Search Results (2,459)

Background/Objectives: While ART effectively suppresses HIV viremia, many PLWH exhibit persistent immune dysfunction. This study aimed to assess immune recovery and immune exhaustion (PD-1/PD-L1 expression) in newly diagnosed versus long-term ART-treated individuals. Methods: We analyzed 79 PLWH: 52 newly diagnosed individuals (12-month follow-up) and 27 long-term-treated patients (Ukrainian refugees). Flow cytometry was used to evaluate CD4+ and CD8+ counts, the CD4+/CD8+ ratio, and PD-1/PD-L1 expression on CD3+, CD4+, and CD19+ lymphocytes. ART regimen and HIV subtype were included as covariates in linear regression models. Results: At 12 months, CD4+ counts were similar between groups (median 596.5 vs. 621 cells/μL, p = 0.22), but newly diagnosed patients had higher CD8+ counts (872 vs. 620 cells/μL, p = 0.028) and a lower CD4+/CD8+ ratio (0.57 vs. 1.05, p = 0.0027). Immune exhaustion markers were significantly elevated in newly diagnosed individuals: CD4+ PD-1+ T cells (24.4% vs. 3.85%, p = 0.0002) and CD3+ PD-1+ T cells (27.3% vs. 12.35%, p < 0.0001). Linear regression confirmed group membership independently predicted higher CD3+ (β = +21.92, p < 0.001), CD4+ (β = +28.87, p < 0.0001), and CD19+ (β = +8.73, p = 0.002) percentages. Lipid parameters and SCORE2 did not differ significantly. Conclusions: Despite virologic suppression and CD4+ recovery, immune exhaustion markers remain elevated in newly diagnosed PLWH, suggesting incomplete immune normalization. Traditional parameters (CD4+ count and CD4+/CD8+ ratio) may not fully capture immune status, warranting broader immunologic profiling in HIV care. Full article

Outdoor access, often referred to as pop holes, is widely used to improve the production and welfare of hens. Such cage-free environments present an opportunity for precision flock management via best environmental control practices. However, outdoor access disrupts the integrity of the indoor environment, including properly planned ventilation. Moreover, complaints exist that hens do not use the holes to access the outdoor environment due to the strong incoming airflow through the outdoor access, as they behave as uncontrolled air inlets in a negative pressure ventilation system. As the egg industry transitions to cage-free systems, there is an urgent need for validated computational fluid dynamics (CFD) models to optimize ventilation strategies that balance animal welfare, environmental control, and production efficiency. We developed and validated CFD models of a cage-free hen house with outdoor access by specifying real-world conditions, including two exhaust fans, sidewall ventilation inlets, wire-meshed pens, outdoor access, and plenum inlets. The simulations of four ventilation scenarios predict the measured air flow velocity with an error of less than 50% for three of the scenarios, and the simulations predict temperature with an error of less than 6% for all scenarios. Plenum-based systems outperformed sidewall systems by up to 136.3 air changes per hour, while positive pressure ventilation effectively mitigated disruptions to outdoor access. We expect that knowledge of improved ventilation strategy will help the egg industry improve the welfare of hens cost-effectively. Full article

In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems. Full article

Carbon monoxide (CO) is a major toxic gas emitted from vehicle exhaust, industrial processes, and incomplete fuel combustion, posing serious environmental and health risks. Catalytic oxidation of CO into less harmful CO₂ is an effective strategy to reduce these emissions. In this study, we investigated the catalytic performance of platinum (Pt) single atoms doped on C₃N monolayers with various vacancy defects, including single carbon (C_V) and nitrogen (N_V) vacancies, using density functional theory (DFT) calculations. Our results demonstrate that Pt@N_V-C₃N exhibited the most favorable catalytic properties, with the highest O₂ adsorption energy (−3.07 eV). This performance significantly outperforms Pt atoms doped at other vacancies. It can be attributed to the strong binding between Pt and nitrogen vacancies, which contributes to its excellent resistance to Pt aggregation. CO oxidation on Pt@N_V-C₃N proceeds via the Eley–Rideal (ER2) mechanism with a low activation barrier of 0.41 eV for the rate-determining step, indicating high catalytic efficiency at low temperatures. These findings suggest that Pt@N_V-C₃N is a promising candidate for CO oxidation, contributing to developing cost-effective and environmentally sustainable catalysts. The strong binding of Pt atoms to the nitrogen vacancies prevents aggregation, ensuring the stability and durability of the catalyst. The kinetic modeling further revealed that the ER2 mechanism offers the highest reaction rate constants over a wide temperature range (273–700 K). The low activation energy barrier also facilitates CO oxidation at lower temperatures, addressing critical challenges in automotive and industrial pollution control. This study provides valuable theoretical insights for designing advanced single-atom catalysts for environmental remediation applications. Full article

Understanding the differential impacts of emission sources of volatile organic compounds (VOCs) on formaldehyde (HCHO) levels is pivotal to effectively mitigating key photochemical radical precursors, thereby enhancing the regulation of atmospheric oxidation capacity (AOC) and ozone formation. This investigation systematically selected and analyzed year-long VOC measurements across three urban zones in Shenzhen, China. Photochemical age correction methods were implemented to develop the initial concentrations of VOCs before source apportionment; then Positive Matrix Factorization (PMF) modeling resolved six primary sources: solvent usage (28.6–47.9%), vehicle exhaust (24.2–31.2%), biogenic emission (13.8–18.1%), natural gas (8.5–16.3%), gasoline evaporation (3.2–8.9%), and biomass burning (0.3–2.4%). A machine learning (ML) framework incorporating Shapley Additive Explanations (SHAP) was subsequently applied to evaluate the influence of six emission sources on HCHO concentrations while accounting for reaction time adjustments. This machine learning-driven nonlinear analysis demonstrated that vehicle exhaust nearly always emerged as the primary anthropogenic contributor in diverse functional zones and different seasons, with gasoline evaporation as another key contributor, while the traditional reactivity metric method, ozone formation potential (OFP), tended to underestimate the role of the two sources. This study highlights the primacy of strengthening emission reduction of transportation sectors to mitigate HCHO pollution in megacities. Full article

Network Intrusion Detection Systems (NIDS) driven by Machine Learning (ML) algorithms are usually trained using publicly available datasets consisting of labeled traffic samples, where labels refer to traffic classes, usually one benign and multiple harmful. This paper studies the generalizability of models trained on such datasets. This issue is crucial given the application of such a model to actual internet traffic because high-performance measures obtained on datasets do not necessarily imply similar efficiency on the real traffic. We propose a procedure consisting of cross-validation using various sets sharing some standard traffic classes combined with the t-SNE visualization. We apply it to investigate four well-known and widely used datasets: UNSW-NB15, CIC-CSE-IDS2018, BoT-IoT, and ToN-IoT. Our investigation reveals that the high accuracy of a model obtained on one set used for training is reproducible on others only to a limited extent. Moreover, benign traffic classes’ generalizability differs from harmful traffic. Given its application in the actual network environment, it implies that one needs to select the data used to train the ML model carefully to determine to what extent the classes present in the dataset used for training are similar to those in the real target traffic environment. On the other hand, merging datasets may result in more exhaustive data collection, consisting of a more diverse spectrum of training samples. Full article

Evaporative cooling (EC) offers an energy-efficient alternative to direct expansion (DX) cooling but suffers from high water consumption. This limitation can be mitigated by pre-cooling incoming fresh air using cooler exhaust air via energy recovery. This study presents and optimizes a solar-driven EC system integrated with underfloor air distribution (UFAD) to enhance thermal comfort and minimize water use in a temporary office in Riyadh’s arid climate. A 3D CFD model was developed and validated against published data to simulate indoor airflow, providing data for thermal comfort evaluation using the predicted mean vote model in cases with and without energy recovery. A year-round hourly energy analysis revealed that the solar-driven EC-UFAD system reduces grid power consumption by 93.5% compared to DX-based UFAD under identical conditions. Energy recovery further cuts annual EC water usage by up to 31.3%. Operational costs decreased by 84% without recovery and 87% with recovery versus DX-UFAD. Full article

Membrane technology is primarily used for the separation and purification of biotechnological products, which contain proteins and enzymes. Membrane fouling during crossflow filtration remains a significant challenge. This study aims to initially validate crossflow filtration models, particularly related to pore-blocking mechanisms, through a comparative analysis with dead-end filtration models. One crossflow microfiltration (MF) and six consecutive ultrafiltration (UF) stages were implemented to concentrate laccase extracts from Pleurotus ostreatus 202 fungi. The complete pore-blocking mechanism significantly impacts the MF, UF 1000, UF 100 and UF 10 stages, with the highest related filtration constant (Kb_F) estimated at 12.60 × 10⁻⁴ (m⁻¹). Although the intermediate pore-blocking mechanism appears across all filtration stages, UF 100 is the most affected, with an associated filtration constant (Ki_F) of 16.70 (m⁻¹). This trend is supported by the highest purification factor (6.95) and the presence of 65, 62 and 56 kDa laccases in the retentate. Standard pore blocking occurs at the end of filtration, only in the MF and UF 1000 stages, with filtration constants (Ks_F) of 29.83 (s^−0.5m^−0.5) and 31.17 (s^−0.5m^−0.5), respectively. The absence of cake formation and the volume of permeate recovered indicate that neither membrane was exposed to exhaustive fouling that could not be reversed by backwashing. Full article

Urban socioeconomic modeling has predominantly concentrated on extensive location and neighborhood-based features, relying on the localized population footprint. However, networks in urban systems are common, and many urban modeling methods do not account for network-based effects. Additionally, network-based research has explored a multitude of data from urban landscapes. However, achieving a comprehensive understanding of urban mobility proves challenging without exhaustive datasets. In this study, we propose using commute information records from the census as a reliable and comprehensive source to construct mobility networks across cities. Leveraging deep learning architectures, we employ these commute networks across U.S. metro areas for socioeconomic modeling. We show that mobility network structures provide significant predictive performance without considering any node features. Consequently, we use mobility networks to present a supervised learning framework to model a city’s socioeconomic indicator directly, combining Graph Neural Network and Vanilla Neural Network models to learn all parameters in a single learning pipeline. In experiments in 12 major U.S. cities, the proposed model achieves considerable explanatory performance and is able to outperform previous conventional machine learning models based on extensive regional-level features. Providing researchers with methods to incorporate network effects in urban modeling, this work also informs stakeholders of wider network-based effects in urban policymaking and planning. Full article

The gut microbiota and its interaction with the nervous system through the gut–brain axis (MGB) have been the subject of growing interest in biomedical research. It has been proposed that modulation of microbiota using probiotics could offer a promising therapeutic alternative for mood regulation and the treatment of anxiety and depression disorders. The findings indicate that several probiotic strains, such as Lactobacillus and Bifidobacterium, have demonstrated anxiolytic and antidepressant effects in pre and clinical studies. These effects seem to be mediated by the regulation of the hypothalamic–pituitary–adrenal axis (HPA), the synthesis of neurotransmitters such as serotonin (5-HT) and Gamma-amino-butyric acid (GABA), as well as the modulation of systemic inflammation. However, the lack of standardization in dosing and strain selection, in addition to the scarcity of large-scale clinical studies, limit the applicability of these findings in clinical therapy. Additional research is required to establish standardized therapeutic protocols and better understand the role of probiotics in mental health. The aim of this narrative review is to discuss the relationship between the gut microbiota and the MGB axis in the context of anxiety and depression disorders, the underlying neurobiological mechanisms, as well as the preclinical evidence for the effect of probiotics in modulating these disorders. In this way, an exhaustive search was carried out in scientific databases including PubMed, ScienceDirect, Scopus, and Web of Science. Preclinical research evaluating the effects of different probiotic strains in animal models during chronic treatment was selected, excluding those studies that did not provide access to the full text. Full article

To enhance the survivability of armed helicopters in high-threat environments, integrated infrared (IR) suppressors are increasingly adopted to reduce thermal signatures. However, such integration significantly alters the exhaust flow field, which may in turn affect both the infrared and acoustic characteristics of the helicopter. This study investigates the aerodynamic, infrared, and acoustic impacts of an integrated IR suppressor through the comparative analysis of two helicopter configurations: a conventional design and a design equipped with an integrated IR suppressor. Full-scale models are used to analyze flow field and IR radiation characteristics, while scaled models are employed for aeroacoustic simulations. The results show that although the integrated IR suppressor increases flow resistance and reduces entrainment performance within the exhaust mixing duct, it significantly improves the thermal dissipation efficiency of the exhaust plume. The infrared radiation analysis reveals that the integrated suppressor effectively reduces radiation intensity in both the 3~5 μm and 8~14 μm bands, especially under cruise conditions where the exhaust is more efficiently cooled by ambient airflow. Equivalent radiation temperatures calculated along principal axes confirm lower IR signatures for the integrated configuration. Preliminary acoustic analyses suggest that the slit-type nozzle and integrated suppressor layout may also offer potential benefits in jet noise reduction. Overall, the integrated IR suppressor provides a clear advantage in lowering the infrared observability of armed helicopters, with acceptable aerodynamic and acoustic trade-offs. These findings offer valuable guidance for the future development of low-observable helicopter platforms. Full article

Intensive energy consumption in the building sector remains one of the primary contributors to climate change and global warming. Within Renewable Energy Communities (RECs), improving energy management is essential for promoting sustainability and reducing environmental impact. Accurate forecasting of energy consumption at the community level is a key tool in this effort. Traditionally, engineering-based methods grounded in thermodynamic principles have been employed, offering high accuracy under controlled conditions. However, their reliance on exhaustive building-level data and high computational costs limits their scalability in dynamic REC settings. In contrast, Artificial Intelligence (AI)-driven methods provide flexible and scalable alternatives by learning patterns from historical consumption and environmental data. This study investigates three Machine Learning (ML) models, Decision Tree (DT), Random Forest (RF), and CatBoost, and one Deep Learning (DL) model, Convolutional Neural Network (CNN), to forecast community electricity consumption using real smart meter data and local meteorological variables. The study focuses on a REC in Loureiro, Portugal, consisting of 172 residential users from whom 16 months of 15 min interval electricity consumption data were collected. Temporal features (hour of the day, day of the week, month) were combined with lag-based usage patterns, including features representing energy consumption at the corresponding time in the previous hour and on the previous day, to enhance model accuracy by leveraging short-term dependencies and daily repetition in usage behavior. Models were evaluated using Mean Squared Error (MSE), Mean Absolute Percentage Error (MAPE), and the Coefficient of Determination $R^2$. Among all models, CatBoost achieved the best performance, with an MSE of 0.1262, MAPE of 4.77%, and an $R^2$ of 0.9018. These results highlight the potential of ensemble learning approaches for improving energy demand forecasting in RECs, supporting smarter energy management and contributing to energy and environmental performance. Full article

Ships play a crucial role in modern society, serving purposes such as marine transportation, tourism, and exploration. Malfunctions or defects in the main engine, which is a core component of ship operations, can disrupt normal functionality and result in substantial financial losses. Consequently, early fault diagnosis of abnormal engine conditions is critical for effective maintenance. In this paper, we propose a deep hybrid model for fault diagnosis of ship main engines, utilizing exhaust gas temperature data. The proposed model utilizes both time-domain features (TDFs) and time-series raw data. In order to effectively extract features from each type of data, two distinct feature extraction networks and an attention module-based classifier are designed. The model performance is evaluated using real-world cylinder exhaust gas temperature data collected from the large ship low-speed two-stroke main engine. The experimental results demonstrate that the proposed method outperforms conventional methods in fault diagnosis accuracy. The experimental results demonstrate that the proposed method improves fault diagnosis accuracy by 6.146% compared to the best conventional method. Furthermore, the proposed method maintains superior performanceeven in noisy environments under realistic industrial conditions. This study demonstrates the potential of using exhaust gas temperature using a single sensor signal for data-driven fault detection and provides a scalable foundation for future multi-sensor diagnostic systems. Full article

This study investigates the effects of diffusion modeling and swirl intensity on flow fields and NO emissions in CH₄/NH₃ non-premixed swirling flames using large eddy simulations (LESs). Simulations are performed for a 50/50 ammonia–methane blend at three global equivalence ratios of 0.77, 0.54, and 0.46 and two swirl numbers of 8 and 12, comparing the unity Lewis number (ULN) and mixture-averaged diffusion (MAD) models against the experimental data includes OH-PLIF and ON-PLIF reported in a prior study by the KAUST group. Both models produce similar flow fields, but the MAD model alters the flame structure and species distributions due to differential diffusion (DD) and limitations in its Flamelet library. Notably, the MAD library lacks unstable flame branch solutions, leading to extensive interpolation between extinction and stable branches. This results in overpredicted progress variable source terms and reactive scalars, both within and beyond the flame zone. The ULN model better reproduces experimental OH profiles and localizes NO formation near the flame front, whereas the MAD model predicts broader NO distributions due to nitrogen species diffusion. Higher swirl intensities shorten the flame and shift NO production upstream. While a low equivalence ratio provides enough air for good mixing, lower ammonia and higher NO contents in exhaust gases, respectively. Full article

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials. This formulation allowed us to employ analytical tools from statistical mechanics to identify optimal ordering strategies via the minimization of a free energy functional. Our approach not only sheds new light on the structural characteristics of optimal planning but also introduces the concept of configurational entropy as a measure of decision variability and robustness. Numerical simulations and analytical approximations demonstrate the efficacy of the lattice gas model in capturing key features of the problem and suggest promising avenues for extending the framework to more complex settings, including multi-item systems and time-varying demand. This work represents a significant step toward bridging physical sciences with supply chain optimization, offering a robust theoretical foundation for both future research and practical applications. Full article