bluuebunny/crossref_metadata_2025 huggingface dataset:
S2HConv-Caps-BiGRU: Deep Learning-Based Heterogeneous Face Recognition Model with Divergent Stages
Authors: Balayesu Narasimhula , Reddy Avuthu Avinash
Published: July 2024

Real-time images of faces captured in different spectrum bands are considered heterogeneous images. Heterogeneous Face Recognition (HFR) matches faces across domains and is crucial to public safety. This paper proposes an HFR approach based on Deep Neural Networks (DNN). Feature maps are extracted from two images, such as gallery and sketch images, using Squirrel Search Heterogeneous Convolutional-Capsule- Bidirectional Gated Recurrent Unit (S2HConv-Caps-BiGRU). As a method of efficiently recognizing faces, coupled representation similarity metric (CRSM) will use the measure for the similarity of two feature maps. The experimental results will be evaluated to state-of-the-art (SOTA) statistical measures in terms of accuracy, recall, Jaccard score, dice score, mean square error (MSE), image similarity, performance and root mean square error (RMSE). Compared to other SOTA, the model produces the best results. The accuracy value of a CUFS dataset is 98.7%.
A Systematic Review on Drug-to-Drug Interaction Prediction and Cryptographic Mechanism for Secure Drug Discovery Using AI Techniques
Authors: Soni Sharmila K. , Thanga Revathi S. , Kiran Sree Pokkuluri
Published: July 2024

Recently, poly-pharmacy persistence has greatly improved in treating multiple diseases effectively. However, determining potential drug–drug interaction (DDIs) during the drug design is critical for controlling the target clinical drug during secure testing. In the medical field, DDIs are significant for disease diagnosis and treatment, mainly aiding researchers in predicting the link between biomolecules for efficient drug discovery (DD). Artificial intelligence (AI) has recently witnessed researchers accurately predict DDIs at minimum time consumption. Although the AI models show accurate results by aiding physicians to determine poly-pharmacy, several unresolvable issues remain in promoting reliability due to high error, complexity and cost-effectiveness. This paper aims to provide a comprehensive review using AI techniques (machine learning (ML)–deep learning (DL) models) and security enhancement techniques to improve DDI prediction and DD, respectively. The recent state of DDI prediction and the security concerns are presented initially, along with a short discussion about the need for effective techniques. Then, the critical evaluation related to existing studies is analyzed and compared to current issues faced in those existing studies. Various pharmaceutical drugs and their pros are also surveyed in addition to the security analysis for the newly invented drugs. Several assessment measures for the surveyed techniques are also conquered and put forth the need for advancements in future techniques effectively. The performance variations produced by the existing studies are also surveyed, and their use in the medical industry is also provided in this review study. The review of this research encourages the researchers to analyze the various issues faced in the pharmaceutical industry so that a novel technique can be introduced in the upcoming studies.
Planar doodles: Their properties, codes and classification
Authors: Bartholomew Andrew , Fenn Roger
Published: July 2024

In this paper, we present those properties of planar doodles, especially when regarded as four-valent graphs, that enable us to classify them into prime and super-prime doodles by analogy to a knot sum. We describe a method for partially characterizing a doodle diagram by a doodle code that describes the complementary regions of the diagram and use that code to enumerate all possible prime and super-prime doodle diagrams via their dual graph. In addition, we explore the relationship between planar doodles and twin groups, and note that a theorem of Tutte means that super-prime doodles have a Hamiltonian circuit and we give examples of prime doodles without a Hamiltonian circuit.
Integrated Optimization of Quality Control and Maintenance Policies for Light-Emitting Devices
Authors: Georgantzinos S. K. , Tseni A. D. , Papadopoulos C. , Giannikos I.
Published: July 2024

This research represents a comprehensive exploration of optimization policies pertaining to burn-in, quality control, and preventive maintenance for light-emitting devices. Utilizing a MATLAB-based computational model, we embarked on an in-depth investigation of the complex interplay and dependencies between these policies. Our findings illustrated that, with optimal burn-in time, cut-off threshold, and replacement interval, the total cost per usage duration was significantly reduced. Additionally, we investigated the system performance with and without quality control. It was observed that an increase in burn-in time corresponded to an increase in cost. The absence of quality control resulted in a similar pattern with the replacement period, where the cost remained constant after a certain replacement period. Conversely, the implementation of quality control indicated a direct relationship between burn-in time and cost, and an inverse relationship between replacement period and cost.
Estimation of Stress–Strength Reliability for Power Transformed Perks Distribution with Applications
Authors: Haridoss Venugopal , Jose Sudheep , Xavier Thomas
Published: July 2024

The power transformation of the Perks distribution, its survival, and hazard functions are computed. The shapes of hazard function are obtained analytically which shows that this model has increasing and decreasing hazard rates. In the stress–strength reliability estimation, point and interval estimates are computed using maximum likelihood and asymptotic confidence interval methods, respectively. The performance of the estimates is evaluated through the Monte Carlo simulation and the applications of the proposed distribution to three real datasets have been demonstrated.
EFFECTS OF INAUDIBLE THETA BINAURAL BEAT ON BRAIN WAVES
Authors: KIM KYU-BEOM , KIM YE-JIN , KIM JI-SU , CHOI MI-HYUN , KIM HYUNG-SIK , CHUNG SOON-CHEOL
Published: July 2024

Auditory binaural beats (BBs) stimulation is a method used to support the assumption that artificial external stimuli can generate specific brain waves. This study aimed to examine the effects of BBs that induce brain waves only based on a different frequency using an inaudible baseline frequency. The experiment was conducted among 18 adults in their 20s (11 male and 7 female). A baseline frequency of 18,000[Formula: see text]Hz and BBs with a difference frequency of 5[Formula: see text]Hz was presented, and the changes in electroencephalogram were observed. Power spectrum analysis was performed, and the absolute power of the theta-band frequency range (4–7[Formula: see text]Hz) was averaged. Absolute theta power was compared between phases without binaural beats (non-BBs) and with binaural beats ([Formula: see text]-BBs) using the Wilcoxon signed-rank test. Theta waves in [Formula: see text]-BBs significantly increased in most brain regions (central, parietal, temporal, and occipital) compared to those in non-BBs. This study is significant because it focused solely on the effects of the BBs-inducing theta waves using a frequency difference, excluding the effect of sound perception.
ADVANCED TECHNIQUE AND MULTI-W-SHAPED SLIT GAUGE FOR QUANTIFYING SPECT SPATIAL RESOLUTION OF GAMMA CAMERA SCAN DATA: AN ENHANCED STUDY
Authors: LIN YA-HUI , CHEN KE-LIN , HSIAO KAI-YU , HUANG HSIN-TZU , CHEN CHING-YUAN , LIN TSUNG-HAN , LIN CHIH-SHENG , PAN LUNG-KWANG , PENG BING-RU
Published: July 2024

The advanced technique comprising an enhanced multi-W-shaped gauge and Taguchi-based analytical program of gamma camera (GC) spatial resolution optimization was proposed and successfully applied to the routine diagnosis of nuclear examination. The indigenous gauge was revised from a V-shaped gauge for computer tomography (CT) scanning. The multi-W-shaped slit had a gradually narrowing gap between two adjacent V-slits; once the gap was beyond the GC recognizable limit, the acquired imaging blurred, and the discrimination between V-slits was lost. The five factors of GC data collecting were assigned as A (detector to target distance), B (total counts), C (zoom of ROI), D (matrix size), and E (energy width). Taguchi’s orthogonal array ([Formula: see text]) of factor combinations was adopted to arrange them into 18 groups. The reproducibility was ensured by the manual ranking of the acquired SPECT imaging quality by three seasoned radiology experts in three discrete periods. Next, Matlab and OriginPro software packages performed the SPECT analysis via the Poisson distribution program, identifying the peak center and FWHM of peaks of interest in SPECT imaging to quantify the spatial resolution and calculate the minimum detectable difference (MDD). The smallest MDD of 6.21[Formula: see text]mm revealed the optimal combination of factors for GC data collecting: A1 (DTD 5[Formula: see text]cm), B3 (TC 225), C3 (Zm 1.0), D1 (MS [Formula: see text]), and E2 (EW 10%). The conventional preset and group 7 (with the highest signal-to-noise ratio) had MDD values of 8.19[Formula: see text]mm and 7.62[Formula: see text]mm.
A MENTAL HEALTH DETECTION MODEL OF COLLEGE STUDENTS UNDER THE BEHAVIOR OF LEARNING BURNOUT
Authors: LU XIAOBING , GUO XUAN
Published: July 2024

With the increase in academic pressure on college students, some students show signs of academic burnout, which may lead to mental health problems. This study aims to explore the mental health status of college students under the manifestation of academic burnout and to propose a mental health detection model based on electroencephalography (EEG) signals. In this study, college students with the manifestation of study burnout were selected as research subjects, and their EEG signals were collected to identify and analyze their psychological state using a mental health recognition model. In the selection of mental health recognition models, this study used a variety of classical models for comparative analysis, including support vector machine (SVM), k-nearest neighbor (KNN), logistic regression (LR), convolutional neural network (CNN), long and short-term memory network (LSTM), and convolutional neural network-long and short-term memory network (CNN-LSTM). Experimental results show that the CNN-LSTM model performs best in identifying students with mental health problems, and its accuracy and stability are better than other models. Through this study, we conclude that academic burnout is closely related to college students’ mental health problems, and EEG signals combined with the CNN-LSTM model can effectively identify the mental health status of these students. This finding provides a new method and tool for monitoring college students’ mental health, which helps detect and intervene in students’ mental health problems early and promote their healthy growth and development.
OPTIMIZATION OF END TRAJECTORY OF MEDICAL ROBOT ARM BASED ON HUMAN BIOMIMETICS
Authors: WANG ZHILIN , SU DONGHAI
Published: July 2024

The end effector of a medical robotic arm based on human bionics is responsible for surgical operations, and reasonable end effector trajectory operation is significant to robots. A deep learning method based on Faster R-CNN network is proposed for optimizing the end trajectory of a seven-degrees-of-freedom biomimetic medical robotic arm. In this method, RPN replaces conventional search in deep learning, and RoI Pooling completes network partitioning, thereby improving the efficiency of deep learning. The experimental results show that, with four data types as inputs: velocity, angular velocity, acceleration, and angular acceleration, the Faster R-CNN network achieves effective optimization results. The end trajectory of the seven-degrees-of-freedom biomimetic medical robotic arm becomes smooth, and the impact force during the speed reversal process is significantly reduced.
A HEART FAILURE PREDICTION ALGORITHM BASED ON IMPROVED REINFORCEMENT LEARNING FRAMEWORK
Authors: ZHANG YIJIE , YANG XIANGBO
Published: July 2024

Reducing the occurrence of diseases has become an important research direction in today’s social medicine. Accurate prediction of heart failure can greatly reduce the complication rate, allowing patients to know the condition in advance and receive treatment. To deal with heart failure, this paper proposes a heart failure prediction algorithm based on a reinforcement learning framework. This paper combines and improves the reinforcement learning algorithm and the swarm intelligence (SI) optimization algorithm, aiming to use the reinforcement learning algorithm to improve the global optimization capability of the SI optimization algorithm. In order to better improve the global search ability of the SI optimization algorithm, the penalty function will be replaced by a combination mode of dynamic and static reward. The improved reinforcement learning algorithm framework is significantly better than the previous algorithm framework and has been used to predict heart failure. The experimental results have proven the effectiveness of the algorithm and its accuracy in prediction of heart failure.
The Great Transition in Indian Museums: From NMML to PMML
Authors: SINGH ADITI
Published: July 2024

The transition of the Nehru Memorial Museum and Library (NMML) to Pradhanmantri Sanghrahalaya/Prime Ministers Museum and Library Society (PMML) in 2023 (while the Pradhanmantri Sangrahalaya was inaugurated on April 14, 2022, the NMML was officially renamed as the PMML with effect from August 14, 2023) signifies a marked change in the museum ecosystem of India. It reflects a shift in how India’s history and politics are presented to the citizenry. Several governments have undertaken similar renaming exercises globally in the past. The move of changing the status of NMML from that of a distinguished establishment highlighting the life and work of India’s first Prime Minister, Jawaharlal Nehru, to one of a museum dedicated to all former Prime Ministers of India has elicited both commendation and criticism. The proponents of the idea laud the all-inclusive representation of all Prime Ministers in India, saying that it gives a fuller account of Indian governance and political history over time, whereas those against it argue that this change could undermine the focus on Nehru’s legacy. This paper argues that this is a huge departure from how museums depict India’s political past. This shift is not simply bureaucratic; rather, it holds deep symbolic meaning about different notions of nationhood and its historical recollection in India. Thus, this research examines government institutions’ roles in museum gatekeeping and consequences of governmental decision-making about renaming particular establishments. This paper not only describes how certain instruments (here, museums) are mobilized to construct the “truth,” it also broaches the theme that even such instruments (technologies of truth-production) have their own histories (susceptible to change in form and content through time/contexts).
ColorVideoVDP: A visual difference predictor for image, video and display distortions
Authors: Mantiuk Rafal K. , Hanji Param , Ashraf Maliha , Asano Yuta , Chapiro Alexandre
Published: July 2024

ColorVideoVDP is a video and image quality metric that models spatial and temporal aspects of vision for both luminance and color. The metric is built on novel psychophysical models of chromatic spatiotemporal contrast sensitivity and cross-channel contrast masking. It accounts for the viewing conditions, geometric, and photometric characteristics of the display. It was trained to predict common video-streaming distortions (e.g., video compression, rescaling, and transmission errors) and also 8 new distortion types related to AR/VR displays (e.g., light source and waveguide non-uniformities). To address the latter application, we collected our novel XR-Display-Artifact-Video quality dataset (XR-DAVID), comprised of 336 distorted videos. Extensive testing on XR-DAVID, as well as several datasets from the literature, indicate a significant gain in prediction performance compared to existing metrics. ColorVideoVDP opens the doors to many novel applications that require the joint automated spatiotemporal assessment of luminance and color distortions, including video streaming, display specification, and design, visual comparison of results, and perceptually-guided quality optimization. The code for the metric can be found at https://github.com/gfxdisp/ColorVideoVDP.
DressCode: Autoregressively Sewing and Generating Garments from Text Guidance
Authors: He Kai , Yao Kaixin , Zhang Qixuan , Yu Jingyi , Liu Lingjie , Xu Lan
Published: July 2024

Apparel's significant role in human appearance underscores the importance of garment digitalization for digital human creation. Recent advances in 3D content creation are pivotal for digital human creation. Nonetheless, garment generation from text guidance is still nascent. We introduce a text-driven 3D garment generation framework, DressCode, which aims to democratize design for novices and offer immense potential in fashion design, virtual try-on, and digital human creation. We first introduce SewingGPT, a GPT-based architecture integrating cross-attention with text-conditioned embedding to generate sewing patterns with text guidance. We then tailor a pre-trained Stable Diffusion to generate tile-based Physically-based Rendering (PBR) textures for the garments. By leveraging a large language model, our framework generates CG-friendly garments through natural language interaction. It also facilitates pattern completion and texture editing, streamlining the design process through user-friendly interaction. This framework fosters innovation by allowing creators to freely experiment with designs and incorporate unique elements into their work. With comprehensive evaluations and comparisons with other state-of-the-art methods, our method showcases superior quality and alignment with input prompts. User studies further validate our high-quality rendering results, highlighting its practical utility and potential in production settings. Our project page is https://IHe-KaiI.github.io/DressCode/.
StopThePop: Sorted Gaussian Splatting for View-Consistent Real-time Rendering
Authors: Radl Lukas , Steiner Michael , Parger Mathias , Weinrauch Alexander , Kerbl Bernhard , Steinberger Markus
Published: July 2024

Gaussian Splatting has emerged as a prominent model for constructing 3D representations from images across diverse domains. However, the efficiency of the 3D Gaussian Splatting rendering pipeline relies on several simplifications. Notably, reducing Gaussian to 2D splats with a single viewspace depth introduces popping and blending artifacts during view rotation. Addressing this issue requires accurate per-pixel depth computation, yet a full per-pixel sort proves excessively costly compared to a global sort operation. In this paper, we present a novel hierarchical rasterization approach that systematically resorts and culls splats with minimal processing overhead. Our software rasterizer effectively eliminates popping artifacts and view inconsistencies, as demonstrated through both quantitative and qualitative measurements. Simultaneously, our method mitigates the potential for cheating view-dependent effects with popping, ensuring a more authentic representation. Despite the elimination of cheating, our approach achieves comparable quantitative results for test images, while increasing the consistency for novel view synthesis in motion. Due to its design, our hierarchical approach is only 4% slower on average than the original Gaussian Splatting. Notably, enforcing consistency enables a reduction in the number of Gaussians by approximately half with nearly identical quality and view-consistency. Consequently, rendering performance is nearly doubled, making our approach 1.6x faster than the original Gaussian Splatting, with a 50% reduction in memory requirements. Our renderer is publicly available at https://github.com/r4dl/StopThePop.
Learning Images Across Scales Using Adversarial Training
Authors: Wolski Krzysztof , Djeacoumar Adarsh , Javanmardi Alireza , Seidel Hans-Peter , Theobalt Christian , Cordonnier Guillaume , Myszkowski Karol , Drettakis George , Pan Xingang , Leimkühler Thomas
Published: July 2024

The real world exhibits rich structure and detail across many scales of observation. It is difficult, however, to capture and represent a broad spectrum of scales using ordinary images. We devise a novel paradigm for learning a representation that captures an orders-of-magnitude variety of scales from an unstructured collection of ordinary images. We treat this collection as a distribution of scale-space slices to be learned using adversarial training, and additionally enforce coherency across slices. Our approach relies on a multiscale generator with carefully injected procedural frequency content, which allows to interactively explore the emerging continuous scale space. Training across vastly different scales poses challenges regarding stability, which we tackle using a supervision scheme that involves careful sampling of scales. We show that our generator can be used as a multiscale generative model, and for reconstructions of scale spaces from unstructured patches. Significantly outperforming the state of the art, we demonstrate zoom-in factors of up to 256x at high quality and scale consistency.
Deep Sketch Vectorization via Implicit Surface Extraction
Authors: Yan Chuan , Li Yong , Aneja Deepali , Fisher Matthew , Simo-Serra Edgar , Gingold Yotam
Published: July 2024

We introduce an algorithm for sketch vectorization with state-of-the-art accuracy and capable of handling complex sketches. We approach sketch vectorization as a surface extraction task from an unsigned distance field, which is implemented using a two-stage neural network and a dual contouring domain post processing algorithm. The first stage consists of extracting unsigned distance fields from an input raster image. The second stage consists of an improved neural dual contouring network more robust to noisy input and more sensitive to line geometry. To address the issue of under-sampling inherent in grid-based surface extraction approaches, we explicitly predict undersampling and keypoint maps. These are used in our post-processing algorithm to resolve sharp features and multi-way junctions. The keypoint and undersampling maps are naturally controllable, which we demonstrate in an interactive topology refinement interface. Our proposed approach produces far more accurate vectorizations on complex input than previous approaches with efficient running time.
Quality Implications of Foreign Metallic Particles in the Membrane Electrode Assembly of a Fuel Cell
Authors: Kumar Nitish , Chen Yixuan , Bahrami MohammadAmin , Lowe Olivia C. , Orfino Francesco P. , Dutta Monica , Lauritzen Michael , Setzler Erin , Agapov Alexander L. , Kjeang Erik
Published: July 2024

Foreign metallic particles unintentionally trapped within the membrane electrode assembly (MEA) may adversely affect quality and yield of high-volume fuel cell production, for instance by damaging the membrane or releasing metallic cation contaminants. The present work aims to understand the impacts of 55 ± 5 μm Fe and SS316L metallic particles present at the membrane - cathode catalyst layer (CCL) interface during fuel cell fabrication, conditioning, and diagnostics. In-situ X-ray computed tomography imaging of particle-laden MEAs within a customized small-scale fuel cell fixture reveals that Fe particles undergo complete dissolution within the first air starve cycle of the conditioning phase. After dissolution, legacy particles are observed to incur considerable damage within the MEA, including void spaces at the membrane-CCL interface, membrane thinning, CCL cracks, and membrane rupture. In stark contrast, the SS316L particles feature negligible dissolution during fuel cell conditioning and diagnostics and remain largely intact, merely causing membrane-CCL delamination in their vicinity. Post-operation chemical analysis by laser ablation inductively coupled plasma mass spectrometry indicates Fe ion concentrations in the range of 800–950 ppm and 10–25 ppm for the Fe and SS316L laden MEAs, respectively, which correlates to visual observations of particle dissolution and slight reductions in fuel cell performance.
Effects of in utero exposure to Δ-9-tetrahydrocannabinol on cardiac extracellular matrix expression and vascular transcriptome in rhesus macaques
Authors: Le Hillary H. , Shorey-Kendrick Lyndsey E. , Hinds Monica T. , McCarty Owen J. T. , Lo Jamie O. , Anderson Deirdre E. J.
Published: July 2024

Prenatal cannabis use is increasing and despite the public health relevance, there is limited safety data regarding its impact on offspring cardiovascular health outcomes. We used a translational, nonhuman primate model of daily edible Δ-9-tetrahydrocannabinol (THC) consumption during pregnancy to assess its effects on the fetal cardiovascular system. THC-exposed fetal vascular tissues displayed upregulation of genes involved in cellular metabolism and inflammation, suggesting that prenatal THC exposure may impact fetal vascular tissues.
Age- and sex-dependent cardiovascular impact of maternal perinatal stress and altered dopaminergic metabolism in the medulla oblongata of the offspring
Authors: Czarzasta Katarzyna , Sztechman Dorota , Zera Tymoteusz , Wojciechowska Malgorzata , Segiet-Swiecicka Agnieszka , Puchalska Liana , Momot Karol , Joniec-Maciejak Ilona , Machaj Ewa , Sajdel-Sulkowska Elizabeth M.
Published: July 2024

We demonstrate that maternal perinatal stress is associated with sex- and age-dependent impact on the cardiovascular system in their offspring. The effect was most significant in adolescent female and adult male offspring. Observed changes in hemodynamic parameters and dopaminergic activity of the medulla oblongata are novel results relevant to understanding the cardiovascular impact of maternal perinatal stress on the offspring. The cardiovascular changes observed in adult offspring suggest a potential long-term, multigenerational impact of maternal perinatal stress.
Prioritized adjustments in posture stabilization and adaptive reaching during neuromuscular fatigue of lower-limb muscles
Authors: Nardon Mauro , Sinha Oindrila , Kpankpa John , Albenze Eliza , Bonnet Cédrick T. , Bertucco Matteo , Singh Tarkeshwar
Published: July 2024

We assessed motor adaptation during force-field reaching following exercise-induced neuromuscular fatigue (NMF) on postural muscles. NMF impaired adaptation in performance. Similarly, diverging activation strategies were observed in the muscles. No effects were seen on measures of postural control. These results suggest the remodulation of motor commands to the muscles in the presence of NMF, which may be relevant in settings where participants could be exposed to NMF while learning, such as sports and rehabilitation.
Leaf Area Estimation of Cenostigma pyramidale: A Native Brazilian Caatinga Species
Authors: Pompelli Marcelo F. , Jarma-Orozco Alfredo , Jaraba-Navas Juan , Pineda-Rodríguez Yirlis , Rodríguez-Páez Luis
Published: July 2024

Global climatic changes are shifting, leading to increased temperatures and unpredictable rainfall changes. Tropical biomes, including the Caatinga in Northeast Brazil, face the risk of aridification. The Caatinga, covering 10.7% of Brazil, is an area that is highly susceptible to desertification, with a projected 5°C temperature rise. The region’s unique biodiversity faces threats from human activities, including deforestation and habitat fragmentation. Cenostigma pyramidale is a Brazilian native species, very common in the Caatinga ecosystem. The plant is vital for its adaptability to dry season and seasonality and presents a potential for use in reforestation programs. However, it also faces challenges like deforestation and habitat fragmentation. This study aims to estimate the leaf area of C. pyramidale using nondestructive and cost‐effective methods. The proposed model, based on leaf length and width, provides reliable and accurate leaf area (LA) estimates. Then, this study recommends the equation LA = 0.746 × (LW)0.979 as the best for estimating leaf area due to its high accuracy and biological consistency. The equation LA = 0.725 × (LW) is recommended as a simpler equation because the linear equation is easier to do mainly in land cases or in more extensive experiments. The equation LA = 0.645 × (L)1.652 eliminates 50% of the allometric process because only one leaf dimension is used to estimate leaf area. The leaf area estimation model provides a practical tool for researchers studying plant physiology and agronomic decision‐making. The findings emphasize the importance of understanding the impact of climate change on species distribution, especially in vulnerable regions like the Caatinga.
The Impact of Growth Factors in Platelet‐Rich Plasma Combination Therapy for Androgenic Alopecia
Authors: Ren Jie , Sun Jingxuan , Li Zhenghui , Zhao Yi , Tuan Hsiaohan
Published: July 2024

The efficacy of platelet‐rich plasma (PRP) therapy for androgenic alopecia (AGA) varies among diverse populations. While prior research has emphasized the pivotal role of growth factors as active components in PRP, the specific relationship between growth factors and treatment outcomes of AGA remains unclear. This study aims to explore how the efficacy of PRP therapy for AGA is influenced by the types and concentrations of growth factors. The analysis of PRP samples from 46 AGA patients involved assessing seven growth factors using an enzyme‐linked immunosorbent assay. Patients received a course of three PRP treatments along with traditional medicines. The assessment of treatment outcomes involved conducting trichoscopy tests before and after the treatment, measuring both hair density (HD) and hair caliber (HC). The findings revealed that HD increased in 36 patients, positively correlating with glial cell‐derived neurotrophic factor (GDNF) concentration (p = 0.005). In addition, HC increased in 35 patients, demonstrating a positive correlation with platelet‐derived growth factor‐BB (PDGF‐BB) concentration (p < 0.05). Notably, a gender‐based analysis identified a statistically significant difference in HC increase post‐PRP therapy (p = 0.005). In addition, no correlations were observed between demographic factors and changes in HD/HC (p > 0.05). The study confirms the beneficial influence of certain growth factors in PRP on AGA treatment outcomes. Future research should further clarify their mechanisms in promoting hair growth, paving the way for the development of novel therapeutic agents.
Conceptual design model of engaging gamification mechanic for online courses
Authors: Mohd Yusoff Azizul , Salam Sazilah , Mohamad Siti Nurul Mahfuzah , Lip Rashidah , Pudjoatmodjo Bambang , Rahmalan Hidayah , Mazlan Azlimi
Published: July 2024

Online learning, or e-learning, delivers educational content and teaching through various formats, ranging from self-paced courses to synchronous virtual classrooms. Gamification, the incorporation of game-like elements into non-game contexts, enhances engagement through rewards, reputation points, and goal setting. In higher education, researchers seek effective methods to stimulate learning and boost learner engagement. This study employs the analytic hierarchy process (AHP) to identify suitable gamification elements for three types of learner interaction, breaking down the decision-making problem into a hierarchy. Through a pairwise comparison matrix, priorities among hierarchy elements are established. The research involves 36 learners from a technical and vocational education and training (TVET) Public University, selecting the top best six gamification mechanics for each construct: virtual goods, wally’s game, rewards, trophies-badges, skill points, and peer grading. The proposed conceptual design will be implemented in online courses to assess learning engagement in cognitive, behavioural, and affective domains in higher education.
Comparative field assessment of grounding enhancement material for electrical earthing system
Authors: Zhe Kang Lim , Chun Lim Siow , Muhammad Usman , Aman Fazlul , Nor Normiza Mohamad
Published: July 2024

Grounding enhancement material (GEM) is used to lower the earthing resistance value of a given earthing system. In this paper, a commercially available GEM is experimented at the field alongside with Sodium Chloride, Copper II Sulphate and planting soil. The well established Wenner’s 4 pole method and fall of potential method was employed to measure the soil resistivity and earthing resistance respectively. It was found that the salts i.e., Sodium Chloride and Copper II Sulphate are superior in reducing the earthing resistance as reduction of more than 85% were observed. However, the commercial GEM has exhibited the most stable earthing resistance value over a period of 101 days, exhibiting the lowest standard deviation. This seems to suggest that the commercial GEM has superior moisture retention capability. This study also proven that Sodium Chloride can be dissolved by heavy downpour and replenishing it periodically is needed in a tropical country like Malaysia with regular thunderstorms and heavy downpours.
Development and implementation of a low-cost metal detector device
Authors: Salah Wael A. , Shabaneh Arafat A. A.
Published: July 2024

Metal detectors contribute to safety, protection, and detection in a variety of disciplines by locating and identifying metal items, playing an important role in which the metal detectors appear in security, archaeology, and industrial applications respectively. The necessity for identifying different types of metals and the need for a high level of security system led to the need of affordable and sensitively metal detecting devices. In this paper, the magnetic pulse induction (PI) technology is used in the development of metal detectors. The primary control circuit is utilizing an Arduino controller which allows the input signal’s to be controlled and monitored using a liquid-crystal display (LCD) and mobile application. A voltage sensor for measuring the analog output from the circuit and capturing the information to the Arduino by employing a Bluetooth module. The Arduino controller estimate the percentage of the signal’s strength and display it on the LCD. Simultaneously, the signal could be sent to the mobile application through Bluetooth in order for the application to display the strength in the form of a spectrum of colors. The results of testing applied to the proposed prototype reveal that the system is running with a satisfactory accuracy and sensitivity.
Developing a secure voice recognition service on Raspberry Pi
Authors: Le Van-Hoan , Luc Nhu-Quynh , Quach Duc-Huy
Published: July 2024

In this study, we present a novel voice recognition service developed on the Raspberry Pi 4 model B platform, leveraging the fast Fourier transform (FFT) for efficient speech-to-digital signal conversion. By integrating the hidden Markov model (HMM) and artificial neural network (ANN), our system accurately reconstructs speech input. We further fortify this service with dual-layer encryption using the Rivest–Shamir–Adleman (RSA) and advanced encryption standard (AES) methods, achieving encryption and decryption times well suited for real-time applications. Our results demonstrate the system's robustness and efficiency: speech processing within 1.2 to 1.9 seconds, RSA 2048-bit encryption in 2 to 6 milliseconds, RSA decryption in 6 to 10 milliseconds, and AES-GCM 256-bit encryption and decryption in approximately 2.6 to 3 seconds.
Energy efficiency based RPL protocol using grasshopper optimization algorithm
Authors: Matada Murigendraiah Savitha , I. Basarkod Prabhugoud
Published: July 2024

The routing protocol for low-power and lossy networks (RPL) is necessary for the internet of things (IoT) because it offers scalable, reliable, and energy-efficient routing capabilities. The trickling algorithm generates a destination-oriented directed acyclic graph (DODAG) with the broadcasting of suppression. However, broadcast suppression is insufficient when addressing network coverage and optimization problems based on uneven node distribution. Network congestion develops in large-scale IoT implementations where many devices are interconnected and congestion causes data transmission delays, decreased overall reliability, and higher latency. In this paper, the grasshopper optimization algorithm with the DODAG (GOA-DODAG) is proposed to determine optimization problems and energy-efficient reliable routing paths which include coverage-based dynamic trickling technique to construct DODAG energy-efficient without affecting the coverage of network and data routing reliability. The GOA-DODAG achieves a 98% packet delivery ratio (PDR) while consuming 0.48 mJ, which is more preferable in comparison to the existing methods like efficient-routing protocol for low-power and lossy networks (E-RPL), reliable and energy-efficient RPL (REFER), elaborated cross-layer RPL objective function to achieve energy efficiency (ELITE).
On the development of an autonomous hexacopter drone for animal detection and collision avoidance system
Authors: Alshbatat Abdel Ilah Nour , Awawdeh Moath
Published: July 2024

Traffic accidents caused by collisions with animals are a significant global concern for authorities. The economic impact is substantial, including costs for treating injuries, rehabilitating victims, repairing vehicle damage, and addressing fatalities. As a contribution towards finding a solution, a wireless sensor network (WSN) and an autonomous, power-efficient, and economically feasible drone are presented. WSN is responsible for acquiring relevant information from the farm and communicating the data to the aerial drone denoted as a hexacopter; which is functioning as a mobile roadside device for transferring the warning information to the passing drivers so as to avoid any collision with animals. The proposed system involves the design of a lightweight camel-based sub-system to trigger drones for monitoring herds, a drone-based sub-system for tracking and ensuring safety in agricultural areas, and a vehicle-based sub-system to communicate collision warnings where an alarming protocol has been developed. The whole system has been designed, implemented, tested, and verified in an actual flight test. Experimental results indicate that the system has a unique capability. It can mitigate the number of accidents involving vehicles and animals, especially camels, and thus reduce the economic cost of damages associated with the problem.
Design of higher gain 1×4 cylindrical dielectric resonator antenna for mm-wave base stations
Authors: El Hasnaoui Yassine , Mazri Tomader
Published: July 2024

In this work, a cylindrical-shaped dielectric resonator antenna (DRA) with four elements is designed and presented at 28 GHz for millimeter-wave applications. The microstrip line is designed to excite the dielectric resonator (DR) with a relative permittivity of 8.3, and then the proposed single cylindrical DR antenna has been mounted on a FR4-Epoxy substrate with a relative permittivity of 4.4 and a thickness of 1.8 mm. However, the optimized single element was used to create a particular array for improving the gain and achieving the required antenna performance. The resulting antenna enabled a response range for the reflection coefficient from 26.8 GHz to 30.6 GHz, which covers the operational frequency. This allows for a maximum gain of 19.07 dB, an impedance bandwidth of 3.8 GHz, and a total efficiency of 80.62%, meeting the requirements for millimeter-wave and fifth-generation applications.
A new approach to joint resource management in MEC-IoT based federated meta-learning
Authors: Samafou Faustin , Amine Adoum Bakhit , Abba Ari Ado Adamou , Marius Fidel Faitchou , Moungache Amir , Armi Nasrullah , Mourad Gueroui Abdelhakh
Published: July 2024

MEC and IoT are rapidly expanding technologies that offer numerous opportunities to enhance efficiency and application performance. However, the huge volume of data generated by IoT devices, coupled with computational and latency constraints, poses data processing challenges. To address this within the MEC architecture, deploying computing servers at the network edge near IoT devices is a promising approach. This reduces latency and traffic load on the core network while improving the user experience. However, offloading computations task from IoT devices to MEC servers and efficiently allocating computing resources is a complex problem. IoT tasks may have specific requirements in terms of latency, bandwidth and energy efficiency, while computing resources and capacities maybe limited or shared between several users. We propose an approach called FedMeta2Ag, which we evaluate using the MNIST database. With 20 epochs, the training accuracy reached 91.5%, while the test accuracy achieved 92.0%. Performance consistently improved during the initial 20 iterations and gradually stabilized thereafter. Additionally, we compared the performance of our proposed model with existing methods, finding that our approach outperforms existing models in predicting performance more accurately. Thus, this approach effectively meets the demanding performance requirements of wireless communication systems.
ABSA of Indonesian customer reviews using IndoBERT: single- sentence and sentence-pair classification approaches
Authors: Yulianti Evi , Nissa Nuzulul Khairu
Published: July 2024

Aspect-based sentiment analysis (ABSA) task is important to identify user satisfaction from customer reviews by recognizing the sentiments of all aspects discussed in the reviews. This work investigates a novel study on the effectiveness and efficiency of three IndoBERT-based models for solving the ABSA task in Indonesian language. IndoBERT is a state-of-the-art transformer-based model, i.e., bidirectional encoder representations from transformers (BERT), that was pre-trained on Indonesian language. Our first model utilizes IndoBERT in a feature-based mode, paired with the convolutional neural network (CNN) and machine learning models, for single-sentence classification. Next, our second model is obtained by fine- tuning the IndoBERT model for a typical single-sentence classification to build an end-to-end model. At last, our third model also adopts a fine-tuning approach to use IndoBERT, but for sentence-pair classification by utilizing auxiliary sentences. Our results demonstrate that the third model, the fine- tuned IndoBERT for sentence-pair classification, gains the highest effectiveness. It demonstrates significant improvement over deep learning baselines (Word2Vec-CNN-XGBoost) by 23.6% and transformer-based baselines (mBERT-aux-NLIB) by 2.2% in terms of F-1 score. When considering both effectiveness and efficiency, the results show that the best- performing model is our second model, the fine-tuned IndoBERT for single- sentence classification.
Harnessing DBSCAN and auto-encoder for hyper intrusion detection in cloud computing
Authors: Kaliyaperumal Prabu , Periyasamy Sudhakar , Periyasamy Muthusamy , Alagarsamy Abinaya
Published: July 2024

The widespread availability of internet services has led to a surge in network attacks, raising serious concerns about cybersecurity. Intrusion detection systems (IDS) are pivotal in safeguarding networks by identifying malicious activities, including denial of service (DoS), distributed denial of service (DDoS), botnet, brute force, probe, remote-to-local, and user-to-root attacks. To counter these threats effectively, this research focuses on utilizing unsupervised learning to train detection models. The proposed method involves employing auto-encoders (AE) for attack detection and density-based spatial clustering of applications with noise (DBSCAN) for attack clustering. By using preprocessed and unlabeled normal network traffic data, the approach enables the identification of unknown attacks while minimizing the impact of imbalanced training data on model performance. The auto-encoder method utilizes the reconstruction error as an anomaly detection metric, while DBSCAN employs a density-based approach to identify clusters, manage noise, accommodate diverse shapes, automatically determine cluster count, ensure scalability, and minimize false positives. Tested on standard datasets such as KDDCup99, UNSW-NB15, CICIDS2017, and CSE-CIC-IDS2018, this proposed model achieves accuracies exceeding 98.36%, 98.22%, 98.45%, and 98.51%, respectively. These results demonstrate the effectiveness of unsupervised learning in detecting and clustering novel intrusions while managing imbalanced data.
Hybrid RNNs and USE for enhanced sequential sentence classification in biomedical paper abstracts
Authors: Ndama Oussama , Bensassi Ismail , En-Naimi El Mokhtar
Published: July 2024

This research evaluates a number of hybrid recurrent neural network (RNN) architectures for classifying sequential sentences in biomedical abstracts. The architectures include long short-term memory (LSTM), bidirectional LSTM (BI-LSTM), gated recurrent unit (GRU), and bidirectional GRU (BI-GRU) models, all of which are combined with the universal sentence encoder (USE). The investigation assesses their efficacy in categorizing sentences into predefined classes: background, objective, method, result, and conclusion. Each RNN variant is used with the pre-trained USE as word embeddings to find complex sequential relationships in biomedical text. Results demonstrate the adaptability and effectiveness of these hybrid architectures in discerning diverse sentence functions. This research addresses the need for improved literature comprehension in biomedicine by employing automated sentence classification techniques, highlighting the significance of advanced hybrid algorithms in enhancing text classification methodologies within biomedical research.
Effect of spacing, concentration of NaCl solution, and biasing of graphite electrodes towards conductometric sensor response
Authors: Zulkarnain Izzah Wadhihah , Abdullah Wan Fazlida Hanim , Halim Ili Shairah Abdul , Muslan Muhammad Izzat Alif
Published: July 2024

An electrical conductivity (EC) sensor is a conductometric sensor used to measure a solution's ability to transmit electrical charges. However, EC sensing accuracy and stability are not consistent due to many factors, such as gap spacing between electrodes, concentration of the solution, and electrical biassing. This study investigates the influence of the gap spacing between electrodes, the concentration of the solution, frequency, and voltage input applied to the EC sensor electrode on EC sensor measurement and provides insights into the relationship between these parameters and the sensor's performance using the voltage divider rule which is the simpler way to measure the conductivity of the solution. From this investigation, gap spacing between the electrodes, the concentration of the solution, frequency below 50 Hz, and voltage input have been found to directly affect the EC sensor measurement. However, there is no significant change in EC sensor measurement regarding the frequency applied to graphite electrodes when the frequency is above 50 Hz. The findings of this study highlight the complex interplay between the physical setup parameters and EC sensor measurement.