bluuebunny/crossref_metadata_2025 huggingface dataset:
A Bag-of-Words Approach for Information Extraction from Electricity Invoices
Authors: Sánchez Javier , Cuervo-Londoño Giovanny A.
Published: May 2024

In an era marked by digitization and automation, extracting relevant information from business documents, like electricity invoices, remains a major challenge. To address this, we need to use machine-learning techniques to automate the process, reduce manual labor, and minimize errors. This work introduces a new method for extracting key values from this type of invoice, including customer personal data, bill breakdown, electricity consumption, or marketer data. We evaluate several machine learning techniques, such as Naive Bayes, Logistic Regression, Random Forests, or Support Vector Machines. Our approach uses a bag-of-words strategy and custom-designed features specifically tailored for electricity data. We tested the method on the IDSEM dataset, which includes 75.000 electricity invoices with eighty-six different fields. The method converts PDF invoices into text and processes each word separately using a context of eleven words. The results of our experiments indicate that Support Vector Machines and Random Forests perform exceptionally well in capturing numerous values with high precision. The study also explores the advantages of our custom features and evaluates the performance of the model on unseen documents. The precision obtained with Support Vector Machines is 91,86%, peaking at 98,47% for one document template. These results demonstrate the effectiveness of our method in accurately extracting key values from invoices.
The Association between Physical Fighting and Victimisation by Traditional Bullying and Cyberbullying among Adolescents in 27 European Countries: The Moderating Effects of Perceived Social Support and Public Education Spending
Authors: Chirwa Masauso , Chanda Patrick , Nakazwe Kalunga Cindy , Kabembo Ireen Manase , Mwale Ackson Tyson , Nkole Bruce
Published: May 2024

Literature indicates scarcity of cross-national research on the association between physical fighting and bullying victimisation among adolescents, and the moderating role of perceived social support and education spending on this association. Using multilevel binary logistic regression, this study examined the association of physical fighting with traditional bullying and cyberbullying victimisation, with public education spending and perceived social support from families and teachers as moderators. Country-level data were combined with 2017/18 Health Behaviour in School-aged Children (HBSC) survey data from 162,792 adolescents (11-, 13- and 15-year-olds) in 27 European countries. Results showed that physical fighting was positively and significantly associated with cyberbullying and traditional bullying victimisation. These results signify that physical fighting is a robust predictor of bullying victimisation. The findings demonstrate that perceived social support and education spending buffered the association of physical fighting with traditional bullying and cyberbullying victimisation. The findings imply that social support not only prevents adolescents from engaging in physical fights, but also reduces the risk of bullying victimisation. Accordingly, to deter adolescents from engaging in physical violence and reduce the risk of bullying victimisation, there is need to fully comprehend the influence of physical fighting on bullying victimisation, and the moderating role of social support.
Simulation of the Active Corrector of Power Factor as a Means of Improving the Electromagnetic Compatibility and Energy Efficiency of Electric Locomotive’s Auxiliary Electric Drive
Authors: Lavrentyev Anatoliy , Pustovetov Mikhail
Published: May 2024

The object of study in the article is the auxiliary variable-frequency electric drive onboard of an AC electric locomotive, and more precisely, the structure and parameters of the control system of the active corrector of power factor (ACPF), which is part of the electric drive, as a unit that ensures electromagnetic compatibility of the electric drive with other equipment on board the electric locomotive and catenary and its energy efficiency. The topic of the article's research is the substantiation of the structure of the ACPF control system, the choice of types (transfer functions) of controllers and the calculation of their parameters. The research method chosen was the calculation of the transfer functions of the controllers of the cascaded loops control system, followed by computer simulation using the OrCAD software. The results of simulating a cascaded two loops control system of an ACPF with different types of controllers are presented and analytically compared. It is concluded that it is advisable to use a cascaded two loops control system, containing a voltage control loop and a current control loop. The best results show the simulation when using an aperiodic current controller (A-controller) and a proportional-integral voltage controller (PI-controller). The data obtained serve as the basis for further improvement of the operation of the ACPF by enhancing and optimizing the settings, structure and algorithms of the control system, which, with the selected hardware of power converter unit, will allow the ACPF to perform its functions with a high degree of reliability in an entire range of disturbing and control influences.
OBJECTIVE: Assessment of the Influence of Myopia, at the Level of the Retina and Optic Nerve in Patients Diagnosed with Open Angle Glaucoma
Authors: Guzman Moscoso Rocio , Moreno Guerrero Antonio , Martos Crespo Francisco
Published: May 2024

MATERIAL AND METHOD: This is an observational analytical cohort study carried out on a total of 160 patients, divided into 4 groups: healthy, with myopia, with primary open-angle glaucoma (POAG), and with POAG and myopia together. The study variables were Bruch's membrane opening area (BMO), the thickness of the RNFL and the RGC layer using the Glaucoma Premium program of the OCT Spectralis of Heidelberg.
RESULTS: Regarding the analysis between age and the thickness of the RNFL, significant results were found in the healthy and glaucoma groups with myopia, for the rest of the groups, although there are no significant results, the correlation is negative, the older the younger is the thickness. Regarding intraocular pressure (IOP), significant results have been found in the nasal sector in the glaucoma group. In the glaucoma with myopia group, statistically significant results have been obtained when analyzing the thickness of the RNFL with refraction. In the analysis between the thickness of the RNFL and the RGC layer, significant results were obtained in the myopia and glaucoma groups; in the group of glaucoma with myopia, it occurs in the superior temporal sector.
CONCLUSION: In the case of myopic patients with glaucoma, damage at the functional, both the thickness of the RNFL, the RCG and the BMO-MRW are greater than other groups. All these alterations can be evaluated and monitored with the OCT Spectralis thanks to the programs it includes, making this instrument one of the best for an early diagnosis of glaucoma and the damage produced in myopic patients.
A Covid-19 Prediction Approach: Prediction of Infectious Disease Probability Using Machine Learning
Authors: Asghari Ilani Mohsen , Moftakhar Tehran Saba , Kavei Ashkan , Radmehr Arian
Published: May 2024

Despite the availability of COVID-19 vaccines, the global pandemic remains a significant health challenge. Timely detection of the virus is crucial for effective containment efforts, prompting the exploration of machine learning (ML) models for COVID-19 detection. highlights the ongoing significance of COVID-19 as a global health crisis despite the availability of vaccines. To address the need for timely virus detection, the study explores the efficacy of machine learning (ML) models, including DNN, Voting, Bagging, SVC_rbf, SVC_linear, SVC_polynomial, and SVC_sigmoid, in predicting COVID-19 infection probability. With a dataset comprising 4000 samples split into 3200 for training and 800 for testing, the analysis reveals that DNN and Voting models demonstrate the highest performance, achieving an impressive accuracy of 89%. These results underscore the potential of ML techniques, particularly DNN and ensemble methods like Voting, in facilitating early COVID-19 detection and contributing to effective pandemic management. The study emphasizes the importance of continued research to further refine and optimize ML-based COVID-19 detection systems.
Gap Filling of Missing and Outlier Values of Rotorcraft Flight Data using Multilayer Perceptron
Authors: Jeong Seon Ho , Kang Dongsoo , Lee Ikgyu , Lee Yongmin , Kim Jeong Ho , Hwang Yun-Young
Published: May 2024

Rotorcrafts operate HUMS for fault diagnosis, but the capabilities of HUMS analysts are important due to the complex mechanical configuration of rotorcraft and the difficulty of analysis. Accordingly, an advanced algorithm is needed to identify faults, and flight data can be used to develop the algorithm. However, flight data contains abnormal or missing data due to problems such as sensor defects, which can be a major problem in algorithm development. Therefore, in this paper, we study a gap filling method using MLP to obtain normal data when there are problems such as abnormal or missing data in rotorcraft flight data. Existing methods typically fill the gap through interpolation or curve fitting using the before and after values of missing data. However, in this study, normal values were estimated using the values of other parameters at the moment of missing or abnormal data occurred. As a result of evaluating the performance of the trained MLP using normal data, it was confirmed that the output of the model well estimates the changing trend of the target. And when abnormal data was applied to the trained model, it was confirmed that point outliers were eliminated and appropriate values were found in the case of missing data.
Development Strategy Based on Combination of Carbon Emission and Vibrancy Typology — a Multi-sourced Data-Driven Approach in Beijing, China
Authors: Xia Jingyi , Wang Jiali , Lai Yuan
Published: May 2024

Confronting the dual challenges of rapid urbanization and climate change, although extensive research has investigated the influencing factors and practical strategies of urban carbon emissions and urban vibrancy, respectively, the unclear mutual nexus between them and the development strategy of collaborative optimization requires further in-depth analysis. This study explores the delicate balance between urban vibrancy and low-carbon sustainability within the confines of Beijing’s Fifth Ring Road. By integrating OpenStreetMap, land use, population, and building carbon emission data, we have developed a reproducible method to estimate total carbon emission and emission intensity per capita. Furthermore, we have introduced vibrancy index data to distinguish the vibrancy evaluation of residential and non-residential land and applied cross-combinational classification technology to dissect the spatial correlation between urban carbon emissions and urban vibrancy. The results reveal that the four combination typologies show more significant differences and regularity in residential land. Based on the discovery of spatial correlation, this study puts forward corresponding development strategy suggestions for each typology based on geographical location and the requirements of urban development policies of these four combination typologies. In conclusion, our study highlights the importance of integrating carbon emissions and urban vibrancy comprehensively in sustainable urban planning and proposes that various land use combinations need targeted development strategies to achieve this goal, which need to consider population, energy, service facilities, and other diverse aspects.
Cost-Effectiveness of Lung Cancer Screening with Low-Dose Computed Tomography: Comparing the NLST and NELSON Trial Protocols
Authors: Rajabi Tanya , Szilberhorn László , Győrbíró Dávid , Tatár Manna , Vokó Zoltán , Nagy Balázs
Published: May 2024

We aimed to directly compare the cost-effectiveness of the NELSON and NLST screening protocols, two trials influencing lung cancer screening implementation internationally. A decision-analytic model analyzing the cost-effectiveness of the NELSON protocols was manipulated to reflect the protocols of the NLST, while maintaining features specific to the Hungarian healthcare setting. In the NELSON protocol, there are three possible outcomes to the initial round of screening: positive, negative, or indeterminate, indicating an uncertain degree of suspicion for lung cancer. This pro-tocol differs from the NLST protocol, in which the only possible screening outcomes are positive or negative, with no indeterminate option. The NLST pathway for smokers aged 55-74 resulted in a 43 € increase in total average lifetime costs compared to the Hungarian screening pathway and re-sulted in a lifetime gain of 0.006 QALYs. The incremental costs and QALYs yield an ICER of 7875 €/QALY. Our results demonstrate that assigning any suspicious LDCT screen as a positive result (NLST protocol) rather than indeterminate (NELSON protocol) can yield a slight QALY gain that is worth the additional use of resources according to Hungary’s willingness to pay threshold. Our study provides insight on the cost-effectiveness, advantages, and disadvantages of various LDCT screening protocols for lung cancer and can assist other countries as they implement their screening programs.
Convolutional Neural Network Based Malaysian Sign-Language Recognition Web Application
Authors: Basheer Riskhan , Aziz Mochamad Azkal Azkiya , Arifa Habiba
Published: May 2024

The deaf-mute and individuals with hearing disabilities who communicate through sign language should not be overlooked or excluded. It is a social responsibility to strive for an inclusive world without inequality. Advances in technology offer various solutions to address the communication gap, one of which is the use of computer-based sign language recognition systems. This project proposes a Malaysian Sign Language recognition model utilizing MobileNetV1, a convolutional neural network-based algorithm, to interpret sign language in real-time settings through a website application using Streamlit. The outcome of the model test shows that it has achieved a recognition rate of 96 percent. Although the proposed model has demonstrated the ability to recognize sign language, it is suggested that further improvement can be achieved by enhancing the quality and diversity of the dataset. The implementation of a more comprehensive dataset would lead to improved performance and increased accuracy of the model. This project underscores the importance of considering and accommodating the needs of the deaf-mute and individuals with hearing disabilities and highlights the potential of technology to bridge communication barriers and achieve a more inclusive society.
Target Identification in Breast Cancer through Network Pharmacology
Authors: Sonawane Mandakini , Badhe Pravin , Mali Prashant
Published: May 2024

In this study, we utilized the UniProt database to extract breast cancer genes Total 2658 genes in Homo sapiens are obtain. Then 166 genes are taken and after delating duplicates 158 genes were remains. These genes were then analyzed for their biological pathways using Gene Ontology enrichment analysis through the Genecodis platform, resulting in the identification of 12 unique genes involved in disease pathways. Protein-protein interaction (PPI) network analysis was conducted using the STRING database and Cytoscape software, identifying ANLN,TREM1,LZTFL1,OXSM,PLA2G3,TAS2R13,ABCB10,ECT2,TRPC,BCAS3,PLA2G3,ZDHHC7 and ERBB2 as potential gene targets for breast cancer.
Properties of a Pressureless Sintered 2Y-TZP Material Combining High Strength and Toughness
Authors: Kern Frank , Osswald Bettina
Published: May 2024

Yttria stabilized zirconia materials are frequently used in mechanical engineering and biomedical applications. Demanding loading conditions require materials combining a high level of strength and fracture toughness. A ready to press alumina doped 2 mol% yttria stabilized zirconia powder was shaped by axial pressing and sintering in air at 1250 - 1500 °C for 2 h. The best combination of strength and toughness value of 7.8 MPa√m. Materials sintered in the middle of the chosen temperature range combine fully density, high transformability and small grain size. Toughness measurements by direct crack length measurements delivered unrealistically high fracture toughness values.
Novel Methodology for the Design of Personalized Cancer Vaccine Targeting Neoantigens: Application to Pancreatic Ductal Adenocarcinoma.
Authors: Savsani Kush , Dakshanamurthy Sivanesan
Published: May 2024

Personalized cancer vaccines have emerged as a promising avenue for cancer treatment or prevention strategies. This approach targets the specific genetic alterations in individual patient’s tumors, offering a more personalized and effective treatment option. Previous studies have shown that generalized peptide vaccines targeting a limited scope of gene mutations are ineffective, emphasizing the need for personalized approaches. While studies have explored personalized mRNA vaccines, personalized peptide vaccines have not yet been studied in this context. Pancreatic ductal adenocarcinoma (PDAC) remains challenging in oncology, necessitating innovative therapeutic strategies. In this study, we developed a personalized peptide vaccine design methodology, employing RNA sequencing (RNAseq) to identify prevalent gene mutations underlying PDAC development in a specific patient solid tumor tissue. We performed RNAseq analysis for trimming adapters, read alignment, and somatic variant calling. We also developed a Python program, called GeneFinder, which validates the alignment of the RNAseq analysis. The Python program is freely available to download. Using chromosome number and locus data, GeneFinder identifies the target gene along the UCSC hg38 reference set. Based on the gene mutation data, we developed a personalized PDAC cancer vaccine that targets 100 highly prevalent gene mutations in the individual patient. We predicted peptide-MHC binding affinity, immunogenicity, antigenicity, allergenicity, and toxicity for each epitope. Then, we selected the top 50 and 100 epitopes based on our previously published vaccine design methodology. Finally, we generated pMHC-TCR 3D molecular model complex structures which are freely available fo download. The designed personalized cancer vaccine contains epitopes commonly found in PDAC solid tumor tissue. Our personalized vaccine was composed of neoantigens, allowing for a more precise and targeted immune response against cancer cells. Additionally, we identified mutated genes which were also found in the reference study where we obtained the sequencing data, thus validating our vaccine design methodology. This is the first study designing a personalized peptide vaccine using human patient data to identify gene mutations associated with the specific tumor of interest.
Efficient Generalized EEG-Based Drowsiness Detection Approach with Minimal Electrodes
Authors: Zayed Aymen , Belhadj Nidhameddine , Ben Khalifa Khaled , Bedoui Mohamed Hedi , Valderrama Sakuyama Carlos Alberto
Published: May 2024

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.
Is There an Added Value of Quantitative DCE-MRI by Magnetic Resonance Dispersion Imaging for Prostate Cancer Diagnosis?
Authors: Jager Auke , Oddens Jorg R. , Postema Arnoud W. , Miclea Razvan L. , Schoots Ivo , Nooijen Peet , van der Linden Hans , Barentsz Jelle , Heijmink Stijn , Wijkstra Hessel , Mischi Massimo , Turco Simona
Published: May 2024

In this multicenter, retrospective study we evaluated the added value of magnetic resonance dispersion imaging (MRDI) to standard multiparametric MRI (mpMRI) for PCa detection. The study included 76 patients - 51 with clinically significant prostate cancer (csPCa), who underwent radical prostatectomy and had an mpMRI including dynamic contrast enhanced-MRI. Two radiologists performed three separate randomized scorings based on mpMRI, MRDI and mpMRI+MRDI. Radical prostatectomy histopathology was used as the reference standard. Imaging and histopathology were both scored according to the Prostate Imaging-Reporting and Data System V2.0 sector map. Sensitivity and specificity for PCa detection were evaluated for mpMRI, MRDI and mpMRI+MRDI. Inter- and intra-observer variability for both radiologists were evaluated using Cohen’s Kappa. On a per patient level, sensitivity for csPCa for radiologist 1 (R1) for mpMRI, MRDI and mpMRI+MRDI were 0.94, 0.82 and 0.94, respectively. For the second radiologist (R2) these were 0.78, 0.94 and 0.96. R1 detected 4% additional csPCa cases using MRDI compared to mpMRI and R2 detected 20% extra csPCa cases using MRDI. Inter-observer agreement was significant only for MRDI (Cohen’s kappa = 0.4250, p=0.004). The results of this study show the potential of MRDI to improve interobserver variability and the detection of csPCa.
Advances on Catalyst Support Modification and their Effect on Fischer Tropsch Synthesis: A Review
Authors: Thibanyane Ntebogang , Gorimbo Joshua , Yao Yali
Published: May 2024

One important goal of research on heterogeneous catalysis for Fischer Tropsch synthesis (FTS) is to understand the connection linking catalyst morphology and its activity. Development of these effective catalysts highlights the potential for understanding the effectiveness of promoted supported catalysts and revealing their complex and dynamic effects in FTS. The metal-support interaction (MSI) is influenced by the physicochemical and textural features provided by the modified support and can directly influence the sizes of active metal crystallites, reactants and/or products mass transfer, dispersion, mechanical strength, stability and consequently the adsorption-desorption characteristics of the catalyst. Herein, this article explores the general strategies for modifying silica supports (achieved through doping, coating, or promoting the support or support precursor) and how these strategies can be manipulated to further alter catalyst performance and product selectivity by enhancing the amount of active metal species and improving the intrinsic turnover frequency (TOF). Furthermore, the review emphasises a new strategy (the inverse model of the silica support modification) aiming at overcoming the challenges posed by active metal coating or coverage, as well as pore blockages by promoters that occur during conventional preparation of promoted silica supported FT catalysts.
Overview of Hydrogels as Transdermal Delivery System for Pharmaceutical and Cosmetic Applications, and Hyaluronic Acid as a Polymer Model in Hydrogel Preparation
Authors: Rashid Fatimah , Carter Paul , Childs Stephen
Published: May 2024

Hydrogels have gained significant attention as vehicles for transdermal delivery systems. These polymeric-structured substances have been implemented in pharmaceutical and cosmetic applications. Many factors are employed in their preparation. Light, pH, temperature and specific molecules ligands are commonly used in the production of hydrogels. The Transdermal delivery system (TDD) is a widely used route, used to deliver actives/drugs via the skin. Hydrogels as a TDD have been shown to be efficient in enhancing the efficacy in delivering the actives and drugs. The aim of this review is to discuss the hydrogels application in both dermatological therapeutic and cosmetic applications, covering the types, and properties of hydrogel polymer networks. Furthermore, this review presents one of the most extensively used polymers (Hyaluronic acid HA) in this field and discusses the properties, preparation methods and applications of HA hydrogel patches as a delivery system, including methods of loading the actives and the releasing of these actives from them.
Evaluation of Y2O3:Eu@SiO2 Nanoparticles as Photosensitizer for X-Ray Activated Photodynamic Therapy in Cancer
Authors: Zhang Hannah J. , Ide Tristan , Sivasubramanian Maharajan , Leoni Lara , Souris Jeffrey S. , Brahmbhatt Divya , Tanios Eve , Pan Ariel , Cheng Shih-Hsun , Chen Nai-Tzu , Lo Leu-Wei , Chen Chin-Tu
Published: May 2024

Traditional photodynamic therapy (PDT), a clinical cancer therapeutic approach using light in killing cancer cells, is often limited by insufficient light penetration, inefficient photosensitizer energy transfer, and inadequate oxygen in hypoxic tumors. Employing X-ray to activate photosensitizer in PDT can potentially overcome these limitations. Here, we compared protocols to synthesize a novel nano-photosensitizer composed of a europium-doped yttrium oxide core with a silica shell (Y2O3:Eu@SiO2) which can be utilized in such therapeutic method, i.e., X-ray activated PDT (XPDT). Transmission electron microscopy (TEM) and dihydroethidium (DHE) were utilized to evaluate the effects of various synthesis conditions on the characteristics of Y2O3:Eu@SiO2 and their ability to generate reactive oxygen species (ROS). The in vivo tumor suppression effects of the Y2O3:Eu@SiO2 were assessed by 18F-fluorothymidine position emission tomography (18F-FLT PET) in human ovarian cancer xenografts using radiation sensitive CAOV3 and radiation resistant SKOV3 cell lines. Our data revealed a positive correlation between nanoparticle condensation time and core size as well as a negative correlation between urea concentrations and core size. Addition of cetyltrimethylammonium bromide (CTAB) during core synthesis enhanced particle dispersion. Two silica coating conditions showed the greatest efficiency in the enhancement of ROS generation. Furthermore, our in vivo study revealed suppression of tumor growth by Y2O3:Eu@SiO2 administration when compared to non-treated control. Silica shell provides flexibility to conjugate moieties that specifically target to cancers. This nano-photosensitizer demonstrates the significant promise for overcoming limitations of conventional PDT and combating various cancers in clinical applications.
Analysis of User Experience data and Methodology of application to improve the development of User Interface
Authors: Ismailova Ellada , Ermakov Andrei
Published: May 2024

Design plays a critical role and creates a positive user experience, establishing a solid brand identity and staying competitive in the digital world. This research investigates the impact of user data collecting and analysis in improving user experience. The Data-driven User interface design can create an optimized and relevant UI with individual preferences, which leads to increasing user engagement and usability. The proposal explores methodologies to implement data collection and user research for tailoring digital products.
Pengembangan Media Animasi berbasis Videoscribe mengenai Kesehatan Reproduksi pada Peserta Didik Kelas X IPS di Salah Satu SMA di Kota Tangerang
Authors: Yunararizky Yulia , Cahyawulan Wening
Published: May 2024

Penelitian ini bertujuan untuk menghasilkan media animasi berbasis videoscribe mengenai Kesehatan reproduksi pada peserta didik kelas X IPS di salah satu SMA di Kota Tangerang. Penelitian ini merupakan penelitian pengembangan yang berpedoman pada model pengembangan ADDIE yang terdiri dari lima tahap, yaitu Analyze, Design, Development, Implementation dan Evaluation. Namun dikarenakan keterbatasan waktu dan kondisi sedang masa pandemi Covid-19 maka penelitian ini hanya dilakukan sampai pada tahap Development. Hasil penelitian dan pengembangan media animasi ini dikembangkan dengan teknik pengumpulan data studi lapangan dan angket penilaian oleh ahli materi dinyatakan bahwa media animasi berbasis videoscribe ini “Layak” untuk digunakan dengan presentase kelayakan 65% dan presentase 65% oleh ahli media dinyatakan “Layak”.
Распространение вариантов вируса SARS-COV-2, вызывающих интерес (VOI) и находящихся под наблюдением (VUM), на основе количества их геномов, депонированных в базу данных GISAID за неделю с 18 по 24 мая 2024 г.
Authors: Дмитриева Людмила , Чумачкова Елена , Краснов Ярослав , Осина Наталья , Зимирова Анастасия , Иванова Александра , Карнаухов Игорь , Караваева Татьяна , Щербакова Татьяна , Кутырев Владимир
Published: May 2024

В обзоре представлена информация по циркулирующим в настоящее время вариантам вируса SARS-COV-2 Omicron вызывающих интерес (VOI) и находящихся под наблюдением (VUM), геномные последовательности которых размещены в международной базе данных GISAID за неделю с 18 по 24 мая 2024 г.
Анализ динамики эпидемического процесса COVID-19 в мире за неделю с 18.05.2024 г. по 24.05.2024 г.
Authors: Зимирова Анастасия , Иванова Александра , Дмитриева Людмила , Чумачкова Елена , Карнаухов Игорь , Щербакова Светлана , Кутырев Владимир
Published: May 2024

В обзоре представлен анализ эпидемиологической ситуации в странах мира по COVID-19 за неделю с 18.05.2024 г. по 24.05.2024 г.
Quadruple Disruption of Superior Shoulder Suspensory Complex With Proximal Humerus Open Fracture-Dislocation
Authors: Quirarte Jaime A. , Wait Trevor J. , Jakkaraju Sohan Kumar , Smith Matthew D. , Salazar Luis M. , Delbello Robert G. , Dutta Anil K.
Published: May 2024

Case:

A 34-year-old man was a restrained passenger involved in a high-speed rollover motor vehicle crash. The patient sustained a type 5 AC joint separation, severely comminuted intra-articular glenoid fracture with extension to the coracoid process base, displaced open scapular body fracture, a posterior shoulder dislocation of the glenohumeral joint, and a 2-part proximal humerus fracture.

Conclusion:

To our knowledge, this is the first report describing this injury pattern involving the superior shoulder suspensory complex with an associated open proximal humerus fracture-dislocation.
Stable Lateral Compression Pelvic Ring Injury Requiring Operative Treatment due to Bladder Impingement
Authors: Collins Andrew P. , Abdelfattah Waleed , Dasari Suhas P. , Unno Florence , Firoozabadi Reza , Kleweno Conor P. , Psutka Sarah P. , Lack William D.
Published: May 2024

Case:

A 25-year-old man sustained a stable lateral compression Type I (LC I) pelvic ring injury upon missing the landing of a downhill ski jump. He presented with painful voiding from a displaced bony fragment, partially impaling the bladder wall. With operative fixation of the fracture and urologic co-management, the patient had excellent outcomes at 1-year follow-up.

Conclusion:

We describe a rare urologic injury in the setting of an LC I pelvic ring injury. In the setting of an otherwise stable pelvic ring injury, careful review of imaging, detailed clinical history, and physical examination remain critical to optimizing patient outcomes.
Enhanced Efficiency of Tesla Turbines with Variable Disc Configurations in Indonesia
Authors: Rosyid Abdul , Fahruddin A’rasy
Published: May 2024

The rapid development of technology increases the demand for energy while non-renewable sources decline. This study explores the Tesla turbine, which converts fluid energy into mechanical energy using circular discs stacked with specific gaps. We tested three configurations with 4, 6, and 8 discs, each 0.3 mm thick, at Universitas Muhammadiyah Sidoarjo. Using direct observation, experimental testing, and literature review, we measured turbine performance with a digital tachometer and spring scale. Results showed that modifying the disc number effectively influenced energy output, offering insights for enhancing Tesla turbine efficiency as a renewable energy solution.
Highlight:

Tesla turbine converts fluid energy to mechanical using circular discs.
Tested configurations: 4, 6, and 8 discs, each 0.3 mm thick.
Study offers insights into enhancing Tesla turbine efficiency

Keywoard: Tesla Turbine, Energy Conversion, Fluid Dynamics, Renewable Energy, Disc Configuration
Dijitalleşmenin Etkisinde İnsan Kaynakları Uygulamaları, Çalışanların Yetkinlikleri ve İyi Oluş Halleri
Authors: Yalırsu Hazal Yaren , Ulukaya Hazel , Gök Sibel , Başaran Altan
Published: May 2024

İnsan kaynakları yönetimi, teknolojik gelişmelerle birlikte dijitalleşme sürecinden etkilenmektedir. Dijitalleşen insan kaynakları uygulamaları, işlerin daha hızlı, hatasız ve efektif yapılmasına imkan tanımasının yanı sıra insan kaynakları çalışanlarından ve yöneticilerinden beklenen yetkinliklerde değişime yol açmakta ve çalışanların iyi oluş hallerini de etkilemektedir. Bu çalışmada, örgütlerde dijitalleşen insan kaynakları uygulamalarını tespit etmek; dijital dönüşüm sürecindeki insan kaynakları çalışanlarında aranan yetkinlikleri ve bu yetkinliklerdeki değişimleri incelemek; dijital dönüşümün çalışanların iyi oluş halleri üzerindeki etkilerini ortaya koymak amaçlanmaktadır. Bu doğrultuda nitel araştırma yöntemiyle İstanbul ilinde çeşitli sektörlerde görev yapan 40 insan kaynakları çalışanı ve yöneticisiyle derinlemesine görüşmeler gerçekleştirilmiştir. Araştırma sonucunda elde edilen bulgulara göre insan kaynakları uygulamalarındaki dijital dönüşümün oldukça sınırlı olduğu ve çalışanlarda aranan yetkinliklerden, teknik yetkinliklerin en çok ön plana çıktığı tespit edilmiştir. Ayrıca, dijital dönüşümle birlikte uzaktan çalışmanın, iyi oluş halini bir yandan çalışanların işlerini kolaylaştırması ve zaman tasarrufu sağlaması nedeniyle olumlu etkilediği diğer yandan esnek çalışma saatlerini artırması ve yüz yüze iletişimi azaltması nedeniyle de olumsuz etkilediği tespit edilmiştir. Dijitalleşen insan kaynakları uygulamalarının tespitine, bu süreçte aranan yetkinliklerin öneminin vurgulanmasına ve dijitalleşme sürecinin çalışanların iyi oluş hallerini etkileyen durumların belirlenmesine yönelik ulusal literatürde bir çalışmaya rastlanmamış olması nedeniyle bu çalışmanın literatüre katkı sağlaması beklenmektedir.
A Column Encryption-Based Privacy-Preserving Framework for Hadoop Big Data Sets
Authors: Baig Hidayath Ali
Published: May 2024

أدى النمو الهائل للإنترنت وإنترنت الأشياء والحوسبة السحابية في الآونة الأخيرة إلى ارتفاع كبير في البيانات عبر مختلف قطاعات الأعمال والصناعة. أصبحت البيانات الضخمة اتجاها متزايدا في السنوات الأخيرة، حيث جذبت انتباه الأكاديميين وقادة الشركات والمسؤولين الحكوميين في جميع أنحاء العالم. Hadoop هو إطار عمل شائع الاستخدام لمعالجة البيانات الضخمة. إن توسيع البيانات هذا لديه القدرة على توفير مزايا كبيرة ومفيدة، وقد تم تحقيق بعض النجاح المبكر من الناحية الفنية في التعامل مع مثل هذه الكمية الكبيرة من البيانات. فإلى جانب فوائده العديدة، فإن له أيضًا عددًا كبيرًا من العيوب. وتشمل هذه، على سبيل المثال لا الحصر، تخزين البيانات وتبادلها وتنظيمها ونقلها وتحليلها وتصورها وأمنها وخصوصيتها. في هذا البحث، يتم دراسة الآثار المترتبة على الخصوصية لتحليلات البيانات الضخمة. تقترح العديد من المنشورات طرقًا لتأمين البيانات الضخمة. كل تقنية لها مزايا وعيوب. بغض النظر عن قوانين الخصوصية، يجب على مطوري التطبيقات حماية البيانات الحساسة.
Analyzing the Eﬃciency of a New Image Encryption Method Based on Aboodh Transformations
Authors: Rasappan Suresh , Murugesan Regan , Kumaravel Sathish Kumar , Mohan Kala Raja , Nagaram Nagadevi Bala
Published: May 2024

الهدف من هذا البحث هو تطوير طريقة تشفير فريدة من نوعها تستخدم طريقة Aboodh وتحويلها العكسي مع طريقة S-Box. تشير نتائج التقييمات إلى أن هذا العمل مناسب للاستخدام في تطبيقات التشفير الآمنة، ويوفر أدلة فيما يتعلق ببناء نظام تشفير للصور بناءً على السلوكيات المعقدة التي يوضحها. بعد تطبيق المنهجيات التي تم تقديمها لتصوير البيانات المأخوذة من مواقف الحياة الواقعية، تم تقييم النتائج باستخدام مجموعة واسعة من المعايير الإحصائية ومعايير الأداء. نتائج هذا التحقيق تؤدي إلى تحسين موثوقية نظام التشفير.
Investigation on the Impact Fracture Mechanism of Rock in Microgravity Environment Using the Continuum-Discontinuum Element Method
Authors: Zhang Qunlei , Fang Jin , Feng Chun , Jia Mintao , Cheng Pengda , Wang Shuang
Published: May 2024

In-depth analysis of asteroid samples will provide a key scientific basis for understanding its evolution history, resource utilization, and life origin. Obtaining rock samples from asteroids is getting more and more attention. However, successful sampling in asteroid is difficult due to extremely complex environmental conditions. In this paper, the dynamic processes of rock failure by sampling device are investigated by a continuum-discontinuum element method (CDEM) to instruct the rock sampling in asteroid. The deformation and failure of rock samples under different conditions (impact speeds, impact positions, rock cut-slot numbers, cut-slot spacings, cut-slot depths and cut-slot widths, etc.) are analyzed, and the relationships between the rock failure and hammer impact method are obtained. The results show that the cut-slot formed by grinding wheel increases the free surface of rock sample, which is beneficial to impact fracture and asteroid sampling. The relative distance between cut-slot free surface and impact position significantly affects the rock fracture under impact loading. The fracture degree of rock with single cut-slot is obviously smaller than that of rock with double cut-slots, the cut-slot scheme of double grinding wheels is more suitable for asteroid sampling. Under the impact loading, the rock fracture is negatively correlated with the cut-slot spacing formed by double grinding wheel cutting, the effective spacing of double cut-slots is 12 mm. The fracture unit number of rock varies nonlinearly with an increase of the depth from impact position, there is an optimal cut-slot depth corresponding to a certain impact velocity, and the rock crushing efficiency of asteroid sampling can be improved as the cut-slot depth matches the crack propagation depth.
The Effect of Repeated Low-Level Red Light (RLRL) Therapy on Myopia Outcomes in Children Aged 6 - 14: A Systematic Review
Authors: Liu Sian , Chao Melissa , Li Yannan
Published: May 2024

Introduction: Myopia, or nearsightedness, is a significant public health concern, affecting billions worldwide. In particular, school-aged children demonstrate alarming increases in myopia rates. Despite various interventions including atropine eye drops, orthokeratology, and single vision spectacles (SVS), no intervention has fully stopped or prevent myopia progression in children. Repeated Low-Level Red-Light (RLRL) therapy, exposing eyes to red light, offers a potential intervention to control myopia in children.
Objective: This protocol outlines our approach to evaluate RLRL therapy's effectiveness in myopia control in children age 6 – 14 years by comparing RLRL with SVS to SVS alone. The effectiveness will be measured by the mean difference in Spherical Equivalent Refraction (SER) and axial length (AL).
Methods and Analysis: Our systematic review will adhere to the PRISMA guidelines. We will search MEDLINE, Embase, Web of Science, Scopus, China National Knowledge Infrastructure, and Wanfang Data Knowledge Service Platform, and various trial registries to identify potentially relevant articles. Articles will be screened according to a pre-defined criterion, and relevant articles will be evaluated using the Cochrane Risk of Bias Tool 2 (RoB 2) criteria. If possible, a meta-analysis and/or subgroup analyses will be performed.
Implications: Findings from this review will contribute to evidence-based strategies for myopia control, potentially benefiting millions of children worldwide. Dissemination plans include publication in a peer-reviewed journal and presentation at relevant seminars and conferences.
The Impact of Global Value Chain Participation on Export Volatility: A Country-level Panel Data Analysis
Authors: Song Joon-Heon
Published: May 2024

Amid rising geopolitical tensions, the expansion of global trade and production networks has significantly amplified the co-movement of economic fluctuations. This study examines the impact of global value chain (GVC) participation on export volatility by analyzing panel data covering 1995 to 2020 across 74 countries. Using a novel econometric model, we isolate the effects of forward and backward GVC engagement and their interactions, enriching the analysis by reviewing factors previously identified as influencing export volatility. These findings illustrate that forward GVC participation exacerbates export volatility, whereas backward GVC engagement has no statistically significant effect. However, the interaction between forward and backward participation significantly diminishes export volatility. This nuanced understanding of the effects of GVC participation offers significant insights into enhancing export stability through diversified GVC integration, thereby contributing valuable perspectives to the field of international economics on the complexity of global trade dynamics and the crucial role of strategic GVC engagement in building resilient economies.
JEL Code: F10, F14, F62
Enhancing Metro Rail Efficiency: A Predictive Maintenance Approach Leveraging Machine Learning and Deep Learning Technologies
Authors: Nair Vishak , M Premalatha , R Srinivasa Perumal , M Braveen
Published: May 2024

This paper looks into the modeling and implementation of a predictive maintenance system of an air production unit for a metro rail designed to suit the challenges detailed by the industrial sector. Using modern machine learning, deep learning, and AI techniques, the system identifies the faulty equipment well in advance when applied to the huge volume of sensor data. One of the major functionalities of the system is an interface designed to alert users, whereby the instant alerts are made to the maintenance personnel for faster intervention, minimization of the possible downtime. The basis of the study is the application of the predictive maintenance system within the unit of air production. It indicates great efficacy toward the prediction of a failure. A wide variety of ML and deep learning models were experimented with and fine-tuned carefully by training and evaluation over the training set and also over the testing set to ensure predictive accuracies. For example, from the above comparative model analysis, the most suitable predictive approach was indicated through the use of accuracy. Deep Learning Models, including LSTM, RNN, and BiLSTM, have been exceedingly good, with all the above models giving an accuracy of above 99.7 percent. Notably, Adaboost, a Boosting technique also has performed well. The culmination of this project highlights the pivotal role of AI and ML technologies in advancing predictive maintenance strategies within the industrial sector. The findings illustrate the potential of these technologies to transform maintenance practices, optimize operational processes and contribute to the overall sustainability of industrial operations. This paper contributes valuable insights into the feasibility and effectiveness of AI-driven predictive maintenance systems.
Symbiosis of antiferromagnetism and ferrimagnetism in adjacent honeycomb layers
Authors: Szaller David , Prodan Lilian , Geirhos Korbinian , Felea Viorel , Skourski Yurii , Gorbunov Denis , Foerster T , Helm Toni , Nomura Toshihiro , Miyata Atsuhiko , Zherlitsyn Sergei , Wosnitza Jochen , Tsirlin Alexander , Tsurkan Vladimir , Kézsmárki István
Published: May 2024

Ferro/ferri- and antiferromagnetic orders are typically exclusive in nature, thus, their co-existence in atomic-scale proximity is expected only in heterostructures. Breaking this paradigm and broadening the range of unconventional magnetic states, we report here on the observation of a new, atomic-scale hybrid spin state. This ordering is stabilized in three-dimensional crystals of the polar antiferromagnet Co$_2$Mo$_3$O$_8$ by magnetic fields applied perpendicular to the \emph{Co} honeycomb layers and possesses a spontaneous in-plane ferromagnetic moment. Our microscopic spin model, capturing the observed field dependence of the longitudinal and transverse magnetization as well as the magnetoelectric/elastic properties, reveals that this novel spin state is composed of an alternating stacking of antiferromagnetic and ferrimagnetic honeycomb layers. The strong intra-layer and the weak inter-layer exchange couplings together with competing anisotropies at octahedral and tetrahedral \emph{Co} sites are identified as the key ingredients to stabilize antiferromagnetic and ferrimagnetic layers in such close proximity. We show that the proper balance of magnetic interactions can extend the stability range of this hybrid phase down to zero magnetic field. The possibility to realize a layer-by-layer stacking of such distinct spin orders via suitable combinations of microscopic interactions opens a new dimension toward the nanoscale engineering of magnetic states.
Exploring Piperine as a Potential Treatment for Polycystic Ovarian Syndrome: Insights from In-silico Docking Studies
Authors: Francis Rahul , Kalyanaraman Ramanathan , Boominathan Vasuki , Parthasarathy Sudharsan , Chavaan Ashajyothi , Ansari Irfan Aamer , Ansari Siddique Akber , Alkahtani Hamad M , Chandran Janani , Tharumasivam Siva Vijayakumar
Published: May 2024

Polycystic Ovarian Syndrome (PCOS) is a multifaceted metabolic and hormonal condition that impacts women in their procreative ages, identified by ovarian dysfunction, hyperandrogenaemiaoverweight and insulin insensitivity.The piperine, an important alkaloid compound of black pepper has shown promise in modulating various physiological processes. In this work, employed computational docking studies to explore the potential of piperine as a treatment for PCOS. Utilizing computational methods, we analyzed the binding interactions between piperine and key molecular targets implicated in PCOS pathogenesis, including hyperandrogenism, and "oligomenorrhea. The network pharmacology analysis report found 988 PCOS-related genes, 108 hyperandrogenism-related genes, and 377 oligomenorrhea-related genes, and we finally shortlisted 5 common genes in PCOS, hyperandrogenism, and "oligomenorrhea": NR3C1, PPARG, FOS, CYP17A1, and H6PD. Our results reveal favorable binding affinities with PPARG (-8.34 Kcal/mol) and H6PD (-8.70 Kcal/mol) and interaction patterns, suggesting the potential of piperine to modulate these targets. Moreover, the reliabilityof the piperine-target interactions was revealed by molecular simulations studies. These findings support further experimental investigations to validate the therapeutic efficacy of piperine in PCOS management. The integration of computational approaches withexperimental studies has the potential to lay the groundwork for the creation of new therapies specifically targeting PCOS and related endocrine disorders.