bluuebunny/crossref_metadata_2025 huggingface dataset:
Decoding Qur'anic Linguistics through Fi'il Tsulasi Mazid in Surah Az-Zukhruf
Authors: Nashrulloh Mohammad Muzakki , Fauji Imam
Published: May 2024

This study examines the occurrences and benefits of fi'il tsulasi mazid (trilateral verbs with additional letters) in Surah Az-Zukhruf, focusing on Arabic language learning and Qur'anic comprehension. Through a detailed analysis, we identified 57 instances of these verbs and categorized them based on their forms and types. Results indicate that the most common verb types are af'ala yuf'ilu and ifta'ala yafta'ilu, while others include fa'ala yufa'ilu and faa'ala yufaa'ilu. Various benefits of these verb forms, such as litta'diyah, littikhad, and littahawul, were identified. This research provides valuable insights for Arabic language educators, allowing them to better explain linguistic changes and improve students' understanding of Arabic as the language of the Qur'an.
Hightligh:
1. Detailed Analysis: Studying 57 instances of fi'il tsulasi mazid.2. Common Verb Types: Identifying prevalent verb types.3. Educational Implications: Enhancing Arabic language teaching for Qur'anic comprehension.
Keywords: Arabic linguistics, Qur'an, Fi'il tsulasi mazid, Surah Az-Zukhruf, Language education
Investigating How Student Interest and Teacher Competence Shape Science Education
Authors: Tristianti Rosulita , Efendi Nur
Published: May 2024

This study investigates the relationship between students' learning interest, perceptions of teacher competence, and cognitive learning outcomes in science education. Using a non-experimental quantitative approach with 90 randomly sampled students from a total population of 336, data were collected through questionnaires and analyzed using product moment correlation and multiple correlation. The results highlight significant correlations between students' learning interests, perceptions of teacher competency, and cognitive learning outcomes, emphasizing the importance of considering these factors in designing effective science education methodologies to enhance student engagement and optimize learning outcomes.
 
Highlight:

Engagement in science correlates with student interest in learning.
Teacher competence impacts science learning outcomes significantly.
Quantitative research informs effective science education methods.

 
Keyword: Science Education, Student Learning Interest, Teacher Competence, Cognitive Learning Outcomes, Quantitative Research
Calculation a new formula to determine the chlorophyll-a content without extraction by modeling meta analysis data in Arthrospira platensis Gomont
Authors: Tunca Hatice , DİNÇ Tarık , YANIK Huriye , SEVİNDİK Tuğba ONGUN , KESKİN Semra YILMAZER
Published: May 2024

Spectrophotometric methods are frequently used to monitor growth and development in algae cultures of toxicity studies. Chlorophyll-ameasurement is one of the most important parameters determining the growth in cyanobacteria cultures. In determining this parameter, primarily extraction with solvents and daily measurements should be carriedout during the study. The use of this method causes both labor and material consumption. Since the chlorophyll-acontent is related to the doubling time and initial culture concentration, it is possible to measure chlorophyll-aby determining these values by modeling. Mathematical model analysis has gained importance in phycology studies with the development of hardware and software depending on technology. Microbial processes modeling allows us to have information about the growth behavior of microalgae and to obtain effective results in the output of algal culture. For this purpose, the available data were adapted to Gompertz, Logistic, Weibull, Exponential and Richards models in multiple pesticide applications. By means of the regression formula obtained from the doubling time values of OD560 and chlorophyll-a, the chlorophyll-a can be easily determined by OD560 in forward studies.
Mapping Computational Cognitive Profiles of Aging to Dissociable Brain and Sociodemographic Factors
Authors: Marzuki Aleya , Wong Kean Yung , Chan Jee Kei , Na Sze Yie , Thanaraju Arjun , Phon-Amnuaisuk Paveen , Vafa Samira , Jenkins Michael , Chia Yook Chin , Schaefer Alexandre
Published: May 2024

Aging is associated with declines in cognition and brain structural integrity. However, there is equivocality over 1) the specificity of affected domains in different people, 2) the location of associated patterns of brain structural deterioration, and 3) the sociodemographic factors contributing to ‘unhealthy’ cognition. We aimed to identify cognitive profiles displayed by older adults and determine brain and sociodemographic features potentially shaping these profiles. A sample of Southeast-Asian older adults (N = 386) participated in a multi-session study comprising cognitive testing, neuroimaging, and a structured interview. We used computational models to extract latent mechanisms underlying cognitive flexibility and response inhibition. Data-driven methods were used to construct cognitive profiles based on standard performance measures and model parameters. We also investigated grey matter volume and machine-learning derived ‘brain-ages’. A profile associated with poor set-shifting and rigid focusing was associated with widespread grey matter reduction in cognitive control regions. A slow responding profile was associated with advanced brain-age. Both profiles were correlated with poor socioeconomic standing and cognitive reserve. This study furthers understanding of how distinct aging profiles of cognitive impairment uniquely correspond to specific vs. global brain deterioration and the significance of socioeconomic factors in informing cognitive performance in older age.
Evaluation of Branch-Level Green Banking in Bangladesh through Life Cycle Assessment Approach
Authors: Chowdhury Kishwar Jahan , Roy Papon , Ahmed Gias Uddin , Hossain Mohammad Mosharraf
Published: May 2024

Green banking becoming an everyday practice around the globe and similar to developing nations like Bangladesh, this study embarks on an ambition to assess the environmental burden of the banking sector. Leveraging Life Cycle Assessment (LCA), a methodology widely used for environmental footprint assessment delves into the resource utilization, waste generation, and overall environmental emissions of Bangladeshi banking systems. This national inventory will not only compare the environmental performances of urban vs. rural and private vs. public banks but also serve as a crucial stepping stone for future research in sustainable banking practices. Overall, this study aims to help policymakers and financial institutions with substantial insights, guiding them to thrive in their operation in a direction where economic prosperity and environmental sustainability are in harmony. This study follows the ISO guideline to conduct LCA of the banking system in Bangladesh at different branch levels. The total deposit of money per year in million BDT is considered a functional unit and activities within the country are considered. In the assessment, OpenLCA is used for modeling while ReCiPe-2008 is used for assessing the lifecycle impact of the entire process. The study results reveal that Marine ecotoxicity is the primary contributor (1.1 kg 1,4 -DCB) to the overall banking system in Bangladesh, followed by freshwater ecotoxicity (0.81 kg 1,4-DCB) and Human carcinogenic toxicity (0.61 kg 1,4-DCB. However, the Global warming potential is identified as 27.22 kg CO2-eq for every million BDT deposited in an economic year. The hotspot analysis of the study indicates that in most impact categories, electricity production has the most significant environmental footprint. However, computer use and fossil fuel production play a noteworthy role in the categories of human non-carcinogenic toxicity and fossil resource depletion, because of their wide range of materials used in production. The study concludes that public and rural commercial banks poses more loopholes to improve for greener banking practices as public banks and rural branches serve more customers with fewer facilities over a large area.
Personality Perceptions from Voices and Faces – Impressions and Kernels of Truth
Authors: Skuk Verena G. , Jacob Isabell , Wientzek Rebecca , Ward Robert , Schweinberger Stefan R
Published: May 2024

We investigated the perception of Big Five personality traits from trait-average voices when traits were based either on speakers´ self-ratings (Exp. 1, E1) or on other perceivers’ ratings of perceived personality of the original voice samples (E2). Trait-average voices were created from a voice database of 93 speakers (40 male, 53 female) using TANDEM-STRAIGHT n-way morphing. For speaker sex, trait and for two sentences, we created five-voice averages from speakers scoring either high or low on the target trait. We then measured perceivers´ ability to discriminate high and low trait-averages per trait. We also assessed facial trait perception (E3) using the paradigm and the full facial composite images by Kramer and Ward (2010). In trait-average voices based on self-ratings (E1), extraversion (for female speakers) and neuroticism (for male speakers) were the only traits that could be discriminated above chance levels. For trait-average voices which were based on other perceivers´ personality ratings of individual voices (E2), all Big Five traits were discriminated with high accuracy, demonstrating stereotyping in the sense of consistent (though not necessarily valid) personality impressions from voices. By comparison with E1, we found substantially better perception of self-rated traits from faces (E3), for all traits except for openness, replicating Kramer and Ward (2010). Individual differences in trait perception were substantial, and there were small but significant correlations between facial and vocal trait perception skills in both E1 and E2. Overall, the present methodological approach offers a promising window into personality perception from voices.
Carotenoids; a unique biomarker for monitoring Peridinium dinoflagellate blooms in freshwaters
Authors: Mudiyanselage Anuththara Sandunmali Vasana Gunawardana Menik Hitimami , Sanjaya Kelum , Atapath Keerthi Sri Senarath , Masakorala Kanaji , Mudiyanselage Ajith Lalith Weerasinghe Yapa Yapa , Gamage Shirani Manel Kumari Widana
Published: May 2024

Peridinium is a rare but, toxic bloom-forming dinoflagellate in freshwaters. Its toxic effects were reported from several countries including Sri Lanka although not-much attened. In this study, we developed a remote sensing-based empirical model to quantify Peridinium using Maussakelle Reservoir in Sri Lanka as the model. Since carotenoids are the major light-harvesting accessary pigments of Peridinium and many other dinoflagellates, it serves as a unique biomarker. Thus, spectral signatures of carotenoids allowed us to distinguish Peridinium in the background of chlorophyll-dominated mix population of phytoplankton. Ground data and Sentinel-2 satellite images were collected when a high density of Peridinium and carotenoid pigment levels were present and a set of linear regression models were developed. Among the models, that developed with B2 and B3 bands of Sentinel-2 better regressed with measured carotenoid (R2 = 0.93, p < 0.001). The relationship between measured and model-predicted carotenoid concentrations displayed a correlation (R2) of 0.86 and root mean squared error (RMSE) of 2.82. Further, a second regression model was developed to predict Peridinium cell density using carotenoid as a proxy. The established relationship was strong and significant (R2 = 0.85, p < 0.001). Then a final empirical model was derived by coupling the two regression models to quantify Peridinium cell density (R2 = 0.71, p < 0.001). We highlight that this method would be a novel approach that directs reliable and accurate prediction and quantification of carotenoid pigments and Peridinium cell density in freshwaters.
NHEJ and HDR occurring simultaneously during gene integration into the genome of Aspergillus niger
Authors: Fritsche Susanne , Reinfurt Aline , Fronek Felix , Steiger Matthias G.
Published: May 2024

Non-homologous end joining (NHEJ) and homology-directed repair (HDR) are two mechanisms in filamentous fungi to repair DNA damages. NHEJ is the dominant response pathway to rapidly join DNA double-strand breaks, but often leads to insertions or deletions. On the other hand, HDR is more precise and utilizes a homologous DNA template to restore the damaged sequence. Both types are exploited in genetic engineering approaches ranging from knock-out mutations to precise sequence modifications. In this study, we evaluated the efficiency of a HDR based gene integration system designed for the pyrG locus of Aspergillus niger. While gene integration was achieved at a rate of 91.4%, we also discovered a mixed-type repair (MTR) mechanism with simultaneous repair of a Cas9-mediated double-strand break by both NHEJ and HDR. In 20.3% of the analyzed transformants the donor DNA was integrated by NHEJ at the 3’ end and by HDR at the 5’ end of the double-strand break. Furthermore, sequencing of the locus revealed different DNA repair mechanisms at the site of the NHEJ event. Together, the results support the applicability of the genome integration system and a novel DNA repair type with implication on the diversity of genetic modifications in filamentous fungi.
Development and validation of an automated Tomotherapy planning method for cervical cancer
Authors: Han Feiru , Xue Yi , Huang Sheng , Lu Tong , Yang Yining , Cao Yuanjie , Chen Jie , Hou Hailing , Sun Yao , Wang Wei , Yuan Zhiyong , Tao Zhen , Jiang Shengpeng
Published: May 2024

Purpose
This study aimed to develop an automated Tomotherapy (TOMO) planning method for cervical cancer treatment, and to validate its feasibility and effectiveness.
Materials and Methods
The study enrolled 30 cervical cancer patients treated with TOMO at our center. Utilizing scripting and Python environment within the RayStation (RaySearch Labs, Sweden) treatment planning system (TPS), we developed automated planning methods for TOMO and volumetric modulated arc therapy (VMAT) techniques. The clinical manual TOMO (M-TOMO) plans for the 30 patients were re-optimized using automated planning scripts for both TOMO and VMAT, creating automated TOMO (A-TOMO) and automated VMAT (A-VMAT) plans. we compared it with M-TOMO and A-VMAT plans. The primary evaluated relevant dosimetric parameters and treatment plan efficiency were assessed using the two-sided Wilcoxon signed-rank test for statistical analysis,with a P-value < 0.05 indicating statistical significance.
Results
A-TOMO plans maintained similar target dose uniformity compared to M-TOMO plans, with improvements in target conformity and faster dose drop-off outside the target, and demonstrated significant statistical differences (P+<0.01). A-TOMO plans also significantly outperformed M-TOMO plans in reducing V50Gy, V40Gy, and Dmean for the bladder and rectum, as well as Dmean for the bowel bag, femoral heads, and kidneys (all P+<0.05). Additionally, A-TOMO plans demonstrated better consistency in plan quality. Furthermore, the quality of A-TOMO plans was comparable to or superior than A-VMAT plans. In terms of efficiency, A-TOMO significantly reduced the time required for treatment planning to approximately 20 minutes.
Conclusion
We have successfully developed an A-TOMO planning method for cervical cancer. Compared to M-TOMO plans, A-TOMO plans improved target conformity and reduced radiation dose to OARs. Additionally, the quality of A-TOMO plans was on par with or surpasses that of A-VMAT plans. The A-TOMO planning method significantly improved the efficiency of treatment planning.
An African Perspective on Publication of Postgraduate Residents’ Dissertation: A qualitative study exploring residents’ research experiences, barriers to publication and strategies to improve publication rates from Kenyan medical residents
Authors: Macharia Brigette , Kamya Dorothy , Waweru-Siika Wangari , Kathomi Caroline
Published: May 2024

Background: Postgraduate medical residents in Kenya must complete and submit a research dissertation as part of their Master of Medicine studies. However, the rate of subsequent publication of this research is lower than the publication rate in higher income settings, denying Kenyans the potential benefit of population-specific data. This study was carried out to explore residents’ research experiences and reasons for the low publication rate and to identify strategies to improve the publication rate of residents’ research in low-income settings.
Methods: In-depth interviews of 8 faculty and academic staff and 18 former residents were conducted to investigate their experiences of conducting, supervising and publishing research. Strategies to improve to improve publications rates were explored.
Results: The former residents described difficult research journeys – from concept development to final submission of dissertation – which discouraged them from seeking publication. Many faculty and staff lacked time or sufficient expertise to successfully guide residents to publication. Departmental research culture, faculty expertise as supervisors and prioritisation of clinical work over research and lack of dedicated research time impacted both residents’ and faculty attitudes to research. Strategies to improve publication rates focused on developing faculty research expertise, more protected research time, and a more structured approach to teaching research methodology, including academic writing skills.
Conclusions: Residents in low middle income countries such as Kenya encounter systemic and personal challenges to successful publication of research. The ease or difficulty of a resident’s research journey influences their attitudes to subsequent publication. Strategies to improve publication rates can improve the dissemination of relevant research data in such settings.
Effect of esaxerenone on circulating microparticles and vascular endothelial function in diabetic mice
Authors: Taguchi Kumiko , Kondo Hiroyuki , Matsumoto Takayuki , Kobayashi Tsuneo
Published: May 2024

Endothelial dysfunction exacerbates hypertension and other vascular complications in diabetes mellitus (DM). Circulating microparticles (MPs) and extracellular vesicles released in patients with DM have emerged as novel regulators of endothelial dysfunction. The obstruction of mineralocorticoid receptors (MRs) is a potential therapeutic approach to reduce cardiovascular complications. Their impact on the obstruction of MRs on circulating MPs and endothelial dysfunction in DM remains unclear. DM was induced in mice through a single intravenous dose of streptozotocin (STZ; 200 mg/kg). Esaxerenone (ESAX; 3 mg/kg/day), a MR blocker was administered via diet for 8 weeks. In this study, the aortas of the DM group showed the endothelial dysfunction and the administration of ESAX ameliorated the endothelial-dependent responses. Moreover, ESAX influences the impaired endothelial-dependent responses of DM-derived MPs. Interestingly, MP levels increased in DM whereas decreased after ESAX administration. In the aorta, the DM-derived MPs increased the expression of intercellular adhesion molecule-1 (ICAM-1). ESAX inhibited the adhesion of DM-derived MPs. Moreover, the ICAM-1 inhibitor A205804 shows similar effects as ESAX. These results indicate that the release and adhesion properties of MPs can be partially obstructed by ESAX via the ICAM-1 signaling pathway, which clarifies the other functions beyond the anti-hypertensive effects of ESAX.
Influence of force parameters of rotary friction welding on the microstructure and mechanical properties of welded joints of high-strength drill pipes
Authors: Atamashkin Artem , Priymak Elena , Tulibaev Egor , Syomka Yaroslav , Trushov Vladimir
Published: May 2024

The present investigation provides an assessment of the influence of the force parameters of rotary friction welding (RFW), namely the friction force and the forging force during welding tubular samples with a diameter of 73 mm and a wall thickness of 9 mm from Cr-Mn-Mo steel G105 according to API 5DP in connection with AISI 4340 The microstructure, microhardness, tensile mechanical properties and impact strength of welded joints after welding and after post-weld stress-relieving tempering were studied. The evolution of the microstructure was studied using optical and scanning electron microscopy. The length of microstructural features of the joint such as thermomechanically affected zone (TMAZ) and heat affected zone (HAZ) were measured. The welded joint of G105 and AISI4340 steels under the studied conditions has equal tensile strength compared to the base G105 steel, both in the initial state after welding and after post-weld tempering. It has been shown that carbide particles of various morphologies, separated during welding and subsequent tempering, play a major role in the processes of strengthening the thermomechanical affected zone (TMAZ). The tensile failure location is fixed in the G105 base steel. It has been established that welding power parameters influence the impact toughness of the steel interface zone: with an increase in welding force parameters, an increase in impact toughness and fracture ductility occurs. The highest impact strength occurs after the following welding parameters: friction force 145 kN, forging force 280 kN, rotation speed during friction 600 rpm, burn-off length 7 mm and subsequent tempering at 550oC. Fractographic studies have established that the fracture surface after this treatment consists entirely of small dimples of ductile fracture. This mode can be recommended as optimal for obtaining an equal-strength connection with high impact strength.
Assessing prognosis in HCV-induced early-stage liver cirrhosis: An integrated model based on CX3CR1-associated immune infiltration genes
Authors: Cai Haozheng , Zhang Jing , Chen Chuwen , Shen Junyi , Zhang Xiaoyun , Peng Wei , Li Chuan , Lv Haopeng , Wen Tianfu
Published: May 2024

Chemokine (C-X3-C motif) Receptor 1 (CX3CR1) is a chemokine receptor that functions primarily by mediating the chemotaxis and adhesion of immune cells. However, the role of CX3CR1 in hepatitis C virus (HCV)-induced early-stage liver cirrhosis remains unexplored. GSE15654 retrieved from the GEO. Cox regression model, CIBERSOT and LASSO technique was utilized to identify CX3CR1-associated prognostic genes. Surgical resection samples were collected for verification. High expression of CX3CR1 in the liver was linked to worse prognosis in individuals with HCV-induced early-stage liver cirrhosis. CX3CR1-associated immune infiltration genes(IIGs), namely ACTIN4, CD1E, TMCO1, LOC400499, MTHFD2, and WSF1, were identified, showing specific expression in the livers of individuals with post-hepatic cirrhosis and liver failure compared to HC. Notably, high infiltration of plasma cells and low infiltration of monocytes were predictive of poor prognosis in early-stage cirrhosis. The combined risk model predicted that high expression of CX3CR1-associated IIGs and increased infiltration of plasma cells were associated with unfavorable prognosis in individuals with HCV-induced early-stage liver cirrhosis. Elevated expression of CX3CR1 is a risk factor for individuals with HCV-induced early-stage liver cirrhosis. The developed combined risk model effectively predicted the prognosis of such individuals.
Urediniospore size variation and morphological novelties of Hemileia vastatrix on Coffea spp. in Mexico
Authors: Pelayo–Sánchez Gabriela , Yáñez-Morales María de Jesús , Solano–Vidal Roney , Silva–Rojas Hilda Victoria , Alvarado–Rosales Dionicio , Morales–Rodriguez Simón , Jiménez–García Luis Felipe , Lara–Martínez Reyna , Ramírez-Ramírez Iván , Valdez–Carrasco Jorge M.
Published: May 2024

We analyzed size variation of Hemileia vastatrix coffee leaf rust urediniospores. Further, the morphological structure and vegetative hyphae of uredinia were examined. The study included isolates collected in 17 coffee cultivars grown at various altitudes (229 to 1,649 m) in 14 regions of four Mexican states. In 2015, 265 leaf samples with rust symptoms were collected from Coffea arabica, Coffea canephora, and Catimores (C. arabica × Timor hybrid) trees (n = 3,808 leaves). Thirty spores per sample, on a final subset of 65 leaves (n = 1,950 spores), were measured and grouped using the Ward centroid method. Uredinia morphology and vegetative hyphae in lesions and tissue sections were examined by electron microscopy. Pathogen identity was confirmed based on symptoms, morphology, and ITS-rRNA sequence analysis. We identified eight significant groups of spores based on size. Groups 8h and 3a had a smallest and largest spore size respectively, and groups 1b–7f having a variable intermediate size. Novel structures were observed on uredinia: double-cell walls on pedicel and urediniospores, split septum between spore and attached pedicel, apical half-septum wall covering the pedicel, and urediniospore ventral side protuberances and hilum; and on lesions: leaf surface and intracellular hyphae anastomoses, and haustoria anastomoses. It is suggested that urediniospore size variation may facilitate interaction to the diversity of coffee cultivars and environmental conditions, and doble cell wall involved for spore development, release, and protection; and anastomoses are likely sources of H. vastatrix acquire nonsexual variation. These findings encourage further analysis for management coffee rust disease.
Efficacy of Machine Learning in Simulating Precipitation and Its Extremes Over the Capital Cities in North Indian States
Authors: Tandon Aayushi , Awasthi Amit , Pattnayak Kanhu Charan
Published: May 2024

Climate change-induced precipitation extremes have become a pressing global concern. This study investigate the predictability of precipitation patterns and its extremes using MERRA2 datasets across North Indian states for the period 1984 to 2022 utilizing machine learning (ML) models. A strong positive correlations of precipitation 0.4 was found with dew point temperature and relative humidity significant at 0.05. In simulating precipitation, Random Forest Classifier (RFC) achieved the highest accuracy (~ 83%) for Rajasthan and Uttar Pradesh, while Support Vector Classifier (SVC) performed best (79–83% accuracy) for other states. However, the ML models exhibited about 5% lower skill in higher elevated stations as compared to the lower elevated stations, its due to the different atmospheric mechanisms control differently over the lower and higher topography. For extreme precipitation events (10th and 95th percentiles of intensity), RFC consistently outperformed SVC across all states. It demonstrated superior ability to distinguish extreme from non-extreme events (Area under curve ~ 0.90) and better model calibration (Brier Scores ~ 0.01). The developed ML models successfully simulated precipitation and extreme patterns, with RFC excelling at predicting extreme precipitation events. These findings can contribute to disaster preparedness and water resource management efforts in the region with varied topography and complex terrain.
Performance evaluation of Cement Fly-ash Gravel piles of recycled construction and demolition waste aggregates under erosion conditions
Authors: Zhang Shiping , Yu Xiaolu , Zhang Junhui , Shi Jinxing , Xu Zhan , Cabrera Milkos Borges
Published: May 2024

Construction and demolition waste (C&DW) has been mainly used in the field of building materials and road engineering, but relatively few applications in soft soil foundation treatment. This study was aimed to the performance evaluation of the Cement Fly-ash Gravel (CFG) piles using recycled C&DW aggregates in soft ground foundations in inland saline areas, in coasts with high water content and rich in a large number of erosive salt ions. In this paper, erosion experiment simulation conditions were set, the test object was recycled aggregate CFG piles, and the control group was modified aggregate CFG piles and natural aggregate CFG piles under the same conditions. Through the compressive strength test and water absorption test on the pile specimens under different erosion conditions, the changes in the strength and water content of the piles before and after the erosion were obtained. The prediction formulae of water absorption and compressive strength of the piles in the submerged environment and the compressive strength of the piles in the salt-ionized environment were proposed, which reveal the performance deterioration of the recycled aggregate CFG piles. The experimental results showed that the water absorption rate of CFG piles with recycled aggregate under long-term water immersion conditions firstly increased and then tended to stabilize with the immersion time. The change of its compressive strength firstly increased and then declined and finally tended to stabilize. The change of the compressive strength gradually decreased with the salt ion concentration under the salt ion erosion environment.
Initiatives to promote uptake and adherence to Option B+ program among rural pregnant women living with HIV in Malawi
Authors: Chilanga Emmanuel , Mbeya Ruth Agather
Published: May 2024

Background
Malawi is the first country in the world to introduce Option B+, a test-and treat program for preventing vertical transmission of HIV from mothers to young children during pregnancy or breastfeeding. The program has significantly reduced the number of children that contract HIV from their mothers. Although this is the case, gaps remain as the program is marred with challenges such as clients opting out, low drug adherence, and default. This study examines the effectiveness of individual and community level approaches used to promote uptake of Option B+ program through reduction of intimate partner violence (IPV) in central Malawi.
Methods
A retrospective narrative qualitative research method was used to understand strategies that HIV positive pregnant women and community stakeholders used to improve uptake of Option B+ program. Twenty-five participants that were directly involved in Option B+ program as beneficiaries and service providers were interviewed. The interviews were audio recoded and transcribed verbatim. Data was manually coded to derive key themes and concepts.
Results
Prevalent forms of IPV against HIV pregnant mothers during initial stage of Option B+ program was identified. They include husband restricting women from taking Antiretroviral Therapy (ART), declining the use of condoms, and reducing family support. Various risk-taking behaviours was used to minimize IPV when some pregnant women were diagnosed HIV positive. These include opting out of the program, concealing the HIV serostatus and ART drugs. The integration of community based reproductive health initiatives such as expert clients, male championship, and safe motherhood into Option B+ program reduced cases of IPV and improved couple uptake of HIV testing.
Conclusion
Integration of Option B+ program with community based reproductive health promotion initiatives has a potential of reducing IPV against HIV positive pregnant women. The strategy can also increase the number of couples that can adopt Option B+ program in rural communities.
Secure and Efficient Routing in Wireless Sensor Networks: LD2FA-PSO Intrusion Detection and Cluster HeadSelection
Authors: Dubey Ayushi
Published: May 2024

A wireless sensor network comprised of numerous sensors randomly distributed within a specific region, which aimed at detecting and collecting information within that region. Although they cannot be recharged or replaced, but sensor devices can detect, store, and transport data and energy, Data communication is another potential use of case for sensor devices. Since energy resources are limited in sensors, wireless sensor networks are constantly developing energy-efficient protocols to extend their ability to last long. Developing the routing protocol is extremely essential in order to maximize the longevity of the network by optimizing its energy efficiency. The key essential role play by Wireless sensor networks (WSNs) in the monitoring of the environment, which gains special focus on attaining energy efficiency and guaranteeing the secure transferring of data as key objectives in their design. Attacks that are network-based there has been gains increased focus, as the complexity of computer networks continues to grows. This, in turn, has prompted research activities that are focused at developing Intrusion Detection Systems that are more resistant to vulnerabilities. This research aims to develop an automated intrusion detection system (IDS) that enhances energy efficiency in wireless sensor networks (WSNs) using LD2FA and Particle Swarm Optimization. The Learning Dynamic Deterministic Finite Automata (LD2FA) that has been presented which is capable of dynamically adapting to the nature of the network. It offers insights into the examination of nodes, packets, and routes in order to efficiently discover and eliminate intrusions. Data transmission that is more energy-efficient as a consequence is accomplished via the use of improved routing. The essential goal of the LD2FA-PSO approach is to optimize routing patterns in order to ensure that transfer of data is as energy-efficient as possible. DFA strengthen the intrusion detection system's which has the ability to recognize and to eliminate variety of network security threats, which includes denial of service attacks, worms, viruses, and malware, while PSO refines the routing process and picks the most effective data transfer channels.
Analysis of cuproptosis-related genes signature in prognosis and immune landscape in Head and neck squamous cell carcinoma
Authors: Ai Maomao , Guo Ruoyu , Lin Ying , Wu Jian , Liu Zeyang , Yu Feng
Published: May 2024

Background: Cuproptosis is a copper-dependent cell death mechanism that is associated with tumor progression, prognosis, and immune response. The potential role of cuproptosis-related genes (CRGs) in the tumor microenvironment in Head and neck squamous cell carcinoma (HNSC) is unclear，and a gene marker is needed to predict clinical outcomes of HNSC.
Patients and methods: In total, 546 HNSC samples were collected from The Cancer Genome Atlas database(TCGA) and 98 samples from Gene Expression Omnibus datasets(GEO).The CRGs were identified in the literature, while the member genes of Signaling by Receptor Tyrosine Kinases(RTKs) pathway were identified by Metascape, Gene-related prognostic models were established and validated in a training set and GEO verification set. Samples were classified using R software packages. A nomogram and calibration curve were constructed to predict patient survival probability to improve the clinical applicability of the genes. Through unsupervised clustering analysis, identified three distinct Cuproptosis Clusters, we further explored the differences of immune cell infiltration, and immunotherapy among the three Clusters.
Results: 4487 differentially expressed genes were identified between 44 adjacent non-tumorous tissues and 502 tumor tissues samples. 11 gene(FLT3, MAP2K2, CDKN2A,PTPRU,IRS2, FGF9,FGFR2,IL6, JUND, FDX1, SHC3)signature was identified.The gene signature used to establish a Prognostic Risk Score,then to construct cases into high- and low-risk groups in terms of overall survival. The 5-year survival AUC in the training and verification sets were 0.693 and 0.642. The corresponding multifactor COX regression analysis found that low-risk group, pathologic N2c, lymphovascular-invasion(NO) and age had clinical independence. The low-risk group has higher ESTIMATEScore and ImmuneScore (P<0.05).
Conclusion: This study revealed the potential effects of CRGs and RTKs gene signature on the TME, clinicopathological features, and prognosis of HNSC，and may offer novel insights into the cancer treatment.
Practical Object and Flow Structure Segmentation using Artificial Intelligence
Authors: KHOJASTEH Ali Rahimi , de Water Willem van , Westerweel Jerry
Published: May 2024

This paper explores integrating artificial intelligence (AI) segmentation models, particularly the Segment Anything Model (SAM), into fluid mechanics experiments. SAM's architecture, comprising an image encoder, prompt encoder, and mask decoder, is investigated for its application in detecting and segmenting objects and flow structures. Additionally, we explore the integration of natural language prompts, such as BERT, to enhance SAM's performance in segmenting specific objects. Through case studies, we found that SAM is robust in object detection in fluid experiments. However, segmentations related to flow properties, such as scalar turbulence and bubbly flows, require fine-tuning. To facilitate the application, we have established a repository (\href{https://github.com/AliRKhojasteh/Flow_segmentation}{github.com/AliRKhojasteh/Flow\_segmentation}) where models and usage examples can be accessed.
The Multi-attribute Impact of Hyperlinks in Blogs: An Emotion-Centric Approach
Authors: Mbila-Uma Stella , Koleoso Oyindamola , Umoga Ifeanyichukwu , Alassad Mustafa , Agarwal Nitin
Published: May 2024

As a digital social medium, blogs have transformed into arenas where individuals can express their perspectives, concepts, and feelings. This research delves into the spread of information in blog posts related to the Uyghur discourse, utilizing topic modeling to classify and prioritize pertinent content. The analysis reveals a significant emphasis on the Chinese Government’s suppression of Uyghur Muslims, as demonstrated by the hyperlinks embedded within the blog network. We mapped the discovered topics onto a network of hyperlinks, forming a topic-induced hyperlink network. Additionally, we conducted a toxicity analysis, which revealed a high level of toxicity in blog posts during that period. A morality assessment was also performed, revealing exceedingly high scores for all vices in the blog posts. Furthermore, we conducted an emotion analysis on the text and image data, which underscored a dominant sentiment of sadness pervading the blog posts and corresponding images. This reflects the severity of the Uyghur persecution. The research highlights the effectiveness of topic modeling, toxicity evaluation, and hyperlink analysis in shedding light on intricate social issues. Future studies could augment these results by expanding keyword scope and refining image recognition models within the context of Uyghur discourse.
Neuroinflammatory Responses and Blood–Brain Barrier Injury in Chronic Alcohol Exposure: Role of Purinergic P2X7 Receptor Signaling
Authors: Togre Namdev S. , Melaka Naveen , Bhoj Priyanka S. , Mogadala Nikhita , Winfield Malika , Trivedi Jayshil , Grove Deborah , Kotnala Sudhir , Rom Slava S , Sri Uma , Persidsky Yuri
Published: May 2024

Alcohol consumption leads to neuroinflammation and blood‒brain barrier (BBB) damage, resulting in neurological impairment. We previously demonstrated that ethanol-induced disruption of barrier function in human brain endothelial cells was associated with mitochondrial injury, increased ATP and extracellular vesicle (EV) release, and purinergic receptor P2X7R activation. Therefore, we aimed to evaluate the effect of P2X7r blockade on peripheral and neuro-inflammation in EtOH-exposed mice. In a chronic intermittent ethanol (CIE)-exposed mouse model, P2X7R was inhibited by two different methods: Brilliant Blue G (BBG) or gene knockout. We assessed blood ethanol concentration (BEC), plasma P2X7R and P-gp, number of extra-cellular vesicles (EV), serum ATP and EV-ATP levels. Brain microvessel gene expression and EV mtDNA copy numbers were measured by RT2 PCR array and digital PCR, respectively. A RT2 PCR array of brain microvessels revealed significant upregulation of proinflammatory genes involved in apoptosis, vasodilation, and platelet activation in CIE-exposed animals, which were decreased 15–50-fold in BBG-treated CIE-exposed animals. Plasma P-gp levels and serum P2X7R shedding were significantly increased in CIE-exposed animals. Pharmacological or genetic suppression of P2X7R decreased P2X7R shedding to levels equivalent to those in control group. The increase in EV number and EV-ATP content in the CIE-exposed mice was significantly reduced by P2X7R inhibition. CIE mice showed augmented EV-mtDNA copy numbers which were reduced in EVs after P2X7R inhibition or receptor knockout. These observations suggested that P2X7R signaling plays a critical role in ethanol-induced brain injury. Increased eATP, EV-ATP, EV numbers, and EV-mtDNA copy numbers highlight a new mechanism of brain injury during alcohol exposure via P2X7R and biomarkers of such damage. In this study, for the first time, we report the in vivo involvement of P2X7R signaling in CIE-induced brain injury.
Modeling ML Efficiency in Telecommunications
Authors: Corona Julio , Teixeira Rafael , Antunes Mário , Aguiar Rui L.
Published: May 2024

Machine learning (ML) models are envisioned to play a fundamental role in Beyond 5G networks. However, training these models presents significant challenges due to the vast amounts of data involved and the complexity of the algorithms, requiring substantial training time and computing resources. This study offers a comprehensive analysis of how dataset characteristics, model complexity, and the number of iterations influence model performance and training time. Furthermore, we take pioneering steps towards modeling the performance and training time of ML models, specifically within the telecommunications sector. Our findings can pave the way for informed decision-making processes in telecommunications, enabling a comprehensive understanding of expected performance and training time requirements. Leading to reduced development times and optimized energy consumption and resource allocation.
Low-intensity endurance exercise increases physical functioning among breast cancer survivors: a randomized controlled trial
Authors: Mulero-Portela Ana L. , Colón-Santaella Carmen L. , Rogers Lara Q. , Missaghian Mariam
Published: May 2024

Purpose: This study assessed the effect of low- versus moderate-intensity endurance exercise on the physical functioning of women who are breast cancer survivors.
Methods: Women who had completed treatment for breast cancer at stages 0 to III, aged 50 years and above, were randomly assigned to a low-intensity or moderate-intensity exercise group. Both groups participated in seven intervention sessions distributed over 26 weeks. Women were instructed to walk for 30 minutes five days a week. Flexibility, strengthening, and balance exercises were included for both groups. The Theory of Planned Behavior guided the interventions. Outcomes were assessed at baseline, post-intervention, and 12 months after baseline. The primary outcomes were PROMIS Physical Functioning, PROMIS Global Health, and the six-minute walk test.
Results: Sixty-five of 101 women randomized (64.4%) completed the study. No statistically significant between-group differences were found at post-intervention or 12 months. In both exercise groups, statistically significant improvements were demonstrated for the PROMIS Physical Functioning at post-intervention (p = 0.01 for low intensity; p=.002 for moderate intensity), and the PROMIS Global Health physical health subscale (p = 0.001 for low intensity; p=.005 for moderate intensity). No statistically significant within-group changes were observed in the PROMIS Global Health mental subscale or the six-minute walk test.
Conclusion: A seven-intervention session program at both low and moderate-intensity walking significantly improved physical functioning in breast cancer survivors.
Implications for cancer survivors: Women who are breast cancer survivors aged 50 years and above who would like to increase their physical functioning, have the option of exercising at both low and moderate intensity.
Trial registration number: ClinicalTrials.gov identifier: NCT02982564
Date of registration: December 5, 2016
Evaluation of Water Quality Assessment Through Machine Learning: A Water Quality Index-Based Approach
Authors: Islam Md. Jahidul , Salekin Siraj Us , Abdullah Md Shahriar , Zaman Nafis , Khan Abdullah Al Ahad
Published: May 2024

Water is an essential element for the survival of all forms of life. The lack of access to clean and safe water can cause various waterborne diseases. Water quality monitoring is vital for ensuring access to clean and safe water. The Water Quality Index (WQI) is a widely used tool to assess water quality, but traditional Water Quality Index (WQI) methods, despite their utility, often suffer from inconsistencies and limitations. Moreover, these methods are not immune to laboratory and human errors. This study aimed to addresses these challenges by integrating advanced machine learning (ML) techniques to refine WQI predictions. Using a dataset comprising physicochemical parameters, such as pH, Cl-, SO42-, Na+, K+, Ca + 2, Mg + 2, Total Hardness & Total Dissolved Solids from diverse water sources, authors implemented several ML algorithms—including Gradient Boosting, Random Forest, and XGBoost—enhanced with explainable AI (XAI). To develop the prediction models, the dataset was divided into three groups: training (70%), testing (15%) and validating (15%). In order to evaluate the models’ performance, the RMSE, MSE, MAE, and R2 metrics were used in this study. The results of model performance indicated that the Gradient Boosting model has superior predictive capabilities after fine-tuning with 96% accuracy on the test dataset. This study suggests a shift towards leveraging ML for more reliable water quality evaluations, promoting enhanced decision-making in environmental health policies.