Search Results (387)

The world population is expected to grow to over 10 billion by 2050 and therefore impose further stress on food production. Precision agriculture has become the main approach used to enhance productivity with sustainability in agricultural production. This paper conducts a technical review of how robotics, artificial intelligence (AI), and thermal imaging (TI) technologies transform precision agriculture operations, focusing on sensing, automation, and farm decision making. Agricultural robots promote labor solutions and efficiency by utilizing their sensing devices and kinematics in planting, spraying, and harvesting. Through accurate assessment of pests/diseases and quality assurance of the harvested crops, AI and TI bring efficiency to the crop monitoring sector. Different deep learning models are employed for plant disease diagnosis and resource management, namely the VGG16 model, InceptionV3, and MobileNet; the PlantVillage, PlantDoc, and FieldPlant datasets are used respectively. To reduce crop losses, AI–TI integration enables early recognition of fluctuations caused by pests or diseases, allowing control and mitigation in good time. While the issues of cost and environmental variability (illumination, canopy moisture, and microclimate instability) are taken into consideration, the advancement in artificial intelligence, robotics technology, and combined technologies will offer sustainable solutions to the existing gaps. Full article

Aligned with Saudi Arabia’s Vision 2030 and its corresponding global direction, this study aimed to identify and evaluate an environmentally friendly and alternative material to replace conventional synthetic polymers for polymer flooding. Extracting biopolymer solution, characterizing rheological properties, and conducting core-flooding experiments (seawater flood (SWF), secondary polymer flood (PF), and tertiary polymer flood) were experimentally investigated under simulated reservoir conditions (75 °C, 165,000 ppm TDS brine, and 2000 psi pore pressure). Biopolymer solutions were successfully generated from powdered pomegranate peels, and rheological characterizations of solutions with different shear rates, temperatures, and pomegranate-peel concentrations were investigated. Results revealed that significant shear-thinning behavior was pronounced in the biopolymer solutions, where 7% solution was selected for core-flooding tests. 7% solution exhibited 14.4 cP apparent viscosity at 13.2 s⁻¹ shear rate and 75 °C, indicating good thermal stability. Interfacial tension (IFT) results demonstrated high IFTs compared to the required IFT to reduce capillary forces, indicating that improved mobility control through viscosity enhancement serves as dominant EOR mechanism. The results indicated that PF yielded a higher ultimate oil recovery (62.2%) compared to SWF (47.6%) and tertiary polymer flood (58.0%). Results demonstrated that significant pressure fluctuations during polymer injection were observed, highlighting injectivity challenges. From all results, pomegranate peels would be potentially used to generate a biopolymer solution and replace environmentally hazardous materials. Full article

Efficient spectrum allocation and telecom traffic steering in densified heterogeneous maritime communication networks remains a critical challenge due to user mobility, dynamic interference, and congestion across terrestrial, aerial, and sea-based transmitters. This paper introduces the Moving Colorable Graph (MCG) framework, a dynamic graph-theoretical representation of interferences that extends conventional graph coloring to capture the spatiotemporal evolution of heterogeneous wireless links under varying channel and traffic conditions. The formulated spectrum allocation problem is inherently non-convex, as it involves discrete frequency assignments, mobility-induced dependencies, and interference coupling among multiple transmitters and users, thus requiring suboptimal yet computationally efficient solvers. The proposed approach models resource assignment as a time-dependent coloring problem, targeting to optimally support users’ diverse demands in dense port-area networks. Considering a realistic port-area network with coastal, sea and Unmanned Aerial Vehicle (UAV) radio coverage, we design and evaluate three MCG-based algorithm variants that dynamically update frequency assignments, highlighting their adaptability to fluctuating demands and heterogeneous coverage domains. Simulation results demonstrate that the selective reuse-enabled MCG scheme significantly decreases network outage and improves user demand satisfaction, compared with static, greedy and heuristic baselines. Overall, the MCG framework may act as a flexible scheme for mobility-aware and congestion-resilient resource management in densified and heterogeneous maritime networks. Full article

Attention and brain focus are essential in human activities that require learning. In higher education, a popular means of acquiring knowledge and information is through serious games. The need for integrating digital learning tools, including serious games, into university curricula has been demonstrated by the students’ preferences that are oriented more towards engaging and interactive alternatives than traditional education. This study examines real-time attention measurement in serious games using wearable brain–computer interfaces (BCIs). By capturing electroencephalography (EEG) signals non-invasively, the system continuously monitors players’ cognitive states to assess attention levels during gameplay. The novel approach proposes adaptive attention measurements to investigate the ability to maintain attention during cognitive tasks of different durations and intensities, using a single-channel EEG system—NeuroSky Mindwave Mobile 2. The measures have been achieved on ten volunteer master’s students in Computer Science. Attention levels during short and intense tasks were compared with those recorded during moderate and long-term activities like watching an educational lecture. The aim was to highlight differences in mental concentration and consistency depending on the type of cognitive task. The experiment was designed following a unique protocol applied to all ten students. Data were acquired using the NeuroExperimenter software 6.6, and analytics were performed in RStudio Desktop for Windows 11. Data is available at request for further investigations and analytics. Experimental results demonstrate that wearable BCIs can reliably detect attention fluctuations and that integrating this neuroadaptive feedback significantly enhances player focus and immersion. Thus, integrating real-time cognitive monitoring in serious game design is an efficient method to optimize cognitive load and create personalized, engaging, and effective learning or training experiences. Beta and attention brain waves, associated with concentration and mental processing, had higher values during the gameplay phase than in the lecture phase. At the same time, there are significant differences between participants—some react better to reading, while others react better to interactive games. The outcomes of this study contribute to the design of personalized learning experiences by customizing learning paths. Integrating NeuroSky or similar EEG tools can be a significant step toward more data-driven, learner-aware environments when designing or evaluating educational games. Full article

Accurate prediction of downstream vehicle arrivals is pivotal for intelligent signal control, yet many advanced controllers depend on high-resolution trajectories that are rarely available outside connected-vehicle settings. We present a deployable alternative that converts ubiquitous Automatic License Plate Recognition (ALPR) timestamps into the predictive inputs required by modern controllers. The method couples a Hidden Markov Model (HMM) for separating free-flow samples from signal-induced delays with a dynamic platoon-dispersion model that is re-estimated online in a rolling window to forecast downstream arrival profiles in real time. In a Simulation of Urban Mobility (SUMO) corridor testbed, the proposed framework consistently outperforms fixed-kernel dispersion and fixed-travel-time baselines, reducing RMSE by 57–75% and MAE by 53–73% across demand levels; ablation results confirm that HMM-based filtering is the dominant contributor to the gains. Robustness experiments further show stable parameter estimation under low ALPR matching rates, indicating suitability for real-world conditions where data quality fluctuates. Because it operates with existing roadside cameras and lightweight inference, the framework is readily integrable into adaptive signal strategies and broader smart-city traffic management. By turning discrete ALPR events into reliable arrival predictions, it bridges the gap between advanced signal control and today’s sensing infrastructure, enabling cost-effective real-time signal optimization in data-constrained urban networks. Full article

Mobile banking applications play a crucial role in providing users with access to financial services, and the quality of user experience is a key factor for their sustainability. This study investigates the predictability of application quality signals derived from user ratings of five leading mobile banking apps in Türkiye. The main problem addressed is understanding how these user-driven quality indicators evolve over time and identifying effective methods for forecasting them. This research problem is critical for understanding how banks can monitor customer satisfaction and reputational risk in real time, as fluctuations in app ratings directly affect user trust and engagement. For this purpose, daily average rating series collected from the Google Play Store were analyzed using LSTM-based time series models, and the results were benchmarked against the seasonal naïve (SNaive) method. The findings show that LSTM consistently achieved lower error rates across all banks, with particularly reliable forecasts for YapıKredi and Akbank, where MAPE values ranged between 16% and 28%. However, low R² values for some banks suggest limitations in long-term forecasting. The contribution of this study lies in demonstrating that user experience signals in mobile banking can be systematically monitored from a time series perspective, and that LSTM-based approaches provide a more effective method for capturing these quality dynamics. Full article

Rodents are the most diverse group of mammals, yet the natural history of many species remains poorly understood due to their elusive behavior. In this study, we examined the population structure, home range, space use, and food selection of two sympatric sigmodontine rodents, Euryoryzomys russatus and Sooretamys angouya, in the Morro Grande Forest Reserve, Brazil. E. russatus was more abundant than S. angouya, with its capture rates influenced by temperature. In contrast, the population variation of S. angouya showed no clear relationship with the assessed biotic (fruits and arthropods) or abiotic factors (temperature and precipitation), suggesting different primary regulatory factors for its population or a more generalist ecological strategy. The two species exhibited vertical stratification in space use: S. angouya displayed scansorial and arboreal locomotion, while E. russatus remained strictly terrestrial. Home range size, space use, and mobility were primarily influenced by resource availability, reproductive cycles, and individual body size. Our findings provide insights into the life strategies of these species, specifically regarding their vertical stratification in space use and their distinct responses to environmental resource fluctuations, enhancing our understanding of how sympatric rodents navigate shared spatial and temporal environments. Full article

Active distribution systems (ADS) are increasingly strained by rising energy demand and the widespread deployment of distributed energy resources (DERs) and electric vehicle charging stations (EVCS), which intensify voltage deviations, power losses, and peak demand fluctuations. This study develops a coordinated optimization framework for Mobile Battery Energy Storage Systems (MBESS) and Dynamic Feeder Reconfiguration (DFR) to enhance network performance across technical, economic, and environmental dimensions. A Non-dominated Sorting Genetic Algorithm III (NSGA-III) is employed to minimize six objectives the active and reactive power losses, voltage deviation index (VDI), voltage stability index (FVSI), operating cost, and CO₂ emissions while explicitly modeling the MBESS transportation constraints such as energy consumption and single-trip mobility within coupled IEEE 33-bus and 33-node transport networks, which provide realistic mobility modeling of energy storage operations. The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is applied to select compromise solutions from Pareto fronts. Simulation results across six scenarios show that the coordinated MBESS–DFR operation reduces power losses by 27.8–30.1%, improves the VDI by 40.5–43.2%, and enhances the FVSI by 2.3–2.4%, maintaining all bus voltages within 0.95–1.05 p.u. with minimal cost (0.26–0.27%) and emission variations (0.31–0.71%). The MBESS alone provided limited benefits (5–12%), confirming that coordination is essential for improving efficiency, voltage regulation, and overall system sustainability in renewable-rich distribution networks. Full article

Agricultural robots operating in greenhouse environments face substantial challenges in detecting tomato stems, including fluctuating lighting, cluttered backgrounds, and the stems’ inherently slender morphology. This study introduces EfficientV1-C2fDWR-IRMB-YOLO (EDI-YOLO), an enhanced model built on YOLOv8n-seg. First, the original backbone is replaced with EfficientNetV1, yielding a 2.3% increase in mAP50 and a 2.6 G reduction in FLOPs. Second, we design a C2f-DWR module that integrates multi-branch dilations with residual connections, enlarging the receptive field and strengthening long-range dependencies; this improves slender-object segmentation by 1.4%. Third, an Inverted Residual Mobile Block (iRMB) is inserted into the neck to apply spatial attention and dual residual paths, boosting key-feature extraction by 1.5% with only +0.7GFLOPs. On a custom tomato-stem dataset, EDI-YOLO achieves 79.3% mAP50 and 33.9% mAP50-95, outperforming the baseline YOLOv8n-seg (75.1%, 31.4%) by 4.2% and 2.6%, and YOLOv5s-seg (66.7%), YOLOv7tiny-seg (75.4%), and YOLOv12s-seg (75.4%) by 12.6%, 3.9%, and 3.9% in mAP50, respectively. Significant improvement is achieved in lateral branch segmentation (60.4% → 65.2%). Running at 86.2 FPS with only 10.4GFLOPs and 8.0 M parameters, EDI-YOLO demonstrates an optimal trade-off between accuracy and efficiency. Full article

Understanding the influence of weather on human well-being and mobility is essential to promoting healthier lifestyles. In this study we employ data collected from 151 participants over a continuous 30-day period in Switzerland to examine the effects of weather on well-being and mobility. Physiological data were retrieved through wearable devices, while mobility was automatically tracked through Google Location History, enabling detailed analysis of participants’ mobility behaviors. Mixed effects linear models were used to estimate the effects of temperature, precipitation, and sunshine duration on well-being and mobility while controlling for potential socio-demographic confounders. In this work, we demonstrate the feasibility of combining multi-source physiological and location data for environmental health research. Our results show small but significant effects of weather on several well-being outcomes (activity, sleep, and stress), while mobility was mostly affected by the level of precipitation. In line with previous research, our findings confirm that normal weather fluctuations exert significant but moderate effects on health-related behavior, highlighting the need to shift research focus toward extreme weather variations that lie beyond typical seasonal ranges. Given the potentially severe consequences of such extremes for public health and health-care systems, this shift will help identify more consistent effects, thereby informing targeted interventions and policy planning. Full article

With the rapid development of Internet of Vehicles (IoV) technology, massive data are continuously integrated into intelligent transportation systems, making efficient computing resource allocation a critical challenge for enhancing network performance. Due to the dynamic and real-time characteristics of IoV tasks, existing static offloading strategies fail to effectively cope with the complexity caused by network fluctuations and vehicle mobility. To address this issue, this paper proposes a task offloading algorithm based on the dynamic adjustment of the parallel factor in directed acyclic graphs (DAG), referred to as Dynamic adjustment of Parallel Factor (DPF). By leveraging edge computing, the proposed algorithm adaptively adjusts the parallel factor according to the dependency relationships among subtasks in the DAG, thereby optimizing resource utilization and reducing task completion time. In addition, the algorithm continuously monitors network conditions and vehicle states to dynamically schedule and offload tasks according to real-time system requirements. Compared with traditional static strategies, the proposed method not only significantly reduces task delay but also improves task success rates and overall system efficiency. Extensive simulation experiments conducted under three different task load conditions demonstrate the superior performance of the proposed algorithm. In particular, under high-load scenarios, the DPF algorithm achieves markedly better task completion times and resource utilization compared to existing methods. Full article

Short-term passenger flow prediction provides critical data-driven support for optimizing resource allocation, guiding passenger mobility, and enhancing risk response capabilities in urban rail transit systems. To further improve prediction accuracy, this study proposes a hybrid SMA-VMD-Informer-BiLSTM prediction model. Addressing the challenge of error propagation caused by non-stationary components (e.g., noise and abrupt fluctuations) in conventional passenger flow signals, the Variational Mode Decomposition (VMD) method is introduced to decompose raw flow data into multiple intrinsic mode functions (IMFs). A Slime Mould Algorithm (SMA)-based optimization mechanism is designed to adaptively tune VMD parameters, effectively mitigating mode redundancy and information loss. Furthermore, to circumvent error accumulation inherent in serial modeling frameworks, a parallel prediction architecture is developed: the Informer branch captures long-term dependencies through its ProbSparse self-attention mechanism, while the Bidirectional Long Short-Term Memory (BiLSTM) network extracts localized short-term temporal patterns. The outputs of both branches are fused via a fully connected layer, balancing global trend adherence and local fluctuation characterization. Experimental validation using historical entry flow data from Weihouzhuang Station on Xi’an Metro demonstrated the superior performance of the SMA-VMD-Informer-BiLSTM model. Compared to benchmark models (CNN-BiLSTM, CNN-BiGRU, Transformer-LSTM, ARIMA-LSTM), the proposed model achieved reductions of 7.14–53.33% in $f_{mse}$, 3.81–31.14% in $f_{rmse}$, and 8.87–38.08% in $f_{mae}$, alongside a 4.11–5.48% improvement in $R^2$. Cross-station validation across multiple Xi’an Metro hubs further confirmed robust spatial generalizability, with prediction errors bounded within $f_{mse}$: 0.0009–0.01, $f_{rmse}$: 0.0303–0.1, $f_{mae}$: 0.0196–0.0697, and $R^2$: 0.9011–0.9971. Furthermore, the model demonstrated favorable predictive performance when applied to forecasting passenger inflows at multiple stations in Nanjing and Zhengzhou, showcasing its excellent spatial transferability. By integrating multi-level, multi-scale data processing and adaptive feature extraction mechanisms, the proposed model significantly mitigates error accumulation observed in traditional approaches. These findings collectively indicate its potential as a scientific foundation for refined operational decision-making in urban rail transit management, thereby significantly promoting the sustainable development and long-term stable operation of urban rail transit systems. Full article

In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks. Full article

Ultrathin oxide semiconductors are promising channel materials for next-generation thin-film transistors (TFTs), but their performance is severely limited by bulk and interface defects as the channel thickness approaches a few nanometers. In this study, we show that high-pressure hydrogen annealing (HPHA) effectively mitigates these limitations in 3.6 nm thick ZnO TFTs. HPHA-treated devices exhibit a nearly four-fold increase in on-current, a steeper subthreshold swing, and a negative shift in threshold voltage compared to reference groups. X-ray photoelectron spectroscopy reveals a marked reduction in oxygen vacancies and hydroxyl groups, while capacitance–voltage measurements confirm more than a three-fold decrease in interface trap density. Low-frequency noise analysis further demonstrates noise suppression and a transition in the dominant noise mechanism from carrier number fluctuation to mobility fluctuation. These results establish HPHA as a robust strategy for defect passivation in ultrathin oxide semiconductor channels and provide critical insights for their integration into future low-power, high-density electronic systems. Full article

In this paper, climate evolution is revisited in terms of the theory of dynamical systems, which has been successfully used in predictions of catastrophic events such as avalanches, landslides, or economy and civilization collapses. Such tipping events are announced by warning signs, named “pre-critical fluctuations” or “critical softening”, allowing a tipping date estimate through well-known equations. In the case of climate, the warning signs are extreme events of increasing amplitudes. We show that in such a context, numerical simulations can hardly predict incoming tipping points, due to a divergence in computational time at the singularity. Based on the dynamical systems theory, a recent publication from Copenhagen University shows that the Atlantic Meridional Oceanic Circulation is likely to collapse well before the end of the century, triggering switchover cascades, eventually culminating in global climate tipping. Paleoclimatic studies also show that tipping events occurred in the past, particularly during the PETM period 56 Myrs ago. If this was to happen now, average global temperatures might reach an unbearable level, with a deadline much closer than expected. This extreme emergency has major consequences on the implementation times of sustainability policies and in energy production, mobility, agriculture, housing, etc., that absolutely must be operational on time. Full article