Search Results (353)

We evaluated the inclusion of hydrolyzed feather meal (HFM) as a partial replacement for fishmeal in diets for Macrobrachium rosenbergii post-larvae (PL) over a 32-day nursery feeding trial. Five experimental diets with increasing HFM levels (control, 1.5%, 3.0%, 4.5%, and 6.0%) were tested. Survival rates ranged from 73.3 ± 5.44% to 83.3 ± 3.84% without significant differences among groups. Dietary HFM inclusion levels above 3.0% significantly improved prawn performance, including final weight (up to 2.18-fold higher than control), length (1.13-fold), antenna length (1.18-fold), biomass gain (2.14-fold), and feed conversion ratio (1.59-fold lower). Prawn-fed diets at 6.0% HFM showed the highest performance among all experimental groups. No significant effects were observed on antioxidant biomarkers or digestive enzymes in prawns hepatopancreas, which suggests no imbalance in the antioxidant system or impairment of digestive function. Likewise, carcass proximate composition remained stable across experimental groups. These findings suggest that HFM at 3.0–6.0% dietary inclusion levels is a potential alternative to fishmeal in nursery-phase diets for M. rosernbergii PL, promoting prawn growth and welfare and maintaining health and carcass quality. Notably, to the best of our knowledge, this is the first study demonstrating the potential effective use of HFM in feeding the nursery phase of M. rosernbergii. Full article

Background/Objectives: The Bentall procedure represents the gold standard therapy in patients with ascending aorta or aortic root aneurysm combined with aortic valve disease precluding a valve-sparing procedure. The aim of this study was to compare early and late outcomes in patients undergoing a Bentall procedure with either a biological or a mechanical valved conduit. Methods: All patients undergoing the Bentall procedure with either a biological or a mechanical valved conduit at our institution between 2001 and 2022 were retrospectively reviewed. A propensity-score (PS) matching analysis was performed to account for imbalances between the two groups. Clinical outcomes of interest included mortality and reintervention. Results: 548 patients underwent the Bentall procedure with a biological (n = 356, 65%) or a mechanical (n = 192, 35%) valved conduit during the study period. After PS-matching, two homogeneous groups of 154 patients were obtained, and no difference was observed in mean survival time between patients with mechanical Bentall and patients with biological Bentall (16 ± 0.8 vs. 16.3 ± 0.7 years, respectively; p = 0.72). Patients with a mechanical Bentall had a significantly higher mean survival time free from reintervention compared to patients with a biological Bentall (23.6 ± 0.4 vs. 21.4 ± 0.7 years, respectively, p = 0.02). PS-adjusted Cox regression showed that age >65 years, postoperative ECMO, and CVA were predictive risk factors of mortality. Conclusions: Bentall operation is a safe procedure for the treatment of ascending aorta and aortic root disease with good early and long-term survival and a low rate of reintervention. PS-matched analysis showed no difference in mortality between patients with a mechanical Bentall and patients with a biological Bentall; however, patients with a mechanical Bentall had a lower rate of reintervention. Full article

As a key node in port logistics systems, ship anchorage is often faced with congestion caused by ship flow fluctuations, multi-priority scheduling imbalances and the poor adaptability of scheduling models to complex environments. To solve the above problems, this paper constructs a ship scheduling algorithm based on a Markov-modulated fluid priority queue, which describes the stochastic evolution of the anchorage operation state via a continuous-time Markov chain and abstracts the arrival and service processes of ships into a continuous fluid input and output mechanism modulated by the state. The algorithm introduces a multi-priority service strategy to achieve the differentiated scheduling of different types of ships and improves the computational efficiency and scalability based on a matrix analysis method. Simulation results show that the proposed model reduces the average waiting time of ships by more than 90% compared with the M/G/1/1 and RL strategies and improves the utilization of anchorage resources by about 20% through dynamic service rate adjustment, showing significant advantages over traditional scheduling methods in multi-priority scenarios. Full article

Manhole covers are crucial for maintaining urban operations and ensuring residents’ travel. The traditional inspection and maintenance management system based on manual judgment has low efficiency and poor accuracy, making it difficult to adapt to the rapidly expanding urban construction and complex environment of manhole covers. To address these challenges, an intelligent management model based on the improved YOLOv8 model is proposed for three types of urban high-frequency defects: “breakage, loss and shift”. We design a lightweight dual-stream feature extraction network and use EfficientNetV2 as the backbone. By introducing the fused MBConv structure, the computational complexity is significantly reduced, while the efficiency of feature extraction is improved. An innovative foreground attention module is introduced to adaptively enhance the features of manhole cover defects, improving the model’s ability to identify defects of various scales. In addition, an optimized feature fusion architecture is constructed by integrating NAS-FPN modules. This structure utilizes bidirectional feature transfer and automatic structure search, significantly enhancing the expressiveness of multi-scale features. A combined loss function design using GIoU loss, dynamically weighted BCE loss, and Distribution Focal Loss (DFL) is adopted to address the issues of sample imbalance and inter-class differences. The experimental results show that the model achieved excellent performance in multiple indicators of manhole cover defect recognition, especially in classification accuracy, recall rate, and F1-score, with an overall recognition accuracy of 98.6%. The application of the improved model in the new smart management system for urban manhole covers can significantly improve management efficiency. Full article

Background/Objectives: The industrial extraction and purification processes of Cannabis sativa L. compounds are critical steps in creating formulations with reliable and reproducible therapeutic and sensorial attributes. Methods: For this study, standardized preparations of chemotype I were chemically analyzed, and the sensory attributes were studied to characterize the extraction and purification processes, ensuring the maximum retention of cannabinoids and minimization of other secondary metabolites. The industrial process used deep-cooled ethanol for selective extraction. Results: Taking into consideration that decarboxylation occurs in the process, the cannabinoid profile composition was preserved from the herbal substance to the herbal preparations, with wiped-film distillation under deep vacuum conditions below 0.2 mbar, as a final purification step. The profiles of the terpenes and cannabinoids in crude and purified Full-spectrum Extract Cannabis Oil (FECO) were analyzed at different stages to evaluate compositional changes that occurred throughout processing. Subjective intensity and acceptance ratings were received for taste, color, overall appearance, smell, and mouthfeel of FECO preparations. Conclusions: According to sensory analysis, purified FECO was more accepted than crude FECO, which had a stronger and more polarizing taste, and received higher ratings for color and overall acceptance. In contrast, a full cannabis extract in the market resulted in lower acceptance due to taste imbalance. The purification process effectively removed non-cannabinoids, improving sensory quality while maintaining therapeutic potency. Terpene markers of the flower were remarkably preserved in SOMAÍ’s preparations’ fingerprint, highlighting a major qualitative profile reproducibility and the opportunity for their previous separation and/or controlled reintroduction. The study underscores the importance of monitoring the extraction and purification processes to optimize the cannabinoid content and sensory characteristics in cannabis preparations. Full article

Convolutional neural networks (CNNs) have established themselves over time as a fundamental tool in the field of copy-move forgery detection due to their ability to effectively identify and analyze manipulated images. Unfortunately, they still represent a persistent challenge in digital image forensics, underlining the importance of ensuring the integrity of digital visual content. In this study, we present a systematic evaluation of the performance of a convolutional neural network (CNN) specifically designed for copy-move manipulation detection, applied to three datasets widely used in the literature in the context of digital forensics: CoMoFoD, Coverage, and CASIA v2. Our experimental analysis highlighted a significant variability of the results, with an accuracy ranging from 95.90% on CoMoFoD to 27.50% on Coverage. This inhomogeneity has been attributed to specific structural factors of the datasets used, such as the sample size, the degree of imbalance between classes, and the intrinsic complexity of the manipulations. We also investigated different regularization techniques and data augmentation strategies to understand their impact on the network performance, finding that adopting the L2 penalty and reducing the learning rate led to an accuracy increase of up to 2.5% for CASIA v2, while on CoMoFoD we recorded a much more modest impact (1.3%). Similarly, we observed that data augmentation was able to improve performance on large datasets but was ineffective on smaller ones. Our results challenge the idea of universal generalizability of CNN architectures in the context of copy-move forgery detection, highlighting instead how performance is strictly dependent on the intrinsic characteristics of the dataset under consideration. Finally, we propose a series of operational recommendations for optimizing the training process, the choice of the dataset, and the definition of robust evaluation protocols aimed at guiding the development of detection systems that are more reliable and generalizable. Full article

Based on digital patent data from 359 Chinese cities between 2006 and 2022, this paper calculates the gravitational value of the digital economy using a modified gravity model and employs social network analysis and QAP analysis to investigate the correlation network of cities’ digital economy and the influencing factors. The study found the following: (1) Chinese cities have a high level of digital economy, showing a consistent increase in growth rate, and density and relevance are rising without revealing a distinct hierarchical network structure. (2) The inner economic network demonstrates a significant imbalance, as illustrated by the “Matthew effect”. Core cities like Shenzhen and Beijing show greater net spillover, indicating their role as network hubs, while less developed cities have lower net spillover, necessitating improvements in interconnection capacity. (3) Differences in economic scale, population quality, scientific and technological innovation, and infrastructure construction, which have a positive effect, are the main sources of linkage network formation. At the same time, the difference in urbanization rates is stage-specific, reflecting the dual logic of factor complementarity and policy synergy. Overall, this study reveals the dynamic evolution of the digital economic spatial network through city-scale innovation and provides theoretical support for promoting the region’s sustainable and coordinated development. Full article

In the context of the global energy transition, thermal management of electric vehicle batteries faces severe challenges due to temperature rise and energy consumption under dynamic operating conditions. Traditional strategies rely on real-time feedback and suffer from response lag and energy efficiency imbalance. In this study, we propose a neural network-based synergistic optimization method for driving conditions prediction and dynamic thermal management, which collects multi-scenario real-vehicle data (358 60-s condition segments) by naturalistic driving data collection method, extracts four typical conditions (congestion, highway, urban, and suburbia) by combining with K-means clustering, and constructs a BP (backpropagation neural network) model (20 neurons in the input layer and 60 neurons in the output layer) to predict the vehicle speed in the next 60 s. Based on the prediction results, the coupled PID control and temperature feedback mechanism dynamically adjusts the coolant flow rate (maximum reduction of 17.6%), which reduces the maximum temperature of the battery by 3.8 °C, the maximum temperature difference by 0.3 °C, and the standard deviation of temperature fluctuation at ambient temperatures of 25~40 °C is 0.2 °C in AMESim simulation and experimental validation. The results show that the strategy significantly improves battery safety and system economy under complex working conditions by prospectively optimizing heat dissipation and energy consumption, providing an efficient solution for intelligent thermal management. Full article

Introduction: Previous neuroimaging studies have reported structural brain alterations and local network hyperexcitability in terms of increased slow-wave electroencephalography (EEG) activity in patients with borderline personality disorder (BPD). In particular, intermittent rhythmic delta and theta activity (IRDA/IRTA) has drawn attention in mental health contexts due to its links with metabolic imbalances, neuronal stress, and emotional dysregulation—processes that are highly pertinent to BPD. These functional disturbances may be reflected in corresponding structural brain changes. The current study investigated cortical thickness and subcortical volumes in BPD and examined their associations with IRDA/IRTA events per minute, symptom severity, and neuropsychological measures. Methods: Seventy female BPD patients and 36 age-matched female healthy controls (HC) were included (for clinical EEG comparisons even 72 patients were available). IRDA/IRTA rates were assessed using an automatic independent component analyses (ICA) approach. T1-weighted MRI data were obtained using a MAGNETOM Prisma 3T system and analyzed with FreeSurfer (version 7.2) for subcortical structures and CAT12 for cortical thickness and global volume measurements. Psychometric assessments included questionnaires such as Borderline Symptom List (BSL-23) and Inventory of Personality Organization (IPO). Neuropsychological performance was evaluated with the Test for Attentional Performance (TAP), Culture Fair Intelligence Test (CFT-20-R), and Verbal Learning and Memory Test (VLMT). Results: Between-group comparisons exhibited no significant increase in IRDA/IRTA rates or structural abnormalities between the BPD and HC group. However, within the BPD group, cortical thickness of the right isthmus of the cingulate gyrus negatively correlated with the IRDA/IRTA difference (after minus before hyperventilation, HV; p < 0.001). Furthermore, BPD symptom severity (BSL-23) and IPO scores positively correlated with the thickness of the right rostral anterior cingulate cortex (p < 0.001), and IPO scores were associated with the thickness of the right temporal pole (p < 0.001). Intrinsic alertness (TAP) significantly correlated with relative cerebellar volume (p = 0.01). Discussion: While no group-level structural abnormalities were observed, correlations between EEG slowing, BPD symptom severity, and alertness with cortical thickness and/or subcortical volumes suggest a potential role of the anterior cingulate cortex, temporal pole, and cerebellum in emotion regulation and cognitive functioning in BPD. Future research employing multimodal EEG-MRI approaches may provide deeper insights into the neural mechanisms underlying BPD and guide personalized therapeutic strategies. Full article

This study investigates the early hydration characteristics and kinetics of ordinary Portland cement (OPC) and calcium sulfoaluminate cement (CSA) composite pastes. The hydration mechanisms of OPC-CSA systems with different proportions are analyzed through zonal analysis and the Krstulović–Dabić method. The experimental results show that in OPC-dominated systems, an appropriate amount of CSA promotes the rapid hydration of ye’elimite and optimizes the cumulative hydration heat and pore structure. However, excessive CSA inhibits hydration due to alkalinity imbalance. In CSA-dominated systems, 10% OPC increases the alkalinity, promoting ye’elimite to hydrate into ettringite. Higher OPC content hinders the hydration process due to ion concentration imbalance. The kinetics model indicates that CSA accelerates the interfacial reaction and diffusion in the OPC system, while OPC reduces the overall hydration rate of the CSA system. Microscopic analysis confirms that the composite system improves the pore structure through mineral interaction. In the OPC-dominated area, the pore structure is mainly composed of small and dense pores. In the CSA-dominated area, the characteristics of large pores are affected by the expansion properties of CSA and hydration heat. This study constructs a coupling mechanism of alkalinity regulation and crystal nucleus generation, providing a theoretical basis for the design of high-performance composite cement materials. Full article

Background and Aims: Cervical arterial dissections (CeAD) are a common cause of stroke in young adults across both sexes. Whether biological sex plays a role in the pathogenesis and outcome of CeAD remains unclear. Methods: In this retrospective analysis of a cohort of patients with CeAD, clinical, imaging, treatment, and outcome data were compared between females and males using multivariate logistic regressions to identify outcome predictors. Propensity score matching (PSM) was used to adjust for imbalances between the groups. Results: Overall, 135 participants were included (79 males and 56 females, median age 44, interquartile range [IQR] 36, 50.5). Of those, 71 patients (53%) were diagnosed with stroke (median age 46, IQR 39.5, 52, median admission NIHSS 3, IQR 1, 7.5). Males had significantly higher rates of smoking (38% vs. 11%, p = 0.0004) but other baseline characteristics did not differ between the groups. Traumatic dissections were numerically more common in men but the difference between the groups did not reach significance. The presence of flame shaped lesion in the extra cranial vessel was more common among men in the initial analysis of the whole group but did not remain significant after PSM. No differences were observed between the groups regarding treatment strategies including administration of systemic thrombolysis and stent placements. The rates of recurrent stroke and recurrent dissections were similar. Favorable outcomes defined as modified Rankin Score (mRS) ≤ 2 and symptomatic intracranial hemorrhage rates were also similar on the univariate analyses and did not change after PSM. Age (odds ratio [OR] 1.12, 95% confidence intervals [CI] 1.04–1.23) and admission NIHSS (OR 0.74, 95%CI 0.60–0.84) were associated with outcomes on regression analysis whereas female sex was not (OR 0.54, 95% CI 0.03–5.87). Conclusions: CeAD occurs more frequently in males, who are more likely to have associated risk factors and traumatic neck injuries. However, sex does not appear to impact outcome in CeAD patients. Full article

Ting, Tai, Lou, and Ge are types of ancient buildings that represent traditional Chinese architecture and culture. They are primarily constructed using mortise and tenon joints, complemented by brick and stone foundations, showcasing traditional architectural craftsmanship. However, research aimed at conserving, inheriting, and rejuvenating these buildings is limited, despite their status as Provincial Cultural Relic Protection Units of China. Therefore, the aim of this study was to reveal the spatial distribution of Ting, Tai, Lou, and Ge buildings across China, as well as the factors driving differences in their spatial distribution. Tourist experiences and building popularity were also explored. The spatial analysis method (e.g., Standard deviation ellipse and Geographic detector), Word cloud generation, and sentiment analysis, which uses Natural Language Processing techniques to identify subjective emotions in text, were applied to investigated the research issues. The key findings of this study are as follows. The ratio of Ting, Tai, Lou, and Ge buildings in Southeast China to that in Northwest China divided by the “Heihe–Tengchong” Line, an important demographic boundary in China with the ratio of permanent residents in the two areas remaining stable at 94:6, was 94.6:5.4. Geographic detector analysis revealed that six of the seven natural and socioeconomic factors (topography, waterways, roads, railways, population, and carbon dioxide emissions) had a significant influence on the spatial heterogeneity of these cultural heritage buildings in China, with socioeconomic factors, particularly population, having a greater influence on building spatial distributions. All seven factors (including the normalized difference vegetation index, an indicator used to assess vegetation health and coverage) were significant in Southeast China, whereas all factors were non-significant in Northwest China, which may be explained by the small number of buildings in the latter region. The average rating scores and heat scores for Ting, Tai, Lou, and Ge buildings were 4.35 (out of 5) and 3 (out of 10), respectively, reflecting an imbalance between service quality and popularity. According to the percentages of positive and negative reviews, Lou buildings have much better tourism services than other buildings, indicating a need to improve services to attract more tourists to Ting, Tai, and Ge buildings. Four main types of words were used with high frequency in the tourism reviews collected form Ctrip, a popular online travel platform in China: (1) historical stories; (2) tourism; (3) culture; and (4) cities/provinces. Ting and Tai buildings showed similar word clouds, as did Lou and Ge buildings, with only the former including historical stories. Conversely, landmark was a high-frequency word only in the reviews of Lou and Ge buildings. Specific suggestions were proposed based on the above findings to promote tourism and revive ancient Chinese architecture. Full article

Effective wastewater management is critical for mitigating environmental and health impacts in ecologically sensitive regions like the Peruvian Amazon, where rapid urbanization has led to increased discharge of nutrient-rich effluents into freshwater systems. Conventional treatment methods often fail to address nutrient imbalances while generating secondary pollutants. This study aims to evaluate the bioremediation potential of a non-axenic cyanobacterium, Synechococcus sp., isolated from the Amazon Basin, for municipal wastewater treatment within a circular bioeconomy framework. The strain was cultivated in different concentrations of municipal wastewater (25%, 50%, 75%, 100%) from Moronacocha Lake in the Peruvian Amazon to assess growth kinetics, ammonium removal efficiency, and biochemical composition. The cyanobacterium exhibited optimal performance in 25% wastewater, achieving the highest specific growth rate (22.8 × 10⁻² μ·day⁻¹) and biomass increase (393.2%), exceeding even the standard BG-11 medium. This treatment also demonstrated exceptional ammonium removal efficiency (95.4%) and enhanced phycocyanin production (33.6 μg/mg, 56% higher than the control). As wastewater concentration increased, both growth parameters and removal efficiency progressively declined. Biochemical analysis revealed that higher wastewater concentrations resulted in decreased protein content and increased lipid accumulation in the biomass. These findings demonstrate the dual potential of Synechococcus sp. for effective wastewater remediation and production of valuable biomass with modifiable biochemical characteristics, offering a sustainable approach for wastewater management in the Peruvian Amazon region. Full article

High-dimensional complex optimization problems are pervasive in engineering and scientific computing, yet conventional algorithms struggle to meet collaborative optimization requirements due to computational complexity. While Chicken Swarm Optimization (CSO) demonstrates an intuitive understanding and straightforward implementation for low-dimensional problems, it suffers from limitations including a low convergence precision, uneven initial solution distribution, and premature convergence. This study proposes an Adaptive Dynamically Enhanced Variant of Chicken Swarm Optimization (ADVCSO) algorithm. First, to address the uneven initial solution distribution in the original algorithm, we design an elite perturbation initialization strategy based on good point sets, combining low-discrepancy sequences with Gaussian perturbations to significantly improve the search space coverage. Second, targeting the exploration–exploitation imbalance caused by fixed role proportions, a dynamic role allocation mechanism is developed, integrating cosine annealing strategies to adaptively regulate flock proportions and update cycles, thereby enhancing exploration efficiency. Finally, to mitigate the premature convergence induced by single update rules, hybrid mutation strategies are introduced through phased mutation operators and elite dimension inheritance mechanisms, effectively reducing premature convergence risks. Experiments demonstrate that the ADVCSO significantly outperforms state-of-the-art algorithms on 27 of 29 CEC2017 benchmark functions, achieving a 2–3 orders of magnitude improvement in convergence precision over basic CSO. In complex composite scenarios, its convergence accuracy approaches that of the championship algorithm JADE within a 10⁻² magnitude difference. For collaborative multi-subproblem optimization, the ADVCSO exhibits a superior performance in both Multiple Traveling Salesman Problems (MTSPs) and Multiple Knapsack Problems (MKPs), reducing the maximum path length in MTSPs by 6.0% to 358.27 units while enhancing the MKP optimal solution success rate by 62.5%. The proposed algorithm demonstrates an exceptional performance in combinatorial optimization and holds a significant engineering application value. Full article

Due to the inherent limitations of detection networks and the imbalance in training data, small-target detection has always been a challenging issue in the field of target detection. To address the issues of false positives and missed detections in small-target detection scenarios, a new algorithm based on STDA-YOLOv8 is proposed for small-target detection. A novel network architecture for small-target detection is designed, incorporating a Contextual Augmentation Module (CAM) and a Feature Refinement Module (FRM) to enhance the detection performance for small targets. The CAM introduces multi-scale dilated convolutions, where convolutional kernels with different dilation rates capture contextual information from various receptive fields, enabling more accurate extraction of small-target features. The FRM performs adaptive feature fusion in both channel and spatial dimensions, significantly improving the detection precision for small targets. Addressing the issue of a significant disparity in the number of annotations between small and larger objects in existing classic public datasets, a new data augmentation method called Copy–Reduce–Paste is introduced. Ablation and comparative experiments conducted on the proposed STDA-YOLOv8 model demonstrate that on the VisDrone dataset, its accuracy improved by 5.3% compared to YOLOv8, reaching 93.5%; on the PASCAL VOC dataset, its accuracy increased by 5.7% compared to YOLOv8, achieving 94.2%, outperforming current mainstream target detection models and small-target detection algorithms like QueryDet, effectively enhancing small-target detection capabilities. Full article