bluuebunny/crossref_metadata_2025_split huggingface dataset:
Capturing lay perspectives on policy effectiveness: Employers’ and precariously employed workers’ suggested improvements for the workers’ compensation system and work injury processes
Authors: Billias Nicole , MacEachen Ellen 
Published: March 2024

Abstract
Purpose: Precarious employment rates are rising, which challenges the effectiveness of current workers’ compensation systems. Precariously employed workers encounter problems related to short-term and/or temporary employment, and complicated employment relationships. Few voices are captured in the literature from precariously employed workers and employers of these workers regarding day-today experience of workers’ compensation policies. This article describes employers’ and precariously employed workers’ suggested changes and improvements to a workers’ compensation system and other processes related to work injury and return-to-work.
Methods: Semi-structured interviews were conducted in Ontario, Canada with 36 precariously employed workers and 15 employers of precariously employed workers who were recruited via social media, email lists, cold calling, word-of-mouth, and the “snowball” method. Thematic code summaries were analyzed to identify suggested workers’ compensation system improvements that relate to precarious employment.
Results: Employers’ and precariously employed workers’ suggestions fell into the categories of (1) communication, (2) worker health and healthcare, and (3) worker and employer support.
Conclusions: Identifying precariously employed workers and employers of precariously employed workers’ suggestions enables a better understanding of areas of improvement for workers’ compensation policy and how these policy changes could affect these stakeholders with regards to managing workplace rehabilitation and disability.
MCPF: Fault-Tolerant Scheduling of Scientific Workflow on Cloud Computing
Authors: Ulabedin Zain , Khan Pervez , Uddin Burhan 
Published: March 2024

Abstract

Scientific workflow applications entail extensive amounts of tasks and data-sets necessitating systematic processing. Cloud platform is utilized for executing these applications which provide access to extensive amounts of scalable and on demand resources. Running scientific workflow applications on cloud computing experiences a huge amount of failure, i.e., hardware failures, software failures, network failures, etc., due to the large scale heterogeneity and distributed nature. That affects overall workflow execution time, monitory cost, and resource utilization. Numerous fault-tolerance methods are used to resolve and handle failures in cloud computing environment. In this paper, we used the MCPF (Multiple Critical Partitions with Failure) technique. The proposed technique has two phases. In the first phase, the rank of all tasks is calculated by summing the ranks, i.e., downward and upward rank. And then, in the second phase tasks are scheduled based on their ranking on the VMs, which has a lower failure rate. We evaluated the performance of our proposed technique under different conditions using parameters, i.e., makespan and cost. We have compared the results of MCPF with well-known existing HEFT, and RDEARP algorithms. Simulation results obtained through experiments and their comparison with existing techniques lead us to the conclusion that our proposed technique yields better results than existing algorithms.
Development of a LC-UV method for the rapid screening of arbutin, hydroquinone, dexamethasone, hydrocortisone, and clobetasol propionate in skin-whitening cosmetic products
Authors: Tang Erasmus N , Lenga Stephanie , Siani-Yonga Henri G , Ngono-Mballa Rose , Heudi Olivier 
Published: March 2024

Abstract

A simple liquid chromatography method with UV detection was developed for the rapid screening of arbutin, hydroquinone, dexamethasone, hydrocortisone and clobetasol propionate in four skin-whitening cosmetic products. The chromatographic conditions for their screening in less than 15 min were optimized on a Kinetex® 2.6 µm Polar C18 column (150 x 4.6 mm) with a gradient of mobile phase made of 0.025% TFA in water and acetonitrile ) and UV detection was performed at two wavelengths, 240 nm and 270 nm. Under these conditions, arbutin, hydroquinone, dexamethasone, hydrocortisone and clobetasol propionate eluted with retention time of 2.20, 3.75, 11.95, 12.58 and 12.75 min, respectively. By comparing the retention characteristics and UV spectra and the peak purity of the standard with each compound, arbutin, hydroquinone, hydrocortisone and clobetasol were identified in the milk and whitening cream products tested while dexamethasone was not found. An attempt for their quantification showed that hydroquinone was the major ingredients. The preliminary results of study show that our method is suitable for rapid screening of arbutin, hydroquinone, hydrocortisone and clobetasol propionate in skin-whitening cosmetic and, could be applied after validation to their quantitation.
A Deep Learning model for the identification of Potato leaf diseases using Wrapper Feature Selection and Concatenation
Authors: Naeem Muhammad Ahtsam , Saleem Muhammad Asim , Sharif Muhammad Imran , Akbar Shahzad , Sajid Muhammad Zaheer 
Published: March 2024

Abstract
Potato is a popular crop that is cultivated in many different climates. Potato farming has recently gained incredible traction, increasing relevance in international agricultural production. Potatoes are susceptible to several illnesses that stunt their development. This plant has significant leaf disease. Early blight (EB) and late blight (LB) are the two devastating leaf diseases for potato plants. The early detection of these diseases would be beneficial for enhancing the yield of this crop. The ideal solution is image processing to identify and analyze these disorders. Using image processing and machine learning, we detail a method that requires no outside help to detect late-blight potato leaf in this article. The pro- posed method comprises four different phases: (1) Histogram input images may improve from equalization to boost their overall quality; (2) feature extraction is performed using a Deep CNN model, then these extracted features are concatenated; (3) feature selection is performed using wrapper-based feature selection; (4) classification is performed using an SVM classifier and its variants. By utilizing SVM and a meticulously selected set of 550 characteristics, the suggested technique achieves an unprecedented 99% accuracy.
Evolution state prediction of step-like landslide based on an optimized intelligent model
Authors: Ma T. , WU Lizhou , Zhou J.T. , Zhang H. 
Published: March 2024

Abstract

Prediction of landslide evolution state is important for early warning system of landslides. The displacement curve of reservoir landslides has step-like characteristics. However, the mutation point of displacement curve is difficult to predict. An optimized machine learning model based on Extreme Gradient Boosting (XGBoost) and Bayesian method (Baye-XGB) is proposed to predict mutation points of displacement curve. The accuracy of models was testified by the Baishuihe landslide. Rainfall, reservoir water level and former displacement are taken as input parameters. K-means cluster was used to classify mutation points and regular points. XGBoost is used to predict evolution state, and the Bayesian method is applied to search hyperparameters. The results indicate that Baye-XGB is better than other models such as Support vector machine (SVM) and artificial neural network (ANN). The monthly displacement greater than 50mm is classified as a mutation point, the monthly displacement smaller than 50mm is classified as a regular point. The F1-score and AUC of the Baye-XGB are 0.95 and 0.99, respectively. The AUC score of Baye-XGB is improved by 17.86% compared with XGBoost_NoSmote, which means the SMOTE disposition can greatly improve the accuracy. Therefore, Baye-XGB can provide scientific guidelines for landslide earning waring.
Association between health systems and universal health coverage and COVID-19 testing rates in 194 countries: Cross-sectional ecological study
Authors: Lee Sieun , Lee Yuri 
Published: March 2024

Abstract

Background
The COVID-19 pandemic exposed the vulnerabilities of health systems worldwide, underscoring the critical need for robust and resilient healthcare infrastructure in effectively responding to disease outbreaks. This study aimed to address this need by examining the impact of health system indicators and universal health coverage (UHC) on COVID-19 testing rates.
Method
The study used secondary data from international organizations, peer-reviewed journals, and statistical portals. The primary outcome measure was COVID-19 testing rates, and the independent variables encompassed various health system indicators, including healthcare workforce density, the Healthcare Access and Quality Index (HAQ), UHC Coverage Index, and UHC Effective Coverage. The statistical analyses included simple and multiple regression models to determine the effects of these variables on COVID-19 testing rates while accounting for potential covariates.
Findings:
The findings revealed positive associations between health system indicators, UHC, and COVID-19 testing rates. Notably, the HAQ index exhibited the strongest positive correlation with COVID-19 testing rates. Multiple regression models further confirmed the positive relationships between UHC and health system indicators, and COVID-19 testing rates.
Conclusion
The study interpreted the results and underscored the significance of well-functioning health systems and UHC in achieving higher COVID-19 testing rates. This suggests that countries with well-structured health systems, advanced infrastructure, and adequate healthcare workforce are better equipped to conduct efficient testing. Moreover, the study emphasized the link between UHC and COVID-19 testing rates, noting that countries with greater UHC tend to exhibit higher testing rates. These findings contribute to our understanding of the association between health systems, UHC, and diagnostic testing for infectious diseases like COVID-19.
Growth Regulatory Potential Evaluation of Sathavari Palkkashayam (Asparagus recemosus Rhizome Milk Extract) Against Various Breast Cancer Cell Lines
Authors: Raj Ben , George Ajisha C , Cheruvathur Meena K , Alarifi Saud , Ahamed Anis , V Sharmila 
Published: March 2024

Abstract
Herbs and herbal products were used as an integral part of traditional medicine for centuries. In India the traditional medicines have unavoidable uses in all aspect of healthcare system and has become a part of lifestyle. The use spreads from treating common cold to serious illnesses like organ failures. Traditional medicines and treatments during the pregnancy period and postpartum has unique roles in maintaining newborn’s health and lactation improvement in mothers. The Palkashayam formulation of Sathavari (Asparagus recemosus rhizome milk extract) is one of such kind, used exclusively as a lactation enhancer. This study focussed on the effect of a medicine which provide sudden enhancement to mammary glands. The growth regulatory potential of formulation studied on both human and mice breast cancer cell lines SKBR33 and C127i using MTT Assay. Palkashayam prepared with milk and water has exhibited good toxicity against both SKBR3 and C127i. Formulation has expresses 48.04% toxicity against C127i in 24hrs with a dosage of 1mg/ml, whereas in human breast cancer cell line SKBR3, the highest toxicity potential expressed is 33.69% in 24hrs at 1mg/ml. Even though the study haven’t reported any trace of formulation inducing cancer cell growth, in SKBR3 it expressed only 1.12% toxicity at 0.001mg/ml dosage. While considering the generalised use of formulation in postpartum treatments, more extensive studies need to be done to ensure the safer use.
Plotting the Major Proteins of Borealpox Virus
Authors: Thomas Sunil 
Published: March 2024

Abstract
In an era marked by rapid environmental transformations, the viability of numerous species hinges crucially on their ability to navigate and adjust to shifting climatic conditions through migration. Yet, amidst this natural quest for survival, a consequential and often overlooked consequence emerges: the inadvertent transportation of a diverse array of parasites and pathogens across landscapes. This phenomenon poses a substantial and tangible threat to global health, particularly in the wake of recent outbreaks and pandemics fueled by zoonotic viruses, which leap from animals to humans. These viruses, originating in animals but capable of infecting humans, serve as poignant reminders of the intricate relationship between human and wildlife health, and the potential dangers of disregarding this delicate balance. In 2015, the borealpox virus (BRPV) [formerly Alaskapox virus (AKPV)], classified as an Orthopoxvirus, was first identified in a woman living near Fairbanks, Alaska. The BRPV causes borealpox disease. The first recorded fatality attributed to BRPV infection occurred in January 2024. Current evidence suggests that BRPV primarily targets small mammals, particularly red-backed voles and shrews, as evidenced by sampling efforts in the Fairbanks North Star Borough. However, it is likely that the virus is more widespread among Alaska's small mammal populations, potentially leading to undiagnosed human infections. Additionally, domestic pets, such as cats and dogs, may also contribute to the transmission of the virus. To date, no instances of human-to-human transmission have been documented, but the lack of diagnostics and vaccines for BRPV remains a concerning gap. This paper aims to address this issue by reporting the structure and epitopes of the major proteins of BRPV. This data could serve as a foundation for the development of novel diagnostics and vaccines to combat BRPV, thereby mitigating its impact on both human and animal populations.
Robust Obstacle Detection in Hilly Region
Authors: S Nikitha , S Sachin Kumar 
Published: March 2024

Abstract
This Paper presents the critical challenge of robust obstacle detection in hilly terrain, where irregular terrain poses increased risk to drivers. When navigating irregular and steep landscapes, drivers face visibility issues, increasing the risk of collisions. Existing obstacle detection systems reach their limits where obstacle can be hidden. Our proposed system includes an advanced system for hilly regions by integrating sensor technologies with advanced machine learning algorithms. It uses an Arduino microcontroller with ultrasonic sensors and a servo motor for precise obstacle detection as well as an ESP32 Module equipped with camera for capturing real-time obstacles. The YOLOv2 machine learning model process these images to accurately identify obstacle under various environmental conditions. To ensure timely warnings a Bluetooth module allows communication with the smartphone application that informs the driver of road conditions and alerts him to detected obstacles or potential collision risks. The aim of the project is to significantly improve safety by improving the systems adaptability and accuracy in hairpin bends mitigating collisions and saving lives.
Clinical and morphological features and the assessment of survival in patients with iris melanoma
Authors: Saakyan S. V. , Svirina I. V. , Tsygankov A. Yu. , Zharov A. A. , Izmailova N. S. 
Published: March 2024

Purpose. To analyze how the clinical and morphological features are associated with vital prognosis of iris melanoma patients.Material and methods. A retrospective analysis included 84 patients (54 women and 30 men) with iris melanoma treated between 2005 and 2019.The mean age at the time of treatment was 52.3 ± 14.5 years. All patients underwent standard and special ophthalmological examination, including ultrasound biomicroscopy, and followed up for 32 to 196 months (103.1 ± 44 months) after hospital discharge.Results. Pigmented tumors predominated (72.6% of patients), while others showed a slightly pigmented (15.4%) and nonpigmented forms (12.0%). Most of the patients (67.9%) received an organ preserving treatment, while others (32.1%) had to have a liquidating treatment (enucleation) due to anular tumor growth and secondary complications. Uveal melanoma was morphologically verified in all cases: spindle cell type A (14.3%), spindle cell type B (31.0%), mixed cell (42.8%), epithelioid cell (11.9%). The epithelioid cell type of tumor was more common when the tumor spread to the ciliary body rather than in iris melanoma (p = 0,046), but the spindle cell and mixed cell types were more common than the epithelioid cell type in both groups. An anular growth was typical for the epithelioid cell type of tumor (p = 0.006). The presence of vessels in tumor stroma was found to be more frequent in pigmented (p = 0.005) and non-pigmented forms (p = 0.0009). For pigmented tumors, spreading into the ciliary body was characteristic (p = 0.024).Conclusion. A retrospective analysis of clinical and morphological factors of iris melanoma patients with an iridociliary localization should that the specific survival was 98.8%, and overall survival was 87%. The data obtained indicated the importance of timely diagnosis of iris tumors for an organ preservation treatment.
Environmentally friendly energy, extremophilic microorganisms, enzymatic activity, microbial fuel cell, hard-to-decompose substrates
Authors: Dmitrieva Anastasia , Faskhutdinova Elizaveta , Larichev Timothy , Velichkovich Natalia , Boger Veronika , Aksenova Larisa 
Published: March 2024

Traditional energy sources pollute the environment. Microbial fuel cells are an alternative energy source that can reduce the environmental burden. Microbial fuel cells also remove recalcitrant wastes from wastewater. This research featured the enzymatic potential of microbial isolates obtained from the Abakan Arzhan thermal spring.
The study involved isolates of the genera Geobacter, Thermomonas, and Rhodopseudomonas. The keratinolytic analysis was in line with State Standard R 55987-2014. The chitinolytic activity was determined by injecting a bacterial suspension on Petri dishes with a chitin-containing medium. The lipolytic analysis involved cultivating the isolates in Stern’s glycerol fuchsin broth. The xylan hydrolysis depended on the reducing sugars. The cellulase activity was measured according to the standard method recom mended by the International Union of Pure and Applied Chemistry (IUPAC). The catalase potential was evaluated by the gasometric method on 1% gasoline media. The optimal parameters of consortium cultivation were determined by the voltage generated.
The Geobacter isolate had the maximal keratinolytic activity while the Thermomonas isolate demonstrated the maximal protein hydrolysis (80.1 ± 1.5%). Both Geobacter and Rhodopseudomonas showed good lytic activity against chitin with the lysis zone of ≥ 3 mm. The Geobacter isolate demonstrated as many as 350 units of xylanase activity and 365 units of cellulase activity; Ther momonas had 350 units of xylanase activity and 360 units of cellulase activity; Rhodopseudomonas showed 310 units of xylanase activity and 304 units of cellulase activity. The maximal catalase properties belonged to Geobacter (1.40 units) and Thermomonas (1.38 units). The maximal energy generation by bacterial consortia occurred at pH 8 and 45°C after 48 h of cultivation.
In this research, isolates of the genera Geobacter, Thermomonas, and Rhodopseudomonas from the Abakan Arzhan thermal spring were able to remove recalcitrant components, thus demonstrating good prospects for biological treatment of industrial wastewater.
Electrocardiographic Patterns and Ejection Fraction in Patients with
Nonischemic Dilated Cardiomyopathy: A Cross-sectional Retrospective Study
Authors: Elsayed Abdelhameed , Kheirallah Hatim , Alsuwayh Abdulaziz , Osman Ibrahim 
Published: March 2024

Background::
The relationship between ECG changes in dilated nonischemic cardiomyopathy and ejection fraction (EF) is complex and poorly understood. Thus,
this study aimed to identify the most common patterns of ECG associated with dilated nonischemic cardiomyopathy and their relationship to EF.

Methods::
A retrospective cross-sectional study was conducted between 2019 and 2022, including 100 consecutive patients with nonischemic dilated
cardiomyopathy. ECG data were rate, axis, left (LBBB) and right bundle branch block (RBBB), first-degree heart block, premature ventricular
contractions, depth of S wave in leads V1 and V2, length of R wave in leads V2 and V6, the ratio of R/S in the lead V4, left ventricular
hypertrophy (LVH), maximum R wave length in leads I, II, and III, poor R wave progression, ST-segment elevation, and T wave inversion.

Results::
The most common ECG pattern associated with cardiomyopathy was T-wave inversion (47%). The EF was 23.9± 8.87% in patients with LBBB
and 25.5± 8.07% in patients without LBBB (P= 0.361). In patients with T-wave inversion, the EF was 23.4± 8.21%, and it was 26.28± 8.35% in
patients without T-wave inversion (P= 0.086). In patients with LVH, the EF was 23.89± 7.84%, and it was 25.5± 8.66% in patients without LVH
(P= 0.354). A negative nonsignificant correlation existed between SV1, SV2, RV2, RV6, R/S V4, and maximum R in I, II, III, and EF.

Conclusions::
The study showed that T-wave inversion was the most common pattern associated with dilated nonischemic cardiomyopathy. The study provided
insight into the negative correlation between EF and SV1, SV2, RV2, RV6, R/S V4, and maximum R in I, II, and III, although it did not reach a
significant level.
Enhancement of Breast Cancer Screening through Texture and Deep Feature Fusion Model using MLO and CC View Mammograms
Authors: Sasikala S. , Arun Kumar S. 
Published: March 2024

A common cancer subtype found in women with high mortality and
occurrence rates is Breast Cancer (BC). BC ranks second among the causes of high
mortality rates in women. The annual death rate due to breast cancer surpasses that of
any other cancer type. The global survival rate for patients with breast cancer remains
suboptimal. To enhance this survival rate, it is essential to implement intervention
techniques for early detection and treatment. Screening using the Medio-Latera-
-Oblique (MLO) view and the Cranio-Caudal (CC) view improved the detection of
cancer signs in small lesions. This motivated the radiologist to use both mammographic
views for screening and subsequently to acquire additional information. To automate
this sequential screening process, Image Processing, and Artificial Intelligence (AI)
techniques are incorporated into these views individually and their results were fused.
Further, feature fusion from both views is analyzed by researchers to enhance the
overall performance of the system. The proposed model is more concentrated on the
extraction and fusion of deep features from the two views to improve screening
efficacy. The effectiveness of the proposed workflow is assessed on mammogram
images taken from the MLO view and CC views of the DDSM dataset. Medical
imaging data in conjunction with Machine Learning (ML) methods are employed for
breast cancer (BC) detection and classification, but they tend to be time-intensive.
Leveraging Deep Learning (DL) algorithms has the potential to further enhance the
detection accuracy.
;This work focuses on improving the detection performance by using a fusion of texture
and Resnet 50 deep feature of MLO and CC view mammograms followed by Support
Vector Machine (SVM) classification. An improved accuracy of 98.1% is achieved
when compared to existing works. Henceforth, this work can be employed for the early
BC diagnosis.