Search Results (838)

China is the world’s leading producer of apples. However, the current classification of apple maturity is predominantly reliant on manual expertise, a process that is both inefficient and costly. In this study, we utilize a diverse array of apples of varying ripeness levels as the research subjects. We propose a lightweight target detection model, termed BGWL-YOLO, which is based on YOLOv11n and incorporates the following specific improvements. To enhance the model’s ability for multi-scale feature fusion, a bidirectional weighted feature pyramid network (BiFPN) is introduced in the neck. In response to the problem of redundant computation in convolutional neural networks, a GhostConv is used to replace the standard convolution. The Wise-Inner-MPDIoU (WIMIoU) loss function is introduced to improve the localization accuracy of the model. Finally, the LAMP pruning algorithm is utilized to further compress the model size. The experimental results demonstrate that the BGWL-YOLO model attains a detection and recognition precision rate of 83.5%, a recall rate of 81.7%, and an average precision mean of 90.1% on the test set. A comparative analysis reveals that the number of parameters has been reduced by 65.3%, the computational demands have been decreased by 57.1%, the frames per second (FPS) have been boosted by 5.8% on the GPU and 32.8% on the CPU, and most notably, the model size has been reduced by 74.8%. This substantial reduction in size is highly advantageous for deployment on compact smart devices, thereby facilitating the advancement of smart agriculture. Full article

Data-driven forecasting is becoming increasingly central to modern energy management, yet nonspecialists without a background in artificial intelligence (AI) face significant barriers to entry. While Python is the dominant machine learning language, R remains a practical and accessible tool for users with expertise in statistics, engineering, or domain-specific analysis. To inform tool selection, we first provide an evidence-based comparison of R with major alternatives before reviewing 49 peer-reviewed articles published between 2020 and 2025 in Science Citation Index Expanded (SCIE)-level journals that utilized R for energy forecasting tasks, including electricity (regional and site-level), solar, wind, thermal energy, and natural gas. Despite such growth, the field still lacks a systematic, cross-domain synthesis that clarifies which R-based methods prevail, how accessible workflows are implemented, and where methodological gaps remain; this motivated our use of text mining. Text mining techniques were employed to categorize the literature according to forecasting objectives, modeling methods, application domains, and tool usage patterns. The results indicate that tree-based ensemble learning models—e.g., random forests, gradient boosting, and hybrid variants—are employed most frequently, particularly for solar and short-term load forecasting. Notably, few studies incorporated automated model selection or explainable AI; however, there is a growing shift toward interpretable and beginner-friendly workflows. This review offers a practical reference for nonexperts seeking to apply R in energy forecasting contexts, emphasizing accessible modeling strategies and reproducible practices. We also curate example R scripts, workflow templates, and a study-level link catalog to support replication. The findings of this review support the broader democratization of energy analytics by identifying trends and methodologies suitable for users without advanced AI training. Finally, we synthesize domain-specific evidence and outline the text-mining pipeline, present visual keyword profiles and comparative performance tables that surface prevailing strategies and unmet needs, and conclude with practical guidance and targeted directions for future research. Full article

The ongoing global shortage of healthcare personnel, exacerbated by demographic changes and the aftermath of the COVID-19 pandemic, has highlighted the need for efficient workforce utilization and innovative technological support in healthcare. This paper presents LifeLink Monitoring, a prototype of an integrated remote patient monitoring system designed to optimize clinical workflows, support medical personnel, and enhance patient care without replacing human expertise. The system enables real-time patient observation through AI-powered devices, providing automated alerts, live video feeds, and intelligent task management to reduce the burden of non-clinical duties on healthcare professionals. Applications include hospitals, hospices, home care, and remote locations. Key features include seamless integration with medical devices and national health records, advanced computer vision and audio analysis, multi-level deployment models, and a blockchain-secured architecture ensuring high data privacy and cybersecurity standards. Additionally, LifeLink incorporates an entertainment module aimed at improving patient emotional well-being. The solution represents a convergence of artificial and human intelligence to improve healthcare delivery, personnel efficiency, and patient outcomes. Full article

In this work, we build on our recent work toward developing a neural speech recognition (NSR) for Quranic recitations that is accessible to people of any age, gender, or expertise level. The Quran recitations by females and males (QRFAM) dataset, a sizable benchmark dataset of audio recordings made by male and female reciters from various age groups and competence levels, was previously reported in our prior works. In addition to this dataset, we used various subsets of the QRFAM dataset for training, validation, and testing to build several basic NSR systems based on Mozilla’s DeepSpeech model. Our current efforts to optimize and enhance these baseline models have also been presented. In this study, we expand our efforts by utilizing one of the well-known speech recognition models, Whisper, and we describe the effect of this choice on the model’s accuracy, expressed as the word error rate (WER), in comparison to that of DeepSpeech. Full article

The European Green Deal (EGD) aims at driving the green transition in the EU and positions cities as pivotal actors in achieving climate neutrality and environment protection. Despite ambitious policy commitments, significant implementation gaps persist at the local level impeding urban green transitions. This study assesses barriers to the EGD urban implementation by integrating several methods (scoping literature review, expert consultations, and computational network analysis) to identify structural conditions hampering change. Barriers are clustered into five domains and reviewed by experts to distill eight structural conditions perpetuating the status quo of urban development, hindering transformative change. The findings illustrate how the emerged structural conditions, ranked by their in-degree centrality, regard insufficient policy implementation; upgrade of consolidated built environments’ layout; short-term mindset; lack of knowledge and data sharing among stakeholders; silos in policymaking and development processes; competition among stakeholders over space use; limited social acceptance; and limited financial resources. Conversely, high-out-degree barriers—such as limited technical expertise in urban departments and GDP-oriented paradigms—emerge as system triggers where targeted interventions could catalyze change. This research provides actionable insights for policymakers by identifying leverage points which could promote urban green transitions and enhance the EGD local implementation for accelerating urban green transitions. Full article

Background: While widely investigated in implantology and nonsurgical endodontics, evidence on the application of dynamic navigation systems (DNSs) in endodontic surgery remains limited. This systematic review aimed to assess their accuracy and reliability based on two-dimensional and three-dimensional virtual deviations, osteotomy parameters, as well as procedural duration, the impact of the dentist’s level of expertise, endodontic surgery healing outcomes, complications, and dentist- and patient-reported feedback. Methods: Following the PRISMA guidelines, an electronic search was conducted across the PubMed/MEDLINE, Scopus, Web of Science, and PROSPERO (CRD420251056347) databases up to 23 April 2025. Eligible studies involved human subjects (cadaveric or live) undergoing endodontic surgery with dynamic navigation. Extracted data focused on accuracy metrics such as platform/apical depth deviation and angular deflection. Results: Fourteen studies involving 240 roots were included. DNSs showed high accuracy, with mean platform and apical deviations of 1.17 ± 0.84 mm and 1.21 ± 0.99 mm, respectively, and angular deflection of 2.29° ± 1.69°, as well as low global deviations, averaging 0.83 ± 0.34 mm at the platform and 0.98 ± 0.79 mm at the apex. Root-end resections averaged 3.02 mm in length and 7.49° in angle deviation. DNS-assisted steps averaged 5.6 ± 2.56 min. Healing outcomes were favorable and complications were infrequent. Conclusions: DNSs demonstrated satisfactory accuracy and efficiency and, in the included studies, were linked to favorable healing outcomes and a low occurrence of intra- and postoperative complications. Nevertheless, the current evidence is still limited by the small number of available studies, and the heterogeneity in study designs and outcome measures, highlighting the need for further studies to define the clinical implications of DNSs in endodontic surgery. Full article

This study presents a validated drone-based methodology for inspecting fire protection belts in Galicia, Spain, with a focus on secondary protection belts surrounding settlements. Current manual inspection methods are limited by resource constraints and inefficiency, especially given Galicia’s steep slopes and fragmented, vegetated terrain. Our integrated approach combines high-resolution drone imagery, RTK positioning, GIS tools, and the Time2Parcel algorithm, enabling synchronized, parcel-level documentation at cadastral scale and allowing office-based technicians to directly review automatically generated video segments specific to each parcel for inspection verification. The methodology employs a hybrid classification system: automated assessments via orthophoto and LiDAR analysis and manual verification for cases with low confidence scores. Government technicians can perform office-based reviews without GIS expertise; the system automatically matches video to cadastral records, eliminating manual video review. Key results include the Time2Parcel algorithm for automatic video-to-parcel correlation, completion of inspections for 4934 parcels, and an operational efficiency increase of 68–70% reduction in inspection time compared with traditional methods. This workflow enables faster, safer, and more accurate inspections in highly fragmented rural contexts, improving legal compliance and environmental management. Full article

Background/Objective: Lung ultrasound (LUS) is a valuable tool for detecting pulmonary conditions, but its accuracy depends on user expertise. This study evaluated whether an artificial intelligence (AI) tool could improve clinician performance in detecting pleural effusion and consolidation/atelectasis on LUS scans. Methods: In this multi-reader, multi-case study, 14 clinicians of varying experience reviewed 374 retrospectively selected LUS scans (cine clips from the PLAPS point, obtained using three different probes) from 359 patients across six centers in the U.S. and Canada. In phase one, readers scored the likelihood (0–100) of pleural effusion and consolidation/atelectasis without AI. After a 4-week washout, they re-evaluated all scans with AI-generated bounding boxes. Performance metrics included area under the curve (AUC), sensitivity, specificity, and Fleiss’ Kappa. Subgroup analyses examined effects by reader experience. Results: For pleural effusion, AUC improved from 0.917 to 0.960, sensitivity from 77.3% to 89.1%, and specificity from 91.7% to 92.9%. Fleiss’ Kappa increased from 0.612 to 0.774. For consolidation/atelectasis, AUC rose from 0.870 to 0.941, sensitivity from 70.7% to 89.2%, and specificity from 85.8% to 89.5%. Kappa improved from 0.427 to 0.756. Conclusions: AI assistance enhanced clinician detection of pleural effusion and consolidation/atelectasis in LUS scans, particularly benefiting less experienced users. Full article

General models of talent development have highlighted the importance of a community of practice to nurture talent potential, with recent studies refining factors that contribute to the developmental journey. In music, an early model described three phases of talent development, while current research has focused on transitions between these. Choral music research has investigated conductors’ expertise and choristers’ experiences, highlighting positive social impacts for children in addition to the development of choral skills. The purpose of this qualitative case study was to investigate talent development of 11 elite middle school choristers utilising interviews. Thematic analyses identified four themes and 10 sub-themes, demonstrating that choristers followed a developmental pathway similar to choral conductors, acquiring vocal competence and mastery, nurturing a sense of belonging to a choral community, participating in meaningful experiences, and becoming advanced choristers through intensive training. Chorister talent development was also linked to personality development, with transformation in choral identity leading to growth in personal confidence. This study extends research into choral talent development by documenting the voices of middle school children participating in an advanced choir, showing that high levels of performance can be achieved through expert choral coaching and without sacrificing the enjoyment of singing. Full article

With the continuous evolution of traffic regulations due to societal and technological changes in South Korea, traffic safety education significantly contributes to promoting compliance and preventing confusion among drivers. However, little research has explored the effectiveness of traffic safety education across different device environments, particularly among actual drivers using virtual reality (VR). In this study, we aimed to develop a VR-based driver safety training education application to address the limitations of existing traffic safety education and to empirically assess its effectiveness in contrast to mobile-based education. An experiment was conducted involving participants with varying levels of driving expertise to analyze the interaction between device type and driver experience. The results demonstrated that the VR-based education was more effective than mobile-based approaches in enhancing learner interest and reducing confusion. Notably, experienced drivers showed significant improvements in transfer of learning and a clearer understanding of traffic regulations. This study provides empirical evidence to support the design and implementation of VR-based education tools to improve learning effectiveness and driver regulatory compliance. Full article

Genealogy is the study of family history and ancestral lineage, tracing relationships across generations through records and narratives. The digital revolution has shifted genealogical research from traditional archives to online platforms. Grounded in knowledge co-creation theory, this study examined the role of social media communities and their managers as knowledge hubs within the genealogical ecosystem. Its central innovation lies in identifying two emerging actors in modern genealogical knowledge ecology: the online community as a hub of expertise and the community manager as a key figure in knowledge creation. Drawing on interviews with fifteen Facebook managers of genealogical communities from diverse Jewish backgrounds worldwide, the study explored their perceptions of online genealogical spaces and their roles as facilitators of knowledge. Participants demonstrated a high level of professionalism and thoughtful engagement with sources; however, verifying the accuracy of genealogical claims was not within the scope of this study. Interviews were conducted in English and Hebrew based on participant preference. Thematic analysis revealed five key areas: two focused on the community’s role as a knowledge hub for both members and outsiders, and three on the manager’s role through self-perception, member engagement, and strategic initiatives. As part of a broader dissertation, this chapter deepens understanding of collaborative, community-driven genealogical knowledge in the age of social media. Full article

The Bric-a-Brick project aims to transform the role of the bricklayer by focusing on ergonomics and human–machine collaboration. By merging human expertise with robotics, the authors of this paper present a cost-effective prototype that aims to reduce the safety risks associated with some of the work performed by regular bricklayers and to lower the future incidence of musculoskeletal disorders commonly associated with the profession. The Bric-a-Brick project aims to reach, by 31 December 2026, Technology Readiness Level 9 (TRL 9): full technological maturity for implementation in real-world environments. To achieve this goal, Bric-a-Brick was developed as a promising intermediate validation activity. While the current system is still under development and much is yet to be achieved, initial results demonstrate strong potential. Further refinement is expected to enhance its reliability and effectiveness. Full article

Background: The adoption of artificial intelligence (AI) in humanitarian logistics is essential for improving coordination and decision making, especially in the challenging landscape of disaster-relief settings. However, the current literature offers limited empirical evidence with respect to the specific impact of AI on coordination and decision making for real-life humanitarian problems. Based on evidence from the humanitarian sector, this paper focuses on how AI could help humanitarian organizations collaborate better, streamline relief supply-chain operations and use resources more effectively. Methods: Twelve key themes influencing AI integration are identified by the study using a Grounded Theory (GT) approach based on interviews with experts from the humanitarian sector. These themes include data reliability, operational limitations, ethical considerations and cultural sensitivities, among others. Results: The findings suggest that AI improves forecasting, planning and inter-organizational coordination and is especially useful during the preparedness and mitigation stages of relief operations. Successful adoption, however, depends on adjusting tools to actual field conditions, building trust and training and striking a balance between algorithmic support and human expertise. Conclusions: The paper offers useful and practical advice for humanitarian organizations looking to use AI technologies in an ethical way while taking into account workforce capabilities, cross-agency cooperation and field-level realities. Full article

Introduction: Decision-making regarding radiotherapy techniques for patients with nasopharyngeal cancer requires a comparison of photon and proton plans generated using planning software, which requires time and expertise. We developed a fully automated decision tool to select patients for proton therapy that predicts proton therapy (XT) and photon therapy (PT) dose distributions using only patient CT image data, predicts xerostomia and dysphagia probability using predicted critical organ mean doses, and makes decisions based on the Netherlands’ National Indication Protocol Proton therapy (NIPP) to select patients likely to benefit from proton therapy. Methods: This study used 48 nasopharyngeal patients treated at the Cancer Hospital of the Chinese Academy of Medical Sciences. We manually generated a photon plan and a proton plan for each patient. Based on this dose distribution, photon and proton dose prediction models were trained using deep learning (DL) models. We used the NIPP model to measure xerostomia levels 2 and 3, dysphagia levels 2 and 3, and decisions were made according to the thresholds given by this protocol. Results: The predicted doses for both photon and proton groups were comparable to those for manual plan (MP). The Mean Absolute Error (MAE) for each organ at risk in the photon and proton plans did not exceed 5% and showed a good performance of the dose prediction model. For proton, the normal tissue complication probability (NTCP) of xerostomia and dysphagia performed well, p > 0.05. There was no statistically significant difference. For photon, the NTCP of dysphagia performed well, p > 0.05. For xerostomia p < 0.05 but the absolute deviation was 0.85% and 0.75%, which would not have a great impact on the prediction result. Among the 48 patients’ decisions, 3 were wrong, and the correct rate was 93.8%. The area under curve (AUC) of operating characteristic curve (ROC) was 0.86, showing the good performance of the decision-making tool in this study. Conclusions: The decision tool based on DL and NTCP models can accurately select nasopharyngeal cancer patients who will benefit from proton therapy. The time spent generating comparison plans is reduced and the diagnostic efficiency of doctors is improved, and the tool can be shared with centers that do not have proton expertise. Trial registration: This study was a retrospective study, so it was exempt from registration. Full article

Background/Objectives: Radiology often presents communication challenges due to its technical complexity, particularly for patients, trainees, and non-specialist clinicians. This study aims to evaluate the effectiveness of RadioBot, an AI-powered chatbot developed on the Botpress platform, in enhancing radiological communication through natural language processing (NLP). Methods: RadioBot was designed to provide context-sensitive responses based on guidelines from the American College of Radiology (ACR) and the Radiological Society of North America (RSNA). It addresses queries related to imaging indications, contraindications, preparation, and post-procedural care. A structured evaluation was conducted with twelve participants—patients, residents, and radiologists—who assessed the chatbot using a standardized quality and satisfaction scale. Results: The chatbot received high satisfaction scores, particularly from patients (mean = 4.425) and residents (mean = 4.250), while radiologists provided more critical feedback (mean = 3.775). Users appreciated the system’s clarity, accessibility, and its role in reducing informational bottlenecks. The perceived usefulness of the chatbot inversely correlated with the user’s level of expertise, serving as an educational tool for novices and a time-saving reference for experts. Conclusions: RadioBot demonstrates strong potential in improving radiological communication and supporting clinical workflows, especially with patients where it plays an important role in personalized medicine by framing radiology data within each individual’s cognitive and emotional context, which improves understanding and reduces associated diagnostic anxiety. Despite limitations such as occasional contextual incoherence and limited multimodal capabilities, the system effectively disseminates radiological knowledge. Future developments should focus on enhancing personalization based on user specialization and exploring alternative platforms to optimize performance and user experience. Full article