Search Results (11,530)

Milk lactose concentration has been proposed as a noninvasive indicator of metabolic health in dairy cows, particularly during early lactation when metabolic demands are elevated. This study aimed to investigate the relationship between milk lactose levels and physiological, biochemical, and behavioral parameters in early-lactation Holstein cows. Twenty-eight clinically healthy cows were divided into two groups: Group 1 (milk lactose < 4.70%, n = 14) and Group 2 (milk lactose ≥ 4.70%, n = 14). Both groups were monitored over a 21-day period using the Brolis HerdLine in-line milk analyzer (Brolis Sensor Technology, Vilnius, Lithuania) and SmaXtec intraruminal boluses (SmaXtec Animal Care Technology^®, Graz, Austria). Parameters including milk yield, milk composition (lactose, fat, protein, and fat-to-protein ratio), blood biomarkers, and behavior were recorded. Cows with higher milk lactose concentrations (≥4.70%) produced significantly more milk (+12.76%) and showed increased water intake (+15.44%), as well as elevated levels of urea (+21.63%), alanine aminotransferase (ALT) (+22.96%), glucose (+4.75%), magnesium (+8.25%), and iron (+13.41%) compared to cows with lower lactose concentrations (<4.70%). A moderate positive correlation was found between milk lactose and urea levels (r = 0.429, p < 0.01), and low but significant correlations were observed with other indicators. These findings support the use of milk lactose concentration as a practical biomarker for assessing metabolic and physiological status in dairy cows, and highlight the value of integrating real-time monitoring technologies in precision livestock management. Full article

The varying characteristics of grid-connected energy resources necessitate a clear and effective approach for managing and scheduling generation units. Without proper control, high levels of renewable integration can pose challenges to optimal dispatch, especially as more generation sources, like wind and solar PV, are introduced. As a result, conventional power sources require an advanced management system, for instance, a virtual power plant (VPP), capable of accurately monitoring power supply and demand. This study thoroughly explores the dispatch of battery energy storage systems (BESSs) and diesel generators (DGs) through a distributionally robust joint chance-constrained optimization (DR-JCCO) framework utilizing the conditional value at risk (CVaR) and heuristic-X (H-X) algorithm, structured as a bilevel optimization problem. Furthermore, Binomial expansion (BE) is employed to linearize the model, enabling the assessment of BESS dispatch through a mathematical program with equilibrium constraints (MPECs). The findings confirm the effectiveness of the DRO-CVaR and H-X methods in dispatching grid network resources and BE under the MPEC framework. Full article

Background/Objectives: The gene pool of the Apennine wolf is affected by admixture with domestic variants due to anthropogenic hybridisation with dogs. Genetic monitoring at the population level involves assessing the extent of admixture in single individuals, ranging from pure wolves to recent hybrids or wolf backcrosses, through the analysis of nuclear and mitochondrial DNA (mtDNA) markers. Although individually non-diagnostic, mtDNA is nevertheless essential for completing the final diagnosis of genetic admixture. Typically, the identification of wolf mtDNA haplotypes is carried out via sequencing of coding genes and non-coding DNA stretches. Our objective was to develop a fast real-time PCR assay to detect the mtDNA haplotypes that occur exclusively in the Apennine wolf population, as a valuable alternative to the demanding sequence-based typing. Methods: We validated a qualitative duplex real-time PCR that exploits the combined presence of diagnostic point mutations in two mtDNA segments, the NDH-4 gene and the control region, and is performed in a single-tube step through TaqMan-MGB chemistry. The aim was to detect mtDNA multi-fragment haplotypes that are exclusive to the Apennine wolf, bypassing sequencing. Results: Basic validation of 149 field samples, consisting of pure Apennine wolves, dogs, wolf × dog hybrids, and Dinaric wolves, showed that the assay is highly specific and sensitive, with genomic DNA amounts as low as 10⁻⁵ ng still producing positive results. It also proved high repeatability and reproducibility, thereby enabling reliable high-throughput testing. Conclusions: The results indicate that the assay presented here provides a valuable alternative method to the time- and cost-consuming sequencing procedure to reliably diagnose the maternal lineage of the still-threatened Apennine wolf, and it covers a wide range of applications, from scientific research to conservation, diagnostics, and forensics. Full article

Malignant mediastinal tumors are a group representing some of the most demanding oncological challenges for early, multi-level, and successful management. The timely identification of any suspicious clinical symptomatology is urgent in achieving an accurate, staged histological diagnosis, in order to follow up with an equally detailed medical therapeutic plan (interventional or not) and determine the principal goals regarding efficient overall treatment in these patients. We report a case of a 24-year-old male patient with an incident-free prior medical history. An initial chest X-ray was performed after the patient reported short-term, consistent moderate chest pain symptomatology, early work fatigue, and shortness of breath. The following imaging procedures (chest CT, PET-CT) indicated the presence of an anterior mediastinal mass (meas. ~11 cm × 10 cm × 13 cm, SUV: 8.7), applying additional pressure upon both right heart chambers. The Alpha-Fetoprotein (aFP) blood levels had exceeded at least 50 times their normal range. Two consecutive diagnostic attempts with non-specific histological results, a negative-for-malignancy fine-needle aspiration biopsy (FNA-biopsy), and an additional tumor biopsy, performed via mini anterior (R) thoracotomy with “suspicious” cellular gatherings, were performed elsewhere. After admission to our department, an (R) Video-Assisted Thoracic Surgery (VATS) was performed, along with multiple tumor biopsies and moderate pleural effusion drainage. The tumor’s measurements had increased to DMax: 16 cm × 9 cm × 13 cm, with a severe degree of atelectasis of the Right Lower Lobe parenchyma (RLL) and a pressure-displacement effect upon the Superior Vena Cava (SVC) and the (R) heart sinus, based on data from the preoperative chest MRA. The histological report indicated elements of a combined, non-seminomatous germ-cell mediastinal tumor, posthuberal-type teratoma, and embryonal carcinoma. The imminent chemotherapeutic plan included a “BEP” (Bleomycin^®/Cisplatin^®/Etoposide^®) scheme, which needed to be modified to a “VIP” (Cisplatin^®/Etoposide^®/Ifosfamide^®) scheme, due to an acute pulmonary embolism incident. While the aFP blood levels declined, even reaching normal measurements, the tumor’s size continued to increase significantly (DMax: 28 cm × 25 cm × 13 cm), with severe localized pressure effects, rapid weight loss, and a progressively worsening clinical status. Thus, an emergency surgical intervention took place via median sternotomy, extended with a complementary “T-Shaped” mini anterior (R) thoracotomy. A large, approx. 4 Kg mediastinal tumor was extracted, with additional RML and RUL “en-bloc” segmentectomy and partial mediastinal pleura decortication. The following histological results, apart from verifying the already-known posthuberal-type teratoma, indicated additional scattered small lesions of combined high-grade rabdomyosarcoma, chondrosarcoma, and osteosarcoma, as well as numerous high-grade glioblastoma cellular gatherings. No visible findings of the previously discovered non-seminomatous germ-cell and embryonal carcinoma elements were found. The patient’s postoperative status progressively improved, allowing therapeutic management to continue with six “TIP” (Cisplatin^®/Paclitaxel^®/Ifosfamide^®) sessions, currently under his regular “follow-up” from the oncological team. This report underlines the importance of early, accurate histological identification, combined with any necessary surgical intervention, diagnostic or therapeutic, as well as the appliance of any subsequent multimodality management plan. The diversity of mediastinal tumors, especially for young patients, leaves no place for complacency. Such rare examples may manifest, with equivalent, unpredictable evolution, obliging clinical physicians to stay constantly alert and not take anything for granted. Full article

In the era of “carbon dioxide peaking and carbon neutrality”, low-carbon development of road traffic and transportation has now become a rigid demand in China. Considering the fact that socioeconomic and demographic characteristics vary significantly across Chinese cities, proper city-level transportation development strategies should be established. Using detailed data from cities at prefecture level and above in China, this study investigates the spatially heterogeneous effects of various factors on road traffic CO₂ emissions. Another theoretical issue is concerned with the analytic method for zonal CO₂ emission modeling. We combine the concepts of geographically weighted regression (GWR) and machine learning for nonparametric regression, proposing a modified random forest (RF) algorithm, named “geographically weighted random forest” (GWRF). Our empirical analysis indicates that, when an appropriate weight parameter is applied, GWRF is able to achieve significantly superior performance compared to both the traditional RF and GWR methods. Moreover, the influences of various explanatory variables on CO₂ emissions differ across cities. These findings suggest that low-carbon transportation strategies should be customized to reflect regional heterogeneity, rather than relying on a unified national policy. Full article

With the rise of the low-altitude urban traffic system, urban air mobility (UAM) has developed rapidly. As a critical component of the UAM system, the strategic layout of vertiports helps divert ground traffic pressure. To satisfy various demand patterns, different vertiport levels are needed, so we focus on the hierarchical vertiport location problem. Considering the capacity limitation, a median location model is established to minimize vertiport construction cost, passenger commuting cost, and penalty cost. For the nonlinear term in the objective function, the Big-M method is employed. Based on the reformulated model, we improve the branch-and-bound algorithm (LVBB) to solve it, where the Lagrange relaxation method is used to decompose the large-scale problem into parallel subproblems and compute the lower bound, and the variable neighborhood search algorithm is used to obtain the upper bound. Numerical experiments are performed in the 11 administrative districts of Nanjing, China. The results demonstrate that the proposed location scheme effectively balances vertiport construction cost and passenger commuting cost while satisfying capacity limitations. It also significantly reduces commuting time to improve passenger satisfaction. This scheme can offer strategic guidance for infrastructure planning in UAM. Full article

As the demand for more accurate crime prediction and risk assessment grows, researchers have been developing smarter models that blend statistical methods with machine learning. This study compares a hybrid ARIMA-ANN model with traditional classification techniques to see which best forecast monthly crime risk levels in Galați County, Romania. The analysis is based on a newly compiled dataset of 132 monthly observations from January 2014 to December 2024, which combines a broad array of social, economic, and environmental data points. The main variable, ‘Crime risk’, is based on normalized counts of offenses per capita and divided into five balanced levels: very low, low, moderate, high, and very high. The hybrid ARIMA-ANN model merges the strengths of statistical time series analysis with the flexible learning ability of artificial neural networks. Performance is evaluated against multinomial logistic regression, decision trees, random forests, and support vector machines. Overall, the results show that an ARIMA-ANN model consistently outperforms traditional methods, especially in recognizing patterns over time, seasonal trends, and complex nonlinear relationships in crime data. This study not only sets a new benchmark for crime analytics in Romania but also offers a flexible, scalable framework for classifying crime risk levels across different regions. Full article

The rapid electrification of transportation and the proliferation of rooftop solar photovoltaics (PVs) in urban environments are reshaping the operational dynamics of power distribution networks. However, the inherent uncertainty in electric vehicle (EV) behavior—including arrival times, charging preferences, and state-of-charge—as well as spatially and temporally variable solar generation, presents a profound challenge to existing scheduling frameworks. This paper proposes a novel data-driven distributionally robust optimization (DDRO) framework for solar-powered EV charging coordination under spatiotemporal uncertainty. Leveraging empirical datasets of EV usage and solar irradiance from a smart city deployment, the framework constructs Wasserstein ambiguity sets around historical distributions, enabling worst-case-aware decision-making without requiring the assumption of probability laws. The problem is formulated as a two-stage optimization model. The first stage determines day-ahead charging schedules, solar utilization levels, and grid allocations across an urban-scale distribution feeder. The second stage models real-time recourse actions—such as dynamic curtailment or demand reshaping—after uncertainties are realized. Physical grid constraints are modeled using convexified LinDistFlow equations, while EV behavior is segmented into user classes with individualized uncertainty structures. The model is evaluated on a modified IEEE 123-bus feeder with 52 EV-PV nodes, using 15 min resolution over a 24 h horizon and 12 months of real-world data. Comparative results demonstrate that the proposed DDRO method reduces total operational costs by up to 15%, eliminates voltage violations entirely, and improves EV service satisfaction by more than 30% relative to deterministic and stochastic baselines. This work makes three primary contributions: it introduces a robust, tractable optimization architecture that captures spatiotemporal uncertainty using empirical Wasserstein sets; it integrates behavioral and physical modeling within a unified dispatch framework for urban energy-mobility systems; and it demonstrates the value of robust coordination in simultaneously improving grid resilience, renewable utilization, and EV user satisfaction. The results offer practical insights for city-scale planners seeking to enable the reliable and efficient electrification of mobility infrastructure under uncertainty. Full article

With the rising performance demands in road engineering, traditional experiments often fail to reveal the microscopic mechanisms behind asphalt behavior. Molecular dynamics (MD) simulation has emerged as a valuable complement, enabling molecular-level insights into asphalt’s composition, structure, and aging mechanisms. This review summarizes the recent advances in applying MD to asphalt research. It first outlines molecular model construction approaches, including average models, three- and four-component systems, and modified models incorporating SBS, SBR, PU, PE, and asphalt–aggregate interfaces. It then analyzes how MD reveals the key performance aspects—such as high-temperature stability, low-temperature flexibility, self-healing behavior, aging processes, and interfacial adhesion—by capturing the molecular interactions. While MD offers significant advantages, challenges remain: idealized modeling, high computational demands, limited chemical reaction simulation, and difficulties in multi-scale coupling. This paper aims to provide theoretical insights and methodological support for future studies on asphalt performance and highlights MD simulation as a promising tool in pavement material science. Full article

In modern vehicles, Autonomous Driving Systems (ADSs) are designed to operate partially or fully without human intervention. The ADS pipeline comprises multiple layers, including sensors, perception, localization, mapping, path planning, and control. The Robot Operating System (ROS) is a widely adopted framework that supports the modular development and integration of these layers. Among them, the path-planning and control layers remain particularly challenging due to several limitations. Classical path planners often struggle with non-smooth trajectories and high computational demands. Meta-heuristic optimization algorithms have demonstrated strong theoretical potential in path planning; however, they are rarely implemented in real-time ROS-based systems due to integration challenges. Similarly, traditional PID controllers require manual tuning and are unable to adapt to system disturbances. This paper proposes a ROS-based ADS architecture composed of eight integrated nodes, designed to address these limitations. The path-planning node leverages a meta-heuristic optimization framework with a cost function that evaluates path feasibility using occupancy grids from the Hector SLAM and obstacle clusters detected through the DBSCAN algorithm. A dynamic goal-allocation strategy is introduced based on the LiDAR range and spatial boundaries to enhance planning flexibility. In the control layer, a modified Pure Pursuit algorithm is employed to translate target positions into velocity commands based on the drift angle. Additionally, an adaptive PID controller is tuned in real time using the Differential Evolution (DE) algorithm, ensuring robust speed regulation in the presence of external disturbances. The proposed system is practically validated on a four-wheel differential drive robot across six scenarios. Experimental results demonstrate that the proposed planner significantly outperforms state-of-the-art methods, ranking first in the Friedman test with a significance level less than 0.05, confirming the effectiveness of the proposed architecture. Full article

Driven by the rapid development of e-commerce and intelligent logistics, the volume of express delivery services has surged, making the efficient and accurate identification of shipping information a core requirement for automatic sorting systems. However, traditional Optical Character Recognition (OCR) technology struggles to meet the accuracy and real-time demands of complex logistics scenarios due to challenges such as image distortion, uneven illumination, and field overlap. This paper proposes a three-level collaborative recognition method based on deep learning that facilitates structured information extraction through regional normalization, dual-path parallel extraction, and a dynamic matching mechanism. First, the geometric distortion associated with contour detection and the lightweight direction classification model has been improved. Second, by integrating the enhanced YOLOv5s for key area localization with the upgraded PaddleOCR for full-text character extraction, a dual-path parallel architecture for positioning and recognition has been constructed. Finally, a dynamic space–semantic joint matching module has been designed that incorporates anti-offset IoU metrics and hierarchical semantic regularization constraints, thereby enhancing matching robustness through density-adaptive weight adjustment. Experimental results indicate that the accuracy of this method on a self-constructed dataset is 89.5%, with an F1 score of 90.1%, representing a 24.2% improvement over traditional OCR methods. The dynamic matching mechanism elevates the average accuracy of YOLOv5s from 78.5% to 89.7%, surpassing the Faster R-CNN benchmark model while maintaining a real-time processing efficiency of 76 FPS. This study offers a lightweight and highly robust solution for the efficient extraction of order information in complex logistics scenarios, significantly advancing the intelligent upgrading of sorting systems. Full article

Given the increases in renewable energy penetration, appropriately allocating transmission costs is important in generation and transmission investment decisions. This study examines how a generator-side transmission charge affects investment decisions by power generation companies (PC) and the transmission system operator (TSO) under two frameworks differing in who decides investment timing. We compare two frameworks: (1) TSO determines investment timing and the PC determines capacity (TL framework); (2) PC determines investment timing and capacity (GL framework). We examine how variations in generator-side charges and demand uncertainty affect the optimal investment timing, capacity, and social surplus. Regarding investment timing, increases in the generator-side charge led to earlier investment in the TL framework but delayed investment in the GL framework. Concerning investment capacity, the TL framework yielded greater capacity with low uncertainty, while the GL framework supported greater capacity with high uncertainty. The magnitude of the relative social surplus of the two frameworks was reversed according to the generator-side charge and uncertainty. Specifically, the GL framework became increasingly superior to the TL framework as uncertainty increased, and this advantage was amplified by a higher generator-side charge. Policymakers should consider uncertainty and calibrate the level of generator-side charge and the allocation of decision-making authority. Full article

The intensifying global demand for sustainable and nutrient-dense food sources necessitates the exploration of underutilized local resources. Arthrospira platensis var. toliarensis, a cyanobacterium endemic to Madagascar, was evaluated for its nutritional, functional, and environmental potential under small-scale, low-input outdoor cultivation. The study assessed growth kinetics, physicochemical parameters, and composition during two contrasting seasons. Biomass increased 7.5-fold in 10 days, reaching a productivity of 7.8 ± 0.58 g/m²/day and a protein yield of 4.68 ± 0.35 g/m²/day. The hot-season harvest showed significantly higher protein content (65.1% vs. 44.6%), enriched in essential amino acids. On a dry matter basis, mineral profiling revealed high levels of sodium (2140 ± 35.4 mg/100 g), potassium (1530 ± 21.8 mg/100 g), calcium (968 ± 15.1 mg/100 g), phosphorus (815 ± 13.2 mg/100 g), magnesium (389.28 ± 6.4 mg/100 g), and iron (235 ± 9.1 mg/100 g), underscoring its value as a micronutrient-rich supplement. The hydroethanolic extract had the highest polyphenol content (4.67 g GAE/100 g of dry extract), while the hexanic extract exhibited the strongest antioxidant capacity (IC₅₀ = 101.03 ± 1.37 µg/mL), indicating fat-soluble antioxidants. Aflatoxin levels (B1, B2, G1, and G2) remained below EU safety thresholds. Compared to soy and beef, this strain showed superior protein productivity and water-use efficiency. These findings confirm A. platensis var. toliarensis as a promising, ecologically sound alternative for improving food and nutrition security, and its local production can offer substantial benefits to smallholder livelihoods. Full article

To promote high-quality agricultural development and implement the “storing grain in the land” strategy, the construction of Well-Facilitated Farmland (WFF) plays a critical role in enhancing grain production capacity and optimizing the spatial distribution of food supply, thereby contributing to national food security. However, accurately assessing the potential impact of WFF construction on China’s grain production and regional self-sufficiency by 2030 remains a significant challenge. Existing studies predominantly focus on the provincial level, while fine-grained analyses at the city level are still lacking. This study quantifies the potential increase in grain production in China under the 2030 WFF construction target by employing effect size analysis, multi-weight prediction, and Monte Carlo simulation across multiple spatial scales (national, provincial, and city levels), thereby addressing the research gap at finer spatial resolutions. By integrating 2030 population projections and applying a grain self-sufficiency calculation formula, it further evaluates the contribution of WFF to regional grain self-sufficiency: (1) WFF could generate an additional 31–48 million tons of grain, representing a 5.26–8.25% increase; (2) grain supply in major crop-producing regions would expand, while the supply–demand gap in balanced regions would narrow; and (3) the number of cities with grain self-sufficiency ratios below 50% would decrease by 11.1%, while those exceeding 200% would increase by 25.5%. These findings indicate that WFF construction not only enhances overall grain production potential but also facilitates a transition from “overall supply-demand balance” to “structural security” within China’s food system. This study provides critical data support and policy insights for building a more resilient and regionally adaptive agricultural system. Full article

The demand for sustainable animal production is increasing. Microalgae such as Chlorella and Spirulina show promise as sustainable and functional ingredients in animal (poultry) feed. However, little is known about consumer perceptions regarding the use of algae in broiler diets and potential effects of algae on chicken meat. Residents of Flanders (Belgium) were surveyed to evaluate consumer knowledge, attitudes and willingness to buy chicken meat produced with algae-supplemented feed. Demographic data were collected, and both descriptive and inferential statistics were applied to assess influencing factors (n = 275 respondents who purchase chicken meat). While most respondents (69.6%) had tasted macroalgae (seaweed), only 11.4% and 24.6% indicated having tasted Chlorella and Spirulina before, respectively. Health, taste and safety were the most important drivers for consuming algae. Meat quality was the most important factor when purchasing chicken meat, while organic production was least valued. Regarding algae-fed chicken, 72.5% expressed willingness to purchase meat labeled as such, and 83.7% would buy algae-fed chicken regardless of its color. Sustainability beliefs significantly influenced willingness to accept a yellower meat color (β = 0.42 to 0.66, p < 0.001). Educational level and age also played a role, with higher-educated consumers showing greater acceptance. The influence of age was also related to the price of the meat, with consumers over 30 expressing a greater willingness to pay more than young people (under 30). Despite limited general knowledge about microalgae, the consumers surveyed are open to the idea of algae-fed chicken meat, particularly when it is framed as more sustainable. Clear ingredient labeling and consumer education may further support market acceptance. Full article