bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Work Place Environment Factors on Performance of Employees: Empirical Study on Wollo University Staffs
Authors: Alemu Kidanie Aragaw 
Published: April 2022

Abstract
Workplace environment is the environment in which people work that include physical setting, job profile, culture and market condition while performance is the development of quantified objectives. This study focuses on measuring the effect of workplace environment factors on performance of Wollo University: the case of College of Business and Economics employees. The researcher would use a causal research design and apply multistage sampling technique, and collect data from 66 selected employees by adopting questionnaire data collection method. The researcher planned to analyze the research data by using simple descriptive statistics and inferential statistics. Forty seven percent of the respondents had worked with the university for less than 5 years; 28.8% of the respondents had worked for the university between 6–10 years, and 24.2% of the respondents had worked for the university for more than 10 years. The findings indicated all workplace environment factors had positive linear relationship with their work place performance in the university. The findings on the modeling of the employees’ performance against the workplace environment factors showed at 5% significant level the multiple linear regression model was statistically significant (p-value 0.001). Among the workplace environment factors at 5% significant level employees’ performance was affected by their work life balance (p-value 0.002). Most of the respondents had worked in the university for more than six years. Work life balance of employees and reward for employees had strong linear association with their workplace performance. In the university the employees’ performance was affected by their workplace environment factors.
Mitigating health disparities in hospital readmission
Authors: raza shaina 
Published: April 2022

Abstract
Background: The management of hyperglycemia in hospitalized patients has a significant impact on both morbidity and mortality. This study used a large clinical database to predict the need for diabetic patients to be hospitalized, which could lead to improvements in patient safety. These predictions, however, may be vulnerable to health disparities caused by social determinants such as race, age, and gender. These biases must be removed early in the data collection process, before they enter the system and are reinforced by model predictions, resulting in biases in the model’s decisions. In this paper, we propose a machine learning pipeline capable of making predictions as well as detecting and mitigating biases. This pipeline analyses clinical data, determines whether biases exist, removes them, and then make predictions. We demonstrate the classification accuracy and fairness in model predictions using experiments Results: The results show that when we mitigate biases early in a model, we get fairer predictions. we also find that as we get better fairness, we sacrifice a certain level of accuracy, which is also validated in the previous studies. Conclusion: We invite the research community to contribute to identifying additional factors that contribute to health disparities that can be addressed through this pipeline
From Measuring User Satisfaction to Improving Quality of Care: Community Verifications and Performance-Based Financing in District Hospitals in Mali
Authors: ZITTI Tony , RIDDE Valéry , COULIBALY Abdourahmane 
Published: April 2022

Abstract
Context: User satisfaction in health facilities is increasingly taken into account to improve the quality of care. In Mali, performance-based financing (PBF) aims to improve the quality of care by increasing the motivation, responsibility, and accountability of health workers. Community verification (CV) is one of the main functions of the PBF. CV makes it possible to monitor the effectiveness of the services reported by the health facilities and to conduct satisfaction surveys of the users who have received care. This paper aims to analyze the CV process during the implementation of the PBF in the Koulikoro region of Mali.Methods: We adopted a qualitative approach based on a multiple case study design corresponding to the district hospitals (DH) and stakeholder groups. We conducted thirty-nine semi-structured interviews with investigators, patients, and health workers in three of the ten DHs in the Koulikoro region. We designed three interview guides and inductively analyzed our data.Results: The CV process was well perceived and welcomed. However, the vast majority of interviewers reported difficulties locating some patients in the community for several reasons: incorrect spelling of names, illegible cards, lack of telephone contact information and incomplete information on the cards, and cases of homonymity. All of this was due to poor record-keeping at the health facilities. Some investigators fraudulently filled out forms without investigating users in the communities. They justified this by the lack of time available to them to conduct the CV. No feedback on the results of the CV was possible from the health workers.Conclusion: The CV is an essential component of PBF implementation. The validity, reliability and efficiency of the measurement instruments must be considered in user satisfaction surveys. In addition, emphasis should be placed on data analysis and the use of CV results.
Area deprivation and COVID-19 incidence and mortality in Bavaria, Germany: a Bayesian geographical analysis
Authors: Manz Kirsi Marjaana , Mansmann Ulrich , Schwettmann Lars , Maier Werner 
Published: April 2022

Abstract
Background
Area deprivation has been shown to be associated with various adverse health outcomes including communicable as well as non-communicable diseases. Our objective was to assess potential associations between area deprivation and COVID-19 standardised incidence and mortality ratios in Bavaria over a period of nearly two years. Bavaria is the federal state with the highest infection dynamics in Germany.
Methods
In this retrospective, observational ecological study, we estimated the strength of associations between area deprivation and standardised COVID-19 incidence and mortality ratios (SIR and SMR) in Bavaria, Germany. We used official SARS-CoV-2 reporting data aggregated in monthly periods between March 2020 and December 2021. We employed unstructured random effects models to study global trends and the Besag-York-Mollie (BYM) model to account additionally for neighbourhood structures. Area deprivation was assessed using the quintiles of the 2015 version of the Bavarian Index of Multiple Deprivation (BIMD 2015) at district level, analysing the overall index as well as its single domains.
Results
SIRs and SMRs were higher in more deprived than in less deprived districts. Aggregated over the whole period, the SIR increased by 1.04 (95% confidence interval (95% CI): 1.01 to 1.07, p = 0.002), and the SMR by 1.11 (95% CI: 1.07 to 1.16, p < 0.001) per BIMD quintile. Looking at individual months revealed clear linear association between the BIMD quintiles and the SIR and SMR in the first, second and last quarter of 2021. During the summer periods in 2020 and 2021 no effect could be assessed. Effects were also observed for the domains income, employment, and social capital deprivation.
Conclusions
In more deprived areas in Bavaria, Germany, higher incidence and mortality ratios were observed during the COVID-19 pandemic with particularly strong associations during infection waves 3 and 4 in 2020/2021. Only high infection levels reveal the effect of causal risk factors and socioeconomic inequalities. There may be confounding between the high BIMD and border regions in the north and east of Bavaria, making the relationship between area deprivation and infection burden more complex.
Secure Connectivity Protocol (SCP): A game theory approach to mitigate malicious activity in Vehicular Ad-hoc Networks
Authors: J naskath , B Paramasivan , Begum Alif Siddiqa 
Published: April 2022

Abstract
In multifaceted systems like Vehicular Ad-hoc Networks (VANETs), vehicles are mobile in nature. Coordination among mobile vehicles is tremendously high in VANETs, incorporated with abundant security and resource utilization challenges. Hence, it is obligatory to design a secure connectivity conventionality to safeguard the normal vehicles from malicious attacks. Game theory assists to solve selfishness issues between the vehicles that deal with the rational and strategic behavior of the vehicles. For each vehicle, the game theory approach publishes the best actions of their strategies. The strategy profile of regular and malicious vehicle is revealed using dynamic Bayesian signaling game. Perfect Bayesian equilibrium (PBE) gives an unmistakable answer for flagging games to solve the problem of inadequate data by joining methodologies and result of players that establish harmony. Normal vehicles ought to be helpful during steering and update their result, while malevolent vehicles try to limp out of the game or behave as a regular vehicle. To identify and visualize this malicious activity, the proposed game theory model portraits a Bayesian signaling game to induce better data exchange and communication among the vehicles with high efficiency. Ordinary vehicles screen constantly the behavior of their neighboring vehicles utilizing the substructure of Bayes rule.
Thermodynamic characterization of synthetic lead-arsenate apatites with different halogen substitutions
Authors: Puzio Bartosz , Zhang Lei , Szymanowski Jennifer E.S. , Burns Peter C. , Manecki Maciej 
Published: April 2022

AbstractThermodynamic parameters have been measured for synthetic analogs of the mimetite-group minerals Pb5(AsO4)3X (X = OH, Cl, Br, I) belonging to the apatite supergroup. Phases precipitated from aqueous solutions under ambient conditions with well characterized structures and compositions were studied. For each phase, dissolution enthalpy was experimentally determined by oxide melt drop solution calorimetry in a molten solvent of sodium molybdate (3Na2O·4MoO3) at 976 K. The enthalpy of formation from the elements ΔHf,elo was calculated using thermochemical cycles and was −3030.6 ± 11.5, −3026.6 ± 15.8, −2967.6 ± 25.0, and −2993.1 ± 12.2 kJ/mol for Pb5.00(AsO4)3.00OH0.86(CO3)0.07, Pb5.00(AsO4)3.00Cl0.80(CO3)0.10, Pb5.00(AsO4)3.00Br0.80(CO3)0.10, and Pb5.00(AsO4)3.00I0.45OH0.35(CO3)0.10, respectively. These ΔHf,elo values exhibit typical trends for apatites: they increased (were less negative) with the increasing molar mass and ionic radius of X and decreased with the electronegativity and ionization energy of X. The compilation and comparison of data for Ca-, Pb-, P-, and As-apatites revealed correlations indicating that thermodynamic enthalpic stability is largely influenced by chemical factors (e.g., differences in electronegativities of the elements, ionization energy, or ionic characteristics of the bonds) and to a lesser extent by physical and geometric parameters in the crystal structure related to the mass and size of the X anion. Using the correlations, it was possible to estimate the value of hitherto unknown ΔHf,elo for Pb5(AsO4)3F, −3144.3 ± 66.5 kJ/mol. The observed relationships apply to the entire apatite supergroup and can be used to predict the values of ΔHf,elo for phases that have not been studied experimentally. The new data on environmentally significant phases will contribute to the modeling of mineral-water interactions, particularly for potential use in the remediation of soils and wastes contaminated with Pb and As and in the immobilization of radioactive waste containing I-129.
Drought monitoring and its effects on vegetation and water extent changes using remote sensing data in Urmia Lake watershed, Iran
Authors: Helali Jalil , Asaadi Shahab , Jafarie Teimour , Habibi Maral , Salimi Saadoun , Momenpour Seyed Erfan , Shahmoradi Salah , Hosseini Seyed Asaad , Hessari Behzad , Saeidi Vahideh 
Published: April 2022

Abstract
The assessment of drought hazards is important due to their socio-economic impacts on water resources, agriculture, and ecosystems. In this study, the effects of drought on changing water area and canopy of the Lake Urmia watershed in the northwest of Iran have been monitored and evaluated. For this purpose, the Standardized Precipitation Index (SPI) was calculated in short and medium periods (1-month and 3-month) to determine the dry-spell periods in the Lake Urmia basin. In reviewing this analysis, the annual average has been examined and evaluated. Furthermore, Moderate Resolution Imaging Spectroradiometer (MODIS) and remote sensing data were used to calculate the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Normalized Difference Water Index (NDWI), and the Temperature–Vegetation–Dryness Index (TVDI) to identify the area of water body, water level, and vegetation changes during 20 years (2000–2020). The Pearson correlation coefficient was also employed to explore the relationship between the drought and the remote sensing-derived indices. According to the results of drought analysis, 2000, 2002, 2004, 2006, 2008, 2010, 2012, 2014, 2016, 2018, and 2020 had experienced dry spells in the Lake Urmia basin. The NDWI changes also showed that the maximum area of the Lake Urmia happened in 2000, and its minimum was recorded in 2014. The variation of NDVI values showed that the highest values of vegetation cover were estimated to be 2,850 km2 in.2000, and its lowest value was 1,300 km2 in.2014. The maximum EVI and TDVI were calculated in 2000, while their minimum was observed in 2012 and 2014. Also, the correlation analysis showed that the SPI had the highest correlation with NDVI. Meanwhile, 1-month SPI had a higher correlation than the 3-month SPI with NDVI and EVI. As a concluding remark, NDVI and NDWI were more suitable indices to monitor the changes in vegetation and drought-related water area. The results can be used to make sound decisions regarding the rapid assessment of remote sensing-derived data and water-related indices.
Recent Advancement of Polymersomes as Drug Delivery Carrier
Authors: Singh Kuldeep , Biharee Avadh , Vyas Amber , Thareja Suresh , Jain Akhlesh Kumar 
Published: April 2022

Background:
Biomedical applications of polymersomes have been explored, including drug and
gene delivery, insulin delivery, hemoglobin delivery, the delivery of anticancer agents, and various diagnostic
purposes.

Objectives:
Polymersomes, which are self-assembled amphiphilic block copolymers, have received a lot of attention
in drug delivery approaches. This review represents the methods of preparation of polymersomes, including
thin-film rehydration, electroformation, double emulsion, gel-assisted rehydration, PAPYRUS method,
and solvent injection methods, including various therapeutic applications of polymersomes.

Methods:
Data was searched from PubMed, Google Scholar, and Science Direct through searching of the
following keywords: Polymersomes, methods of preparation, amphiphilic block copolymers, anticancer drug
delivery

Results:
Polymersomes provide both hydrophilic and hydrophobic drug delivery to a targeted site, increasing
the formulation's stability and reducing the cytotoxic side effects of drugs.

Conclutions:
Polymersomes have the potential to be used in a variety of biological applications, including drug
and gene delivery, insulin delivery, hemoglobin delivery, delivery of anticancer agents, as well as in various
diagnostic purposes. Recently, polymersomes have been used more frequently because of their stability, reducing
the encapsulated drug's leakage, site-specific drug delivery, and increasing the bioavailability of the drugs
and different diagnostic purposes. The liposomes encapsulate only hydrophilic drugs, but polymersomes encapsulate
both hydrophilic and hydrophobic drugs in their cores.
Neuroproteomics Chip-Based Mass Spectrometry and Other Techniques for Alzheimer’s Disease Biomarkers – Update
Authors: Pomilio Alicia B. , Vitale Arturo A. , Lazarowski Alberto J. 
Published: April 2022

Background:Alzheimer's disease (AD) is a progressive neurodegenerative disease of growing interest given that there is cognitive damage and symptom onset acceleration. Therefore, it is important to find AD biomarkers for early diagnosis, disease progression, and discrimination of AD and other diseases.Objective:The objective of this study is to update the relevance of mass spectrometry for the identification of peptides and proteins involved in AD useful as discriminating biomarkers.Methods:Proteomics and peptidomics technologies that show the highest possible specificity and selectivity for AD biomarkers are analyzed, together with the biological fluids used. In addition to positron emission tomography and magnetic resonance imaging, MALDI-TOF mass spectrometry is widely used to identify proteins and peptides involved in AD. The use of protein chips in SELDI technology and electroblotting chips for peptides makes feasible small amounts (μL) of samples for analysis.Results:Suitable biomarkers are related to AD pathology, such as intracellular neurofibrillary tangles; extraneuronal senile plaques; neuronal and axonal degeneration; inflammation and oxidative stress. Recently, peptides were added to the candidate list, which are not amyloid-β or tau fragments, but are related to coagulation, brain plasticity, and complement/neuroinflammation systems involving the neurovascular unit.Conclusion:The progress made in the application of mass spectrometry and recent chip techniques is promising for discriminating between AD, mild cognitive impairment, and matched healthy controls. The application of this technique to blood samples from patients with AD has shown to be less invasive and fast enough to determine the diagnosis, stage of the disease, prognosis, and follow-up of the therapeutic response.
Managing Apoptosis in Lung Diseases using Nano-assisted Drug Delivery System
Authors: Shukla Monu K. , Dubey Amit , Pandey Sadanand , Singh Sachin K. , Gupta Gaurav , Prasher Parteek , Chellappan Dinesh K. , Oliver Brian G. , Kumar Deepak , Dua Kamal 
Published: April 2022

Abstract:
Several factors exist that limit the efficacy of lung cancer treatment. These may be tumor-specific
delivery of therapeutics, airway geometry, humidity, clearance mechanisms, presence of lung diseases, and
therapy against tumor cell resistance. Advancements in drug delivery using nanotechnology based multifunctional
nanocarriers, have emerged as a viable method for treating lung cancer with more efficacy and fewer adverse
effects. This review does a thorough and critical examination of effective nano-enabled approaches for
lung cancer treatment, such as nano-assisted drug delivery systems. In addition, to therapeutic effectiveness,
researchers have been working to determine several strategies to produce nanotherapeutics by adjusting the
size, drug loading, transport, and retention. Personalized lung tumor therapies using sophisticated nano modalities
have the potential to provide great therapeutic advantages based on individual unique genetic markers and
disease profiles. Overall, this review provides comprehensive information on newer nanotechnological prospects
for improving the management of apoptosis in lung cancer.
Urine Metabolites as a Predictor of Warfarin Response Based on INR in Atrial
Fibrillation
Authors: Bawadikji Abdulkader Ahmad , Teh Chin-Hoe , Kader Muhamad Ali Bin Sheikh Abdul , Sulaiman Syed Azhar Syed , Ibrahim Baharudin 
Published: April 2022

Background:
Warfarin is an anticoagulant with wide inter-individual variations in drug responses monitored
based on the International Normalized Ratio (INR). It is commonly prescribed for atrial fibrillation (AF) and
stroke. Oral anticoagulants (e.g., warfarin) reduce the risk of getting a stroke but increase the risk of hemorrhage.
The proton nuclear magnetic resonance (1H-NMR) pharmacometabonomics technique is useful for determining drug
responses. Furthermore, pharmacometabonomics analysis can help identify novel biomarkers of warfarin outcome/
INR stability in urine.

Objectives:
The focus of this research was to determine if urine metabolites could predict the warfarin response
based on INR in patients who were already taking warfarin (identification; phase I) and to determine if urine metabolites
could distinguish between unstable and stable INR in patients who had just started taking warfarin (validation;
phase II).

Methods:
A cross-sectional study was conducted. Ninety urine samples were collected for phase 1, with 49 having
unstable INR and 41 having stable INR. In phase II, 21 urine samples were obtained, with 13 having an unstable INR
and eight having a stable INR. The metabolites associated with unstable INR and stable INR could be determined
using univariate and multivariate logistic regression analysis.

Results:
Multivariate logistic regression (MVLR) analysis showed that unstable INR was linked with seven regions.

Discussion:
The urine pharmacometabonomics technique utilized could differentiate between the urine metabolite
profiles of the patients on warfarin for INR stability.

Conclusion:
1H-NMR-based pharmacometabonomics can help lead to a more individualized, controlled side effect
for warfarin, thus minimizing undesirable effects in the future.
MetaConClust - Unsupervised Binning of Metagenomics Data using Consensus Clustering
Authors: Sharma Anu , Sinha Dipro , Mishra Dwijesh Chandra , Rai Anil , Lal Shashi Bhushan , Kumar Sanjeev , Farooqi Moh. Samir , Chaturvedi Krishna Kumar 
Published: April 2022

Background:Binning of metagenomic reads is an active area of research, and many unsupervised machine learning-based techniques have been used for taxonomic independent binning of metagenomic reads.Objective:It is important to find the optimum number of the cluster as well as develop an efficient pipeline for deciphering the complexity of the microbial genome.Method:Applying unsupervised clustering techniques for binning requires finding the optimal number of clusters beforehand and is observed to be a difficult task. This paper describes a novel method, MetaConClust, using coverage information for grouping of contigs and automatically finding the optimal number of clusters for binning of metagenomics data using a consensus-based clustering approach. The coverage of contigs in a metagenomics sample has been observed to be directly proportional to the abundance of species in the sample and is used for grouping of data in the first phase by MetaConClust. The Partitioning Around Medoid (PAM) method is used for clustering in the second phase for generating bins with the initial number of clusters determined automatically through a consensus-based method.Results:Finally, the quality of the obtained bins is tested using silhouette index, rand Index, recall, precision, and accuracy. Performance of MetaConClust is compared with recent methods and tools using benchmarked low complexity simulated and real metagenomic datasets and is found better for unsupervised and comparable for hybrid methods.Conclusion:This is suggestive of the proposition that the consensus-based clustering approach is a promising method for automatically finding the number of bins for metagenomics data.