Search Results (5,978)

Deep learning-based industrial product surface defect detection methods are replacing manual inspection, while the issue of small object detection remains a key challenge in the current field of surface defect detection. The feature pyramid structures demonstrate great potential in improving the performance of small object detection and are one of the important current research directions. Nevertheless, traditional feature pyramid networks still suffer from problems such as imprecise focus on key features, insufficient feature discrimination capabilities, and weak correlations between features. To address these issues, this paper proposes a plug-and-play guided focus feature pyramid network, named GF-FPN. Built on the foundation of FPN, this network is designed with a bottom-up guided aggregation network (GFN): through a lightweight pyramidal attention module (LPAM), star operation, and residual connections, it establishes correlations between objects and local contextual information, as well as between shallow-level details and deep-level semantic features. This enables the feature pyramid network to focus on key features, enhance the ability to distinguish between objects and backgrounds, and thereby improve the model’s small object detection performance. Experimental results on the self-built TinyIndus dataset and NEU-DET demonstrate that the detection model based on GF-FPN exhibits more competitive advantages in object detection compared to existing models. Full article

Objective: To provide a narrative overview of the scientific knowledge on Last Aid Courses and experiences from different countries. Background: The levels of death literacy, grief literacy, and knowledge about palliative care are low in many countries around the world. For many people, dying, death, and grief are still a taboo. Public Palliative Care Education (PPCE), the public knowledge approach, and the Last Aid Course (LAC) aim to increase death literacy, grief literacy, and public knowledge about palliative care. Methods: A literature search in the databases PubMed/Medline, CINAHL, and PsycInfo was undertaken. Other additional sources were found by hand searching, books, reference lists, and the internet. A narrative overview of the existing literature on LAC and Public Palliative Care Education (PPCE) is provided. Experiences with PPCE and LAC from different countries are presented. Based on the findings, a future agenda for research on PPCE and LAC is presented. Results and Discussion: PPCE and LAC have been introduced in 23 countries. A total of 17 articles and reviews on Last Aid were included. Research on the effects of LAC in different countries and cultural issues connected to LAC are ongoing. Conclusions: Since 2015, LACs have been introduced in 23 different countries. The LAC, the LAC-KT, and PPCE may enhance the public debate on dying, death, grief, and palliative care and may empower people to contribute to end-of-life care in the community. Future research on PPCE, the LAC, and the LAC-KT should focus on retention over time and the long-term effects of the courses. Full article

Background: Our growing understanding of the brain basis of mind has seen an interest in evolutionarily ancient structures, most notably the brainstem. This paper offers an interesting example of this underexplored territory, by considering the mesencephalic component of the trigeminal nucleus. This largely uncelebrated brainstem structure is central to control of the jaw, and for the foundational acts of eating, oral exploration, and biting. Objectives: This paper explores the interesting anatomy of the mesencephalic trigeminal: unique in the nervous system as a centrally located sensory ganglion, which combines sensory and motor function for the jaw. An unexplored aspect of its anatomy is that the mesencephalic component of the nucleus lies directly adjacent to the brain’s core system for the experience of emotion, the peri-acqueductal gray (PAG). Results: The data suggest a role for the jaw, and more broadly the oral cavity, in relation to a range of feeling states, from pleasure to aggression. This is supported by behavioural and classic neuropsychological findings, such as the Klüver-Bucy syndrome. However, the proposal is not well-supported by findings of direct connections between the trigeminal nucleus and the PAG. Conclusions: While these contrasting findings present a conundrum, there may be a role for non-synaptic signalling, of the sort increasingly understood to be important for interoception and homeostasis. Full article

Objectives: Innovative, evidence-based interventions for developmental dyslexia (DD) are necessary. While traditional methods remain valuable, newer approaches, such as cognitive–motor training, show the potential to improve literacy skills for those with DD. Verbal Working Memory–Balance (VWM-B) is a novel cognitive–motor training program that has demonstrated positive effects on reading, cognitive functions, and motor skills in children with DD. This extension study explored the neural mechanisms of VWM-B through voxel-to-voxel intrinsic functional connectivity (FC) analysis in children with DD. Methods: Resting-state fMRI data from 16 participants were collected in a quasi-double-blind randomized clinical trial with control and experimental groups, pre- and post-intervention measurements, and 15 training sessions over 5 weeks. Results: The mixed ANOVA interaction was significant for the right and left postcentral gyrus, bilateral precuneus, left superior frontal gyrus, and left posterior division of the supramarginal and angular gyri. Decreased FC in the postcentral gyri indicates reduced motor task engagement due to automation following VWM-B training. Conversely, increased FC in the bilateral precuneus, left superior frontal gyrus, and left posterior divisions of the supramarginal and angular gyri suggests a shift of cognitive resources from motor tasks to the cognitive functions associated with VWM-B. Conclusions: In conclusion, the study highlights that cognitive–motor dual-task training is more effective than single-task cognitive training for improving cognitive and motor functions in children with DD, emphasizing the importance of postural control and automaticity in dyslexia. The trial for this study was registered on 8 February 2018 with the Iranian Registry of Clinical Trials (IRCT20171219037953N1). Full article

Electroencephalography (EEG)-based motor imagery (MI) brain–computer interfaces (BCIs) hold considerable potential for applications in neuro-rehabilitation and assistive technologies. Yet, their development remains constrained by challenges such as low spatial resolution, vulnerability to noise and artifacts, and pronounced inter-subject variability. Conventional approaches, including common spatial patterns (CSP) and convolutional neural networks (CNNs), often exhibit limited robustness, weak generalization, and reduced interpretability. To overcome these limitations, we introduce EEG-GCIRNet, a Gaussian connectivity-driven EEG imaging representation network coupled with a regularized LeNet architecture for MI classification. Our method integrates raw EEG signals with topographic maps derived from functional connectivity into a unified variational autoencoder framework. The network is trained with a multi-objective loss that jointly optimizes reconstruction fidelity, classification accuracy, and latent space regularization. The model’s interpretability is enhanced through its variational autoencoder design, allowing for qualitative validation of its learned representations. Experimental evaluations demonstrate that EEG-GCIRNet outperforms state-of-the-art methods, achieving the highest average accuracy (81.82%) and lowest variability (±10.15) in binary classification. Most notably, it effectively mitigates BCI illiteracy by completely eliminating the “Bad” performance group (<60% accuracy), yielding substantial gains of ∼22% for these challenging users. Furthermore, the framework demonstrates good scalability in complex 5-class scenarios, performing competitive classification accuracy (75.20% ± 4.63) with notable statistical superiority (p = 0.002) against advanced baselines. Extensive interpretability analyses, including analysis of the reconstructed connectivity maps, latent space visualizations, Grad-CAM++ and functional connectivity patterns, confirm that the model captures genuine neurophysiological mechanisms, correctly identifying integrated fronto-centro-parietal networks in high performers and compensatory midline circuits in mid-performers. These findings suggest that EEG-GCIRNet provides a robust and interpretable end-to-end framework for EEG-based BCIs, advancing the development of reliable neurotechnology for rehabilitation and assistive applications. Full article

Background and Objectives: The insertion of endosseous implants requires the alveolar bone to be drilled, which produces alterations of the osseous neoalveolus approximately 1 mm deep, an area that will later be subjected to osseous renewal. The healing of the bone around the inserted implant is complex and depends on numerous factors, amongst which the size of the insertion orifice relative to the diameter of the implant, the design, and the pace and depth of the threads play an essential part. Therefore, the aim of this paper is to investigate from a histologic point of view the osseointegration of the implants inserted in a rabbit cortical bone by creating a 150 µm high healing chamber. Materials and Methods: 5 mm-long and 2 mm-wide titan implants were inserted into the femur of 15 12-month-old rabbits by using a drill with a 1.8 mm diameter, obtaining a spiralled healing chamber 150 µm high. The animals were euthanized after 7, 14, and 28 days according to effective legal and ethical protocols. The bone around the implants was severed 5 µm thick. After coloring with the Tricrom Goldner method, the sections that intercepted most centrally the intervention area were examined and photographed with an Olympus microscope. Results: The histologic result showed osseous healing within the healing chamber in the third to the endosteum of the implant after 7 days from the insertion. After 14 days, the osseous healing spread to 2/3 of the healing chamber. After 28 days, the whole healing chamber was occupied by bone. Conclusions: The healing chamber favored proper conditions for osseous healing, which began at the level of the endosteum. This statement is based on the histologic findings of bone formation after 7 days only in the third of the endosteum of the healing chamber. A 150 µm height of the healing chamber obtained in the rabbit cortical bone does not pose a risk of connective tissue proliferation. Full article

Background/Objectives: Diaschisis, reduced neural activity, perfusion, and metabolism in structurally intact but anatomically connected regions, is a network-level consequence of focal brain injury. Despite the extensive literature, its prevalence across imaging modalities and diaschisis subtypes has not been systematically synthesized. This review aims to identify convergent evidence for diaschisis after ischemic stroke and clarify how its detection relates to neuroanatomical disconnection, clinical factors, and imaging methods. (PROSPERO: CRD420251017909). Methods: PubMed and Embase were searched through February 2025 for studies reporting quantitative measures of diaschisis using perfusion, metabolic, or functional imaging. Pooled prevalence and modality-specific estimates were calculated. Subgroup analyses examined diaschisis subtypes, stroke severity, age, and study quality. Results: Sixty-six studies (3021 patients) were included. Overall pooled prevalence was 53% (95% CI: 47–58%). Crossed cerebellar diaschisis was most frequently studied (49%), while thalamic and other remote patterns showed comparable or higher effect sizes. Detection varied primarily by imaging modality: ASL MRI (67%) and PET (58%) showed the highest sensitivity; SPECT (53%) and CTP (49%) were intermediate; DSC-PWI had the lowest (28%). In contrast, age had no measurable effect and stroke severity only modestly increased detection, suggesting that diaschisis is driven predominantly by neuroanatomical disconnection rather than demographic or clinical variables. Egger’s tests indicated minimal publication bias. Conclusions: Diaschisis is a common manifestation of network vulnerability after ischemic stroke, determined chiefly by lesion topology and long-range anatomical connectivity. Detection depends more on imaging physiology than patient characteristics. Standardized definitions and longitudinal multimodal studies are needed to clarify its temporal evolution and clinical significance. Full article

Rapid evolution of electricity distribution networks challenges the maintenance of up-to-date information in electricity utility databases. This hinders the ability of utilities to understand phase connectivity and topology of users in their distribution networks. Extensive research has been conducted to develop smart meter data-driven phase identification and topology determination approaches as alternatives to the conventional, time-consuming, and expensive approach of manual inspection. However, the majority of such approaches are challenged by low levels of smart meter penetration in distribution networks, entailing further investigation. The objective of this paper is to contribute to this challenge by proposing an alternative smart meter data-driven approach of user phase identification, verification, and topology determination and testing the method on a real Australian distribution network under varied levels of smart meter penetration. This paper first presents a smart meter data-driven user phase identification tool using k-means clustering. Then, a smart meter data-driven user phase verification and topology determination approach is introduced by analyzing voltage-to-power sensitivities obtained from linear regression. Four distinct linear regression models are developed and compared to recognize relevant parameters and input variables leading to the most reliable sensitivities. The overall process proposed in this study demonstrated high accuracy at original smart meter penetration of 75% of the case study DN. The performance at reduced smart meter penetrations of 50% and 25% is also examined and discussed in the paper. Full article

Background/Objectives: Unmet healthcare needs, driven by structural and patient-level barriers, are particularly critical in chronic obstructive pulmonary disease (COPD). However, limited research has examined how academic themes on this topic connect and evolve over time. This study analyzed the structure and temporal shifts in research trends on unmet healthcare needs in COPD to identify key concepts and topics and policy implications. Methods: We systematically searched PubMed, Embase, and CINAHL (12–15 March 2025) to identify English-language abstracts on unmet healthcare needs in COPD. Eligible studies were peer-reviewed articles with an English-language abstract that examined unmet healthcare needs from the patient perspective. In total, 451 abstracts were analyzed using text network analysis and Latent Dirichlet Allocation. Topic distributions before and after the coronavirus disease pandemic were assessed using chi-square tests, and findings were interpreted within Penchansky and Thomas’s 5A healthcare access framework. Results: Six topics emerged: socioeconomic disparities, early diagnosis and symptom management, guideline-based information and technology use, integrated care for advanced COPD, access to pulmonary rehabilitation, and equitable medication availability. These topics mapped onto all five access dimensions, underscoring the multidimensional nature of unmet healthcare needs. Network analysis identified management, diagnosis, symptoms, exacerbation, and other related terms as central hubs in the discourse. Post-pandemic, research shifted toward digital information delivery, technology adoption, and equitable pharmacotherapy. Conclusions: Findings suggest that reducing unmet healthcare needs in COPD requires integrated systems that address both disease complexity and access barriers. Targeted, multidisciplinary, and policy-driven interventions in highly central domains are needed to reduce disparities and improve outcomes. This study also confirmed a post-pandemic shift in research priorities, emphasizing the need for equitable and adaptive healthcare policies. Full article

Major depressive disorder (MDD), identified by the World Health Organization as the leading cause of disability worldwide, remains underdiagnosed due to the lack of objective diagnostic tools. Electroencephalogram (EEG) signals offer potential biomarkers, yet conventional analyses often overlook the brain’s nonlinear dynamics. In this study, we analyzed resting-stage EEG data to identify four microstate types in MDD patients. Symmetrical microstate–brain networks were then constructed for each microstate by using time series of four types of microstates as dynamic windows. Then, we compared microstate features (duration, occurrence, coverage, transition probability) and brain network parameters (clustering coefficient, characteristic path length, local and global efficiency) between MDD patients and healthy controls to analyze the characteristics of the changes in the brain activities of the patients with MDD and the topological patterns of the functional connectivity. The comparative analysis showed that MDD patients showed more frequent microstate transitions and reduced network efficiency, suggesting elevated energy consumption and impaired neural integration, which may imply a cognitive shift in MDD patients toward internal focus and psychological withdrawal from external stimuli. By integrating microstate and brain network features, we captured the temporal and spatial characteristics of MDD-related brain activity and validated their diagnostic utility using our previously proposed multiscale spatiotemporal convolutional attention network (MSCAN). Our MSCAN achieved an accuracy of 98.64% for MDD detection, outperforming existing approaches. Our study can offer promising implications for the intelligent diagnosis of MDD and a deeper understanding of its neurophysiological underpinnings. Full article

Public open spaces play a vital role in supporting social connection and leisure among residents, enhancing quality of life while contributing to both economic growth and environmental health. The rapid global urbanization underscores the critical link between urban environments and human health, which demands focusing on sustainable, health-conscious urban planning. Accordingly, Public and green spaces are vital in this context, as recognized by global agendas like the Sustainable Development Goals (SDG) 11.7. This research aims to objectively evaluate the availability of public open spaces (POS) in Alexandria, Egypt. This study will utilize Geographic Information System (GIS) to formulate a methodology that incorporates spatial data analysis for quantifying public open spaces and assessing the proportion of the population with convenient access to these areas, evaluating their coverage, service area isochrones, spatial distribution, and proximity to residential areas. The study will benchmark its findings against global standards to expose critical spatial inequalities within cities of the Global South. The primary aim is to present evidence-based recommendations for sustainable urban public space design, tackling availability and accessibility issues to improve the well-being of Alexandria’s expanding urban population. This research offers a scientific foundation to inform policy and decision-making focused on creating more equitable, healthier, and resilient urban environments. Full article

Considering the global objective of carbon emission reduction, this paper focuses on optimizing the operational efficiency of grid-connected electric vehicles (EVs) and promoting sustainable energy integration and thus proposes a novel dual-incentive mechanism combining real-time pricing (RTP) and carbon quotas. A core of this study is the development of a bilevel programming model that effectively captures the strategic interaction between power suppliers (PS) and microgrid (MG) users. At the upper level, the model enables the PS to optimize electricity prices, achieving both revenue maximization and grid balance maintenance; at the lower level, it supports MGs in rational scheduling of EV charging/discharging, photovoltaic and wind energy (PWE) utilization, and load consumption, ensuring the fulfillment of user demands while maximizing MG profits. To address the non-convex factors in the model that hinder an efficient solution, another key is the design of a bilevel distributed genetic algorithm, which realizes efficient decentralized decision making and provides technical support for the practical application of the model. Through comprehensive simulations, the study verifies significant quantitative outcomes. The proposed algorithm converges after only 61 iterations, ensuring efficient solution performance. The average purchase price of electricity from the PS for the MG is USD 1.1, while the selling price of PWE sources from MG for the PS is USD 0.6. This effectively promotes the MG to prioritize the consumption of PWE sources and encourages the PS to repurchase the electricity generated by PWE sources. On average, carbon emissions decreased by approximately 300 g each time slot, and the average amount of carbon trading was around USD 8. Ultimately, this research delivers a practical and impactful solution for the development of MGs and the advancement of carbon reduction goals. Full article

In the context of green transition and digital transformation, forestry is becoming a strategic area of application of current modern technologies. The Internet of Things (IoT), artificial intelligence (AI), big data analysis (Big Data) and Digital Twins define the basic infrastructure of smart forestry. By connecting sensors, drones and satellites, IoT allows for continuous monitoring of forest ecosystems, risk anticipation and decision optimization in real-time. The purpose of this study is to perform a comprehensive narrative analysis of the relevant scientific literature from the recent period (2020–2025) regarding the application of IoT in forestry, highlighting the conceptual, technological and institutional developments. Based on a selection of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) (29 full-text articles), four major axes are analyzed: (A) forest fire detection and prevention; (B) climate-smart forestry and carbon accounting; (C) forest digitalization through the concepts of Forest 4.0, Forest 5.0 and Digital Twins; (D) sustainability and digital forest policies. The results show that IoT is a catalyst for the sustainable transformation of the forest sector, supporting carbon accounting, climate-risk reduction and data-driven governance. The analysis highlights four major developments: the consolidation of IoT–AI architectures, the integration of IoT and remote sensing, the emergence of Forest 4.0/5.0 and Digital Twins and the growing role of governance and data standards. These findings align with the objectives of the EU Forest Strategy 2030 and the European Green Deal. Full article

Objectives: To evaluate the changes in retinal function and neural conduction along the visual pathways after 12 months of treatment with Citicoline oral solution in patients with open-angle glaucoma (OAG). Methods: In this randomized, prospective, double-blind study, 29 OAG patients were enrolled. Fifteen patients (Citicoline Group, 15 eyes) received Citicoline oral solution (Neurotidine^®, 500 mg/day), and 14 patients (Placebo Group, 14 eyes) received placebo for 12 months. Visual field (VF), pattern electroretinogram (PERG), visual evoked potentials (VEP), and Retinocortical Time (RCT) were assessed at baseline and after 6 and 12 months. Brain Diffusion Tensor Imaging (DTI)-Magnetic Resonance Imaging (MRI) was performed at baseline and at 12 months. Results: PERG, VEP, and RCT baseline values were comparable between groups (p > 0.01) at baseline. After 12 months of Citicoline treatment, significant (p < 0.01) increases in PERG P50–N95 and VEP N75-P100 amplitudes, and significant shortening of PERG P50, VEP P100 implicit times and RCT were observed. VEP implicit times shortening significantly correlated with the changes in VF Mean Deviation, and RCT shortening was associated with changes in DTI-MRI metrics in the lateral geniculate nucleus and on optic radiations, without reaching the level of significance. No significant changes were found in the Placebo Group. Conclusions: In OAG, Citicoline oral solution enhances retinal function likely through neuromodulation processes and changes post-retinal visual pathway connectivity. This could explain the improvement of visual field defects. Full article

Integrating connected and autonomous vehicle dedicated lanes (CAVDLs) into existing road networks under mixed traffic conditions presents a complex challenge, often requiring a balance of multiple conflicting objectives. This study develops a dynamic multi-objective optimization framework, formulated as a mixed-integer nonlinear programming problem, to determine the optimal network-wide deployment of CAVDLs. The framework integrates three core components: an endogenous demand model capturing connected and autonomous vehicle (CAV)/human-driven vehicle (HDV) mode choice, a multi-class dynamic traffic assignment model that adjusts lane capacity based on CAV-HDV interactions, and an NSGA-III algorithm that minimizes total system travel time, total emissions, and construction costs. Results of a case study indicate the following: (i) sensitivity analysis confirms that user value of time is the most critical factor affecting CAV adoption; the model’s endogenous consideration of this variable ensures alignment between CAVDL layouts and actual demand; (ii) the proposed Pareto-optimal solution reduces total travel time and emissions by approximately 31% compared to a no-CAVDL scenario, while cutting construction costs by 23.5% against a single-objective optimization; (iii) CAVDLs alleviate congestion by reducing bottleneck duration and peak density by 36.4% and 16.3%, respectively. The developed framework provides a novel and practical decision-support tool that explicitly quantifies the trade-offs among traffic efficiency, environmental impact, and infrastructure cost for sustainable transportation planning. Full article