bluuebunny/crossref_metadata_2025 huggingface dataset:
Sequelae of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection among Kidney Transplant Recipients: A Large Single-Center Experience
Authors: Zona Emily E. , Gibes Mina L. , Jain Asha S. , Danobeitia Juan S. , Garonzik-Wang Jacqueline , Smith Jeannina A. , Mandelbrot Didier A. , Parajuli Sandesh
Published: May 2024

Background. Kidney transplant recipients (KTRs) are a vulnerable immunocompromised population at risk of severe COVID-19 disease and mortality after SARS-CoV-2 infection. We sought to characterize the post-infection sequelae in KTRs at our center. Methods. We studied all adult KTRs (with a functioning allograft) who had their first episode of SARS-CoV-2 infection between 04/2020 and 04/2022. Outcomes of interest included risk factors for hospitalization, all-cause mortality, COVID-19-related mortality, and allograft failure. Results. Of 979 KTRs with SARS-CoV-2 infection, 381 (39%) were hospitalized. In the multivariate analysis, risk factors for hospitalization included advanced age/year (HR: 1.03, 95% CI: 1.02–1.04), male sex (HR: 1.29, 95% CI: 1.04–1.60), non-white race (HR: 1.48, 95% CI: 1.17–1.88), and diabetes as a cause of ESKD (HR: 1.77, 95% CI: 1.41–2.21). SARS-CoV-2 Vaccination was associated with decreased risk of hospitalization (HR: 0.73, 95% CI: 0.59–0.90), all-cause mortality (HR: 0.52, 95% CI: 0.37–0.74), and COVID-19-related mortality (HR: 0.47, 95% CI: 0.31–0.71) in the univariate and multivariate analyses. Risk factors for both all-cause and COVID-19-related mortality in the multivariate analyses included advanced age, hospitalization, and respiratory symptoms for hospital admission. Furthermore, additional risk factors for all-cause mortality in the multivariate analysis included being a non-white recipient and diabetes as a cause of ESKD, with being a recipient of a living donor as protective. Conclusions. Hospitalization due to COVID-19-associated symptoms is associated with increased mortality. Vaccination is a protective factor against hospitalization and mortality.
Accounting for BEV Users’ Risk Attitudes and Charging Inertia in En Route Charging Choice Behavior
Authors: Jin Zhicheng , Li Hao , Chen Di , Yu Lu , Tu Huizhao
Published: May 2024

This paper innovatively explores BEV (battery electric vehicle) users’ risk attitudes and charging inertia, examining their effects on en route charging and charging route choice behavior. An attitudinal survey was conducted to explore the two latent variables of risk attitudes and charging inertia in relation to socioeconomic and travel‐related characteristics. ICLV (Integrated choice and latent variable) models are adopted to estimate the latent variables and the charging choice behavior simultaneously. Specifically, uncertainty in energy consumption is first considered in the ICLV model, which is represented by the available range (AR) uncertainty. Multinomial logit (MNL) models directly incorporating socioeconomic attributes are employed as a reference for comparison with ICLV models. Results illustrate that risk attitudes and charging inertia both play significant roles in modeling en route charging choice behavior. Risk‐averse users and users having charging inertia value AR uncertainty more. Battery range, charging frequency, and income emerge as the most crucial factors influencing users’ intention to charge en route. The results show significant heterogeneity of BEV users in attitudes and charging choice behavior, underscoring the importance of accounting for the heterogeneity in en route charging demand estimation and deployment optimization of public charging stations, particularly for medium‐to long‐distance trips.
Metabolic Reprogramming of Tumor-Associated Macrophages Using Glutamine Antagonist JHU083 Drives Tumor Immunity in Myeloid-Rich Prostate and Bladder Cancers
Authors: Praharaj Monali , Shen Fan , Lee Alex J. , Zhao Liang , Nirschl Thomas R. , Theodros Debebe , Singh Alok K. , Wang Xiaoxu , Adusei Kenneth M. , Lombardo Kara A. , Williams Raekwon A. , Sena Laura A. , Thompson Elizabeth A. , Tam Ada , Yegnasubramanian Srinivasan , Pearce Edward J. , Leone Robert D. , Alt Jesse , Rais Rana , Slusher Barbara S. , Pardoll Drew M. , Powell Jonathan D. , Zarif Jelani C.
Published: May 2024

Glutamine metabolism in tumor microenvironments critically regulates antitumor immunity. Using the glutamine-antagonist prodrug JHU083, we report potent tumor growth inhibition in urologic tumors by JHU083-reprogrammed tumor-associated macrophages (TAMs) and tumor-infiltrating monocytes. We show JHU083-mediated glutamine antagonism in tumor microenvironments induced by TNF, proinflammatory, and mTORC1 signaling in intratumoral TAM clusters. JHU083-reprogrammed TAMs also exhibited increased tumor cell phagocytosis and diminished proangiogenic capacities. In vivo inhibition of TAM glutamine consumption resulted in increased glycolysis, a broken tricarboxylic acid (TCA) cycle, and purine metabolism disruption. Although the antitumor effect of glutamine antagonism on tumor-infiltrating T cells was moderate, JHU083 promoted a stem cell–like phenotype in CD8+ T cells and decreased the abundance of regulatory T cells. Finally, JHU083 caused a global shutdown in glutamine-utilizing metabolic pathways in tumor cells, leading to reduced HIF-1α, c-MYC phosphorylation, and induction of tumor cell apoptosis, all key antitumor features. Altogether, our findings demonstrate that targeting glutamine with JHU083 led to suppressed tumor growth as well as reprogramming of immunosuppressive TAMs within prostate and bladder tumors that promoted antitumor immune responses. JHU083 can offer an effective therapeutic benefit for tumor types that are enriched in immunosuppressive TAMs.
Supplementary Figure S10 from LP-184, a Novel Acylfulvene Molecule, Exhibits Anticancer Activity against Diverse Solid Tumors with Homologous Recombination Deficiency
Authors: Kulkarni Aditya , Zhou Jianli , Biyani Neha , Kathad Umesh , Banerjee Partha P. , Srivastava Shiv , Prucsi Zsombor , Solarczyk Kamil , Bhatia Kishor , Ewesuedo Reginald B. , Sharma Panna
Published: May 2024

Figure S10 shows mouse body weight changes in a TNBC PDX model following treatment with LP-184, Olaparib or the combination
Efficacy and Safety of Atezolizumab and Bevacizumab in Appendiceal Adenocarcinoma
Authors: Hornstein Nicholas J. , Zeineddine Mohammad A. , Gunes Betul B. , Pellatt Andrew J. , Knafl Mark , Zhu Haifeng , Willett Anneleis F. , Yousef Abdelrahman , Liu Suyu , Sun Ryan , Futreal Andrew , Woodman Scott E. , Taggart Melissa W. , Overman Michael J. , Halperin Daniel M. , Raghav Kanwal P. , Shen John Paul
Published: May 2024

Purpose:

Appendiceal adenocarcinoma (AA) remains an orphan disease with limited treatment options for patients unable to undergo surgical resection. Evidence supporting the efficacy of combined VEGF and PD-1 inhibition in other tumor types provided a compelling rationale for investigating this combination in AA, where immune checkpoint inhibitors have not been explored previously.

Experimental Design:

We conducted a prospective, single-arm phase II study evaluating efficacy and safety of atezolizumab in conjunction with bevacizumab (Atezo+Bev) in advanced, unresectable AA.

Results:

Patients treated with the Atezo+Bev combination had 100% disease control rate (1 partial response, 15 stable disease) with progression-free survival (PFS) of 18.3 months and overall survival not-yet-reached with median duration of follow-up of 40 months. These survival intervals were significantly longer relative to a clinically and molecularly matched synthetic control cohort treated with cytotoxic chemotherapy designed for colorectal cancer (PFS of 4.4 months, P = 0.041).

Conclusions:

In light of recent data demonstrating a lack of efficacy of 5-fluorouracil–based chemotherapy, Atezo+Bev is a promising treatment option for patients with low-grade unresectable AA; further study is warranted.

Significance:

AA remains an orphan disease with limited systemic therapy options for patients who are not candidates for surgical resection. These data suggest activity from combined VEGF and PD-L1 inhibition that warrants further study.
Liver Resection in Synchronous Bilobar versus Unilobar Colorectal Liver Metastases: A Retrospective Analysis of Oncological Outcomes and Patient Survival
Authors: Stoess Christian , Mirschinka Benjamin , Ollesky Johanna , Steffani Marcella , Seyfried Nick , Kaufmann Benedikt , Friess Helmut , Hüser Norbert , Nitsche Ulrich , Hartmann Daniel
Published: May 2024

Introduction: Resection of colorectal liver metastasis has emerged as the standard treatment. Our study compares oncological outcomes of patients with resected synchronous bilobar versus unilobar colorectal liver metastasis. Methods: This retrospective study presents long-term follow-up data of 105 consecutive patients with primary colorectal cancer and synchronous liver metastasis. All patients underwent primary tumor and metastasis resections between 2007 and 2019. Results: Fifty-five patients with bilobar and 50 patients with unilobar colorectal liver metastases were included. No significant difference in overall, tumor-specific, or recurrence-free survival was observed between patients with bilobar and unilobar metastases. After case-control matching, the results were confirmed in patients with similar tumor burdens. In the multivariate analysis, chemotherapy following liver metastasis resection was a significant prognostic factor associated with improved overall survival (hazard ratio 0.518, 95% confidence interval: 0.302–0.888, p = 0.017). Conclusion: Overall survival, as well as tumor-specific and recurrence-free survival, did not differ between patients with unilobar and bilobar liver metastasis. These findings contribute to the understanding that primary tumor and metastasis resection in eligible patients improve long-term outcomes.
PQ enhancement in grid connected EV charging station using novel GVCR control algorithm for AUPQC device
Authors: Dharavatu Anil Kumar , Ramavathu Srinu Naik
Published: May 2024

The rapid increase of environmental impacts together global warming is conquered by substantial selection of electric-vehicles (EV’s) over the internal-combustion engine (ICE) vehicles. The replacement of these vehicles in transportation industry has led to reducing the running cost, ecological emissions, vehicle maintenance. The EV’s are operated by available battery energy and energized through utility-grid integrated EV charging stations. It is noted that, such charging stations may introduce power-quality issues, highly impacting the electric-grid due to presence of power electronic conversion devices in EV charging stations. The primary emphasis of power-quality impacts on electrical distribution grid are counteracted by employing active universal power-quality conditioner (AUPQC) device. The main role of AUPQC has been selected for mitigation of various PQ problems on both electric-grid side and charging station by using feasible control objective. In this work, a novel generalized voltage-current reference (GVCR) control objective has been proposed for extraction of fundamental reference voltage-current signals. The key findings are simple mathematical notations, no transformations, fast response, low dv/dt switch stress, low switching loss and maximum efficiency. The main goal is design, operation and performance of proposed GVCR controlled AUPQC device has been validated under integration of various EV chargers to electric-grid by using MATLAB/Simulink computing tool, simulation results are presented for analysis and interpretation.
Investigation of linear models for control of water flow and temperature in a water supply system
Authors: Asset Askhat , Mansurova Madina , Zhmud Vadim , Kopesbaeva Aksholpan , Dzheksenbaev Nurbolat
Published: May 2024

In some cases, the object model is a set of parallel models of the same general appearance, but with different parameters. The most common model is a model in the form of a serial connection of a first- or second-order filter and a delay link. An example is the water supply system of a large residential building or a group of houses. From the most general considerations, we can expect that such an object can be approximately described by a simpler model, replacing the sum of identical-looking models with different parameters with a single model of this type with averaged parameters, however, finding many parameters simply in the form of an average is, apparently, an unreasonable approach. It seems more reasonable to find the parameters by the approximating model by numerical optimization, in which the integral from the module or from the square of the deviation of the output signal of such a model from the output signal of the exact model is minimized when the test signal is applied. For linear models, the most reasonable test signal is a single step effect. This article tests this hypothesis and provides the results of this test.
Integration of statistical methods and neural networks for temperature regulation parameter optimization
Authors: Kaddar Leila Benaissa , Khelifa Said , Zareb Mohamed El Mehdi
Published: May 2024

Temperature control plays a crucial role in various industrial processes, ensuring optimal performance and product quality. The conventional approach to optimizing temperature controller parameters involves manual tuning, which can be time-consuming, labor-intensive, and often lacks precision. This paper introduces an innovative methodology for optimizing the parameters of a temperature controller by integrating statistical methods in the preparation of the experimental plan utilized by neural networks. The integration of statistical techniques in designing the experimental framework enhances the efficiency of data collection, providing a robust foundation for subsequent analysis. The neural network leverages this well-structured dataset to model and optimize the temperature controller parameters, resulting in improved precision and performance. The synergistic integration of statistical methods and neural networks not only streamlines the optimization process but also enhances the reliability of the temperature control system. The effectiveness of the proposed approach is demonstrated through case studies on the Procon level/flow and temperature 38-003 process. The results show significant improvements in temperature control performance, with reduced process variability and faster response times.
CNN and Adaboost fusion model for multiface recognition based automated verification system of students attendance
Authors: Hussain Hassan Nashaat M. , Moussa Mahmoud A. , M. Mahmoud Mohamed Hassan
Published: May 2024

In recent times, companies and institutions globally are increasingly adopting automated systems for recording employee attendance due to the inefficiency and error-prone nature of traditional methods. Face recognition is the fastest, most natural, and most accurate way to identify someone, despite its difficulty. Remote deployment and control of the technology using internet of things (IoT) protocols provides real-time attendance data worldwide. We use the Haar-cascade algorithm to detect and extract features and the adaptive boost algorithm confused with convolutional neural network (CNN) algorithm to recognize the face in our proposed smart attendance system. Per frame, the proposed system recognizes multiple faces. Face recognition in 18 conditions was designed into the proposed system to ensure its versatility. The system's graphical user interface (GUI) was made for average users. This work is more important because IoT technology records student attendance and sends data to authorities. We use Raspberry Pi 4 and camera module for our suggested system. Python and OpenCV libraries tested the multiple face image recognition proposal in 18 situations under four conditions. Single-face image recognition was compared to other methods. In most cases, the proposed method was 100% accurate and outperformed related methods.
Biomarkers of attention bias during public speaking anxiety
Authors: Razak Akmal , Feroz Farah Shahnaz , Subramaniam Siva Kumar , Shahbodin Faaizah , Rajkumar Sujatha
Published: May 2024

The analysis of brain signals and their properties yields significant insights into the fundamental neural impairments associated with attention bias in individuals suffering from public speaking anxiety (PSA). This study aims to identify electroencephalogram (EEG) and performance biomarkers of attention bias in individuals with public speaking anxiety using the ex-Gaussian modeling technique, frontal alpha asymmetry (FAA) and delta-beta correlation (DBC). 12 subjects with high (H) PSA and 12 subjects with low (L) PSA performed the modified emotional stroop task. EEG data were captured using the low-cost 14-channel emotiv Epoc+. Results showed that the ex-Gaussian sigma was higher in the emotional condition in the high public speaking anxiety (HPSA) group, indicating attention bias. The study also found higher right FAA in HPSA compared to LPSA group. There was a negative correlation between σ and alpha power in the left region of the brain in the HPSA group, potentially related to attentional bias. Moreover, there was a notable trend towards significantly heightened DBC in the frontal and central regions of the brain among HPSA subjects. In conclusion, in biomedical engineering, the ex-Gaussian model, FAA and DBC are useful because they can identify EEG and performance biomarkers of attention bias in people with PSA.
High-gain UWB elliptical and circular slotted antipodal Vivaldi antenna for through wall detection
Authors: Ahmed Sajjad , Katiran Norshidah , Joret Ariffuddin , Mohd Shah Shaharil , Ahmed Arslan , Tusin Najwanisa
Published: May 2024

The paper describes a high-gain ultra-wideband (UWB) elliptical and circular slotted antipodal Vivaldi antenna (ECS-AVA) that is designed for through-wall detection systems. The antenna flares are loaded with elliptical and circular slots to improve the gain and broaden the bandwidth. To validate the efficacy of the designed antenna, a prototype of ECS-AVA is fabricated and subjected to measurements. The experimental findings suggest that the designed antenna can handle signals effectively across a range from 3.1 GHz to 10.6 GHz, as shown by its measured impedance bandwidth, with │S11│≤ -10 dB. The obtained measurements results are consistent with the results of the CST simulation. The proposed antenna exhibits improved radiation patterns in the UWB band with peak gain values ranging from 4.8 dB to 11.9 dB.
Energy management enhancement of a smart home supplied by renewable energy system
Authors: Shakir Hasan Hammoodi , Salem Fatma Ben
Published: May 2024

Solar energy is a reliable and eco-friendly solution for power outages in Karbala, Iraq. This study presents a smart grid technology model for energy management in electrical systems, optimizing power schemes and economic benefits through a unique spatial distribution approach in Iraq, with the primary objective of ensuring consistent base loads for smart homes while achieving other economic goals. The algorithm’s effectiveness was tested in three different scenarios. The energy was supplied by the national grid and battery bank-powered base loads. Meteorological data, including temperature and solar radiation, was gathered from a station in Karbala city for testing and evaluation. The study found that energy consumption decreased by 85% in April, with solar energy accounting for 37% of the total consumption. Smart homes saved 48% of energy, reducing reliance on the grid to 15%, as well as the reduction of energy consumption reached up to 47% and 60% in January and July, respectively, with solar energy estimated at 14% and 26% in those months.
Artificial intelligence powered internet of vehicles: securing connected vehicles in 6G
Authors: Kumar Raja Depa Ramachandraiah , Abas Zuraida Abal , Akula Chandra Sekhar , Kumar Yellapalli Dileep , Kumar Goshtu Hemanth , Eswari Venappagari
Published: May 2024

The rapid advancements in automotive technology and the emergence of next-generation networks such as 5G and 6G are laying the foundation for the internet of vehicles (IoV), a revolutionary concept to transform transportation systems. The convergence of artificial intelligence (AI) and connected vehicles IoV is driving a paradigm shift in the transportation sector, especially in the dynamic framework of 5G and future 6G networks. This survey paper provides a thorough survey of the evolving AI-based IoV security landscape. We explore key areas of 5G/6G networks, focusing on the complex interplay of machine learning (ML) and deep learning (DL) in enhancing vehicle-to-everything (V2X) security and connected vehicles. Addressing the unique challenges of 6G, this paper outlines future directions for improving security and highlights open research issues. This comprehensive survey, which aims to provide information and guidance to both researchers and practitioners, contributes to a detailed understanding of the security issues associated with connected vehicles in the emerging 6G era.
Evaluation of machine learning algorithms in the early detection of Parkinson's disease: a comparative study
Authors: Zapata-Paulini Joselyn , Cabanillas-Carbonell Michael
Published: May 2024

Parkinson's is a neurodegenerative disease that generally affects people over 60 years of age. The disease destroys neurons and increases the accumulation of α-synuclein in many parts of the brain stem, although at present its causes remain unknown. It is therefore a priority to identify a method that can detect the disease, and this is where machine learning models become important. This study aims to perform a comparative analysis of machine learning models focused on the early detection of Parkinson's disease. Logistic regression (LR), support vector machines (SVM), decision trees (DT), extra trees classifiers (ETC), K-nearest neighbors (KNN), random forests (RF), adaptive boosting (AdaBoost) and gradient boosting (GB) algorithms are described and developed to identify the one that offers the best performance. In the training stage, we used the Oxford University dataset for Parkinson's disease detection, which has a total of 23 attributes and 195 records on patient voice recordings. The article is structured into six sections, such as introduction, related work, methodology, results, discussions, and conclusions. The metrics of accuracy, sensitivity, F1 count, and precision were used to measure the models' performance. The results position the KNN model as the best predictor with 95% accuracy, precision, sensitivity, and F1 score.
Unveiling deep learning powers: LSTM, BiLSTM, GRU, BiGRU, RNN comparison
Authors: Shaikh Zakir Mujeeb , Ramadass Suguna
Published: May 2024

Deep learning algorithms have revolutionized various fields by achieving remarkable results in time series analysis. Among the different architectures, recurrent neural networks (RNNs) have played a significant role in sequential data processing. This study presents a comprehensive comparison of prominent RNN variants: long short-term memory (LSTM), Bidirectional LSTM (BiLSTM), gated recurrent unit (GRU), bidirectional GRU (BiGRU), and RNN, to analyze their respective strengths and weaknesses of national stock exchange India (NSEI). The Python application developed for this research aims to evaluate and determine the most effective algorithm among the variants. To conduct the evaluation, data from the public domain covering the period from 1/1/2004 to 30/06/2023 is collected. The dataset considers significant events such as demonetization, market crashes, the COVID-19 pandemic, downturns in the automobile sector, and rises in unemployment. Stocks from various sectors including banking, automobile, oil and gas, metal, and Pharma are selected for analysis. Finally, the results reveal that algorithm performance varies across different stocks. Specifically, in certain cases, BiLSTM outperforms, while in others, both BiGRU and LSTM are surpassed. Notably, the overall performance of simple RNN is consistently the lowest across all stocks.
Hyperspectral image construction in different spectral bands of tea leafs for identifying the tea type using O-ConvNet-RF model
Authors: Gongalla Likitha , Bordoloi Monali
Published: May 2024

Tea, a commonly consumed beverage, is susceptible to being sold in adulterated or expired forms by third-party vendors. Hyperspectral imaging across different wavelength bands has proven to precisely assess the diverse types of tea and their corresponding financial gains. This study aims to employ a deep learning methodology in conjunction with hyperspectral imaging for efficiently classifying tea leaves. A novel approach is proposed, wherein a waveband convolutional neural network is utilized to generate hyper spectral images of tea leaf samples with enhanced resolution. The model known as optimized-convolutional neural network-random forest O- (ConvNet-RF) demonstrated exceptional performance, achieving high accuracy, impressive recall, F1 score, and notable sensitivity rate, outperforming existing alternative methods. The tea leaf types, namely green, yellow, and black, were accurately identified using a combination of the random forest (RF) model and the O-ConvNet-RF model. The tree-based classification method for the identification of tea leaves demonstrated superior performance as compared to alternative machine learning models. In general, this study presents a successful methodology for the classification of tea leaves, with potential implications for consumer processing and distributor profit analysis.
Grid impact analysis on wind power plant interconnection in strengthening electricity systems
Authors: Senen Adri , Kurniawan Arif , Dini Hasna Satya , Anggaini Dwi
Published: May 2024

The Timor system is one of the large systems in the East Nusa Tenggara region. Based on the general plan for electricity supply for 2021-2030, there is a plan to interconnect a 2x11 MW wind power plant. The addition of wind power plants will pose a considerable threat to the system due to the intermittency nature of renewable energy plants. Therefore, a comprehensive grid impact study is needed to convince network managers that adding wind farms will not cause disruptions to the system either locally or in general and is expected to strengthen the electricity system. The power flow simulation results, installing a 2x11 MW wind farm on the Timor system can improve voltage quality and reduce losses on both 70 and 150 kV systems. For transient stability, the frequency value on the Timor system still meets the grid code requirements. In addition, the simulation results of the intermittency impact of the wind power plant output show that the Timor system is still in a stable condition. The stability of the rotor angle of the existing power plant when the transient stability simulation is carried out shows that it is still in a balanced condition.
Breast cancer relapse disease prediction improvements with ensemble learning approaches
Authors: Sahoo Ghanashyam , Nayak Ajit Kumar , Tripathy Pradyumna Kumar , Pati Abhilash , Panigrahi Amrutanshu , Rath Adyasha , Moharana Bhimasen
Published: May 2024

Diagnosis and prognosis are especially difficult areas of medical research related to cancer due to the high incidence of breast cancer, which has surpassed all other cancers in terms of female mortality. Another factor that has a substantial influence on the quality of life of cancer patients is the fear that they may experience a relapse of their disease. The objective of the study is to give medical practitioners a more effective strategy for using ensemble learning techniques to forecast when breast cancer may recur. This research aimed to investigate the usage of deep neural networks (DNNs) and artificial neural networks (ANNs) in addition to machine learning (ML) based approaches, including bagging, averaging, and voting, to enhance the efficacy of breast cancer relapse diagnosis on two breast cancer relapse datasets. Results from the empirical study demonstrate that the proposed ensemble learning-enabled approach improves accuracies by 96.31% and 95.81%, precisions by 96.70% and 96.15%, sensitivities by 98.88% and 98.68%, specificities by 84.62% in both, F1-scores by 97.78% and 97.40%, and area under the curve (AUCs) of 0.987 and 0.978, with University Medical Centre, Institute of Oncology (UMCIO) and Wisconsin prognostic breast cancer (WPBC) datasets respectively. Consequently, these improved disease outcomes may encourage physicians to use this model to make better treatment choices.
Enhancing learner performance prediction on online platforms using machine learning algorithms
Authors: Jebbari Mohammed , Cherradi Bouchaib , Hamida Soufiane , Ouassil Mohamed Amine , El Harrouti Taoufiq , Raihani Abdelhadi
Published: May 2024

E-learning has emerged as a prominent educational method, providing accessible and flexible learning opportunities to students worldwide. This study aims to comprehensively understand and categorize learner performance on e-learning platforms, facilitating timely support and interventions for improved academic outcomes. The proposed model utilizes various classifiers (random forest (RF), neural network (NN), decision tree (DT), support vector machine (SVM), and K-nearest neighbors (KNN)) to predict learner performance and classify students into three groups: fail, pass, and withdrawn. Commencing with an analysis of two distinct learning periods based on days elapsed (≤120 days and another exceeding 220 days), the study evaluates the classifiers’ efficacy in predicting learner performance. NN (82% to 96%) and DT (81%-99.5%) consistently demonstrate robust performance across all metrics. The classifiers exhibit significant performance improvement with increased data size, suggesting the benefits of sustained engagement in the learning platform. The results highlight the importance of selecting suitable algorithms, such as DT, to accurately assess learner performance. This enables educational platforms to proactively identify at-risk students and offer personalized support. Additionally, the study highlights the significance of prolonged platform usage in enhancing learner outcomes. These insights contribute to advancing our understanding of e-learning effectiveness and inform strategies for personalized educational interventions.
Performance analysis of the proxy-based and collusion-resistant revocable CPABE framework
Authors: Chawla Shobha , Gupta Neha
Published: May 2024

An efficient revocation of access rights in ciphertext policy attribute-based encryption (CPABE) schemes has multiple challenges, particularly for lightweight devices. Thus, extensive research on the existing studies enforcing and governing access control has been conducted. The methodologies used in the existing CPABE (bilinear pairing cryptography based) schemes to revoke users at the system and attribute levels have been focused on in the current study. The existing studies have been examined on the basis of the following parameters for revocation: type of revocation addressed, level of collusion resistance, dynamicity achieved, scalability of revocation, and computational cost incurred. It has been observed in the study that no single scheme achieves all the revocation properties and addresses both types of revocation. The module proposed in proxy-based and collusion-resistant multi-authority revocable CPABE (PCMR-CPABE) efficiently addresses both types of revocation and is fully collusion-resistant, dynamic, and scalable. The present paper extends the study on PCMRCPABE and presents a performance analysis of the module in terms of functional specifications and computational cost. The presented analysis has compared the performance of the existing cutting-edge schemes with the PCMR-CPABE module and has proved that the proposed module is better in terms of functionality and is computationally inexpensive.
Cervical cancer: empowering diagnosis with VGGNet transfer learning
Authors: Joshi Vaishali M. , Mulmule Pallavi V. , Gandhi Swati A. , Patil Alaknanda S.
Published: May 2024

This study addresses the critical issue of cervical cancer, which stands as the fourth most prevalent cancer among women. With early detection being pivotal for successful treatment, the research focuses on evaluating the effectiveness of deep learning-based models in cervical cancer detection. Leveraging the widely employed Papanicolaou (Pap) smear test, the study proposes a transfer learning approach, incorporating contrast limited adaptive histogram equalization for image enhancement. Convolutional neural network models, including AlexNet, visual geometry group (VGGNet)-16, and VGGNet-19, are employed to accurately distinguish between cancerous and non-cancerous cervical cell images. The evaluation metrics encompass accuracy, precision, sensitivity, specificity, F1-score, and the matthew correlation coefficient (MCC). Notably, the findings reveal the exceptional performance of the VGGNet-19 model, achieving an accuracy of 98.71%, sensitivity of 98.33%, and specificity of 99% for a single smear cell. This research marks a significant advancement in the application of deep learning for precise cervical cancer detection. The promising results underscore the potential of these models to enhance early diagnosis and contribute to improved treatment outcomes, thereby addressing a crucial aspect of women's health.
Elevating smart city mobility using RAE-LSTM fusion for next-gen traffic prediction
Authors: Rafalia Najat , Moumen Idriss , Raji Fatima Zahra , Abouchabaka Jaafar
Published: May 2024

The burgeoning demand for efficient urban traffic management necessitates accurate prediction of traffic congestion, spotlighting the essence of time series data analysis. This paper delves into the utilization of sophisticated deep learning methodologies, particularly long short-term memory (LSTM) networks, convolutional neural networks (CNN), and their amalgamations like Conv-LSTM and bidirectional-LSTM (Bi-LSTM), to elevate the precision of traffic pattern forecasting. These techniques showcase promise in encapsulating the intricate dynamics of traffic flow, yet their efficacy hinges upon the quality of input data, emphasizing the pivotal role of data preprocessing. This study meticulously investigates diverse preprocessing techniques encompassing normalization, transformation, outlier detection, and feature engineering. Its discerning implementation significantly heightens the performance of deep learning models. By synthesizing advanced deep learning architectures with varied preprocessing methodologies, this research presents invaluable insights fostering enhanced accuracy and reliability in traffic prediction. The innovative RD-LSTM approach introduced herein harnesses the hybridization of a reverse AutoEncoder and LSTM models, marking a novel contribution to the field. The implementation of these progressive strategies within urban traffic management portends substantial enhancements in efficiency and congestion mitigation. Ultimately, these advancements pave the way for a superior urban experience, enriching the quality of life within cities through optimized traffic management systems.
Advancing medical imaging with GAN-based anomaly detection
Authors: Ounasser Nabila , Rhanoui Maryem , Mikram Mounia , El Asri Bouchra
Published: May 2024

Anomaly detection in medical imaging is a complex challenge, exacerbated by limited annotated data. Recent advancements in generative adversarial networks (GANs) offer potential solutions, yet their effectiveness in medical imaging remains largely uncharted. We conducted a targeted exploration of the benefits and constraints associated with GAN-based anomaly detection techniques. Our investigations encompassed experiments employing eight anomaly detection methods on three medical imaging datasets representing diverse modalities and organ/tissue types. These experiments yielded notably diverse results. The results exhibited significant variability, with metrics spanning a wide range (area under the curve (AUC): 0.475-0.991; sensitivity: 0.17-0.98; specificity: 0.14-0.97). Furthermore, we offer guidance for implementing anomaly detection models in medical imaging and anticipate pivotal avenues for future research. Results unveil varying performances, influenced by factors like dataset size, anomaly subtlety, and dispersion. Our findings provide insights into the complex landscape of anomaly detection in medical imaging, offering recommendations for future research and deployment.
Harnessing the power of blockchain to strengthen cybersecurity measures: a review
Authors: Turab Nidal , Owida Hamza Abu , Al-Nabulsi Jamal I.
Published: May 2024

As the digital environment continues to evolve with the increasing frequency and complexity of cybersecurity threats, there is growing interest in using blockchain (BC) technology. BC is a technology with desirable properties such as decentralization, integrity, and transparency. The decentralized nature of BC eliminates single points of failure, reducing the vulnerability of critical systems to targeted attacks. The complex and rapidly evolving nature of cyber threats requires an earlier and adaptive approach. This review paper examined several papers collected from official websites. Focusing on using BC technology to improve cybersecurity, the main keywords of the review paper were BC technology, supply chain management, proof of work, and proof of stake. This review paper aims to investigate the security components through a threat assessment that compares the security of BC in different classes and real attack environments. It highlights the potential of BC to strengthen cybersecurity measures, citing unique features. The review paper also points out that there is a lack of focus on addressing security challenges related to computer data and digital systems and calling for a deeper discussion on problem-solving.
Hardware-realized secure transceiver for human body communication in wireless body area networks
Authors: Nataraju Chaitra Soppinahally , Sreekantha Desai Karanam , Sairam Kanduri VSSSS
Published: May 2024

Wireless body area networks (WBANs), featuring wearable and implantable devices for collecting physiological data are increasingly critical in healthcare for enabling continuous remote monitoring, diagnostic improvements, and treatment optimization. Secure communication within WBANs is essential to protect sensitive health data from unauthorized access and manipulation. This paper introduces a novel secure digital (SD)- human body communication (HBC) Transceiver (TR) system, tailored for WBAN applications, that prioritizes security and offers significant enhancements in size, power efficiency, speed, and data transmission efficiency over current solutions. Leveraging a combination of frequency-selective (FS) digital transmission with walsh codes (WCs) or quadrature amplitude modulation (QAM), and incorporating one-round encryption and decryption modules, the system complies with the IEEE 802.15.6 standard, ensuring broad compatibility. Specifically, the QAM-based SD-HBC TR system exhibits a 4% reduction in chip area, a 7.6% increase in operating frequency, a 3.4% decrease in power consumption, a 27.5% reduction in latency, and improvements of 33% in throughput and 35.5% in efficiency. Importantly, it achieves a bit error rate (BER) of up to 10-8 , demonstrating high reliability across communication methods. This research significantly advances secure communication in WBANs, offering a promising approach for enhancing the reliability, efficiency, and security of healthcare monitoring technologies.
Inventory model having preservation technology with fix lifetime under two level trade credit policy
Authors: Sharma Deep Kamal , Singh Ompal
Published: May 2024

Supply-chain management involves moving storage supplies from origin to consumption, with manufacturers running production based on quadratic demand, distributors and retailers monitoring inventory. When a new product is released, demand often rises linearly and then declines dramatically when an alternative becomes available. Shortages are not allowed. Players' inventory will decrease at a rate of (1/(1+m-t)), where m is fixed lifetime, greater than the replenishment time. Deteriorating goods experience constant mass loss or usefulness, but preservation technology can help the damaged item to be consumed. Retailers with direct customer relationships can reduce stock spoilage through good warehouses. Manufacturers' storage systems have a higher deterioration rate. Two-tier trade credit financing is examined in this model. Distributors offer specific credit terms to stores, while manufacturers provide a grace period for invoicing. Distributors and retailers must pay interest on unsold inventories if invoices aren't settled on time. An integrated storage system reduces costs by minimizing costs through multiple shipments from manufacturers to distributors and retailers, and by adjusting replenishment times for each player. The resolution process is designed so that the supply chain operator gets the best possible decision. Therefore, results are authorized using mathematical examples for different scenarios. Management decisions are suggested.
Proposal for an e-learning system model based on the invocation and semantic discovery of web services
Authors: Halim Mohamed , Tahiri Abdelmajid , El Ghzizal Yassir , Adadi Nouha , Chenouni Driss
Published: May 2024

Service-oriented computing (SOC) provides a new framework for designing distributed web applications and software in a flexible, scalable, and cost-effective manner. Its use is widespread to efficiently integrate existing Web services and create high value-added applications. This model, proven in various fields such as e-commerce, also shows significant advantages in the field of e-learning. This approach highlights the discovery and use of Web services listed in specialized directories. In fact, this paper proposes a framework for exploring web services associated with education. This approach is based on the application of a matching algorithm to select the services best suited to the needs of users of the online learning system, as well as the ontology of the e-learning domain and the semantic descriptions of the web services via web ontology language for web services (OWL-S).
Prácticas pedagógicas que posibilitan la formación investigativa de docentes
Authors: Moya Garzón Yuly Paulin , Rengifo Guerrero Elizabeth
Published: May 2024

El presente artículo recoge de manera general las reflexiones que se plantearon en la investigación “Hacia la comprensión de las prácticas pedagógicas que posibilitan la formación investigativa de docentes en la Licenciatura en Electrónica de la Universidad Pedagógica Nacional”, que se llevó a cabo en el marco de la línea de investigación en Educación, perteneciente a la cohorte 23 de la Maestría en Desarrollo Educativo y Social del CINDE–UPN.
Global South: ¿una agenda política para pensar la comunicación?
Authors: Peres-Neto Luiz
Published: May 2024

In recent years the idea of the global south has become more visible in many media and communication publications, especially in the context of the global north. Based on this premise, this article proposes to present the main lines employed under the global south's idea, and, therefore, discuss its appropriation by the field of communication, in the light of Ibero-American communicology. Finally, it is stressed to what extent the global south represents a political agenda, able to articulate dialogues between the Ibero-American communicational production - epistemic and methodological traditions- and the agenda of the global north, its limitations, and potentialities from the experience of a researcher subjected to both logics.
Publicidade contemporânea e semiótica peirceana: uma proposta metodológica
Authors: Pompeu Bruno , Sato Silvio Koiti
Published: May 2024

Este artigo pretende apresentar a contribuição da semiótica de Charles Sanders Peirce para um entendimento mais amplo e menos superficial da publicidade atual, compreendendo-a como uma modalidade de linguagem que permeia ininterruptamente a vida e o mundo. Entendemos que o legado teórico-metodológico de Peirce cresce em relevância para refletir as transformações por que passa a comunicação publicitária na contemporaneidade, tanto no seu plano expressivo, quanto nas suas lógicas mais profundas. Para isso, propomos uma metodologia de análise semiótica publicitária que vai além de esmiuçar elementos constituintes de uma peça específica, incorporando a referencialidade cada vez mais expandida da publicidade e sua surpreendente potência de geração de sentido.
Sonoridades fronteiriças: fruição e rearticulações da música pop e da música popular
Authors: Carvalho Nilton Faria de
Published: May 2024

This study evaluated empirical data from musical listening workshops to analyze the communicational aspects of hybrid musical productions by artists from the global South. The discourse of workshop participants open a premise to consider the mixture of rhythms, instrumentation, timbres, and bodies as the production of situated knowledge about different cultures. Thus, the activities in the workshops represent other community dynamics and new ways of populating the experience of musical fruition. Nomadism (Deleuze and Guattari) and cultural frontiers (Lotman) were used as theoretical concepts to understand the production of some artists and the shared enjoyment of their works in the workshops. This study aimed to analyze how this communicational experience can rearticulate subjectivities in a sense of alterity considering Latin American and decolonial perspectives.
A Monarquia do Norte (1919) no discurso iconográfico da Ilustração Portuguesa
Authors: Sousa Jorge Pedro , Cardoso Fátima Lopes , Azevedo Celiana
Published: May 2024

On January 19, 1919, rebellious troops proclaimed, in Oporto, the reinstitution of the Monarchy, soon followed by other garrisons in the north of Portugal. Monarchist rule in almost all of the north of the country, particularly in Oporto, had no equivalent in the south. The ensuing confrontation between monarchist insurgents and republican forces led to the end of the Northern Monarchy on February 13. This research aimed to unveil, through a quantiqualitative study, the structure of the iconographic discourse that Ilustração Portuguesa, the only large-circulation contemporary illustrated magazine, built about this episode. It was accepted the starting hypothesis, that the discourse of the Ilustração Portuguesa was aligned with the republican governmental power, which controlled the capital city, Lisbon, where the magazine had its headquarters. The magazine's iconographic narrative glorifies the Republic and its "heroes," transforming the publication into an instrument of “infopropaganda” at the service of the established power in Lisbon.
Públicos Digitais, Contestação Digital: uma nova transformação estrutural da esfera pública?
Authors: Celikates Robin
Published: May 2024

Neste artigo o autor aborda a questão da digitalização da esfera pública a partir das transformações dos métodos de confronto, incluindo desobediência civil. Ao olhar mais de perto algumas das características estruturais da digitalização, busca então enfrentar os problemas conceituais, normativos e políticos levantados por ela. Dentro do limitado espaço desta contribuição, no entanto, as seguintes reflexões poderão ser somente tentativas e servir como preliminares para discussões futuras.