(1) Background: Second Victims are health care workers negatively affected by adverse events and are prone to developing dysfunctional coping strategies compromising not only their own, but also patient safety. An effective way to address this issue is the implementation of peer support programs. This paper reports on the effectiveness of a peer support program at the Austrian Hietzing Clinic (KHI). (2) Methods: Telephone interviews were carried out after peer support sessions using a questionnaire. Descriptive analysis was carried out reporting frequencies and proportions for nominal variables, means with standard deviations for ordinal and interval-scaled variables. (3) Results: So far, 45 peer supporters have completed telephone interviews after a peer support session. The main reasons for the session were bad news from the private sphere and death of a patient. The peer supporters rated the helpfulness of the sessions for their colleagues as very high while not feeling very burdened themselves afterwards. (4) Conclusions: Underutilization of the program is probable. However, the high rating of helpfulness of the carried-out sessions by the peer supporters in combination with them not feeling very burdened, shows that the program is safe for the peer supporters and an upscaling is warranted.

With the rapid developments that have been made in computer vision and computer technology, semantic segmentation of high-resolution images has emerged as a mainstream and challenging task. The, segmentation of water areas remains a complex task due to the similar features between water areas or between water areas and other land objects. Meanwhile, since coastlines and riverbanks are typically presented in irregular shapes, it is difficult to obtain the categories of pixels at boundaries. This paper proposed the use of a Water-Land Boundary Attention Network (WLBANet) to improve the accuracy of pixels at boundaries between water and land. This network also reinforced the performance of contextual information extraction into distinguishing between water areas and other land objects. To prove the validity of this proposed network, WLBANet was performed on the Water Land Dataset (WLD), which is also by this paper, and which contains water areas and other land objects categories. The results demonstrates that the proposed method has significant effectiveness.

Managing a portfolio whose value closely tracks an index by trading only in a subset of the index constituents involves an NP-hard optimisation problem. In the prior literature, it has been suggested to solve this problem using sequential Monte Carlo (SMC, a.k.a. particle filter) methods. However, this literature does not take transaction costs into account, although transaction costs are the primary motivation for attempting to replicate the index by trading in a subset, rather than the full set of index constituents. This paper extends the SMC approach to index tracking to allow for proportional transaction costs and implements this extended method on empirical data for a variety stock indices. In addition to providing a more practically useful tracking strategy by allowing for transaction costs, we find that including a penalty for transaction costs in the optimisation objective can actually lead to better tracking performance.

Deploying search and rescue (SAR) dogs in disaster situations is crucial for effective rescue missions. This study delves into the challenges and obstacles encountered during the use of SAR dogs, particularly focusing on the earthquake that hit Turkey in February 2023. By employing a case study approach, the research explores various factors that impact the efficiency and safety of SAR dog operations, such as environmental conditions, equipment needs, cultural perceptions, and logistical hurdles. Environmental conditions such as extreme weather, hazardous materials, and unstable structures present significant risks to both the dogs and their handlers. The adequacy of equipment, including protective gear and medical supplies, is critical for ensuring the safety and efficiency of rescue operations. Logistical challenges, including the transportation and care of dogs in disaster zones, further complicate their deployment. Cultural barriers also play a crucial role, as perceptions of dogs vary widely across different communities, potentially affecting the acceptance and integration of canine units in rescue efforts. The case study of the earthquake in Turkey highlights these issues, showcasing the successes and setbacks experienced by international rescue teams. The findings indicate that although SAR dogs are invaluable in locating survivors, they face significant dangers, including hazardous materials, extreme weather, and physical injuries. Additionally, cultural differences and logistical issues complicate their deployment. This paper highlights the necessity for improved training, protective measures, and international standards to ensure SAR dogs' well-being and effectiveness in disaster response efforts. The study's implications suggest that addressing these challenges can markedly enhance the preparedness and resilience of SAR teams, leading to more efficient and safer rescue operations in future disaster scenarios.

This study critically explores the legislative frameworks and national regulations of Serbia, Croatia, and Slovenia concerning the engagement, training, and responsibilities of search and rescue dogs and their handlers. The goal is to offer a thorough understanding of how these dogs and their handlers are engaged, trained, and managed in each country. By carefully analyzing the legal foundations, the study aims to clarify the rights, duties, and responsibilities of all parties involved, ensuring that search and rescue dog deployments are conducted within a structured and lawful framework. Such an understanding is crucial for improving disaster management and ensuring a coordinated response to various emergencies. The findings reveal that although each country has strong legal provisions for deploying search and rescue dogs, there are significant differences in the detail and scope of these regulations. Slovenia is notable for its detailed guidelines, regular evaluations, and licensing processes, setting a high standard. Croatia benefits from strong institutional support and active international collaboration, which greatly enhances its disaster response capabilities. On the other hand, Serbia, despite having a solid foundational framework, could improve by better coordinating with non-governmental organizations and aligning more closely with international standards. Key areas for improvement include harmonizing standards, enhancing training programs, increasing public awareness, better resource allocation, and adopting new technologies. By addressing these issues, Serbia, Croatia, and Slovenia can further strengthen their legal and institutional frameworks, ensuring their search and rescue teams are better prepared for disasters. This study emphasizes the need for continuous improvement through collaboration, standardization, and innovation to maximize the effectiveness of search and rescue operations involving dogs.

The purpose of this study is to establish whether digital literacy and insurtech adoption influence insurance inclusion in Uganda. Principally, we sought to determine whether insurtech adoption mediates the nexus between digital literacy and insurance inclusion. This study adopted a cross-sectional and quantitative correlational approach. The study’s sample was 391 individuals who had used digital platforms such as mobile phones and computers to access insurance products and services in Uganda. Data were collected using structured survey questionnaires. Partial Least Squares Structural Equation Modelling (PLSEM) was employed to test the hypothesised relationships. The results demonstrate that both digital literacy and insurtech adoption significantly and positively influence insurance inclusion. We also found digital literacy to be a significant and positive determinant of insurtech adoption. Markedly, it was found that insurtech adoption mediates the association between digital literacy and insurance inclusion in Uganda. However, this study was conducted in a developing country with an underdeveloped insurance market and with low technological advancement. This may affect generalisation of the study findings. This study’s novelty lies in establishing how digital literacy and insurtech adoption interplay to influence insurance inclusion in Uganda. This is the first study to examine the effect of digital literacy and insurtech adoption on insurance inclusion.

Telemedicine has been increasingly considered an effective strategy to support patients with several diagnosis and therapies, avoiding the on-site visit and drastically reducing the risks to share infections. In the recent years, a number of different technologies have been developed and applied to clinical studies, with the aim to investigate new ways for increase the early diagnoses and monitor the clinical evolution of the most predictable diseases directly from the patients’ home. Telemedicine refers to all those technological interfaces used to remotely perform clinical procedures. Telemedicine applications could be useful in several medical fields, such as preventive medicine, home patient care, education for healthcare professionals and patients, research, public health, and health management. On the other hand, the synergy of these different technologies has a significant impact on the clinical risks analysis, particularly useful in frail patients. Although Telemedicine shows to have important advantages, undoubtedly important issues still remain. The present work aims to shed light on the main advantages and disadvantages related to of each application’s features, which may serve as a useful tool for researchers.

Disasters, whether natural or human-induced, pose significant threats to human lives, property, and infrastructure. This study examines the effectiveness of search and rescue (SAR) dogs in locating survivors during disasters, with a focus on the experiences in Serbia, Croatia, and Slovenia. The primary goals are to evaluate training protocols, operational challenges, success rates, and the overall impact of SAR dogs in disaster scenarios. The findings indicate that SAR dogs greatly enhance the efficiency of disaster response operations. Their superior sense of smell, agility, and ability to manoeuvre through complex terrains make them crucial for quickly locating survivors. However, the performance of SAR dogs is affected by various factors, including the quality of their training, environmental conditions, and the coordination between handlers and dogs. The research highlights several challenges encountered by SAR dogs, such as extreme weather conditions, hazardous terrains, fatigue, and psychological stress. Additionally, limited resources and inconsistent training standards can hinder their effectiveness. The study underscores the importance of standardized training protocols, increased resource allocation, and better coordination among SAR teams. By addressing these challenges, the efficiency of SAR dogs can be further optimized, resulting in more effective disaster responses and higher survival rates. SAR dogs play an essential role in disaster management in Serbia, Croatia, and Slovenia. Ongoing investment in their training and welfare, along with improved operational protocols, will ensure their crucial contribution to saving lives during disasters. This study contributes to a broader understanding of the effectiveness of SAR dogs and provides practical recommendations for enhancing disaster response strategies.

The evaluation of crowdfunding campaigns varies from person to person; some investors are more interested in the creativity of the project, and others are more concerned with the profiles of entrepreneurs. The study investigated how entrepreneurs’ face disclosure influenced the success of crowdfunding. Secondary data was collected from multiple crowdfunding platforms for projects in Africa, that is, cross-country data from 54 African countries, to overcome data limitations from a single country. Econometrics analysis revealed that the facial disclosure of entrepreneurs increases the probability of crowdfunding success. Images, videos, and backers had a positive influence on the success of crowdfunding. On the contrary, the duration of the crowdfunding campaign was negatively associated with its success. To reduce the knowledge asymmetry between creators and backers, those prepared to start a crowdfunding project must provide as much information as possible to show their abilities. This study contributes to understanding the role of disclosing an entrepreneur’s profile on economic exchanges in the success of online crowdfunding. The signalling and agency theories support the finding. The findings were limited to crowdfunding projects on the African continent and are not necessarily applicable to some developed countries.

This paper presents the results of quantitative research examining the impacts of demographic and socioeconomic factors on community resilience to disasters. The survey was carried out utilising a questionnaire distributed and subsequently collected online from 321 participants during January 2024. The study employed an adapted version of the '5S' social resilience framework, encompassing five sub-dimensions – social structure (10 variables), social capital (9 variables), social mechanisms (17 variables), social equity and diversity (13 variables), and social belief (13 variables). This customized framework includes 62 indicators, providing a thorough assessment of community (social) disaster resilience in the research context. To explore the relationship between predictors and community (social) disaster resilience in Serbia, their attitudes towards the mentioned five sub-dimensions, as well as their engagement in preventive measures and their perception of resilience to various disasters with a particular focus on demographic and socio-economic impacts, statistical methods including t-tests, one-way ANOVA, Pearson’s correlation, and multivariate linear regression were employed. The results of the multivariate regressions across various community disaster resilience subscales indicate that age emerged as the most significant predictor for the social structure subscale. At the same time, education stood out as the primary predictor for the social capital subscale. Additionally, employment status proved to be the most influential predictor for both social mechanisms and social equity-diversity subscales, with property ownership being the key predictor for the social beliefs sub-scale. Despite being considered in the analyses, gender, marital status, income level, and number of household members did not emerge as the most significant predictors across the investigated community (social) disaster resilience subscales. The determined mean value of the community (social) disaster resilience index is M = 2.62, and the findings reveal that participants assigned the highest ratings to the social beliefs subscale (M = 2.76), while the lowest values were observed in the social structure subscale (M = 2.46). Findings can be used to create strategies and interventions aimed at enhancing the resilience of communities in Serbia by addressing the intricate interplay between demographic characteristics, socio-economic factors, and their ability to withstand, adapt to, and recover from different disasters.

This paper aims to develop a hierarchical risk assessment model using the newly-developed evidential reasoning (ER) rule, which constitutes a generic conjunctive probabilistic reasoning process. In this paper, we first provide a brief introduction to the basics of the ER rule and emphasize the strengths for representing and aggregating uncertain information from multiple experts and sources. Further, we discuss the key steps of developing the hierarchical risk assessment framework systematically, including (1) formulation of risk assessment hierarchy, (2) representation of both qualitative and quantitative information, (3) elicitation of attribute weights and information reliabilities, (4) aggregation of assessment information using the ER rule and (5) quantification and ranking of risks using utility-based transformation. The proposed hierarchical risk assessment framework can potentially be implemented to various complex and uncertain systems. A case study on the fire/explosion risk assessment of marine vessels demonstrates the applicability of the proposed risk assessment model.

Complex atypical endometrial hyperplasia (CAH) carries a high probability of cancer. Intraoperative evaluation of endometrial cancer in cases of CAH has not been reliable. The safety and sensitivity of sentinel lymph node (SLN) sampling has been validated. We aim to evaluate the efficacy and safety of SLN sampling in CAH managed by the da Vinci robotic platform. A total of 113 patients with a preoperative diagnosis of CAH were included in this retrospective cohort study. All underwent a robot assisted total laparoscopic hysterectomy and bilateral salpingo-oophorectomy with 69 patients undergoing SLN sampling. Statistical analysis calculated the probability of cancer, SLN map rate, and surgical complications. Predictors of cancer were evaluated. Forty-seven percent of the entire cohort were diagnosed with endometrial cancer. Median age was 63 years in the SLN cohort (N = 69) and 61 in the No SLN cohort (N = 44) (p= 0.363).  Median BMI was 34 Kg/m2 in the SLN cohort and 40 in the No SLN cohort (p= 0.004). Bilateral SLN map was 92.8% and unilateral SLN map rate was 7.2%. There were no grade 3-4 complications in the SLN cohort, and 4 grade 3-4 complications in the No SLN group (p=0.021). A preoperative diagnosis of CAH bordering on or cannot rule out cancer was the only predictor of cancer.

In the context of healthcare technology management (HTM) in Saudi Arabia and the Gulf region, this study addresses a significant gap by exploring both external and internal risk factors affecting HTM performance. Previous research has primarily focused on modeling or predicting failures in medical devices, mostly examining internal (endogenous) factors that impact device performance and the development of optimal service strategies. However, a comprehensive investigation of external (exogenous) factors has been notably absent. This research introduces a novel hierarchical risk management framework, designed to accommodate the broad array of healthcare technologies, not limited to medical devices. It significantly advances the field by thoroughly investigating and validating a comprehensive set of 53 risk factors and assessing their influence on HTM. Additionally, this study embraces the perspective of enterprise risk management (ERM) and expands it to identify and incorporate a wider range of risk factors, offering a more holistic and strategic approach to risk assessment in healthcare technology management. The findings reveal that several external and internal factors previously underexplored significantly impact HTM performance. Notably, the Fuzzy AHP survey identified “design risk” under facility and environmental risks as the highest risk for HTM in the region. Furthermore, the study reveals that three out of the top ten risks are related to “facility and internal environmental” factors, six are related to technological endogenous factors, and only one is related to managerial factors. This distribution underscores the critical areas for intervention and the need for robust facility and technology management strategies. In conclusion, this research not only fills a critical void by providing a robust framework for healthcare technology risk assessment but also broadens the scope of risk analysis to include a wider array of technologies, thereby enhancing the efficacy and safety of healthcare interventions in the region. Additionally, the proposed hierarchy provides insights into the underlying risk factors for healthcare technology management, with potential applications extending beyond the regional context to a global scale. Moreover, the equation we propose offers a novel perspective on the key risk factors involved in healthcare technology management, presenting insights with far-reaching implications applicable not only regionally but also on a global level. This framework also supports sustainability goals by encouraging the efficient and responsible utilization and management of healthcare technologies, essential for ensuring the long-term economic and environmental sustainability of medical technology use.

Abstract: Integration of Medical Cyber Physical Systems (MCPS) and Internet-of-Medical Devices (IoMT) with conventional hospital networks have facilitated easy and speedy data collection, vertical and horizontal connectedness and collaborations among healthcare providers. Federated identity management (FIM) provides a solution towards the identity management challenge arising from this integration of millions of MCPSs to get personalized care and frictionless experiences for the patients, doctors, and employees. FIM protocols such as OAuth, Security Assertion Markup Language (SAML) are highly susceptible to cyber attacks like theft of barrier token, replay attack, message insertion, and Man-in-Middle attacks. IoMT devices have vulnerabilities in their firmware, operating systems, data encryption, data at store and data transmission. Combined vulnerabilities of FIM framework and IoMT devices creates major cyber risks for the current digital healthcare system. Therefore, a comprehensive and evidenced based cyber risk assessment is an urgent need for a cyber-safe digital health care system that can avoid frequent life threatening situations. This paper proposes a comprehensive and evidenced based cyber risk assessment approach for FIM and IoMT based collaborative digital healthcare systems. The novelty of the proposed approach is that it considers three dimensional vulnerabilities arising from existing IT communication protocols and infrastructure, IoMT and MCPS medical devices, protocols of FIM and their combined impact on hospitals and provides corresponding recommendations of security controls. The proposed approaches combine two industry standards including Cyber Resilience Review (CRR) asset management, NIST SP 800-30 to take advantage of both approaches. We have used a large number of IoMT and MCPS devices from multiple providers for threat modelling and produced evidence based cyber-risks using attack trees and detailed attack sequence diagrams to validate proposed approaches. The corresponding recommendation of security controls will support healthcare professionals and providers significantly for improving both the patient and medical device safety management within the FIM enabled healthcare ecosystem.

One of the major concerns in structural health management (SHM) is the early detection of a growing crack. Using this, future damage due to crack propagation can be mitigated or eliminated by implementing proper repair and maintenance. Acoustic Emission (AE) is a non-destructive testing (NDT) method with potential applications for locating and monitoring fatigue cracks. The research presented in this paper focuses on SHM using AE. A novel AE signal analysis approach was proposed in order to detect crack initiation and assess small crack lengths. Experimental investigation indicated that initiation of a crack could be identified through the statistical analysis of the resulting features of the AE signals. A probabilistic AE-based model for small fatigue crack sizing was developed and the uncertainties of the model were estimated. In addition, a probabilistic model validation approach was implemented to confirm accurate estimation of the results. The outcome of this research can be used to evaluate the integrity of structures under fatigue loading. The proposed approach can also be applied as an approach to manage health and assess prognosis of structures.

Abstract: Background: Gender dimorphism and marital status on colorectal cancer mortality have 9 been previously documented, but the relationship between these factors and DNA mismatch repair 10 protein (MMRP) expression status is unknown. Methods: Colectomy specimens were reviewed ret- 11 rospectively for patients between 2018 to 2023 with demographics including race/ethnicity, gender, 12 marital status, faith, body mass index, pathologic staging, and MMRP expression status. Statistical 13 analyses were performed by using baseline characteristics tables and various programs in R-pack- 14 age. Results: A total 1018 colectomies were reviewed, and the tumor stages were significantly higher 15 in right colon (stage 3 and 4) than those in left colon and rectosigmoid colon (p<0.01). Marital status 16 was significantly associated with patients’ gender, age, tumor size and tumor stages (all p<0.01). 17 MMRP status were available in 775 cases, and 139 (17.9%) were MMRP deficient, and 636 (82%) 18 MMRP proficient. MMRP deficiency was significantly associated with older female patients, larger 19 tumor in size, higher tumor stages, higher histologic grade and more common in right colon (all 20 p<0.01). In addition, MMRP deficiency was statistically associated with higher percentage of di- 21 vorced and widowed patients (p<0.01). Multivariate linear regression analysis revealed persistent 22 association of MMRP deficiency with tumor size, tumor grade, tumor stage and nodal metastasis, 23 but the associations with gender and marital status no longer existed. Conclusion: The differences 24 in prevalence of CRC by gender and marital status and tumor MMRP status illustrate the im- 25 portance of these factors on tumor stages and nodal metastasis but these associations were more 26 complex with other confounding factors.

This study offers a concise summary of the research that has been done on alternative methods of HRM. A survey of 47 articles on the topic of creative human resource management uncovered three primary schools of thinking. At its core, innovation in HRM entails nothing more than that. One such topic worthy of investigation is the way human resource management approaches new situations. Third, research on inventiveness in the business world has given rise to the concept of "innovative HRM." Each of these three approaches, as well as the theoretical underpinnings of each, will be covered in this article. The note wraps up with a discussion of the significance of the findings for next research as well as clinical practice.

While digitization can make the health industry faster and more efficient, there are many financial, social and organizational obstacles and barriers to its implementation. This article offers a systematic review of the main troubles detected from the literature review. Also there is a proposal of new kind of restrictions to pay attention. Finally, its offer an heterodox reading about the problem analyzed.

Academics in healthcare grapple with the challenge of efficiently managing time while addressing diverse roles and responsibilities, leading to heightened levels of anxiety and stress within the academic community. The primary objective of this study is to offer a sustainable and systemic solution to the prevalent time-management challenges in healthcare academia, through the development of a predictive model. Employing systemic analysis software and adopting a systemic management approach, we crafted a model to address academic time-management concerns. The dynamics of time allocation for academicians are governed by three fundamental pillars: teaching, research, and administrative tasks. Significantly prioritizing administrative tasks depletes valuable academic time, making time management within academia excessively costly. This occurs as highly skilled individuals find themselves diverted towards administrative duties at the expense of their academic roles diminishing resilience levels. The proposed Timebooster Academic Systemic Model (TASM) advocates for the cultivation of soft skills in academia, en-compassing prioritization, seeking assistance, and addressing tendencies of perfectionism and procrastination. Additionally, TASM recommends the implementation of full automation, delegation of administrative responsibilities, and the mitigation of both overt and covert time-consuming elements. Furthermore, TASM reports on the pivotal role of the academic resilient manager-leaders, who actively contribute to their own satisfaction and academic growth by enhancing collaboration and teamwork. Time-management challenges emerge as a critical determinant of academic well-being and resilience. Systemic models, exemplified by TASM, offer a valuable framework for investigating prioritization concerns, identifying administrative impediments in academic processes, and managing low-value time-consuming elements. The TASM model, along with the systemic philosophy underpinning its design, presents an avenue for advancing the exploration of time-management phenomena within the context of healthcare academia.

With opioid overdose rates on the rise, we aim to develop a county-level risk stratification, specifically focusing on access to Medications for Opioid Use Disorder (MOUD) and high overdose rates. We examined over 15 million records from the South Carolina Prescription Tracking System (SCRIPTS) across 46 counties. Additionally, we incorporated data from opioid treatment programs, healthcare professionals prescribing naltrexone, clinicians with buprenorphine waivers, and county-level overdose fatality statistics. To assess the risk of opioid misuse, we classified counties into high-risk and low-risk categories based on prescription rates, overdose fatalities, and treatment service availability. Utilizing statistical methods, we compared prescription patterns and outcomes between these groups. Our study shows that between 2017 and 2021, opioid prescriptions decreased from 64,223 to 41,214 per 100,000 residents, while fentanyl-related overdose deaths increased by 312%. High-risk counties have significantly higher rates of fentanyl prescriptions and rely more on out-of-state doctors. They also exhibit higher instances of doctor shopping and had fewer medical doctors per capita, with limited access to MOUD. To effectively combat the opioid crisis, we advocate for improved local healthcare infrastructure, broader treatment access, stricter management of out-of-state prescriptions, and vigilant tracking of prescription patterns. Tailored local strategies are essential for mitigating the opioid epidemic in these communities.

This paper presents a co-designed solution aimed at improving community healthcare assistance management. To enhance patients’ self-awareness of their health status, encouraging active participation in care process, and to support nurses in delivering patient-centered care, we have developed an innovative platform with a highly customized app. This platform was designed using a multi-disciplinary bottom-up approach. Patient data collection and processing facilitate the automation of care timeline planning, based on real time patients’ needs and the available resources. To achieve this goal different components have been considered: real-time health data collection and processing, patient care planning, decision support for nurses, secure communication for data transmission, and user-friendly interfaces to ensure easy access to platform functionalities.

This paper systematically discusses the application and development of federated learning in data privacy protection and data value sharing. With the rapid development of global information technology, especially the explosive growth of data from Internet of Things devices, data security and privacy protection are facing unprecedented challenges. This paper first analyzes the growth trend of global data volume and its importance to next generation technologies such as artificial intelligence technologies such as deep learning. Second, the paper provides an in-depth look at the impact of current data privacy regulations on data flows and value creation, particularly the EU's GDPR and China's Data Security and Personal Information Protection Law. Then, this paper introduces in detail federated learning, as a new distributed machine learning paradigm, which effectively solves the contradiction between existing data sharing and privacy protection by protecting individual data privacy and realizing global model collaborative construction. Finally, this paper discusses the combination of blockchain technology and federated learning, and proposes BeFL architecture as a new secure, decentralized and trusted federated learning system, which is expected to provide a comprehensive solution for large-scale data processing and value creation in multi-party scenarios. The research in this paper not only deepens the understanding of federation learning in theory, but also provides important reference and enlightenment for future research and application in related fields.

The healthcare system is crucial to the development, standard of living, and sustainability of any country. Amid the pandemic, most developed countries are experiencing varying levels of difficulty managing their healthcare systems. Overloading of healthcare services is common, leading to prolonged waiting times for medical services; thus, the wastage of hospital resources must be addressed seriously. In this paper, we examine the problem of no-shows in outpatient clinics. Through a review of the literature and practical industrial experience, we identify the processes of these clinics. We then propose a robust optimisation approach for overbooking using a traditional overbooking model and a robust system. This model aims to mitigate the significant parameter uncertainties encountered during the pandemic. Considering risk aversion, the optimal policy for overbooking can be established while accounting for the costs associated with overbooking. The main contribution of this paper is the introduction of an alternative method to address the uncertainty of no-shows using an overbooking technique.

The quality of healthcare services is a very important indicator of social welfare in developed and emerging countries. Good quality levels are critical to ensuring patient safety, improving health outcomes, increasing patient satisfaction, optimizing resource utilization, and meeting regulatory requirements. The stable and sustainable operation of medical institutions urgently requires the establishment of a reliable and effective medical quality performance evaluation platform. However, the complexity of the evaluation process, involving numerous evaluators and the consideration of different subjective and objective criteria, poses significant challenges and essentially constitutes a group multi-criteria decision-making problem. This article uses the overall medical environment of the institution, medical service attitude, medical security, administrative service measures, etc. as measurement standards to deal with this challenge. It proposes a medical service quality measurement model suitable for both qualitative and quantitative analysis. In view of the heterogeneity of information, the two-tuple fuzzy language calculation method is introduced to enable evaluators to skillfully manage diverse information and effectively avoid potential information omission problems in the subjective evaluation integration process. The effectiveness and feasibility of the model were demonstrated by applying it to the operational framework of a medical institution in central Taiwan.

In recent years, the digital economy has played an increasingly important role in enhancing productivity, fostering growth, and facilitating the emergence of digital banking. With the widespread adoption of digital financial services for financial inclusion, the banking sector is experiencing a demand for digital transformation. It is important to note that despite the many advantages of this change, it also disrupts the familiar banking experience that customers have grown to expect, as well as introducing potential risks to maintaining customer protection at previous levels. Many critics have raised concerns regarding customer protection and potential exposure to various risks that may arise from digital financial services, which may result in both financial and non-financial losses. These risks can impact customer retention as dissatisfied customers may switch to alternative service providers. Additionally, reputation can be affected since merely offering digital financial services may not sufficiently safeguard customers. To remain competitive, financial institutions must deliver a secure experience that aligns with customer expectations while considering economic factors and societal needs. This is why it is essential to comprehend the severity of the risk factors that influence the protection of customers in the realm of digitalized banking services and products. In this study, five factors have been identified that influence customer protection when using digital financial services in Pakistan. Based on the analysis conducted using SmartPLS, it appears that all the factors proposed in this study have a significant and positive impact on the protection of customers. In particular, the information provided remains of paramount importance when it comes to determining the level of protection that a customer is entitled to within digital financial services, followed by responsiveness, privacy, authentication, and encryption mechanisms. Due to these findings, the implementation of enhanced information security management principles becomes imperative for the development and progress of the Pakistani digital banking industry.

This pioneering study explores the geospatial and temporal patterns of natural and human-induced disasters from 1900 to 2024, providing essential insights into their global distribution and impacts. Utilizing the comprehensive international EM-DAT database, our analysis reveals significant trends and disparities in disaster occurrences and their widespread consequences. Results showed a dramatic escalation in both natural and man-made (technological) disasters over the decades, with notable surges in the 1991-2000 and 2001-2010 periods. A total of 25,836 disasters were recorded worldwide, of which 69.41% were natural disasters (16,567) and 30.59% were man-made (technological) disasters (9,269). The most significant increase in natural disasters occurred from 1961-1970, while man-made (technological) disasters surged substantially from 1981-1990. Seasonal trends reveal that floods peak in January and July, while storms are most frequent in June and October. Droughts and floods are the most devastating in terms of human lives, while storms and earthquakes cause the highest economic losses. The most substantial economic losses were reported during the 2001-2010 period, driven by catastrophic natural disasters in Asia and North America. Our research highlights Asia as the most disaster-prone continent, accounting for 41.75% of global events, with natural disasters making up 61.89% of these events. Oceania, despite experiencing fewer total disasters, shows a remarkable 91.51% of these as natural disasters. Africa is notable for its high incidence of man-made (technological) disasters, which constitute 43.79% of the continent's disaster events. Europe, representing 11.96% of total disasters, exhibits a balanced distribution but tends towards natural disasters at 64.54%. Examining specific countries, China, India, and the United States emerged as the most frequently affected by both natural and man-made (technological) disasters. The impact of these disasters has been immense, with economic losses reaching their highest during the decade of 2010-2020, largely due to natural disasters. The human toll has been equally significant, with Asia recording the most fatalities and Africa the most injuries. Particularly, the decade from 2001 to 2010 experienced the greatest economic losses, primarily driven by natural disasters in Asia and North America. These insights highlight the urgent need for tailored disaster risk management strategies that address the distinct challenges and impacts in various regions. By understanding historical disaster patterns, policymakers and stakeholders can better anticipate and manage future risks, ultimately safeguarding lives and economies.

This study comprehensively explores Serbia's Disaster (Risk) Management System, aiming to critically evaluate its inherent strengths and weaknesses. Using descriptive-analytical methods and qualitative interpretation, SWOT analysis was applied to collect data from scientific papers, books, and institutional reports. This study delves deeply into the nuances of Serbia's Disaster Management Framework, revealing a nuanced landscape of strengths, weaknesses, and strategic improvement opportunities. Identified strengths underscore a robust foundation characterized by proficient professionals, state-of-the-art technology, and cohesive teamwork, all pivotal for effective emergency preparedness and response. Conversely, weaknesses underscore pressing imperatives for enhancements in regulatory enforcement, resource allocation, and infrastructure upgrades, presently hampering the framework's efficacy and response readiness. This research contributes to the ongoing discourse on disaster risk management and serves as a catalyst for further scholarly inquiry and policy formulation. By mapping out the structure of Serbia’s Disaster (Risk) Management Framework and integrating diverse data sources, the study enriches theoretical models and lays the groundwork for practical policy interventions. Its findings, including precise recommendations for policy reform and strategic improvements, provide valuable insights for policymakers, emergency management experts, and stakeholders seeking to enhance disaster preparedness and response capabilities on both national and global scales.

The COVID-19 pandemic over the past years has shown a great need for rapid, low-cost and on-site detection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). In this paper, an aptamer-based colloidal gold nanoparticle lateral flow test strip was well developed to realize the visual detection of wild type SARS-CoV-2 spike protein (SP) and multiple variants. Under the optimal reaction conditions, a low detection limit of SARS-CoV-2 S protein of 0.68 nM was acquired, and the actual detection recovery was 83.3% to 108.8% for real world samples. This suggests a potential tool for the prompt detection of SARS-CoV-2 with good sensitivity and accuracy, and a new method for the development of alternative antibody test strips for the detection of other viral targets.

The utilization of Artificial Intelligence (AI) in biotechnology is widespread, addressing challenges such as elucidating disease pathophysiology and novel therapeutic approaches. AI plays a critical role in tasks such as drug manufacturing, chemical analysis, RNA and DNA sequencing, and enzyme studies by automating processes and expediting data collection and analysis. AI has particularly transformed the analysis of omics data for researchers, providing tools to handle complex and multifaceted datasets effectively. Through AI, integrative analysis of multi-omics data can be conducted in efficient ways. This article discusses the application of multi-omics and AI technologies in studying various diseases related to infection (COVID-19), mutation (cancer), and senescence (alopecia) to understand their pathophysiology. The applications of AI were performed as the aspect of the diagnosis, prognosis, and treatment for each disease. We introduce AI-driven analysis of pathophysiology can overcome traditional limitations in pathology, with diverse features showing potential in defining the causes of diseases, and potentially enhancing primary omics data in the distinguish the subtype of diseases. In this context, integrated approaches using multi-omics data and AI technology could provide fresh biological insights by comprehending the molecular mechanism of diseases and is instrumental in advancing precision medicine. We anticipate that the rapidly advancing AI-based multi-omics data analysis will significantly contribute to promoting healthy longevity.

The Roma population constitutes the largest ethnic minority in Europe, however, data regarding the prevalence of non- communicable diseases are still scarce in the medical literature. The aim of this study was to assess the clinical and metabolic particularities of a Roma population with diabetes, in comparison with a group of Romanian Caucasians. We conducted an observational, transversal study and evaluated 808 adult patients with diabetes mellitus, from a tertiary hospital providing diabetes care, aged between 18 and 89 years old, of whom the majority were men (52.84%). Most of them had type 2 diabetes mellitus (T2DM), reaching 95.1% the Caucasian group and 87.8% among the Roma patients. 81.1% of the Caucasian patients had hypertension, approximately 15% higher than the Roma group, while more than half of the patients in both groups had obesity. There were no differences concerning myocardial infarction, however, the number of patients with history of stroke was 2.1 times higher in the Roma group compared to Caucasians. Chronic kidney disease was present in 22.1% of the Roma patients, 33.25% had retinopathy, and 72.7% presented polyneuropathy. The prevalence of cardiovascular risk factors, cardiovascular disease and microvascular complications among the study Roma population are quite significant, underscoring the importance of ethnic disparities in approaching healthcare management strategies.

Diffuse midline gliomas are among the deadliest human cancers and have had little progress in treatment in the last 50 years. Cell cultures of these tumors have been developed recently, but it is unknown the degree to which such cultures retain the characteristics of the source tumors. DNA methylation profiling offers a powerful tool to look at genome-wide epigenetic changes that are biologically meaningful and can help assess the similarity of cultured tumor cells to their in vivo progenitors. Paraffinized diagnostic tissue from three diffuse intrinsic pontine gliomas with H3 K27M mutations was compared with subsequent passages of neurosphere cell cultures from those tumors. Each cell line was passaged 3-4 times and analyzed with DNA methylation arrays and standard algorithms that provided comparison of diagnostic classification and cluster analysis. All samples tested maintained high classifier scores and clustered within the reference group of H3 K27M-mutant diffuse midline gliomas. There was a gain of 1q in all cell lines, with two cell lines initially manifesting the gain of 1q only during culture. In vitro cell cultures of H3 K27M-mutant gliomas maintain high degrees of similarity in DNA methylation profiles to their source tumor, confirming their fidelity even with some chromosomal changes.

This paper presents a comprehensive analysis of the financial factors and risk management strategies essential for optimizing disaster resilience within the Serbian defence industry's critical infrastructure. The significance of this sector is multi-faceted, impacting national security, economic stability, and technological advancement. Primarily, the Serbian defence industry ensures the preservation of vital defence interests, maintaining Serbia's independence from foreign sources for weapons and military equipment in both peacetime and wartime. Economically, it is a significant employer of the working-age population, directly affecting local employment rates, fostering economic development, and ensuring the sustainable growth of this crucial sector. This, in turn, stimulates broader economic activity and enhances social cohesion while strengthening the national balance of payments through increased export potential. From a technological perspective, the defence industry drives scientific, technological, and industrial development, reinforcing Serbia's global political and military standing within the Western Balkans and on the international stage. Consequently, the paper aims to examine the risk management and protection of the Serbian defence industry's critical infrastructure, offering concrete and actionable measures to improve and develop these systems with a particular emphasis on security. The research's utility and contribution lie in identifying similarities and differences in the operational performance of defence industry companies, a vital segment of the national economy. The presentation of these findings focuses on the protection of critical infrastructure. The results will form the basis for further investigation into the underlying causes of business performance and the effective management of critical infrastructure security.

This qualitative study explores the multifaceted strategies organizations employ to build resilient supply chains through effective procurement risk management. Conducted over a six-month period in 2024, the research involved semi-structured interviews with 25 industry professionals from sectors including manufacturing, retail, healthcare, and technology, alongside document analysis and case studies. The study identifies four key themes: risk identification and assessment, supplier relationship management, digitalization in risk management, and strategic resilience practices. The findings reveal that organizations face diverse risks, including supplier, supply market, and external environmental risks, necessitating comprehensive risk assessment methodologies. Supplier relationship management emerges as a critical factor in mitigating risks, with practices such as regular audits, collaborative risk management, long-term partnerships, and supplier diversification being crucial. The adoption of digital technologies like predictive analytics, blockchain, advanced analytics, and artificial intelligence enhances risk management capabilities, providing data-driven insights and fostering transparency. Strategic resilience practices, including inventory buffering, flexible sourcing, redundancy, and scenario planning, are essential for maintaining supply chain continuity amidst disruptions. The study underscores the importance of an integrated and proactive approach to procurement risk management that leverages robust assessment, strong supplier relationships, digital tools, and strategic planning. These findings contribute to a deeper understanding of how organizations can effectively manage procurement risks and build resilient supply chains in a dynamic global environment. The insights offer valuable guidance for procurement professionals and organizational leaders in developing resilient and adaptable supply chain strategies to navigate the complexities of today's market.

This paper presents a new algorithm for semi-supervised action recognition using adaptive feature analysis. We model the samples of the same action as the same manifold and those of different actions as different manifolds, and integrate feature correlation mining and visual similarity mining into a joint framework. By maximizing the separability between different classes based on the labeled data, the proposed algorithm learns multiple features from labeled data and utilizes the intrinsic geometric structure of the data distribution between labeled and unlabeled data to boost the recognition. We introduce Projected Barzilai-Borwein (PBB) to train a subspace projection matrix as classifier. Extensive experiments on three benchmark datasets demonstrate that our algorithm outperforms the existing semi-supervised action recognition algorithms when there are a few labeled training samples.

Background: IoT and Big Data are newer technologies that can provide substantial support for healthcare systems and help overcome their shortcomings. The aim of this paper was to analyze literature production descriptively, thematically, and chronologically from an interdisciplinary perspective in a holistic way to identify the most prolific research entities and themes. Methods: The synthetic knowledge synthesis qualitatively and quantitatively analyzes the production of literature through a combination of descriptive bibliometrics, bibliometric mapping and content analysis. For this analysis the Scopus bibliometric database was used. Results: In the Scopus database, 2272 publications were found, which were published between 1985 and June 10, 2024. The first article in this field was published in 1985. Until 2012 the production of literature was steady increasing, after that exponential growth began, which reached its peak in 2023. The most productive countries were the USA, India, China, the United Kingdom, South Korea, Germany and Italy. The content analysis resulted in 8 themes (four from the perspective of computer science and four from the perspective of medicine) and 21 thematic concepts (eight from the perspective of computer science and 13 from the perspective of medicine). Conclusions: The results show that IoT and Big Data have become key technologies in preventive health. The study outcomes might represent a starting point for the further development of research that combines multidisciplinary aspects of healthcare.

In today's highly digitized and globalized financial environment, the need to protect payment systems from risk is more urgent than ever. Artificial intelligence (AI) technology is rapidly becoming a key tool in this space, with machine learning algorithms, big data analytics, and real-time monitoring enabling AI to effectively detect and prevent fraudulent activity, optimize risk management processes, and deliver intelligent services. AI not only improves the security and efficiency of payment systems, but also significantly improves customer satisfaction and loyalty. With the continuous development of AI technology and financial payment technology, the financial payment industry will usher in more innovations and changes in the future, further optimizing the payment process, improving payment efficiency, and ensuring payment security.

The Western Balkans region is persistently vulnerable to natural hazards, inadequate infrastructure, and limited institutional capacities, necessitating a collaborative approach to disaster risk management. This paper presents a comprehensive project led by the Scientific-Professional Society for Disaster Risk Management (NSD-URVs) in Belgrade, aiming to establish and strengthen a regional network of experts in disaster risk management. The project focuses on building expertise, facilitating knowledge exchange, promoting expert coordination, and enhancing cross-border collaboration. Key activities include developing a web platform, organising an international conference, and advocating for regional policies that integrate best practices and innovations in disaster risk management. The expected outcomes are the establishment of a cohesive network, enhanced expertise and coordination, and improved preparedness and responsiveness to disasters. By leveraging collective expertise and resources, this initiative aims to fortify the region's resilience against potential disasters. Addressing the identified gaps and implementing the proposed policy recommendations can significantly enhance the region's resilience to natural hazards. Future research should focus on the implementation and impact of these recommendations to ensure continuous improvement in disaster preparedness and response.

Patients with a psychotic disorder frequently report oral health problems, while mental health nurses (MHNs) seem not to be fully aware of these problems and the risk factors. Therefore, this study aims to develop supportive tools for MHNs regarding oral health to increase sensitization among MHNs and to provide MHNs with knowledge to recognize (potential) oral health problems in patients with a psychotic disorder. We used a human-centered design in which the user, MHNs as well as experts by experience, were placed in the centre of the research process. Problems and needs in MHNs working with patients with a psychotic disorder were addressed. To identify key issues of problem as well as the needs in terms of resources, we started with focus groups (n = 9). We analysed the data thematically based on context of patients and MHNs in practice regarding oral health, preferred oral health tools focused on MHN, and intended outcomes of tools. A Multi Criteria Decision Matrix was developed and analysed (n = 9) to identify the most optimal and viable solution based on established criteria and issues that are prevalent in the work of MHNs. Most promising result was the development of a brochure with an awareness screener. The brochure with awareness screener was developed as a low-fidelity prototype for MHNs regarding oral health in patients with a psychotic disorder, based on the latest scientific evidence. After testing it, the feasibility was tested through semi-structured interviews (n=19). MHNs and experts by experience were satisfied with the tool and provided recommendations for adjustments to the content. Significant augmentations to the brochure include a clinical lesson and a toothbrush with toothpaste for patients. We can conclude that a brochure with an awareness screener is feasible for MHNs. Future steps aiming to further refine and optimize care processes for oral health tools in MHNs, should take refining eligibility criteria for psychiatric populations and the language level of the target group of a tool into account.

In today's globalized economy, supply chain disruptions present significant challenges, impacting businesses' operational continuity and efficiency. This study delves into the ways businesses navigate these disruptions, with a particular emphasis on qualitative insights into risk management practices. The complexity and interconnectivity inherent in modern supply chains make them vulnerable to a broad spectrum of disruptions, ranging from natural disasters and geopolitical conflicts to technological failures and pandemics. Effective supply chain risk management requires a structured approach to identifying potential risks, evaluating their likelihood and potential impact, and formulating robust mitigation strategies. Employing a qualitative research methodology, this study gathers in-depth insights from semi-structured interviews with supply chain managers, industry experts, and executives. The findings underscore the necessity of proactive risk identification and comprehensive assessment processes. Advanced technologies, including artificial intelligence (AI), the Internet of Things (IoT), and blockchain, are highlighted as crucial tools for enhancing visibility and responsiveness within supply chains. Strategies such as diversifying suppliers and maintaining safety stock emerge as vital components of risk mitigation, ensuring supply continuity in the face of disruptions. Strong relationships with suppliers are pivotal, facilitating better information sharing and collaborative problem-solving. Leadership commitment to risk management and fostering a culture of resilience within organizations is also critical. Training programs and simulation exercises are identified as effective means of preparing employees to handle disruptions. Furthermore, adherence to regulatory compliance and a focus on sustainability are integral to maintaining long-term stability and reducing the risk of future disruptions.

This research addresses a gap in the literature by conducting a comprehensive analysis of patients’ level of satisfaction with dental care. Methods: By combining quantitative and qualitative survey methods with the PSQ questionnaire this study aims to augment ongoing initiatives to enhance dental patients’ experiences by painting a more comprehensive depiction of patients' level of satisfaction; Results: When asked about their overall level of satisfaction 77,1% of the patients said they received excellent services from the office personnel and 72,2% said they trust their doctors; Conclusions: Assessing patient satisfaction in the realm of dental services quality is crucial for enhancing service quality and accuracy, benefiting both patients and dentists, and ultimately improving public health.

Integrated disaster risk reduction in schools represents a key component of safety strategies within the educational sector of every country. In this study, a series of 850 face-to-face interviews (650 with students and 200 with teachers) were conducted throughout 2023 in 10 out of the total 18 municipalities in the Western Morava Basin of the Republic of Serbia. The paper proposes two central hypotheses regarding school-based disaster risk reduction. Firstly, it suggests that gender, age, parent's employment, academic achievement, living situation, paternal and maternal education levels, and engagement with social media collectively influence students' perspectives on this matter. Secondly, it posits that gender, age, marital status, parenthood, and educational background significantly impact teachers' viewpoints on school-based disaster risk reduction. Multivariate linear regression was used to explore predictors of students' and teachers' insights on school-based disaster risk reduction. Various statistical tests including Chi-square, t-tests, one-way ANOVA, and Pearson's correlation were employed to investigate the influence of demographic and socioeconomic factors on these insights. The results of multivariate regression analyses indicate that age, gender and marital emerge as the primary predictors across various facets of students' and teachers' insights on school-based disaster risk reduction (awareness of disasters, disaster education activities, attitudes on disaster risk reduction education, enhancement of disaster information accessibility, etc). The results of this study provide insight into the key factors influencing students' and teachers' perceptions of disaster risk reduction in schools. As such, they can serve as a basis for improving educational programs, developing policies and strategies, normative educational frameworks, teacher training, and further research in disaster education.

Supply chain risk management (SCRM) is a critical aspect of contemporary business operations, necessitated by the complex and interconnected nature of global supply chains. This qualitative research delves into the intricacies of SCRM through in-depth interviews with industry experts, aiming to unravel the multifaceted nature of supply chain risks and the strategies employed for mitigation. Key themes emerged, including the diverse sources of risks encompassing operational, financial, environmental, and geopolitical factors, highlighting the need for a comprehensive approach to risk management. Challenges such as limited visibility, resource constraints, and organizational silos were identified, underscoring the importance of addressing these barriers to enhance SCRM effectiveness. Strategies for risk mitigation encompassed technological investments for enhanced visibility and predictive capabilities, collaboration and information sharing among supply chain partners, and the integration of sustainability principles into risk management practices. Leadership, organizational culture, and continuous learning emerged as critical factors in driving effective SCRM practices, emphasizing the need for proactive and adaptable approaches to navigate evolving risks. Overall, this study contributes to the existing body of knowledge on SCRM by providing valuable insights for practitioners and academics, and underscores the importance of holistic and proactive approaches to enhance supply chain resilience and agility in today's dynamic business environment.

Abstract: Global ecological concern regarding the transfer of fouling organisms to ship hulls is increasing. This study investigated the species composition, dominant species, distribution patterns, community structure, and life-cycle differences of hull-fouling macroinvertebrates on five research vessels (R/Vs: Isabu, Onnuri, Eardo, Jangmok 1, and Jangmok 2) operated by the Korea Institute of Ocean Science and Technology (KIOST). Hull-fouling macroinvertebrates were collected three to five times on quadrats from the upper and middle sectors of the hull sides, bottom, and niche areas (the propellers and shafts and the thrusters). A total of 47 macroinvertebrate species were identified, represented by 8,519 individuals(ind.)/m2 and a biomass of 1,967 gWWt/m2 on the five vessels. The number of species, density, and biomass were greater on the coastal vessels the Eardo, Jangmok 1, and Jangmok 2 than on the ocean-going vessels the Isabu and Onnuri. Among the coastal vessels, barnacles were the most abundant and had the greatest density, while mollusks had the highest biomass. Differences between hull sectors showed that the highest species abundance and density appeared on all hulls in ports and bays where the Jangmok 1 operated, while the highest species abundance, density, and biomass were identified in the niche areas of the Eardo, which operated farther from the coast. The hull-fouling macroinvertebrates that exceeded 1% of all organisms were the barnacles Amphibalanus amphitrite, Balanus trigonus, and Amphibalanus improvisus; the polychaete Hydroides ezoensis; the bivalves Magallana gigas and Mytilus galloprovincialis; and the amphipod Jassa slatteryi. The dominant species were cosmopolitan and globally distributed, and many of them were cryptogenic. Six native species were identified: M. gigas, H. ezoensis, the amphipod Melita koreana, the isopod Cirolana ko-reana, and the barnacle B. trigonus and F. kondakovi. Eight non-indigenous species (NIS) were de-tected: barnacles A. amphitrite and A. improvisus, the bivalve M. galloprovincialis, the polychaete Perinereis nuntia, amphipods J. slatteryi and Caprella californica, and bryozoans Bugulina californica and Bugula neritina. Of the fouling macroinvertebrates found on the vessel hulls, 13% were native and 17% were NIS. The nMDS ordination analysis of the five vessels showed four clusters of fouling macroinvertebrate communities: two ocean clusters (the Isabu and Onnuri), one coastal cluster (the Eardo and Jangmok 2), and one local cluster (the Jangmok 1). More diverse communities developed on the hulls of vessels that operated locally rather than globally or in deep oceans. Spearman rank correlation showed that biological indices such as number of species, density, and biomass correlated highly and positively with the numbers of days on coastal operations and negatively with the number days on ocean operations. The species diversity index correlated positively with the total number of anchoring days and coastal operation days and negatively with the total number of operation days and ocean operation days. The macroinvertebrates differed by the area of operation, the port of anchorage, the number of days in operation and at anchor, and the hull sectors. There is no previous research data on hull-fouling macroinvertebrates in the Republic of Korea, and this study provides a basis for future studies to identify introduced species and their differences based on operation area.

With the burgeoning growth of online retail, the issue of counterfeit products has become a significant threat to both consumer trust and brand integrity, posing challenges to sustainable market practices. Online retail platforms are promoted to redesign their channel structures and enhance brand traceability to tackle counterfeits using blockchain technology. To investigate the interactions between blockchain technology and the channel structures of online retail platforms, we design a game theory model incorporating a manufacturer with(out) blockchain participation selling products via an online retail platform that operates an agency channel and/or a reselling channel. We show that the critical trade-off of blockchain participation comprises the brand assurance effect of the blockchain, the channel competition intensification effect, and the blockchain adoption cost. The platform is capable of strategically designing the channel structure to encourage (while discouraging) blockchain participation by the manufacturer. Our analysis highlights the complementarity between blockchain participation and the channel structure. Furthermore, the effect of the consumer’s belief in the reselling channel, the commission fee in the agency channel, and the substitutability between channels are investigated in depth. The research provides valuable insights for online retail platforms and policymakers seeking to foster a sustainable and trustworthy e-commerce ecosystem.

The qualitative research on the influence of supply chain risk management (SCRM) on marketing strategies during economic uncertainty explores the complex interplay between these two critical areas. Conducted through in-depth interviews with industry professionals across various sectors, the study examines how organizations manage supply chain risks and adapt their marketing strategies in response to challenges such as natural disasters, geopolitical tensions, economic downturns, and pandemics. The findings reveal that these risks significantly impact marketing efforts by causing supply shortages, delays, increased costs, and demand fluctuations, necessitating rapid adjustments in promotional activities, pricing strategies, and communication with customers. Key strategies for integrating SCRM with marketing include leveraging technology for real-time supply chain monitoring, building strong supplier relationships, diversifying supply sources, and enhancing communication and collaboration between supply chain and marketing teams. These practices enable organizations to enhance their resilience and responsiveness, ensuring that marketing strategies are aligned with the evolving realities of the supply chain. The study also identifies challenges such as organizational silos, lack of cross-functional collaboration, resistance to change, and limited technological capabilities, which can hinder effective integration. Addressing these barriers through fostering collaboration, investing in technology, and promoting change management is essential for achieving successful integration. The outcomes of this integration include improved supply chain resilience, enhanced customer satisfaction, increased marketing agility, and a stronger competitive position. The research underscores the importance of a holistic and adaptive approach to integrating SCRM with marketing strategies, providing valuable insights for organizations seeking to navigate economic uncertainty and sustain their growth in a rapidly changing environment.

Pulmonary sarcoidosis is a multisystem granulomatous interstitial lung disease (ILD) with a variable presentation and prognosis. Early accurate detection of pulmonary sarcoidosis may prevent progression to pulmonary fibrosis, a serious and potentially life-threatening form of the disease. However, the lack of a gold-standard diagnostic test and specific radiographic findings pose challenges in diagnosing pulmonary sarcoidosis. Chest computed tomography (CT) imaging is commonly used but requires expert, chest-trained radiologists to differentiate pulmonary sarcoidosis from lung malignancies, infections, and other ILDs. In this work, we developed a multichannel, CT and radiomics-guided ensemble network (RadCT-CNNViT) with visual explainability for pulmonary sarcoidosis vs. lung cancer (LCa) classification using chest CT images. We leverage CT and hand-crafted radiomics features as input channels, and a 3D convolutional neural network (CNN) and vision transformer (ViT) ensemble network for feature extraction and fusion before a classification head. The 3D CNN sub-network captures localized spatial information of lesions, while the ViT sub-network captures long-range, global dependencies between features. Through multichannel input and feature fusion, our model achieved the highest performance with accuracy, sensitivity, specificity, and combined AUC of 0.93±0.04, 0.94±0.04, 0.93±0.08 and 0.97 respectively in a 5-fold cross-validation study with pulmonary sarcoidosis (n=126) and LCa (n=93) cases. The model offers promising potential for improving the diagnosis of pulmonary sarcoidosis. A detailed ablation study showing the impact of CNN+ViT compared to CNN or ViT alone, and CT+radiomics input, compared to CT or radiomics alone, is also presented in this work.

This study examines the differential impact of proactive and reactive job crafting behaviors on job well-being, drawing from the perspective of boundary theory. The increasing popularity of work connectivity behavior after-hours (WCBA) after work has attracted widespread attention from scholars on the relationship between WCBA and employee well-being. One view suggests that the impact of WCBA is negative, while another view suggests it is positive. Obviously, the impact of (WCBA) on well-being at work is still contradictory. To clarify the complexity of the impact of WCBA on well-being at work, based on boundary theory, we divided WCBA into proactive WCBA (PC) and reactive WCBA (RC), and examined the double-edged sword effect of WCBA on well-being at work, as well as the mediating mechanisms of job control and work-to-home conflict, and the moderated effects of boundary segmentation preferences. This study uses empirical sampling method to collect data from 125 employees for a period of five days for quantitative research. The results show that, first, PC has an inverted U-shaped effect on job control, and job control is negatively related to well-being at work. Thus the mediating effect of job control is significant. Second, RC has a negative impact on job control, and there is also a negative relationship between job control and well-being at work. Therefore, the mediating effect of job control is significant. Third, PC and RC are positively correlated with work-to-home conflict, and work-to-home conflict has a significant positive impact on well-being at work. Therefore, the mediating effect of work-family conflict is significant. Fourth, the study also found that integration preference moderates the relationship between work-to-home conflict and PC on well-being at work, that is, the mediating effect of work-to-home conflict is stronger for employees with low integration preference. This study enriches our understanding of WCBA, PC and RC from the perspective of proactive and passive employee behaviors. The study also provides a new interpretation of the impact of WCBA on well-being at work and offers valuable insights for the sustainable development in digital social transformation and the application of boundary theory and the theory of empowerment-subjugation in achieving the United Nations Sustainable Development Goals. In addition, the study deepens our understanding of the heterogeneous regulatory role played by work-family integration preferences in influencing well-being at work under different types of WCBA.

Chronic lymphocytic leukemia (CLL) is the most common form of leukemia in adults, characterized by abnormal growth of mature B cells in the blood, bone marrow, and lymphoid tissues. Although CLL primarily affects the hematopoietic system and lymph nodes, renal involvement is exceptionally rare and presents diagnostic and treatment challenges. Potential causes of renal failure in CLL patients include sepsis, dehydration, tumor lysis syndrome (TLS), compression of the ureter by enlarged lymph nodes, and side effects of cancer therapy. This report presents a case of CLL with renal infiltration leading to severe renal failure, discussing the clinical presentation, diagnostic workup, and management of this condition.

An observational cohort study of patients diagnosed with endometrial cancer (EC) stage IA G1, or atypical endometrial hyperplasia (AHE), undergoing organ-preserving treatment, was conducted. Objective of the study: to determine CDO1, PITX2, and CDH13 gene methylation levels in early endometrial cancer and atypical hyperplasia specimens obtained before organ-preserving treatment in the patients with adequate response and with insufficient response to hormonal treatment. Materials and methods: 41 endometrial specimens obtained during diagnostic uterine curettage in women with EC (n=28) and AEH (n=13), willing to preserve reproductive function, were studied; 18 specimens of endometrial cancer IA stage G1 from peri- and early postmenopausal women (comparison group) were included in the study. The control group included 18 endometrial specimens from healthy women obtained by diagnostic curettage for missed abortion and/or intrauterine adhesions. Methylation levels were analyzed using the modified MS-HRM method. Results: All 13 women with AEH had complete response (CR) to medical treatment. In the group undergoing organ-preserving treatment for uterine cancer IA stage G1 (n=28), 14 patients had complete response (EC CR group) and 14 had not (non-CR). It was found that all groups had statistically significant differences in CDO1 gene methylation levels with the control group (p&lt;0.001) except for the EC CR group (p=0.21). The p-value of difference between EC CR and EC non-CR groups was &lt;0.001. The differences in PITX2 gene methylation levels between the control and study groups were also significantly different (p&lt;0.001), except for AEH group (p=0.21). For the difference between EC CR and EC non-CR groups, the p-value was 0.43. For CDH13 gene methylation levels, statistically significant differences were found between the control and EC non-CR groups (p&lt;0.001), and the control and EC comparison groups (p=0.005). When comparing the EC CR group with EC non-CR group, the p-value for this gene was &lt;0.001. Simultaneous assessment of CDO1 and CDH13 genes methylation allowed to accurately distinguish between EC CR and EC non-CR groups (AUC=0.96). Conclusion: Assessment of CDO1 and CDH13 gene methylation in endometrial specimens from patients with endometrial cancer (IA stage G1), planned for medical treatment, can predict the treatment outcome.

Brazil has ideal conditions for the large-scale use of cabotage cargo transportation: a vast coastline with almost 7,400 km, approximately 70% of the population resident in a coastal range of up to 200 km from the coast and a strong concentration of activities economic along the coast. However, there is an absolute domain of road modal for the transportation of goods, and cabotage is underused. The purpose of the study was to raise the main barriers that negatively impact the performance of Brazilian cabotage. To this end, it was used to bibliographic and documentary research, based on studies already published in journals, doctoral theses, master's dissertations, congress annals, technical reports and research on government-related government agencies sites, navigation companies and entities related to cabotage. The main barriers that contribute to the low use of cabotage in Brazil were identified and analyzed, as well as possible alternatives of solution for sustainable development of this mode of transport. Such a study will certainly serve as subsidies for the definition of public policies aimed at better use of Brazilian cabotage, as well as to encourage navigation companies to invest more in the transport of goods by cabotage, in order to contribute to the reduction of trucks on highways Brazilian and for environmental preservation.

To decrease privacy risks, consumers may choose to misrepresent when they are asked to offer personal information. This paper examines the impact of consumer misrepresentation on a monopolistic firm and consumers with an economic model. The results show that consumer misrepresentation may benefit the firm but hurt consumers. In addition, consumers misrepresentation may encourage the firm to provide higher personalized service level in certain scenarios, such as, when the unit cost of personalized service is low. Thirdly, when consumers misrepresent, and the firm only covers part of the market, a greater unit value of consumer’s privacy information will reduce the firm’s profit, but a greater unit cost of personalized service increases the firm’s profit.

At present, farmland abandonment（FA）is a severe phenomenon in China, imposing a serious restriction on agricultural production. In this context, it is of great significance to explore the logical relationship between high-standard farmland construction (HSFC) and FA to optimize land resource allocation and guarantee national food security. Based on 838 samples of farmers in the main rice production area of the Yangtze River Basin in China, this paper employs the Tobit model, the mediating effect model, and the moderated mediating effect model to analyze the impact of HSFC on FA at the micro level. The results show that (1) HSFC inhibits FA, and FA proportion decreases by 1.14% for every 1% increase in HSFC proportion, and the robustness test and endogeneity treatment also yield consistent conclusions. (2) Agricultural socialization services (ASS) play a positive mediating role in the influence path. HSFC promotes farmers' purchase of ASS, which in turn inhibits FA. (3) The agricultural labor transfer distance (ALTD) plays a positive moderating role in the relationship between HSFC and FA. The farther the distance, the more likely HSFC can promote farmers' purchase of ASS and inhibit FA. The results provide enlightenment on how to precisely implement HSFC policy, that is, to inhibit FA by improving the construction of high-standard farmland and the post-construction management as well as protection system. Moreover, this study helps guiding the development of ASS and optimizing the institutional environment for agricultural labor transfer, creating conditions for agricultural scale management.

Atrial fibrillation (AF) is a common arrhythmia associated with various risk factors and significant morbidity and mortality. This article presents findings from a study involving 115 patients with permanent AF. The study examined demographics, risk factors, associated pathologies, complications, and anticoagulant therapy. The results showed a slight predominance of AF in males, with the highest incidence in individuals aged 75 and above. Common risk factors included arterial hypertension, dyslipidaemia, diabetes mellitus type 2, and obesity. Comorbidities such as congestive heart failure, mitral valve regurgitation, and pulmonary disorders were prevalent among the patients. Major complications included congestive heart failure, stroke, and myocardial infarction. Thromboembolic and bleeding risk assessment using CHAD2DS2-VASc and HAS-BLED scores demonstrated a high thromboembolic risk in all patients. The majority of patients were receiving novel oral anticoagulants (NOACs) before admission, while NOACs were also the most prescribed antithrombotic therapy at discharge. The study highlights the importance of risk factor management and appropriate anticoagulant therapy in AF patients to reduce complications and improve outcomes.

The thyroid gland secretes essential hormones for the growth regulation and the metabolic activity of the body. Thyroid dysfunction includes many conditions that can indirectly affect dental treatments. Therefore, the practitioner must be aware of these pathologies and their signs and symptoms in order to elaborate specific dental treatment plans, thus avoiding any per- and/or postoperative complications. Moreover, the dentist can help in the screening of undiagnosed thyroid dysfunction among patients.

The healthcare system is intricate and dynamic. For the purpose of organising and planning a system, where allocating funds appropriately is essential and therefore managerial skills play a critical role. A certain portion of each nation's GDP is allotted to the health sectors. Managers and decision-makers in the healthcare systems are using resources effectively, yet the quantity of funding they receive from the government or other stakeholders is insufficient sometimes. A sound understanding of both basic and advanced economics is necessary for the care management, to make cost-effective judgements in public health. Healthcare officials at all levels (regional, state, federal, or international), need to be knowledgeable about public health issues and the related modern economic climates. Business executives and healthcare managers need effective practical knowledge to handle partner-financial allocation (demand-supply chain) and handle economic volatility.

With the fast development of blockchain technology in latest years, its application in scenarios that require privacy, such as health area, became encouraged and widely discussed. This paper presents an architecture to ensure the privacy of health-related data, which are stored and shared within a blockchain network in a decentralized manner, through the use of encryption with the RSA, ECC and AES algorithms. Evaluation tests were performed to verify the impact of cryptography on the proposed architecture in terms of computational effort, memory usage and execution time. The results demonstrate an impact mainly on the execution time and on the increase in the computational effort for sending data to the blockchain, however, justifiable considering the privacy and security provided with the architecture and encryption.

A new model for international bed numbers is applied to local area populations for English Clinical Commissioning Groups (CCGs) where the Age Standardized Mortality Rate (ASMR) is used as a measure of population health and hence for total occupied hospital bed demand (elective plus emergency admission types, including acute, rehabilitation, mental health, and maternity), rather than simple bed availability. A 100-unit increase in ASMR (European Standard population) leads to a 15.3 – 30.7 (feasible range) unit increase in occupied beds per 1,000 deaths. ASMR shows why the Australian states of the Northern Territory and Tasmania have an intrinsic higher bed demand than the other states – although in both cases the bed planning methodologies failed to indicate this need and both states show a large bed deficit. Due to the high volatility in bed demand the average bed demand for 2019 was determined by a linear trend using data for 2014/15 to 2019/20 — as the pre-COVID-19 baseline. The slope of the relationship between occupied beds per 1,000 deaths and deaths per 1,000 population shows a power law function. As is to be expected there is scatter around the trend line which will partly arise from uncertainty in the expected occupied beds in 2019, fluctuations in both ASMR and deaths per 1,000 population from one year to another, changes in the number of persons on the elective, outpatient and diagnostic waiting lists, and local area variation in births and consequently upon maternity, neonatal and pediatric bed demand. Additional variation in bed demand will arise from differences in the level of funding for social care, especially elderly care, and to a lesser extent upon patients using private health care, and charitable hospices for palliative care. However, after including the change in bed demand arising from ASMR the model is sufficiently accurate to identify gross differences in bed demand as a prelude to further investigation. The model requires further extension to cover specialty-level demand, and perhaps segregation between elective and emergency types of inpatient demand. A method for benchmarking the whole hospital’s average bed occupancy is given which enables them to run at optimum efficiency and safety. Using this method English hospitals can be shown to operate at highly disruptive and unsafe levels of bed occupancy leading to deleterious ‘turn-away’. Turn-away implies bed unavailability for the next arriving patient. The problems associated with crafting effective bed planning are illustrated using the English NHS as an example.

Under the new pattern of “double carbon” development, good ESG performance is the best way to promote the sustainable development of enterprises, and the ESG investment strategies are directly affected by the strategic vision of managers. Based on the upper echelons theory and stakeholder theory, this paper takes Chinese A-share listed companies from 2011 to 2022 as samples to empirically analyze the impact of managerial myopia on corporate ESG performance. The results show that managerial myopia significantly inhibits corporate ESG performance, mainly by inhibiting corporate green investment and green innovation sustainability. Furthermore, for state-owned enterprises (SOE), heavy polluting enterprises (HPE) and non-high-tech enterprises, the inhibitory effect of manager myopia on ESG performance is stronger. When the enterprise is in the growth stage, the above inhibition effect is more severe. For external governance, the greater the analyst attention and public environmental attention are, the more conducive they are to alleviating the restraining effect of managerial myopia on enterprise ESG performance. Therefore, effectively improving the time cognition level of managers and strengthening external supervision have become important measures for comprehensively optimizing the ESG performance.

Xinjiang, as a climate-sensitive region in northwest China, holds significant importance in studying extreme weather events for agricultural production and socioeconomic development. Based on data from 52 meteorological stations and 23 extreme climate indicators in Xinjiang from 1960 to 2021, the 5a moving average, linear trend estimation, climate tendency rate, and GIS spatial analysis methods are used to analyze the distribution and change characteristics of extreme climate. The results revealed that from 1960 to 2021, TXx, TNx, TXn, TNn, FD, ID, TN10p, TX10p, and WSDI showed a significant upward trend, while SU, TR, TN90p, TX90p, and CSDI showed a significant downward trend. What is more, the range of changes in the extreme temperature indexes from 1990 to 2021 is higher than that of 1960 to 1989. The high-value areas of extreme indexes are primarily concentrated in the northern part, while the high-value areas of cold event indexes are mainly distributed in the southern part. The upward/downward trends all accounting for over 80.00% of the entire region. The Rx1day, Rx5day, PRCPTOT, R95p, R99p, R10, R20, and R25 showed a significant growth trend from 1960 to 2021, and the range of change in the extreme precipitation indexes from 1990 to 2021 was lower than that from 1960 to 1989. Moreover, their high-value areas and high climate tendency areas are mainly concentrated in the northern and western parts of Xinjiang, with upward trends all accounting for over 71.00% of the entire region. The research results provide theoretical foundations for formulating climate risk strategies in the northwest region of China.

The collaboration of multimodal transport documents is the foundation for conducting multimodal transport operations. Blockchain technology can effectively address trust issues and information sharing difficulties in current multimodal transport document collaboration. However, in current blockchain-based electronic document research, the bottleneck problem lies in the collaboration of multiparty in multimodal transport under the "one-bill coverage system". We design a blockchain platform architecture for multimodal transport, reshaping collaboration processes for different modes of transport based on the blockchain platform. It proposes a collaboration model for multimodal transport under the "one-bill coverage system" and performs model solving and case analysis. The obtained collaboration strategies are embedded into the platform's application layer. Finally, using Solidity language, it writes smart contracts related to multimodal transport under the "one-bill coverage system " and deploys and executes them on the Remix platform.

Productivity has been modest in construction due to many reasons, especially project-specific operations and fragmentation. The main aim of this research is to map the challenges towards industrialization in construction through a comparative study of the manufacturing industry. An inductive approach and a qualitative method are followed by applying semi-structured inter-views, and node-based thematic analysis. The key elements of industrialization in mature man-ufacturing companies are benchmarked. The findings indicate a specific order of steps for indus-trialization in construction. Data and information management with achieved systematization have been sources of productivity in manufacturing companies. However, the systematization starts from the offering, construction companies need to define their offering, that is the products, and define their respective data. Only accurate and good quality data on products enables the systematization of order delivery processes, which are the engine for a company’s success or failure. Continuous efficiency improvement is only possible with systematized processes and respective data related to the processes. The originality involves clarifying the industrialization of construction companies via products, respective data, and processes considering industrialized manufacturing companies.

In the past two decades, several floods have affected people and their properties in Burkina Faso, with the unprecedented flooding occurring in Ouagadougou in September 2009. So far, most studies have focused on Ouagadougou and surrounding localities and have paid little attention to other flood-prone regions in Burkina Faso. Specifically, there is a data and knowledge gap about the flood risk in the Haut-Bassins region, which in turn hampers the development of mitigation strategies and risk reduction measures in communities. This study demonstrates how data collected at the household level can be used to understand flood risk and its components at the village level in this data-scarce region. Using an indicator-based method, we analyze both flood risk and flood risk perception at the village level. Moreover, we determine the factors influencing flood risk perception at the household level using an ordered logit model. We find that 12 out of the 14 villages considered, experience high levels of flood risk. The management of runoff from the nearest urban areas and poorly designed civil engineering infrastructures, such as roads, were highlighted by the households as significant factors that increase their vulnerability. Hence, dependencies in space and between urban and rural areas may play a substantial role in modifying risk. The perceived flood risk consistently exceeds the estimated flood risk, with an insignificant positive correlation between both risk indices. Regression results indicate that flood risk perception is mainly influenced by informational and behavioral factors of the households. The findings of this study can provide valuable information to municipal and regional authorities involved in disaster risk management in the study area. Moreover, the method is transferable to other data-scarce regions

This study investigates the complex role of financial risks in the context of public–private partnerships (PPPs) in India and their relationship to the amount and extent of investments made by private partners (s). We hypothesize that subsidies, bid criteria, and risk have a positive relationship with investments made by private partners. A project’s leverage moderates these relationships. Regression was used to analyze the hypotheses using SPSS 24 and PROCESS Macro. Data from the World Bank were collected from 2009 to 2019 for the study variables. Interestingly, our research finds support for these relationships that may seem contrary to some commonly-held notions of risk relationships, such as – the government using ‘viability gap’ funding to attract private investment and ‘leverage’ does not moderate the relationship between risk assumed and private investment. This study is among the first to recognize and elaborate on financial risk relationships, specifically in the context of Indian PPPs. These findings are significant for both private and public participants in terms of financial considerations in PPP projects, especially in emerging markets.

In the rapidly evolving FinTech sector, risk management is paramount for dealers and brokers. This paper underscores the significance and progression of risk management within this realm. Initially, it reviews the traditional risk management technologies and their inherent limitations. The discourse then shifts to contemporary solutions, specifically emphasizing the role of artificial intelligence, machine learning algorithms, and big data analytics in forecasting and mitigating potential risks. The paper also presents case studies of companies effectively utilizing these advanced techniques, highlighting their benefits in real-world scenarios. Additionally, it addresses forthcoming challenges, including regulatory compliance, data privacy issues, and the imperative for strengthened cybersecurity measures. The conclusion acknowledges that the adoption of these innovative technologies not only boosts operational efficiency but also markedly diminishes financial risks, thereby contributing to a more stable and reliable financial market

Dogs population increased exponentially in the past decade while the birth rate decreased gradually, especially in the developed countries. Due to dogs’ characteristics of loyalty and obedience, most pet owners treated their dogs as their family members or companions, therefore they are willing to select and purchase safe, high quality and nutrition dog food to their dogs. However, there is lack of regulation or standard to reinforce the production or distribution of dog food in Hong Kong. Consequently, this paper will first review the relationship between high dog population and declining birth rate, types of dog food and typical ingredients. Secondly, the potential health implications of various “toxic” food ingredients for dog, hazard analysis of dog food and its testing methods will be investigated. Lastly, this paper will summarize worldwide existing standards and regulations, quality and hygiene management system related to dog food safety to provide insights and suggestions to Hong Kong government about the importance and urgency of implementing regulations and policies to oversee the imported and local manufactured dog food.

This paper aims to assess whether the financial performance of Portuguese companies that publish sustainability reports (SR) differs from the financial performance of companies that do not publish SR. We use two methodological procedures. First, we conducted a univariate analysis to test the differences in the financial performance according to the disclosure of SR. Second, we conducted a multivariate analysis using a multiple linear regression that explains financial performance by the disclosure of SR, as well as control variables such as sector, size, leverage, growth, and liquidity. Findings indicate that financial performance of companies that disclose SR does not significantly differ from the financial performance of companies that do not disclose SR. The results are robust to both methodological procedures, as well as to the sample split by sectors.

The Solvency II regulatory framework specifies procedures and parameters for determining solvency capital requirements (SCRs) for insurance companies. The proposed standard SCR calculations involve two steps. The Value–at–Risk (VaR) of each risk driver is measured and, in a second step, all components are aggregated to the company’s overall SCR, using the Standard Formula. This formula has two inputs: the VaRs of the individual risk drivers and their correlations. The appropriate calibration of these input parameters has been the purpose of various Quantitative Impact Studies that have been conducted during recent years. This paper demonstrates that the parameter calibration for the equity–risk module—overall, with about 25%, the most significant risk component—is seriously flawed, giving rise to spurious and highly erratic parameter values. As a consequence, an implementation of the Standard Formula with the currently proposed calibration settings for equity–risk is likely to produce inaccurate, biased and, over time, highly erratic capital requirements.

This paper presents a comprehensive study on applying machine learning (ML) techniques for real-time credit card fraud detection. With the increasing prevalence of credit card fraud, financial institutions face the challenge of detecting fraudulent transactions promptly and accurately. This research evaluates various ML models, including Logistic Regression, Decision Trees, Random Forests, XGBoost, and Deep Convolutional Neural Networks, for their effectiveness in identifying fraudulent activities. Utilizing PySpark for processing large-scale transaction data, the study highlights the importance of real-time analysis, adaptive learning, and handling imbalanced datasets. Key findings reveal that XgBoost, with its balance of accuracy and complexity, emerged as the most promising model, achieving an accuracy rate of 98% in detecting fraudulent transactions. The paper further discusses the potential of ensemble methods, graph-powered systems, and intelligent sampling in enhancing fraud detection capabilities. Challenges such as overfitting, data access, and the need for real-time analysis are addressed. Future research directions include implementing models in live transaction environments, expanding model applicability across financial platforms, and developing advanced anomaly detection methodologies. This study underscores the pivotal role of machine learning in safeguarding financial transactions against fraud, offering significant implications for consumers, financial institutions, and the broader financial ecosystem.

Understanding and improving seismic resilience in regions prone to earthquakes is crucial for mitigating the effects of natural disasters. This study zeroes in on Montenegro, a region frequently affected by seismic activity, with the goals of critically evaluating current resilience levels, pinpointing key preparedness gaps, and assessing the efficacy of existing response strategies. Employing qualitative methods, the research involved semi-structured interviews with residents from Montenegro's most vulnerable areas, including Nikšić, Podgorica, Bar, Kotor, Cetinje, Budva, Herceg Novi, and Berane. These participants, selected for their firsthand experiences with significant earthquake impacts, contributed to a deeper understanding through discussions that aligned with a broad conceptual framework, exploring everything from the nuances of citi-zen resilience to local government responses and personal preparedness. The study aimed to gather in-depth views on community resilience during earthquakes, evaluate citizens' knowledge and preparedness for such disasters, and examine local governance strategies for enhancing seismic resilience. It revealed varying resilience levels across communities, with some displaying well-structured strategies while others showed notable deficiencies. The findings underscore the urgent need for community-specific preparedness programs and improvements in both structural and educational frameworks to enhance resilience. This research highlights the necessity of a comprehensive approach that includes detailed risk assessments, tailored preparedness training, and stronger public awareness efforts. Additionally, it emphasizes strengthening local governments' capabilities to enact and sustain effective response measures to ensure ongoing community resilience against future seismic challenges.

Medical waste management is crucial issues for keeping our life safe and safety. Without effective measures for waste management, we cannot keep ourselves safe from different types of contagious diseases; viruses, bacteria. Knowledge sharing assists healthcare staff in identifying and mitigating hazards on appropriate handling, segregation and disposal medical waste. The aim of the study is to identify a multifaceted analysis of knowledge sharing, training, communication and attitudes for effective medical waste management. This study selected 153 medical workers randomly, including nurses, lab technologists, and cleaners, from hospitals, diagnostic centers, and clinics in the northern part of Bangladesh. The study design of this research is mixed-methods. This research adopts a survey and interview as a research strategy. The samples are collected randomly. SPSS software packages, MS Excel Office, and PLS-SEM are used to analyze data. Most of the respondents always share their knowledge face-to-face according to the findings. The result also shows that there are positive relationship between sources of knowledge (p: 0.046), awareness and knowledge (p: 0.049), attitudes of waste management (p: 0.025) and effective medical waste management. However, there are no relationships between communication and collaboration (p: 0.943), the practice of waste management (p: 0.155), supervision and staff monitoring (p: 0.527), training and education (p: 0.656) and effective medical waste management. Its focus on the northern part of Bangladesh introduces regional specificity, potentially limiting the applicability of results to other regions. The knowledge-sharing practices for waste management among medical staff are examined within this research, along with the relationship between attitude, awareness, and knowledge, staff supervision and monitoring, practice, training, and education and effective medical waste management that have not been studied before home and abroad. This analysis highlights the critical need for an integrated approach to medical waste management, emphasizing the importance of continuous education, open communication, and cultivating a culture that prioritizes environmental sustainability. The insights from this study offer practical recommendations for healthcare institutions to improve their medical waste management practices, ultimately leading to enhanced health outcomes and better environmental protection.

This study introduces an integrated method for managing process risks in a Business Process Reengineering (BPR) project, using Robust Data Envelopment Analysis (RDEA) and machine learning (ML). The goal is to prioritize risks based on three standard factors of PFMEA: Severity, Occurrence, and Detection (S-O-D), and incorporating two additional factors (Breakdown Cost and Breakdown Duration) seen as undesirable outputs. The model also accounts for the effect of uncertainty on expert-estimated values by applying disturbance percentages in the linear PFMEA-RDEA model. A machine learning model is proposed to predict new values if partial or total modifications have been made to the processes. The approach was implemented in an au-tomotive sector company, and the results showed the impact of uncertainty on values by com-paring different approaches such as RPN, PFMEA-DEA, and PFMEA-RDEA. A new reduced risk categorization was achieved, who allowed for decision-makers to focus on necessary actions for reengineering.

BackgroundIntracranial hemorrhage (ICH) in pregnancy, estimated at 1 in 10,000 cases, presents significant diagnostic challenges prenatally despite advanced imaging techniques such as ultrasound (US) and MRI. Detecting ICH is crucial for pregnancy management and future treatment decisions aimed at improving fetal survival and reducing brain damage. This report presents the diagnosis and outcomes of two cases of prenatal ICH.Cases presentationThe first case involves a 30-year-old pregnant woman with irregular prenatal care diagnosed with ICH at 32 weeks of gestation via US and MRI. She chose to continue the pregnancy, delivering a 3160g male infant at 36 weeks via cesarean section. Following NICU care including resuscitation and ventriculoperitoneal shunt placement, the infant was discharged. Subsequent examinations showed a reduction in ventricle size. In the second case, a 27-year-old woman taking acenocoumarol for a mechanical heart valve developed fetal subdural hemorrhage detected by US and MRI. She opted to terminate the pregnancy, resulting in a stillborn male infant weighing 1530g.ConclusionFetal ICH presents with varying severity and prognostic implications, diagnosed and graded using US. Fetal cranial MRI may help clarify the etiology. Management remains controversial, with termination of pregnancy potentially warranted in severe cases due to poor prognosis. Further research is needed to refine management and improve outcomes in fetal ICH.

Amidst the escalating demands for efficient Intelligent Transportation Systems (ITS) management, the significance of accurate Origin-Destination (OD) data prediction has emerged as a paramount concern, given its indispensable role in the ITS domain. This paper introduces a groundbreaking integrated model, creatively fusing the unique strengths of Graph Convolutional Network (GCN), Residual Neural Network (ResNet), and Long Short-Term Memory Network (LSTM) into a single architecture, which we name GraphResLSTM, to exploit road segment average speed data for OD prediction. Compared with conventional road segment traffic volume data, average speed data is more readily available, significantly reducing the data accessibility barrier for OD prediction. We underscore the critical role of selection of key road segments and employs a combination of Entropy Weight Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to identify 41 representative road segments from the experimental road network. In the experimental phase, we simulate 86400000 seconds of traffic conditions, generating a total of 1440000 data records. Utilizing these simulated data, we design and conduct comparative experiments between models as well as data types. The results demonstrate that both the GraphResLSTM model and road segment average speed data outperform other models and data types notably in OD prediction tasks.

We study a two-echelon supply chain made up of a supplier and a manufacturer, both of which can reduce their component/product carbon emissions. With the vertical technology spillovers, we explore the optimal decisions of centralized and decentralized supply chains with price dependent demand and propose coordination strategy for the decentralized supply chain. Considering the cost contraction effectiveness of the technology spillovers, the centralized and decentralized game theoretic models of a two-echelon supply chain are developed to investigate optimal decisions of pricing and carbon emissions reduction. Through a systematic comparison and numerical analysis, we show that the profits of both players and the entire supply chain improve with the effect of technology spillovers increasing. Carbon emissions reduction will be taken by various protective measures so that the supplier and the manufacturer who do not innovate can hardly share the results of innovating via the “free-riding” methods when the technology spillover is relatively small. We also propose a revenue-cost sharing contract through bargaining to enhance the performance of the decentralized supply chain.

The global antibiotic resistance crisis, driven by antibiotic overuse and misuse, is multifaceted. This study aimed to assess the microbiological and genetic characteristics of raw retail pork meat, including the isolation, antibiotic susceptibility testing (AST), whole genome sequencing (WGS) of selected indicator bacteria, antibiotic residue testing, and metagenomic sequencing. Samples were purchased from 10 randomly selected retail stores in Gauteng, South Africa. Samples were aseptically separated, with a portion sent to an external laboratory for isolating indicator bacteria and antibiotic residue testing. Identification of the isolated bacteria was reconfirmed using matrix-assisted laser desorption/ionization-time of flight mass spectrometry (MALDI-TOF MS). AST was performed using the Microscan Walkaway system. WGS and metagenomic sequencing were performed using the Illumina NextSeq 550 instrument. The isolated E. coli and enterococci exhibited little phenotypic resistance, with WGS revealing tetracycline resistance genes. Isolated bacteria and meat samples all harboured tetracycline resistance genes, and the antibiotic residue concentrations were within acceptable limits for human consumption. In the metagenomic context, most identified bacteria were of food/meat spoilage and environmental origin, with the resistome analysis primarily indicating beta-lactam, tetracycline and multidrug resistance genes. Further research is needed to understand the broader implications of these findings on environmental health and antibiotic resistance.

Prostate cancer is the second leading cause of death in males in America, with advanced prostate cancers exhibiting a 5-year survival rate of only 32%. Castration-resistance is often developed during the course of treatment, though its influences require further understanding. This study explores the human microbiome for its implications in castration-resistance and metastasis in prostate cancer. RNA sequencing data was downloaded for bone and soft tissue biopsies of patients with metastatic castration-resistant prostate cancer. These sequences were mapped to bacterial sequences to yield species-level abundance approximations. Numerous species were found to correlate to the expression of known markers of castration-resistance, including AR, PI3K, and AKT. Castration-resistance-associated signaling pathways were also enriched with these species, including PI3K-AKT signaling and endocrine resistance. For their implications in cancer aggression and metastasis, cancer stem cell markers were further explored for relation to these species. EGFR and SLC3A2 were widely downregulated with greater abundance of most species. Our results suggest that the microbiome is heavily associated with castration-resistance and stemness in prostate cancer. By considering the microbiome’s importance in these factors, we may better understand the highly aggressive and highly invasive nature of prostate cancer, allowing for needed improvement in the treatment of this disease.

This paper presents quantitative research results regarding the influence of demographic factors on the earthquake risk perception of the citizens of Belgrade. This research aims to determine how much the citizens of Belgrade are aware of the risk and prepared to react in the event of an earthquake. The relationship between gender, age, level of education, and facility ownership with risk perception was examined. T-test, One-way ANOVA, and Pearson correlation coefficient were used to examine the relationship between the variables and the earthquake risk perception. The survey was conducted using a questionnaire that was given and then collected online among 235 Belgrade respondents during September 2020. The questions were divided into three categories. The first part of the questionnaire was consisted of general questions about the demographic characteristics of the respondents, then the questions that would determine the level of awareness of the respondents about earthquakes, and finally, the questions for determining the respondents' preparedness. The results of the research show that women have a higher perception of risk. It has been proven that the youngest respondents from the age category of 18-30 have the lowest risk perception. The influence of education level in no case showed a statistically significant correlation with risk perception.

By introducing environmental mediator, for avoiding randomness in selecting indicators and subjectivity in deciding interaction path coefficients to reveal interaction mechanism among forest industry, ecology and environmental capacity, structural Pressure–State–Impact–Response (PSIR) and quantitative Structural Equation Modeling (SEM) are utilized with data on thirty–one provinces of China. We can find that: (i) forest industry has a negative influence on ecology, whereas ecology has a positive influence on industry; (ii) the destructive influence that industry has on ecology is much greater than the positive effect ecology has on industry; and (iii) environmental capacity plays a partial mediating role between industry and ecology. According to these results we can conclude that: (i) forest industry and ecology are not in a symbiotic relationship and have not reached a level of ecological security; (ii) the interaction between industry and ecology is in a period of transition from antagonistic to beneficial; and (iii) industry dominates ecology. However, we prospect the following advice: (i) methods of forest industry should be changed; (ii) new modes of industrial integration and circular economy should be developed; and (iii) the ability of the environment to sustain itself should be enhanced. The level of forest ecological security to discuss the symbiotic mechanism of industry and ecology will propose to be measured in future.

Proactive deprescribing is the process of stopping a medicine before harm occurs. This study aimed to specify and validate with an international sample of healthcare professionals, a proactive deprescribing process of steps and constituent activities. We developed a proactive deprescribing process framework of steps which we populated with literature-derived activities required to be undertaken by healthcare professionals. We distributed a survey to healthcare professionals in-ternationally requesting for each activity; the frequency of its occurrence in practice and whether it was important. Extended response questions invited barriers and enablers to deprescribing. The 263 survey respondents were from 25 countries. A proactive deprescribing process was developed comprising four steps: (1) Identify a patient for potential stop of a medicine, (2) Evaluate a patient for potential stop of a medicine, (3) Stop a medicine(s), (4) After a medicine has been stopped, and 17 activities. All activities were considered important by ≥70% respondents. Nine activities re-quired healthcare professionals to undertake in direct partnership with the patient and/or care-giver, of which seven were only sometimes undertaken. Deprescribing interventions should focus on addressing the barriers and enablers of healthcare professionals undertaking the activities that require direct partnership with the patient and/or caregiver.

Our aim is at three feels: i) study safe haven properties of sectoral CDS; ii) study hedging effectiveness and measure the diversification benefits of introducing them in both Conventional and Islamic stock markets portfolios and iii) compare these results with VSTOXX, Gold and Bitcoin indices. To do this, we estimate time varying hedge ratio using (A)DCC and GOGARCH models and then deduce hedging effectiveness.. We follow the same methodology for each sub-period and each refit to test the effect of bubble crypto currency and COVID-19 pandemic on hedging effectiveness. Empirical results show the superiority of industrial CDS sectoral index to hedge both conventional and Islamic stock markets whether long or short term despite that their performances were reduced slightly during the second and third periods. VSTOXX and Others sectoral CDS indices offers Hedging performances that are generally better than Bitcoin and gold.

Despite being stored at 113 K and atmospheric pressure, LNG cold potential is not exploited to reduce green ships’ energy needs. An innovative system based on three Organic Ranking Cycles integrated into the regasification equipment is proposed to produce additional power and recover cooling energy from condensers. A first-law analysis identified ethylene and ethane as suitable working fluids for the first and the second ORC making available freshwater and ice. Propane, ammonia and propylene could be arbitrarily employed in the third ORC for air-conditioning. An environmental analysis that combines exergy efficiency, ecological indices and hazard aspects for marine environment and ship’s passengers, indicated propylene as safer and more environmentally friendly. The exergy analysis confirms more than 20% of the LNG potential can be recovered from every cycle to produce a net clean power of 76 kW, whereas 270 kW can be saved by recovering condensers' cooling power to satisfy some ship needs. Assuming the sailing mode, a limitation of 162 kg in LNG consumptions was determined, avoiding the emission of 1584 kg of CO2 per day. Marine thermal pollution is reduced by 3.5 times by recovering the working fluids' condensation heat for the LNG pre-heating.

Big Data and blockchain technology are coming together to revolutionize how decisions are made in a decentralized way across various industries. This review looks at how these technologies, along with distributed systems, can improve data security, transparency, and real-time processing, making decision-making more efficient and informed. The integration enhances data security with unchangeable records, increases transparency and traceability, and supports real-time data analysis. However, there are challenges to overcome, including scalability, data privacy, interoperability, regulatory compliance, and high costs. By examining case studies such as Estonia's healthcare system, IBM and Walmart's Food Trust, and the Brooklyn Microgrid project, we explore the practical applications and benefits of combining Big Data with blockchain. Despite these hurdles, the review finds that the ongoing advancements and innovative solutions in these technologies offer significant promise. They are set to drive the adoption and effectiveness of decentralized decision-making, ultimately leading to better efficiency and outcomes across multiple sectors.

Background. The Stanford Health Assessment Questionnaire-Disability Index (HAQ) is widely used to measure functional ability in persons with Rheumatoid Arthritis (RA). The instrument was developed with limited involvement from persons with RA, and their perception of the instrument has not been studied in depth. The aim of this study was to explore how persons with RA experience the use of the HAQ in care. Methods. Forty persons with RA were purposefully recruited to participate in semi-structured interviews. The interviews were then analyzed qualitatively using thematic analysis. Results. The participants questioned the relevance of the HAQ but nevertheless experienced that the instrument had a profound effect on their understanding of health and how care is delivered. The analysis resulted in three themes: Problems with individual items, meaning of the summative score, and effects on care and health perceptions. Conclusions. To make the HAQ relevant to persons with RA, it needs to be revised or to include an option to select items most meaningful to the respondent. To ensure relevance, the HAQ update should preferably be co-created by researchers, clinicians and persons with RA.

Recycling waste products is an environmental-friendly activity that can bring benefits to accompany, saving manufacturing costs and improving economic efficiency. For the beer industry, recycling bottles can reduce manufacturing costs and reduce the industry's carbon footprint. This paper presents a model for a multi-objective collection-distribution center location and allocation problem in a closed loop supply chain for the beer industry, in which the objective is to minimize total costs and transportation pollution. Uncertainties in the form of randomness and fuzziness are jointly handled in this paper to ensure a more practical problem solution, for which returned bottle sand unusable bottles are considered fuzzy random variables. A heuristic algorithm based on priority-based global-local-neighbor particle swarm optimization (pb-glnPSO) is applied to ensure reliable solutions for this NP-hard problem. A case study on a beer operation company is conducted to illustrate the application of the proposed model and demonstrate the priority-based global-local-neighbor particle swarm optimization.

Background: A Morel–Lavallée lesion (MLL) is a traumatic, internal closed degloving injury that results from shearing of subcutaneous tissue away from underlying fascia, usually associated with high-velocity trauma. Case presentation: A 52-year-old man was transported to hospital for further follow-up of a superficial femoral artery injury. Examination on admission showed bruising seen around the left thigh. Contrast-enhanced computed tomography of the left thigh revealed hematoma with extravasation from a branch of the left superficial femoral artery, but no apparent aneurysms or femoral fractures. The patient was treated with compression bandages and discharged on hospital day 6. He left hospital able to walk unaided. He subsequently complained of left thigh pain with swelling and visited the hospital several times. He was treated at another outpatient clinic and was diagnosed with MLL. Single-use disposable portable negative pressure wound therapy (NPWT) was applied for about 2 months, achieving a stable wound site. Conclusion: We encountered a case of MLL successfully treated with single-use portable NPWT. This report presents the case of a patient with isolated MLL without fracture in the left thigh, in which the diagnosis was missed on initial admission. Finally, the patient received minor interventions including drainage along with tissue debridement and portable NPWT. We reviewed the relevant literature and summarize the pathogenesis, clinical manifestations, and treatment strategies for MLL. Emergency physicians require an increased understanding of MLL to avoid misdiagnosis and missed diagnosis.

The wellness economy has developed rapidly recently, particularly in wellness tourism. Wellness tourism transforms sustainable strategy through the availability of extended multi-actor engagement in service innovation to further the tourism experience. Therefore, when operating a wellness tourism destination, it is necessary to encourage multi-actor engagement in service innovation to achieve sustainable development. This paper introduces a mixed research method with the SWOT-AHP-QSPM, as it qualitatively analyzes the advantages, disadvantages, opportunities, and threats, constructs an evaluation index system, conducts quantitative analysis using the analytic hierarchy process, determines the index weight and weighted score, and calculate the sustainable development strategy, which is appropriate for wellness development of Chongqing Tea Mountain and Bamboo Forest National Forest Park. Purposive field surveys and personal interviews were conducted in Chongqing forest tourism destinations, involving 356 effective respondents. The research reveals that multi-actor engagement significantly enhances sustainable development. These findings bridge service innovation knowledge gaps and provide valuable insights for forest tourism destination management and sustainable policy strategies to elevate multi-actor engagement in forest sites.

In the extended malfatti problem, the numbers of circles in the triangle are triangle numbers, because the tangency properties among the internal circles and 3 sides of the triangle have special type of structure. That is, the corner circle tangent to two sides of the triangle and two boundary circles, the boundary circles tangent to one side of the triangle and 4 other circles, and the inner circles always tangent to 6 other circles. The circles we find in extended general Malfatti’s problem has the property that the smallest radius and the largest radius for the circle has big difference. In this paper, we proposed algorithms to solve the problem that the tangency properties among circles and sides of triangle is not fixed, so that the number of circles in the triangle is not necessary a triangle numbers. The purpose of this change is try to find the radii of the circles in the triangle is within a small range.

It has been empirically observed that the upper tail of the firm size distribution follows either the Pareto distribution or the Zipf distribution, with both patterns being explained by Gibrat’s law (Gabaix, 1999). This article analyzed firm revenue data from China, spanning from 2005 to 2013, to examine the whole range of the firm size distribution. Our empirical analysis revealed that firm revenue data over these years is well fitted by a three-parameter generalized Pareto distribution, with the fitted parameters indicating a dichotomy: The size distribution of large-sized firms, namely the upper tail, is asymptotically characterized by a Pareto distribution or a Zipf distribution, whereas the size distribution of smaller and medium-sized firms is approximated by an exponential distribution. This finding suggests that Gibrat’s law should be extended to account for the emergence of a generalized Pareto distribution.

Driving factors of smart city construction are exploratively studied by grounded theory method based on text sources from journal papers in SCI and CSSCI databases. Initial scale of driving factors about smart city construction is obtained on the basis of above analyses. This paper modifies measuring items of the initial scale with a small sample pretest and reliability test, then forms final items by exploratory factor analysis. According to the above scale, questionnaires are designed to obtain empirical data, and confirmatory factor analysis is used to verify further validity and reliability of the scale. The results show that driving factors of smart city construction include three main dimensions: problem-oriented factors, business- or technology-driven factors and endogenous development requirements.

High Resolution Melting Analysis (HRM) has been pointed as a suitable alternative method to detect and identify Leishmania species. Herein, we aimed to evaluate the sensitivity, specificity, accuracy, and limitations of a HSP70-HRM protocol both as a diagnostic scheme applied in clinical samples and as a species typing tool for laboratory research and reference services. Our data reveal the pronounced species-typing potential of the HSP70-HRM in DNA from cultured parasites. For clinical samples, however, we advise caution due to the parasite load-dependent accuracy. In the light of these findings and considering the importance of parasite load determination for clinical and research purposes we recommend the integration of the presented typing scheme and the previously published Leishmania quantifying approach as combined tools for clinicians, surveillance, and research.

Enterprise risk management (ERM) was introduced in the 1990s and has become an expectation by boards of directors and regulators as a sign of good management and good corporate governance. However, many organizations struggle to implement ERM, and still seek practical ad-vice on ERM implementation. This article explains many of the reasons why organizations are unsuccessful in their efforts at implementation and provides practical solutions by an experienced risk manager and consultant, an ex-Chief Risk Officer, and an academic, all who has writ-ten extensively in the subject. This article should be of interest to practitioners involved in implementing ERM, to consultants in ERM, and to academics teaching courses on ERM, risk management, and related topics. This article also provides a base against which further future re-search can be done as ERM best practices continue to evolve.

Endometriosis is a prevalent chronic inflammatory disease characterized by a considerable delay between initial symptoms and diagnosis through surgery. The pressing need for a timely, non-invasive diagnostic solution underscores the focus of current research efforts. This study examines the diagnostic potential of the menstrual blood lipidome. The lipid profile of 39 samples (23 women with endometriosis and 16 patient of control group) was acquired using reverse-phase high-performance liquid chromatography-mass spectrometry with LipidMatch processing and identification. Profiles were normalized based on total ion counts. Significant differences in lipids were determined using the Mann-Whitney test. Lipids for the diagnostic model, based on logistic regression, were selected using a combination of variance importance projection filters and Akaike information criteria. Levels of ceramides, sphingomyelins, cardiolipins, triacylglycerols, acyl- and alkenyl-phosphatidylethanolamines, and alkenyl-phosphatidylcholines increased, while acyl- and alkyl-phosphatidylcholines decreased in cases of endometriosis. PE P-16:0/18:1 and CL 16:0_18:0_22:5_22:6 serve as marker lipids in the diagnostic model, exhibiting a sensitivity of 81% and specificity of 85%. The diagnostic approach based on dried spots of menstrual blood holds promise as an alternative to traditional non-invasive methods for endometriosis screening.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

BACKGROUND Detecting total cholesterol in dried blood spots could aid in identifying individuals with a high likelihood of familial hypercholesterolemia and could be used as a screening measure. This study aims to assess the diagnostic accuracy of dried blood spots on Whatman 903 paper cards using a manual enzymatic technique. METHODS A total of 394 samples collected as serum and dried blood spots were compared. Cholesterol was determined in serum using the automated reference method, while cholesterol on paper was measured using a manual enzymatic method. Within- and between-day diagnostic variability were analyzed. The correlation between both methods was assessed using Passing-Bablok regression and Bland-Altman plot. Internal validation of our correlation formula was performed on 149 samples, along with external validation of the formula proposed by Corso et al. RESULTS The within- and between-day coefficient of variation was found to be lower than 10.14% and 14.09%, respectively. Method comparison yielded an area under the curve of 0.976, with a standard error of 0.009. Passing-Bablok regression indicated a precision of 0.803 and an accuracy of 0.96. Internal validation precision was measured at 0.716. Regarding internal validation, calculated cholesterol with our regression line did not differ from serum cholesterol analysis, while significant differences were observed in calculated cholesterol levels using the external formula. CONCLUSIONS Total cholesterol analysis in dot blood spots demonstrates high precision and reproducibility. This method reliably enables the incorporation of this biological marker into neonatal screening for familial hypercholesterolemia detection.

Background: Physiotherapy and chronic low back pain (CLBP) form a broad and fast developing research area. The aim of the article was to holistically, thematically and chronologically analyze and synthesise literature production and identify most prolific research entities and research themes. Methods: The article quantitatively and quantitatively analyzed research literature production harvested from the Scopus bibliometric database. using a triangulation of bibliometric and thematic analysis. For this, Excell and VOSwiever software were used. Results: In the Scopus database, 2843 data sources were found, which were published between 1974 and February 26, 2024. The growth trend has been linearly positive since the beginning of publication, and after 2018, exponential growth began. A review of the most prolific entities showed that the most literature was published in America, Europe and Australasia. The thematic analysis of the information sources identified six main themes, while the chronological analysis revealed three main areas of developments: assessment tools, CLBP processing and study methodology. Conclusions: The results of the bibliometric study presents a good starting point for further research providing Taxonomy and research landscapes as a holistic framework offering multidisciplinary knowledge about CLBP, while chronological analysis provides a basis for identifying prospective research trends.

The impact of the agriculture net carbon effect is crucial for climate change mitigation, yet research on this topic in China is currently insufficient. In this study, the carbon balance ratio (CBR) is used to characterise the net carbon effect of agriculture, and the spatial Durbin model and threshold regression model were used to empirically analyse the CBR with a sample of 30 provincial-level regions in China from 2004 to 2019. The aim was to investigate the mechanism of the influence of the land management scale (SCALE) of farming on the CBR. The results of the study indicate that national farming generally has a net sink effect, with large changes in interannual fluctuations; the expansion of SCALE has an obvious role in increasing sinks and reducing emissions, and has a positive spatial spillover effect on CBR, with significant spatial heterogeneity. Furthermore, as the SCALE expands, the direction of the influence of rural residents’ income and education level on CBR changes in the opposite direction, showing a significant non-linear relationship characteristic. However, in most areas, the SCALE threshold has not yet been reached, suggesting that the improvement in education levels can still offset the negative impact of economic income growth in the short term. The study recommends paying attention to the role of farmers’ human capital, changing production methods, and implementing carbon peaking and carbon neutrality goals to enhance the net sink effect of the farming industry, which would be achieved by increasing land management scale.

To decrease oral pathogens and increase the number of beneficial bacteria, probiotics and bioactive compounds made via microbial bioconversion are recently used. In addition, the interest regarding probiotics-mediated bioconversion with popular medicinal plants is increasing. These compounds produced during the process exhibit synergistic effect and enhance a variety of biological activities including pathogen inhibition in host. Therefore, we developed bioconverted milk with high inhibitory activity against oral pathogens and α-glucosidase. Lactiplantibacillus plantarum SMFM2016-RK was chosen as the probiotic due to its beneficial characteristics such as high acid and bile tolerance, antioxidant activity, and α-glucosidase inhibition. Based on the minimal bactericidal concentration against three oral pathogens, the following appropriate concentrations of Artemisia herba-alba extract were added to milk: 5 mg/mL of A. herba-alba ethanol extract and 25 mg/mL of A. herba-alba hot-water extract. Four bioconverted milks (BM), BM1, BM2, BM3, and BM4, were produced by combining L. plantarum SMFM2016-RK alone, L. plantarum SMFM2016-RK and ethanol extract, L. plantarum SMFM2016-RK and hot-water extract, and L. plantarum SMFM2016-RK with both extracts. As a result of antimicrobial activity, BM3 inhibited the growth of Aggregatibacter actinomycetemcomitans and Porphyromonas gingivalis the most, and BM4 suppressed the growth of Fusobacterium nucleatum the most. In addition, bioconverted milk containing A. herba-alba (BM2, BM3, and BM4) inhibited α-glucosidase more effectively than BM1. In conclusion, A. herba-alba-added milk bioconverted by L. plantarum SMFM2016-RK displayed the growth inhibitory effect on oral pathogens and the α-glucosidase inhibitory activity, thus it necessitates to evaluate the effects on the alleviation of oral diseases and glycemic control through future animal experiments.

The integration of deep learning models into financial risk prediction and analysis has significantly transformed traditional approaches. While conventional quantitative methods often rely on simplistic metrics like maximum drawdown, the advent of deep learning necessitates a more nuanced evaluation, emphasizing the model's generalization ability, especially during market crises such as stock market crashes. This paper explores the critical aspects of evaluating deep learning models' risk control capabilities in finance, underscoring the importance of understanding both statistical metrics and generalization abilities, particularly in adverse market conditions. By examining deep learning models' performance in scenarios like stock market crashes and highlighting the significance of cross-validation techniques, this study aims to offer practitioners insights into constructing robust risk management systems. It advocates for a comprehensive approach integrating quantitative analysis with macroeconomic factors to enhance financial risk prediction and analysis in volatile markets. The experimentation reveals that different deep generative models excel in various aspects of financial time series analysis, with generative adversarial networks (GANs) demonstrating superior performance in predicting Value at Risk (VaR) and variational autoencoders (VAEs) excelling in return rate prediction. Moreover, integrating multiple models further enhances predictive performance, leveraging the strengths of each model to compensate for individual weaknesses. Overall, this paper underscores the potential and significance of deep generative models in financial time series analysis, offering a roadmap for improved risk management and decision-making in financial markets.

The sustainable development of resource-based cities has long been a focal point for both governmental policies and academic research. A wealth of academic literature identifies the 'resource curse' as a critical barrier to sustainable development in these urban areas. Despite extensive studies, a consensus on effective strategies to circumvent or resolve this challenge remains elusive. Recent empirical data reveals a noticeable pattern of economic growth differentiation in China's resource-based cities. Why do some resource-based cities experience economic decline while others achieve sustainable economic growth? Therefore, this paper analyzes the reasons for the divergence of economic growth in resource cities from the perspective of heterogeneity. The results of the study show that the phenomenon of the resource curse is conditional, and the heterogeneity of demo-graphic changes, geographic location and institutional environment has a significant impact on the economic growth of resource cities. These factors are investigated to elucidate the observed diversification and heterogeneity in the economic growth of resource-based cities. Building upon this analysis, the paper proposes a framework for differentiated sustainable development pathways tailored to the unique contexts of these cities. This approach not only contributes to the current understanding of the 'resource curse' but also offers novel insights for future research aiming to adopt a more nuanced and context-specific perspective in examining sustainable urban development.