Search Results (7,918)

Cervical cancer is a significant health challenge, yet it can be effectively prevented through early detection. Cytology-based screening is critical for identifying cancerous and precancerous lesions; however, the process is labor-intensive and reliant on trained experts to scan through hundreds of thousands of mostly normal cells. To address these challenges, we propose a novel distribution-augmented approach using contrastive self-supervised learning for detecting abnormal squamous cervical cells from cytological images. Our method utilizes color augmentations to enhance the model’s ability to differentiate between normal and high-grade precancerous cells; specifically, high-grade squamous intraepithelial lesions (HSILs) and atypical squamous cells–cannot exclude HSIL (ASC-H). Our model was trained exclusively on normal cervical cell images and achieved high diagnostic accuracy, demonstrating robustness against color distribution shifts. We employed kernel density estimation (KDE) to assess cell type distributions, further facilitating the identification of abnormalities. Our results indicate that our approach improves screening accuracy and reduces the workload for cytopathologists, contributing to more efficient cervical cancer screening programs. Full article

The detection and analysis of cancer cell exosomes with high sensitivity and precision are pivotal for the early diagnosis and treatment strategies of prostate cancer. To this end, a microfluidic chip, equipped with a cactus-like array substrate (CAS) based on surface-enhanced Raman spectroscopy (SERS) was designed and fabricated for the detection of exosome concentrations in Lymph Node Carcinoma of the Prostate (LNCaP). Double layers of polystyrene (PS) microspheres were self-assembled onto a polyethylene terephthalate (PET) film to form an ordered cactus-like nanoarray for detection and analysis. By combining EpCAM aptamer-labeled SERS nanoprobes and a CD63 aptamer-labeled CAS, a ‘sandwich’ structure was formed and applied to the microfluidic chips, further enhancing the Raman scattering signal of Raman reporter molecules. The results indicate that the integrated microfluidic sensor exhibits a good linear response within the detection concentration range of 10⁵ particles μL⁻¹ to 1 particle μL⁻¹. The detection limit of exosomes in cancer cells can reach 1 particle μL⁻¹. Therefore, we believed that the CAS integrated microfluidic sensor offers a superior solution for the early diagnosis and therapeutic intervention of prostate cancer. Full article

Background: In the digital age, safeguarding children’s mental health (CMH) has emerged as one of the most pressing challenges. The rapid evolution of social media (SM) from a basic networking platform to a multifaceted tool has introduced numerous conveniences. However, it has also posed significant challenges to children’s mental well-being. Methods: Given the intricate relationship between the widespread use of SM and mental health issues in children, this study conducted a systematic scoping review to examine the literature on the impact of SM on CMH from 2014 to 2024. Literature searches were performed across five databases (Web of Science, ScienceDirect, Scopus, PubMed, and APA PsycInfo), and the retrieved studies were screened, extracted, and analyzed by the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Extension for Scoping Review (PRISMA-ScR) guidelines. Results: The review identified a complex relationship between SM use and CMH. Different SM platforms exhibited varying effects on children. Frequent SM use was strongly associated with lower self-esteem, depressive symptoms, anxiety, and other mental health challenges in children. Conversely, moderate use of SM facilitated social interactions and emotional expression, which may have a positive influence on mental health. Additionally, factors such as social support from family and school were found to play a critical role in mitigating the negative effects of SM on CMH. Conclusions: To enhance CMH, it is essential to guide children in the appropriate use of SM, promote awareness of privacy protection, and ensure adequate family and social support. Future research should further investigate the specific mechanisms underlying SM use and its differential effects on children across varying age groups and regions. Full article

Monitoring the dynamic behaviors of self-aligning roller bearings (SABs) is vital to guarantee the stability of various mechanical systems. This study presents a novel self-powered, intelligent, and self-aligning roller bearing (I-SAB) with which to monitor rotational speeds and bias angles; it also has an application in fault diagnosis. The designed I-SAB is compactly embedded with a novel sweep-type triboelectric nanogenerator (TENG). The TENG is realized within the proposed I-SAB using a comb–finger electrode pair and a flannelette triboelectric layer. A floating, sweeping, and freestanding mode is utilized, which can prevent collisions and considerably enhance the operational life of the embedded TENG. Experiments are subsequently conducted to optimize the output performance and sensing sensitivity of the proposed I-SAB. The results of a speed-sensing experiment show that the characteristic frequencies of triboelectric current and voltage signals are both perfectly proportional to the rotational speed, indicating that the designed I-SAB has the self-sensing capability for rotational speed. Additionally, as both the bias angle and rotational speed of the SAB increase, the envelope amplitudes of the triboelectric voltage signals generated by the I-SAB rise at a rate of 0.0057 V·deg⁻¹·rpm⁻¹. To further demonstrate the effectiveness of the triboelectric signals emitted from the designed I-SAB in terms of self-powered fault diagnosis, a Multi-Scale Discrimination Network (MSDN), based on the ResNet18 architecture, is proposed in order to classify the various fault conditions of the SAB. Using the triboelectric voltage and current signals emitted from the designed I-SAB as inputs, the proposed MSDN model yields excellent average diagnosis accuracies of 99.8% and 99.1%, respectively, indicating its potential for self-powered fault diagnosis. Full article

This perspective begins with an overview of the major impact that the dendron, dendrimer, and dendritic state (DDDS) discovery has made on traditional polymer science. The entire DDDS technology is underpinned by an unprecedented new polymerization strategy referred to as step-growth, amplification-controlled polymerization (SGACP). This new SGACP paradigm allows for routine polymerization of common monomers and organic materials into precise monodispersed, dendritic macromolecules (i.e., dendrons/dendrimers) with nanoscale sizes and structure-controlled features that match and rival discrete in vivo biopolymers such as proteins and nucleic acids (i.e., DNA, siRNA, mRNA, etc.). These dendritic architectures exhibit unprecedented new intrinsic properties widely recognized to define a new fourth major polymer architecture class, namely: Category (IV): dendrons, dendrimers, and random hyperbranched polymers after traditional categories: (I) linear, (II) cross-linked, and (III) simple-branched types. Historical confusion over the first examples of the structure confirmed and verified cascade, dendron, dendrimer, and arborol syntheses, while associated misuse of accepted dendritic terminology is also reviewed and clarified. The importance of classifying all dendrons and dendrimers based on branch cell symmetry and the significant role of critical nanoscale-design parameters (CNDPs) for optimizing dendritic products for pharma/nanomedicine applications with a focus on enhancing stealth, non-complement activation properties is presented. This is followed by an overview of the extraordinary growth observed for amphiphilic dendron/dendrimer syntheses and their self-assembly into dendritic supramolecular assemblies, as well as many unique applications demonstrated in pharma and nanomedicine, especially involving siRNA delivery and mRNA vaccine development. This perspective is concluded with optimistic expectations predicted for new dendron and dendrimer application roles in pharma, nanomedicine, and life sciences. Full article

The rapid detection and accurate localization of wildfires are critical for effective disaster management and response. This study proposes an innovative Unmanned aerial vehicles (UAVs)-based fire detection system leveraging a modified Miti-DETR model tailored to meet the computational constraints of drones. The enhanced architecture incorporates a redesigned AlexNet backbone with residual depthwise separable convolution blocks, significantly reducing computational load while improving feature extraction and accuracy. Furthermore, a novel residual self-attention mechanism addresses convergence issues in transformer networks, ensuring robust feature representation for complex aerial imagery. The model, which was trained on the FLAME dataset encompassing diverse fire scenarios, demonstrates superior performance in terms of Mean Average Precision (mAP) and Intersection over Union (IoU) metrics compared to existing systems. Its capability to detect and localize fires across varied backgrounds highlights its practical application in real-world scenarios. This advancement represents a pivotal step forward in applying deep learning for real-time wildfire detection, with implications for broader emergency management applications. Full article

Dealing with uncertainty is a pivotal skill for adaptive decision-making across various real-life contexts. Cognitive models suggest that individuals continuously update their knowledge based on past choices and outcomes. Traditionally, uncertainty has been linked to negative states such as fear and anxiety. Recent evidence, however, highlights that uncertainty can also evoke positive emotions, such as surprise, interest, excitement, and enthusiasm, depending on one’s task expectations. Despite this, the interplay between mood, confidence, and learning remains underexplored. Some studies indicate that self-reported mood does not always align with confidence, as these constructs evolve on different timescales. We propose that mood influences confidence, thereby enhancing decision flexibility—defined as the ability to switch effectively between exploration and exploitation. This increased flexibility is expected to improve task performance by increasing accuracy. Our findings support this hypothesis, revealing that confidence modulates exploration/exploitation strategies and learning rates, while mood affects reward perception and confidence levels. These findings indicate that metacognition entails a dynamic balance between exploration and exploitation, integrating mood states with high-level cognitive processes. Full article

In industries spanning manufacturing to software development, defect segmentation is essential for maintaining high standards of product quality and reliability. However, traditional segmentation methods often struggle to accurately identify defects due to challenges like noise interference, occlusion, and feature overlap. To solve these problems, we propose a cross-hierarchy feature fusion network based on a composite dual-channel encoder for surface defect segmentation, called CFF-Net. Specifically, in the encoder of CFF-Net, we design a composite dual-channel module (CDCM), which combines standard convolution with dilated convolution and adopts a dual-path parallel structure to enhance the model’s capability in feature extraction. Then, a dilated residual pyramid module (DRPM) is integrated at the junction of the encoder and decoder, which utilizes the expansion convolution of different expansion rates to effectively capture multi-scale context information. In the final output phase, we introduce a cross-hierarchy feature fusion strategy (CFFS) that combines outputs from different layers or stages, thereby improving the robustness and generalization of the network. Finally, we conducted comparative experiments to evaluate CFF-Net against several mainstream segmentation networks across three distinct datasets: a publicly available Crack500 dataset, a self-built Bearing dataset, and another publicly available SD-saliency-900 dataset. The results demonstrated that CFF-Net consistently outperformed competing methods in segmentation tasks. Specifically, in the Crack500 dataset, CFF-Net achieved notable performance metrics, including an Mcc of 73.36%, Dice coefficient of 74.34%, and Jaccard index of 59.53%. For the Bearing dataset, it recorded an Mcc of 76.97%, Dice coefficient of 77.04%, and Jaccard index of 63.28%. Similarly, in the SD-saliency-900 dataset, CFF-Net achieved an Mcc of 84.08%, Dice coefficient of 85.82%, and Jaccard index of 75.67%. These results underscore CFF-Net’s effectiveness and reliability in handling diverse segmentation challenges across different datasets. Full article

Background/Objectives: Supervised rehabilitation has been reported to improve motor and functional outcomes after lumbar spine surgery. However, the effects of supervised rehabilitation on psychosocial and participation-related outcomes are still debated. This study aimed to systematically review the effects of supervised rehabilitation on psychosocial and participation-related outcomes in patients after lumbar spine surgery. Methods: A systematic literature search was carried out using PubMed, EMBASE, CINAHL, PEDro, CENTRAL, and Google Scholar databases from inception to March 2024. Randomized controlled trials investigating the effects of supervised rehabilitation on psychosocial and participation-related outcomes after lumbar spine surgery were included. Methodological quality was assessed through the revised Cochrane risk of bias tool for randomized trials. Pooled effects were reported as the standardized mean difference (SMD) with a 95% confidence interval (CI₉₅) or reported qualitatively in the presence of clinical heterogeneity. The certainty of the evidence was rated through the GRADE approach. Results: Fifteen studies (1297 patients) were included. Very low evidence supported supervised rehabilitation to improve quality of life at 1 year (SMD: −0.28; CI₉₅ from −0.49 to −0.07, I² = 32%), while low evidence supported supervised rehabilitation to enhance self-efficacy at 6 months (SMD: −1.13; CI₉₅ from −1.54 to −0.72) and 1 year (SMD −1.03, CI₉₅ from −1.43 to −0.63). No effects of supervised rehabilitation were found on quality of life at 6 months or in terms of fear-avoidance belief, catastrophizing, anxiety, depression, and return to work at 6 months and 1 year (very low to low evidence certainty). Conclusions: Supervised rehabilitation improved quality of life and self-efficacy in patients after lumbar spine surgery. However, the certainty of the evidence ranged from very low to low, and further studies are needed. Full article

Substantial evidence globally confirms the benefits of physical activity (PA) interventions for the physical and mental health of overweight and obese children and adolescents. However, current research has yet to determine which behavior change techniques (BCTs) are most effective in PA interventions for this population. This systematic review aims to evaluate the application of BCTs in PA interventions for overweight and obese children and adolescents and to identify the most effective BCTs using the promise ratio. Five electronic databases (PubMed, Embase, Cochrane Central, Web of Science, and PsycINFO) were searched up to May 31, 2024, to identify intervention studies meeting the eligibility criteria for promoting PA in the target population. Thirteen studies were included (nine randomized controlled trials and four quasi-experimental studies). The quality of the included studies was assessed using a revised version of the Cochrane Risk of Bias tool and the Risk of Bias in Non-randomized Studies tool. Among the 24 BCTs implemented, the most frequently applied were goal setting (behavior), instruction on how to perform the behavior, feedback on behavior, and self-monitoring of behavior. Action planning, social support, and material incentives showed the most significant potential to promote PA. These findings provide valuable insights for designing future PA interventions for this group, with the potential to improve health outcomes and enhance PA participation among obese children and adolescents. Full article

Creative self-efficacy and social skills are two elements that can significantly enhance personal and professional development. The main objective of this research is to analyze the relations established between creative self-efficacy and social skills with other variables such as self-esteem, academic performance, and life satisfaction. The participants included in the methodology of this study are a total of 238 Portuguese university students. The instruments used were the Creative Self-Efficacy Scale, the Social Skills Questionnaire (CHASO), the Rosenberg Self-Esteem Scale (RSES), the Satisfaction with Life Scale (SWLS), and a series of ad hoc questions to assess academic performance. The results obtained indicate the existence of significant relations between creative self-efficacy and social skills, with the latter also being positively associated with self-esteem and life satisfaction. Analyses indicate that there are significant differences according to gender, academic performance, and the level of self-esteem of the participants. In addition, variables such as self-esteem, academic performance, and fluency act as predictors of life satisfaction. The importance of further exploring and understanding the complex relationship between creative self-efficacy, social skills, and individual well-being in the university context is discussed. Full article

Corporations are increasingly looking beyond inter-company collaborations to multidimensional collaborative activities between departments and organizational members within the company to strengthen innovative market competitiveness. High-tech corporations, specifically, are adopting collaborative learning approaches to promote work capability among engineers from the perspective of developing new technologies and increasing productivity. This study aimed to empirically verify the effect of the service quality of collaborative learning programs on the innovative work behavior of engineers in high-tech companies. Job autonomy, self-efficacy, and learning transfer were set as mediating variables and their effects on innovative work behavior were analyzed. The sample comprised 298 high-tech engineers in South Korea. Collaborative learning service quality was found to have a direct positive effect on job autonomy, self-efficacy, and learning transfer. However, job autonomy and self-efficacy did not influence innovative work behavior through learning transfer. On the other hand, collaborative learning quality had a positive effect on innovative work behavior through learning transfer. Thus, improving the service quality of collaborative learning programs in high-tech corporations can enhance learning transfer within the organization, leading to innovative business outcomes. Moreover, to maximize the effectiveness of collaborative learning, the service quality of learning programs can be improved by prioritizing learning transfer rather than job autonomy or self-efficacy. Full article

Nurse turnover presents a significant challenge for healthcare organizations worldwide, impacting patient care quality and organizational stability. Understanding the determinants of nurse turnover, particularly job satisfaction, intention to stay, organizational commitment, and general self-efficacy, is crucial for developing effective retention strategies. This study aimed to explore the relationships among job satisfaction, intention to stay, organizational commitment, general self-efficacy, and demographic variables. A cross-sectional, correlational research design was employed, with data collected through validated questionnaires distributed to a total convenience sample of 227 clinical nurses in Riyadh, Saudi Arabia between July 2023 and August 2023. Validated measurement tools, including the Job Satisfaction Index, the Intent to Stay Scale, the Organizational Commitment Scale, and the General Self-Efficacy Scale, were utilized. Descriptive statistics were employed to summarize demographic information, and a correlation analysis was conducted to explore the relationships between the study variables. The findings revealed moderate levels of job satisfaction, intention to stay, organizational commitment, and general self-efficacy among the clinical nurses, with significant positive correlations observed among these variables as well as the nurses’ sociodemographic characteristics. Notably, clinical nurses constituted a significant portion of the sample, suggesting the need for targeted interventions tailored to this demographic group as well as non-Saudi nurses (expatriate nurses), particularly in enhancing their organizational commitment and self-efficacy. The study found significant and positive associations between the four study variables and the nurses’ demographic characteristics. Tailored interventions addressing job satisfaction, intent to stay, organizational commitment, self-efficacy, and demographic variables are essential for mitigating nurse turnover. By fostering a supportive work environment and implementing targeted retention strategies informed by demographic insights and determinants of turnover, healthcare organizations can enhance nurse retention rates and ensure a stable and fulfilled nursing workforce. Full article

This paper presents a comparative study of the Vertex Block Descent (VBD) and Position-Based Dynamics (PBD) algorithms, focusing on their performance in physical simulation tasks. Unity, a versatile physics engine, served as the simulation platform for the experiments. Among various types of physical simulations of deformable objects, fluids, and cloth dynamics, cloth simulations were chosen for implementation with both algorithms. The experimental setup ensured identical parameters, including time steps and movement behavior, for both algorithms across scenarios involving hanging, object-to-object collisions, and self-collisions. The results indicate that while the performance difference in frames per second (fps) between the two algorithms is negligible for simulations with a small number of nodes, the VBD algorithm consistently outperforms the PBD algorithm as the node count increases. Furthermore, this study provides practical guidelines for maintaining real-time performance, detailing the maximum node count each algorithm can support, while sustaining a minimum threshold of 30 fps, which is necessary for real-time applications. The comparison was conducted using CPU-based computation to establish a baseline for future studies in GPU-accelerated environments, where parallel processing is expected to further highlight the performance advantages of VBD. Future work will extend this research by evaluating additional physical simulation models, including the Mass-Spring System and Extended Position-Based Dynamics (XPBD), and developing optimizations to enhance the efficiency and scalability of these algorithms. Full article

Natural polyphenolic compounds play a pivotal role in biological processes and exhibit notable antioxidant activity. Among these compounds, chlorogenic acid stands out as one of the most widespread and important polyphenols. The accurate detection of chlorogenic acid is crucial for ensuring the quality and classification of the raw materials used in its extraction, as well as the final products in the food, pharmaceutical, and cosmetics industries that contain this bioactive compound. Raman spectroscopy emerges as a powerful analytical tool, particularly in field applications, due to its versatility and sensitivity, offering both qualitative and quantitative analyses. By using the self-assembly of gold nanoparticles at liquid–liquid interfaces and the developed “aqua-print” process, we propose a facile and inexpensive route to fabricate enhanced substrates for surface-enhanced Raman spectroscopy with high reproducibility. To ensure substrate reliability and accurate molecule detection in SERS experiments, a benchmarking procedure was developed. This process involved the use of non-resonant rhodamine 6G dye in the absence of charge transfer and was applied to all synthesized nanoparticles and fabricated substrates. The latter revealed the highest enhancement factor of 4 × 10⁴ for 72 nm gold nanoparticles among nanoparticle diameters ranging from 14 to 99 nm. Furthermore, the enhanced substrate was implemented in the detection of chlorogenic acid with a concentration range from 10 μM to 350 μM, demonstrating high accuracy (R² > 99%). Raman mapping was employed to validate the good uniformity of the signal (the standard deviation was below 15%). The findings of this study were also supported by DFT calculations of the theoretical Raman spectra, demonstrating the formation of the chlorogenic acid dimer. The proposed method is strategically important for the development of the class of in-field methods to detect polyphenolic compounds in raw materials such as plants, extracted plant proteins, and polyphenolic compounds. Full article