bluuebunny/crossref_metadata_2025_split huggingface dataset:
School culture as barometer of learners’ success: case study of Eastern Cape selected schools
Authors: Mkhomi Moses Sipho , Mzokwana Nondwe Nomnikelo 
Published: April 2024

The South African government attaches high priority on education hence a bigger share of the country’s budget is allocated to education. Literature presents a growing concern that South African public schools are not performing well with a number of contributing factors for learner poor performance. However, despite poor learner performance in some schools, there remain a number of schools who continuously produce pleasing results. It against this background that this paper sought to determine whether school culture has a contribution to Grade12 learners’ academic performance in selected secondary schools in the Eastern Cape Province. This paper used the Social Interactionist Theory (Mead, 1934) as it lenses, which seeks to understand events and circumstances around us and influence behaviour. This paper is located in the interpretivist paradigm and employed qualitative approach to gain experiences and opinions of participants on the influence of school culture 5 schools were sampled with 40 participants who responded through semi-structured interviews. Among the findings that emerged from the study was the critical participation of the stakeholders in school programmes, the vision and mission of the school to ensure learner success and maintenance of proud legacy of the school. The paper therefore, recommends cascading focus to the lower grades to continuously sustain school culture that enhances good learner performance.
Pengelompokan Kecamatan di Kabupaten Sleman Berdasarkan Jenis Disabilitasnya Tahun 2022 Menggunakan K-Means Clustering
Authors: Mulia Hidayahti Rate Mulia Hidayahti , Edy Widodo  
Published: April 2024

Penyandang disabilitas masih jarang diberikan ruang yang cukup dalam masyarakat. Masyarakat sering kali mengabaikan dan meremehkan keberadaan mereka. Mereka sering dipandang sebagai kelompok yang lemah dan hanya perlu dikasihani karena keterbatasannya. Hak- hak mereka sebagai manusia sering terabaikan, seperti hak untuk hidup, hak atas pendidikan dan kesehatan, dan hak untuk mengakses layanan publik. Peneliti ingin meneliti jenis disabilitas menurut Kecamatan yang ada di Kabupaten Sleman dengan variabel yang digunakan yaitu tuna netra, bisu tuli, cacat tubuh, mental retardasi, penyakit kronis, dan cacat ganda. Tujuan dari penelitian ini adalah melakukan pengelompokan Kecamatan di Kabupaten Sleman berdasarkan jenis disabilitas dengan harapan mempermudah Badan Perencanaan Pembangunan Daerah (BAPPEDA) Kabupaten Sleman dalam pengelompokan dan dapat mempermudah kegiatan dalam memenuhi hak-hak penyandang disabilitas di Kabupaten Sleman. Pengelompokan yang dilakukan guna untuk mengetahui karakteristik tiap-tiap cluster dengan melihat rata-rata dari masing-masing jenis disabilitas per Kecamatan yang ada di Kabupaten Sleman pada tahun 2022 disetiap clusternya. Penelitian ini menggunakan metode k-means clustering dan diperoleh hasil yaitu terdapat 3 Kecamatan pada cluster 1, 5 Kecamatan pada cluster 2, dan 9 Kecamatan pada cluster 3.
In vitro Effects of Postmetabolites from Limosilactobacillus fermentum 53 on the Survival and Proliferation of HT-29 Cells
Authors: Moskova-Doumanova Veselina Svetoslavova , Vaseva Anita Stoyanova , Veleva Ralitsa Krumcheva , Mladenova Kirilka Stefanova , Melnishka Denitsa Ruenova , Doumanov Jordan , Videv Pavel Veselinov , Topouzova-Hristova Tanya Ivanova , Dobreva Lili Ivailova , Atanasova Nikoleta Trifonova , Danova Svetla Trifonova 
Published: April 2024

Naturally fermented dairy products are an important component of the human diet. They are a valuable source of nutrients as well as vitamins and minerals. Their importance as a source of probiotic bacterial strains should not be overlooked. A number of studies highlights the positive effects of species of the Lactobacillacea on the intestinal microbiota, the overall homeostasis of the body, as well as a complementary treatment for some diseases. However, data on the effects on intestinal epithelial cells of postmetabolites released by bacteria are incomplete. This is likely due to the fact that these effects are species and strain specific. In the present study, we investigated the effects of postmetabolites produced by a pre-selected candidate probiotic strain Limosilactobacillus fermentum on HT-29 intestinal epithelial cells. Our data showed a pronounced proliferative effect, evaluated by flow cytometry, quantification of cell population, and determination of mitotic index. This was accompanied by stabilization of the cell monolayer, measured by increase of TEER (trans epithelial electric resistance) and reorganization of actin filaments. The data obtained are a clear indication of the positive effects, that the products secreted by L. fermentum strain 53 have on the intestinal epithelial cells.
New Insights on Catalysts-Supported In-Situ Upgrading of Heavy Oil and Hydrogen Generation during In-Situ Combustion Oil Recovery
Authors: Fassihi M. R. , Moore R. G. , Pereira Almao P. , Mehta S. A. , Ursenbach M. G. , Mallory D. G. 
Published: April 2024

Summary
As part of greenhouse gas reduction initiatives, there have been many publications on carbon sequestration, reducing the carbon footprint of oil and gas operations, and generating carbonless fuel [e.g., hydrogen (H2)] by means of in-situ processes. In-situ upgrading (ISU) can help with these aspects by converting bitumen and heavy oil into low sulfur, low N2, and low asphaltene products, generating fewer emissions and producing hydrogen as a byproduct, thus helping with utilization of vast resources of energy that would otherwise be wasted due to extreme measures of no fossil fuel policies. In addition, such processes could produce more valuable products, enhanced shipping/pipelining, and less demanding downstream processing.
In this paper, we provide new insights into the results of several combustion tube tests that were performed for Alberta Ingenuity Centre for In Situ Energy, using different heavy oils with fresh supported catalysts. The catalysts were placed in the production end of the combustion tube so oil would pass over the catalyst bed before being produced. In practice, solid catalyst particles could be placed into the oil-bearing formation adjacent to the producing wellbore, ensuring that crude oil will flow over the catalysts during oil production. In this paper, we use many laboratory results that have never been published before. The objective is to understand whether using catalysts has merit in our future oil production activities under the current environmental restrictions. A commercial Ni/Mo catalyst was used in these tests. The results of these tests indicated at least temporary significant occurrence of reactions such as hydroprocessing (HP) and hydrotreating reactions, such as hydrocracking, hydrodesulfurization (HDS), hydrodenitrogenation (HDN), and hydrodeoxygenation. They also generated a significant volume of hydrogen in situ.
We will discuss the impact of pressure, temperature, water injection, and dispersed vs. supported catalysts on the degree of oil upgrading. Also, the key parameters that could impact in-situ hydrogen generation will be presented. Specifically, the role of reactions such as aquathermolysis, thermal cracking, water-gas shift (WGS, defined later) reaction, and coke gasification will willbe discussed. Note that the products of these reactions could undergo additional methanation (ME) reactions, which could reduce the H2 concentration in the produced gas. Finally, methods of upscaling these results to the field conditions will be presented.
Open-label randomised clinical trial investigating whether Robot-Assisted Kidney Transplantation can reduce surgical complications compared to Open Kidney Transplantation (ORAKTx): Study protocol for a randomised clinical trial
Authors: Ortved Milla , Dagnæs-Hansen Julia , Stroomberg Hein Vincent , Kistorp Thomas , Rohrsted Malene , Sørensen Søren Schwartz , Røder Andreas 
Published: April 2024

Abstract
Background: Kidney transplantation is the ultimate treatment for end-stage kidney disease. Function of the kidney graft is not only dependent on medical factors but also a complication-free surgical procedure. In the event of major surgical complications, the kidney graft is potentially lost and the patient will return to the waiting list which may be long. To optimise peri-operative care and reduce complications, robot-assisted kidney transplantation (RAKT) has been introduced as an alternative to open kidney transplantation (OKT), but to our knowledge no randomised clinical trials are comparing RAKT to OKT. We will explore whether robot-assisted surgery can reduce 30-day surgical complications compared to open surgery in kidney transplantation.
Methods: This a single-site, open-label, randomised clinical trial comparing RAKT to OKT. Participants are adult recipients of kidney transplantation and all participants are recruited from Copenhagen University Hospital – Rigshospitalet, Denmark. The study plans to include 106 participants who will be randomised in a 1:1 manner between OKT and RAKT. Primary outcomes are vascular- and major surgical complications at 30 days post-operatively. Participants will be followed for two years to evaluate secondary outcomes including recovery, late complications and kidney graft function. This is designed as a superiority trial and planned analyses are as intention-to-treat.
Discussion: Studies indicate RAKT can reduce several surgical complications, but the lack of RCTs limits the extrapolation of these results to justify the replacement of an open approach with a robot-assisted one. Ultimately, the introduction of new surgical techniques should be as vigorously tested as any other new treatments. However, reducing surgical complications that compromise graft viability could lead to improved patient care and survival.
Trial registration: The trial was prospectively registered on February 15th 2023 on clinicaltrials.gov with NCT-identifier 05730257.
Transcriptome- and DNA methylation-based cell-type deconvolutions produce similar estimates of differential gene expression and differential methylation
Authors: Hannon Emily R , Marsit Carmen J , Dent Arlene E , Embury Paula , Ogolla Sidney , Midem David , Williams Scott M , Kazura James W 
Published: April 2024

Abstract
Background
Changing cell-type proportions can confound studies of differential gene expression or DNA methylation (DNAm) from peripheral blood mononuclear cells (PBMCs). We examined how cell-type proportions derived from the transcriptome versus the methylome (DNAm) influence estimates of differentially expressed genes (DEGs) and differentially methylated positions (DMPs).
Methods
Transcriptome and DNAm data were obtained from PBMC RNA and DNA of Kenyan children (n = 8) before, during, and 6 weeks following uncomplicated malaria. DEGs and DMPs between time points were detected using cell-type adjusted modeling with Cibersortx or IDOL, respectively.
Results
Most major cell types and principal components had moderate to high correlation between the two deconvolution methods (r = 0.60–0.96). Estimates of cell-type proportions and DEGs or DMPs were largely unaffected by the method, with the greatest discrepancy in the estimation of neutrophils.
Conclusion
Variation in cell-type proportions is captured similarly by both transcriptomic and methylome deconvolution methods for most major cell types.
Multivariate Analysis of Water Quality in the Dhaleshwari River, Bangladesh: Identifying Pollution Sources and Environmental Implications
Authors: Ali Md. Sohrab , Begum Shaheda , Rabbi Fahim Muntasir , Sumaia Mrs , Hasan Md. Kamrul , Rahman Md. Alinur , Rahman Mohammad Mahfuzur , Rahaman Md. Hasibur 
Published: April 2024

Abstract

The Dhaleshwari river, coursing through the central part of Bangladesh, is significantly impacted by rampant industrialization and various anthropogenic activities. This study aims to evaluate the current water quality status of the Dhaleshwari river, considering seasonal variations, and identify potential pollution sources using physicochemical parameters, multivariate statistical analysis, and the Weight Arithmetic Water Quality Index method. Sampling was conducted at six locations along the Dhaleshwari river from 2021 to April 2022, encompassing both dry and wet seasons. Eleven physicochemical parameters were tested following standard procedures. Correlation coefficients revealed strong positive relationships between EC, TDS, TS, BOD5, COD, and Chloride. Additionally, strong negative relationships were observed between DO, BOD5, COD, and TSS. The calculated WQI values demonstrated that the overall water quality of the Dhaleshwari river is very poor. Seasonal variation in water quality was significant, with the worst quality observed during the dry season; however, water quality during the wet season was also unsatisfactory. Cluster analysis grouped the six sampling stations into four distinct clusters based on water quality similarities across both seasons. Geospatial analysis shows the distribution of water quality in the study locations. Principal Components Analysis (PCA) indicated that direct wastewater discharge from surrounding industries, particularly tanneries, as well as municipal wastewater, were the major pollution sources. In conclusion, this study sheds light on the deteriorating water quality of the Dhaleshwari river due to industrialization and human activities. The findings underscore the urgent need for targeted interventions to mitigate pollution sources and improve the overall health of the river ecosystem.
Hybrid deep learning combined with traditional financial models: Application of RNN models and GARCH-Family Model for Natural Gas Price Volatility Forecasting
Authors: Chen Yufeng , Fan Xingang 
Published: April 2024

Abstract

The natural gas market has significant commonalities with the general financial market, especially its time series data are often non-stationary and show different fluctuation characteristics due to different market conditions. Therefore, accurate forecasting of natural gas price volatility requires a correct handling of the unique characteristics of its time series. In this paper, GARCH model and TGARCH model are specially selected to capture the volatility heteroscedasticity generated in different market scenarios, and IGARCH model is used to ensure that the model can still maintain high prediction accuracy when the time series is non-stationary. In order to deal with the long-term dependence of natural gas prices on time series, this paper introduces the LSTM model and the GRU model, both of which are variants of recurrent neural network (RNN). Thus we obtain the GARCH-IGARCH-TGARCH-LSTM/GRU model. It is worth noting that this model is applied to the field of natural gas price volatility prediction for the first time, which provides a new research perspective for in-depth understanding and accurate prediction of natural gas market volatility. We use the natural gas futures price index from June 2013 to June 2023 for the simulation test. Using 100 repeated experiments, we verify the robustness of the GARCH-IGARCH-TGARCH-GRU model in volatility forecasting and demonstrate its superior forecasting accuracy with a mean square error (MSE) of 0.22 and a mean absolute error (MAE) of 0.13. In the face of market breaks and extreme events, the integrated model shows higher adaptability and robustness. This study not only provides a powerful volatility forecasting tool for natural gas market participants, but also provides a strong demonstration of the universality of this type of model.
Developing a Modified Deep Belief Network with metaheuristic optimization Algorithm for predicting Alzheimer disease using Electroencephalogram
Authors: Jayagopal Prabhu , Mohan Prakash , Rajasekar Vijay Anand , SathishKumar Sree Dharinya , Mathivanan Sandeep Kumar , Mallik Saurav , Qin Hong 
Published: April 2024

Abstract

A neurological brain disorder that progresses over time is Alzheimer's disease. Alzheimer's disease can take years to identify, comprehend, and manifest—even in cases where signs are obvious. On the other hand, technological developments like imaging methods aid in early detection. But frequently, the results are unreliable, which delays the course of treatment. By dividing resting-state electroencephalography (EEG) signals into three groups—AD, healthy controls, and mild cognitive impairment (MCI)—this work offers a novel perspective on the diagnosis of Alzheimer's disease (AD). In order to overcome data limits and the over-fitting issue with deep learning models, we looked at augmenting the one-dimensional EEG data of 100 patients (49 AD participants, 37 MCI subjects, and 14 HC subjects) with overlapping sliding windows. Better results and early intervention could arise from this for persons afflicted with the illness. This research has the potential to significantly advance the early diagnosis of Alzheimer's disease and lay the groundwork for the creation of more precise and trustworthy diagnostic instruments for this debilitating condition. This study presents a Modified Deep Belief Network (MDBN) with a metaheuristic optimization method for detecting face expression and Alzheimer's disease using EEG inputs. The recommended method extracts significant features from EEG data in a novel way by applying the Improved Binary Salp Swarm Algorithm (IBSSA), which combines the MDBN and the metaheuristic optimization algorithm. The performance of the suggested technique MDBN-IBSSA for Alzheimer's disease diagnosis is evaluated using two publicly available datasets. The proposed technique's capacity to discriminate between healthy and ill patients is proved by the MDBN-IBSSA accuracy of 98.13%, f-Score of 96.23%, sensitivity of 95.89%, precision of 95.671%, and specificity of 97.13%. The experimental results of this study show that the MDBN-IBSSA algorithm proposed for AD diagnosis is effective, superior, and applicable.
Overexpression of ATF3 enhanced the therapeutic effects of human adipose-derived stem cells on erectile dysfunction associated with cavernous nerve injury
Authors: Lin Jiajing , Lu Yawen , Lin Zelong , Wu Yunlin , Jin Long , Fan Wenbei , Chen Bohao , An Geng , Li Chaohui , Fan Yong 
Published: April 2024

Abstract

Background Erectile dysfunction (ED) frequently arises as a prevalent complication following prostatectomy. Local administration of stem cells via penile injection has shown effectiveness in addressing erectile dysfunction stemming from bilateral cavernous nerve injury (BCNI-ED). Moreover, stem cell therapy, incorporating targeted genetic alterations, has the capacity to improve therapeutic results. This investigation involved the utilization of human Adipose-Derived Stem Cells (hASCs) that were genetically modified to overexpress activating transcription factor 3 (ATF3) and subsequently injected into the cavernous bodies of rat penises to enhance the therapeutic efficacy in cases of BCNI-ED. Methods Human adipose-derived stem cells (hASCs) were isolated, cultured, and characterized using flow cytometry, as well as subjected to osteogenic and adipogenic induction. Subsequently, the hASCs were transfected with lentivirus and assessed for the expression of nerve-associated growth factors. Following this, the treated hASCs were administered into the penises of rats with bilateral cavernous nerve injury-induced erectile dysfunction (BCNI-ED) to assess their therapeutic efficacy. The intracorporeal pressure/mean arterial pressure ratio (ΔICP/MAP) was utilized as a measure of erectile function. In vivo small animal imaging was conducted to monitor the distribution of the hASCs. Results hASCs showed increased expression of CD90, CD73, CD105 and decreased expression of CD45. ATF3-modified hASCs expressed nerve growth-related factors and had improved therapeutic potential in improving ΔICP/MAP ratio and α-SMA content while decreasing Collagen I content. In vivo imaging of small animals showed hASCs residing within the cavernous, with no group differences. Conclusions ATF3 enhanced neural-related trophic factor expression in hASCs, boosting their therapeutic potential of adipose stem cells on erectile dysfunction associated with cavernous nerve injury.
Genome resequencing and genome-wide polymorphisms in mosquito vectors Aedes aegypti and Aedes albopictus from south India
Authors: Acharya Preeti , Singh Upasana Shyamsunder , Rajamannar Veeramanoharan , Muniaraj Mayilsamy , Nayak Binata , Das Aparup 
Published: April 2024

Abstract

Aedes aegypti and Aedes albopictus mosquitoes spread major vector-borne viral diseases in tropical and sub-tropical regions of the globe. In this study, we sequenced the genome of Indian Ae. aegypti and Ae. albopictus and mapped to their reference genomes. Comparative genomics were performed between our strain and the reference strains. A total of 14,416,484 single nucleotide polymorphisms (SNPs) and 156,487 insertions and deletions (InDels) were found in Ae. aegypti, and 28,940,433 SNPs and 188,987 InDels in Ae. albopictus. A special emphasis was given to gene families involved in mosquito digestion, development and innate immunity that could be putative candidates for vector control. Serine protease cascades and their inhibitors called serpins, play a central role in these processes. We extracted high-impact variants in genes associated with serine proteases and serpins. This study reports for the first time a high coverage genome sequence data of an Indian Ae. albopictus mosquito. The results from this study will provide insights into Indian Aedes specific polymorphisms and the evolution of immune related genes in mosquitoes, that can serve as a resource for future comparative genomics and for those pursuing development of targeted biopesticides for effective mosquito control strategies.
Deep Learning-based Study on Assessment and Enhancement Strategy for Geological Disaster Emergency Evacuation Capacity in Changbai Mountain North Scenic Area
Authors: Zheng Erzong , Zhang Yichen , Zhang Jiquan , Zhu Jiale , Yan Jiahao , Liu Gexu 
Published: April 2024

Abstract

This research focuses on enhancing emergency evacuation capabilities under geological disasters in the northern scenic area of Changbai Mountain. The research leverages advanced technology, employing the Real-Enhanced Super-Resolution Generative Adversarial Network (Real-ESRGAN), significantly enhancing image clarity. It is then combined with deep learning methods for a comprehensive assessment of emergency evacuation capacity, facilitating a more accurate understanding of the current scenic area situation. Introducing a 3D model based on elevation, slope, and slope direction enhances the geographic overview, offering a 360° view for a more detailed understanding of terrain and geomorphological features. Considering geographic and climatic factors, the study proposes targeted improvements tailored to the specific characteristics of the scenic area. The innovation of this study lies in its successful resolution of remote sensing image blurring using Real-ESRGAN, introducing an advanced method for small-area studies. Through these integrated tools and methods, the study significantly enhances the accuracy of data processing, assessment, and decision support. It demonstrates an integrated approach to scenic area research, contributing to geohazard management, emergency planning, and the overall safety of the Changbai Mountain scenic area.