Search Results (14,675)

An advanced artificial intelligence (AI)-powered mobile automated impurity removal system was developed and integrated into potato harvesting machinery for decentralized agricultural environments in Japan. As opposed existing stationary AI systems in centralized processing facilities, this mobile prototype enables on-field impurity removal in real time through a systematic dual-evaluation methodology. The system integrates the YOLOX-small architecture with precision pneumatic actuators and achieves 40–50 FPS processing under dynamic field conditions. Algorithm validation across 10 morphologically diverse potato varieties (Danshaku, Harrow Moon, Hokkaikogane, Kitaakari, Kitahime, May Queen, Sayaka, Snowden, Snow March, and Toyoshiro) using count-based analysis showed exceptional recognition, with potato misclassification rates of 0.08 ± 0.03% (range: 0.01–0.32%) and impurity detection rates of 89.99 ± 1.25% (range: 80.00–93.30%). Cross-farm validation across seven commercial farms in Hokkaido confirmed robust algorithm consistency (PMR: 0.08 ± 0.03%, IDR: 90.56 ± 0.82%) without farm-specific calibration, establishing variety-independent and environment-independent operation. Field validation using weight-based analysis during actual harvesting at 1–4 km/h confirmed successful AI-to-field translation, with 0.22–0.42% potato misclassification and adaptive impurity removal of 71.43–85.29%. The system adapted intelligently, employing conservative sorting under high-impurity loads (71.43% removal, 0.33% misclassification) to prioritize potato preservation while maximizing efficiency under standard conditions (85.29% removal, 0.30% misclassification). The dual-evaluation framework successfully bridged the gap between AI accuracy in laboratory settings and effectiveness in agricultural operations. The proposed AI algorithm surpassed project targets for all tested conditions (>60% impurity removal, <1% potato misclassification). This successful integration demonstrates technical feasibility and commercial viability for widespread agricultural automation, with a validated 50% reduction in labor (four workers to two workers). This implementation provides a comprehensive validation methodology for next-generation autonomous harvesting systems. Full article

The transition to renewable energy systems and electric mobility depends on the effectiveness, reliability, and durability of lithium-ion battery technology. Accurate modeling and control of battery systems are essential to ensure safety, efficiency, and cost-effectiveness in electric vehicles and grid storage. In engineering and materials science, battery models depend on physical parameters such as capacity, energy, state of charge (SOC), internal resistance, power, and self-discharge rate. These parameters are affected by measurement uncertainty. Despite the widespread use of lithium-ion cells, few studies quantify how measurement uncertainty propagates to derived battery parameters and affects predictive modeling. This study quantifies how uncertainty in voltage, current, and temperature measurements reduces the accuracy of derived parameters used for simulation and control. This work presents a comprehensive uncertainty analysis of 18650 format lithium-ion cells with nickel cobalt aluminum oxide (NCA), nickel manganese cobalt oxide (NMC), and lithium iron phosphate (LFP) cathodes. It applies the law of error propagation to quantify uncertainty in key battery parameters. The main result shows that small variations in voltage, current, and temperature measurements can produce measurable deviations in internal resistance and SOC. These findings challenge the common assumption that such uncertainties are negligible in practice. The results also highlight a risk for battery management systems that rely on these parameters for control and diagnostics. The results show that propagated uncertainty depends on chemistry because of differences in voltage profiles, kinetic limitations, and temperature sensitivity. This observation informs cell selection and testing for specific applications. Improved quantification and control of measurement uncertainty can improve model calibration and reduce lifetime and cost risks in battery systems. These results support more robust diagnostic strategies and more defensible warranty thresholds. This study shows that battery testing and modeling should report and propagate measurement uncertainty explicitly. This is important for data-driven and physics-informed models used in industry and research. Full article

Steel surface defect classification is critical for industrial quality control, yet existing methods struggle to balance accuracy and efficiency for real-time deployment in vision-based sensor systems. This paper presents MobileSteelNet, a lightweight deep learning framework that introduces two novel modules: multi-scale feature fusion (MSFF), for integrating multi-stage features; and Cross-Interactive Efficient Multi-Scale Attention (CIEMA), which unifies inter-channel interaction, parallel multi-scale spatial extraction, and grouped efficient computation. Experiments on the NEU-DET dataset demonstrate that MobileSteelNet achieves 91.36% average accuracy, surpassing ResNet-50 (88.01%) and lightweight networks, including MobileNetV2 (86.08%). Notably, it achieves 93.70% accuracy on Scratch-type defects, representing an 82.12 percentage point improvement over baseline MobileNetV1. With a model size of only 8.2 MB, MobileSteelNet maintains superior performance while meeting lightweight deployment requirements, making it suitable for edge deployment in vision sensor systems for steel manufacturing. Full article

Within the framework of the carbon market mechanism, corporate investments to secure forestry carbon credits play a pivotal role in mobilizing social capital for ecological construction and realizing the value of ecosystem services. This study integrates information decision theory and Bayesian network analysis to simulate corporate investment decision-making for forestry carbon sequestration within China’s carbon market. Through this approach, we explore the decision-making mechanisms behind corporate investments in forestry carbon sequestration and conduct decision simulations. The findings reveal several key insights: (1) External factors, including tax incentives, consumer preference for low-carbon products, and societal environmental awareness, exert a significant impact on the valuation of forestry carbon sequestration investments. Internally, the challenge posed by technological costs in achieving emission reductions significantly influences the evaluation of forestry carbon sequestration investments. (2) Investment value judgments are shaped by the nature of the decision-making problem, which inherently involves a synergistic relationship. (3) Corporations recognize the importance of forestry carbon sequestration in reducing the costs of emission reduction, formulating low-carbon development plans, expanding investment opportunities, and enhancing the quality of forestry carbon sequestration. (4) The collective value judgment of corporates regarding forestry carbon sequestration in terms of cost reduction for emission reduction, low-carbon development planning, investment opportunity expansion, and corporate image enhancement significantly influences their investment decisions in forestry carbon sequestration. (5) Corporate investment decisions exhibit a strong preference for market-based pricing and risk-sharing mechanisms. Consequently, enhancing the carbon information disclosure system and the carbon market trading mechanism, as well as establishing price protection and income stabilization expectations for forestry carbon sequestration, can encourage corporates to make investments in this area. This not only aids in the green, low-carbon transformation of businesses but also addresses the challenge of positive externalities associated with forestry carbon sequestration through market-oriented solutions. Full article

This study explores how teachers’ work sustainability is shaped through everyday governance practices within private higher education institutions in China. Using a constructivist grounded theory approach, the analysis draws on long-term fieldwork and in-depth interviews with teachers, administrators, leaders, and students from two private colleges. The findings suggest that teachers’ difficulties do not stem from isolated adverse incidents, but rather from an ongoing organizational process embedded in routine management practices. Evaluation-centered promotion systems, relationship-based governance, and data-driven oversight interact to restructure how teaching work is organized, recognized, and assessed. Professional contributions are frequently treated as negotiable outcomes subject to managerial discretion, while informal alignment practices and selective monitoring gradually narrow teachers’ space for professional judgment and initiative. Despite accumulating dissatisfaction, most teachers remain in their positions. Occupational identity, social expectations, and constrained labor mobility limit realistic exit options, transforming short-term accommodation into prolonged endurance. In this context, teacher retention reflects not organizational stability, but the persistence of governance conditions that challenge the long-term sustainability of teachers’ work. By examining how routine management practices gradually reshape teachers’ work, this study highlights an overlooked dimension of sustainability in higher education: the long-term viability of teachers’ professional lives within existing governance arrangements. Unlike studies that conceptualize teachers’ difficulties through the lens of workplace bullying or interpersonal conflict, this study focuses on how ordinary governance practices shape long-term work sustainability without overt confrontation. Full article

This research introduces an advanced attention-driven model designed to optimize mobile shelf warehouse order-picking. Our model incorporates an enhanced masking mechanism and context-aware decoder, streamlining the order-picking process. In essence, our model presents an attention model based heuristic solution to the long-standing problem of order-picking optimization, leveraging the latest in attention-based deep learning techniques. The attention model is combined with Apriori and the Adaptive Large Neighborhood Search (ALNS) algorithm to solve the bilevel combinatorial optimization model for mobile shelves. Compared to existing methods, our innovative model shows superior performance, offering significant potential in warehousing solutions. Full article

Frailty is a multidomain reduction in physiologic reserve that impacts recovery and can contribute to poor outcomes following trauma beyond what chronological age, comorbidities, or injury severity predicts. In geriatric trauma patients, a large proportion are frail or prefrail on initial encounter in the emergency department, and because there are opportunities for actionable management plans, major trauma guidelines endorse systematic screening integrated into coordinated geriatric trauma care. We reviewed the literature and identified practical instruments used in the acute trauma setting for risk stratification. Additionally, we highlight the feasibility of using these instruments, as some can be completed via patient report, proxy input, or chart review when cognition, language, or caregiver availability limits history-taking. Implementation efforts succeed when shared mental models are leveraged and screening is embedded in the electronic health record system, linked to order sets and trigger-based pathways that offer downstream goal-directed care management, such as early mobility, delirium prevention, nutrition, medication review, and comprehensive geriatric assessment. Additionally, we highlight the importance of initiating early goals-of-care discussions and coordinating care with palliative care services. Resource-limited systems can preserve the same architecture by using nurse-led or allied staff-led screening, tele-geriatric consultation, and virtual interdisciplinary huddles. Lastly, we expand upon opportunities for longitudinal post-discharge follow-up. We describe how targeted initiatives translate research into practice, improve outcomes, and support longitudinal reassessment through in-person and telehealth follow-up visits. Full article

This paper presents the development of a robot-oriented Distributed Acoustic Sensing (DAS) system designed for environmental monitoring and hazard detection on ground robotic platforms. Unlike conventional DAS solutions primarily intended for stationary or quasi-stationary infrastructures, the proposed approach explicitly accounts for robot-induced mechanical vibrations, mobility constraints, and limited onboard resources. A dedicated anti-jitter signal processing pipeline combined with edge-based data processing is introduced to suppress motion-induced strain components while preserving weak external acoustic signals. The system integrates optical fiber deployment along the robot structure using flexible guides and vibration-isolated clamps, ensuring stable mechanical coupling under continuous motion. Experimental validation, including laboratory tests and preliminary outdoor field trials, demonstrates reliable detection of acoustic events in the 10–200 Hz frequency range, with reduced processing latency of 80–100 ms and a detection reliability of up to 95%. Comparative analysis with conventional sensors confirms the advantages of the proposed DAS-based approach in terms of sensitivity, spatial coverage, and robustness. The results demonstrate the feasibility and effectiveness of DAS technology for real-time sensing applications on mobile robotic platforms. Full article

Vehicle-to-grid (V2G) services are gaining attention as a strategy to integrate electric vehicles (EVs) into sustainable energy systems. Although technological aspects have been widely studied, methodologies for identifying optimal V2G hubs and forecasting the energy available for grid transfer remain limited. This study introduces a data-driven approach to (i) identify the optimal V2G region based on the aggregated parking duration using floating car data (FCD; collected from GPS-enabled vehicles); (ii) estimate the surplus battery capacity of electric vehicles in that region; and (iii) forecast the energy transferable to the grid. The methodology applies spatial k-means clustering to define candidate zones, computes aggregated parking durations, and selects the optimal hub. The surplus energy is estimated considering the daily mobility needs of users, 20% reserve, and transfer rates. For forecasting, autoregressive integrated moving average (ARIMA) and long short-term memory (LSTM) models are implemented and compared. The proposed methodology has been applied to a real case study, using 58 days of FCD observations. The empirical findings of this study show the goodness of the proposed methodology, and the opportunity offered V2G technology to support the sustainable use of energy. The ARIMA model demonstrated a superior forecasting performance with an RMSE of 52.424, MAE of 36.05, and MAPE of 12.98%, outperforming LSTM (RMSE of 99.09, MAE of 80.351, and MAPE of 53.20%) under the current data conditions. The results of this study suggest that for supporting grid needs of a medium-sized city, V2G plays a key role, and at the current status of the EV penetration, the use of FCD and predictive approaches is paramount for making an informed decision. Full article

Advanced oxidation processes using porphyrin-based heterogeneous catalysts hold promise for removing hazardous pollutants from wastewater. Their high visible-light absorption coefficients enable absorption of light from the solar spectrum. Moreover, their conjugated aromatic skeletons and intrinsic electronic properties facilitate the delocalization of photogenerated electrons during photodegradation. Delaying the recombination of photogenerated electron–hole pairs by introducing specific materials increases efficiency, as separated charges have more time to participate in redox reactions, boosting photocatalytic activities. However, applying these photocatalysts for wastewater treatment is challenging owing to facile agglomeration, deactivation, and recovery of the photocatalyst for reuse, which can significantly increase the overall cost. Therefore, new photocatalytic systems comprising porphyrin molecules must be developed. For this purpose, porphyrins can be conjugated to nanomaterials to create hybrid materials with photocatalytic efficiencies superior to those of free-standing starting porphyrins. Various transition metal oxides (TiO₂, ZnO, and Fe₃O₄) nanoparticles, main-group-element oxides (Al₂O₃ and SiO₂) nanoparticles, metal plasmons (silver nanoparticles), carbon-based platforms (graphene, graphene oxide, and g-C₃N₄), and polymer matrices have been used as nanostructured solid supports for the successful fabrication of porphyrin-conjugated hybrid materials. The conjugation of porphyrin molecules to solid supports improves the photocatalytic degradation activity in terms of visible-light conversion ability, recyclability, active porous sites, substrate mobility, separation of photogenerated charge species, recovery for reuse, and chemical stability, along with preventing the generation of secondary pollution. This review discusses the ongoing development of porphyrin-conjugated hybrid nanomaterials for the heterogeneous photocatalytic degradation of organic dyes, pharmaceutical pollutants, heavy metals, pesticides, and human care in water. Several important results and advancements in the field allow for a more efficient wastewater remediation process. Full article

Fibromyalgia is a complex chronic disorder involving persistent widespread pain accompanied by functional limitations, cognitive impairments, and alterations in neural processing. Previous research indicates that exercise-based interventions can play a key role in alleviating symptom burden and enhancing physical performance; however, there is limited evidence regarding their impact on neurophysiological mechanisms. Creative dance, in combination with strength training, may stimulate both motor and cognitive systems, promoting brain plasticity and functional improvements. This study will analyze the effects of a six-week strength and creative dance program on physical fitness under single- and dual-task conditions in women with fibromyalgia and will explore the associated changes in brain electrical activity and autonomic modulation. Methods: This randomized controlled trial will be divided into an exercise group (n = 22) and a control group (n = 22). The 6-week supervised intervention consists of two 60-minute sessions per week, combining strength exercises and creative dance. Primary outcomes include physical fitness tests (strength, mobility, balance, and agility gait test in single-task and dual-task), fibromyalgia symptoms, and quality of life. Secondary outcomes include changes in electroencephalography, heart rate variability, physical activity level, and fear of falling. Statistical analyses will compare within- and between-group differences using non-parametric tests and effect sizes. It is hypothesized that the intervention will improve physical fitness and dual-task performance, alongside increases in brain activity power. This study may provide insights into the neurophysiological mechanisms underlying the benefits of exercise benefits in fibromyalgia. Full article

This essay contends that Jean-Luc Godard’s late digital cinema elaborates a geopolitical aesthetics in which Europe confronts the return of its repressed histories through the very instability of the digital image. While Europe has long functioned in Godard’s work as both theme and epistemic horizon—echoing the Hegelian cartographies—Film Socialisme (2010) and The Image Book (2018) transform this Eurocentrism into a site of crisis. In these films, what Fredric Jameson terms the “political unconscious” (1981) emerges through the spectral return of Palestine and the Arab world, compelling a reckoning with colonial legacies and the limits of representation. The digital turn proves decisive. Godard mobilizes pixelation, saturation, glitch, and decomposed sound to reveal what might be called the technological unconscious of the medium. I develop the concept of “Digital Orientalism” to designate how Orientalist chronotopes persist in the digital age yet are unsettled by Godard’s experimental manipulation of audiovisual fragments. Through close readings of Film Socialisme and The Image Book, which incorporates works by Arab filmmakers including Youssef Chahine, Nacer Khemir, Ossama Mohammed, and Wiam Simav Bedirxan, I show how Godard’s fractured montages produce symptomatic cartographies of the world-system where repression, memory, and accident collide. Full article

Existing origin-destination (OD) forecasting models struggle to jointly capture local topology and global flow patterns in urban mobility. Therefore, we developed a multi-view spatio-temporal network (MVSTN), a novel dual-branch spatio-temporal model that integrates a graph convolutional network-based local spatial relationship module for static and dynamic graph modeling, and a self-attention-based global similarity module for learning latent mobility similarities. MVSTN achieves superior performance on multiple real-world datasets, particularly in long-term forecasts, highlighting its practical value for intelligent transportation systems. Full article

Organic-rich shale, as a significant alternative energy source, possesses abundant resources. Classified by maturity, it comprises three categories: medium-high maturity shale oil, medium-low maturity shale oil, and oil shale. Medium-high maturity shale oil faces challenges such as tight reservoirs and poor fluidity; medium-low maturity shale oil is characterized by a high proportion of retained hydrocarbons and poor mobility; and oil shale requires high-temperature conversion. Addressing the inherent characteristics of these three resource types, this paper systematically reviews the theoretical foundations and key technologies from two dimensions: “CO₂ injection for medium-high maturity shale oil extraction” and “in situ conversion of medium-low maturity shale/oil shale”. The results indicate that CO₂ injection technology for medium-high maturity shale oil utilizes its supercritical diffusion properties to reduce miscibility pressure by 40–60% compared to conventional reservoirs, efficiently displacing crude oil in nanopores while establishing a geological storage system for greenhouse gases, thereby pioneering an integrated “displacement–drive–storage” model for carbon-reduced oil production. The autothermic pyrolysis in situ conversion process for medium-low maturity shale/oil shale significantly reduces costs by leveraging the oxidation latent heat of kerogen. Under temperature and pressure conditions of 350–450 °C, the shale pore network expansion rate reaches 200–300%, with permeability increasing by two orders of magnitude. Assisted natural gas injection further optimizes the thermal field distribution within the reservoir. Future research should focus on two key directions: synergistic cost reduction and carbon sequestration through CO₂ injection, and the matching of in situ conversion with complex fracture networks. This study delineates key technological pathways for the low-carbon and efficient development of different types of organic-rich shale, contributing to energy security. Full article

Vehicle repositioning is essential for improving efficiency and service quality in ride-sharing platforms, yet existing approaches typically optimize proxy rewards that fail to reflect human-centered preferences such as wait time, service coverage, and unnecessary empty travel. We propose the first two-stage Bilevel Reinforcement Learning (RL) from Human Feedback (RLHF) framework for preference-aligned vehicle repositioning. In Stage 1, a value-based Deep Q-Network (DQN)-RLHF warm start learns an initial preference-aligned reward model and stable reference policy, mitigating the reward-model drift and cold-start instability that arise when applying on-policy RLHF directly. In Stage 2, a Kullback–Leibler (KL)-regularized Proximal Policy Optimization (PPO)-RLHF algorithm, equipped with action masking, behavioral-cloning anchoring, and alternating forward–reverse KL, fine-tunes the repositioning policy using either Large Language Model (LLM)-generated or rubric-based preference labels. We develop and compare two coordination schemes, pure alternating (PPO-Alternating) and k-step alternating (PPO-k-step), demonstrating that both yield consistent improvements across all tested arrival scales. Empirically, our framework reduces wait time and empty-mile ratio while improving served rate, without inducing trade-offs or reducing platform profit. These results show that human preference alignment can be stably and effectively incorporated into large-scale ride-sharing repositioning. Full article