Search Results (202)

This theoretical essay aims to analyze some of the socio-economic innovations introduced by Platform Capitalism Specifically, it focuses on two main aspects: first, the digital platform as a radical organizational innovation. Digital platforms represent a structural novelty in the market economy, signaling a new organization of production and labor. Second, the essay examines the role of platforms in directly generating value through the concept of “network value”. To this end, it explores the function of “business intelligence” as a strategic and competitive tool. Finally, the paper discusses the key issues associated with platform capitalism, which could threaten its social sustainability and contribute to economic and financial instability. These issues include the increasing commodification of everyday activities, the devaluation of paid labor in favor of free production driven by platform users (the so-called prosumers), and the emergence of proprietary and financial monopolies. Hence, digital platforms do not inherently ensure comprehensive social and environmental sustainability unless supported by targeted economic policy interventions. Conclusively, it is emphasized that defining robust social welfare frameworks—which account for emerging value creation processes—is imperative. Simultaneously, policymakers must incentivize the proliferation of cooperative platforms capable of fostering experimental circular economy models aligned with ecological sustainability. Full article

Foraging behavior in hens is an important indicator of animal welfare. It involves both the search for food and exploration of the environment, which provides necessary enrichment. In addition, it has been inversely linked to damaging behaviors such as severe feather pecking. Conventional studies rely on manual observation to investigate foraging location, duration, timing, and frequency. However, this approach is labor-intensive, time-consuming, and subject to human bias. Our study developed computer vision-based methods to automatically detect foraging hens in a cage-free research environment and compared their performance. A cage-free room was divided into four pens, two larger pens measuring 2.9 m × 2.3 m with 30 hens each and two smaller pens measuring 2.3 m × 1.8 m with 18 hens each. Cameras were positioned vertically, 2.75 m above the floor, recording the videos at 15 frames per second. Out of 4886 images, 70% were used for model training, 20% for validation, and 10% for testing. We trained multiple You Only Look Once (YOLO) object detection models from YOLOv9, YOLOv10, and YOLO11 series for 100 epochs each. All the models achieved precision, recall, and mean average precision at 0.5 intersection over union (mAP@0.5) above 75%. YOLOv9c achieved the highest precision (83.9%), YOLO11x achieved the highest recall (86.7%), and YOLO11m achieved the highest mAP@0.5 (89.5%). These results demonstrate the use of computer vision to automatically detect complex poultry behavior, such as foraging, making it more efficient. Full article

Blood is a widely used sample for diagnosing diseases such as malaria and diabetes. While diagnostic techniques have advanced, sample preparation remains labor-intensive, requiring steps like mixing and centrifugation. Microfluidic technologies have automated parts of this process, including cell lysis, yet challenges persist. Electrical lysis offers a chemical-free, continuous approach, but lysing small cells like red blood cells requires high electric fields, which can damage electrodes and cause system failures. Here, we present a microfluidic device utilizing ion concentration polarization (ICP) for rapid blood cell lysis at 75 V. Fluorescence imaging confirmed the formation of an ion depletion region near the Nafion^® nanochannel membrane, where the electric field was concentrated across the entire microchannel width. This phenomenon enabled the efficient trapping and lysis of blood cells under these conditions. Continuous blood injection achieved a lysis time of 0.3 s with an efficiency exceeding 99.4%. Moreover, lysed cell contents accumulated near the Nafion membrane, forming a concentrated lysate. This approach eliminates the need for high-voltage circuits or chemical reagents, offering a simple yet effective method for blood cell lysis. The proposed device is expected to advance lab-on-a-chip and point-of-care diagnostics by enabling rapid and continuous sample processing. Full article

Automated waste classification is an essential step toward efficient recycling and waste management. Traditional deep learning models, such as convolutional neural networks, rely on extensive labeled datasets to achieve high accuracy. However, the annotation process is labor-intensive and time-consuming, limiting the scalability of these approaches in real-world applications. Zero-shot learning is a machine learning paradigm that enables a model to recognize and classify objects it has never seen during training by leveraging semantic relationships and external knowledge sources. In this study, we investigate the potential of zero-shot learning for waste classification using two vision-language models: OWL-ViT and OpenCLIP. These models can classify waste without direct exposure to labeled examples by leveraging textual prompts. We apply this approach to the TrashNet dataset, which consists of images of municipal solid waste organized into six distinct categories: cardboard, glass, metal, paper, plastic, and trash. Our experimental results yield an average classification accuracy of 76.30% with Open Clip ViT-L/14-336 model, demonstrating the feasibility of zero-shot learning for waste classification while highlighting challenges in prompt sensitivity and class imbalance. Despite lower accuracy than CNN- and ViT-based classification models, zero-shot learning offers scalability and adaptability by enabling the classification of novel waste categories without retraining. This study underscores the potential of zero-shot learning in automated recycling systems, paving the way for more efficient, scalable, and annotation-free waste classification methodologies. Full article

The commercialization of selective broccoli harvesters, a critical response to agricultural labor shortages, is hampered by end effectors with large operational envelopes and poor adaptability to complex field conditions. To address these limitations, this study developed and evaluated a novel end-effector with an integrated transverse cutting mechanism and a foldable grasping cavity. Unlike conventional fixed cylindrical cavities, our design utilizes actuated grasping arms and a mechanical linkage system to significantly reduce the operational footprint and enhance maneuverability. Key design parameters were optimized based on broccoli morphological data and experimental measurements of the maximum stem cutting force. Furthermore, dynamic simulations were employed to validate the operational trajectory and ensure interference-free motion. Field tests demonstrated an operational success rate of 93.33% and a cutting success rate of 92.86%. The end effector successfully operated in dense planting environments, effectively avoiding interference with adjacent broccoli heads. This research provides a robust and promising solution that advances the automation of broccoli harvesting, paving the way for the commercial adoption of robotic harvesting technologies. Full article

In this study, free compression tests were conducted to examine the changes in the strength of soil after adding 24 mm long basalt fiber (1%), lime (3%, 6%, 9% by dry weight), and tuff (10%, 20%, 30% by dry weight) before curing and after 28, 42, and 56 days of curing. Instead of the K + BF 1% + SL 9% mixture, where the SL ratio is high, it has been revealed that T, which has a lower SL content and is environmentally friendly (as in the K + BF 1% + SL 6% + T 10% mixture), can be used considering environmental factors and costs. However, due to the length and cost of experimental studies, the use of artificial intelligence to reduce the need for physical tests/experiments and to accelerate processes will provide savings in terms of labor, time, and cost. Unconfined compressive strength (q_u) prediction was performed using the artificial neural network (ANN) technique. The accuracy of the ANN model was proven using the R and MSE metrics. In addition, a q_u prediction of the mixture with 30% water content was performed according to the curing times. The experimental and predicted q_u values for the curing times were compared and presented. Full article

Cage-free egg production is now the predominant system in Australia. However, the occurrence of floor eggs (FE), which are eggs laid outside designated nest boxes, presents a major challenge for these producers. To understand factors that may be associated with the laying of FE, a national scoping survey of cage-free egg-laying flocks was undertaken. Forty-three flocks across multiple farms were surveyed via a phone-based interview using predetermined questions. Floor egg levels ranged from 0.01–17%. There was no difference in floor egg levels between the breeds of brown-egg-laying hens. Age at peak lay did not alter the level of FE, but higher rate of peak lay had a weak association with fewer FE (r = −0.31, p = 0.049). Larger flocks had a lower percentage of FE (r = −0.5, p = 0.002), and farmers of larger sized flocks considered a lower level of floor eggs to be acceptable. Farms with tunnel-ventilated sheds reported fewer FE compared to those using other ventilation systems (p = 0.013). Higher floor egg levels were associated with increased labor costs (p = 0.023). These findings suggest that shed design and environmental management may be leveraged to reduce floor egg occurrence and improve operational efficiency in cage-free systems. Full article

Effective monitoring of fish passage through river barriers is essential for evaluating fishway performance and supporting adaptive river management. Traditional methods are often invasive, labor-intensive, or too costly to enable widespread implementation across most fishways. Infrared (IR) beam-break counters offer a promising alternative, but their adoption has been limited by high costs and a lack of flexibility. We developed and tested a novel, low-cost infrared beam-break counter—FishTracker—based on open-source Raspberry Pi and Arduino platforms. The system detects fish passages by analyzing interruptions in an IR curtain and reconstructing fish silhouettes to estimate movement, direction, speed, and morphometrics under a wide range of turbidity conditions. It also offers remote access capabilities for easy management. Field validation involved controlled tests with dummy fish, experiments with small-bodied live specimens (bleak) under varying turbidity conditions, and verification against synchronized video of free-swimming fish (koi carp). This first version of FishTracker achieved detection rates of 95–100% under controlled conditions and approximately 70% in semi-natural conditions, comparable to commercial counters. Most errors were due to surface distortion caused by partial submersion during the experimental setup, which could be avoided by fully submerging the device. Body length estimation based on passage speed and beam-interruption duration proved consistent, aligning with published allometric models for carps. FishTracker offers a promising and affordable solution for non-invasive fish monitoring in multispecies contexts. Its design, based primarily on open technology, allows for flexible adaptation and broad deployment, particularly in locations where commercial technologies are economically unfeasible. Full article

Diffusion Tensor Imaging (DTI) is a crucial imaging technique for accurately assessing pathological changes in Cervical Spondylotic Myelopathy (CSM). However, the segmentation of spinal cord DTI images primarily relies on manual methods, which are labor-intensive and heavily dependent on the subjective experience of clinicians, and existing research on DTI automatic segmentation cannot fully satisfy clinical requirements. Thus, this poses significant challenges for DTI-assisted diagnostic decision-making. This study aimed to deliver AI-driven segmentation for spinal cord DTI. To achieve this goal, a comparison experiment of candidate input features was conducted, with the preliminary results confirming the effectiveness of applying a diffusion-free image (B0 image) for DTI segmentation. Furthermore, a deep-learning-based model, named SCS-Net (Spinal Cord Segmentation Network), was proposed accordingly. The model applies a classical U-shaped architecture with a lightweight feature extraction module, which can effectively alleviate the training data scarcity problem. The proposed method supports eight-region spinal cord segmentation, i.e., the lateral, dorsal, ventral, and gray matter areas on the left and right sides. To evaluate this method, 89 CSM patients from a single center were collected. The model demonstrated satisfactory accuracy for both general segmentation metrics (precision, recall, and Dice coefficient) and a DTI-specific feature index. In particular, the proposed model’s error rate for the DTI-specific feature index was evaluated as 5.32%, 10.14%, 7.37%, and 5.70% on the left side, and 4.60%, 9.60%, 8.74%, and 6.27% on the right side of the spinal cord, respectively, affirming the model’s consistent performance for radiological rationality. In conclusion, the proposed AI-driven segmentation model significantly reduces the dependence on DTI manual interpretation, providing a feasible solution that can improve potential diagnostic outcomes for patients. Full article

Weeds present a pervasive challenge in agricultural fields. The integration of herbicide-resistant crops with chemical weed management offers an effective solution for sustainable weed control while reducing labor inputs, particularly in large-scale intensive farming systems. Consequently, the development of herbicide-resistant cultivars has emerged as an urgent priority. In this study, we found that the G311E mutant gene of Arabidopsis MATE (multidrug and toxic compound extrusion) family transporter DTX6, designated DTX6m, confers robust resistance to bipyridyl herbicides paraquat and diquat in rice. DTX6m-overexpression lines exhibited marked resistance to these two herbicides, tolerating diquat concentrations up to 5 g/L, which is five-fold higher than the recommended field application dosage. Agronomic assessments demonstrated that grain yields of DTX6m-overexpressing plants were statistically equivalent to those of wild-type plants. Moreover, the plants displayed beneficial phenotypic changes, such as accelerated flowering and a slight reduction in height. Seed morphometric analysis indicated that in comparison with the wild-type control, DTX6m-transgenic lines exhibited altered grain dimensions while maintaining consistent 1000-grain weight. Nutritional assays further demonstrated that DTX6m increased the levels of free amino acids in seeds, while normal protein and starch contents were retained. Collectively, these results establish that DTX6m effectively boosts rice resistance to paraquat and diquat, validating DTX6m as a candidate gene for engineering plant herbicide resistance and also implying a potential role for DTX6m in amino acid homeostasis in plants. Full article

This study explores the potential of freely available tools for collecting, processing, and applying information in the management of mechanized fieldwork. A hierarchical approach was developed, integrating operational, logistical, and strategic levels of decision-making based on crop type, land conditions, machinery, labor, and time constraints. Various technological and technical solutions were evaluated through simulations and manual data processing. The proposed methodology was applied to a real-world case in Kalipetrovo, Bulgaria. The results include a 3.5-fold reduction in required tractors and a 50% decrease in tractor driver needs, achieved through extended working hours and shift scheduling. Additional benefits were identified from replacing conventional tillage with deep tillage, resulting in higher fuel consumption but improved soil preparation. Detailed resource schedules were created for machinery, labor, and fuel, highlighting seasonal peaks and optimization opportunities. The approach relies on spreadsheets and free AI-assisted platforms, proving to be a low-cost, accessible solution for mid-sized farms lacking advanced digital infrastructure. The findings demonstrate that structured information integration can support the effective renewal and utilization of tractor and machinery fleets while offering a scalable basis for decision support systems in agricultural engineering. Full article

Free trade zone (FTZ) policies and port integration play critical roles in advancing international shipping and port development. While Free trade zones (FTZs) promote trade liberalization and attract investment to support port infrastructure, port integration helps alleviate excessive competition, reduce redundant labor, and minimize resource inefficiencies. Given these dynamics, it is important to examine how FTZs and port integration differentially shape shipping capacity and port competitiveness across China’s coastal provinces. To this end, this study introduces a comprehensive evaluation framework for port competitiveness, which considers both port operation–related factors and the external environment. The framework employs a combination of principal component analysis and the entropy weight method to assess port competitiveness in coastal regions. The findings reveal that comprehensive port service capacity and management efficiency capacity have the most significant influence on port competitiveness, outweighing the impact of other evaluated indicators. It also reveals that the development of China’s coastal ports is regionally unbalanced, with strong competitiveness in the Yangtze River Delta, Pearl River Delta, and Bohai Rim clusters, moderate development in the southeastern cluster, and relatively weak performance in the Beibu Gulf cluster. Both FTZ and port integration policies can promote port competitiveness to some extent, especially for professional technical support and services, digital management, and overall management efficiency. The dynamics of port competitiveness under the FTZs are higher than those under port integration. The research results deepen the understanding of the roles of FTZ and port integration policies in promoting the competitiveness of ports in various regions and provide insights for ports to seize opportunities and enhance development. The reinforcement of industrial synergies with neighboring regions and the formation of complementary development patterns enhance their overall competitiveness. Exploring new modes aligned with the advancement of FTZs and port integration can further stimulate regional economic development and support national opening-up strategies. Full article

Real-time estimation of fish growth offers multiple benefits in indoor aquaculture, including reduced labor, lower operational costs, improved feeding efficiency, and optimized harvesting schedules. This study presents a low-cost, vision-based method for estimating the body length and weight of olive flounder (Paralichthys olivaceus) in tank environments. A 5 × 5 cm reference grid is placed on the tank bottom, and images are captured using two fixed-position RGB smartphone cameras. Pixel measurements from the images are converted into millimeters using a calibrated pixel-to-length relationship. The system calculates fish length by detecting contour extremities and applying Lagrange interpolation. Based on the estimated length, body weight is derived using a power regression model. Accuracy was validated using both manual length measurements and Bland–Altman analysis, which indicated a mean bias of −0.007 cm and 95% limits of agreement from −0.475 to +0.462 cm, confirming consistent agreement between methods. The mean absolute error (MAE) and mean squared error (MSE) were 0.11 cm and 0.025 cm², respectively. While optimized for benthic species such as olive flounder, this system is not suitable for free-swimming species. Overall, it provides a practical and scalable approach for non-invasive monitoring of fish growth in commercial indoor aquaculture. Full article

The growth velocity of the crystal–melt interface during solidification is one of the important parameters that determine the crystal growth morphology. However, both experimental investigations and theoretical calculations are time-consuming and labor-intensive. Moreover, machine learning (ML)-based methods are severely limited by the limited amount of available experimental data. In this work, the crystal–melt interface velocity of four alloy systems under different values of undercooling was calculated by molecular dynamics simulation. The results showed a similar trend to the experimental data. A framework including molecular dynamics (MD) calculation and a transfer learning (TL) model was proposed to predict the interface velocity of binary alloys during free solidification. In order to verify the effectiveness of the model, eight ML models were constructed based on pure experimental data for model comparison. The prediction ability of the different models was assessed from two perspectives: interpolation and extrapolation. The results show that, regardless of whether it is interpolation or extrapolation, the TL model driven by both physical information and experimental data is superior to ML models driven solely by experimental data. The interpretability analysis method reveals the specific role of feature values in the interface velocity prediction of binary alloys. Full article

This study is situated against the background of shortages in the agricultural labor force and shortages of cultivated land. In order to improve the intelligence level and operational efficiency of agricultural machinery and solve the problems of difficulties in recognizing navigation lines and a lack of real-time performance of transplanters in the crop-free ridge environment, we propose a crop-free-ridge navigation line recognition method based on an improved YOLOv8 segmentation algorithm. First, this method reduces the parameters and computational complexity of the model by replacing the YOLOv8 backbone network with MobileNetV4 and the feature extraction module C2f with ShuffleNetV2, thereby improving the real-time segmentation of crop-free ridges. Second, we use the least-squares method to fit the obtained point set to accurately obtain navigation lines. Finally, the method is applied to testing and analyzing the field experimental ridges. The results showed that the average precision of the improved neural network model using this method was 90.4%, with a Params of 1.8 M, a FLOPs of 8.8 G, and an FPS of 49.5. The results indicate that the model maintains high accuracy while significantly outperforming Mask-RCNN, YOLACT++, YOLOv8, and YOLO11 in terms of computational speed. The detection frame rate increased significantly, improving the real-time performance of detection. This method uses the least-squares method to fit the 55% ridge contour feature points under the picture, and the fitting navigation line shows no large deviation compared with the image ridge centerline; the result is better than that of the RANSAC fitting method. The research results indicate that this method significantly reduces the size of the model parameters and improves the recognition speed, providing a more efficient solution for the autonomous navigation of intelligent carrier aircraft. Full article