bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Performance of Data Mining Techniques for Cyberbullying Detection of Arabic Social Media Text
Authors: Hussien Omar Kamal Eldien , Aboutabl Amal Elsayed , Haggag Riham Mohamed Younis 
Published: October 2023

Cyberbullying has spread like a virus on social media platforms and is getting out of control. According to psychological studies on the subject, the victims are increasingly suffering, sometimes to the point of committing suicide among the victims. The issue of cyberbullying on social media is spreading around the world. Social media use is growing, and it can have useful and negative implications when you take into account how social media platforms are abused through different forms of cyberbullying. Although there is a lot of cyberbullying detection in English, there are few studies in the Arabic language. Data Mining techniques are often used to solve and detect this problem. In this study, different data mining algorithms were used to detect cyberbullying in Arabic texts.. Our study was conducted The Bullying datasets consisted of 26,000 comments written in Arabic and were collected from kaggle.com, the Cyber_2021 dataset consisted of 13,247 comments collected via github.com, and the Data 2022 dataset consisted of 47,224 comments collected via Instagram. Various extraction features CountVectorizer and Tf-Idf were used Accuracy, precision, recall, and the F1 score were used to evaluate classifier performance. In the study, Bagging Classifier achieve high results of Bullying dataset from Kaggle Accuracy 96.04, F1-Score 95.98, Recall 96.04, Precision 95.95, SVC model gave the highest results of Cyber_2021 dataset from Github an Accuracy 98.49, F1-Score 98.49, Recall 98.49, Precision 98.50, while Data 2022 dataset from (Instagram) achieving an Accuracy of 77.51, F1-Score 76.60, Recall 77.51, and Precision 77.24. Were achieved for Tf-Idf Vectorizer. Tf-Idf Vectorizer the best to all results than count Vectorizer .
Wave-Atom and Cycle-Spinning-Based Noise Reduction in Mammography Images
Authors: Sarada Ch. , Ashwini P. , Srividya K. 
Published: October 2023

Image denoising is crucial in medical image processing. Digital mammography depends significantly on de-noising for computer-aided-detection of malignant cells like Microcalcifications. In this work, we proposed an unique hybrid approach to reduce Gaussian noise in digital mammograms by combining the wave-atom translation and cycle spinning methods. Pictures denoised by thresholding of coefficients would produce pseudo-Gibbs events because wave atoms are not translationally invariant. Circular motion is applied to keep away the artefacts. Experimental results clearly establish that the method is effective at filtering out background noise while maintaining the integrity of edges and enhancing picture quality. Mini-Mias pictures with variable quantities of Gaussian Noise are used to evaluate and analyse the performance using peak signal-to-noise ratio and structural similarity index. The provided technique outperforms several current filters in terms of evaluated results of peak signal-to-noise ratio and structural similarity index.
Performance and Analysis of a U-Net Model for Automated Skin Lesion Segmentation
Authors: Patil Sheetal Nana , Patil Hitendra D. 
Published: October 2023

A greater proportion of people are affected by skin cancer, particularly melanoma, which has a higher tendency to metastasize. For Dermatologist, Visual inspections are most challenging & complex task for melanoma detection. To solve this problem, dermoscopic images are analyzed and segmented. Due to the sensitivity involved in surgical operations, existing techniques are unable to achieve higher accuracy. As a result, computer-aided systems are essential to detect & segment dermoscopic images.
In this paper, for segmentation 5000 skin images were taken from the HAM10000 dataset. Prior to segmentation, preprocessing is done by resizing images. A novel U Net structure is a fully convolutional network is presented & implemented using up-sampling and down-sampling technique with Rectified Linear Units (ReLU) for activation functions. The outcomes of proposed methodology shows performance improvement for skin-lesion segmentation with 94.7 % pixel accuracy & 89.2 % dice coefficient compared with existing KNN & SVM techniques.
Unmanned Aerial Vehicle Design for Smart City Application
Authors: Doraswamy Birasalapati , Giriprasad M. N. , Krishna K. Lokesh 
Published: October 2023

Nowadays, Unmanned Aerial Vehicles (UAVs) or drones are also one of the applications to provide the required services and to gather information from the target location. Because smart city applications effectively deal the drone interaction and enhance the human lifestyle with drones. Moreover, UAVs are generally utilized due to their privacy threats, lower cost, pose security, and versatility, which request dependable detection at lower altitudes. However, the less sensing module in the drone has earned the low sensing accuracy of location tracking. So, this paper aims to develop a novel Firefly-based Recurrent Neural Mechanism (FRNM) to enrich the sensing capacity of the drone vehicle. In addition, the sound of the research is medicine delivery through UAVs in emergencies. This UAV system is one of the most crucial features to delivering essential medical items aids by reaching properly correspondent patients. Moreover, the client's needs are stored in the FRNM cloud then that stored data is trained to the UAV machine. Hereafter, based on the trained details, the drone can reach the destination and has delivered the requested medicine to the specific clients. The planned design is drawn in Network Simulator (NS2) environment, and the robustness of the projected replica is valued by calculating the chief parameters. Hereafter, the improvement score was valued by the comparison assessment. Hence, the FRNM has reported the finest performance by earning less location finding duration, running period, and error rate.
Protein Kinase C Epsilon Overexpression in Prostate Adenocarcinoma is Associated with Oncogenesis
Authors: Trehan Deepika , Rehman Sayeed ur , Batra Janendra K , Agrawal Usha 
Published: October 2023

Background: PKCε, an isozyme of serine-threonine kinase, has been implicated in epithelial cancer metastasis and progression. This study investigates the impact of the oncogenic PKCε, overexpressed abnormally in human Prostate tumor samples and cell lines, to understand its efficacy. Methods: The microarray dataset, GSE86257, was processed for normalization. The identification of upregulated and downregulated genes was based on FDR >1 and p <0.05 values. Cytoscape analysis and functional enrichment of significant genes were done. The identified genes were validated on the TCGA dataset and survival analysis was performed by Kaplan-Meier analysis. Results: A total of 1524 DEGs were identified with 728 upregulated genes and 818 downregulated genes. The two significant modules with MCODE score:9.0 and Venn analysis provided cyclin-dependent kinase inhibitor protein (CDK1), Cyclin B1 (CCNB1), Phospholipase C Gamma 1 (PLCG1), Cyclin Dependent Kinase 9 (CDK9), Phosphoinositide-3-Kinase Regulatory Subunit 3 (PIK3R3), H4 Clustered Histone 6 (H4C6), Phospholipase C Gamma 2 (PLCG2) as most interacting genes. TCGA data analysis and Prognostic analysis revealed CCNBI, CDK9, and PLCG1 associated with poor prognosis. Conclusion: PKCε regulates genes that are responsible for cancer progression. Therefore, targeting PKCε in Prostate cancer may serve as an important regulatory effect and may improve the prognosis of the disease.
Deep Cervical Muscle Strengthening Exercise on Neck Pain Disability: A Randomized Controlled trail study
Authors: Krishnan Dr Madhan , Paluchamy Thenmozhi 
Published: October 2023

Objective: The study aimed to investigate the effect of deep cervical flexor muscle strengthening exercises on neck pain disability among undergraduate students. Materials and Methods: A randomized controlled trial was adopted with 60 participants who met the inclusion and were randomly allocated into the study group (n=30) and the control group (n=30). Neck pain disability was evaluated before, one month after the initiation of deep cervical flexor muscle strengthening exercise. Results: Majority (>70%) of the participants were female, with a mean age group of 18.72, and used mobile phones and computers with improper ergonomics. There were significant (p<0.001) changes after one month in the overall neck disability index within the study group and between the study and control groups compared by paired t and independent t-test. Conclusion: This finding concludes that the outcome effectively reduces neck pain and its associated disability and could be incorporated into routine day-to-day practice to reduce neck pain, thereby minimizing the disability.
Purification of gas mixtures based on the removal of small impurities from a counterflow membrane module: engineering calculation method
Authors: Okunev Alexandr   Yu. 
Published: October 2023

One of the tasks of the membrane gas separation process is the extraction of low-content highly penetrating impurities from gas mixtures.
The gas separation process is efficiently organized using hollow fiber-type membrane modules.
In this study, an attempt was made to develop an engineering method for calculating the purification of gas mixtures based on the removal of small impurities from a countercurrent membrane module, which would enable the calculations related to the separation mode using analytical dependencies, without the use of numerical integration procedures and iterations.
A mathematical model representing the gas separation process from binary mixtures having low content of a highly penetrating component was analyzed to address the challenges related to cleaning, purifying, and drying gas mixtures. Parametric studies related to the process were carried out and an engineering calculation method that enabled calculating separation in the module without using complex, resource-intensive algorithms having analytical formulas was developed herein. During the parametric study, the separation of a binary gas mixture was considered a limiting case. The proposed method was tested with respect to the example of air drying in a module comprising a (polyvinyltrimethylsilane) PVTMS membrane, which was an approximation of a binary mixture. The numerical and analytical results of the calculation are consistent with each other, and the calculation herein based on the developed method requires several orders of magnitude fewer calculations, that can even be performed manually.
Symplastic leiomyoma: a rare clinicopatholgical diagnosis
Authors: Shenoy Kavitha V. , N. Usha , Priyadarshini Sarah Grace 
Published: October 2023

Although leiomyomas are common this case is being reported due to its uniqueness as it posed challenges at all levels of pathological rarity and management. Symplastic leiomyoma is an unusual benign variant of leiomyoma with less likelihood of malignant transformation. In our case report we present a 28-year-old nulligravida who presented to our OPD with Fibroid uterus detected on scan and myomectomy specimen done revealed a rare pathological diagnosis of symplastic leiomyoma and her followup.
Realigning online teacher training modules to COVID-19: An evaluation of the online training module among pre-service and in-service teachers in Pakistan
Authors: Mohebi Laila , AlMohsen Fatima , Alshamsi Alanood 
Published: October 2023

This paper aims to have a critical evaluation of an online training module among pre-service and in-service teachers in Pakistan. The aim of this study was to provide a thorough examination of the various aspects that contribute to both satisfaction and dissatisfaction with the training module. This evaluation is critical in the process of facilitating the accommodation of the synchronous and asynchronous online models that have established a permanent mark in education years after the COVID-19 pandemic lockdowns. A total of thirty educators who registered for the online training module were selected as participants for the study, in which open-ended online questionnaires were employed as the primary method of data collection. This research employed Braun and Clarke's technique, which involves a six-step theme analysis process. The results revealed the mixed feelings of satisfaction and dissatisfaction with adopting training modules. The motivation behind satisfaction stemmed from several factors, including the pertinence of content structure, adaptability, proficient utilization of media, and a determined attitude. Conversely, dissatisfaction was driven by the inadequacy of the content in addressing issues they thought were pertinent for teachers in the 21st century. The study underlined the crucial role of content design in online instruction, highlighting the need for robust planning to create accessible, engaging, and flexible modules compatible with standard devices like smartphones. The study proposes the ongoing adjustment of online modules to cater to the requirements of learners in the 21st century. This is because online modules have great potential for establishing a permanent position in the training of pre-service and in-service teachers.
Novel Framework for Multi-Scale Occupancy Sensing for distributed monitoring in Internet-of-Things
Authors: Mane Preethi K , Rao K Narasimha 
Published: October 2023

Abstract

Occupancy sensing is one of the integral parts of modern evolving security surveillance and monitoring system used over different types of infrastructure. With an aid of multiple form of occupancy sensors, the prime idea of occupancy sensing is to identify the presence or absence of occupants in specifically monitored area followed by transmitting back the sensing information either for storage or for prompting a set of commands from the connected control units. Review of existing schemes exhibits the presence of adoption of multiple methodologies over different variants of use-cases; however, they are quite case specific, uses expensive deployment process, and performs highly sophisticated operation. At present, there are no studies specifically reported of using multi-scale occupancy sensing suitable for large and distributed environment of Internet-of-Things (IoT). Therefore, the proposed study introduces a mechanism of novel multi-scale occupancy sensing considering a use case of smart university campus, although, it can be implemented over any form of different infrastructures too connected over IoT environment. The proposed scheme is implemented considering different types of cost-effective sensors, handheld devices and access points in order to identify the state of occupancy in large number of rooms present in the campus. The sensed data from distributed connected campus are aggregated over cloud server where they are subjected to suitable preprocessing to increase the data quality suitable for reliable prediction. Multiple set of potential learning-based schemes are integrated with proposed model to explore best fit model. This assessment scenario is not found reported in existing scheme to perform classification of states of occupancy. The study outcome shows Convolution Neural Network and Long Short-Term Memory to accomplish higher accuracy compared to other learning approach.
Iroquois homeobox 4 (IRX4) Derived Micropeptide Promotes Prostate Cancer Progression and Chemoresistance Through Wnt Signaling Dysregulation
Authors: Batra Jyotsna , Fernando Achala , Liyanage Chamikara , Sriniva Srilakshmi , Panchadsaram Janaththani , Rothnagel Joseph A , Clements Judith 
Published: October 2023

Abstract

Background
Genome-wide association studies have implicated Iroquois (IRX) gene clusters in cancer susceptibility, yet their functional roles remain unclear. Micropeptides (miPEPs), short open reading frames (sORFs) encoding small peptides, have emerged as regulators of diverse biological processes. However, the significance of cancer-related miPEPs in tumorigenesis and therapy response remains unexplored.
Methods
Here, we utilized a Sequential Window Acquisition of all Theoretical Mass Spectra (SWATH-MS/MS)-based proteomic approach to discover miPEPs generated by IRX clusters in prostate, breast, endometrial, and ovarian cancers. The differential expression of identified miPEPs was measured across different cancer subtypes, grades, and stages, and identified IRX4_PEP1, a miPEP derived from IRX4 as a promising candidate. The role of IRX4_PEP1 was evaluated in prostate cancer (PCa) in vitro via functional assays and comprehensive pathway analysis. Additionally, the expression of IRX4_PEP1 was evaluated in PCa patient samples for it's potential diagnostic and prognostic significance.
Results
We identified 17 miPEPs from IRX clusters in prostate, breast, endometrial, and ovarian cancers. Among these, IRX4_PEP1 exhibited significant roles in PCa progression. IRX4_PEP1 promoted PCa cell proliferation, migration, and invasion by interacting with heterogeneous nuclear ribonucleoprotein K (hnRNPK). Notably, IRX4_PEP1 disrupted Wnt signaling by interacting with Catenin beta 1 (β catenin; CTNB1), elevating PCa stemness markers, and fostering docetaxel resistance. Clinically, IRX4_PEP1 expression was elevated in PCa tissues, correlating positively with disease aggressiveness. CTNB1 and hnRNPK levels also correlated positively with IRX4_PEP1 in PCa tissues.
Conclusions
These findings highlight IRX4_PEP1's role in PCa stemness and chemoresistance, suggesting it as a therapeutic target and potential diagnostic marker. This study unveils the intricate links between miPEPs, gene clusters, and cancer progression, offering insights for innovative interventions.
The Promotoer, a brain-computer interface-assisted intervention to promote upper limb functional motor recovery after stroke: a statistical analysis plan for a randomized controlled trial
Authors: Cipriani Marta , Pichiorri Floriana , Colamarino Emma , Toppi Jlenia , Tamburella Federica , Lorusso Matteo , Bigioni Alessandra , Morone Giovanni , Tomaiuolo Francesco , Santoro Filippo , Cordella Daniele , Molinari Marco , Cincotti Febo , Mattia Donatella , Puopolo Maria 
Published: October 2023

Abstract
Background Electroencephalography (EEG)-based Brain-Computer Interfaces (BCIs) allow to modulate the sensorimotor rhythms and are potential technologies for promoting post-stroke motor function recovery. The Promotoer study is planned to assess short and long-term efficacy of the Promotoer system, an EEG-based BCI to assist motor imagery (MI) practice, in enhancing post-stroke functional hand motor recovery. This paper details the statistical analysis plan of the Promotoer study.Methods The Promotoer study is a randomized, controlled, assessors blinded, single-centre, superiority trial, with 2 parallel groups and 1:1 allocation ratio. Subacute stroke patients are randomized to EEG-based BCI-assisted MI training or to MI training alone (ie., no BCI). Internal pilot study for sample size re-assessment is planned. Primary outcome is the effectiveness of the Upper extremity-Fugl-Meyer Assessment (UE-FMA) score. Secondary outcomes include clinical, functional and user experience scores assessed at the end of intervention and at follow-up. Neurophysiological assessments are also planned. Effectiveness formulas have been specified, and intention-to-treat and per-protocol populations have been defined. Statistical methods for comparisons of groups and for development of predictive score of important improvement are described. Explorative subgroup analyses and methodology to handle missing data are considered.Discussion The Promotoer study will produce robust evidence for short/long-term efficacy of the Promotoer system in subacute stroke patients undergoing rehabilitation. Moreover, the development of predictive score of response will allow the transfer of the Promotoer system to optimal clinical practice. The statistical analysis plan, by fully describing statistical principles and procedures for the analysis of data, provides transparency on the analysis of data.Trial registration: ClinicalTrials.gov NCT04353297. Registered on April 15, 2020, https://clinicaltrials.gov/study/NCT04353297
Escherichia coli Microcin B17 as a chassis for the development of novel antimicrobial peptides
Authors: Pinheiro Vitor , Masschelein Joleen , Withanage Shamal 
Published: October 2023

Abstract
Antimicrobial resistance remains an urgent and growing global health problem. Peptides with antimicrobial activity are widespread in nature but are rarely harnessed for therapeutic applications, making them an underexplored and untapped resource. Many harbour post-translational modifications, which are essential for function and have evolved alongside specialized export and immunity machineries – leading to the current paradigm that natural sequences cannot be changed in vivo, and curtailing their development as antimicrobial agents. Nonetheless, we found sufficient ambiguity in literature to question if that remained true for E. coli microcin B17 (MccB17). We demonstrate that its natural machinery for synthesis, export, and immunity can be co-opted as a platform for novel azole-modified antimicrobial peptide discovery, exploring sequences unrelated to wild-type. We show a dense functional space, establishing the possibility that, through directed evolution, antimicrobial discovery could match the speed with which resistance emerges.
New Porous Amine Functionalized Biochar based Desiccated Coconut Waste as Efficient CO2 Adsorbents
Authors: Zakaria Dina Sofiea , Rozi Siti Khalijah Mahmad , Halim Hairul Nazirah Abdul , Mohamad Sharifah , Zheng Ghee Kang 
Published: October 2023

Abstract
Environmental problems such as climate change and global warming caused by greenhouse gases, mainly CO2, have become a worldwide topic of concern. Adsorption is a promising method for CO2 capture. In this research, a series of amine functionalized on biochar based desiccated coconut waste (amine-biochar@DCW) namely ethylenediamine functionalized biochar@DCW (EDA-biochar@DCW), diethylenetriamine functionalized biochar@DCW (DETA-biochar@DCW), triethylenetetramine functionalized biochar@DCW (TETA-biochar@DCW), tetraethylenepentamine functionalized biochar@DCW (TEPA-biochar@DCW), and pentaethylenehexamine functionalized biochar@DCW (PEHA-biochar@DCW) adsorbents were synthesized and characterized. From the characterization analyses, series of amine-biochar@DCW adsorbents had better developed pore structure and larger specific surface area than that of pristine desiccated coconut waste (DCW). Furthermore, the results showed that the increase percentage of elemental of C and N as well the presence peaks NH stretching, NH bending, CN stretching, and CN bending, revealing the presence of amine on the surface of biochar@DCW. From the CO2 adsorption experiment, among amine modified biochar adsorbents, TETA-biochar@DCW had higher CO2 adsorption capacity (61.78 mg/g) with mass ratio (m:m) of biochar@DCW:TETA (1:2). The adsorption kinetics on the TETA-biochar@DCW was best fitted by the pseudo-second model, suggesting the adsorption process occurs through chemisorption. Additionally, TETA-biochar@DCW depicts high selectivity towards CO2 gas and good reusability after five CO2 adsorption–desorption cycles.