Search Results (117,947)

This paper examines the impact of government-led green certification on enterprise green transformation, utilizing data from A-share-listed firms in China and focusing on the Green Factory Certification program launched by the Ministry of Industry and Information Technology in 2016. We find that government-led green certification has a significant environmental incentive effect, with certified green factories significantly accelerating enterprise green transformation compared to non-certified enterprises. These findings provide a strong response to the ongoing debate regarding the environmental performance of green certification policies. Unlike general green certification, government-led green certification incorporates both internal incentives and external monitoring. Green factory certification reduces financing costs and promotes internal green innovation, while also attracting the attention of green investors and media scrutiny, which in turn accelerates enterprise green transformation. Additionally, green factory certification has differential effects on enterprise green transformation. The effect of green factory certification on enterprise green transformation is more pronounced in regions with high public environmental concern, non-heavily polluting industries, non-state-owned enterprises, and large-scale enterprises. This paper broadens the policy pathways for enterprise green transformation from the perspective of government-led green certification, offering valuable insights for promoting such transformations. Full article

Background: Globally, acute malnutrition remains a significant public health challenge. Severe acute malnutrition (SAM) is the most lethal type of acute malnutrition. This study aimed to produce pooled estimates of the effectiveness of integrated management of acute malnutrition (IMAM) programs in addressing SAM in Somalia. Methods: Medline, Embase, Cochrane, Web of Science, CINAHL, ProQuest, Google Scholar, eLENA, and the UNICEF website were searched with no language or date restrictions. Random effect models were used to estimate the pooled estimates of outpatient therapeutic program (OTP) and stabilization centres (SC) performance outcomes (I² > 50%). Results: Of 186 identified studies, three included data from IMAM programs in Somalia but were excluded, as they had incomplete data. Included were seven datasets identified through the authors’ list, which screened 36.34 million and admitted 2.31 million (6.35%) children aged 6–59 months. The pooled estimates of IMAM performance outcomes [%, (95% confidence interval)] for OTPs and SCs were, respectively, 2.45 (2.18, 2.56) and 4.11 (95%CI: 3.33, 4.90) for relapse, 95.39 (94.87, 95.90) and 80.81 (79.25, 82.37) for recovery, 0.18 (0.15, 0.21) and 1.73 (1.51, 1.95) for death, 2.57 (2.34, 2.79) and 2.75 (2.37, 3.12) for defaulter, 1.86 (1.55, 2.17), and 0.84 (0.66, 1.02) for non-recovery. Conclusions: OTP and SC performance outcomes in Somalia exceeded the Sphere Minimum Standards and compare favourably with results from the region. The adaptation of IMAM programs to meet multiple challenges in Somalia, supported by well-designed, coordinated, standardized, integrated, and harmonized implementation plans, is a strength. Full article

Background: While long-term opioid therapy is a widely utilized strategy for managing chronic pain, many patients have understandable questions and concerns regarding its safety, efficacy, and potential for dependency and addiction. Providing clear, accurate, and reliable information is essential for fostering patient understanding and acceptance. Generative artificial intelligence (AI) applications offer interesting avenues for delivering patient education in healthcare. This study evaluates the reliability, accuracy, and comprehensibility of ChatGPT’s responses to common patient inquiries about opioid long-term therapy. Methods: An expert panel selected thirteen frequently asked questions regarding long-term opioid therapy based on the authors’ clinical experience in managing chronic pain patients and a targeted review of patient education materials. Questions were prioritized based on prevalence in patient consultations, relevance to treatment decision-making, and the complexity of information typically required to address them comprehensively. We assessed comprehensibility by implementing the multimodal generative AI Copilot (Microsoft 365 Copilot Chat). Spanning three domains—pre-therapy, during therapy, and post-therapy—each question was submitted to GPT-4.0 with the prompt “If you were a physician, how would you answer a patient asking…”. Ten pain physicians and two non-healthcare professionals independently assessed the responses using a Likert scale to rate reliability (1–6 points), accuracy (1–3 points), and comprehensibility (1–3 points). Results: Overall, ChatGPT’s responses demonstrated high reliability (5.2 ± 0.6) and good comprehensibility (2.8 ± 0.2), with most answers meeting or exceeding predefined thresholds. Accuracy was moderate (2.7 ± 0.3), with lower performance on more technical topics like opioid tolerance and dependency management. Conclusions: While AI applications exhibit significant potential as a supplementary tool for patient education on opioid long-term therapy, limitations in addressing highly technical or context-specific queries underscore the need for ongoing refinement and domain-specific training. Integrating AI systems into clinical practice should involve collaboration between healthcare professionals and AI developers to ensure safe, personalized, and up-to-date patient education in chronic pain management. Full article

Kazakh Sign Language (KSL) is a crucial communication tool for individuals with hearing and speech impairments. Deep learning, particularly Transformer models, offers a promising approach to improving accessibility in education and communication. This study analyzes the syntactic structure of KSL, identifying its unique grammatical features and deviations from spoken Kazakh. A custom parser was developed to convert Kazakh text into KSL glosses, enabling the creation of a large-scale parallel corpus. Using this resource, a Transformer-based machine translation model was trained, achieving high translation accuracy and demonstrating the feasibility of this approach for enhancing communication accessibility. The research highlights key challenges in sign language processing, such as the limited availability of annotated data. Future work directions include the integration of video data and the adoption of more comprehensive evaluation metrics. This paper presents a methodology for constructing a parallel corpus through gloss annotations, contributing to advancements in sign language translation technology. Full article

Many regions worldwide are exposed to multiple omnipresent hazards occurring in complex interactions. However, multi-hazard assessments are not yet fully integrated into current planning tools, particularly when referring to transboundary areas. This work aims to enable spatial planners to include multi-hazard assessments in their climate change adaptation measures using available data. We focus on a set of hazards (e.g., extreme heat, drought, landslide) and propose a four-step methodology to (i) harmonise existing data from different databases and scales for multi-hazard assessment and mapping and (ii) to read identified multi-hazard bundles in homogeneous territorial areas. The methodology, whose outputs are replicable in other EU contexts, is applied to the illustrative case of Northeast Italy. The results show a significant difference between hazards with a ‘dichotomous’ spatial behaviour (shocks) and those with a more complex and nuanced one (stresses). The harmonised maps for the single hazards represent a new piece of knowledge for our territory since, to date, there are no comparable maps with this level of definition to understand hazards’ spatial distribution and interactions between transboundary areas. This study does present some limitations, including putting together data with a remarkable difference in definition for some hazards. Full article

In the evolving landscape of fandom economics, fan-driven crowdfunding has emerged as a powerful force, transforming audiences from passive consumers into active participants in celebrity branding. This study examines the roles of trust, loyalty, and perceived value in shaping crowdfunding participation within Chinese Weibo K-pop communities, where fans collectively finance large-scale public advertisements for their idols. Using structural equation modeling (SEM) on survey data from 260 participants, our findings reveal that trust and loyalty within fan communities significantly enhance engagement in crowdfunding activities, reinforcing perceived idol value and financial commitment to promotional campaigns. These insights position fan crowdfunding as a key driver of digital-era consumer culture, illustrating its impact on idol branding, online community dynamics, and the monetization of fandom engagement. By bridging fandom studies, digital marketing, and behavioral economics, this research provides theoretical advancements and practical strategies for entertainment agencies and celebrity managers seeking to harness the economic power of fan-driven marketing. As fandoms continue to reshape traditional advertising models, understanding the mechanisms behind community-based crowdfunding offers new avenues for brand engagement, audience monetization, and sustainable fan participation in the global entertainment industry. Full article

Background/Objectives: We aimed to evaluate asprosin and peptide tyrosine–tyrosine (PYY) levels in schizophrenia patients and the relationships between these levels and clinical severity, as well as whether these two hormones have a role in determining the disease and/or the phases of the disease. Methods: This study included 50 patients with schizophrenia in the remission phase, 50 in the acute phase, and 50 controls. The Positive and Negative Syndrome Scale (PANSS) was filled out for patients. The patients’ biochemical parameters and asprosin and PYY levels were measured. Results: Levels of asprosin and PYY were significantly different in all three groups (p < 0.001, p < 0.001). In the remission phase group, asprosin levels had a negative effect on PANSS general symptomatology scores (p: 0.002, p < 0.001). In the acute phase group, while PYY levels showed a negative effect on PANSS general symptomatology scores (p: 0.031), asprosin levels had a negative effect on all subscales of PANSS (p < 0.001). In the acute phase, a one-unit decrease in asprosin levels was associated with a 93% increase in PANSS total scores. The results of the receiver operating characteristic (ROC) analysis to distinguish the acute phase showed that PYY could not be used for diagnosis (p: 0.066), but asprosin was associated with the acute phase of schizophrenia (p < 0.001) and both asprosin and PYY were associated with the disease (p < 0.001, p < 0.001). Conclusions: We think that both asprosin and PYY can be used as potential biomarkers to identify schizophrenia, and only asprosin to identify the phases of the disease. PYY and asprosin levels may be markers that can be used to determine clinical severity. Full article

Due to low contrast, chromatic aberration, and generally small objects in underwater environments, a new underwater fish detection model, BGLE-YOLO, is proposed to investigate automated methods dedicated to accurately detecting underwater objects in images. The model has small parameters and low computational effort and is suitable for edge devices. First, an efficient multi-scale convolutional EMC module is introduced to enhance the backbone network and capture the dynamic changes in targets in the underwater environment. Secondly, a global and local feature fusion module for small targets (BIG) is integrated into the neck network to preserve more feature information, reduce error information in higher-level features, and increase the model’s effectiveness in detecting small targets. Finally, to prevent the detection accuracy impact due to excessive lightweighting, the lightweight shared head (LSH) is constructed. The reparameterization technique further improves detection accuracy without additional parameters and computational cost. Experimental results of BGLE-YOLO on the underwater datasets DUO (Detection Underwater Objects) and RUOD (Real-World Underwater Object Detection) show that the model achieves the same accuracy as the benchmark model with an ultra-low computational cost of 6.2 GFLOPs and an ultra-low model parameter of 1.6 MB. Full article

Burst pressure is one of the critical strength parameters used in the design and operation of pressure vessels because it represents the maximum pressure that a vessel can withstand before failing. Historically, the Barlow formula was used as a design base for estimating burst pressure. However, it does not consider the plastic flow response for ductile steels and is applicable only to thin-walled cylinders (i.e., the diameter to thickness ratio D/t ≥ 20). A new multiaxial plastic yield theory was developed to consider the plastic flow response, and the associated theoretical (i.e., Zhu–Leis) solution of burst pressure was obtained and has gained extensive applications in the pipeline industry because it was validated by different full-scale burst test datasets for large-diameter, thin-walled pipelines in a variety of steel grades from Grade B to X120. The Zhu–Leis flow theory of plasticity was recently extended to thick-walled pressure vessels, and the associated exact flow solution of burst pressure was obtained and is applicable to both thin and thick-walled cylindrical shells. Many full-scale burst tests are available for thin-walled line pipes in the pipeline industry, but limited pressure burst tests exist for thick-walled vessels. To validate the newly developed exact solutions of burst pressure for thick-walled cylinders, this paper conducts a series of burst pressure tests on small-diameter, thick-walled pipes. In particular, six burst tests are carried out for three thick-walled pipes in Grade B carbon steel. These pipes have a nominal diameter of 2.375 inches (60.33 mm) and three nominal wall thicknesses of 0.154, 0.218, and 0.344 inches (3.91, 5.54, and 8.74 mm), leading to D/t = 15.4, 10.9, and 6.9, respectively. With the burst test data, comparisons show that the Zhu–Leis flow solution of burst pressure matches well the burst test data for thick-walled pipes. Thus, these burst tests validate the accuracy of the Zhu–Leis flow solution of burst pressure for thick-walled cylindrical vessels. Full article

Plant-based products are gaining increasing popularity, making a vegetarian diet a fundamental part of nutrition among many social groups. The aim of this study was to assess the quality of selected vegetarian products available on the Polish market and their homemade counterparts. Additionally, consumer preferences and dietary behaviors regarding vegetarian diets and products available on the Polish market were analyzed. The consumer evaluation of the intensity of selected sensory attributes using the five-point scale method showed that, among the hummus samples, the natural hummus received the highest rating among all the tested products. In the falafel group, the homemade falafel received the highest scores. The consumer preference assessment using the ranking method, which considered the taste of the products, indicated that traditional hummus received the highest scores. In the falafel group, the highest number of points was awarded to the homemade falafel and the chickpea–spelt falafel. The majority of respondents declared that the taste of the tested products was a very important quality determinant. The choice of plant-based products made by consumers primarily depends on individual dietary preferences. The key determinant influencing consumers when selecting plant-based products is taste, which plays a crucial role in their decision to repurchase. Full article

Remote sensing (RS) has been widely used to monitor cyanobacterial blooms in inland water bodies. However, the accuracy of RS-based monitoring varies significantly depending on factors such as waterbody type, sensor characteristics, and analytical methods. This study comprehensively evaluates the current capabilities and challenges of RS for cyanobacterial bloom monitoring, with a focus on achievable accuracy. We find that chlorophyll-a (Chl-a) and phycocyanin (PC) are the primary indicators used, with PC demonstrating greater accuracy and stability than Chl-a. Sentinel and Landsat satellites are the most frequently used RS data sources, while hyperspectral images, particularly from unmanned aerial vehicles (UAVs), have shown high accuracy in recent years. In contrast, the Medium-Resolution Imaging Spectrometer (MERIS) and Moderate-Resolution Imaging Spectroradiometer (MODIS) have exhibited lower performance. The choice of analytical methods is also essential for monitoring accuracy, with regression and machine learning models generally outperforming other approaches. Temporal analysis indicates a notable improvement in monitoring accuracy from 2021 to 2023, reflecting advances in RS technology and analytical techniques. Additionally, the findings suggest that a combined approach using Chl-a for large-scale preliminary screening, followed by PC for more precise detection, can enhance monitoring effectiveness. This integrated strategy, along with the careful selection of RS data sources and analytical models, is crucial for improving the accuracy and reliability of cyanobacterial bloom monitoring, ultimately contributing to better water management and public health protection. Full article

Background/Objectives: Kinesiophobia, or the fear of movement, is a significant problem in the rehabilitation of patients after a stroke, especially in individuals with diabetes, who have an increased risk of health complications. The aim of the study was to validate the Tampa Scale for Kinesiophobia (TSK) for assessing kinesiophobia in the context of patients with diabetes complicated by stroke to ensure its adequacy and reliability in this specific group of patients. Methods: After considering exclusion criteria, 166 patients with type 2 diabetes after ischemic stroke, hospitalized in the neurological rehabilitation ward, were included in the analysis. A survey using the TSK was conducted in the study group. A reliability analysis of the questionnaire was conducted, and then exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were used to disclose the number of factors that characterize the study group. Results: The Cronbach’s alpha value for the entire scale is 0.875. The value for all the questions on the scale was also above 0.86, so they are considered reliable. Removing any question does not increase the value of Cronbach’s alpha or Guttman index. Based on the scree plot, two factors were identified. The first factor includes 12 items and forms a physical factor, while the second factor includes 5 items and forms a psychological factor. The fit of the two-factor model was checked using confirmatory factor analysis. The final two-factor model has an acceptable fit. All the factor loadings are statistically significant. The factor loadings range from 0.262 to 0.729 for the physical factor and from 0.543 to 0.822 for the psychological factor. Conclusions: The TSK is a reliable and valid tool for assessing the level of kinesiophobia in a group of patients with type 2 diabetes complicated by stroke. The results of the study using this tool may contribute to the development of more effective therapeutic strategies that take into account the specific physical and psychological needs of this group of patients. Full article

The disambiguation of word senses (Word Sense Disambiguation, WSD) plays a crucial role in various natural language processing (NLP) tasks, such as machine translation, sentiment analysis, and information retrieval. Due to the complex morphological structure and polysemy of the Korean language, the meaning of words can change depending on the context, making the WSD problem challenging. Since a single word can have multiple meanings, accurately distinguishing between them is essential for improving the performance of NLP models. Recently, large-scale pre-trained models like BERT and GPT, based on transfer learning, have shown promising results in addressing this issue. However, for languages with complex morphological structures, like Korean, the tokenization mismatch between pre-trained models and fine-tuning data prevents the rich contextual and lexical information learned by the pre-trained models from being fully utilized in downstream tasks. This paper proposes a novel method to address the tokenization mismatch issue during the fine-tuning of Korean WSD, leveraging BERT-based pre-trained models and the Sejong corpus, which has been annotated by language experts. Experimental results using various BERT-based pre-trained models and datasets from the Sejong corpus demonstrate that the proposed method improves performance by approximately 3–5% compared to existing approaches. Full article

Scaled integument of six species of the genus Acanthocercus and Laudakia nupta, family Agamidae was studied using light and scanning electron microscopy. Gross observation revealed the presence of two types of modified scales in the males. The enlarged scales covered with an extremely thick β-corneous layer were detected in the dorsal and ventral surfaces of the tail base and on the palmar and plantar limb surfaces of all species. After detachment of the β-layer, the surface of such scales was covered with high papillae (“papillary scales”). The callous scales were found in the precloacal region of Acanthocercus species and in both precloacal and mid-ventral regions of L. nupta. Modified scales were found in some females and subadut specimens, and absent in juveniles. A prominent papillary layer characterized the dermis of both scale types. It was assumed that well-developed dermal papillae in such scales expended the total surface area of stratum germinativum and created a pool of proliferated cells in the interpapillary loops to increase the production of differentiating keratinocytes. These processes were undoubtedly associated with the formation of a thick and resistant corneous layer that distinguished both types of scales. Functional role, a taxonomic value, and analogies with normal and pathological epidermis in birds and mammals are discussed. Full article

The increasing complexity of interconnected systems in the Internet of Things (IoT) demands advanced methodologies for real-time security and management. This study presents MAS-LSTM, an anomaly-detection framework that combines multi-agent systems (MASs) with long short-term memory (LSTM) networks. By training agents on IoT traffic datasets (NF-ToN-IoT, NF-BoT-IoT, and their V2 versions), MAS-LSTM offers scalable, decentralized anomaly detection. The LSTM networks capture temporal dependencies, enhancing anomaly detection in time-series data. This framework overcomes key limitations of existing methods, such as scalability in heterogeneous traffic and computational efficiency in resource-constrained IIoT environments. Additionally, it leverages graph signal processing for adaptive and modular detection across diverse IoT scenarios. Experimental results demonstrate its effectiveness, achieving F1 scores of 0.9861 and 0.8413 on NF-BoT-IoT and NF-ToN-IoT, respectively. For V2 versions, MAS-LSTM achieves F1 scores of 0.9965 and 0.9678. These results highlight its robustness in handling large-scale IIoT traffic. Despite challenges in real-world deployment, such as adversarial attacks and communication overhead, future research could focus on self-supervised learning and lightweight architectures for resource-constrained environments. Full article