bluuebunny/crossref_metadata_2025 huggingface dataset:
On the pH-Dependence of the Hupd Peak of Pt-Group Nanoparticles
Authors: Oschinski Hedda , Beinlich Simeon D. , Reuter Karsten , Hörmann Nicolas G.
Published: November 2024

Understanding the electrochemical behavior of hydrogen adsorption at Pt-group metal surfaces, particularly in the context of non-well-defined nanoparticle surfaces, is crucial for advancing electrocatalytic applications such as the hydrogen evolution reaction (HER). This study investigates the non-Nernstian pH shifts observed for underpotential deposited Hupd-like cyclic voltammetry peaks on Pt, Ir, Pd, and Rh nanoparticles. Utilizing density functional theory calculations, we explore the potential-dependent stability of H and OH adsorbates at undercoordinated surface sites, emphasizing the role of non-ideal electrosorption valencies in these shifts. Our results support that the peaks arise predominantly from a direct H-OH replacement process and suggest the primary influence of partial charge transfer. The theoretical predictions show good agreement with experimental observations across various Pt-group metals, even on non-well-defined surfaces, and provide insights into cation-specific effects at Pt across the entire pH scale. This work not only clarifies the origin of the Hupd-like peak within the water stability region but also offers a foundation for understanding cation effects in HER kinetics, paving the way for more detailed analyses of cation type, concentration, and interfacial solvent structure.
Bioinformatics and Machine Learning–Based Identification of Critical Biomarkers and Immune Infiltration in Venous Thromboembolism
Authors: Li Yajing , Deng Hongru
Published: November 2024

Objective: This study aims to use bioinformatics and machine learning algorithms to screen and analyze the key genes involved in venous thromboembolism (VTE) and explore the relationship between these biomarkers and immune cell infiltration.

Methods: The gene expression profile with the identifier GSE19151 was downloaded from the GEO database. Differential expression analysis using the limma package was conducted to identify genes that were differentially expressed between VTE and normal samples. Biological activities of these genes were then investigated through GO analysis utilizing the R language package. KEGG and GSEA were also performed to identify key signaling pathways. Furthermore, machine learning techniques were employed to determine hub gene signatures related to VTE, and ROC curves were used to validate the findings. To compare the immune infiltration of healthy and VTE samples, single sample gene set enrichment analysis (ssGSEA) was applied. Lastly, the Spearman correlation coefficient was used to assess the relationship between the expression of hub genes and immune cell infiltration.

Results: A total of 628 differentially expressed genes (DEGs) were discovered between the VTE samples and normal samples. GO analysis identified protein polyubiquitination, lysosomal lumen acidification, organellar ribosome, mitochondrial ribosome, ammonium transmembrane transporter activity, and immunoglobulin binding as the processes with the highest abundance of DEGs. KEGG pathway analysis revealed that DEGs were enriched in ribosome, COVID‐19, viral infection, oxidative phosphorylation, Parkinson’s disease, nonalcoholic fatty liver disease, apoptosis, and cancer. The most prominent KEGG pathways associated with VTE were ribosome, Parkinson’s disease, oxidative phosphorylation, Alzheimer’s disease, and Huntington’s disease according to GSEA findings. DLST and LSP1 were identified as hub gene signatures in VTE by machine learning integrative analysis, and ROC curves confirmed their diagnostic value. Results from ssGSEA indicated a significant difference in the degree of immune cell infiltration between VTE and normal samples, with the expression of DLST and LSP1 positively correlated with the content of some immune cells. The R package, code, and analysis results used in this paper are available on https://github.com/doctorlaby/my-project.

Conclusion: Our research is the first to utilize machine learning techniques in identifying DLST and LSP1 as significant biomarkers of VTE. With our findings, we have uncovered new insights into the underlying causes of VTE and potential treatments for affected patients.
Effectiveness of Two Wavelengths of Diode Laser and Amorphous Calcium Phosphate–Casein Phosphopeptide Mousse in the Treatment of Dentinal Hypersensitivity: A Randomized Clinical Study
Authors: Nambiar Manjusha , Shetty Bhavya , Fazal Ibrahim , Khan Safiya Fatima , Shah Mehul A. , Kamath Vignesh , Faruk Shahaziya , Jalaj Vaishnavi , N. Sowmya
Published: November 2024

Objective: In office methods for immediate relief of dentinal hypersensitivity (DH) has long been an area of research. This study compared the efficacy of 660 nm diode laser, 980 nm diode laser, and amorphous calcium phosphate–casein phosphopeptide (ACP–CPP) agent in the treatment of DH.

Materials and Methods: A total of 39 patients with minimum three hypersensitive teeth in at least one quadrant were selected and randomly divided into three groups; Group A, B, and C patients were treated by 660 nm diode laser, 980 nm diode laser, and ACP–CPP agent, respectively. All the hypersensitive teeth were stimulated with tactile; thermal and air stimuli, and the pretreatment and posttreatment hypersensitivity scores were evaluated with the numeric rating scale (NRS) at baseline, 30 min, 1 week, 1 month, 3 months, and 6 months. The Shapiro–Wilk test was used to determine the uniformity of the data. The Chi‐square (χ2) test of significance was used to compare proportions. For intergroup comparison, the Kruskal–Wallis test and the Mann–Whitney U test were utilized.

Results: At 30 min and 1 week, the 980 nm laser was more effective than the 660 nm laser, but there was no statistically significant difference between the two groups at 1, 3, and 6 months. Throughout the trial period, the 660 nm and 980 nm diode lasers were more effective than the ACP–CPP agent in lowering DH.

Conclusions: 660 nm diode laser, 980 nm diode laser, and ACP–CPP agent showed positive prospective as effective desensitizers when utilized as a clinical procedure.
Heavy Metals Pollution and Potential Health Risks: The Case of the Koche River, Tatek Industrial Zone, Burayu, Ethiopia
Authors: Temesgen Mathewos , Alemu Tegenu , Shasho Enkosa
Published: November 2024

This study aimed to determine the levels of some heavy metals in the Koche River and the potential health risks. A replica of water samples was taken from 12 sampling sites purposely selected in the dry season. Heavy metal levels were determined using a flame atomic absorption spectrophotometer following the APHA (1998) procedure. Heavy metal pollution index (HPI), heavy metal evaluation index (HEI), chronic daily intake (CDI), hazard quotient (HQ), hazard index (HI), total hazard index (THI), and incremental lifetime cancer risk were calculated on the basis of the results. The heavy metals detected were Fe > Mn > Cu > Zn > Cr. The Cr, Fe, Mn, and Cu contents were above the maximum allowed limit of WHO for drinking and irrigation water at most of the sampling sites. The HPI and HEI values also surpassed the maximum limit of the study sites. The highest HPI and HEI values were found at the Yam1site. Oral ingestion represented 99.55% and 97.85% of CDItotal (CDIingestion + CDIdermal contact) in adults and children, respectively. The mean CDItotal and the noncarcinogenic risk values were found in the order of Fe > Mn > Cu > Zn > Cr in both ages. CDI, HQ, HI, and THI scores were higher in children. The HIoral and THI values were also higher than 1 in both ages except in DK 2, Sour 1, and Sour 2 sites. However, the HQdermal level was higher than 1 only for Cr in children. The ELCR obtained also indicated a high carcinogenic risk of Cr (0.75 ± 0.44 and 1.15 ± 0.66 in adults and children, respectively). In general, most of the study sites had heavy metal pollution levels that exceeded the maximum allowed limit. Therefore, effective management of sources of pollution and continuous monitoring of river quality to minimize health risks are very important.
Phase Ib Pharmacodynamic Study of the MNK Inhibitor Tomivosertib (eFT508) Combined With Paclitaxel in Patients With Refractory Metastatic Breast Cancer
Authors: Ferrario Cristiano , Mackey John , Gelmon Karen A. , Levasseur Nathalie , Sorensen Poul H. , Oo Htoo Zarni , Negri Gian L. , Tse Veronica W.L. , Spencer Sandra E. , Cheng Grace , Morin Gregg B. , del Rincon Sonia , Cotechini Tiziana , Gonçalves Christophe , Hindmarch Charles C.T. , Miller Wilson H. , Amiri Mehdi , Basiri Tayebeh , Villareal-Corpuz Victor , Sperry Sam , Gregorczyk Kevin , Spera Gonzalo , Sonenberg Nahum , Pollak Michael
Published: November 2024

Purpose:

Preclinical data motivate clinical evaluation of inhibitors of MAPK-interacting kinases 1 and 2 (MNK1/2). We conducted a phase 1b clinical trial to study target engagement and safety of tomivosertib, a MNK1/2 inhibitor, alone and in combination with paclitaxel.

Patients and Methods:

Eligible patients had metastatic breast cancer resistant to standard-of-care treatments. Biopsies were obtained at baseline and during treatment with tomivosertib, and then tomivosertib was continued with the addition of paclitaxel until disease progression or toxicity. Serum drug levels were measured, and pharmacodynamic endpoints included IHC, proteomics, translatomics, and imaging mass cytometry.

Results:

Tomivosertib alone and in combination with paclitaxel was well tolerated. There was no pharmacokinetic interaction between the drugs. We observed a clear reduction in phosphorylation of eIF4E at S209, a major substrate of MNK1/2, and identified tomivosertib-induced perturbations in the proteome, translatome, and cellular populations of biopsied metastatic breast cancer tissue.

Conclusions:

We conclude that tomivosertib effectively inhibits MNK1/2 activity in metastatic breast cancer tissue and that it can safely be combined with paclitaxel in future phase II studies. We demonstrate feasibility of using proteomic profiles, translatomic profiles, and spatial distribution of immune cell infiltrates for clinical pharmacodynamic studies.
Calculating Future 10-Year Breast Cancer Risks in Risk-Adapted Surveillance: A Method Comparison and Application in Clinical Practice
Authors: Zachariae Silke , Quante Anne S. , Kiechle Marion , Rhiem Kerstin , Fehm Tanja N. , Schröder Jörg-Gunther , Horvath Judit , Leinert Elena , Dikow Nicola , Ronez Joelle , Schönfeld Mirjam , van Mackelenbergh Marion T. , Schatz Ulrich A. , Meisel Cornelia , Aktas Bahriye , Witt Dennis , Mehraein Yasmin , Weber Bernhard H.F. , Solbach Christine , Speiser Dorothee , Hoyer Juliane , Faigle-Krehl Gesine , Much Christiane D. , Müller-Rausch Alma-Verena , Villavicencio-Lorini Pablo , Banys-Paluchowski Maggie , Pieh Daniel , Schmutzler Rita K. , Fischer Christine , Engel Christoph
Published: November 2024

The German Consortium for Hereditary Breast and Ovarian Cancer (GC-HBOC) has successfully implemented risk-adapted breast cancer surveillance for women at high breast cancer risk in Germany. Women with a family history of breast and ovarian cancer but without pathogenic germline variants in recognized breast cancer risk genes are recommended annual breast imaging if their predicted 10-year breast cancer risk is 5% or higher, using the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA) breast cancer risk model, as outlined in the current GC-HBOC guideline. However, women who initially do not meet this risk threshold may do so later, even if there is no new cancer in their family. To determine when this threshold is crossed, one could annually repeat BOADICEA calculations using an aging pedigree: the “prediction by aging pedigree” (AP) approach. Alternatively, we propose a simplified and more practical “'conditional probability” (CP) approach, which calculates future risks based on the initial BOADICEA assessment. Using data from 6,661 women registered with GC-HBOC, both methods were compared. Initially, 74% of women, ages 30 to 48 years, had a 10-year breast cancer risk below 5%, but 53% exceeded this threshold at an older age based on the AP approach. Among the women with an initial risk below the threshold, the CP approach revealed that 99% of women exceeded the 5% threshold at the same or an earlier age compared with the AP approach (88% of cases were within the same year or 1 year earlier). The CP approach has been implemented as a user-friendly web application.

Prevention Relevance: The German Consortium for Hereditary Breast Cancer recommends annual breast imaging for women if their 10-year breast cancer risk is 5% or higher. Women who initially do not meet this risk threshold may do so later. We propose a simple method to determine future risks based on initial risk assessments.
Table S3 from ETx-22, a Novel Nectin-4–Directed Antibody–Drug Conjugate, Demonstrates Safety and Potent Antitumor Activity in Low-Nectin-4–Expressing Tumors
Authors: Lopez Marc , Crompot Emerence , Josselin Emmanuelle , Farina Anne , Rubis Marion , Castellano Remy , Fares Joanna , Wehbe Maria , Collette Yves , Charafe Emmanuelle , Blanchin Stéphanie , Romagne François , Pálfi Anikó , Hechler Torsten , Pahl Andreas , Azim Hatem A. , Lhospice Florence , Mamessier Emilie , Bertucci François , Elands Jack , Préville Xavier , Olive Daniel
Published: November 2024

Supplementary Table 3 shows the affinities (Octet) of anti-hnectin-4 mAbs for monomeric and dimeric human nectin-4 
Data from ETx-22, a Novel Nectin-4–Directed Antibody–Drug Conjugate, Demonstrates Safety and Potent Antitumor Activity in Low-Nectin-4–Expressing Tumors
Authors: Lopez Marc , Crompot Emerence , Josselin Emmanuelle , Farina Anne , Rubis Marion , Castellano Remy , Fares Joanna , Wehbe Maria , Collette Yves , Charafe Emmanuelle , Blanchin Stéphanie , Romagne François , Pálfi Anikó , Hechler Torsten , Pahl Andreas , Azim Hatem A. , Lhospice Florence , Mamessier Emilie , Bertucci François , Elands Jack , Préville Xavier , Olive Daniel
Published: November 2024

Nectin-4 is a cell adhesion molecule expressed at various levels in many solid tumors, including urothelial cancer. As a means to reduce on-target skin toxicity observed with enfortumab vedotin (EV), an anti–nectin-4–monomethyl auristatin E (MMAE) antibody–drug conjugate (ADC) approved for patients with advanced urothelial cancer, 15A7.5, an anti–nectin-4 mAb that exhibited differential nectin-4–binding between tumors and primary keratinocytes, was selected for the development of ETx-22. Exatecan, a topoisomerase I inhibitor, was chosen as payload. ETx-22 ADC induced rapid and long-lasting tumor regression in various patient-derived xenograft models expressing low to high levels of nectin-4 and also in a monomethyl auristatin E–resistant xenograft model. ETx-22 has a highest nonseverely toxic dose of more than 20 mg/kg in nonhuman primates (NHP) without signs of significant skin toxicity. ETx-22 represents a valuable therapy for the treatment of patients with nectin-4–expressing tumors, including those that are resistant to EV treatment.
Significance:
ETx-22, a novel ADC combining a tumor nectin-4–specific antibody and an innovative linker to exatecan, demonstrates significant and durable responses in low-target–expressing tumor models that are resistant to MMAE-based EV and has a better toxicity profile. This new ADC has the potential to benefit additional patient populations beyond its current indication.
Advancement in self-powered implantable medical systems
Authors: Abu Owida Hamza , Al-Nabulsi Jamal , Turab Nidal , Al-Ayyad Muhammad , Al Hawamdeh Nour , Alshdaifat Nawaf
Published: November 2024

Many different elements of patient monitoring and treatment can be supported by implantable devices, which have proven to be extremely reliable and efficient in the medical profession. Medical professionals can use the data they collect to better diagnose and treat patients as a result. The devices’ power sources, on the other hand, are battery-based, which introduces a slew of issues. As part of this review, we explore the use of harvesters in implanted devices and evaluate various materials and procedures and look at how new and improved circuits can enable the harvesters to sustain medical devices.
Electrical active power optimization of the SEIG-WTS based on perturb and observe method
Authors: Borja Borja Mario G. , Lescano Sergio , Torres Alvarado Sally , Yancachajlla Tito Ubaldo , Luyo Jaime
Published: November 2024

This paper proposes an electrical active power optimization for self-excited induction generator-wind turbine system (SEIG-WTS) using a perturb and observe (PO)-based maximum power point tracking. The main advantage is the optimization of the SEIG active power of SEIG-WTS and the simply practical implementation with rotor speed sensor, current sensor and three phase inverter. The active power optimization of SEIG-WTS is achieved by perturbing angular magnetic field speed with stator reference voltage. To test the effectiveness of the proposal, numeric simulations were carried out under very challenging conditions with wind speed profile in steps and actual wind speed profile. The proposal reaches the maximum power in 7 seconds for hardest condition when the system works at high rotor speed. The proposal is useful for the development of maximum power point tracking control (MPPT) controllers due to its simplicity in implementation.This paper proposes an electrical active power optimization for self-excited induction generator-wind turbine system (SEIG-WTS) using a perturb and observe (PO)-based maximum power point tracking. The main advantage is the optimization of the SEIG active power of SEIG-WTS and the simply practical implementation with rotor speed sensor, current sensor and three phase inverter. The active power optimization of SEIG-WTS is achieved by perturbing angular magnetic field speed with stator reference voltage. To test the effectiveness of the proposal, numeric simulations were carried out under very challenging conditions with wind speed profile in steps and actual wind speed profile. The proposal reaches the maximum power in 7 seconds for hardest condition when the system works at high rotor speed. The proposal is useful for the development of maximum power point tracking control (MPPT) controllers due to its simplicity in implementation.
A new deep learning approach for predicting high-frequency short-term cryptocurrency price
Authors: Akouaouch Issam , Bouayad Anas
Published: November 2024

Cryptocurrencies are known for their volatility and instability, making them an attractive but risky investment for traders, analysts, and researchers. As the allure of Bitcoin (BTC) and other cryptocurrencies continues to grow, so does the interest in predicting their prices. To forecast the market rate and sustainability of cryptocurrencies, this study uses machine learning-based time series analysis. The study employs forecast periods ranging from 1 to 10-minutes to categorize the consistency of the market. High-frequency pricing of cryptocurrencies is anticipated with a timestep of up to 10 seconds using various deep learning (DL) models. A hybrid model combining long short-term memory (LSTM) and gated recurrent unit (GRU) is created and compared with standard LSTM and GRU models. Mean squared error (MSE) is the benchmark for estimating the models' performance. The study achieves better results than benchmark models, with MSE values for BTC, Cardano (ADA), and Cosmos (ATOM) in a 5-minute window size being 0.000192, 0.000414, and 0.000451, respectively, and for a 10-minute window size being 0.000212, 0.000197, and 0.000746. Compared to existing models, the suggested model offers a high price predicting accuracy. This study on crypto price prediction using machine learning applications is a preliminary investigation into the topic.
A common vocabulary for semantic interoperability of Moroccan e-government services
Authors: Laaz Naziha , Benaddi Hanane
Published: November 2024

Interoperability is a critical factor for the success of e-government services, as it enables different public information systems to communicate in a consistent and accurate manner. Governments are making significant efforts to improve their public e- services interactions and promote e-government interoperability. Morocco has developed an e-government interoperability framework that lists compliance rules and references for the development of public information systems. Unfortunately, Moroccan public administrations still work independently and operate as siloed organizations. To deal with this problem, it is essential to implement a common vocabulary (CV) for public services that public administrations can share to formalize public data, enhance exchange between information systems, and ensure data interoperability. In this light, this work presents a CV to standardize public services data, define concepts and relationships. The standardized vocabulary is defined using RDF/XML serialization format and incorporates fundamental declarations to ensure digital communication in Moroccan public services. The approach is illustrated through a case study of e-health service. The study shows the potential added value of creating a national vocabulary. It helps public administrations to structure data, interoperate more effectively and accelerate digital transformation.
Hybrid deep learning: a comparative study on ai algorithms in natural language processing for text classification
Authors: Mahmudul Hasan Md. , Kumar Das Rajesh , Hassan Mocksidul , Razia Sultana , Ferdous Ani Jannatul , Akter Khushbu Sharun , Islam Mirajul
Published: November 2024

The objective of this research project is to assess the effectiveness of various machine learning algorithms, including deep learning and combination approaches, in performing tasks such as categorizing products into specific categories using data from an e-commerce platform named "OTHOBA." In this study, a dataset consisting of 19,087 data samples is used to evaluate the effectiveness of seven supervised machine learning models. Among these models are three based on deep learning: long short-term memory (LSTM), bidirectional long short-term memory (Bi-LSTM), and 1D convolutional (Conv1D), as well as a multi-layer model that combines Conv1D and LSTM approaches. The task at hand is the classification of product categories. The LSTM-based model demonstrates the highest accuracy rate of 96.23% among the deep learning models, while the logistic regression (LR) models achieve the highest accuracy scores of 97.00% for product category classification. Overall, the proposed models and techniques show significant progress in natural language processing (NLP) research for text classification, specifically in English, and have practical applications for e-commerce sites.
Analysis of alternatives methodology for large-scale information system implementation
Authors: Arisal Andria , Setiadi Bambang , Muslim Ichwanul
Published: November 2024

According to the Presidential Decree, central and local governments must implement electronic-based government systems or sistem pemerintahan berbasis elektronik (SPBE). However, the independent implementations have created various similar applications to support the same field of governmental activities. The situation creates difficulties in achieving effectiveness, integration, sustainability, efficiency, accountability, interoperability, and security of governmental services. Therefore, a common application will be developed for each governmental activity to improve interoperability and data integration. On the other hand, central or local governments must consider the suitable implementation of their public service information systems. This manuscript guides the determination of alternatives using cost, benefit, and risk analysis. We use the proposed guidance for a case study because sistem pengelolaan pengaduan pelayanan publik nasional-layanan aspirasi dan pengaduan online rakyat (SP4N-LAPOR!) has been regulated as the common application for Public Service Complaints Management using PermenPANRB No. 680, 2020. The application of the proposed guidance shows that it can help the stakeholder quantitatively decide on an alternative implementation of the application for the public service complaints management system.
Leveraging pretrained transformers for enhanced air quality index prediction model
Authors: Velusamy Santhana Lakshmi , Madhaya Shanmugam Vijaya
Published: November 2024

Air pollution mitigation is essential to ensure sustainable development, as it directly affects climate change, economic productivity, and social well-being. Despite the availability of numerous prediction techniques, machine learning (ML) remains the optimal solution for forecasting air pollution. Constructing a prediction model for a region with limited data poses a challenge. This study presents a novel technique that combines temporal fusion transformer (TFT) with transfer learning to create an inventive air quality index (AQI) prediction model, effectively utilizing temporal insights and prior knowledge. The TFT is an advanced deep neural architecture engineered to enhance time series forecasting through the fusion of sequence modelling and global temporal patterns. By fusing TFT with transfer learning, the research pioneers a fresh approach to AQI prediction for region with data scarcity issue, capitalizing on cross-domain knowledge transfer. Utilizing meteorological and pollutant data from the Cochin region, a hybrid AQI prediction model is constructed through TFT and transfer learning. Employing a preexisting TFT model trained on Trivandrum data, transfer learning technique is utilized to adapt the model for predicting AQI in the Cochin region. The study demonstrates that integrating TFT with transfer learning yields superior accuracy compared to an exclusive TFT-based approach.
Initial study of general theory of complex systems: physical basis and philosophical understanding
Authors: Esenovich Suleimenov Ibragim , Arshavirovich Gabrielyan Oleg , Serikuly Bakirov Akhat
Published: November 2024

The fundamental difference between neural networks containing and not containing feedback between elements is analyzed. It is shown that in the first of these cases, quantitative relationships describing the functioning of the neural network can be obtained based on an analogy with the theory of noise-resistant codes. In the second case, an analogy with electronic circuits that form memory cells (triggers) is valid. It is shown that feedback between elements of even the simplest neural networks can lead to the appearance of multidimensional hysteresis, when, with the same state of inputs, the system can be in several qualitatively different states, the transition between which can be abrupt. In this case, the state of the neural network outputs depends not only on the current state of its inputs, but also on the path along which this state was formed. The results obtained are used for the philosophical substantiation of a new approach to the interpretation of complex systems of various natures, which are considered analogs of neural networks. According to it, a system that can store and processing information should be considered "complex".
Intrusion detection system in lightweight devices: issues and challenges
Authors: Musa Shanono Nuruddeen , Muslim Zulkiflee , Azman Abu Nur , Rahayu Selamat Siti , Nahar Haniza
Published: November 2024

Intrusion detection system (IDS) is a crucial component in ensuring the security of computer networks. It helps in identifying and responding to unauthorized access attempts or malicious activities within a network. The focus of this systematic review is on IDS specifically designed for lightweight devices. This systematic review aims to provide an abstract understanding of the current state of IDSs for lightweight devices. It involves a comprehensive analysis of existing research papers, evaluating the methodologies, techniques, and performance metrics used in these IDS solutions. The goal of the systematic review is to provide a critical assessment and analysis of the literature on IDS in lightweight devices, closing the research gap in this field. The review analyzed and evaluated 55 studies out of 678 initially identified. The findings of the study are presented in the paper, which includes insights into the state-of-the-art proposals in the field, challenges and limitations of existing solutions, and recommendations for future research directions. The outcome of this paper can help the advancement of IDS for lightweight devices.
Implementation of radio frequency identification technology for a secure and intelligent shopping cart
Authors: N. Arinze Stella , U. Okafor Patrick , R. Obi Emenike , O. Nwajana Augustine
Published: November 2024

Shopping at supermarkets has become a daily activity in urban areas of Enugu, Nigeria. However, there is always a huge rush in most mega supermarkets during times of discount offers, weekends and holidays resulting in long queues due to the barcode billing process. This research proposes a way of reducing the time spent at the billing counter using a radio frequency identification (RFID) smart-based shopping cart. To achieve this objective, an RFID tag, RFID reader, Arduino microcontroller and light-emitting diode (LED) display were used to develop a smart shopping trolley. RFID tag was placed on each of the eight products displayed for sale. RFID reader reads all the products that were placed on the cart and the details of the product such as the name, quantity, cost, and total cost was displayed on the LED. The smart shopping trolley system also incorporates an alarm system that triggers off when the RFID tag is removed from a product to avoid shoplifting and make the system secure for the owners of the supermarket. The result showed that the billing of the products was done directly from the smart shopping cart. The system was compared with the conventional barcode system and was found to overcome the limitations of time-consuming billing procedures.
EXIT chart analysis of regular and irregular LDPC convolutional codes on AWGN channel
Authors: Laouar Oulfa , Amamra Imed , Derouiche Nadir
Published: November 2024

Low-density parity-check (LDPC) codes are widely recognized for their excellent forward error correction, near-Shannon-limit performance, and support for high data rates with effective hardware parallelization. Their convolutional counterpart, LDPC convolutional codes (LDPC-CCs), offer additional advantages such as variable codeword lengths, unlimited parity-check matrices, and simpler encoding and decoding. These features make LDPC-CCs particularly suitable for practical implementations with varying channel conditions and data frame sizes. This paper investigates the performance of LDPC-CCs using the extrinsic information transfer (EXIT) chart, a graphical tool for analyzing iterative decoding. EXIT charts visualize mutual information exchange and help predict convergence behavior, estimate performance thresholds, and optimize code design. Starting with the EXIT chart principles for LDPC codes, we derived the mutual information functions for variable and check nodes in regular and irregular LDPC-CC tanner graphs. This involved adapting existing EXIT functions to the periodic parity-check matrix of LDPC-CCs. We compare regular and irregular LDPC-CC constructions, examining the impact of degree distributions and the number of periods in the parity-check matrix on convergence behavior. Our simulations show that irregular LDPC-CCs consistently outperform regular ones, and the EXIT chart analysis confirms that LDPC-CCs demonstrate superior bit error rate (BER) performance compared to equivalent LDPC block codes.
Hardware-based efficient Mickey-128 stream cipher with unrolling factors for throughput enhancement
Authors: Ananth Raghavendra , Rao Malode V. Panduranga , Swamy Ramaiah Narayana
Published: November 2024

The emerging trend known as "ubiquitous computation" aims to incorporate intelligent gadgets into commonplace items. The lightweight cryptographic techniques are being researched and developed to minimize the gadgets' resources and a perpetual desire to reduce production expenses. A key element of symmetric cryptography, the stream cipher has unique benefits in terms of scalability as well as performance. The Mickey-128 stream cipher is designed and implemented in this manuscript. Additionally, unrolling features are incorporated with Mickey-128 cipher to enhance the throughput. The Mickey-128 contains a 128-bit key, an initialization vector (IV), and two clocking registers (R and S) with mapping units. The finite state machine (FSM) controller initializes and controls the key, IV and RS- registers data. The proposed Mickey-128 cipher runs on an Artix-7 field programmable gate array (FPGA) at 639.1 MHz and uses less than 1% of the chip's area (Slices). For unrolling factors 8 and 16, the Mickey-128 cipher achieves a throughput of 5.12 Gbps and 10.23 Gbps, accordingly. Finally, a comparison is made between the proposed Mickey-128 cipher and the existing ciphers' better hardware efficiency and throughput.
Improving the performance of a U-shaped patch antenna using metamaterials for biomedical applications
Authors: Siraj Younes , Foshi Jaouad , Saidi Alaoui Kaoutar
Published: November 2024

This paper discusses the performance improvement of a patch antenna using metamaterials (MTM). The suggested antenna is a U-shaped patch antenna with a modified ground plane dedicated to biomedical applications. The size of the antenna is 40×20 mm2 with a FR4 substrate (εr=4.3, tanδ=0.02, H=1.6 mm) designed for operation at 2.4 GHz (ISM Band) and 6.23 GHz frequencies. The proposed MTM is 2×2 array positioned under the antenna at a distance of 2 mm. The integration of the MTM enhances clearly the antenna performance especially the return loss, voltage standing wave ratio (VSWR) and the gain. However, the reflection coefficient was enhanced from -10.71 dB to -36.63 dB at 2.45 GHz and from -13.88 dB to -36.54 dB at 6.23 GHz, the VSWR improved from 1.66 to 1.03 at 2.45 GHz and from 1.75 to 1.04 at 6.23 GHz. Additionally, the peak gain also was increased from 1.77 dB to 3.48 dB. The obtained results confirm the suitability of the suggested antenna for biomedical applications.
Deep hybrid neural network for automatic classification of heart arrhythmias using 12-lead electrocardiograms
Authors: Sultan Daniyar , Baikuvekov Meirzhan , Omarov Batyrkhan , Kassenkhan Aray , Nuralykyzy Saltanat , Zhekambayeva Maigul
Published: November 2024

This research introduces a novel convolutional neural network-bidirectional long short-term memory (CNN-BiLSTM) hybrid network for the automatic classification of heart arrhythmias using 12-lead electrocardiograms (ECGs). By merging the spatial feature extraction capabilities of CNNs with the temporal precision of BiLSTM networks, our approach sets a new standard in cardiac diagnostics. The proposed model was tested against the comprehensive CPSC2018 dataset, demonstrating superior performance with an accuracy of 90.67%, precision of 93.27%, recall of 96.35%, and an F-score of 94.78%, surpassing existing state-of-the-art methods. These results underscore the effectiveness of integrating spatial and temporal data analysis, offering a robust and reliable tool for medical practitioners. This study represents a significant advancement in automated ECG analysis, paving the way for improved diagnosis and treatment of heart diseases, and contributing to enhanced patient outcomes in cardiac care.
A scoring approach for detecting fake reviews using MRCS similarity metric enhanced by personalized k-means
Authors: Ennaouri Mohammed , Zellou Ahmed
Published: November 2024

Online commerce has grown in the digital age, and as a result, consumers now depend more than ever on other consumer feedback to make informed purchasing decisions. However, as the importance of reviews has increased, so has the prevalence of fake ones, which now infiltrate platforms and manipulate users' perceptions. This presents a significant challenge to preserving confidence and integrity in online marketplaces. This study addresses the difficulty of identifying fake reviews by introducing a distinctive methodology that incorporates advanced natural language processing (NLP) tools. By including a new metric, mean review cosine similarity (MRCS), which enhances textual similarity assessment for more accurate detection, we improve the identification process. Additionally, an exaggeration detection technique is included, enhancing the model's capacity to identify deceptive variations in review content. Furthermore, an adaptive clustering method differs from traditional k-means classification through modifying clusters to adjust to the constant evolution of misleading linguistic patterns. Empirical validation on the Yelp labeled dataset demonstrates the approach's accuracy (90%), with high precision (89%), recall (95%), and F1 score (92%), indicating its effectiveness and highlighting areas for further refinement.
KawanSurya: an Android-based mobile app for assessing the techno-economic potential of rooftop photovoltaic
Authors: Tanoto Yusak , Marvel Christopher , Tumbelaka Hanny H
Published: November 2024

Many developing countries, including Indonesia, are progressing poorly in residential rooftop photovoltaic (PV) adoption, including on-grid systems. On the customer side, the decision to implement on-grid rooftop PV or rely only on power from the utility grid has often been made without appropriate knowledge of techno-economic considerations. This includes the impression of high system costs. This paper introduces KawanSurya: PV calculator, a solar rooftop PV techno-economic application for Android mobile phones, designed to help residential customers assess the potential of installing on-grid rooftop PV systems. The tool allows users to select a specific geographic location, calculate daily load profiles, and determine available roof areas. It uses irradiance data from the PVGIS API and HOMER’s solar PV output equation to determine hourly PV output power. Simulation results for a typical 2,200 VA household show a payback period of 9.44 years or beyond, significantly influenced by electrical load profiles and bill reduction factors. A 65% bill reduction factor and similar load profile prolong the payback period, while a 0% billing reduction factor or uncompensated electricity sales may exceed the project’s lifetime.
Modern artificial intelligence technics for unmanned aerial vehicles path planning and control
Authors: Zamoum Yasmine , Baiche Karim , Benkeddad Youcef , Bouzida Brahim , Boushaki Razika
Published: November 2024

Unmanned aerial vehicles (UAVs) require effective path planning algorithms to navigate through complex environments. This study investigates the application of Deep Q-learning and Dyna Q-learning methods for UAV path planning and incorporates fuzzy logic for enhanced control. Deep Q-learning, a reinforcement learning technique, employs a deep neural network to approximate Q-values, allowing the UAV to improve its path planning capabilities by maximizing cumulative rewards. Conversely, Dyna Q-learning leverages simulated scenarios to update Q-values, refining the UAV’s decision-making process and adaptability to dynamic environments. Additionally, fuzzy logic control is integrated to manage UAV movements along the planned path. This control system uses linguistic variables and fuzzy rules to handle uncertainties and imprecise information, enabling real-time adjustments to speed, altitude, and heading for accurate path following and obstacle avoidance. The research evaluates the effectiveness of these methods individually, with a focus on model-free learning in a gradual training approach, and compares their performance in terms of path planning accuracy, adaptability, and obstacle avoidance. The paper contributes to a deeper understanding of UAV path planning techniques and their practical applications in various scenarios.
Distributed denial-of-service attack detection short review: issues, challenges, and recommendations
Authors: Ahasan Habib AKM , Imtiaz Ahmed , Tripura Dhonita , Omar Faruk Md. , Anwar Hossain Md. , Ara Iffat , Sarker Sohag , Zainul Abadin A F M
Published: November 2024

An attacker can attack a network in several methods when there are a lot of device connections. Distributed denial-of-service (DDoS) attacks could result from this circumstance, which could damage resources and corrupt data. Therefore, irregularity in traffic data must be detected to identify malicious behavior in a network, which is critical for maintaining the integrity of current cyber-physical systems (CPS) as well as network security. This article attempts to study and compare various approaches to detecting DDoS attacks and expresses data paths for packet filtering for high-speed networks (HSN) performance, using machine or deep learning techniques used in intrusion detection systems (IDSs) and flow-based IDSs. The study presents a comprehensive DDoS attack taxonomy, categorizes detection strategies, and highlights the HSN accuracy assessment features. By exposing the problems and difficulties associated with DDoS attacks on HSN, several investigation paths are proposed to assist researchers in determining and developing the best solution.
A novel hybrid SMOTE oversampling approach for balancing class distribution on social media text
Authors: Raveendhran Nareshkumar , Krishnan Nimala
Published: November 2024

Depression is a frequent and dangerous medical disorder that has an unhealthy effect on how a person feels, thinks, and acts. Depression is also quite prevalent. Early detection and treatment of depression may avoid painful and perhaps life-threatening symptoms. An imbalance in the data creates several challenges. Consequently, the majority learners will have biases against the class that constitutes the majority and, in extreme situations, may completely dismiss the class that constitutes the minority. For decades, class disparity research has employed traditional machine learning methods. In addressing the challenge of imbalanced data in depression detection, the study aims to balance class distribution using a hybrid approach bidirectional long short-term memory (BI-LSTM) along with synthetic minority over-sampling and Tomek links and synthetic minority over-sampling and edited nearest neighbors’ techniques. This investigation presents a new approach that combines synthetic minority oversampling technique with the Kalman filter to provide an innovative extension. The Kalman-synthetic minority oversampling technique (KSMOTE) approach filters out noisy samples in the final dataset, which consists of both the original data and the artificially created samples by SMOTE. The result was greater accuracy with the BI-LSTM classification scheme compared to the other standard methods for finding depression in both unbalanced and balanced data.
Doppler radar-based pothole sensing using spectral features in k-nearest neighbors
Authors: Aiman Dani Asmadi Muhammad , Zainuddin Suraya , Mohd Nasir Haslinah , Syafiza Md Isa Ida , Emileen Abd Rashid Nur , Pasya Idnin
Published: November 2024

Potholes, resulting from wear, weather, and traffic, pose a substantial road safety concern, driving up maintenance costs and government liabilities. Numerous studies have explored pothole detection systems, however, there is a limited focus on radar-based approaches. This study investigates the use of Doppler radar mounted on moving vehicles to collect asphalt road surface data, with the aim to leverage this unique perspective point. Spectral features from power spectral density (PSD) are extracted and explored by incorporating Doppler signal PSD features into a k-nearest neighbors (KNN) within a machine learning framework for road condition classification. Six KNN algorithms are applied, and results indicate that potholes exhibit distinct spectral differences characterized by higher variability, with fine KNN performing the best, achieving an accuracy rate of 95.38% on the test dataset. In summary, this research underscores the effectiveness of Doppler radar-based pothole sensing and emphasizes the significance of algorithm and feature selection for achieving accurate results, proposing the viability of radar systems and machine learning.
A stereo-vision system for real-time person detection in ADAS applications using a fine-tuned version of YOLOv5
Authors: Rachidi Oumayma , Ed-Dahmani Chafik , Bououlid Idrissi Badr
Published: November 2024

Pedestrian detection holds significant importance in advanced driver assistance systems (ADAS) applications, and presents a challenging task in this field. While the advent of deep learning has facilitated the introduction of various pedestrian detectors characterized by accuracy and low inference speed, there persists a need for further improvements. Notably, ADAS requires accurate detection of pedestrians in various environmental conditions that can adversely impact the model’s performance, such as poor lighting, and bad weather. Furthermore, an imperative requirement involves the incorporation of distance estimation in conjunction with pedestrian detection, with an extension of detection capabilities to encompass cyclists and riders, who are equally crucial for ensuring road safety. Therefore, this paper introduces a stereovision system designed for the detection of pedestrians, cyclists, and riders. The initial phase, involves improving the performance of you only look once version 5 (YOLOv5s) through a fine-tuning process with a custom dataset integrating augmentation techniques to common objects in context (COCO) dataset. The detector is trained using Google Colab, and tested in real-time with a Raspberry Pi 4 model B, 8 G RAM. A comparative analysis is conducted between the YOLOv5s and the fine-tuned model to prove the accuracy of our approach. The results showcase a high performance of the detector reaching an accuracy exceeding 79%.
Optimal deployment of solar PV power plants as fast frequency response source for a frequency secure low inertia power grid
Authors: K. Wamukoya Brian , K. Kaberere Keren , M. Muriithi Christopher
Published: November 2024

Modern power systems are witnessing increased uptake of solar photovoltaic power plants (SPVPPs) replacing conventional synchronous generators (SGs). SPVPPs lack any rotating parts resulting in no natural rotational inertia contribution to the grid. Reduced inertia makes the power system more dynamic, making it susceptible to frequency instability caused by minor disturbances. This problem is majorly addressed by limiting the penetration of SPVPPs to ensure a minimum level of critical inertia is maintained or by providing additional virtual inertia from an energy storage system. However, the SPVPPs can be configured to operate below maximum power point tracking (MPPT) (deloaded mode) to provide a reserve capacity that can rapidly be deployed as fast frequency response (FFR) in case of a frequency event. This paper presents a strategy to optimize the FFR capacity of a deloaded SPVPP using particle swarm optimization (PSO) algorithm. DIgSILENT PowerFactory was used to model the deloaded SPVPP and run time domain simulations. PSO algorithm was implemented using a Python script in PowerFactory. The proposed strategy was applied on a modified IEEE 39 bus test system. The results show that optimal deloading of SPVPP can help to successfully arrest frequency decline, reduce power curtailment while adhering to the prescribed constraints.