Search Results (177)

In recent years, the traditional orchard sprayer has had problems, such as waste of liquid agrochemicals, low target coverage, high manual dependence, and environmental pollution. In this study, an automatic swing-arm sprayer for orchards was developed based on the standardized pear orchard in Pinggu, Beijing. Firstly, the structural principles of a crawler-type traveling system and swing-arm sprayer were simulated using finite element software design. The combination of a diffuse reflection photoelectric sensor and Arduino single-chip microcomputer was used to realize real-time detection and dynamic spray control in the pear canopy, and the sensor delay compensation algorithm was used to optimize target recognition accuracy and improve the utilization rate of liquid agrochemicals. Through the integration of innovative structural design and intelligent control technology, a vertical droplet distribution test was carried out, and the optimal working distance of the spray was determined to be 1 m; the nozzle angle for the upper layer was 45°, that for the lower layer was 15°, and the optimal speed of the swing-arm motor was 75 r/min. Finally, a particle size test and field test of the orchard sprayer were completed, and it was concluded that the swing-arm mode increased the pear tree canopy droplet coverage by 74%, the overall droplet density by 21.4%, and the deposition amount by 23% compared with the non-swing-arm mode, which verified the practicability and reliability of the swing-arm spray and achieved the goal of on-demand pesticide application in pear orchards. Full article

Accurate and rapid aquatic organism recognition is a core technology for fisheries automation and aquatic organism statistical research. However, due to absorption and scattering effects, images of aquatic organisms often suffer from poor contrast and color distortion. Additionally, the clustering behavior of aquatic organisms often leads to occlusion, further complicating the identification task. This study proposes a lightweight object detection model, Mobile-YOLO, for the recognition of four representative aquatic organisms, namely holothurian, echinus, scallop, and starfish. Our model first utilizes the Mobile-Nano backbone network we proposed, which enhances feature perception while maintaining a lightweight design. Then, we propose a lightweight detection head, LDtect, which achieves a balance between lightweight structure and high accuracy. Additionally, we introduce Dysample (dynamic sampling) and HWD (Haar wavelet downsampling) modules, aiming to optimize the feature fusion structure and achieve lightweight goals by improving the processes of upsampling and downsampling. These modules also help compensate for the accuracy loss caused by the lightweight design of LDtect. Compared to the baseline model, our model reduces Params (parameters) by 32.2%, FLOPs (floating point operations) by 28.4%, and weights (model storage size) by 30.8%, while improving FPS (frames per second) by 95.2%. The improvement in mAP (mean average precision) can also lead to better accuracy in practical applications, such as marine species monitoring, conservation efforts, and biodiversity assessment. Furthermore, the model’s accuracy is enhanced, with the mAP increased by 1.6%, demonstrating the advanced nature of our approach. Compared with YOLO (You Only Look Once) series (YOLOv5-12), SSD (Single Shot MultiBox Detector), EfficientDet (Efficient Detection), RetinaNet, and RT-DETR (Real-Time Detection Transformer), our model achieves leading comprehensive performance in terms of both accuracy and lightweight design. The results indicate that our research provides technological support for precise and rapid aquatic organism recognition. Full article

Background/Objectives: Artificial intelligence (AI) offers opportunities to enhance healthcare accessibility for people with disabilities (PwDs). However, their application in Saudi Arabia remains limited. This study explores PwDs’ experiences with AI technologies within the Kingdom’s Vision 2030 digital health framework to inform inclusive healthcare innovation strategies. Methods: Semi-structured interviews were conducted with nine PwDs across Riyadh, Al-Jouf, and the Northern Border region between January and February 2025. Participants used various AI-enabled technologies, including smart home assistants, mobile health applications, communication aids, and automated scheduling systems. Thematic analysis following Braun and Clarke’s six-phase framework was employed to identify key themes and patterns. Results: Four major themes emerged: (1) accessibility and usability challenges, including voice recognition difficulties and interface barriers; (2) personalization and autonomy through AI-assisted daily living tasks and medication management; (3) technological barriers such as connectivity issues and maintenance gaps; and (4) psychological acceptance influenced by family support and cultural integration. Participants noted infrastructure gaps in rural areas, financial constraints, limited disability-specific design, and digital literacy barriers while expressing optimism regarding AI’s potential to enhance independence and health outcomes. Conclusions: Realizing the benefits of AI for disability healthcare in Saudi Arabia requires culturally adapted designs, improved infrastructure investment in rural regions, inclusive policymaking, and targeted digital literacy programs. These findings support inclusive healthcare innovation aligned with Saudi Vision 2030 goals and provide evidence-based recommendations for implementing AI healthcare technologies for PwDs in similar cultural contexts. Full article

Citizen science now relies heavily on digital platforms to engage the public in environmental data collection. Yet, many projects face declining participation over time. This study examines the effect of three elements of gamification—points, daily streaks, and real-time leaderboards—on student engagement, achievement, and immersion during a five-day campus-wide intervention utilising the GAME and a spatial crowdsourcing app. Employing a convergent mixed-methods design, we combined behavioural log analysis, validated psychometric scales (GAMEFULQUEST), and post-experiment interviews to triangulate both quantitative and qualitative dimensions of engagement. Results reveal that gamified elements enhanced students’ sense of accomplishment and early-stage motivation, which is reflected in significantly higher average scores for goal-directed engagement and recurring qualitative themes related to competence and recognition. However, deeper immersion and sustained “flow” were less robust with repetitive task design. While the intervention achieved only moderate long-term participation rates, it demonstrates that thoughtfully implemented game mechanics can meaningfully enhance engagement without undermining data quality. These findings provide actionable guidance for designing more adaptive, motivating, and inclusive citizen science solutions, underscoring the importance of mixed-methods evaluation in understanding complex engagement processes. While the sample size limits the statistical generalizability, this study serves as an exploratory field trial offering valuable design insights and methodological guidance for future large-scale, controlled citizen science interventions. Full article

Wind power generation provides clean and renewable electricity for microgrids, but its intermittency and uncertainty pose challenges to the operation and power quality of microgrids. Accurate forecasting is conducive to maintaining the stability of microgrids and improving the efficiency of energy management. Therefore, this study proposes a dual-branch frequency transformer (DBFformer), which leverages multi-scale spectral transformation and the multi-head attention mechanism to improve the prediction accuracy of microgrid wind turbines. In the encoder, two parallel branches are designed to extract the global features and local dynamic features of meteorological data based on Fourier transform and wavelet transform, respectively. In the decoder, an exponential smoothing attention (ESA) mechanism and a frequency attention (FA) mechanism are introduced to extract multi-scale temporal features. ESA enhances the model’s ability to capture long-term growth trends, whereas FA focuses on periodic pattern recognition. Additionally, to further optimize the model’s performance, a periodic weight coefficient (PWC) mechanism is employed to dynamically adjust the fusion coefficients to further improve the fusion performance and prediction accuracy. The factors influencing wind turbine power are analyzed; then, the most relevant factors are selected for the experiment. According to the experimental results, the proposed DBFformer accurately predicts the output power of wind turbines and exhibits superior performance. It achieves lower mean squared error (MSE) and mean absolute error (MAE) values than other state-of-the-art models. Specifically, its MSE values are 0.195, 0.216, 0.457, and 0.583, and the corresponding MAE values are 0.318, 0.335, 0.474, and 0.503 for different rated wind turbines. Furthermore, comprehensive ablation experiments validate that the dual-branch structure, frequency transformations, dual-attention mechanisms, and PWC module have a positive impact on the proposed model. Therefore, this research offers a novel and effective approach for wind power forecasting and supports the broader goal of integrating clean energy into microgrids. Full article

This article introduces a freely available Spanish–Mixtec parallel corpus designed to foster natural language processing (NLP) development for an indigenous language that remains digitally low-resourced. The dataset, comprising 14,587 sentence pairs, covers Mixtec variants from Guerrero (Tlacoachistlahuaca, Northern Guerrero, and Xochapa) and Oaxaca (Western Coast, Southern Lowland, Santa María Yosoyúa, Central, Lower Cañada, Western Central, San Antonio Huitepec, Upper Western, and Southwestern Central). Texts are classified into four main domains as follows: education, law, health, and religion. To compile these data, we conducted a two-phase collection process as follows: first, an online search of government portals, religious organizations, and Mixtec language blogs; and second, an on-site retrieval of physical texts from the library of the Autonomous University of Querétaro. Scanning and optical character recognition were then performed to digitize physical materials, followed by manual correction to fix character misreadings and remove duplicates or irrelevant segments. We conducted a preliminary evaluation of the collected data to validate its usability in automatic translation systems. From Spanish to Mixtec, a fine-tuned GPT-4o-mini model yielded a BLEU score of 0.22 and a TER score of 122.86, while two fine-tuned open source models mBART-50 and M2M-100 yielded BLEU scores of 4.2 and 2.63 and TER scores of 98.99 and 104.87, respectively. All code demonstrating data usage, along with the final corpus itself, is publicly accessible via GitHub and Figshare. We anticipate that this resource will enable further research into machine translation, speech recognition, and other NLP applications while contributing to the broader goal of preserving and revitalizing the Mixtec language. Full article

In recent years, research on human activity recognition (HAR) on smartphones has received extensive attention due to its portability. However, the discrimination issues between similar activities such as leaning forward and walking forward, as well as going up and down stairs, are hard to deal with. This paper conducts HAR based on the sensors of smartphones, i.e., accelerometers and gyroscopes. First, a feature extraction method for sensor data from both the time domain and frequency domain is designed to obtain more than 300 features, aiming to enhance the accuracy and stability of recognition. Then, the LightGBM (version 4.5.0) algorithm is utilized to comprehensively analyze the above-mentioned extracted features, with the goal of improving the accuracy of similar activity recognition. Through simulation experiments, it is demonstrated that the feature extraction method proposed in this paper has improved the accuracy of HAR. Compared with classical machine learning algorithms such as random forest (version 1.5.2) and XGBoost (version 2.1.3), the LightGBM algorithm shows improved performance in terms of the accuracy rate, which reaches 94.98%. Moreover, after searching for the model parameters using grid search, the prediction accuracy of LightGBM can be increased to 95.35%. Finally, using feature selection and dimensionality reduction, the efficiency of the model is further improved, achieving a 70.14% increase in time efficiency without reducing the accuracy rate. Full article

The Trombe Wall (TW) has gained recognition for its simplicity, efficiency, and zero operational costs, making it a key contributor to Sustainable Development Goals (SDGs) 7 and 11 by enhancing energy access and providing sustainable heating solutions. This passive solar technology is particularly beneficial in rural areas, offering cost-effective thermal comfort while minimizing environmental impact. This study evaluates the performance of three TW configurations attached to a room, designed with inclined glazing relative to the vertical air layer and stone layers at the bottom acting as thermal mass, commonly used in rural installations in Peru. Using 2D Computational Fluid Dynamics, the analysis compares an inclined heated wall with guided vanes featuring three or five blades to a configuration without vanes. Results show that the three-blade guided flow configuration achieves the highest temperature rise of 4 °C, with a reference temperature of 20 °C, under an absorber heat flux of 200–400 W/m², albeit with a slightly lower flow rate of 0.17–0.23 kg/s compared to the configuration without guided flow. The maximum thermal efficiency of 57.90% was observed for the three-blade configuration, which is 2.26% higher than the efficiency of the configuration without guided flow, under an absorber heat flux of 400 W/m². The obtained path-lines reveals that the three-blade configuration minimizes flow detachment, nearly eliminates recirculation near the bottom corner of the glazing, and reduces the separation bubble at the top corner of the massive wall near the outlet. These findings highlight the potential of guided vanes to enhance the performance of Trombe Walls in rural settings. Full article

This study explores the sustainability of Islamic decorative arts by examining the symbolic, cultural, and spiritual dimensions of botanical decorations in Makkah’s architectural heritage. Grounded in Carl Jung’s theory of the collective unconscious and Lamya Al-Faruqi’s philosophy of Tawhid, the research uncovers the profound psychological and spiritual meanings embedded in these motifs. Employing a qualitative methodology, the study integrates symbolic analysis, cultural interpretation, and historical documentation, supported by digital design tools, to assess the relevance of these decorations in contemporary urban contexts. Findings reveal that botanical motifs, such as palm trees and pinecones, reflect universal archetypes of resilience and growth while symbolising divine unity through abstraction and harmony. The research highlights their integral role in architectural structures and their potential in cultural tourism and educational initiatives. However, challenges such as urbanisation necessitate urgent documentation and innovative preservation strategies. This study offers valuable insights into sustaining Makkah’s architectural identity by bridging psychological and philosophical perspectives. Its recommendations align with Saudi Vision 2030 and global sustainability goals, advocating for the revival and integration of these motifs into modern urban design to ensure the continued appreciation and recognition of Makkan architectural heritage. Full article

Due to complex traffic conditions, freeway curves are associated with higher crash rates, particularly for trucks, which poses significant safety risks. Predicting truck crash rates on curves is essential for enhancing freeway safety. However, geometric design consistency indicators (GDCIs) are limited in terms of their ability to evaluate safety levels. To address this, this study identifies key factors influencing truck crash rates on curves and proposes a new safety evaluation indicator, the mean speed change rate (MSCR). A vague set, as an extension of the fuzzy set, was employed to integrate the MSCR and GDCI to identify high-risk curves. The factors contributing to differences in crash rates between the curves to the left and right are also analyzed. To assess the proposed approach, a case study was conducted using truck traffic data extracted from floating car data (FCD) collected on 32 freeway curves. The results demonstrate that the deflection angle, radius, and deflection direction are key contributions to truck crash risks. Importantly, the recognition accuracy of the MSCR indicator for crash risks on curves to the left and right is improved by 11.8% and 18.2% compared with GDCIs. Combining the proposed MSCR indicator with GDCIs can more comprehensively evaluate the safety of curves, with recognition accuracy rates of 88.2% and 27.3%, respectively. The indicator change value of the curves to the left are always larger, and the difference is more obvious as the geometric indicator changes. The MSCR indicator provides a more comprehensive curve safety assessment method than existing indicators, which is expected to promote the formulation of curve safety management strategies and further achieve sustainable development goals. Full article

The aging population has created a significant challenge affecting the world; social and healthcare systems need to ensure elderly individuals receive the necessary care services to improve their quality of life and maintain their independence. In response to this need, developing integrated digital solutions, such as IoT based wearable devices combined with artificial intelligence applications, offers a technological platform for creating Ambient Intelligence (AI) and Assisted Living (AAL) environments. These advancements can help reduce hospital admissions and lower healthcare costs. In this context, this article presents an IoT application based on MEMS (micro electro-mechanical systems) sensors integrated into a state-of-the-art microcontroller (STM55WB) for recognizing the movements of older individuals during daily activities. human activity recognition (HAR) is a field within computational engineering that focuses on automatically classifying human actions through data captured by sensors. This study has multiple objectives: to recognize movements such as grasping, leg flexion, circular arm movements, and walking in order to assess the motor skills of older individuals. The implemented system allows these movements to be detected in real time, and transmitted to a monitoring system server, where healthcare staff can analyze the data. The analysis methods employed include machine learning algorithms to identify movement patterns, statistical analysis to assess the frequency and quality of movements, and data visualization to track changes over time. These approaches enable the accurate assessment of older people’s motor skills, and facilitate the prompt identification of abnormal situations or emergencies. Additionally, a user-friendly technological solution is designed to be acceptable to the elderly, minimizing discomfort and stress associated with using technology. Finally, the goal is to ensure that the system is energy-efficient and cost-effective, promoting sustainable adoption. The results obtained are promising; the model achieved a high level of accuracy in recognizing specific movements, thus contributing to a precise assessment of the motor skills of the elderly. Notably, movement recognition was accomplished using an artificial intelligence model called Random Forest. Full article

In this paper, the authors draw attention to the significant impact of the human factor during collision situations in maritime navigation. The problems in the communication process between navigators are so excessive that the authors propose automatic communication. This is an alternative method to the current one. The presented system comprehensively performs communication tasks during a sea voyage. To reach the mentioned goal, AI methods of natural language processing and additional properties of metaontology (ontology supplemented with objective functions) are applied. Dedicated to maritime transport applications, the model for translating a natural language into an ontology consists of multiple steps and uses AI methods of classification for the recognition of a message from the ship’s bridge. The reverse model is also multi-stage and uses a created rule-based knowledge base to create natural-language sentences built on the basis of the ontology. Validation of the model’s accuracy results was conducted through accuracy assessment coefficients for information classification, commonly used in science. Receiver operating characteristic (ROC) curves represent the results in the datasets. The presented solution of the designed architecture of the system as well as algorithms developed in the software prototype confirmed the correctness of the assumptions in the described study. The authors demonstrated that it is feasible to successfully apply metaontology and machine learning methods in the proposed prototype software for ship-to-ship communication. Full article

Fuel cell hybrid vehicles offer significant potential in heavy-duty transportation due to their high efficiency, extended range, and zero emissions, making them a key enabler of sustainable transportation. To enhance the energy consumption economy and lifecycle economy of fuel cell hybrid mining trucks (FCHMTs) while reducing total operating costs and promoting environmental sustainability, this paper proposes an approximate globally optimal energy management strategy (EMS) based on a rule-interposing balance cost minimization strategy (AGO-BCMS). First, an FCHMT power system model is established, including degradation models for the fuel cell and battery. Then, the global optimality of dynamic programming (DP) is utilized to extract the fuel cell output characteristics under different battery states and vehicle power demands. Subsequently, optimal rules are designed and embedded into the cost minimization optimization model to plan the fuel cell output range under actual driving conditions. Simultaneously, dynamic threshold updates are performed based on vehicle driving condition phase recognition. Finally, energy distribution optimization is calculated using sequential quadratic programming (SQP). This strategy not only improves the economic viability of FCHMTs but also contributes to the broader goals of advancing sustainable transportation solutions. The proposed strategy was validated under both single round-trip and continuous operational conditions. Simulation results show that, under single round-trip conditions, the proposed strategy reduces the total operational cost by 3.13%, 4.09%, and 10.90% compared to balance cost-minimization strategies (BCMS), equivalent consumption minimization strategy (ECMS), and rule-based strategies, respectively. Under continuous operational conditions, the total cost is reduced by 3.61%, 6.63%, and 15.90%, respectively. Full article

The growing recognition of renewable energy’s importance, particularly its role in sustainability, has propelled wind energy to a prominent position. Receiving substantial global policy support due to its unique advantages, wind energy has seen a significant increase in installed turbine capacity. Consequently, expectations for the foundational bearing performance of these turbines have heightened, reflecting the enhanced focus on sustainable energy solutions. In response to these demands, this research introduces an innovative single pile foundation design that aims to elevate bearing capabilities to new heights. This research delves into the horizontal bearing properties of this novel foundation and the stress-strain dynamics of geotechnical materials under loading conditions. To achieve this, we utilize the Gudehus-Bauer subplastic model, specifically tailored for coastal sands within the ABAQUS finite element analysis software. Calibration and verification of the Gudehus-Bauer model’s parameters were meticulously conducted based on laboratory tests focusing on the coastal sands of the Yangtze River basin in China, enabling the development of a precise finite element model for the new single pile foundation in sandy coastal soils. Our findings reveal that this reinforced single pile foundation not only mirrors the horizontal bearing capacity and failure mechanisms of traditional designs but also surpasses them in performance. Numerically, this innovative structure boasts a remarkable 19.34% increase in horizontal ultimate bearing capacity and a minimum of 21.91% reduction in maximum displacement compared to standard single piles. These results underscore the superior horizontal bearing performance of our novel foundation design, which not only enhances structural integrity but also aligns with the principles of sustainable engineering by optimizing material usage, reducing environmental impact, and contributing to the broader goal of promoting renewable energy as a sustainable energy source. Full article

This study proposes a new approach and explores how pattern recognition enhances collaboration between users and Agile teams in software development, focusing on shared resources and decision-making efficiency. Using domain-specific modeling languages (DSMLs) within a security-by-design framework, the research identifies patterns that support team selection, effort estimation, and Agile risk management for Afghanistan’s ministries. These patterns align software development with governmental needs by clarifying stakeholder roles and fostering cooperation. The study builds on the p-mart-Repository-Programs (P-MARt) repository, integrating data mining, algorithms, and ETL (Extract, Transform, Load) processes to develop innovative methodologies. These approaches enable dynamic knowledge management, refine documentation, and improve project outcomes. Central to this effort is our new Pattern Shared Vision Refinement (PSVR) approach, which emphasizes robust collaboration, data security, and adaptability. By addressing challenges unique to governmental operations, PSVR strengthens Agile practices and ensures high-quality software delivery. By analyzing historical trends and introducing new strategies, the study underscores the critical role of pattern recognition in aligning development processes with organizational goals. It demonstrates how systematic pattern identification can optimize interaction and secure stakeholder consensus, ultimately enhancing software engineering outcomes in Afghanistan’s governmental context. Full article