavior, aiding screening, process optimization, and time-resolved structural studies. Full article

This paper applies a Convolutional Neural Network (CNN)-based vehicle security enhancement model, with a specific focus on the South African context. While conventional security systems, including immobilizers, alarms, steering locks, and GPS trackers, provide a baseline level of protection, they are increasingly being circumvented by technologically adept adversaries. These limitations have spurred the development of advanced security solutions leveraging artificial intelligence (AI), with a particular emphasis on computer vision and deep learning techniques. This paper presents a CNN-based Vehicle Security Enhancement Model (CNN-based VSEM) that integrates facial recognition with GSM and GPS technologies to provide a robust, real-time security solution in South Africa. This study contributes a novel integration of CNN-based authentication with GSM and GPS tracking in the South African context, validated on a functional prototype.The prototype, developed on a Raspberry Pi 4 platform, was validated through practical demonstrations and user evaluations. The system achieved an average recognition accuracy of 85.9%, with some identities reaching 100% classification accuracy. While misclassifications led to an estimated False Acceptance Rate (FAR) of ~5% and False Rejection Rate (FRR) of ~12%, the model consistently enabled secure authentication. Preliminary latency tests indicated a decision time of approximately 1.8 s from image capture to ignition authorization. These results, together with positive user feedback, confirm the model’s feasibility and reliability. This integrated approach presents a promising advancement in intelligent vehicle security for regions with high rates of vehicle theft. Future enhancements will explore the incorporation of 3D sensing, infrared imaging, and facial recognition capable of handling variations in facial appearance. Additionally, the model is designed to detect authorized users, identify suspicious behaviour in the vicinity of the vehicle, and provide an added layer of protection against unauthorized access. Full article

The increasing demand for red wines, supported by their complex sensory features and rich biochemical composition, has encouraged cultivation in non-traditional viticultural regions. This study investigates the antioxidant potential and volatile composition of three red grape cultivars (Feteasca neagra, Merlot, and Pinot noir) cultivated in the Tarnave Vineyard, Romania, a region historically dedicated to white wines but now increasingly favorable to red varieties due to climate change. Antioxidant capacity, assessed via DPPH, Trolox equivalent antioxidant capacity (TEAC), and Ferric reducing antioxidant power (FRAP) assays, identified Feteasca neagra as the most potent (IC₅₀: 115.32 µg/mL; FRAP: 13.45 mmol TE/L). Gas chromatography–mass spectrometry (GC–MS) profiling identified 61 volatile compounds, with Pinot noir showing the highest concentration (99,018.57 µg/L). Multivariate analysis (ANOVA, PCA) confirmed significant varietal differences and terroir-specific influences on wine composition. Pinot noir was characterized by high levels of higher alcohols, esters, and lactones, yielding a floral and fruity aroma, while Feteasca neagra exhibited intense color, high flavonoid content (notably malvidin-3-glucoside), and vanilla–herbal notes. Merlot presented a balanced sensory profile with significant phenolic acid content. These findings highlight the chemical and sensory potential of the Tarnave Vineyard for premium red wine production. Full article

Waveforms define the shape, structure, and frequency characteristics of signals, whereas modulation schemes determine how information symbols are mapped onto these waveforms for transmission. Their appropriate selection plays a critical role in determining the efficiency, robustness, and reliability of data transmission. In wireless communications, the choice of waveform influences key factors, such as network capacity, coverage, performance, power consumption, battery life, spectral efficiency (SE), bandwidth utilization, and the system’s resistance to noise and electromagnetic interference. This paper provides a comprehensive analysis of the waveforms and modulation schemes used across successive generations of mobile cellular networks, exploring their fundamental differences, structural characteristics, and trade-offs for various communication scenarios. It also situates this analysis within the historical evolution of mobile standards, highlighting how advances in modulation and waveform technologies have shaped the development and proliferation of cellular networks. It further examines criteria for waveform selection—such as SE, bit error rate (BER), throughput, and latency—and discusses methods for assessing waveform performance. Finally, this study presents a comparative evaluation of modulation schemes across multiple mobile generations, focusing on key performance metrics, with the BER analysis conducted through MATLAB simulations. Full article

Composite materials are replacing traditional metals across various industries as they offer lighter weight and affordability, as well as excellent mechanical properties. In the present work, a hybrid composite was developed by combining randomly oriented S-glass fibers and coconut fibers within an epoxy matrix by using the hand lay-up method. The laminate was prepared by using two sheets of raw coconut fiber and eight layers of 200 GSM S-glass fiber, maintaining an epoxy-to-hardener ratio of 10:1. The laminate was cured under a hydraulic press at 80 °C for two hours and then post-cured at a temperature of 100 °C for four hours. In order to assess the performance of the composites, a series of tests, including mode II interlaminar fracture toughness, tensile strength, impact resistance, and hardness, as well as thermal conductivity, were performed. SEM analysis of the fracture surfaces confirmed the combined presence of fiber pull-out and good fiber–matrix bonding, supporting the observed improvements in mechanical properties. The results indicate that the hybrid composite has clear advantages over the composites reinforced with individual fibers alone. It showed a 358% higher tensile strength, a 30% increment in impact strength, and roughly 31% better flexural strength as compared to the coconut fiber composite. In comparison to the glass fiber composite, the hybrid composite offered enhanced toughness and better thermal stability, along with lower material costs and improved sustainability due to the addition of the natural fibers. Considering the rising need for lightweight, strong, and eco-friendly materials for industries, this fabricated hybrid composite appears to be a promising option for structural applications in fields like automotive, aerospace, and construction, where reducing weight without compromising strength is essential. Full article

Dehydroepiandrosterone (DHEA) and its sulfate ester form DHEAS, are multifunctional steroid hormones primarily produced in the adrenal cortex, with additional synthesis in peripheral tissues. DHEA/DHEAS serve as precursors to sex steroids and exhibit neuroprotective, anti-inflammatory, and immune-modulating effects. DHEA levels decline significantly with age, a phenomenon termed “adrenopause”, prompting interest in supplementation to mitigate age-related symptoms. Particularly in postmenopausal women, DHEA has shown potential benefits in treating genitourinary syndrome of menopause (GSM), including improved vaginal health, lubrication, and sexual function. While intravaginal DHEA appears effective and safer than systemic estrogen therapy, especially for women with estrogen sensitivity, results remain mixed for oral administration. DHEA and DHEAS exhibit diverse neuroactive properties through modulation of GABA-A, NMDA, and sigma-1 receptors. These neurosteroids contribute to neuroprotection, synaptic plasticity, and mood regulation. Altered DHEA/DHEAS levels have been implicated in neurodegenerative disorders and depression, with emerging evidence supporting their potential therapeutic value. In addition, DHEA plays a multifaceted role in aging-related physiological changes. It supports muscle anabolism, bone density maintenance, cardiovascular protection, and immune regulation. Though supplementation shows potential benefits, especially in conjunction with resistance training, results remain discrepant. Current evidence has revealed that the therapeutic effects of DHEA supplementation are inconsistent in different human systems among different studies. The diversity of results is mainly due to heterogeneous receptor distribution, various action pathways, and distinct tissue responses in different systems. Further research is needed to define its efficacy and dosage across various systems. Full article

The growing demand for autonomous and energy-efficient telemetry systems in Industrial Internet of Things (IIoT) applications highlights the limitations of GSM-dependent infrastructure. This research proposes and validates a secure and infrastructure-independent telemetry transmission architecture based on Raspberry Pi and LoRa technology. The system integrates lightweight symmetric encryption (AES-128 with CRC-8) and local data processing, enabling long-range communication without reliance on cellular networks or cloud platforms. A fully functional prototype was developed and tested in real urban environments with high electromagnetic interference. The experimental evaluation was conducted over distances ranging from 10 to 1100 m, focusing on the Packet Delivery Ratio (PDR), Packet Error Rate (PER), and Packet Loss Rate (PLR). Results demonstrate reliable communication up to 200 m and high long-term stability, with a 24 h continuous transmission test achieving a PDR of 97.5%. These findings confirm the suitability of the proposed architecture for secure, autonomous IIoT deployments in infrastructure-limited and noisy environments. Full article

The growing adoption of the Internet of Things (IoT) demands scalable, energy-efficient communication for autonomous devices. Narrowband IoT (NB-IoT), as a low-power wide-area technology, offers reliable connectivity but remains difficult to integrate in MicroPython systems due to the absence of high-level GSM libraries. This paper introduces a modular, object-oriented MicroPython library that abstracts AT command handling, automates network configuration, and supports protocols such as MQTT and Blynk. The architecture features a layered, hardware-agnostic core and device-specific adapters, enhancing portability and extensibility. The library includes structured exception handling and automated retries to improve system reliability. Empirical validation using a Raspberry Pi Pico and SIM7020E module in a typical IoT scenario demonstrated an up to 81% reduction in implementation time. By providing a reusable and extensible framework, this work improves developer productivity, enhances error resilience, and establishes a solid foundation for rapid NB-IoT application development. Future directions include cross-hardware validation and AI-assisted code and test generation. Full article

Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder characterized by social communication deficits and repetitive behaviors. Recent studies have suggested that gut microbiota may play a role in the pathophysiology of ASD. This study aims to develop a classification model for ASD diagnosis and to identify ASD-associated biomarkers by analyzing metagenomic data at the taxonomic level. Methods: The performances of five different methods were tested in this study. These methods are (i) SVM-RCE, (ii) RCE-IFE, (iii) microBiomeGSM, (iv) different feature selection methods, and (v) a union method. The last method is based on creating a union feature set consisting of the features with importance scores greater than 0.5, identified using the best-performing feature selection methods. Results: In our 10-fold Monte Carlo cross-validation experiments on ASD-associated metagenomic data, the most effective performance metric (an AUC of 0.99) was obtained using the union feature set (17 features) and the AdaBoost classifier. In other words, we achieve superior machine learning performance with a few features. Additionally, the SHAP method, which is an explainable artificial intelligence method, is applied to the union feature set, and Prevotella sp. 109 is identified as the most important microorganism for ASD development. Conclusions: These findings suggest that the proposed method may be a promising approach for uncovering microbial patterns associated with ASD and may inform future research in this area. This study should be regarded as exploratory, based on preliminary findings and hypothesis generation. Full article

Robust, reliable, and secure communications are essential for efficient railway operation and keeping employees and passengers safe. The Future Railway Mobile Communication System (FRMCS) is a global standard aimed at providing innovative, essential, and high-performance communication applications in railway transport. In comparison with the legacy communication system (GSM-R), it provides high data rates, ultra-high reliability, and low latency. The FRMCS architecture will also benefit from cloud computing, following the principles of the cloud-native 5G core network design based on Network Function Virtualization (NFV). In this paper, an approach to the management of virtualized FRMCS applications is presented. First, the key management functionality related to the virtualized FRMCS application is identified based on an analysis of the different use cases. Next, this functionality is synthesized as RESTful services. The communication between application management and the services is designed as Application Programing Interfaces (APIs). The APIs are formally verified by modeling the management states of an FRMCS application instance from different points of view, and it is mathematically proved that the management state models are synchronized in time. The latency introduced by the designed APIs, as a key performance indicator, is evaluated through emulation. Full article

Introduction: The decline of serum estrogen in postmenopausal women leads to several changes in the vulvovaginal and vesicourethral areas, resulting in the genitourinary syndrome of menopause (GSM), characterized by bothersome symptoms such as vaginal atrophy, lack of lubrication, dyspareunia, urgency, dysuria, and recurrent urinary tract infections. Nevertheless, this condition could also be experienced by younger women affected by hormone-dependent tumors. Although topical estrogens are considered “the gold standard”, hormonal treatments cannot be indicated in cancer survivors. As a result, energy-based devices using radiofrequency and laser technologies have emerged as alternative options. This prospective study aimed to evaluate the benefits of non-invasive monopolar radiofrequency (RF) in women affected by GSM who have contraindications to, did not respond to, or declined local estrogen therapy. Methods: The patients underwent five weekly sessions of second-generation monopolar RF. At baseline and at the fifth session, two validated questionnaires were administered to the patients: the Visual Analogue Scale (VAS) and the Female Sexual Function Index (FSFI-19). On the other hand, the vaginal mucosa status was evaluated by clinicians through the Vaginal Health Index (VHI). At the end of the cycle, the Patient Global Impression of Improvement (PGI-I) questionnaire was collected. Results: Based on 44 patients who completed five sessions of radiofrequency, a significant improvement was observed in sexual function according to the FSFI scale (22.9 vs. 38.6; p < 0.001) and in VVA atrophy symptoms, as documented by the VAS score (223 vs. 125; p < 0.001). The mean VHI score increased by 3 points (p < 0.001). Moreover, according to PGI-I, 96% of patients reported a perceived improvement (PGI-I score ≤ 3). Conclusions: Radiofrequency could provide an innovative and safe therapeutic approach for patients suffering from GSM and VVA, especially when hormonal strategies are unsuitable. Full article

Background/Objectives: Gut microbial dysbiosis, leaky gut, and increased transepithelial translocation of commensal bacteria have been documented in multiple sclerosis (MS). Intrathecal IgGs specific for Akkermansia muciniphila, a gut bacterium, are increased in patients with MS and associated with clinical disability. Our objective here was to explore the putative involvement of intrathecal anti-A. muciniphila IgG in MS pathogenesis by characterizing patients with different anti-A. muciniphila IgG indices. Methods: Serum and intrathecal IgG specific for A. muciniphila and other gut bacteria, as well as routine cerebrospinal fluid (CSF) parameters, were measured in 61 patients with MS. Examination of these patients included immunophenotyping of CSF-infiltrating and paired circulating lymphocytes, intrathecal markers of neurodegeneration and inflammation, and a detailed characterization of demographic, clinical, and magnetic resonance imaging (MRI) features. Results: Plasma blasts (p < 0.01), B cells (p < 0.01), and Th2 cells (p < 0.01), which might be involved in antibody production, were increased in the CSF of these patients, as well as blood pro-inflammatory Th17 cells (p < 0.05). Anti-A. muciniphila IgG indices were negatively associated with blood-brain barrier (BBB) permeability and circulating monocytes (p < 0.001), and positively with brain lesion load (p < 0.01). Conclusions: The differences between patients with low and high anti-A. muciniphila IgG indexes regarding BBB permeability, CSF cell infiltrates, and pro-inflammatory peripheral immune cells, as well as imaging features, support a role of anti-A. muciniphila immune response in MS pathogenesis. Full article

Radio frequency identification (RFID) is popular and attaining momentum in manifold sectors, including, but not limited to, pharmaceuticals, retail, defense, transport, healthcare and currently security. Utilizing RFID solely as a solution does not result in effective security. Conventional systems have integrated only one solution, such as GSM, cryptography, wireless sensors, biometrics or a One-Time Password (OTP); however, the security provided is limited since each incorporated technology has its disadvantages. Our paper proposes improving the conventional methods in the field by proposing an intelligent door-lock system prototype implementing two-step authentication, providing double-layered security provisions in, for instance, highly sensitive zones. The suggested technique, firstly based on RFID technology and then a password (PIN) during the authentication process, results in a hybrid system that is more accurate and efficient compared to a traditional, single-method system. The Arduino micro-controller is interfaced with RFID, with a keypad that receives the input to the micro-controller, a Liquid Crystal Display to output the authentication status and finally a motor connected to the door for automation within a limited time-frame. Adding biometric verification, such as fingerprints and face recognition, can enhance the proposed design further by providing an additional layer of security from external intruders. Full article

The remarkable success of autoregressive Large Language Models (LLMs) is predicated on the causal attention mechanism, which enforces a static and rigid form of informational asymmetry by permitting each token to attend only to its predecessors. While effective for sequential generation, this hard-coded unidirectional constraint fails to capture the more complex, dynamic, and nonlinear dependencies inherent in sophisticated reasoning, logical inference, and discourse. In this paper, we challenge this paradigm by introducing Dynamic Asymmetric Attention (DAA), a novel mechanism that replaces the static causal mask with a learnable context-aware guidance module. DAA dynamically generates a continuous-valued attention bias for each query–key pair, effectively learning a “soft” information flow policy that guides rather than merely restricts the model’s focus. Trained end-to-end, our DAA-augmented models demonstrate significant performance gains on a suite of benchmarks, including improvements in perplexity on language modeling and notable accuracy boosts on complex reasoning tasks such as code generation (HumanEval) and mathematical problem-solving (GSM8k). Crucially, DAA provides a new lens for model interpretability. By visualizing the learned asymmetric attention patterns, it is possible to uncover the implicit information flow graphs that the model constructs during inference. These visualizations reveal how the model dynamically prioritizes evidence and forges directed logical links in chain-of-thought reasoning, making its decision-making process more transparent. Our work demonstrates that transitioning from a static hard-wired asymmetry to a learned and dynamic one not only enhances model performance but also paves the way for a new class of more capable and profoundly more explainable LLMs. Full article

Snow and ice accumulation on transportation infrastructure presents significant safety and maintenance challenges in cold regions, while conventional removal methods are both energy-intensive and environmentally detrimental. This study proposes a passive Heat Pipe–Coupled Geothermal Snow Melting System (HP-GSMS) that harnesses shallow geothermal energy to maintain snow-free surfaces without external energy input. Using Fluent-based CFD simulations, the system’s thermal performance was evaluated under various working fluids (ammonia, carbon dioxide, water) and pipe materials (stainless steel, aluminum). A one-dimensional thermal resistance model validated the CFD results under ammonia–stainless steel conditions, predicting a heat flux of 358.6 W/m² compared to 361.0 W/m² from the simulation, with a deviation of only 0.66%, confirming model accuracy. Ammonia demonstrated superior phase-change efficiency, with the aluminum–ammonia configuration yielding the highest heat flux (up to 677 W/m²), surpassing typical snow-melting thresholds. Aluminum pipes enhanced radial heat conduction without compromising phase stability, while water exhibited poor phase-change performance and CO₂ showed moderate but stable behavior. Additionally, a dynamic three-node RC thermal network was employed to assess transient performance under realistic diurnal temperature variations, revealing surface heat fluxes ranging from 230 to 460 W/m², with a daily average of approximately 340 W/m². These findings demonstrate the HP-GSMS’s practical viability in cold climates and underscore the importance of selecting low-boiling-point fluids and high-conductivity materials for scalable, energy-efficient, and low-carbon snow-melting applications in urban infrastructure. Full article