bluuebunny/crossref_metadata_2025_split huggingface dataset:
Corruption and Social Capital: Findings From Selected OECD Countries
Authors: Bayraktar Astt. Prof. Dr. Murat 
Published: June 2024

Abstract

Corruption, a pervasive global challenge, erodes trust in institutions, hinders development, and deepens inequalities. While numerous factors contribute to its persistence, this abstract explores the potential of social capital, the networks and norms of trust within a society, to act as a powerful antidote. However, the relationship between social capital and corruption is not always straightforward. Informal networks, while fostering trust, can also create opportunities for collusion and exclusion. Moreover, the effectiveness of social capital in combating corruption is contingent upon its equitable distribution within a society. Unequal access to social networks can exacerbate existing power imbalances and hinder collective action against corrupt elites.
Panel regression anaylsis reveals that a 1 unit increase in social capital increases Corruption Perceptions Index (CPI) by 0.138891 units (100 is very clean and 0 is highly corrupt). In conclusion, while social capital is not a panacea for corruption, its potential to foster trust, norms of fairness, and collective action makes it a valuable tool in the fight against this complex phenomenon. Further research is needed to understand the specific contexts and mechanisms through which social capital can be most effectively harnessed to promote transparency, accountability, and ultimately, good governance regarding to fight against corruption.
JEL Codes: D73, H26, O11
Unveiling the Impact of Ownership Structure on SMEs’ Cybersecurity Perceptions
Authors: Pekarcik Marek , Safar Leos , Rutecka Paulina , Morawiec Patryk 
Published: June 2024

Abstract

As we move towards a more digitalized and interconnected world, new cybersecurity challenges emerge. While most research focuses on large companies, this study aims to fill a gap in the existing literature by exploring cybersecurity issues in small and medium-sized enterprises (SMEs), particularly in relation to non-technical, soft-skill, and intellectual capital aspects. This study examines the interplay between cybersecurity awareness, perception, and ownership structure in SMEs in the Silesian Region of Poland. Unlike the majority of cybersecurity literature, our focus is on how ownership structure influences risk perception. We surveyed 200 SMEs and utilized hierarchical and simple linear regression analyses to assess the relationships between these factors and financial performance. Our results indicate that larger enterprises and those without a family-owned structure exhibit significantly higher levels of cybersecurity. Additionally, we found a positive correlation between cybersecurity and a firm's financial performance and overall health. These findings underscore the importance of cybersecurity awareness and practices for the growth and stability of SMEs.
Relationship between resilience, social support and job burnout among journalists in Greece
Authors: Katsiroumpa Aglaia , Bistaraki Angeliki , Moisoglou Ioannis , Giazitzi Dimitra-Ioanna , Kalogeropoulou Maria , Gallos Parisis , Galanis Petros 
Published: June 2024

Abstract

Background
Journalists experience high levels of job burnout. However, little is known about the factors that affect job burnout among journalists.
Aim
To investigate the relationship between resilience, social support and job burnout in a sample of journalists.
Methods
We conducted a cross-sectional study with a convenience sample. We performed our study in Greece during April 2023. We used the Multidimensional Scale of Perceived Social Support, the Brief Resilience Scale and the single item burnout measure to measure social support, resilience and job burnout, respectively. We measured the following demographic variables: gender, age, chronic disease, educational level and job experience.
Results
Among our sample, mean age was 50.4 years (standard deviation = 11.4). More than half of our journalists were females (57.3%). Our journalists experienced moderate to high levels of social support, and moderate levels of resilience. Mean job burnout score was 5.99 (standard deviation = 2.22). In our sample, 58.7% (n = 88) of journalists had a job burnout score ≥ 6 indicating high levels of burnout, while 41.3% (n = 62) had a job burnout score < 6 indicating low levels of burnout. Both multivariable linear and logistic regression models showed a negative relationship between resilience and job burnout (linear regression model: adjusted coefficient beta = -0.585, 95% CI = -1.133 to -0.038, p-value = 0.036), (logistic regression model: adjusted odds ratio = 0.575, 95% CI = 0.330 to 0.999, p-value = 0.049). There were no statistically significant relationships between social support and job burnout in both models.
Conclusions
Our sample experienced high levels of job burnout. We found that more resilient journalists experienced lower levels of job burnout. Social support did not affect levels of job burnout among journalists.
Enhanced groundwater vulnerability assessment to nitrate contamination in Chongqing, Southwest China: Integrating novel explainable machine learning algorithms with DRASTIC-LU
Authors: Liang Yuanyi , Zhang Xingjun , Sun Yigao , Yao Linlin , Gan Lin , Wu Jialin , Chen Si , Li Junyi , Wang Jian 
Published: June 2024

ABSTRACT

Groundwater vulnerability to nitrate assessment serves as a measure of potential groundwater nitrate pollution in a target area. This study applies the DRASTIC-LU framework, nitrate distribution data, and three machine learning models (RF, XGB, SVM) to classify nitrate levels (exceeding 10 mg/L as nitrogen) in Chongqing, China. Model evaluation uses accuracy and F1 score metrics, with RF achieving the highest accuracy (92.9%), kappa (0.857), and AUC (0.948) on test dataset. Furthermore, the SHAP interpreter revealed that aquifer conductivity, lithology, agricultural activities, areas with high-intensity development, and groundwater recharge are the most influential indicators of groundwater vulnerability. The final groundwater vulnerability level distribution map, with a resolution of 1 km × 1 km, reveals that high and extremely high vulnerability levels are concentrated in areas with high-intensity urban development and karst trough valleys in the southeastern, northeastern, and central urban areas. This work represents the first attempt of using machine learning models for groundwater vulnerability assessment in the Chongqing region. It provides theoretical support for the construction layout of groundwater monitoring stations and the prevention and control of groundwater pollution in the future.
Data assimilation with machine learning for constructing gridded rainfall time series data to assess long-term rainfall changes in the northeastern regions in India
Authors: Singh Vishal , Bansal Joshal Kumar , Rani Deepti , Singh Pushpendra Kumar , Nema Manish Kumar , Singh Sudhir Kumar , Jain Sanjay Kumar 
Published: June 2024

ABSTRACT
Data scarcity and unavailability of observed rainfalls in the northeastern states of India limit prediction of extreme hydro-climatological changes. To fill this gap, a data assimilation approach has been applied to re-construct accurate high-resolution gridded (5 km2) daily rainfall data (2001–2020), which include seasonality assessment, statistical evaluation, and bias correction. Random forest (RF) and support vector regression were used to predict rainfall time series, and a comparison between machine learning and data assimilation-based gridded rainfall data was performed. Five gridded rainfall datasets, namely, Indian Monsoon Data Assimilation and Analysis (IMDAA) (12 km2), APHRODITE (25 km2), India Meteorological Department (25 km2), PRINCETON (25 km2), and CHIRPS (25 and 5 km2), have been utilized. For re-constructed rainfall datasets (5 km2), the comparative seasonality and change assessment have been performed with respect to other rainfall datasets. CHIRPS and APHRODITE datasets have shown better similarities with IMDAA. The RF and assimilated rainfall (AR) have superiority based on bias and extremity, and AR data were recognized as the best accurate data (&gt;0.8). Precipitation change analysis (2021–2100) performed utilizing the bias-corrected and downscaled CMIP6 datasets showed that the dry spells will be enhanced. Considering the CMIP6 moderate emission scenario, i.e., SSP245, the wet spell will be enhanced in future; however, when considering SSP585 (representing the extreme worst case), the wet spells will be decreased.
A validation workflow for treatment wetland performance data
Authors: Guillaume-Ruty Sophie Hai Yen , Pueyo-Ros Josep , Comas Joaquim , Forquet Nicolas 
Published: June 2024

ABSTRACT

Treatment wetlands (TWs) effectively remove target pollutants and enhance urban water circularity and resilience. They constitute a prominent solution for urban wastewater treatment, thanks to their adaptability across various types of wastewater, scales and climatic conditions. However, the disparity in TW designs and the focus on a restricted set of variables applicable to research studies impede any comprehensive evaluation and comparison of TW performance. Our study introduces a methodology for data validation, in concurrently establishing a workflow specific to TW. This approach is aimed at defining the scope and relationships within the data, implementing checks and concatenating them into a quality flag, as an initial step towards building reliable statistical models. We underscore the importance of both mobilising comprehensive knowledge and identifying customary, yet implicit, choices intertwined in data processing. As for the application workflow, we collected and analysed data sourced from peer-reviewed papers on horizontal and vertical flow TW. Deficiencies were noted in key data elements like dimensions, concentrations and operational conditions. For the data analysis, relationships are highlighted between variables introduced for modelling purposes. These methodologies and workflows assess the quality of the data, in paving the way towards more dependable statistical models for TW design and implementation.
Regulation of Metastasis and Growth of Colorectal Cancer via Targeting the PTEN Pathway: An Update on the Progress of LncRNA MLLT4-AS1
Authors: Liang Ruipeng , Liu Zhili , Li Bo , Xiang Debing , Wu Chunrong 
Published: June 2024

Background:
Globally, colorectal cancer (CRC) is known as the primary cause of mortality.
Recent studies have reported that long non-coding RNAs (lncRNAs) are essential in assessing
the survival of CRC patients. However, the function of the novel lncRNA MLLT4-AS1 in
CRC is still unknown.

Objective:
This study aimed to identify the expression and the clinical significance of lncRNA
MLLT4-AS1 in CRC.

Methods:
The level of MLLT4-AS1 in CRC was evaluated via the TCGA database. The relative
level of MLLT4-AS1 in CRC cell lines was assessed by RT qPCR analysis. In cell culture, HT29
cells were transfected with MLLT4-AS1 siRNA, negative control, overexpressed MLLT4-AS1, or
PTEN plasmids. Flow cytometry, CCK 8 assay, wound healing analysis, and transwell assay were
used to quantify apoptosis, cell propagation, migration, and invasion, respectively. A nude mouse
xenograft model was developed to evaluate the in vivo impact of MLLT4-AS1 plasmids on tumor
growth. RNA pull-down analysis was used to search for possible targets of MLLT4-AS1.

Results:
MLLT4-AS1 was substantially increased in CRC cell lines and patients. It inhibited CRC
cell apoptosis and accelerated their proliferative, migration, and invasive properties. In in vivo
analysis, MLLT4-AS1 also enhanced the metastasis and proliferation of CRC cells. It was found
that PTEN was substantially enriched by biotin-labeled PTEN, as identified via an RNA pull--
down analysis. The expression of phosphatase and PTEN was suppressed by MLLT4-AS1 by
ubiquitination proteasome-dependent RNA degradation. Thus, PTEN is considered a potential target
of MLLT4-AS1. By targeting PTEN, MLLT4-AS1 intensified the biological behavior of malignant
CRC.

Conclusion:
The study concluded that the MLLT4-AS1/PTEN axis may represent an innovative
therapeutic intervention for CRC patients.
Effect of Water Deficit Stress on Reproductive Stage of Durum Wheat Near Isogenic Lines Carrying the NAM-B1 Gene
Authors: Mbideen Farah O. AL , Aburumman Abeer , Al-Sayaydeh Rabea , Albdaiwi Randa N. , Al-Abdallat Ayed M. 
Published: June 2024

Background
In wheat, the NAC family plays a crucial role in conferring cellular responses to different abiotic stresses, particularly drought. The functional NAM-B1 allele in durum wheat accelerates senescence, while its homologous genes contribute to improved water deficit stress tolerance. Therefore, the main objective of this study was to investigate the role of the NAM-B1 gene in durum wheat subjected to water deficit stress at two reproductive stages.

Materials and Methods
Three Near-Isogenic Lines carrying a functional NAM-B1 allele and their recurrent parent, Langdon (LDN), were used in this study. The study assessed NILs and LDN performance under varied water conditions (well-watered, 60%, and 80% water deficit of pot capacity) during flag leaf and anthesis stages. The agronomic performance of the three NILs and LDN was assessed for 16 traits, including plant height, grain yield, and thousand kernel weight. Additionally, physiological measurements, including stomatal resistance, chlorophyll content (SPAD values), and chlorophyll fluorescence (Fv/Fm) were taken.

Results
Significant genotypic effects were observed on seven agronomic traits, while 15 traits were influenced by the water stress treatment. The NILs exhibited accelerated maturity and a shorter grain-filling period, which were particularly pronounced under stress conditions. Severe water deficit resulted in reduced grain weight and thousand-grain weight in tested genotypes. Interestingly NILs carrying functional NAM-B1 showed taller plants and had higher tiller and spike numbers when compared to LND. Significant genotypic effects were observed for seven traits, and water stress treatments affected 15 traits. The NILs exhibited accelerated maturity and reduced grain weight under severe water deficit. Physiological measurements showed genotype and water-deficit differences, with NIL #504 displaying higher SPAD values, particularly under stress conditions, and a significant genotype X treatment effect was observed for stomatal resistance at the anthesis stage.

Discussion
Limited treatment × genotype interactions for most traits highlight the complexity of drought tolerance in the tested lines, which might be attributed to its quantitative nature influenced by multiple genes. The study emphasizes the necessity for future research to explore the role of other NAM-related genes in response to water deficit stress and their interactive effects with the NAM-B1 gene at different growth stages in wheat plants subjected to diverse stress conditions.

Conclusion
In conclusion, this study reveals pronounced genotypic effects, particularly in agronomic traits, such as maturation rate and grain filling period, under severe water deficit stress. Future studies are needed to understand the genetic mechanisms mediated by NAM-B1 genes in accelerating maturity in response to stress.
The Effectiveness of Eugenia Oleina in Protecting Tropical Residual Slope from Excessive Shear Failure
Authors: Duraisamy Youventharan , Othman Rokiah , Sulaiman Mohd Arif , Jaya Ramadhansyah Putra , Taib Siti Noor Linda , Rodzif Irfan Hakimie Mohd , Kambali Kuraisha 
Published: June 2024

Introduction
Slope stability and soil erosion are major concerns in geotechnical engineering and land management. This research investigates the relationship between soil type and root systems in stabilizing slopes.

Methods
The main aim is to measure the effectiveness of Eugenia Oleina as a bioengineering technique for slope protection. Laboratory tests were conducted to measure soil shear strength, root properties, and the factor of safety (FOS) of tropical slopes before and after plant root implementation.

Results
Results revealed significant differences in cohesion and angle of friction values between unrooted and rooted soil. Apart from that, it was observed that as the root diameter increased, the tensile strength decreased when the applied force increased. The FOS of unrooted soil was higher than rooted soil, indicating greater stability without any vegetation. Furthermore, this study also evaluated the use of bio-anchorage to prevent soil erosion, considering factors such as soil composition, vegetation, and external loads. Finite element analysis was carried out using Plaxis 3D simulations to assess the effectiveness of Eugenia Oleina in controlling slope erosion.

Conclusion
This study contributes valuable insights in choosing suitable plant species for erosion control in tropical soil and guides soil bioengineering practices for slope stability in various soil conditions.
Green Synthesis of NiO Nanoparticles using Pongamia pinnata and their Catalytic Utility in the Synthesis of N-Fmoc/Cbz-protected Amino Acid Derived Sulfides and their Biological Investigations
Authors: Ramya M. , Lalithamba H. S. , Kumari Dalli , Nagendra G. 
Published: June 2024

Introduction:
Synthesis of NiO nanoparticles using environmentally friendly Pongamia pinnata
seeds as a source of fuel was demonstrated using a solution combustion approach.

Method:
The protocol for the synthesis of NiO NPs is simple and efficient. NiO NPs were utilized as
the catalyst for the synthesis of N-protected aminoalkyl sulfides from N-protected alkyl thiols and
bromo esters of amino acids.

Results:
The NiO NPs were characterized using XRD, SEM, and EDX techniques. N-protected aminoalkyl
sulfides were characterized by HRMS, 1H, and 13C NMR techniques and were evaluated for
their in vitro antifungal activities, against A. Niger using Fluconazole as a standard.

Conclusion:
The current study presents an effective approach for synthesizing a new class of sulfides
from N-protected aminoalkyl thiols and bromomethyl esters in the presence of nano NiO as a catalyst.
Data-Driven Identification of Potentially Successful Intervention Implementations Using 5 Years of Opioid Prescribing Data: Retrospective Database Study
Authors: Hopcroft Lisa EM , Curtis Helen J , Croker Richard , Pretis Felix , Inglesby Peter , Evans David , Bacon Sebastian , Goldacre Ben , Walker Alex J , MacKenna Brian 
Published: June 2024

Background
We have previously demonstrated that opioid prescribing increased by 127% between 1998 and 2016. New policies aimed at tackling this increasing trend have been recommended by public health bodies, and there is some evidence that progress is being made.

Objective
We sought to extend our previous work and develop a data-driven approach to identify general practices and clinical commissioning groups (CCGs) whose prescribing data suggest that interventions to reduce the prescribing of opioids may have been successfully implemented.

Methods
We analyzed 5 years of prescribing data (December 2014 to November 2019) for 3 opioid prescribing measures—total opioid prescribing as oral morphine equivalent per 1000 registered population, the number of high-dose opioids prescribed per 1000 registered population, and the number of high-dose opioids as a percentage of total opioids prescribed. Using a data-driven approach, we applied a modified version of our change detection Python library to identify reductions in these measures over time, which may be consistent with the successful implementation of an intervention to reduce opioid prescribing. This analysis was carried out for general practices and CCGs, and organizations were ranked according to the change in prescribing rate.

Results
We identified a reduction in total opioid prescribing in 94 (49.2%) out of 191 CCGs, with a median reduction of 15.1 (IQR 11.8-18.7; range 9.0-32.8) in total oral morphine equivalence per 1000 patients. We present data for the 3 CCGs and practices demonstrating the biggest reduction in opioid prescribing for each of the 3 opioid prescribing measures. We observed a 40% proportional drop (8.9% absolute reduction) in the regular prescribing of high-dose opioids (measured as a percentage of regular opioids) in the highest-ranked CCG (North Tyneside); a 99% drop in this same measure was found in several practices (44%-95% absolute reduction). Decile plots demonstrate that CCGs exhibiting large reductions in opioid prescribing do so via slow and gradual reductions over a long period of time (typically over a period of 2 years); in contrast, practices exhibiting large reductions do so rapidly over a much shorter period of time.

Conclusions
By applying 1 of our existing analysis tools to a national data set, we were able to identify rapid and maintained changes in opioid prescribing within practices and CCGs and rank organizations by the magnitude of reduction. Highly ranked organizations are candidates for further qualitative research into intervention design and implementation.
Public Maternal Health Dashboards in the United States: Descriptive Assessment
Authors: Callaghan-Koru Jennifer A , Newman Chargois Paige , Tiwari Tanvangi , Brown Clare C , Greenfield William , Koru Güneş 
Published: June 2024

Background
Data dashboards have become more widely used for the public communication of health-related data, including in maternal health.

Objective
We aimed to evaluate the content and features of existing publicly available maternal health dashboards in the United States.

Methods
Through systematic searches, we identified 80 publicly available, interactive dashboards presenting US maternal health data. We abstracted and descriptively analyzed the technical features and content of identified dashboards across four areas: (1) scope and origins, (2) technical capabilities, (3) data sources and indicators, and (4) disaggregation capabilities. Where present, we abstracted and qualitatively analyzed dashboard text describing the purpose and intended audience.

Results
Most reviewed dashboards reported state-level data (58/80, 72%) and were hosted on a state health department website (48/80, 60%). Most dashboards reported data from only 1 (33/80, 41%) or 2 (23/80, 29%) data sources. Key indicators, such as the maternal mortality rate (10/80, 12%) and severe maternal morbidity rate (12/80, 15%), were absent from most dashboards. Included dashboards used a range of data visualizations, and most allowed some disaggregation by time (65/80, 81%), geography (65/80, 81%), and race or ethnicity (55/80, 69%). Among dashboards that identified their audience (30/80, 38%), legislators or policy makers and public health agencies or organizations were the most common audiences.

Conclusions
While maternal health dashboards have proliferated, their designs and features are not standard. This assessment of maternal health dashboards in the United States found substantial variation among dashboards, including inconsistent data sources, health indicators, and disaggregation capabilities. Opportunities to strengthen dashboards include integrating a greater number of data sources, increasing disaggregation capabilities, and considering end-user needs in dashboard design.