bluuebunny/crossref_metadata_2025_split huggingface dataset:
Recommendations in Social Media Applications to Ensure Personification and Safety using Machine Learning
Authors: Gawade Prema , Joshi Sarang 
Published: September 2023

Myriads of social media utilization lead to various issues like personalization hacks, data security problems, and safety. A recommendation is of paramount importance to alleviate this problem when there is a huge amount of data and the number of participants on the platform is increasing exponentially. Unfortunately, modern social media research has enhanced the performance and personalization of recommendations in many fields, yet largely underutilizes the power of artificial intelligence to enable personalized recommendations system for social media platforms like WhatsApp, Facebook, Twitter, etc. With advancement inside the global of technology every hour and every day new features are delivered to the list. In a manner, social platforms are merging into our actual existence, and to achieve personification and related safety, users can get any one safety factor from all 6 classes with this approach. This factor provides the basis for personification and the implementation of safety precautions.
This research proposes recommendations for personification in social media applications. The proposed Modified Inception Resnet V4 Convolutional Neural Network (MInReCNN) outperforms embedded media persona analysis and classification through text, image, and video data. Using these prediction classes better decisions can be made in given social media domain.
AIWFF: A Machine Learning based Framework for Automatic Weather Forecasting
Authors: Bharathi K Sowjanya , Babu Boddu Sekhar 
Published: September 2023

In the contemporary era witnessing global warming effects, weather is a dynamic phenomenon which is highly uncertain. The conventional approaches that rely on certain physical processes governing atmosphere are capable of serving a great deal in weather forecasting. However, robustness to perturbations is still desired. In this content Artificial Intelligence (AI) innovations assume significance to bring about more reliable forecasting alternative which may complement conventional methods. In this paper, we proposed a framework known as AI-enabled Weather Forecasting Framework (AIWFF) which exploits machine learning (ML) models that are robust to time series data and underlying perturbations for improving forecasting performance. An algorithm known as Learning based Intelligent Weather Forecasting (LIWF) is proposed and implemented. This algorithm has required pre-processing, feature section and a pipeline of ML models to learn from data and then forecast weather more accurately. Another algorithm known as Hybrid Method for Feature Selection (HMFS) is proposed to leverage training quality in LIWF algorithm. The framework results in three trained knowledge models saved to secondary storage. These models are known as Random Forest Regressor, Linear Regressor and Decision Tree Regressor. An application with Graphical User Interface (GUI) is developed to make use of these knowledge models and provide forecasting on user requests. The empirical results revealed that the proposed framework shows better performance.
Knowledge based quality medical education by utilizing the means of technology application
Authors: Raju V Rama 
Published: September 2023

In the 21 century`s hi-tech world, imparting the scientific medical health educational instruction is changing fast, which is inclined through several indeed many factors which includes altering health care milieu, role-of Doctor of Medicine, transformed societal prospects, swiftly varying remedial-medicinal, medical and therapeutic-science, plus range of didactic (pedagogical) methods, techniques, and prototype models.Changes and transformations in `societal-expectations` set `patient-safety` in front of, also raise `ethical-issues` of learning connections plus measures over live subjects, through venerable teaching is any more suitable. Aims of education through technology in medicine comprise, providing `knowledge-acquisition’, civilizing resulting, development of perceptual disparity, enhancing quality-skills harmonization, working for unusual or grave measures, knowledge side imparting guidance, plus humanizing psycho motor skills – perceptual abilities and motor controls. Thus, various technologies could address the above objectives.Newer technologies like podcast s, videos through flipped class-work-learning’s cellular-mobiles yapps, videogames, computer-simulations model-prototypes, amateur trainers, incorporated model-simulators, virtual-reality, plus smart-wearable devices-Google glass, are few techniques accessible to tackle the varying educational-instructive milieu.This study addresses the application of technology which yields the infrastructural network plus basis for understanding several confronts given that `medical education` pro (in support of the) prospect.
Аpplication of Big Data in laboratory medicine. Russian Journal for Personalized Medicine
Authors: Ovchinnikova M. A. , Zhilenkova Yu. I. , Chernysh N. Yu. 
Published: September 2023

The term “big data” (Big Data) refers to data sets, covering the excessive difference in differences between databases in the storage, management and analysis of information. The emergence of big data application algorithms has become the consumption of resources that use resource resources for information processing and computer calculations for the purpose of big data for statistical processing, analysis, forecasting and decision making. In laboratory practice, with a large amount of practical digital information, the use of big data is not currently widespread. The purpose of this work is to conduct a retrospective review of the literature on the use of big data in the field of laboratory medicine in the period 2018–2023. and evaluating the results of practical developments, benefits and achievements associated with big data analytics in the field of laboratory.
Rancang Bangun Chatbot Pengaduan Kekerasan Perempuan Anak Dengan Metode Fuzzy String Matching Dan Enhanced Confix Stripping Stemmer
Authors: Billah Al Muhtadi , Wulandari Diah Ayu Retnani , Auliya Yudha Alif 
Published: September 2023

Violence against women is a serious issue affecting many individuals in society. In Indonesia, including Kota Probolinggo, the number of cases of violence against women remains high. This research aims to design a chatbot application as a means for reporting violence against women and children (VAWC) in Kota Probolinggo. The application provides an anonymous and safe space for victims to report their complaints without revealing their identities. Additionally, the chatbot delivers information and education about different types of violence against women and preventive measures. The development of the chatbot application utilizes Fuzzy String Matching, Enhanced Confix Stripping Stemmer, and preprocessing methods to enhance data search accuracy and provide appropriate responses to users. The testing results show that the chatbot offers responses with satisfactory accuracy. Leveraging AI technology in the form of a chatbot is expected to provide an effective solution in addressing and preventing violence against women, offering a secure and anonymous platform for victims to report incidents. The application is envisioned to offer better support and assistance to victims of violence.
The chatbot application implements fuzzy string matching, the Levenshtein distance algorithm, and Enhanced Confix Stripping Stemmer to process user input before matching it with the database. During the Enhanced Confix Stripping Stemmer process, sentences are transformed into an array of basic words, enhancing the accuracy of data matching when using fuzzy string matching and the Levenshtein distance algorithm. Testing results indicate a 4.77% difference between manual calculations and the chatbot system. This difference may be attributed to variations in text processing and other factors.
Evaluation of healthcare professionals' knowledge on breastfeeding
Authors: BRAHAM Amira , JDI Imene BEN , ZOUKAR Olfa , BERGAOUI Haifa , CHERIF Amani 
Published: September 2023

Abstract
Background
Less than half of infants are exclusively breastfed for the first six months of life. This raises questions about the role of support provided to breastfeeding mothers in helping them address everyday questions and challenges.
Methods
Our study aimed to evaluate healthcare professionals' knowledge on breastfeeding. Methodology: This was a descriptive study conducted among healthcare professionals. Data collection was done using a questionnaire, and the data were gathered, coded, and entered into SPSS software version 20.
Results
Our study included 86 healthcare professionals, including midwives (n = 39), midwifery students (n = 33), medical interns (n = 10), and obstetrics and gynecology residents (n = 4). The evaluation of knowledge on hygiene and dietary rules yielded an average score of 0.53. The theme related to complications occurring in mothers and infants had an average score of 0.55 out of 1 (0.1; 0.9). Regarding the benefits of breastfeeding, only 49% were aware of the benefits for the mother, compared to 42.3% for the baby. A mean score of 0.51 was attributed to knowledge about treatments that can be taken during breastfeeding.
Conclusions
The successful implementation and continuation of breastfeeding require the collective efforts of various stakeholders, particularly healthcare professionals. Our study highlighted a lack of knowledge among the surveyed healthcare professionals regarding breastfeeding.
Soluble form of Lingo2, an autism spectrum disorder-associated molecule, functions as an excitatory synapse organizer in neurons
Authors: Tomita Taisuke , Yoshida Fumiaki , Nagatomo Ryota , Kimura Misaki , Shun Shiyori , Nao Yosuke , Takatori Sho , Ito Genta , Utsunomiya Shun , Ogawa Koichi , Maeda Kazuma , Takemoto Hiroshi , Lichtenthaler Stefan 
Published: September 2023

Abstract

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by impaired social communication and repetitive behaviors. In recent years, a pharmacological mouse model of ASD involving maternal administration of valproic acid (VPA) has become widely used. Newborn pups in this model show an abnormal balance between excitatory and inhibitory (E/I) signaling in neurons and exhibit ASD-like behavior. However, the molecular basis of this model and its implications for the pathogenesis of ASD in humans remain unknown. Using quantitative secretome analysis, we found that the level of leucine-rich repeat and immunoglobulin domain-containing protein 2 (Lingo2) was upregulated in the conditioned medium of VPA model neurons. This upregulation was associated with excitatory synaptic organizer activity. The secreted form of the extracellular domain of Lingo2 (sLingo2) is produced by the transmembrane metalloprotease ADAM10 through proteolytic processing. sLingo2 was found to induce the formation of excitatory synapses in both mouse and human neurons, and treatment with sLingo2 resulted in an increased frequency of miniature excitatory postsynaptic currents in human neurons. These findings suggest that sLingo2 is an excitatory synapse organizer involved in ASD, and further understanding of the mechanisms by which sLingo2 induces excitatory synaptogenesis is expected to advance our understanding of the pathogenesis of ASD.
A new probability adjustment method for combining conflicting evidences: application in classifiers combination
Authors: Jebari Chaker , Jin LeSheng , Yager Ronald R. 
Published: September 2023

Abstract

Dempster-Shafer (DS) evidence theory has been widely used in information fusion. However, combining conflicting evidences is still an open question despite the existence of many proposed studies to improve the DS theory combination rule. In this paper we propose an improved probability adjustment method to combine evidences based on their ranks. The idea of our method is to divide each piece of evidence by its rank, assuming that the evidences produced by each source of information are sorted descendingly. The obtained evidences are normalized, averaged and then the DS combination rule is applied. The main objective of our method is to employ the evidence rank to adjust it by giving high support to highly ranked evidences and low support to lowly ranked evidences. Through a numerical example, we proved that the proposed method is more efficient and generates better results that those achieved by many other DS based combination methods. Moreover, we applied our method in combining the outputs generated by multiple single-label classifiers. Using many real-world classification datasets, we compared our method with other DS combination methods, non-learning combination rules and ensemble classifiers. The achieved results shows that our proposed method performs better using most of the datasets.
Projected intensification of subsurface marine heatwaves under climate change
Authors: Konsta Katerina , Doxa Aggeliki , Katsanevakis Stelios , Mazaris Antonios D. 
Published: September 2023

Abstract

Marine heatwaves (MHWs) are periods of abnormally warm ocean temperatures that severely impact marine ecosystems. Although they can propagate beneath the ocean’s surface, MHWs are typically assessed using sea surface temperatures. Here, we investigated the future evolution and depth penetration of MHWs across the Mediterranean basin. Our analysis revealed that MHWs will become more intense and persistent by the end of the century, both on the surface and, especially, in deeper waters. Near the surface, we projected on average 51 annual MHW days and a mean intensity of 1.5°C, whereas at depths below 1000 m, MHWs may occur up to 340 days annually, with an intensity of 0.4°C. These findings highlight that MHWs penetrate deep into the ocean, posing a severe threat to marine life and ecosystems that are often overlooked, particularly in marine subsurface environments.
Synthesis of magnetic Fe3O4@SiO2 nanoparticles decorated with polyvinyl alcohol for heavy metal ion removal from aqueous solution
Authors: Asgharinezhad Ali Akbar Akbar , Esmaeilpour Mohsen , Afshar Majid Ghahraman 
Published: September 2023

Abstract
This work describes Fe3O4@SiO2 core-shell nanoparticles functionalized with polyvinyl alcohol and its application for the removal of Cu(П) and Cd(П) ions by analytical methods under diverse experimental parameters. Surface chemistry and morphology during functionalization of Fe3O4@SiO2 NPs were monitored using Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), energy dispersive X-ray analysis (EDX), vibration sample magnetometer (VSM), transmission electron microscope (TEM), field emission scanning electron microscope (FE-SEM), dynamic light scattering (DLS). The performance of the polyvinyl alcohol modified magnetic nanoparticles for metal ions removal was evaluated by changing five process variables: contact time of adsorbent, temperature, pH, adsorbent dosage and the selective removal. The adsorption isotherm studies of the novel adsorbent in removing heavy metals from wastewater showed that the maximum absorption amounts of Cu(П) and Cd(П) were 1.57 and 0.99 mmol/g at 35 oC. According to the results, the prepared composites possess excellent adsorption efficiency to be applied for the preconcentration of heavy metal ions in the field of water treatment. The Fe3O4@SiO2-TCT-PVA adsorbent is completely separated from the aqueous solution under external magnetic field for five consecutive cycles without significant loss of activity.
Healthcare Performance of Leprosy Management in Peripheral Health Facilities of Dhanusa and Mahottari, Nepal
Authors: Mahato Ram Kumar , Ghimire Uttam , Bajracharya Bijay , KC Binod , Bam Deepak , Ghimire Deepak , Pyakurel Uttam Raj , Hayman David TS , Pandey Basu dev , Das Chuman Lal , Paudel Krishna Prasad 
Published: September 2023

Abstract

Background
The global elimination of leprosy transmission by 2030 is a World Health Organization (WHO) target. Nepal’s leprosy elimination program depends on early case diagnosis and the performance of health workers and facilities. The knowledge and skills of paramedical staff (Leprosy Focal Person, LFP) and case documentation and management by health facilities are therefore key to the performance of health care services.
Methods
The performance of health workers and facilities was evaluated through a combined cross-sectional and retrospective study approach of 31 health facilities and their LFPs in Dhanusa and Mahottari Districts in Madhesh Province, Nepal. An average of 6 patients (paucibacillary, PB, or multibacillary, MB) per health facility registered within the 2018/2019 fiscal year were also enrolled in the study. LFP knowledge (e.g., of the three cardinal signs) and skills (e.g., nerve palpation) and facility processes (e.g., record keeping) were scored (e.g., 0, 1) and then rescaled to a proportion, where 1 is perfect. Internal benchmarking was used to guide performance management.
Results
Overall LFP knowledge and skill scores ranged from 0.16 to 0.63 (median 0.53, 95% confidence interval (CI), 0.46-0.6). Case documentation scores ranged from 0.15 to 0.87 (median 0.37, 95% CI 0.36-0.38), case management scores from 0.38 to 0.79 (median 0.54, 95% CI 0.53-0.55) and overall healthcare scores from 0.36-0.62 (median 0.48, 95% CI 0.47-0.49). Leprosy-related training was significantly related to the knowledge and skills of the health workers. All identified cases (n =187) adhered to the complete treatment and release after treatment (RFT) scheme, out of which 84.5% were satisfied with the service they were provided. Leprosy disability and ear hand and feet (EHF) scores were not significantly reduced in treated patients during the study period, but counseling by LFPs significantly improved cases’ positive beliefsand practices regarding self-care.
Conclusion
Overall leprosy care performance was low (43%) and can be improved by evidenced-based training, onsite coaching, monitoring, and supervision to facilitate leprosy transmission elimination. The results highlight many of the challenges facing leprosy elimination programs.
Elevated expression of Toll-like receptor 7 and its correlation with clinical features in patients with primary Sjogren's syndrome
Authors: Yang Huimin , Sun Chao , Wang Xin , Wang Tao , Xie Changhao , Li Zhijun 
Published: September 2023

Abstract
Background
The labial salivary glands (LSGs) are important for the diagnosis, evaluation of therapeutic efficacy, and genetic analyses of primary Sjogren's syndrome (pSS). In autoimmune diseases, the recognition of self nucleic acids and viral RNA and DNA through endogenous Toll-like receptor(TLR) triggers the production of type I IFN and pro-inflammatory cytokines, leading to the occurrence and progression of the disease. Here, we detected the expression of TLR7 in LSGs and analyse its correlation with clinical features and serum cytokines in pSS patients.
Methods
LSGs and serum samples were obtained from 56 pSS patients and 19 non-SS patients (non-pSS patients). The expression of TLR7 in the LSGs was evaluated with immunohistochemistry. The serum levels of interferon-α (IFN-α) and interleukin-6 (IL-6) were quantified by ELISA. Laboratory parameters were measured by clinical standard laboratory techniques.
Results
TLR7-positive cells in pSS were localized in the ductal epithelial cells and lymphocytes of LSGs. The expression of TLR7 was upregulated in pSS patients compared with controls. Patients with anti-Ro52 antibody positivity showed higher TLR7 levels than those who were negative but not those with anti-Ro60 and anti-SSB. TLR7 levels were positively associated with the levels of IgG, IgA, ANA, IL-6, IFN-α and serum globulin but were not associated with IgM, C3, C4, or rheumatoid factor (RF) in serum.
Conclusion
TLR7 may be involved in the inflammatory response and the production of antibodies in pSS and plays an important role in local and systemic pSS manifestations. This research showed that TLR7 is involved in pSS pathogenesis.
Increased levels of APOA4 in cord blood in maternal major depressive disorder
Authors: Matsuo Seiko , Moriyama Yoshinori , Ushida Takafumi , Imai Kenji , Tano Sho , Miki Rika , Yoshida Kosuke , Yokoi Akira , Kajiyama Hiroaki , Kotani Tomomi 
Published: September 2023

Abstract
Background: Prenatal maternal depression affects neurodevelopment in the offspring. This study aimed to investigate the cord blood profile of mothers with major depressive disorder (MDD).
Methods:Liquid chromatography-tandem mass spectrometry (LC-MS) was performed using umbilical cord blood from women with MDD and gestational age-matched controls (Control). The levels of several differentially expressed proteins in cord blood were compared between the two groups using enzyme-linked immunosorbent assays.
Results: The proteomic profiles of cord blood differed between the MDD and Control groups, including the pathways on regulation of plasma lipoprotein particle levels and synapse organisation. Only apolipoprotein A4 (APOA4) levels were significantly increased in the cord blood of the MDD group. APOA4 levels in the maternal serum were also significantly higher in the MDD group than in the control group (p <0.05). APOA4 levels were higher in cord blood than in maternal serum.
Conclusions: APOA4, a biomarker of depression, was increased in neonates at birth exposed to maternal MDD in utero. Thus, our results suggest that the risk of MDD in children born to MDD mothers might be related to increased APOA4 levels at birth, although further studies are required.
Meta-analysis of the Diagnostic Value of exosomal microRNA in Renal Cell Carcinoma
Authors: Qingru Li , Jing Tian , Binyi Li 
Published: September 2023

Abstract
Aim
This study aims to evaluate the feasibility of using extracellular vesicle-derived miRNA as a biomarker for the diagnosis of renal cell carcinoma through meta-analysis.
Methods
Clinical studies on the diagnosis of renal cell carcinoma using extracellular vesicle-derived miRNA were retrieved from various databases including PubMed, Web of Science, Cochrane Library, Embase ,CNKI, Wanfang Database, VIP Database and SinoMed. After screening and extracting relevant data, the included articles’ quality were assessed with QUADAS-2 tool. Meta-disc 1.4 software was used to analyze the heterogeneity of threshold/non-threshold effects in the included literature, and use Stata MP 16.0 to calculate sensitivity, specificity, positive likelihood ratio, negative likelihood ratio, area under the curve, diagnostic odds ratio and publication bias.
Results
A total of 11 articles were included. Results showed that the Spearman correlation coefficient was 0.319 (P = 0.075 > 0.05), indicating no threshold effect. The pooled sensitivity was 0.73 (95%CI: 0.68–0.78), pooled specificity was 0.81 (95%CI: 0.76–0.85), pooled positive likelihood ratio was 3.80 (95%CI: 3.02–4.77), pooled negative likelihood ratio was 0.33 (95%CI: 0.28–0.40), and the diagnostic odds ratio was 11.48 (95%CI: 8.27–15.95). The area under the curve was 0.84 (95%CI: 0.80–0.87). There was no publication bias detected in this study.
Conclusion
The expression levels of extracellular vesicle miRNAs hold an important value in the diagnosis of renal cell carcinoma. However, due to the limited number of included studies and heterogeneity among them, further clinical research is necessary.