bluuebunny/crossref_metadata_2025_split huggingface dataset:
An Empirical Study of Destructive Nodes Characterization in Wireless Networks
Authors: Srinivas Aluvala Et al. 
Published: January 2024

Mobile Ad hoc Networks (MANETs) are deployed in various new public and domestic environments, going up to new needs in terms of concert and effectiveness. According to the wireless dynamic nature, some needed services like security for network maintenance, trust-based routing and resource management among network nodes are not carried out as good as expected. Also, the ad-hoc networks are vulnerable to secure communications and multiple attacks can participate in various layers of a network stack. Destructive nodes have chances to change or discard routing specifications, sometimes it can send false routes to capture source data packets to pass through themselves. Some protocols have been designed to address the complication from secure data communication. Even though, a secure protocol cannot handle all kinds of attack detection and elimination in all situations. New secure data communication wireless protocols need to focus these challenges, because security care is not natively built in MANET. Therefore, in this research paper, analysis of destructive nodes characterization and impacts on wireless networks investigated the multiple attacks behaviour, activities of the attacks all through neighbour selection, path establishment from source to destination, creating awareness of attack presence detection knowledge to the normal devices during path discovery and data transmission mechanisms. Legitimate nodes need to be building with secure transmission knowledge to make sure trusted communication, ensure validation, honesty, and privacy to classify the attacks in MANETs.
Preserve data-while-sharing: An Efficient Technique for Privacy Preserving in OSNs
Authors: Nithish Ranjan Gowda Et al. 
Published: January 2024

Online Social Networks (OSNs) have become one of the major platforms for social interactions, such as building up relationships, sharing personal experiences, and providing other services. Rapid growth in Social Network has attracted various groups like the scientific community and business enterprise to use these huge social network data to serve their various purposes. The process of disseminating extensive datasets from online social networks for the purpose of conducting diverse trend analyses gives rise to apprehensions regarding privacy, owing to the disclosure of personal information disclosed on these platforms. Privacy control features have been implemented in widely used online social networks (OSNs) to empower users in regulating access to their personal information. Even if Online Social Network owners allow their users to set customizable privacy, attackers can still find out users’ private information by finding the relationships between public and private information with some background knowledge and this is termed as inference attack. In order to defend against these inference attacks this research work could completely anonymize the user identity.
This research work designs an optimization algorithm that aims to strike a balance between self-disclosure utility and their privacy. This research work proposes two privacy preserving algorithms to defend against an inference attack. The research work design an Privacy-Preserving Algorithm (PPA) algorithm which helps to achieve high utility by allowing users to share their data with utmost privacy. Another algorithm-Multi-dimensional Knapsack based Relation Disclosure Algorithm (mdKP-RDA) that deals with social relation disclosure problems with low computational complexity. The proposed work is evaluated to test the effectiveness on datasets taken from actual social networks. According on the experimental results, the proposed methods outperform the current methods.
Machine Learning and Deep Learning Models for Predicting the Onset of Diabetes: A Pilot Study
Authors: Reehana Sk Et al. 
Published: January 2024

Diabetes currently one of the most significant worldwide concerns, and its prevalence is only expected to increase in the future years. In order to monitor glucose levels in the blood and set treatment protocols for diabetes, keeping a regular schedule for checking blood glucose levels is essential. The purpose of widespread adoption of digital health in recent years has been to enhance diabetic healthcare for patients, and as a result, a massive quantity of data has been collected that may be used in the ongoing management of this chronic condition. Deep learning, a relatively new kind of machine learning, is one method that has taken advantage of this trend, and its applications seem promising. In this research, we provide a thorough analysis of how deep learning has been used in the study of diabetes thus far. We conducted a comprehensive literature search and found that this method is most often used in the following settings: diabetes diagnosis, glucose control, and the identification of diabetes-related complications. We have described the most important details regarding the learning models used, the development process, the primary outcomes, and the baseline techniques for performance assessment from the 40 original research publications that we selected based on the search. In the reviewed literature, it becomes clear that several deep learning algorithms and frameworks have outperformed traditional machine learning methods to attain state-of-the-art performance on numerous problems involving diabetes. However, we point out several gaps in the existing literature, such as a dearth of readily available data and a lack of clarity in the interpretation of models. In the near future, these obstacles may be surmounted thanks to the fast advancements in deep learning methodologies which will allow for wider application of this technology in therapeutic settings.
A Model for Predicting E-Commerce Product Returns Using Hybrid CNN-GRU
Authors: A.Arulmurugan Et al. 
Published: January 2024

The aim of this work is to predict the product return rate of e-commerce using deep learning algorithms. A novel hybrid model combining Convolutional Neural Network(CNNs) and Gated Recurrent Units (GRUs) is proposed to predict e-commerce product return rates. The developed model is trained and validated on a large e-commerce dataset with features like consumer demographic informations, product details, transaction and product return details, and consumer feedbacks. The return history of every product is learned and products least and most sold is analyzed. Based on all this analysis the return of products in the future is predicted. The proposed work demonstrates that the hybrid CNN-GRU model outperforms conventional models and standalone CNN and GRU architectures with an accuracy of 83%. Also focus is made on understanding the features influencing the product returnsthat help firms to make data driven decisions and minimize product returns . This type of predictive models can be applied to enhance the business strategies in making informed decisions enhancing the overall satisfaction of the consumer and improving the revenue of the business.
An intelligent Espionage Robot Controlled by Mobile Wireless System
Authors: D Ferlin Deva Shahila Et al. 
Published: January 2024

The field of robotics has grown exponentially as a result of the integration of several domains. With advances in technology, a drone, also known as an unmanned aerial vehicle (UAV), can be controlled from 20,000 miles away. Such technologies either use artificial intelligence or radio communication to control their process. Creating one such autonomous robot is the main task. More than a thousand kilometers away, in a distant place, this must be managed. Currently available technologies can only control robots up to 500 yards away. Our communication way is WIFI. These Wi-Fi-connected computers send serial data, and they also communicate with a system using a microcontroller to direct how it operates. The robot senses as well in order to avoid obstacles and determine its location.
Explainable Artificial Intelligence (xAI): Reflections on Judicial System
Authors: Chaudhary G. 
Published: January 2024

Machine learning algorithms are increasingly being utilized in scenarios, such, as criminal, administrative and civil proceedings. However, there is growing concern regarding the lack of transparency and accountability due to the “black box” nature of these algorithms. This makes it challenging for judges’ to comprehend how decisions or predictions are reached. This paper aims to explore the significance of Explainable AI (xAI) in enhancing transparency and accountability within contexts. Additionally, it examines the role that the judicial system can play in developing xAI. The methodology involves a review of existing xAI research and a discussion on how feedback from the system can improve its effectiveness in legal settings. The argument presented is that xAI is crucial in contexts as it empowers judges to make informed decisions based on algorithmic outcomes. However, the lack of transparency, in decision-making processes can impede judge’s ability to do effectively. Therefore, implementing xAI can contribute to increasing transparency and accountability within this decision-making process. The judicial system has an opportunity to aid in the development of xAI by emulating reasoning customizing approaches according to specific jurisdictions and audiences and providing valuable feedback for improving this technology’s efficacy.Hence the primary objective is to emphasize the significance of xAI in enhancing transparency and accountability, within settings well as the potential contribution of the judicial system, towards its advancement. Judges could consider asking about the rationale, behind outcomes. It is advisable for xAI systems to provide a clear account of the steps taken by algorithms to reach their conclusions or predictions. Additionally, it is proposed that public stakeholders have a role, in shaping xAI to guarantee ethical and socially responsible technology.
Analisis Kesulitan Belajar Siswa Materi Indahnya Kebersamaan
Authors: Rahmawati Ari , Sunarsih Eti , Mertika Mertika 
Published: January 2024

Tujuan penelitian ini adalah untuk mengetahui: (1) kesulitan belajar siswa pada mapel IPS pada siswa kelas IV di SDN 89 Singkawang (2) upaya guru mengatasi kesulitan belajar IPS pada siswa kelas IV di SDN 89 Singkawang. Penelitian ini merupakan penelitian deskriptif kualitatif. Teknik pengumpulan data yaitu wawancara, angket, dan dokumentasi. Lokasi penelitian di SDN 89 Singkawang dengan subjek siswa kelas IV berjumlah lima siswa dan guru. Hasil analisis data menunjukkan dari lima siswa terdapat dua siswa mengalami kesulitan belajar berat sedangkan tiga yang lain mengalami kesulitan belajar ringan. Upaya guru dalam mengatasi kesulitan belajar siswa pada mapel IPS diketahui (1) memberikan pertanyaan agar dapat merangsang keaktifan siswa (2) menggunakan media pembelajaran berupa gambar agar fokus siswa dalam belajar dapat diarahkan (3) menerapkan dialog interaktif antar siswa dan guru. Kesulitan belajar yang dialami oleh siswa juga diantisipasi dengan menghadirkan orangtua siswa pada akhir semester dilakukan pertemuan antar pihak sekolah dengan orangtua siswa sebagai kegiatan evalluasi bersama demi kemajuan siswa dalam memperoleh hasil belajar yang maksimal.
Keaktifan Belajar Siswa SD pada Pembelajaran IPA Menggunakan Model Pembelajaran Make A Match
Authors: Anisah Siti , Erviana Lina , Purnamasari Mega Isvandiana 
Published: January 2024

Tujuan penelitian ini untuk mengetahui (1) penggunaan model pembelajaran Make A Match pada pembelajaran IPA kelas V SDN 2 Gunungsari. (2) keaktifan belajar siswa setelah menggunkan model pembelajaran Make A Match pada pembelajaran IPA kelas V SDN 2 Gunungsari. (3) faktor pendukung dan penghambat kekatifan siswa dalam pembelajaran IPA menggunakan model pembelajaran Make A Match kelas V SDN 2 Gunungsari. Penelitian ini menggunakan jenis penelitian deskriptif kualitatif. Teknik pengumpulan data dengan teknik observasi, wawancara, angket dan dokumentasi. Teknik analisis data menggunakan teknik reduksi data, penyajian data, dan penarikan kesimpulan. Hasil analisis data memperoleh 1. Pembelajaran menggunakan model pembelajaran Make A Match dengan media kartu berpasangan mampu menarik perhatian, semangat siswa sehingga pembelajaran tidak merasa bosan dan jenuh. 2. Siswa aktif mengikuti pembelajaran IPA menggunakan model pembelajaran Make A Match. Dapat membuat siswa lebih aktif bertanya, berpendapat, berdiskusi saling bertukar informasi dan dapat memecahkan masalah yang diberikan guru. 3. Faktor pendukung pembelajaran menggunakan model pembelajaran Make A Match yaitu pembelajaran serta materi terarah, tidak kaku, interaksi siswa, tanggung jawab, dan pengetahuan siswa. faktor penghambat pembelajaran menggunakan model pembelajaran Make A Match yaitu ketidak tertiban siswa.
A week in the life of the human brain: stable states punctuated by chaotic transitions
Authors: Wang Maxwell , G'Sell Max , Castellano James F. , Richardson R. Mark , Ghuman Avniel 
Published: January 2024

Abstract
Many important neurocognitive states, such as performing natural activities and fluctuations of arousal, shift over minutes-to-hours in the real-world. We harnessed 3–12 days of continuous multi-electrode intracranial recordings in twenty humans during natural behavior (socializing, using digital devices, sleeping, etc.) to study real-world neurodynamics. Applying deep learning with dynamical systems approaches revealed that brain networks formed consistent stable states that predicted behavior and physiology. Changes in behavior were associated with bursts of rapid neural fluctuations where brain networks chaotically explored many configurations before settling into new states. These trajectories traversed an hourglass-shaped structure anchored around a set of networks that slowly tracked levels of outward awareness related to wake-sleep stages, and a central attractor corresponding to default mode network activation. These findings indicate ways our brains use rapid, chaotic transitions that coalesce into neurocognitive states slowly fluctuating around a stabilizing central equilibrium to balance flexibility and stability during real-world behavior.
Electron Paramagnetic Resonance Studies of Iron ions (Fe3+ - VO) as nanomaterials in Strontium Titanate Oxide (SrTiO3) Crystals at 80K
Authors: Booq Zainab Ibrahim Yousef 
Published: January 2024

Abstract
This work presents the structure of the phase transition of Fe3+ - V0 ions in SrTiO3 Crystals using Bruker EMX EPR spectrometer with an X-band (9.1–9.9 GHz) under nitrogen gas preserved. In the low magnetic field (300 mT) the structure is tetragonal (C4v)-orthorambic (C2v) but in high magnetic field (800 mT-1700 mT), it becomes tetragonal (C2v) only.The spectra exhibit sixteen lines originating from defect centers laying on three original direction equivalent to < 100>, others tilted a way from these directions by orthorambic angle\(\stackrel{-}{\varvec{\varnothing }}\)=±0.8, in addition to a tetragonal angle \(\varvec{\varnothing }\)=±1.2. The observed transition lines occur between the energy levels − 5/2↔-3/2,-5/2↔-1/2, -1/2↔+1/2, -3/2 ↔-1/2.
The spin Hamiltonian parameters are investigated by mathematical model (SM) and simulation programme written in this research. The position of defects Fe3+ - V0 are determined, showing that the paramagnetic Fe3+ ions moves toward the vacancy by 11 pm with a contraction of 10.55%.
Bioconversion of Eel Skin Waste into Valuable Collagen: Isolation, Spectral Characterization, and Biocompatibility Assessment
Authors: Asharaf Fathima , Rajasree Radhika , Rajan Roopa 
Published: January 2024

Abstract

The current investigation was carried out to evaluate the structural and cell proliferation capability of pike conger eel collagen derived by acid and enzymatic extraction methods. The recovery yield of pepsin-soluble collagen (PSC) (40.65%) was significantly higher (p<0.05) than acid-soluble collagen (ASC) (29.9%) on a dry weight basis. In terms of molecular weight pattern, α1 and α2 bands of extracted collagen occurred to be 130KDa and 112KDa, respectively, and were confirmed to have a Type 1 structure. The presence of characteristic peaks of amide bands in Fourier transform infrared spectra (FTIR) and the d value of 12.28 Å and 11.37 Å in X-ray diffraction (XRD) revealed no significant changes in the highly ordered, intact triple helical structure of collagen through pepsin digestion. The higher thermal stability with a denaturation temperature of 33.3°C for ASC and 35.1°C for PSC was well documented in the differential scanning calorimetry (DSC) curve, which showed a substantial correlation with the findings of the imino acid content of both ASC and PSC collagen (203.5 ± 1.06 and 216.81 ± 0.32 residues/1000g respectively). In addition, the nontoxic behavior of isolated collagen samples to L929 fibroblast cell lines emphasized the possible utilization of pike conger eel skin as a safe source of thermally stable collagen.
Deep-Learning-Based Survival Prediction of Patients with Thyroid Cancer
Authors: Zhang Jinrong , Li Li , Zheng Xinkai , He Yong , Wu Shuai , Jiang Yuzhen , Ming Wai-kit , Cheng Leong Nga , Lyu Jun , Deng Liehua 
Published: January 2024

Abstract

Background
This study acquired data pertaining to individuals diagnosed with Thyroid Cancer (TC) from the Surveillance, Epidemiology, and End Results (SEER) database. Subsequently, a deep learning and neural network model known as DeepSurv was employed to forecast the survival rate of TC patients and assess its efficacy.
Methods
Information on individuals diagnosed with TC from the years 2000 to 2019 was collected from the SEER database. The individuals in question were subsequently allocated into training and testing cohorts through a random selection process, maintaining a ratio of 7:3. The outcomes of the DeepSurv model were compared to those of the Cox proportional-hazards (CoxPH) model in order to estimate the chances of survival for TC patients. The accuracy of the model's predictions was evaluated through the examination of calibration curves, the time-dependent area under the receiver operating characteristic curve (AUC), and the concordance index (C-index).
Results
A total of 25,797 individuals diagnosed with TC were included in this study, with 18,057 comprising the training group and 7,740 forming the testing cohort. The CoxPH model exhibited robust correlations between age, gender, marital status, surgical intervention, radiation therapy, tumor extension, and the survival outcomes of TC patients. Notably, the C-index for the CoxPH model was 0.884, indicating a high level of predictive accuracy. Additionally, the training cohort data were used to create the DeepSurv model, which produced a higher C-index of 0.904. The predictive performance of both models was assessed, and the 3-, 5-, and 8-year AUC values were calculated. Regarding the CoxPH model, the corresponding area under the receiver operating characteristic curve (AUC) values were determined to be 0.835, 0.797, and 0.756, respectively. In comparison, the DeepSurv model achieved higher AUC values of 0.942, 0.918, and 0.906. The DeepSurv model demonstrated superior predictive ability for TC patients, as indicated by both the AUC values and the calibration curve, suggesting higher reliability compared to the CoxPH model.
Conclusion
Using TC patient data from the SEER database for research, we built the DeepSurv model, which performed better than the CoxPH model in estimating the survival time of TC patients.
A case of explosive community-acquired pneumonia and septic shock caused by Acinetobacter pitti
Authors: zhan xiaoying , Tian Xin , Zhang Cangjian 
Published: January 2024

Abstract
Acinetobacter pitti, a species of acinetobacter, has a special pathogenesis and is commonly known as nosocomial infection, but community infection is rare.We present a case of an outbreak of community-acquired pneumonia caused by Acinetobacter pitti, which was followed by convulsions and cavitation pneumonia with high virulence manifestations.Pneumonia infected by this bacterium is relatively rare, often manifested as acute onset, severe illness, high fever, shock, multiple organ failure and other manifestations, the mortality rate is very high, as early as possible to identify the infectious pathogen, early treatment can improve the success rate of treatment, significantly improve the prognosis of patients.
Risk Factors of Combined Spinal Epidural Analgesia on Maternal Intrapartum Fever
Authors: Zhao Hanzhen , Wang Cheng , Shi Lu , Tang Lian , Li Jingjing , Yu Yanxia 
Published: January 2024

Abstract

Objective
Combined spinal epidural analgesia (CSEA) has been commonly used as clinical labor anesthesia. Intrapartum fever induced by CSEA frequently occurs and seriously endangers maternal and fetal health. The aim of this study is to explore the risk factors of CSEA on maternal intrapartum fever.
Methods
A total of 200 parturient women from Suzhou Municipal Hospital were included in this study.74 parturient women with intrapartum fever during combined spinal epidural analgesia were included in fever group and 126 parturient women with normal body temperature in control group. The maternal demographic characteristics, details of labor and delivery, anesthetic scheme, fetal weight and Apgar score were collected. Logistic regression and receiver operating characteristic (ROC) curve were used to analyze the related factors of intrapartum fever during labor analgesia. Nomogram and decision curve analysis (DCA) were used to apply and evaluate the predictive model.
Results
Logistic multivariate analysis indicated that the times of vaginal examinations (OR 2.069,95%CI 1.587–2.696), duration of analgesia (OR 2.466,95%CI 1.741–3.492), time from rupture of membrane to delivery (OR 0.909,95%CI 0.855–0.967) and labor induced by water sac(OR 10.806,95%CI 1.384–84.035)were independent risk factors for intrapartum fever. More than 7 times of vaginal examinations, 3.35h of CSEA and 5.26h from rupture of membrane to delivery prompt for higher risk of maternal intrapartum fever.
Conclusion
The times of vaginal examinations, duration of analgesia, time from rupture of membrane to delivery and water sac induced labor are identified as risk factors of CESA-induced intrapartum fever. The predictive model and the nomogram based on the above risk factors contribute to evaluate and restraint intrapartum fever during labor anesthesia.
A fast numerical algorithm for finding all real solutions to a system of N nonlinear equations in a finite domain
Authors: Chueca-Díez Fernando , Gañán-Calvo Alfonso M. 
Published: January 2024

Abstract

A highly recurrent traditional bottleneck in applied mathematics, for which the most popular codes (Mathematica and Matlab) do not offer a solution, is to find all the real solutions of a system of N nonlinear equations in a certain finite domain of the N-dimensional space of variables. We present an algorithm of minimum length and computational weight to solve this problem, resembling a graphical tool of edge detection in an image extended to N dimensions. Once the hypersurfaces (edges) defined by each nonlinear equation have been identified in a single, simultaneous step, the coincidence of the hypersurfaces in the vicinity of all the hyperpoints that constitute the solutions makes the final Newton-Raphson step rapidly convergent to all the solutions with the desired degree of accuracy. As long as N remains smaller than about five, which is often the case for physical systems that depend on fewer than five parameters, this approach demonstrates excellent effectiveness.