Search Results (677)

The trend of decreasing endurance of flash memory makes the overall lifetime of SSDs more sensitive to the effects of wear leveling. Under these circumstances, we observe that existing wear-leveling techniques exhibit anomalous behavior under workloads without clear access skew or under dynamic access patterns and produce high write amplification, as high as 5.4×, negating its intended benefits. We argue that wear leveling is an artifact for maintaining the fixed-capacity abstraction of a storage device, and it becomes unnecessary if the exported capacity of the SSD is to gracefully reduce. We show that this idea of capacity variance extends the lifetime of the SSD, allowing up to 2.94× more writes under real workloads. Full article

With the rapid growth of electricity demand in industrial parks and the increasing penetration of renewable energy, vehicle-to-grid (V2G) technology has become an important enabler for mitigating grid stress while improving charging economy. This paper proposes a multi-objective rolling linear-programming-model-based predictive control (LP-MPC) method for coordinated electric vehicle (EV) scheduling in industrial park microgrids. The model explicitly considers transformer capacity limits, EV state-of-charge (SOC) dynamics, bidirectional charging/discharging constraints, and photovoltaic (PV) generation uncertainty. By solving a linear programming problem in a receding horizon framework, the approach simultaneously achieves load peak shaving, valley filling, and EV revenue maximization with real-time feasibility. A simulation study involving 300 EVs, 100 kW PV, and a 1000 kW transformer over 24 h with 5-min intervals demonstrates that the proposed LP-MPC outperforms greedy and heuristic load-leveling strategies in peak load reduction, load variance minimization, and charging cost savings while meeting all SOC terminal requirements. These results validate the effectiveness, robustness, and economic benefits of the proposed method for V2G-enabled industrial park microgrids. Full article

This study evaluates the innovation capacity of Egypt’s governorates to identify their potential for developing smart cities as innovation hubs. Smart cities represent essential instruments for tackling complicated urban issues like environmental degradation, regional economic disparities, and rapid urbanization. In the framework of Egypt Vision 2030, the establishment of fourteen fourth-generation smart cities is seen as an essential initiative to promote balanced, innovation-driven regional development. However, the absence of a thorough assessment of regional innovation capabilities during the planning phase poses significant concerns regarding the viability of attaining these objectives. A quantitative approach is employed to address this research gap, utilizing a composite Regional Innovation Capacity Index (RICI) as well as conducting cluster analysis and spatial autocorrelation analysis to assess the 27 governorates’ innovation capacities. The findings show significant gaps in innovation capacity among regions, with notable variances in knowledge creation, knowledge utilization, and supportive infrastructure. The findings demonstrate that new smart cities have been developed in some governorates with limited innovation capacity, while high-capacity governorates remain underutilized. These disparities underscore the need for specific policy actions to strengthen innovation ecosystems in lagging regions. The study offers actionable insights on how to match regional innovation capacities with Egypt’s smart city development policy. Full article

Prediabetes increases oxidative stress and the risk of type 2 diabetes and related cardiovascular diseases. Previous trials have shown antioxidant-rich strawberries improve this risk, but effects on antioxidant markers are inconclusive. This 28-week randomized controlled crossover trial evaluated the effects of freeze-dried strawberries (FDS) on fasting glucose, serum antioxidant status, and vascular inflammation in adults with prediabetes not on glucose-lowering medications. Participants were assigned to FDS (32 g/day ~ 2.5 servings of whole strawberries) or control (usual diet, no strawberries) for 12 weeks each, separated by a 4-week washout (n = 25/treatment period). Biomarkers were measured at baseline, 12, 16 (baseline 2), and 28 weeks. A mixed-model analysis of variance detected differences between groups, adjusting for covariates. Compared to control, FDS significantly improved serum superoxide dismutase (0.08 ± 0.04 U/mL), glutathione [(GSH): 1.8 ± 0.96 µmol/L], antioxidant capacity [(AC): 5.9 ± 3.2 µmol/L], β-carotene (113.9 ± 15.8 nmol/L), fasting glucose (97 ± 12 mg/dL), intercellular adhesion molecule [(ICAM): 56.0 ± 21.8 ng/mL], and vascular cell adhesion molecule [(VCAM): 440 ± 163 ng/mL] (all p < 0.05). ICAM was inversely correlated with GSH (r = −0.21), AC (r = −0.15), and β-carotene (r = −0.13) (all p < 0.05). VCAM was inversely correlated with AC (r = −0.12) (p < 0.05). Catalase, glutathione reductase, glutathione peroxidase, α-carotene, P-selectin, and E-selectin were unaffected. Our findings support strawberry intake as a dietary intervention for improving blood glucose control and antioxidant status in adults with prediabetes. Full article

Dollar spot is a severe and widespread turfgrass disease. Ultraviolet-C (UV-C) light treatment offers a promising management strategy, and its integration into autonomous mowers could reduce fungicide use, promoting sustainable and efficient turfgrass management. To ensure effectiveness and optimize intervention timing, monitoring is essential and hyperspectral sensing could represent a valuable resource. This study aimed to develop an innovative approach for the early detection and integrated management of dollar spot in bermudagrass by evaluating (i) the efficacy of an autonomous mower equipped with UV-C lamps in mitigating infections, and (ii) the potential of full-range hyperspectral sensing (350–2500 nm) for disease detection and monitoring. The autonomous mower enabled UV-C treatment with a field capacity of 0.04 ha h⁻¹, requiring 1.3 machines to treat 1 ha day⁻¹, and a primary energy consumption of 55.06 kWh ha⁻¹ for a complete weekly treatment. Full-range canopy hyperspectral data (400–2400 nm) enabled rapid, non-destructive field detection. Permutational multivariate analysis of variance (PERMANOVA) detected significant effects of Clarireedia jacksonii (Cj; dollar spot pathogen) and the Cj × UV-C interaction. Partial least-squares discriminant analysis (PLS-DA) separated Cj+/UV+ and Cj+/UV− plots (Accuracy validation ≈ 0.73; K ≈ 0.69). Investigated spectral indices confirmed Cj × UV-C interactions. Future research should explore how to optimize UV-C application regimes, improve system scalability, and enhance the robustness of hyperspectral models across diverse turfgrass genotypes, growth stages, and environmental conditions. Full article

The nutritional value of beef is highly influenced by its fatty acid composition. This study evaluated whether diet proximate analyses or plasma fatty acid profiles could predict the meat fatty acid composition in young beef cattle finished at pasture or with hay- and concentrate-based diets in stalls. Eighteen crossbred animals (Limousine × Sardo-Bruna) were analyzed for plasma and the intramuscular fat composition of Longissimus thoracis (LT) and Musculus gluteus maximus (MGM). A canonical correlation analysis revealed strong relationships between the dietary antioxidant capacity and meat lipid profiles, particularly for α-linolenic acid and conjugated linoleic acid. The redundancy index indicated that diet explained 38% of the variance in LT fatty acids and 20% in MGM. Partial least squares regression achieved a high precision and accuracy (R² up to 0.94), with a low root mean square error of prediction and high predictive ability (Q² > 0.85), in predicting the intramuscular fatty acid composition from plasma samples. Overall, (i) animals consuming diets with a higher antioxidant capacity and rich in n-3 precursors (ether extract) have healthier fat profiles, and (ii) plasma fatty acid profiling can be a powerful method for monitoring meat quality. This approach provides farmers with a non-invasive tool to improve meat quality management and promote healthier beef products. Full article

The soybean Glycine max (L.) Merrill is a key crop in Brazil’s agricultural sector and is essential for both domestic food security and international trade. However, water stress severely impacts its productivity. In this study, we examined the physiological and biochemical responses of soybean plants to various water regimes via hyperspectral reflectance (350–2500 nm) and machine learning (ML) models. The plants were subjected to eleven distinct water regimes, ranging from 100% to 0% field capacity, over 14 days. Seventeen key physiological parameters, including chlorophyll, carotenoids, flavonoids, proline, stress markers and water content, and hyperspectral data were measured to capture changes induced by water deficit. Principal component analysis (PCA) revealed significant spectral differences between the water treatments, with the first two principal components explaining 88% of the variance. Hyperspectral indices and reflectance patterns in the visible (VIS), near-infrared (NIR), and shortwave-infrared (SWIR) regions are linked to specific stress markers, such as pigment degradation and osmotic adjustment. Machine learning classifiers, including random forest and gradient boosting, achieved over 95% accuracy in predicting drought-induced stress. Notably, a minimal set of 12 spectral bands (including red-edge and SWIR features) was used to predict both stress levels and biochemical changes with comparable accuracy to traditional laboratory assays. These findings demonstrate that spectroscopy by hyperspectral sensors, when combined with ML techniques, provides a nondestructive, field-deployable solution for early drought detection and precision irrigation in soybean cultivation. Full article

The integration of solar generation into national energy balances is associated with a wide range of technical, economic, and organizational challenges, the solution of which requires the adoption of innovative strategies for energy system management. The inherent variability of electricity production, driven by fluctuating climatic conditions, complicates system balancing processes and necessitates the reservation of capacities from conventional energy sources to ensure reliability. Under modern market conditions, the pricing of generated electricity is commonly based on day-ahead forecasts of day energy yield, which significantly affects the economic performance of solar power plants. Consequently, achieving high accuracy in day-ahead electricity production forecasting is a critical and highly relevant task. To address this challenge, a physico-statistical model has been developed, in which the analytical approximation of daily electricity generation is represented as a function of a random variable—cloud cover—modeled by a β-distribution. Analytical expressions were derived for calculating the mathematical expectation and variance of daily electricity generation as functions of the β-distribution parameters of cloudiness. The analytical approximation of daily generation deviates from the exact value, obtained through hourly integration, by an average of 3.9%. The relative forecasting error of electricity production, when using the mathematical expectation of cloudiness compared to the analytical approximation of daily generation, reaches 15.2%. The proposed forecasting method, based on a β-parametric cloudiness model, enhances the accuracy of day-ahead production forecasts, improves the economic efficiency of solar power plants, and contributes to strengthening the stability and reliability of power systems with a substantial share of solar generation. Full article

Background/Objectives: Therapeutic exercises during hospitalization may provide important benefits for onco-hematological patients. However, the most effective protocols and outcomes for evaluation remain unclear. The objective of this study was to evaluate the effects of a structured exercise program during hospitalization. Methods: We conducted a randomized clinical trial with hospitalized onco-hematological patients. The control group performed conventional exercises, while the intervention group received a combined program of aerobic and resistance training. Outcomes included functional capacity, muscle strength, quality of life, and fatigue, assessed at admission and discharge. The sample size was calculated for a moderate effect size (Cohen’s d = 0.50; α = 0.05; power = 80%), yielding a target of 35 participants per group. Data were analyzed using repeated measures analysis of variance, followed by Bonferroni post hoc tests. The significance level was set at 5%. Results: The intervention group showed significant improvements in dyspnea (p = 0.017) and pain (p = 0.024) compared with the control group. In addition, reductions in insomnia (p = 0.021) and improvements in emotional functioning (p = 0.024) were observed. No significant between-group differences were found for fatigue, muscle strength, or functional capacity. Conclusions: A short-term program of aerobic and resistance exercises was safe and improved pain and dyspnea in hospitalized onco-hematological patients, with additional favorable effects on insomnia and emotional function. However, no significant effects were detected in fatigue, muscle strength, or functional capacity, likely due to the short hospitalization period and limited number of sessions. Future studies should consider longer interventions and post-discharge follow-up to clarify the sustainability of these benefits. Full article

Aim: This study analyzed the concurrent validity of the Optojump infrared photocell system for estimating lower limb peak power by comparing it with the 15 s Wingate anaerobic test (WAnT) and examining relationships with sprint performance indicators. Methods: Twelve physically active university students (ten males, two females; age: 23.39 ± 1.47 years; body mass: 73.08 ± 9.19 kg; height: 173.67 ± 6.97 cm; BMI: 24.17 ± 1.48 kg·m⁻²) completed a cross-sectional validation protocol. Participants performed WAnT on a calibrated Monark ergometer (7.5% body weight for males, 5.5% for females), 30 s continuous jump tests using the Optojump system (Microgate, Italy), and 30 m sprint assessments with 10 m and 20 m split times. Peak power was expressed in absolute (W), relative (W·kg⁻¹), and allometric (W·kg^−0.67) terms. Results: Thirty-second continuous jump testing produced systematically higher peak power values across all metrics (p < 0.001). Mean differences indicated large effect sizes: relative power (Cohen’s d = 0.99; 18.263 ± 4.243 vs. 10.99 ± 1.58 W·kg⁻¹), absolute power (d = 0.86; 1381.71 ± 393.44 vs. 807.28 ± 175.45 W), and allometric power (d = 0.79). Strong correlations emerged between protocols, with absolute power showing the strongest association (r = 0.842, p < 0.001). Linear regression analysis revealed that 30 s continuous jump-derived measurements explained 71% of the variance in Wingate outcomes (R² = 0.710, p < 0.001). Sprint performance showed equivalent predictive capacity for both tests (Wingate: R² = 0.66; 30 s continuous jump: R² = 0.67). Conclusions: The Optojump infrared photocell system provides a valid and practical alternative to laboratory-based ergometry for assessing lower limb anaerobic power. While it systematically overestimates absolute values compared with the Wingate anaerobic test, its strong concurrent validity (r > 0.80), large effect sizes, and equivalent predictive ability for sprint performance (R² = 0.66–0.71) confirm its reliability as a field-based assessment tool. These findings underscore the importance of sport-specific, weight-bearing assessment technologies in modern sports biomechanics, providing coaches, practitioners, and clinicians with a feasible method for monitoring performance, talent identification, and training optimization. The results further suggest that Optojump-based protocols can bridge the gap between laboratory precision and ecological validity, supporting both athletic performance enhancement and injury prevention strategies. Full article

Toxic cyanobacteria, including Microcystis, produce harmful toxins that affect aquatic ecosystems and human health. Biotreatment using macrophytes shows promise in mitigating these blooms. This study investigates the bioaccumulation dynamics and biochemical responses of two aquatic macrophytes, Pistia stratiotes and Ceratophyllum demersum, in removing microcystin from contaminated water. P. stratiotes showed high initial bioaccumulation rates with rapid microcystin uptake, which is effective for short-term bioremediation. C. demersum has shown stable bioaccumulation. Biochemical analyses have revealed the activation of plant antioxidant defenses, with both macrophytes showing an increase in carotenoids, glutathione (GSH), and antioxidant enzymes such as superoxide dismutase (SOD) and glutathione-S-transferase (GST) concentrations. In particular, C. demersum has maintained higher antioxidant levels, contributing to its sustained capacity and resilience. Fluctuations in malondialdehyde (MDA) indicated oxidative stress, with P. stratiotes managing such stress through its defenses. Principal Component Analysis (PCA) supports these findings: Pistia’s first two components explained 25.09% and 20.71% of the variance, with Carotenoid and Chl contributing strongly to PC1, and MDA and GST influencing both components. For C. demersum, PC1 and PC2 explained 21.79% and 19.78% of the variance, with Carotenoid and Chl a being major contributors, while SOD and GSH played significant roles in sample differentiation. Integrating both plants into bioremediation strategies could optimize microcystin removal: P. stratiotes offers rapid initial detoxification, while C. demersum ensures continuous, long-term remediation. This combined approach enhances the efficiency and sustainability of phytoremediation. Future research should optimize environmental conditions and explore synergistic effects among multiple plant species for more effective and sustainable bioremediation solutions. Full article

Tanacetum balsamita L. is a medicinal and aromatic plant of high economic value, yet its tissue culture and micropropagation protocols remain poorly developed. This study evaluated and compared two in vitro culture systems, semisolid medium (SS) and Temporary Immersion System (TIS), for enhancing biomass production and growth performance, in terms of relative growth rate (RGR), photosynthetic activity, chlorophyll content, antiradical capacity, and anatomical development. The results demonstrated that the TIS significantly improved RGR, photosynthetic performance, and antiradical activity, and promoted the anatomical development that facilitated greenhouse acclimatization. Machine learning (ML) models, including Multilayer Perceptron (MLP) and Random Forest (RF), were employed to predict morphological and biochemical traits. MLP achieved the highest predictive accuracy (R² > 0.95) and lowest error metrics for complex, nonlinear traits such as chlorophyll content and antiradical activity, whereas RF excelled in predicting morphological traits with more uniform variance, such as leaf number and shoot length. Overall, this study demonstrates that the TIS provides a high-yield, economically crucial strategy for the micropropagation of T. balsamita, and that integrating ML-based predictive modeling can enhance parameter optimization and phenotyping precision. This combined approach offers a valuable framework for advancing tissue culture research in medicinal and aromatic plants through both production efficiency and data-driven decision-making. Full article

Air pollution poses significant risks to built heritage, yet traditional methods for diagnosing degradation patterns remain largely fragmented, often relying on isolated data streams and/or subjective comparative interpretations. This study proposes a novel modular workflow that integrates Raman spectroscopy and micro-XRF spectrometry data with user-defined contextual metadata to automate the characterisation of pollution-induced degradation layers on monuments. This method utilises algorithms for peak detection, dimensionality reduction, unsupervised machine learning clustering, variance analysis across centroids, and correlation analysis, as well as steps for data re-encoding and visualisation of the results, allowing for scalable and reproducible analyses on heterogeneous multidimensional datasets. Applied to case studies from Athens, Piraeus, and Eleusis, Greece, the workflow successfully identified pollution sources and degradation patterns, while also quantifying the contribution of features, including contextual variables such as surface orientation and sampling height. The results validate the method’s capacity to combine molecular and elemental data streams, to enhance interpretive clarity, and to minimise manual effort and subjectivity. This work showcases the potential of algorithmic approaches in cultural heritage diagnostics to adapt dynamically and incorporate additional datasets and informs future applications of automated methods in the broader field of heritage science. Full article

To address the issue of significant perturbations caused by the limited flexibility of clean energy grid integration, along with the combined effects of electric vehicle charging demand and the uncertainty of high-penetration intermittent energy in the integrated energy system (IES), a capacity optimization method for the IES subsystem of a hydrogen-containing chemical park, accounting for strong perturbations, is proposed in the context of the park’s energy usage. Firstly, a typical scenario involving source-load disturbances is characterized using Latin hypercube sampling and Euclidean distance reduction techniques. An energy management strategy for subsystem coordination is then developed. Building on this, a capacity optimization model is established, with the objective of minimizing daily integrated costs, carbon emissions, and system load variance. The Pareto optimal solution set is derived using a non-dominated genetic algorithm, and the optimal allocation case is selected through a combination of ideal solution similarity ranking and a subjective–objective weighting method. The results demonstrate that the proposed approach effectively balances economic efficiency, carbon reduction, and system stability while managing strong perturbations. When compared to relying solely on external hydrogen procurement, the integration of hydrogen storage in chemical production can offset high investment costs and deliver substantial environmental benefits. Full article

Oat × maize addition lines (OMAs) are plants of oat (Avena sativa L.) obtained by wide crossing with maize (Zea mays L.) that retained one or more maize chromosomes in the oat genome, which can result in morphological and physiological changes. The aim of the study was to determine the relationship between phenolics, pigments, sugars, and yield components in 14 OMAs and oat cv. Bingo under soil drought. The plants were sown in pots in a vegetation tunnel. The pots were watered to the level of 70% field water capacity (FWC) and then drought treated to 20% FWC for 2 weeks. Analysis of variance (ANOVA) showed that genotype and treatment significantly influenced the measured parameters. Out of 14 OMAs, lines 9 and 78b showed the highest grain weight and number, with the least amount of biomass loss under drought. These OMAs were the only two to equal or surpass the oat cv. Bingo under drought and control conditions. On average, soil drought caused decrease in biomass and the number and mass of grains (30%, 44%, 46%, respectively). Soil drought increased the amount of sugars by 15% and phenolics by 9% but decreased pigment contents by 8%. According to Pearson’s correlation coefficients, fifteen pairs of traits were positively and statistically significantly correlated in control and drought conditions. Significant relationships were found between the yield components and biochemical parameters on the fourteenth day of drought. A positive correlation occurred between the number and weight of kernels and the content of soluble sugars, chlorophyll a, b, and the sum of a and b. A negative correlation was found between all analyzed yield components and the content of phenolics. The results suggest the possibility of using such biochemical parameters as a quick physiological indicator of plant tolerance to soil drought. Variation in studied OMA lines reveals substantial differences in drought response, offering promising opportunities for targeted selection and breeding strategies. Full article