Search Results (102,362)

This paper examines the current state of Romanian seaports from the perspective of their transformation into smart ports, using the SWOT model to identify the most suitable strategies. It highlights the strengths and weaknesses of existing infrastructure, as well as the opportunities and risks, to outline coherent and sustainable courses of action for future development. A SWOT analysis was conducted based on information collected from a questionnaire sent to members of the maritime port authority, directors, and staff from various departments of the analyzed ports, as well as direct interviews with experts from the three ports. This analysis served as the foundation for developing strategies aimed at accelerating digitization, improving operational efficiency, and reducing environmental impact. The identified strategies were subsequently ranked using the AHP method. The weights assigned to the ten strategies emphasize the relative importance and systemic influence of each one on the process of ports transforming into smart entities. This study makes a significant contribution to the emerging literature on the transformation of Romanian seaports into “smart ports” by approaching this process through the lens of sustainable port development. Full article

The study investigated differences in ruminal and fecal microbiota composition, fermentation traits, and volatile organic compounds (VOC) in Simmental dairy cows classified as high (HME) or low (LME) methane emitters. Methane emissions from 48 cows were quantified using the Laser Methane Smart portable gas detector. The 12 animals with the highest and lowest emissions were selected and assigned to the HME and LME groups, respectively, balanced for body weight, days in milk, and body condition score. Rumen fluid and fecal samples were analyzed for pH, ammonia, volatile fatty acids (VFA), VOC, and microbiota composition. As expected, CH₄ emissions were significantly higher in HME than in LME cows (22.5 vs. 13.2 g/kg DMI; 16.9 vs. 8.4 g/kg FCM). The neutral detergent fiber digestibility was higher in HME cows (51.4% vs. 47.9%). The valeric acid concentration and the acetate-to-propionate ratio were significantly higher in HME cows (3.53 vs. 3.31). The VOC profiles significantly differed between groups in both feces and rumen fluid. The microbiota analysis revealed a significant difference between groups at the order and genus levels (Bray–Curtis dissimilarity). The Shannon index was higher in LME cows (2.08 vs. 1.95). HME cows exhibited a higher abundance of Methanosphaera and Methanobacteriales. Overall, the results indicate that re-shaping the rumen microbial community can play a key role in reducing methane emissions, strengthening the case for microbiome-driven approaches and offering insights that can support mitigation strategies across dairy production systems. Full article

Proton exchange membrane fuel cells (PEMFCs) are attractive energy sources for clean and efficient power generation; however, their nonlinear characteristics and sensitivity to operating condition variations make maximum power point tracking (MPPT) a challenging control problem. Conventional MPPT techniques often exhibit slow convergence, steady-state oscillations, and degraded performance under dynamic fuel flow variations. This paper proposes a machine learning–driven MPPT control strategy for a PEMFC system integrated with a DC–DC boost converter. The MPPT problem is formulated as a supervised classification task, where machine learning classifiers generate duty-cycle commands to regulate the converter and ensure operation at the maximum power point. A detailed PEMFC–converter model is developed in MATLAB/Simulink-2025b, and a dataset of 3000 labeled samples is generated under varying fuel flow conditions. Several classification algorithms, including decision trees, support vector machines (SVM), k-nearest neighbors (kNN), and ensemble learning methods, are systematically evaluated within an identical simulation framework. Simulation results show that the proposed machine learning-based MPPT controller significantly improves dynamic and steady-state performance. Ensemble Boosted Trees achieve the best overall response with a settling time of approximately 32 ms, peak power overshoot below 4.5%, and steady-state power ripple limited to 1.5%. Quadratic SVM and weighted kNN classifiers also demonstrate stable tracking behavior with power ripple below 2.1%, while overly complex models such as Cubic SVM suffer from large oscillations and reduced accuracy. These results confirm that classification-based machine learning offers an effective, fast, and robust MPPT solution for PEMFC systems under dynamic operating conditions. Full article

Mental disorders, including anxiety, depression, and bipolar disorder, are frequently associated with metabolic, inflammatory, and behavioral alterations that modulate their clinical expression and increase the risk of physical comorbidities. This cross-sectional study aimed to characterize the profile of inflammatory, metabolic, and cardiac biomarkers in individuals with mental disorders compared to healthy controls, also considering anthropometric and lifestyle indicators. Fifty volunteers were evaluated and distributed into four groups: control, anxiety, depression, and bipolar disorder. All participants completed the Depression, Anxiety, and Stress Scale—21 items (DASS-21) and underwent blood collection for the assessment of inflammatory biomarkers such as C-Reactive Protein and its high-sensitivity detection (CRP/hs-CRP), Interleukins (IL-6, IL-1β) and Tumor Necrosis Factor alpha (TNF-α), metabolic biomarkers (vitamin D, cortisol, and D-dimer), and cardiac biomarkers such as N-terminal pro-B-type Natriuretic Peptide (NT-proBNP), Creatine Kinase—MB (CK-MB), troponin I (cTnI), and myoglobin (Myo). The results showed a significantly higher body mass index (BMI) in clinical groups, particularly in groups with anxiety and depression. Biomarker analyses revealed significant differences in groups with mental disorders. Elevated levels of CRP (p = 0.0038), hs-CRP (p = 0.0048), and IL-6 (p = 0.0030) were identified in the anxiety group, while the depression group was characterized by reduced vitamin D levels (p = 0.0302). Individuals with bipolar disorder presented significantly higher levels of CK-MB (p = 0.0016), CRP (p < 0.0001), IL-6 (p = 0.0198), and IL-1β (p = 0.0067). It was also observed that most individuals with mental disorders did not engage in physical activity. This inactivity was associated with worse emotional scores, higher systemic inflammation, and vitamin D deficiency. These findings reinforce the existence of an integrated axis between metabolism, inflammation, and behavior, in which excess weight, sedentary lifestyle, and nutritional deficiencies synergistically contribute to the maintenance of psychiatric symptoms and metabolic vulnerability. Integrating biomarkers, BMI, and behavioral factors may aid in identifying clinical subphenotypes and guiding more precise and individualized therapeutic strategies. Full article

To reduce water consumption and potential formation damage associated with conventional water-based fracturing fluids while improving the proppant-carrying and flow adaptability of CO₂-based systems without relying on specialized CO₂ thickeners, a CO₂–water polymer hybrid fracturing fluid was developed using an AM/AA copolymer (poly(acrylamide-co-acrylic acid), P(AM-co-AA)) as the thickening agent for the aqueous phase. Systematic experimental investigations were conducted under high-temperature and high-pressure conditions. Fluid-loss tests at different CO₂ volume fractions show that the CO₂–water polymer hybrid fracturing fluid system achieves a favorable balance between low fluid loss and structural continuity within the range of 30–50% CO₂, with the most stable fluid-loss behavior observed at 40% CO₂. Based on this ratio window, static proppant-carrying experiments indicate controllable settling behavior over a temperature range of 20–80 °C, leading to the selection of 60% polymer-based aqueous phase + 40% CO₂ as the optimal mixing ratio. Rheological results demonstrate pronounced shear-thinning behavior across a wide thermo-pressure range, with viscosity decreasing systematically with increasing shear rate and temperature while maintaining continuous and reproducible flow responses. Pipe-flow tests further reveal that flow resistance decreases monotonically with increasing flow velocity and temperature, indicating stable transport characteristics. Phase visualization observations show that the CO₂–water polymer hybrid fracturing fluid system exhibits a uniform milky dispersed appearance under moderate temperature or elevated pressure, whereas bubble-dominated structures and spatial phase separation gradually emerge under high-temperature and relatively low-pressure static conditions, highlighting the sensitivity of phase stability to thermo-pressure conditions. True triaxial hydraulic fracturing experiments confirm that the CO₂–water polymer hybrid fracturing fluid enables stable fracture initiation and sustained propagation under complex stress conditions. Overall, the results demonstrate that the AM/AA copolymer-based aqueous phase can provide effective viscosity support, proppant-carrying capacity, and flow adaptability for CO₂–water polymer hybrid fracturing fluid over a wide thermo-pressure range, confirming the feasibility of this approach without the use of specialized CO₂ thickeners. Full article

The genus Asparagus L. is a traditional Chinese herb valued for its medicinal and culinary properties, with root tubers being the primary organ of interest. To elucidate the genetic mechanisms underlying tuber formation, we conducted a comparative transcriptome analysis of two species, Asparagus cochinchinensis (Lour.) Merr. and Asparagus taliensis F. T. Wang & Tang ex S. C. Chen, which exhibit distinct differences in root tuber number. High-throughput sequencing generated 6.68 Gb and 7.60 Gb of clean data for the respective species, leading to the annotation of 115,080 non-redundant unigenes. Comparative analysis identified 26,013 differentially expressed genes (DEGs), including 1096 associated with carbohydrate metabolism. Weighted gene co-expression network analysis (WGCNA) revealed that the MEred and Megreenyellow modules which included genes involved in material and energy metabolism were significantly correlated with tuber development. From these modules, we identified two candidate genes involved in carbon and sugar metabolism, designated Ac_uniYEAD and Ac_uniRPE. Quantitative real-time PCR validation confirmed that their expression levels were positively correlated with root tuber number, consistent with the transcriptomic data. These results highlight Ac_uniYEAD and Ac_uniRPE as promising targets for genetic improvement of tuber yield in Asparagus breeding programs. Full article

To address the complex influencing factors, divergent stakeholder demands, and the challenge of quantitative comparison in alignment selection for highway expansion and reconstruction, we systematically reviewed the relevant factors. These factors were classified into four categories—economy, technology, safety, and environment—and comprise 16 subfactors in total. The symmetry of the route selection process is disrupted by the varying priorities of different stakeholders, leading to asymmetric evaluations of the alternatives. Using the G30 Lianhuo Expressway Jingqing section expansion and reconstruction project as a case study, we applied the Analytic Hierarchy Process (AHP) combined with expert judgment to derive weights for each factor. The results indicate that environmental factors carry substantial weight, reflecting increased awareness of environmental protection in contemporary projects. We then developed a comparative model based on the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Applying this model to alignment alternatives between the Jingjiadian and Huachacun sections indicates that Option 4 is the preferred alignment. Overall, the AHP–TOPSIS composite evaluation framework effectively integrates expert knowledge with objective quantitative analysis. It enables the scientific ranking of alternatives and provides decision support for alignment selection in mountainous highways and other linear engineering projects. Full article

Background: The protracted Syrian conflict has severely disrupted healthcare services, compromising the continuity and quality of care for individuals with type 2 diabetes mellitus (T2DM). This study evaluated diabetes-related quality of life, treatment satisfaction, and perceived stress among adults with T2DM receiving care in selected clinics within conflict-affected Syrian regions and examined predictors of these outcomes. Methods: A cross-sectional survey was conducted in July 2024 among 200 adults with T2DM recruited from outpatient clinics, primary healthcare centers, and diagnostic laboratories in Homs and Damascus. Participants completed validated Arabic versions of the Audit of Diabetes-Dependent Quality of Life (ADDQoL), Diabetes Treatment Satisfaction Questionnaire (DTSQs), and Perceived Stress Scale (PSS-10), alongside the collection of sociodemographic and clinical data. Descriptive statistics, univariate analyses, and multivariable linear regression models were applied. As this study used a facility-based purposive sample, its findings may not be generalizable to all individuals with diabetes in Syria. Results: Participants had a mean age of 57.6 ± 11.8 years, and 59.5% were male. Hypertension (70.5%) and obesity (35.5%) were the most common comorbidities, while retinopathy (21.5%), nephropathy (23.5%), and neuropathy (19.5%) were the most frequent complications. The mean ADDQoL Average Weighted Impact score was −3.1 ± 1.3, indicating substantial quality-of-life impairment. The mean DTSQs total score was 30.4 ± 5.6, suggesting moderate satisfaction with treatment despite frequent perceived hyperglycemia. The mean PSS-10 score was 18.8 ± 3.4, with 92.5% of respondents experiencing moderate stress. In multivariable models, poorer quality of life was predicted by older age, rural residence, higher BMI, and depression. Lower treatment satisfaction was associated with rural residence and retinopathy, while higher perceived stress was linked to lower education, obesity, and obstructive sleep apnea. Conclusions: Adults with T2DM attending selected healthcare facilities in conflict-affected Syria experience marked reductions in quality of life, moderate treatment satisfaction, and elevated psychosocial stress. These findings highlight the need for strengthened medication supply chains, improved rural service coverage, and integration of psychosocial support within diabetes care in fragile health systems. Full article

Diabetes mellitus, particularly type 2 diabetes mellitus (T2DM), represents a rapidly expanding global health challenge with substantial public health and economic consequences. Recent advances in antidiabetic therapy—including dipeptidyl peptidase-4 (DPP-4) inhibitors, glucagon-like peptide-1 receptor agonists (GLP-1 RAs), dual GIP/GLP-1 receptor agonists, and sodium–glucose cotransporter-2 (SGLT-2) inhibitors—have transformed diabetes management by providing benefits beyond glycemic control, such as cardiovascular and renal protection, weight reduction, and improved quality of life. As the therapeutic landscape becomes increasingly complex and patient-centered, ensuring the safe and effective use of these agents in real-world settings has emerged as a key concern for pharmacoepidemiology and pharmacovigilance. Community pharmacists, as highly accessible healthcare professionals, play an expanding role in diabetes care through medication optimization, patient education, adherence support, and monitoring of adverse drug reactions in primary care. Evidence from systematic reviews and meta-analyses indicates that pharmacist-led interventions improve glycemic outcomes, enhance self-care behaviors, and facilitate the appropriate adoption of contemporary antidiabetic therapies. This narrative review synthesizes current evidence on novel pharmacological treatments for T2DM and examines the evolving contribution of community pharmacists in translating therapeutic innovation into routine practice. Barriers to implementation and future perspectives for integrating pharmacist-led services into diabetes management and pharmacovigilance frameworks are also discussed. Full article

Natural preservation technologies have emerged as sustainable alternatives for maintaining the postharvest quality of fresh products. This study developed and characterized edible films and coatings produced from purple corn flour (MMH) and ethanolic propolis extract (EEP), and evaluated their effectiveness in extending the shelf life of Fuerte avocado. Film-forming solutions were prepared using three MMH/EEP formulations (100/0, 90/10, and 80/20), and their apparent viscosity was determined. Films obtained by drying at 45 °C for 12 h were analyzed for pH, thickness, tensile strength, solubility, water vapor permeability, and microstructure by SEM. The MMH 80/20 EEP formulation showed the best overall performance and was selected as a coating for avocados stored under ambient and refrigerated conditions. Shelf life was defined based on quantitative criteria, including acceptable limits of weight loss and sensory acceptability. Under these criteria, coated avocados reached a shelf life of 30 days at ambient temperature, compared to 15 days for uncoated fruit, and 72 days under refrigerated storage, compared to 50 days for the control. Additionally, the coating reduced weight loss, preserved moisture, and improved sensory acceptance. Overall, MMH/EEP systems represent a promising natural alternative for the postharvest preservation of avocado. Full article

This paper theoretically and experimentally investigates the effect of condenser location and geometry on the thermal performance of a vapor chamber, as thermal management systems for electronic devices with multiple heat sources under non-uniform heat flux conditions. A weighting factor approach was applied to represent the non-uniform heat input imposed on individual heat sources. The proposed theoretical model was validated through comparison with Lefèvre’s analytical results under the same conditions and experimental data obtained under different condenser locations. It was shown that the wall temperature distribution for the separated condenser configuration was lower than for the concentrated configuration. Using the validated model, the effects of condenser geometry on the temperature uniformity and maximum heat transfer rate of the vapor chamber were analyzed under the capillary limit condition by varying the condenser aspect ratio. The results show that higher aspect ratios improve temperature uniformity due to wider condenser coverage, whereas lower aspect ratios enhance the maximum heat transfer rate by reducing the liquid pressure drop between the evaporator and condenser. Specifically, the maximum heat transfer rate reaches 72.6 W at an aspect ratio of 2.5, which corresponds to a 13.3% increase compared to 64.1 W at an aspect ratio of 8.3. Full article

Background: Poor clinical control in Chronic Obstructive Pulmonary Disease (COPD) is prevalent, yet the interplay of disease severity, modifiable factors, and clinician perception remains poorly understood. This study aimed to determine the frequency of poor control, identify its independent determinants, and characterize the heterogeneity of the poorly controlled population receiving maintenance inhaled therapy with various devices in primary care. Methods: In a multicenter, cross-sectional analysis of 988 patients from the Study SIMPLIFY, clinical control of COPD was classified using the objective RADAR score. We used multivariable logistic regression and Machine Learning (Random Forest with SHAP analysis) to identify determinants of poor control (RADAR ≥ 4) and k-medoids cluster analysis to characterize the poorly controlled subgroup (n = 452). Results: Nearly half the cohort (45.7%, n = 452) had poor clinical control. Agreement between physician-assessed control (five categories) and RADAR classification was 49.3%, with overestimation in 34.0% and underestimation in 16.7% of cases (Cohen’s κ = −0.081; weighted κ = −0.037). The strongest independent determinants were the exacerbator phenotypes (eosinophilic aOR 6.85; non-eosinophilic aOR 4.91). Key modifiable factors included active smoking (aOR 1.92), lower TAI-12 adherence score (per point; aOR 0.96), high dosing frequency (≥4 inhalations/day; aOR 1.54) and high inhaler burden (≥3 devices; aOR 1.84). Machine learning analysis identified clinical phenotype and adherence behavior as the top two scale-independent predictors of poor control. Cluster analysis of the poorly controlled group revealed five reproducible and clinically meaningful phenotypes (C0–C4), primarily separated by treatment complexity, comorbidities, and adherence. Conclusions: Poor clinical control is common and critically under-recognized in primary care patients with COPD on maintenance inhaled therapy. This is driven by a profound clinician perception gap and a failure to address key modifiable determinants, such as high dosing frequency, regimen complexity, and poor adherence, which likely drives therapeutic inertia. Our findings underscore the need to integrate objective tools to unmask poor control and highlight the importance of treatment simplification. The identification of distinct clinical phenotypes provides a roadmap toward a more personalized, evidence-based standard of care. Full article

To address the performance degradation of the traditional Extended Kalman Filter (EKF) in state estimation for sensorless brushless DC motor (BLDC) control under dynamic operating conditions, such as sudden speed and load changes—a degradation caused primarily by model mismatches—this paper proposes an Adaptive Extended Kalman Filter (AEKF) algorithm. The proposed algorithm incorporates a robust weighting strategy based on the Mahalanobis distance and a dynamically adjusted adaptive forgetting factor. This integration establishes an estimation mechanism capable of online updating of the innovation covariance, thereby enhancing the state observer’s adaptability to system uncertainties and external disturbances. Simulation results demonstrate that, compared to the traditional EKF, the designed AEKF algorithm significantly improves the estimation accuracy of rotor position and speed under various operating conditions, including low-speed start-up, speed step changes, and sudden load applications. Furthermore, it accelerates dynamic response, suppresses overshoot, and enhances the system’s disturbance rejection robustness. This work provides an effective state estimation solution for high-dynamic performance sensorless control of BLDC. Full article

Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates a multisource data-fusion approach for improving cropland SOM prediction in Yucheng City, Shandong Province, China. We applied a Local Energy Weighted Discrete Wavelet Transform (LEW-DWT) to fuse multi-temporal Landsat 8 imagery (2014–2023). Quantitative analysis (e.g., Information Entropy and Average Gradient) demonstrated that LEW-DWT effectively preserved high-frequency spatial details and texture features of fragmented croplands better than traditional DWT and simple splicing methods. These were combined with 41 environmental predictors to construct composite Ev–Tn–Mm features (environmental variables, temporal NDVI features, and multi-temporal multispectral information). Random Forest (RF) and Convolutional Neural Network (CNN) models were trained and compared to assess the contribution of the fused data to SOM mapping. Key findings are: (1) Comparative analysis showed that the LEW-DWT fusion strategy achieved the lowest spectral distortion and highest spatial fidelity. Using the fused multitemporal dataset, the CNN attained the highest predictive performance for SOM (R² = 0.49). (2) Using the Ev–Tn–Mm features, the CNN achieved R² = 0.62, outperforming the RF model (R² = 0.53). Despite the limited sample size, the optimized shallow CNN architecture effectively extracted local spatial features while mitigating overfitting. (3) Variable importance analysis based on the RF model reveals that mean soil moisture is the primary single variable influencing the SOM, (relative importance 15.22%), with the NDVI phase among time-series features (1.80%) and the SWIR1 band among fused multispectral bands (1.38%). (4) By category, soil moisture-related variables contributed 45.84% of total importance, followed by climatic factors. The proposed multisource fusion framework offers a practical solution for regional SOM digital monitoring and can support precision agriculture and soil carbon management. Full article

The emerging scope of the Industrial Internet of Services (IIoS) requires a robust intrusion detection system to detect malicious attacks. The increasing frequency of sophisticated and high-impact cyber attacks has resulted in financial losses and catastrophes in IIoS-based manufacturing industries. However, existing solutions often struggle to adapt and generalize to new cyber attacks. This study proposes a unique approach designed for known and zero-day network attack detection in IIoS environments, called Denoising Adaptive Multi-Branch Architecture (DA-MBA). The proposed approach is a smart, conformal, and self-adjusting cyber attack detection framework featuring denoising representation learning, hybrid neural inference, and open-set uncertainty calibration. The model merges a denoising autoencoder (DAE) to generate noise-tolerant latent representations, which are processed using a hybrid multi-branch classifier combining dense and bidirectional recurrent layers to capture both static and temporal attack signatures. Moreover, it addresses challenges such as adaptability and generalizability by hybridizing a Multilayer Perceptron (MLP) and bidirectional LSTM (BiLSTM). The proposed hybrid model was designed to fuse feed-forward transformations with sequence-aware modeling, which can capture direct feature interactions and any underlying temporal and order-dependent patterns. Multiple approaches have been applied to strengthen the dual-branch architecture, such as class weighting and comprehensive hyperparameter optimization via Optuna, which collectively address imbalanced data, overfitting, and dynamically shifting threat vectors. The proposed DA-MBA is evaluated on two widely recognized IIoT-based datasets, Edge-IIoT set and WUSTL-IIoT-2021 and achieves over 99% accuracy and a near 0.02 loss, underscoring its effectiveness in detecting the most sophisticated attacks and outperforming recent deep learning IDS baselines. The solution offers a scalable and flexible architecture for enhancing cybersecurity within evolving IIoS environments by coupling feature denoising, multi-branch classification, and automated hyperparameter tuning. The results confirm that coupling robust feature denoising with sequence-aware classification can provide a scalable and flexible framework for improving cybersecurity within the IIoS. The proposed architecture offers a scalable, interpretable, and risk sensitive defense mechanism for IIoS, advancing secure, adaptive, and trustworthy industrial cyber-resilience. Full article