Search Results (5,940)

The United Nations has pushed for improved mobile connectivity, ensuring that 97% of the world’s population lives within reach of a mobile cellular signal. This is within the framework of objective nine regarding industry, innovation, and infrastructure for sustainable development. The next challenge is for users to know the quality of this service. The Long-Term Evolution (LTE) network’s quality of service (QoS) is evaluated with key performance indicators (KPI) that only specialists can interpret. This work aims to assess the QoS and effectiveness of the fourth-generation (4G) LTE network using a weighted fuzzy inference system. Analytic Hierarchy Process (AHP) is integrated to rank the fuzzy rules. The KPIs that are considered for the evaluation are download speed, upload speed, latency, jitter, packet loss rate, reference received signal power (RSRP), and reference received signal quality (RSRQ). The evaluated data were collected collaboratively with end-user equipment (UEs). Different usage scenarios are contemplated to define the importance according to the positive impact of the QoS of the LTE mobile network. The advantage of the weighted fuzzy inference system concerning the fuzzy inference system is that each KPI is assigned a different weight, which implies having rules with hierarchies. In this way, the weighted fuzzy inference system provides two indices of quality and effectiveness. It can be a valuable tool for end users and regulatory bodies to identify the quality of the LTE mobile network. Full article

Innovation is a key driver of high-quality economic development. Building strong relationships between central and local financial authorities, with a clear division of powers and responsibilities and well-coordinated fiscal resources, is both practical and significant. Such collaboration enhances the government’s ability to support scientific and technological innovation, leading to improved outcomes. In this study, multiple mechanisms of fiscal decentralization and government innovation investment in regional innovation were explored based on Chinese-style fiscal decentralization, the theory of fiscal decentralization, and the innovation system. Provincial panel data from 2008 to 2021 were used to examine both the direct effect of fiscal decentralization and the mediating effect of government innovation investment on regional innovation. The results show that fiscal decentralization distorts the government’s fiscal expenditure behavior, significantly inhibiting regional innovation enhancement. The results of a mechanism analysis demonstrate that fiscal decentralization weakens the government’s support and guidance for scientific and technological innovation, but increasing innovation investment can offset this effect and enhance the regional innovation level. Overall, fiscal decentralization negatively affects regional innovation by inhibiting the government’s innovation investment. To address these challenges, the fiscal system requires deeper reform, adjusting the relationship between central and local governments. Additional measures should include improving the government’s performance appraisal system, guiding local authorities to adopt appropriate performance perspectives, increasing fiscal expenditure and the government’s role in scientific and technological innovation, and enhancing independent scientific and technological innovation. Full article

Digital technologies are fundamentally transforming professions by altering roles and redefining competencies across all sectors. The progression from computerization to digitization, digitalization, and now digital transformation has been driven by the widespread integration of artificial intelligence (AI) and big data analytics (BDA). Industry 4.0 introduced smart automation and connectivity, Industry 5.0 emphasized human–machine collaboration and personalization, and Industry 6.0 now integrates advanced technologies with sustainability and ethical considerations, exerting a profound influence on many professions. This transformation is especially significant in emerging markets, where AI and BDA are overhauling traditional practices and enhancing efficiency but also introducing new challenges. Focusing on the accounting profession, this paper examines AI’s and BDA’s dual impact on the roles and skill sets of professional accountants (PAs). Specifically, it addresses how these technologies shape the activities, interactions, roles, and competencies of PAs in an Industry 6.0 context, as well as the opportunities and challenges that arise. Given the public interest role of PAs in ensuring accuracy and transparency in financial reporting, understanding their perceptions and experiences of digital transformation is essential. The findings reveal that while AI and BDA drive efficiency gains and open strategic pathways, they also risk eroding core traditional accounting competencies, reducing client engagement, and raising ethical concerns such as data security and privacy—all of which can undermine service quality and, ultimately, public trust. These insights underscore the need for responsible AI and BDA integration, particularly in emerging markets, where digital literacy gaps and regulatory limitations may slow adoption. This study offers actionable recommendations for policymakers, educators, and organizations, highlighting the importance of ethical standards, targeted training, and sustainable practices to preserve the relevance and integrity of the accounting profession in an increasingly technology-driven era. Full article

Precision medicine has revolutionised targeted cancer treatments; however, its implementation in ovarian cancer remains challenging. Diverse tumour biology and extensive heterogeneity in ovarian cancer can limit the translatability of genetic profiling and contribute to a lack of biomarkers of treatment response. This review addresses the barriers in precision medicine for ovarian cancer, including obtaining adequate and representative tissue samples for analysis, developing functional and standardised screening methods, and navigating data infrastructure and management. Ethical concerns related to patient consent, data privacy and health equity are also explored. We highlight the socio-economic complexities for precision medicine and propose strategies to overcome these challenges with an emphasis on accessibility and education amongst patients and health professionals and the development of regulatory frameworks to support clinical integration. Interdisciplinary collaboration is essential to drive progress in precision medicine to improve disease management and ovarian cancer patient outcomes. Full article

Climate change is an aspect in our lives that presents urgent challenges requiring innovative approaches and collaborative efforts across diverse fields. Our research investigates the growth and thematic structure of the intersection between climate change research and machine learning (ML). Employing a mixed-methods approach, we analyzed 7521 open-access publications from the Web of Science Core Collection (2004–2024), leveraging both R and Python for data processing and advanced statistical analysis. The results reveal a striking 37.39% annual growth in publications, indicating the rapidly expanding and increasingly significant role of ML in climate research. This growth is accompanied by increased international collaborations, highlighting a global effort to address this urgent challenge. Our approach integrates bibliometrics, text mining (including word clouds, knowledge graphs with Node2Vec and K-Means, factorial analysis, thematic map, and topic modeling via Latent Dirichlet Allocation (LDA)), and visualization techniques to uncover key trends and themes. Thematic analysis using LDA revealed seven key topic areas, reflecting the multidisciplinary nature of this research field: hydrology, agriculture, biodiversity, forestry, oceanography, forecasts, and models. These findings contribute to an in-depth understanding of this rapidly evolving area and inform future research directions and resource allocation strategies by identifying both established and emerging research themes along with areas requiring further investigation. Full article

Improving the chemical structure of the cross-linker is a potential method for reducing reservoir pollution and enhancing the fracturing efficiency of shale reservoirs. In this investigation, a three-dimensional (3-D) spherical cross-linker comprising branched chains was synthesized, and the 3-D structure of the cross-linker was analyzed through scanning electron microscopy (SEM). Furthermore, we constructed a multifunctional coupled collaborative evaluation device that can be used to evaluate numerous properties associated with water-based fracturing fluids, including fluid viscosity, adsorption capacity, and water pollution. Meanwhile, the influence of varying reservoir conditions and cross-linker content on the fluid viscosity of water-based fracturing fluids and the potential for reservoir contamination has been evaluated and elucidated. The results indicated that the synthesized cross-linker exhibited a superior environmental protection of the shale reservoir and an enhanced capacity for thickening fracturing fluids in comparison to commercial cross-linkers. Moreover, cross-linker content, reservoir temperature, reservoir pressure, and fracture width can affect fluid viscosity and reservoir residual in different trends. The addition of 0.3% nano-cross-linker (Synthetic products) to a water-based fracturing fluid resulted in an apparent viscosity of 160 mPa·s at 200 °C, and the adsorption capacity and water content of the shale reservoir were only 0.22 µg/m³ and 0.05 µg/L, respectively. Additionally, an elevation in reservoir temperature resulted in a reduction in the adsorption capacity. However, the cross-linker content in groundwater underwent a notable increase, and the cross-linker residue in water increased by 0.009 µg/L. The impact of reservoir pressure on fluid viscosity and groundwater pollution potential exhibited an inverse correlation compared to that of reservoir temperature, and the above two parameters changed by +18 mPa·s and −0.012 µg/L, respectively. This investigation provides basic data support for the efficient fracturing and reservoir protection of shale reservoirs. Full article

This paper presents a comprehensive exploration of data quality terminology, revealing a significant lack of standardisation in the field. The goal of this work was to conduct a comparative analysis of data quality terminology across different domains and structure it into a hierarchical data model. We propose a novel approach for aggregating disparate data quality terms used to describe the multiple facets of data quality under common umbrella terms with a focus on the ISO 25012 standard. We introduce four additional data quality dimensions: governance, usefulness, quantity, and semantics. These dimensions enhance specificity, complementing the framework established by the ISO 25012 standard, as well as contribute to a broad understanding of data quality aspects. The ISO 25012 standard, a general standard for managing the data quality in information systems, offers a foundation for the development of our proposed Data Quality Data Model. This is due to the prevalent nature of digital systems across a multitude of domains. In contrast, frameworks such as ALCOA+, which were originally developed for specific regulated industries, can be applied more broadly but may not always be generalisable. Ultimately, the model we propose aggregates and classifies data quality terminology, facilitating seamless communication of the data quality between different domains when collaboration is required to tackle cross-domain projects or challenges. By establishing this hierarchical model, we aim to improve understanding and implementation of data quality practices, thereby addressing critical issues in various domains. Full article

Objectives: Current guidelines recommend systematic screening for rheumatic diseases (RDs), including antiphospholipid syndrome (APS), in patients with recurrent pregnancy loss (RPL). However, these recommendations are based on limited evidence, as data on the prevalence of RD in this specific population remain scarce. In particular, the impact of the recent update to the ACR/EULAR classification criteria for APS on the prevalence of RD among RPL patients has yet to be clarified. To address these gaps, this study aims to (i) assess the impact of the 2023 ACR/EULAR APS classification criteria in patients with recurrent pregnancy loss (RPL); and (ii) analyze the prevalence of RD in these patients. Methods: We conducted a retrospective cohort study at Rennes University Hospital. From January 2010 to December 2021, all patients referred to the Clinical Immunology Department for RPL were included. Patients were eligible if they had undergone a full RPL evaluation, according to guidelines. Results: We included 165 women with RPL. APS according to the Sydney criteria was found in 24 (14.5%) patients. No significant differences in obstetric history or clinical signs were observed between APS-positive and APS-negative individuals. Only two patients fulfilled the updated 2023 APS criteria, resulting in 163 (98.8%) patients being classified as having unexplained recurrent pregnancy loss (uRPL). Among them, 108 had a new pregnancy following uRPL, resulting in 87 (81%) live births and 21 (19%) recurrent miscarriages. We did not identify any prognostic factor associated with subsequent pregnancy outcomes, including the patients’ antiphospholipid biological profile. We found a prevalence of non-APS RD of only 2.4% in the study population, including systemic lupus erythematosus, rheumatoid arthritis, and Behçet’s disease. Conclusions: APS was identified in 14.5% of the patients based on the former Sydney criteria and 1.2% according to the revised criteria. The lack of clinical differences between APS and non-APS patients aligns with previously reported limitations of the Sydney criteria in accurately identifying aPLA-related RPL. According to the rarity of APS as per the updated criteria, future large collaborative trials will be needed to further characterize APS-related RPL patients and to determine the best treatment strategy for future pregnancies. Full article

In the context of intensifying global competition, productive entrepreneurship plays an important role in industrial upgrading and sustainable economic development. This study explores how the institutional environment affects productive entrepreneurship through different allocation paths. By integrating multiple institutional logics and using PD-QCA (Panel Data fsQCA) methods, we examined data from 76 countries for the period 2012–2022. The results show that institutional complexity provides entrepreneurs with diversified opportunities and resources, but also brings decision-making challenges due to the conflict of institutional factors. We identified three main models: (1) Government-Market Collaborative Model: driven by government support and property rights protection. (2) Market-Led Model: financial support is crucial. (3) Three-Wheel Drive Model: combines property rights protection, market opening, and social norms that promote entrepreneurship. This study contributes to the literature by revealing the multidimensional effects of institutional complexity and provides practical implications for policymakers seeking to promote high-quality entrepreneurial activity. Limitations include the static nature of the data and the need to further explore dynamic institutional interactions over time. Full article

The importance of the electronics sector in the modern world is unquestionable, as it demonstrates clean technology, dry processes, and efficient design, which favor Industry 4.0 and sustainability. Nonetheless, the large number of instruments developed, and their correspondent quick obsolescence, imply an increment in electronic waste. Therefore, in this work, with the aim of diminishing obsolescence, we developed and customized one application that runs independently of systems and takes advantage of the existing computing structures. The application is a new edge computing structure (the AIFC) that is based on an enterprise service bus (ESB) developed in decentralized microservices. In this study, we conducted action research involving the collaboration of researchers and practitioners, and the tests involved six different scenarios; they used existing low-cost, basic computing environments and ranged from the proof of concept, prototype, minimum viable product, and scalability to the roadmap for the structure implementation. The six scenarios emulated sections of a small and medium-sized enterprise (SME), and all the developed microservices communicate with each other to perform data filtering, processing, storage, query, and sensor data acquisition. The results show that it is possible to carry out these functions with low latency and without any decrement or even increase in performance when compared with more conventional cloud computing structures, and it is also possible to manipulate different products that do not have single, consolidated structures. Moreover, there is no need to update machines or communication structures, which are the main factors of rapid obsolescence. Therefore, following the steps of the AIFC development, the results from the proof of concept to the minimum viable product and scalability tests correspond to a roadmap for a sustainable solution and are an important tool for both Industry 4.0 and SMEs. Full article

The 2022 White House National Strategy on Hunger, Nutrition, and Health outlined goals for ending hunger in the US. Actions fell into five areas, called pillars; the goal of Pillar 5 was to enhance nutrition and food security research. This study leveraged participatory research theory and the National Strategy for developing a statewide, evidence-informed food security research agenda. A mixed-methods study employing the Community Engagement in Research Continuum (CEnR) was implemented. Engagement strategies included collecting baseline data via a statewide survey and encouraging participants of a statewide Summit to participate in a collective prioritization process. Surveys were emailed to a purposive sample of 1575 contacts; 31 registered for the in-person session. A total of 197 records were included in the survey analysis; all 31 registrants attended the in-person session. The research agenda was to include two objectives for implementation over the next two years. Academic and nonacademic stakeholders interacted as peers during the prioritization exercise. The first research objective was to conduct food assistance usage gap analyses; the second was to investigate barriers to collaboration among Idaho’s food security programs. The CEnR proved efficacious for producing a stakeholder-generated research agenda. Leveraging a national framework helped facilitate asset-based, actionable strategies within a proposed timeline. Full article

Introduction: Prostate biopsy results form the mainstay of patient care. However, there is often significant discordance between the biopsied histology and the ‘true’ histology shown on a radical prostatectomy (RP). Discordance in pathology can lead to the mismanagement of patients, potentially missing clinically significant cancer and delaying treatment. There have been many advancements to improve the concordance of pathology and more accurately counsel patients; most notably, the induction of pre-biopsy mpMRIs has become a gold standard to aid in triaging and identifying clinically significant cancers, and also to facilitate ‘targeted’ biopsies. Although there have been multiple reviews on MRI-targeted biopsies, upgrading remains an ongoing phenomenon. Aim: To assess the rates of prostate cancer upgrading and the clinical implication of upgrading on NCCN stratification. Methods: We conducted a retrospective audit of 2994 men with non-metastatic prostate cancer diagnosed between 2010 and 2019 who progressed to a radical prostatectomy within 1 year of diagnosis without alternative cancer treatment from the multi-institutional South Australia Prostate Cancer Clinical Outcomes Collaborative registry. The study compared the histological grading between the biopsies and radical prostatectomies of men with prostate cancer and the varying rates of upgrading and downgrading for patients with and without a pre-biopsy MRI. Data were also obtain on suspected confounding variables; age, PSA, time to RP, T-stage at diagnosis and RP, number of cores, number of positive cores, prostate size, tumour volume and procedure type. The results were assessed through cross tabulation and uni- and multi-variate logistic regression while adjusting for confounders. Results: Upgrading occurred in (926) 30.9% of patients and downgrading in (458) 15.3% of patients. In total, 71% (410/579) of grade group 1 and 24.9% (289/1159) of grade group 2 were upgraded following a radical prostatectomy. By contrast, 33.4% (373/1118) of patients without prebiopsy MRI were upgraded at RP compared to 29.5% (553/1876) of the patients who received a pre-biopsy MRI. When analysed on a uni-variate level, the inclusion of a pre-biopsy MRI demonstrated a statically significant decrease in upgrading of the patient’s pathology and NCCN risk stratification (p = 0.026, OR 0.83, CI 0.71–0.98) (p = 0.049, OR 0.82, CI 0.64–1.01). However, when adjusted for confounders, the use of an MRI did not maintain a statistically significance. Conclusions: When considering the multiple variables associated with tumour upgrading, a pre-biopsy MRI did not show a statistically significant impact. However, upgrading of Gleason Grade Group following a prostatectomy is an ongoing phenomenon which can carry significant treatment implications and should remain a consideration with patients and clinicians when making decisions around treatment pathways. More research is still required to understand and improve biopsy grading to prevent further upgrading from affecting treatment choices. Full article

Cloud–edge–end computing architecture is crucial for large-scale edge data processing and analysis. However, the diversity of terminal nodes and task complexity in this architecture often result in non-independent and identically distributed (non-IID) data, making it challenging to balance data heterogeneity and privacy protection. To address this, we propose a privacy-preserving federated learning method based on cloud–edge–end collaboration. Our method fully considers the three-tier architecture of cloud–edge–end systems and the non-IID nature of terminal node data. It enhances model accuracy while protecting the privacy of terminal node data. The proposed method groups terminal nodes based on the similarity of their data distributions and constructs edge subnetworks for training in collaboration with edge nodes, thereby mitigating the negative impact of non-IID data. Furthermore, we enhance WGAN-GP with attention mechanism to generate balanced synthetic data while preserving key patterns from original datasets, reducing the adverse effects of non-IID data on global model accuracy while preserving data privacy. In addition, we introduce data resampling and loss function weighting strategies to mitigate model bias caused by imbalanced data distribution. Experimental results on real-world datasets demonstrate that our proposed method significantly outperforms existing approaches in terms of model accuracy, F1-score, and other metrics. Full article

This study investigates the relationship between neuroticism and panic-selling behavior among investors, particularly during market downturns. Building on the theoretical framework of behavioral finance, we hypothesize that higher levels of neuroticism are positively associated with an increased likelihood of panic selling. The data for this research were derived from a comprehensive survey titled Survey on Life and Money, which was conducted by Rakuten Securities in collaboration with Hiroshima University in November and December 2023, with a total sample size of 189,524 participants. Our results reveal that 9.46% of the respondents fully or partially panic-sold their stocks during market volatility. Additionally, the respondents demonstrated a tendency toward neuroticism, with an average score of 2.95 out of 5 on the neuroticism scale. Using a probit regression analysis, we examined the dependent variable of panic selling in relation to neuroticism as the independent variable, controlling for various demographic, socioeconomic, and behavioral characteristics. Our findings robustly support this hypothesis, indicating that individuals with higher neuroticism scores are more likely to engage in panic selling during periods of market volatility, with significance at the 5% level. This study contributes to the behavioral finance literature by highlighting the significant role of personality traits in investment decision making and underscores the importance of understanding investor psychology in financial markets. This study emphasizes the need for a nuanced understanding of how individual psychological factors, particularly neuroticism, drive market behavior and influence broader economic stability. Full article

Background: The COVID-19 pandemic highlighted the global challenge of inadequate data on SARS-CoV-2’s effects on pregnant women and their infants. In response, Slovakia, along with other countries, launched a nationwide study to assess the incidence, characteristics, and outcomes of SARS-CoV-2 infection during pregnancy. Methods: We conducted a national observational descriptive study of SARS-CoV-2 positive cases among pregnant women from the pandemic’s onset to its conclusion. In collaboration with the Public Health Office of the Slovak Republic, we identified 1184 pregnant women who tested positive for SARS-CoV-2 and contacted them for participation. Results: Among the 240 participating SARS-CoV-2-positive pregnant women, 13 required hospitalizations, with an increased need for intensive care and respiratory support. However, the absolute risk of poor outcomes remained low. Higher maternal age and infection during the third trimester emerged as key risk factors for hospitalization. A symptomatic course was dominant, with fatigue (70%), headache (58%), and fever (56%) as the leading symptoms. While maternal and neonatal outcomes were generally favourable, a slight increase in caesarean sections and preterm births suggests an indirect impact on maternity care. Vaccination during pregnancy correlated with reduced symptoms and no hospitalizations. Elevated CRP levels were common among infected women, while ultrasound findings remained normal. Conclusions: This study offers a multi-dimensional view of pregnancy during the pandemic, capturing both the mother’s personal concerns and the objective insights from prenatal and labour care settings. The findings suggest that most pregnant women with SARS-CoV-2 experience mild to moderate illness, offering reassurance to clinicians about generally favourable maternal and neonatal outcomes while underscoring the need for vigilance in rare severe cases. Full article