bluuebunny/crossref_metadata_2025 huggingface dataset:
Dismantling Age Myths: Towards a Resilient Multigenerational Manufacturing Sector in Singapore
Authors: Willems Thijs , Poon King Wang
Published: September 2024

This paper examines the strategic importance of fostering a multigenerational workforce to enhance the competitiveness of Singapore’s manufacturing industry in the face of the Fourth Industrial Revolution. Addressing common myths about age and technological adoption, our mixed-methods study reveals that the integration of the diverse strengths of an age-diverse workforce is not only beneficial but also necessary for innovation and sustainable growth. With Singapore’s manufacturing workforce rapidly ageing and fresh talent dwindling, the industry faces challenges like knowledge transfer, automation, and the risk of skills dilution. We argue that a multigenerational approach that combines the expertise of older workers with the digital fluency of the younger generation can create a resilient talent pool essential for continuous expertise and complex problem-solving. The aim of this paper is thus to debunk several myths around an ageing workforce that currently stand in the way of fostering a multigenerational workforce in Singapore’s manufacturing industry. We end the paper by arguing that such a workforce will become increasingly important for other industries as well.
The formation and deformation of the Neogene Pannonian Basin: a structural overview
Authors: Csontos László , Dunkl István , Koroknai Balázs , Tari Gábor , Soós Balázs , Magyari Árpád , Nyíri Dániel , Soták Ján , Wórum Géza , Tóth Tamás , Kovács Gábor
Published: September 2024

The Pannonian Basin is a continental back-arc basin. Palaeomagnetics defined three terranes: Alcapa (Alps–Carapathians–Pannonian), Tisza–Dacia and Dinarides, rotated counterclockwise, clockwise and counterclockwise, respectively, in the Early and Middle Miocene. The timings and the extent of rotations suggest the terranes experienced differential internal rotations.

Metamorphic core complexes occur along the margins and an internal part of the Pannonian Basin. Metamorphic core complexes display extension parallel then perpendicular to the margins. Low-temperature thermochronology defined that synrift exhumation occurred
c.
18–16 Ma, coeval with map-view rotations.

Microtectonic measurements evidenced superimposed fault patterns. When combined with palaeomagnetics, these patterns simplify to north–south compression throughout Late Paleogene–Middle Miocene. Complications result from rotating blocks deforming under this external stress field.

Interpretation of seismic reflection data suggest that synrift phase was characterized by two perpendicular extensions. When rotations are considered, these resolve in east–west stretching. Strike-slip faulting during the Late Miocene accommodated differential movement of terranes pulled eastwards by slab roll-back along the Carpathians.

Several inversions affect Late Miocene–Recent sediments. These are most intense and earliest in the SW Pannonian Basin, but also propagate into the internal parts. Rotational indentation of Adria into the Southern Alps–Western Dinarides is the main cause for inversion. Neotectonics reflect this inversion, that is enhanced by eastwards escaping Eastern Alps.
ProtoRefine: Enhancing Prototypes with Similar Structure in Few-Shot Learning
Authors: Zhou Zhenyu , Liao Qing , Luo Lei , Liu Xinwang , Zhu En
Published: September 2024

Few-shot learning presents a substantial challenge in developing robust models due to the inherent scarcity of samples within each category. To overcome this challenge, metric-based methods have been introduced, classifying images based on the relationships among samples within a given embedding space. While these methods are effective, the limited samples often result in an incomplete representation of the category’s feature space, leading to sub-optimal prototypes for classification. Recognizing this shortcoming, we identified that categories in new tasks often exhibit structural similarities with those in the relative base domain. Driven by this observation, we introduce ProtoRefine. Our approach employs the structural information of categories within the base domain that bear relevance to the new tasks, generating additional sample embeddings. This strategy refines the prototype representation, thus providing a more accurate prototype for category classification. We performed extensive experiments on popular few-shot learning benchmarks, with the results highlighting the effectiveness of ProtoRefine, especially within the 5-way 1-shot settings. Matching the competitive results of state-of-the-art methods, our work underlines the significant advantage of enhancing prototypes with structurally similar information from the base domain in the context of few-shot learning.
Large Language Models for Software Engineering: A Systematic Literature Review
Authors: Hou Xinyi , Zhao Yanjie , Liu Yue , Yang Zhou , Wang Kailong , Li Li , Luo Xiapu , Lo David , Grundy John , Wang Haoyu
Published: September 2024

Large Language Models (LLMs) have significantly impacted numerous domains, including Software Engineering (SE). Many recent publications have explored LLMs applied to various SE tasks. Nevertheless, a comprehensive understanding of the application, effects, and possible limitations of LLMs on SE is still in its early stages. To bridge this gap, we conducted a Systematic Literature Review (SLR) on LLM4SE, with a particular focus on understanding how LLMs can be exploited to optimize processes and outcomes. We selected and analyzed 395 research articles from January 2017 to January 2024 to answer four key Research Questions (RQs). In RQ1, we categorize different LLMs that have been employed in SE tasks, characterizing their distinctive features and uses. In RQ2, we analyze the methods used in data collection, pre-processing, and application, highlighting the role of well-curated datasets for successful LLM for SE implementation. RQ3 investigates the strategies employed to optimize and evaluate the performance of LLMs in SE. Finally, RQ4 examines the specific SE tasks where LLMs have shown success to date, illustrating their practical contributions to the field. From the answers to these RQs, we discuss the current state-of-the-art and trends, identifying gaps in existing research, and highlighting promising areas for future study. Our artifacts are publicly available at
https://github.com/security-pride/LLM4SE_SLR
.
Beware of Words: Evaluating the Lexical Diversity of Conversational LLMs using ChatGPT as Case Study
Authors: Martínez Gonzalo , Hernández José Alberto , Conde Javier , Reviriego Pedro , Merino-Gómez Elena
Published: September 2024

The performance of conversational Large Language Models (LLMs) in general, and of ChatGPT in particular, is currently being evaluated on many different tasks, from logical reasoning or maths to answering questions on a myriad of topics. Instead, much less attention is being devoted to the study of the linguistic features of the texts generated by these LLMs. This is surprising since LLMs are models for language, and understanding how they use the language is important. Indeed, conversational LLMs are poised to have a significant impact on the evolution of languages as they may eventually dominate the creation of new text. This means that for example, if conversational LLMs do not use a word it may become less and less frequent and eventually stop being used altogether. Therefore, evaluating the linguistic features of the text they produce and how those depend on the model parameters is the first step toward understanding the potential impact of conversational LLMs on the evolution of languages. In this paper, we consider the evaluation of the lexical diversity of the text generated by LLMs in English and how it depends on the model parameters. A methodology is presented and used to conduct a comprehensive evaluation of lexical diversity using ChatGPT as a case study. The results show how lexical diversity depends on the version of ChatGPT and some of its parameters, such as the presence penalty, or the role assigned to the model. The dataset and tools used in our analysis are released under open licenses with the goal of drawing much-needed attention to the evaluation of the linguistic features of LLM-generated text.
Evaluating Large Language Models as Virtual Annotators for Time-series Physical Sensing Data
Authors: Hota Aritra , Chatterjee Soumyajit , Chakraborty Sandip
Published: September 2024

Traditional human-in-the-loop-based annotation for time-series data like inertial data often requires access to alternate modalities like video or audio from the environment. These alternate sources provide the necessary information to the human annotator, as the raw numeric data is often too obfuscated even for an expert. However, this traditional approach has many concerns surrounding overall cost, efficiency, storage of additional modalities, time, scalability, and privacy. Interestingly, recent large language models (LLMs) are also trained with vast amounts of publicly available alphanumeric data, which allows them to comprehend and perform well on tasks beyond natural language processing. Naturally, this opens up a potential avenue to explore the opportunities in using these LLMs as virtual annotators where the LLMs will be directly provided the raw sensor data for annotation instead of relying on any alternate modality. Naturally, this could mitigate the problems of the traditional human-in-the-loop approach. Motivated by this observation, we perform a detailed study in this paper to assess whether the state-of-the-art (SOTA) LLMs can be used as virtual annotators for labeling time-series physical sensing data. To perform this in a principled manner, we segregate the study into two major phases. In the first phase, we investigate the challenges an LLM like GPT-4 faces in comprehending raw sensor data. Considering the observations from phase 1, in the next phase, we investigate the possibility of encoding the raw sensor data using SOTA SSL approaches and utilizing the projected time-series data to get annotations from the LLM. Detailed evaluation with four benchmark HAR datasets shows that SSL-based encoding and metric-based guidance allow the LLM to make more reasonable decisions and provide accurate annotations without requiring computationally expensive fine-tuning or sophisticated prompt engineering.
Swift and Cost-Effective Detection of Nitrite in Environmental Samples Using Ru@Pt Modified PGE
Authors: K. Gowri Bala Kumari , Brahman Pradeep Kumar , Akkaraboyina Lakshmi Lavanya , Khandepu Bala Murali Krishna , T. V. Reddy
Published: September 2024

The development of a straightforward method is crucial for detecting and quantifying nitrite ions within the surrounding environment. This study involves the electrochemical fabrication of a bi-metallic alloy composed of Ruthenium and Platinum on a graphene-modified pge, the first-ever electrodeposition on pencil graphite (RuNPs@PtNPs/Gr-CHI). This study aims to establish a highly responsive and specific approach for identifying nitrite ions while demonstrating the efficacy of a commercially available pencil graphite electrode in detecting this environmental contaminant. The prevalence and structural characteristics of bimetallic nanoalloy particles are confirmed through X-ray diffraction, scanning electron microscopy (SEM), and energy-dispersive X-ray spectroscopy (EDS). The composite exhibited a core–shell shape at a size of 26.998 nm. The electrooxidation of nitrite at RuNPs@PtNPs/Gr-CHI/PGE was investigated using differential pulse voltammetry. The results demonstrated a satisfactory linear relationship from 0.025 mM to 1.625 mM. The method revealed a low detection limit of 0.33 μM. The composite electrode exhibited favorable outcomes regarding selectivity, sensitivity (25.5 μAμM−1cm−2), and repeatability, which are desirable characteristics of the electrochemical sensor material. The constructed electrode underwent testing for five weeks to determine the stability. The suggested sensor’s capability is demonstrated by detecting nitrite ions in real samples such as water, soil, and fruit juice.
Impact of TBAI Doping on the Optical and Dielectric Features of PVC/MoO3/NiMoO4/PANI Polymer Composite for Optoelectronic and Energy Storage Applications
Authors: El-Naggar A. M. , Kamal A. M. , Ahmed Sameh I. , Heiba Zein K. , Mohamed Mohamed Bakr
Published: September 2024

Poly (vinyl chloride, PVC)/MoO3/NiMoO4/polyaniline (PANI)/x wt% tetrabutylammonium iodide (TBAI) polymers were formed using casting and hydrothermal methods. The present study examined the nanocomposites’ structural, electrical, and optical features comprising PVC/MoO3/NiMoO4/PANI/x wt%TBAI polymers. X-ray diffraction and scanning electron microscopy were used to investigate the structure and morphology of different samples. The influence of different amounts of TBAI on the linear and nonlinear optical features of PVC/MoO3/NiMoO4/PANI/x wt%TBAI polymers was explored. Adding MoO3/NiMoO4/PANI.TBAI reduced the direct and indirect optical band gaps to their minimum values (3.88, 3.04) eV and (3.58, 2.13) eV, respectively. Doped polymer with MoO3/NiMoO4 has the highest refractive index value. Only PVC filled with MoO3/NiMoO4 exhibits the highest non-linear optical parameters within the visible range. The fluorescence intensity and emitted colors influenced by the kind of dopant. The dielectric constant and ac conductivity values of the host polymer were affected by the amount of TBAI. The maximum energy density value was observed in PVC/MoO3/NiMoO4/PANI/10 wt%TBAI polymer. The Cole-Cole plot demonstrated an irregular shift for doped samples relative to the undoped. The obtained results nominated the nanocomposite films of PVC/MoO3/NiMoO4/PANI/x wt%TBAI to be used in diverse electric and optoelectrical applications.
Wind Load Analysis on Temporary Scaffolding Structures in High‐Altitude Venues Based on Numerical Simulation: A Case Study From the 2022 Beijing Olympics
Authors: Sun Ming-hong , Zhang Lei , Li Xu-yang , Li Xiao-guang , Li Qing-wen
Published: September 2024

Scaffolding is widely used in construction, maintenance, and other activities as a temporary support structure. However, strong winds frequently cause scaffolding structures to collapse. Thus, the influence of wind load on the performance of such temporary support systems cannot be ignored. During the 2022 Beijing Winter Olympics, the scaffolding at the National Alpine Skiing Center in Yanqing District faced challenges from high‐altitude winds. This study evaluated its performance under a wind speed of 33 m/s using ANSYS Workbench. The result show that the maximum wind speed of Model A is 55 m/s, which occurs at the connection between the channel plate and the stand plate. And the maximum wind speeds of Model B and Model C are 47 m/s and 43 m/s, respectively, which both occur at the top of the stand. Compared with Model A and Model B, the wind pressure, stress, and displacement of Model C are larger, in which the negative pressure of Model C is below −3600 Pa and the positive pressure is above 600 Pa at the position of the connection between the stand plate and the rod. The stress at the connection between the channel plate and the stand plate is more than three times of that of the other positions of the plate, and the displacement of the stand plate can reach 68 mm in some areas. Therefore, optimizing the connection part, considering local wind direction when arranging, and placing windproof measures can all reduce the impact of strong winds on the scaffolding.
The Differentiation Potential of Apical Papilla Cells in Relation to Tenascin‐C and Syndecan‐1 Expression and Their Potential Role in Regeneration
Authors: Kodonas K. , Fardi A. , Papadimitriou S. , Gogos C.
Published: September 2024

Introduction: This study investigated the distribution pattern of tenascin‐C and syndecan‐1 in the dental mesenchyme during root development of immature swine teeth in order to define the differentiation dynamics of both pulp tissue progenitors and apical papilla cells, as well as to assess the adequacy criticize of the apical papilla to induce dentin–pulp regeneration.

Methods: Three 7‐month‐old miniature swine were used in this study. A total of 12 teeth, including two immature permanent incisors and two premolar teeth of each case, were extracted and processed for histological and immunohistochemical analysis. Different populations of mesenchymal cells located at the root apex were morphologically evaluated in hematoxylin–eosin serial sections. Additionally, the distribution patterns of tenascin‐C and syndecan‐1 were assessed immunohistochemically.

Results: Syndecan‐1 was strongly expressed in the dental pulp, particularly along the odontoblasts of the root and the newly deposited predentin layer. Tenascin‐C was intensely expressed in the dental pulp. The apical papilla and dental follicle showed no expression of either molecule.

Conclusions: Cell differentiation potential in the developing swine apex is progressively restricted to the newly formed dental pulp, whereas phenotypic expression of apical papilla cells remains undetermined unless the new microenvironment triggers cell differentiation towards the odontoblastic lineage.
Characteristics and Mechanism of Gas Production from Kitchen Waste Using Dielectric Barrier Discharge Nonthermal Plasma
Authors: Wang Siyi , Li Li , Zhao Lulu , Wang Huanran , Li Xianchun
Published: September 2024

Dielectric barrier discharge (DBD) nonthermal plasma is applied for the gasification of kitchen waste. The gas production performance of kitchen waste gasification based on DBD under different reaction ambientes is studied. Density functional theory (DFT) simulations are used to investigate the gas production mechanism via glucose and aspartate. Scanning electron microscopy (SEM) and Raman spectroscopy are used to investigate the physicochemical properties of the produced carbon materials and reveal the plasma reaction mechanism. The plasma treatment of millet and soybean has good gasification effect and can inhibit tar production. N2 and CO2 ambientes are more conducive to the gasification of millet and soybean. The H2 concentration in plasma‐treated millet gasification under plasma in N2 ambient can reach 4%. Glucose is finally decomposed through ring‐opening reaction, decarbonylation reaction, dehydration reaction, and carbon–carbon bond breaking. And the decomposition process of aspartic acid mainly includes decarbonylation and deammonia reaction. With the increase of gas ionization energy, the surface pore structure of the carbon material after plasma treatment is more developed, which indicates that the reaction between plasma and solid is on a layer by layer basis with the solid surface facing the inner surface. This paper aims to provide valuable theoretical support for the application of DBD in the treatment of kitchen waste.
Targeting BTN2A1 Enhances Vγ9Vδ2 T-Cell Effector Functions and Triggers Tumor Cell Pyroptosis
Authors: Le Floch Anne-Charlotte , Imbert Caroline , Boucherit Nicolas , Gorvel Laurent , Fattori Stéphane , Orlanducci Florence , Le Roy Aude , Archetti Lorenzo , Crescence Lydie , Panicot-Dubois Laurence , Dubois Christophe , Vey Norbert , Briantais Antoine , Anastasio Amandine , Cano Carla , Guittard Geoffrey , Frechin Mathieu , Olive Daniel
Published: September 2024

Vγ9Vδ2 T cells are potent but elusive cytotoxic effectors. Butyrophilin subfamily 2 member A1 (BTN2A1) is a surface protein that has recently been shown to bind the Vγ9 chain of the γδ T-cell receptor, but its precise role in modulating Vγ9Vδ2 T-cell functions remains unknown. Here, we show that 107G3B5, a monoclonal BTN2A1 agonist antibody, was able to significantly enhance Vγ9Vδ2 T-cell functions against hematologic or solid cell lines and against primary cells from patients with adult acute lymphoblastic leukemia. New computer vision strategies applied to holotomographic microscopy videos showed that 107G3B5 enhanced the interaction between Vγ9Vδ2 T cells and target cells in a quantitative and qualitative manner. In addition, we found that Vγ9Vδ2 T cells activated by 107G3B5 induced caspase 3/7 activation in tumor cells, thereby triggering tumor cell death by pyroptosis. Together, these data demonstrate that targeting BTN2A1 with 107G3B5 enhances the Vγ9Vδ2 T-cell antitumor response by triggering pyroptosis-induced immunogenic cell death. These new pyroptosis-based therapies have great potential to stimulate the immune system to fight cancer, especially “cold” tumors.

See related Spotlight by Kabelit, p. 1662
Oncogenic Mutant p53 Sensitizes Non–Small Cell Lung Cancer Cells to Proteasome Inhibition via Oxidative Stress–Dependent Induction of Mitochondrial Apoptosis
Authors: Chougoni Kranthi Kumar , Neely Victoria , Ding Boxiao , Oduah Eziafa , Lam Vianna T. , Hu Bin , Koblinski Jennifer E. , Windle Bradford E. , Palit Deb Swati , Deb Sumitra , Nieva Jorge J. , Radhakrishnan Senthil K. , Harada Hisashi , Grossman Steven R.
Published: September 2024

Non–small cell lung cancer (NSCLC) cells with oncogenic mutant p53 (Onc-p53) alleles exhibit significantly higher levels of proteasome activity, indicating that Onc-p53 induces proteotoxic stress which may be leveraged as a therapeutic vulnerability. Proteasome inhibitors (PI) are most active in cells under proteotoxic stress, so we investigated whether PIs exhibit preferential cytotoxicity in Onc-p53 NSCLC cells. Indeed, bortezomib (BTZ) and other PIs exhibited IC50 values 6- to 15-fold lower in Onc-p53 cells versus wild-type (WT) p53 cells. BTZ cytotoxic effects in Onc-p53 cells were abrogated by antioxidants such as N-acetyl-L-cysteine, indicating that oxidative stress is the critical driver of BTZ-dependent cytotoxic effects in Onc-p53 cells. Importantly, we observed oxidative stress–dependent transcriptional induction of the proapoptotic BH3-only protein NOXA, leading to cleavage of caspase-3, consistent with an apoptotic mechanism of cell death in Onc-p53 but not in WT p53 cells treated with BTZ. BTZ-generated oxidative stress was linked to nuclear translocation of NRF2 and transcriptional activation of ATF3, which in turn was required for NOXA induction. Validating the translational potential of BTZ in Onc-p53 NSCLC cells, BTZ and the BH3-mimetic navitoclax were synergistically cytotoxic in Onc-p53 but not in WTp53 cells in vitro, and BTZ effectively limited the growth of Onc-p53 NSCLC xenografts when combined with navitoclax and carboplatin (a standard-of-care chemotherapeutic in NSCLC) in vivo. Our data therefore support further investigation of the therapeutic utility of PIs combined with BH3-mimetics and chemotherapy in Onc-p53 human NSCLC as a novel therapeutic strategy.

Significance:

NSCLC is the leading cause of cancer death due, in part, to a lack of active therapies in advanced disease. We demonstrate that combination therapy with a proteasome inhibitor, BH3-mimetic, and chemotherapy is an active precision therapy in NSCLC cells and tumors expressing Onc-p53 alleles.
Hybrid approach to medical decision-making: prediction of heart disease with artificial neural network
Authors: Bhavekar Girish Shrikrushnarao , Chafle Pratiksha Vasantrao , Goswami Agam Das , Marathula Ganesh Kumar , Hirve Sumit Arun , Karpe Suraj Rajesh , Magar Nitin Sonaji , Farakte Amarsinh Baburao , Pikle Nileshchandra Kalbarao , Shinde Snehal Bankatrao , Gaikwad Amit Kamalakar
Published: September 2024

Heart disease prediction is important in today’s world because it helps to reduce the unpredictable death rate of patients, and cardiac diseases are considered one of the most serious diseases affecting people. Hence, in this paper, a heart disease prediction model is designed for effective prediction of heart diseases by means of machine learning (ML) and deep learning (DL). This prediction uses the proposed method of an artificial neutral network and the Chi2 feature selection method applied to determine which features from the dataset were suitable for prediction. The proposed methodology uses classifiers like support vector machines (SVM), Naive Bayes (NB), logistic regression (LR), random forest (RF), and artificial neural networks (ANN). Python was used to conduct the study that assessed the ANN system proposal with the Cleveland heart disease dataset at the University of California (UCI). Compared to other algorithms, the model achieves an accuracy of 97.64% and takes 0.49 seconds to execute, making it superior in predicting heart disease.
Efficient k-way partitioning of very-large-scale integration circuits with evolutionary computation algorithms
Authors: P. Rajeswari , Chandra S. Theodore , Sasi Smitha
Published: September 2024

The standardization of very-large-scale integration (VLSI) physical architecture for VLSI chips and multichip platforms is now in its early stages of development. The purpose of VLSI partitioning is to divide the circuit into numerous smaller circuits with few connections in between. Partitioning is the fundamental problem in circuit design and division. The efficient method of evolutionary computation may be used to tackle the partitioning problem in VLSI circuit design. It provides a heuristic approach to solve this problem by exploring the solution space and incrementally improving the quality of the solutions. In order to obtain the shortest wire length (WL), area, and connections, an evolutionary optimized simulated annealing memetic algorithm (OSAMA) that incorporates one or more local search phases inside its evolutionary cycle was developed.
PiWall as a home traffic controller: enabling parental control and monitoring
Authors: Abd Razak Nurul Imanina , Kamarudin Shafinah , Shuhud Mohd Ilias M. , Zakaria Muhammad Luqman Mahamad , Mohd Siti Munirah , Wahab Amelia Natasya Abdul
Published: September 2024

The movement control order (MCO) has led to a surge in internet usage among children, becoming the new normal. Most countries heavily rely on online platforms for education, granting children more freedom to explore the internet using electronic devices. Yet, it is challenging for parents to monitor their online activities. Children may stumble upon unsuitable content such as pornography or get redirected to harmful websites through pop-up advertising. To address these concerns, this study proposes PiWall, a home traffic controller. PiWall aims to block undesirable websites and filter out advertisements to help parents manage their home network. Testing shows that PiWall significantly improves the internet browsing experience by reducing website loading times and network traffic, hence offering protection against inappropriate content and malicious attacks. Implementing PiWall as a home traffic controller is a viable alternative to traditional parental controls in creating a safer internet environment for children.
Gaussian filter and CNN based framework for accurate detection of brain tumor by analyzing MRI images
Authors: Sivakumar S , Chaudhari Poonam , Thatavarti Satish , Sucharitha G. , Mahesh Basuthkar , Raghuvanshi Abhishek
Published: September 2024

The diagnosis of cancer can be challenging and time-consuming due to the complex characteristics of tumors and inherent noise in medical imaging. The significance of early detection and localization of tumors must be considered. Radiological imaging techniques can detect and potentially forecast the presence of neoplastic growths at various phases. The expeditiousness of the diagnosis process can be notably enhanced by amalgamating these images with algorithms designed for segmentation and relegation. Early detection of tumors and accurate localization of their position are critical factors. Medical scans, when used with segmentation and relegation procedures, enable the prompt and precise detection of cancerous tumor regions. The identification of malignant tumors enables this achievement. The present article introduces a framework for detecting brain tumors based on a convolutional neural network (CNN). The initial step in processing brain magnetic resonance imaging (MRI) images involves the application of a Gaussian filter to eliminate any noise present. Subsequently, CNN and long short-term memory (LSTM) deep learning methodologies are employed to classify images. CNN has demonstrated improved accuracy in the classification and detection of brain tumors. CNN has achieved an accuracy of 99.25% in cancer image classification. The sensitivity and specificity of CNN are also 98.75% and 99.25%, respectively.
An attention-based channel estimation algorithm for next-generation point to point communication systems
Authors: Olaniyi Kayode A. , Heymann Reolyn , Swart Theo G.
Published: September 2024

Accurate and robust estimation of channel parameters is essential in establishing reliable communication with characteristic optimal resource utilization in next-generation communication systems. Traditional techniques have limitations, such as the need for additional bandwidth and decreased spectral efficiency. Thus, there is a need for novel techniques that enhance the accuracy and robustness of channel parameter estimation in next-generation communication systems. To address this need, we propose in this paper a recurrent neural network (RNN)-based attention mechanism, to improve channel estimation accuracy and robustness in next-generation communication systems. The attention mechanism selectively focuses on the most relevant features while ignoring noise and interference. The attention network weights are initialized and are constantly updated in the course of network training. The weight values determine the significance of the features before passing them to the channel estimator. This allows the algorithm to adapt to varying channel conditions and improve its accuracy in challenging environments. The proposed attention-based algorithm performance is compared with three baseline techniques: learned denoising-based approximate message passing (LDAMP), Wasserstein generative adversarial networks (WGAN), and maximum likelihood (ML). The result evaluations indicate that the attention-based algorithm performs better than the existing artificial intelligence-based channel coding algorithms, in terms of robustness and accuracy.
Deep learning based detection, classification, and location of power system faults
Authors: Sahoo Anjan Kumar , Samal Sudhansu Kumar
Published: September 2024

The identification, categorization, and localization of faults play a crucial role in maintaining the smooth operation of power systems. Distance relays possess a significant capability to withstand power fluctuations, thereby minimizing inadvertent disruptions in transmission lines. Addressing these challenges involves the adoption of advanced fault analysis techniques to enhance the accuracy and speed of relay operations. While modern machine learning (ML) approaches are still nascent in fault analysis, the authors propose a novel deep learning (DL) based long short term memory (LSTM) method for precise fault detection, classification, and rapid fault location estimation. The proposed approach is applied to the Kundur two-area 4 machine 11 bus system covering a distance of 220 km. The LSTM fault detection (LSTM (FD)) module accurately detects and classifies faults, while the LSTM fault location (LSTM (FL)) module precisely estimates fault locations. The effectiveness of the proposed method is verified through a comparative assessment with various traditional ML and DL techniques. The protection modules are also tested under different fault locations, fault resistances, and noisy signals. The features taken into consideration for the operation of the protection modules are different bus voltages, bus currents, zero sequence voltage, zero sequence current, fault inception angle, and fault resistance.
Reconfiguration of the radial distribution network using an artificial rabbits optimization approach
Authors: Rao Ganney Poorna Chandra , Krishna Puvvula Venkata Rama , Rupesh Mailugundla , Karike Swathi , Polisetty Sathyanarayana , Reddy Sareddy Venkata Rami , Sreedhar Jadapalli
Published: September 2024

Lowering system power losses along with improving voltage profile have been major concerns for researchers for the past few decades. The performance of an electrical distribution system (EDS) is dependent on these two factors. This work’s main emphasis is on reconfiguring the radial distribution network (RDN) to diminish system power losses and strengthen the voltage profile. The process of network reconfiguration (NR) involves state transitions of sectionalizing and tie switches while still adhering to the limitations. In this work, the optimal reconfiguration network is determined using the artificial rabbits optimization (ARO) approach. The adopted method is tested using IEEE 119 bus RDN under low, normal, and heavy load conditions. When compared to the current approaches, the adopted methodology produced favorable results.
An ANN enabled joint power allocation and base station switching system for EE heterogeneous networks
Authors: Euttamarajah Shornalatha , Ng Yin Hoe , Tan Chee Keong
Published: September 2024

In recent years, dynamic and complex development in wireless communication in network models or environments led to more tedious and complicated resource management issues (i.e., power allocation and base station switching (BSS)). Conventional solutions often suffer from delays and degraded network service quality. Due to the ability of machine learning in analyzing huge volumes of data and automatically adapt to environmental changes, it emerges as a highly sought-after technique. In this work, we propose a machine learning approach based on feed-forward neural network (FFNN) to predict the active BS sets and estimate the power allocation to each user equipment (UE) within the active BSs for energy-efficiency (EE) maximization of a coordinated multi-point (CoMP-enabled) cellular system with hybrid-powered transmitting nodes in a HetNet-based architecture. By training the neural network model efficiently using a regression-based supervised learning technique that employs various backpropagation algorithms, almost similar EE performance (less than 5% difference) can be achieved with significantly reduced computational complexity and delay compared to the traditional methods, such as the well-known dual decomposition and brute force techniques. The effects of various hyper parameters and back-propagation algorithms are also investigated. Our results demonstrate that the proposed framework is a promising solution for establishing a fully green and intelligent network.
A novel agile method for user stories’ XMI model generation via NLP and MDA
Authors: Kharmoum Nassim , Retal Sara , Hajjaj Mouna , Lagmiri Souad Najoua , Rhazali Yassine
Published: September 2024

Agile software development methodologies have grown in popularity during the past few years. One of the key components of agile development is the use of user stories to describe software requirements. However, creating and managing user stories can be time-consuming and error-prone. In this paper, we present a novel method to generating user stories’ XMI model using natural language processing (NLP) and model-driven architecture (MDA) approach. We devel-oped a method that uses NLP to extract key information from user stories and then applies MDA techniques to generate an XMI model conforming to its pro-posed meta-model. We conducted a case study to illustrate and validate our method, and we analyze and discuss the studied-related work with our proposal. As a result, our method has the potential to make user stories’ models and their meta-models the focus of software development. This will help to streamline the development process by making it easier to construct and transform models in an agile environment with the MDA approach.
Covid-19 forecasting model based on machine learning approaches: a review
Authors: Sayeed Md Shohel , Hishamuddin Siti Najihah , Song Ong Thian
Published: September 2024

As coronavirus disease (Covid-19) it is a contagious disease that is spread by the SARS-CoV-2 virus, one of the most common causes of disease in humans. The disease was initially discovered in Wuhan, China, in 2019, and has now spread throughout the world, including Malaysia. A large number of people have lost their life partners and families because of this disease. Thus, in order for us to stop this epidemic spread, we have to implement social distance. The Covid-19 infection displays this type of behavior, which necessitates the development of mathematical and predictive modeling techniques capable of predicting possible disease patterns or trends, in order to assist the government and health authorities in predicting and preparing for potential outbreaks. The purpose of this paper is to provide an in-depth critique and analysis of the machine-learning approaches that have been implemented by researchers to predict Covid-19, based on existing research. As a result, future researchers will be able to use this paper as a valuable resource for their research related to the Covid-19 forecasting model.
Anomaly intrusion detection using machine learning- IG-R based on NSL-KDD dataset
Authors: Aljammal Ashraf H. , Al-Oqily Ibrahim , Obiedat Mamoon , Qawasmeh Ahmad , Taamneh Salah , Wedyan Fadi I.
Published: September 2024

Cybersecurity is challenging for security guards because of the rising quantity, variety, and frequency of attacks and malicious activities in cyberspace. Intrusion attacks are among the most common types of cyberspace attacks. Therefore, an intrusion detection system (IDS) is in high demand to accurately detect and mitigate their impact. In this paper, an anomaly IDS using machine learning and information gain-rank (IG-R) is proposed to improve the detection accuracy of intrusions. The network security lab-knowledge discovery dataset (NSL-KDD) is used to train and test the proposed IDS. Initially, the information gain (IG) algorithm and Ranker are used to evaluate, rank and reduce the number of selected instances from 41 instances to only 6 instances. Furthermore, many classifiers have been tested and evaluated; such as adaptive boosting (AdaBoostM1), random forest, J48, and naïve Bayes to choose the best performance classifier to be used in the detection process. After applying the IG-R and testing the suggested classifiers, the results showed that the random forest classifier has the best performance over the tested classifiers with TPR, FPR, and accuracy of 99.7%, 0.3%, and 99.7%, respectively, and is recommended to be used in the detection process.
Design and development of automatic voltage regulator using Ziegler-Nichols PID for electrical irons testing
Authors: Sukma Irawan , Suseno Aji Dwi , Muhidin Muhidin , Bakti Prayoga , Ardiatna Wuwus , Supono Ihsan , Firdaus Himma , Mandaris Dwi
Published: September 2024

This research presents an automated voltage regulation system crucial for a power input test of electric irons based on SNI IEC 60335-2-3 clause 11.4. The system is designed with an Arduino-based proportional-integral-derivative (PID) control mechanism to augment voltage stability and meet the standard requirement. The system comprises a microcontroller for PID control, a dimmer as the actuator, and a voltage sensor for error measurement. It utilizes the Ziegler-Nichols (Z-N) oscillation method to determine the PID control parameters. The simulation results identified a third-order transfer function as the best fit for the system, and the optimal PID parameters for the system are Kp=60, Ki=125, and Kd=500. The system was tested under the electric iron's active and non-active conditions. The proposed PID system demonstrated stable responses, effectively regulating the system voltage with minimal overshoot and settling time, and meeting standard requirements even under varying load conditions. It suggests potential applications beyond electric iron testing, promising efficiency improvements in broader household product testing.
Evaluating drones as bird deterrents in industrial environments: multirotor vs fixed-wing efficacy
Authors: Hornain Imran Mohd , Rosely Nik Fadzly N
Published: September 2024

Unmanned aerial vehicles (UAVs) or drones have been proposed as deterrent tools to mitigate pest birds’ problems. Many studies have been conducted to evaluate the efficacy of drones, mainly to protect crops, fishponds and airports. Little information can be acquired on using drones in industrial areas. In this study, two types of drones, categorized as multirotor drones and fixed-wing drones, were used to evaluate their efficacy in reducing pest birds, Asian glossy starling (Aplonis panayensis) flocks in one of the semiconductor factories in Kulim Hi-tech Park, Kedah, Malaysia during dusk. Each drone was evaluated during its five minutes of operation time and five minutes after landing. Control data were also taken to compare drone treatment days with no drone treatment days. Our result shows a significant difference between multirotor drone treatment and control treatment but not between fixed-wing drone treatment and control treatment due to different altitudes applied, ambient light intensity and size of flight path covered. We suggest implementing biomimetic design into drones and applying other conventional ground deterrents to prolong the residual effect of post-treatment.
Real-time object detection and distance measurement for humanoid robot using you only look once
Authors: Dwijayanti Suci , Suprapto Bhakti Yudho , Mutiyara Mutiyara , Rendyansyah Rendyansyah
Published: September 2024

Humanoid robots are designed to mimic human structures and utilize cameras to process visual input to identify surrounding objects. However, previous studies have focused solely on object detection, overlooking both the complexities of real-world implementation and the significance of calculating the distance between objects and the robot. This study proposes a system that employs the you only look once (YOLO) algorithm to detect various objects in the proximity of a robot. Using a dataset of primary data collected in a laboratory, the detected objects are from 12 classes, including humans, chairs, tables, cabinets, computers, books, doors, bottles, eggs, learning modules, cups, and hands, with each class comprising 1500 data points. Two YOLO architectures, namely tiny YOLOv3 and tiny YOLOv4, are assessed for their performance in object detection, with the tiny YOLOv4 demonstrating a superior accuracy of 82.99% compared to tiny YOLOv3. Evaluation under simulated conditions yields an accuracy of 74.16%, while in real-time scenarios, accuracies are 61.66% under bright conditions and 38.33% under dim conditions, affirming tiny YOLOv4’s efficacy. Moreover, this study reveals an average error distance of 31% between an object and the robot in real-time conditions. The developed system enhances human–robot interaction capabilities via data transmission.
Experimental study the performance of a 6-bladed Savonius vertical axis wind turbine using polyvinyl chloride material
Authors: Siregar Izhary , Dwi Nugroho Setyawan , Zaskia Pratiwi Citra , Mawardi Iman , Jibril Ahmad , Suhadi Suhadi
Published: September 2024

The Savonius U type wind turbine is a vertical axis turbine that can operate at low wind speeds. In general, the performance of this turbine is influenced by several factors, one of which is the shape of the turbine blade. This research aims to test the design results of a 6 blade Savonius turbine with a blade length of 50 cm made from polyvinyl chloride (PVC) by varying the dimensions of the blade diameter. The variables that vary between blade length and blade diameter are D/L=0.10, D/L=0.13, D/L=0.18, and D/L=0.20. The aim of this research is to determine the effect of variations in the parameters above on turbine rotation and the electrical power produced in a direct current (DC) generator at each variation in wind speed. From the research results, it is known that the trend graph of the relationship between turbine rotation and wind speed has a linear correlation. In simple terms, this turbine can be applied to DC voltage loads such as lighting using light emitting diode (LED) lamps with a maximum power capacity of ± 16 watts, while the overall efficiency (OE) is 50.25%.
Implementation of deep learning models in FPGA development board for recognition accuracy enhancement
Authors: Jassim Salah Ayad , Khider Ibrahim
Published: September 2024

Deep learning (DL) model performance is intricately tied to the quality of training, influenced by several parameters. Of these, the computing unit employed significantly impacts training efficiency. Traditional setups use central processing units (CPUs) or graphics processing units (GPUs) for DL training. This paper proposes an alternative using field programmable gate arrays (FPGAs) for DL training, leveraging their customizable and parallelizable architecture. FPGA programming allows for tailored circuit designs, optimizing DL training requirements and enabling efficient parallel processing. The use of FPGAs in DL training has garnered attention for their potential in achieving high computational throughput and energy efficiency, attributed to advantages like low latency, high bandwidth, and reconfigurability. By exploiting FPGA parallel processing capabilities, faster training times and the potential for larger, more complex DL models are feasible. This paper provides an overview of state-of-the-art techniques for FPGA-based DL model training, discussing challenges such as hardware architecture design, memory management, and algorithm optimization. Additionally, various FPGA-based DL frameworks and libraries facilitating DL model development and deployment on FPGAs are explored.
Employing PIPRECIA-S weighting with MABAC: a strategy for identifying organizational leadership elections
Authors: Setiawansyah Setiawansyah , Hadad Sitna Hajar , Aldino Ahmad Ari , Palupiningsih Pritasari , Fitri Laxmi Gibtha , Megawaty Dyah Ayu
Published: September 2024

The election of organizational leaders, especially in organizations whose members have diverse backgrounds and interests, can cause various problems. Problems in the selection of school organization leaders include the absence of an objective selection of organizational leadership candidates because they are selected based on comparisons between candidates without considering the criteria in the selection of organizational leadership candidates. Research related to the multi-attributive border approximation area comparison (MABAC) and simplified pivot pairwise relative criteria importance assessment (PIPRECIA-S) methods has never been conducted so far, so it is a reference in conducting this research using the MABAC and PIPRECIA-S methods. This study aims to select the head of the school organization using the MABAC method and PIPRECIA-S weighting can increase the objectivity of the criteria assessment results by relying on calculations from the PIPRECIA-S weighting method. Based on the selection results using the MABAC method and PIPRECIA-S weighting, candidate 1 was recommended as the leader of the school organization because it achieved rank 1 with a total score of 0.293. The contribution of this research is to help in the selection of the head of the organization using the PIPRECIA-S and MABAC methods as a decision-making solution.