bluuebunny/crossref_metadata_2025 huggingface dataset:
Risk Factors of Head-Load Carriage among Farmers: An Analysis of Physiological and Perceptual Responses
Authors: Rubin Benjamin D.
Published: October 2024

Musculoskeletal Disorders (MSDs) significantly threaten the health and well-being of farmers who carry head loads. Despite the prevalence of MSDs, a major barrier to preventing this issue is the lack of awareness and training among farmers. In Mantalongon, Dalaguete, Cebu, vegetables are predominantly transported by head-load carriages, exposing farmers to substantial health risks. This study aimed to evaluate the perceptual and physiological responses of farmers to identify risk factors associated with head-load carrying. The research involved 36 farmers, examining the relationship between MSDs and various task-related variables, health history, and personal profiles. The results indicated that the farmers rated their work intensity as "moderate" on the Borg scale and could carry loads averaging 147% of their body weight. Despite these strenuous conditions, no significant statistical correlation was found between personal profiles, health histories, and reported pain and discomfort. Four primary risk factors were identified: (i) the load weight carried significantly affected the Rating of Perceived Exertion (RPE) (p=0.003), neck discomfort (p=0.002), upper back discomfort (p=0.001), and heart rate variation (p=0.025), (ii) the terrain traveled was associated with upper back discomfort (p=0.025), lower back discomfort (p=0.046), and leg discomfort (p=0.031), (iii) the distance traveled significantly influenced heart rate variation (p=0.001), and (iv) the lifting technique was correlated with lower back discomfort (p=0.023). These findings underscore the urgent need for targeted interventions to mitigate MSD risks among farmers. Implementing comprehensive awareness and training programs can provide farmers with the necessary knowledge and skills to reduce the physical strain associated with carrying head loads, thus improving their health and overall well-being.
GFRP Encasing Efficiency on Enhancement Composite Beams under Static Loading
Authors: Bahlol Fahad M. , Al-Ahmed Ali
Published: October 2024

Structural engineers are increasingly favoring pultruded Glass Fiber-Reinforced Polymer (pultruded GFRP) composite for its lightweight, corrosion resistance, and high strength properties. The utilization of the GFRP material in reinforced concrete structures is not yet well-defined due to a lack of scientific evidence. The study focuses on the structural performance of composite beams made of encased GFRP sections and rebar encased in reinforced concrete. This study highlights the structural significance of embedding GFRP sections in concrete beams composed of GFRP rebars and normal reinforced concrete. To achieve this goal, five different specimens were tested and analyzed under two points of static loading. The experimental program consisted of one reference beam, without the encased GFRP I-section, and four hybrid beams. The study involved installing two types of shear connectors on composite beams with GFRP I-sections to analyze their impact on shear capacity and slide resistance. The experimental findings revealed that encasing the composite beams with GFRP improved their load-bearing and energy dissipation capabilities. Additionally, the shear connectors enhanced the ultimate capacity and eliminated slipping failures. Therefore, there was a strong agreement on the numerical results demonstrating the significance of this work.
Efficient Job Scheduling in Cloud Environments using Reinforcement Learning Actor-Critic Models
Authors: Naik Archana , Sooda Kavitha
Published: October 2024

Optimized scheduling is an important task in the scheduling of job execution on cloud virtual machines, where optimal resource usage and a shorter makespan have become important features. When scheduling jobs, balancing the workload across all available virtual machines provides optimized performance. Reinforcement learning is a better optimization algorithm due to its adaptability to dynamic environments and balancing exploration and exploitation. To perform optimized balancing of job scheduling, an Actor-Critic-based reinforcement algorithm is applied in this work. The Alibaba cloud dataset is used to analyze the algorithm's performance. Policy constraints are made for assigning the number of tasks to the scheduler. During the learning phase, the rewards turn out to be negative. After the learning phase, the rewards stabilize. The results show that the algorithm is able to produce positive reward points. A 5% reduction in the makespan of job execution demonstrates the improvement in scheduling and resource use.
Evaluation of Factors Leading to Time Delays and Cost Overruns in Marine Construction Projects
Authors: Nassar Aymen , Elbisy Ahmed
Published: October 2024

Marine construction projects are among the most complex endeavors undertaken, as they are subject to numerous variables and constraints that make them more vulnerable to cost overruns and time delays than other project types. This study investigates the potential factors that influence time delays and cost overruns in marine construction projects. For this purpose, 43 factors that affect marine construction projects in terms of cost overrun and time delay were identified and categorized into nine main groups through a detailed literature review process, as well as interviews with experts from the marine construction industry. The relative importance of these factors and groups was quantified using the Relative Importance Index (RII) method. The ranking of factors and groups was determined according to their level of effect on time delay and cost overrun. Interestingly, the top five factors for both time delay and cost overrun, although ordered differently, were the same: inflation (e.g., material, equipment, and labor prices), fluctuation in cost (e.g., money exchange rate, taxes and burdens, and interest rates charged by bankers on loans), incompetence or inexperience of contractors (lack of experience and/or managerial skills), poor planning and management of contractor's schedule, and difficulties in project financing by the contractor. Contractor-related factors had the highest RII for time delays, followed by external-related factors with a tiny difference. However, for cost overruns, the ranking of these two groups is reversed. The findings of this study could help organizations that plan to carry out successful and sustainable marine construction projects, ultimately contributing to the development of critical maritime infrastructure.
Optimization by Genetic Algorithm of a Wind Energy System applied to a Dual-feed Generator
Authors: Guediri Mourad , Ikhlef Nabil , Bouchekhou Hocine , Guediri Abdelhafid , Guediri Abdelkarim
Published: October 2024

In an attempt to improve wind energy production using a Doubly Fed Induction Generator (DFIG), this paper presents a model for power maximization through controlling the turbine speed by utilizing a Maximum Peak Power Tracking (MPPT) controller, and through also controlling the stator active and reactive power for DFIG. In the context of increasing the search for new electric energy production sources, including renewable energies, Proportional Integral (PI) contributed to the modeling of the control and improvement of the wind energy conversion system, with the aim of exploiting wind energy to produce clean energy without pollution. To enhance the benefits of classic PI regulators, and so obtain efficient performance, the study seeks to determine the parameters of PI regulators. PI is used for wind turbines without including classical analytical methods for final calculation. Thus, optimization algorithms, namely Genetic Algorithms (GA) or Particle Swarm Optimization (PSO), which seek to minimize the error in a controlled system between the input signal and the output signal, were developed in this study. The basis of this approach is the management of both reactive and active power. In order to increase performance and efficiency, the new approach incorporates GA ideas into the control technology used in the wind turbine. The simulation results derived after this incorporation provide wind turbine systems that are more stable and efficient producing significantly better results than traditional PI regulators. Then, a simulation program, which includes the artificial intelligence controls and GA, is created in Matlab.
Enhancing Milling Surface Finish: The Role of Servo Parameters and Machining Stability
Authors: Su Zheng-Mou , Lin Wei-Zhu , Lin Yung-Chih , Hung Jui-Pin
Published: October 2024

Abnormal machining vibrations and inconsistent machining quality are commonly observed in high-speed machining operations and are often attributed to the inappropriate characteristics of servo dynamics and structural resonance caused by feeding motions with higher jerk. An understanding of the manner in which distinct servo system parameters impact machining, can facilitate the refinement of machine tool tuning, thereby enabling the achievement of desired machining outcomes. In practical terms, the servo parameters, including feeding acceleration and jerk, control gains, and feed-forward compensation, can be appropriately determined based on the characteristics of the machine in question, such as the presence of dynamic errors in positioning and contouring paths. This study aims to evaluate the interactive effects between servo parameters and machining parameters to gain insight into their influence on machining quality. In order to achieve this objective, different servo parameters were tuned to meet various machining requirements, and cutting experiments were conducted with consideration of the cutter stability. The results are expected to provide guidance on the control of servo systems and the optimization of machining parameters, ensuring that servo tuning effectively enhances machine tool cutting performance.
Rain Height and Satellite Interference over Malaysia from 1992 to 2022
Authors: Hasan Nurhayati , Basarudin Hafiz , Yong Xin Yu , Lloyd Ling , Chung Boon Kuang , Mohd Yunus Noor Hidayah , Ramli Aizat Faiz , Hong Seng Gan
Published: October 2024

The impact of climate change on rainfall patterns poses significant challenges to satellite communication infrastructure. This study explores the dynamic relationship between changing climate conditions and their effects on the reliability and efficiency of satellite communication systems. Utilizing an extensive dataset comprising satellite imagery and meteorological records, this study focuses on discerning the evolving rain height patterns in Malaysia. Rain height is one of the crucial parameters used to determine rain fade on satellite communications. The study employs a statistical methodology including predictive modeling using the regression method, to assess the correlation between climate change-induced alterations in rainfall and the resultant impact on rain height. Our findings reveal a strong correlation between climate change which is typically associated with the increase of global temperature (~ 0.5 °C - 1.5 °C) and rain height (~ 4 m increase per year) in Malaysia. Rain attenuation increases by approximately 0.03 dB per year with the increase of rain height and temperature. The research contributes by revising studies on climate change's effect on rain height with 30 years’ worth of meteorological data collected from NOAA. In 2024, the rain height in Malaysia is predicted to be more than 5.4 km. Studies like this one contribute to the effective planning and deployment of satellite communication systems, especially at high-frequency ranges such as Ka and Ku bands, ensuring high-quality and stable communication systems while minimizing loss.
Design and Modeling of a Six-Bar Mechanism for Repetitive Tasks with Symmetrical End-Effector Motion
Authors: Gazo-Hanna Eddie , Saber Ahmed , Amine Semaan
Published: October 2024

This study investigates the dynamics of a novel planar six-bar mechanism with one Degree of Freedom (DoF), incorporating both four-bar and five-bar linkages. Kinematic analysis is performed by setting up closed-loop equations, and the results are validated against SOLIDWORKS software simulations. Additionally, a static stress analysis assesses the structural integrity of the mechanism under operational loads, identifying potential failure points and ensuring design adequacy. Dynamic force analysis is then performed to determine the driving torque of the actuator for the designed amphibious mechanism. These findings highlight the mechanism's potential for precise motion control in compact applications, providing valuable insights into its practical utility in various industrial applications.
An Effective Method for the Determination of the Natural Frequency of piled Pier Segments through Impact Vibration Testing
Authors: Nguyen Thi Bach Duong , Mac Van Ha , Vu Thi Nga , Nguyen Viet Thanh
Published: October 2024

The Impact Vibration Test (IVT) is an effective, accurate, and fast non-destructive evaluation method to indicate the technical condition of structures through the consideration of the health index. This index is the ratio of measured and designed natural frequencies in the absence of the original measured frequency. However, the determination of the natural frequency of the berth’s segments in both numerical and experimental approaches is expensive and time-consuming work. This motivated the present study to develop formulas that allow quick estimation of the natural frequency of the berth’s segments based on one of the horizontal frames under various conditions and cases. In order to execute this task, different wharves with various working levels, geological conditions, characteristics, and spring coefficients were examined. A case study for a wharf under a real incident is also provided to show the procedure and demonstrate its effectiveness. Interestingly, the natural frequency of the horizontal frame can be obtained quickly and simply in comparison with that of the berth’s segment. Therefore, the proposed formulas are expected to significantly enhance the efficiency of field IVT investigations.
Dielectric Modeling of Staphylococcus Aureus Bacteria and Shape Optimization of Electrodes for Isolation in Microfluidic Channel: A Numerical Study
Authors: Ganesan Sanchanna , Vimala Juliet A. , Kumar C. Likith
Published: October 2024

This study investigates the optimization of the electrode shape to isolate bacteria from the blood within a microfluidic channel, employing the dielectrophoretic technique. The examination focuses on the dielectric modeling of Staphylococcus Aureus bacteria to isolate them from blood components, specifically Red Blood Cells (RBC) and platelets. The numerical study utilized the COMSOL Multiphysics software to model three distinct channels, using standard geometric shapes such as triangles, rectangles, and circles as their electrodes. Numerical analyses were carried out to compare the three models and acquire an appropriate electrode configuration to separate Staphylococcus Aureus from blood particles. Simulations were carried out for all three models by modifying the voltages and inlet velocities to obtain their efficiency. The results showed that the circular and triangular-shaped electrode models required a low voltage of 4 V to achieve the desired separation of bacteria from blood particles, RBC, and platelets. In contrast, the rectangular-shaped electrode required a higher voltage of 8 V.
CFD Analysis of Flow Characteristics and Diagnostics of Leaks in Water Pipelines
Authors: Mushumbusi Philbert F. , Chaudhari Ashvinkumar , Leo Judith , Masanja Verdiana G.
Published: October 2024

This study utilizes Computational Fluid Dynamics (CFD) to generate pressure and flow rate values for the analysis of flow characteristics and the diagnosis of leaks in inclined pipelines. The Semi-Implicit Method for Pressure-Linked Equations (SIMPLE) solver in OpenFOAM software was modified to incorporate the effect of pipe orientation angle. Subsequently, the SIMPLE solver was employed to simulate the flow of water through the pipe. It was observed that the position of the leak along the pipe had no effect on the leakage rate, with the exception of pipes of a greater inclination, which were associated with higher leakage rates. A mathematical leak model is proposed based on non-dimensional flow variables and pipe orientation angle. To generate sufficient pressure values and leakage rates, the CFD simulation was performed 70 times. These values were then incorporated into the mathematical model for the leak location to be predicted. The proposed method is applicable to the detection of leakages of varying sizes in pipelines with different orientations. Therefore, knowing the pipe orientation angle and measurements of inlet flow rate, outlet flow rate, and pressure drop, the model can be used to precisely locate leaks in a pipeline.
A Tribological Study on NAB-Y2O3-CNT Composite prepared by the Powder Metallurgy Method
Authors: Hammood Shahad Ali , Al-Dulaimi Kawthar Yahya , Al-Ethari Haydar
Published: October 2024

Nickel Aluminum Bronze (NAB) alloys display characteristics like superior strength and excellent wear resistance. In this work, NAB was reinforced using various volume fractions of yttrium (Y2O3) and/or carbon nanotube (CNT) particles prepared using the Powder Metallurgy (PM) method. General full factorial design was used in MiniTab19 software. The experimental results showed a significant influence of the Y2O3 and CNT particles on mechanical and physical properties. The optimal results were recorded for the sample having 6 vol.% of Y2O3 and 1.5 vol.% of CNT. This sample exhibited the highest effect on characteristics with hardness value of 288 Hv, porosity of 10.2%, surface roughness of 0.15 µm, average particle size of 37.55 µm, wear rate of 0.0000313 g/mm, and friction coefficient of 0.68. The results demonstrated that there is an improvement of 64% in microhardness in comparison with the base alloy. Porosity, surface roughness, wear rate, friction coefficient, and average grain size were reduced by 4%, 81%, 71%, 31%, and 24%, respectively.
Heat Transfer Rate and Fluid Flow Analysis with Design Parameters of Gas Turbine using Beta-clog2-LSTM
Authors: Saraireh Mohammad
Published: October 2024

A Gas Turbine (GT) is a combustion engine that converts fuel into mechanical energy. None of the conventional models has utilized the stator hub, rotor tip leakage, and inter-stage flow for the optimum design of GT. This study performs an effective design parameter analysis for GT with heat transfer rate and fluid flow detection using Betadecay with cloglog-based Long Short-Term Memory (Beta-clog2-LSTM) and Griewank Siberian Tiger Optimization (G-STO). Initially, the design parameters were taken and the geometry of those parameters was created. Afterward, mesh generation was performed using the Linear Weighted Gradient Smoothing Sliding Mesh Interface (LWGSSMI). Then, the boundaries of the generated mesh were detected. Next, numeric modeling was performed deploying Finite Element Analysis (FEA), followed by flow behavior analysis. The optimal parameters were selected by G-STO. Similarly, the data in a heat transfer rate dataset were preprocessed and the features were extracted. Prediction of heat rate was performed using Beta-clog2-LSTM. Finally, the thermal loss was calculated, and a heat exchanger was utilized to mitigate it. The performance analysis demonstrated the robustness of the proposed method by achieving 0.98 prediction accuracy.
Utilizing Machine Learning for the Early Detection of Coronary Heart Disease
Authors: Ghrabat Mudhafar jalil Jassim , Mohialdin Siamand Hassan , Abdulrahman Luqman Qader , Al-Yoonus Murthad Hussein , Abduljabbar Zaid Ameen , Honi Dhafer G. , Nyangaresi Vincent Omollo , Abduljaleel Iman Qayes , Neamah Husam A.
Published: October 2024

Coronary Heart Disease (CHD) is a persistent health issue, and risk prognosis is very important because it creates opportunities for doctors to provide early solutions. Despite such promising results, this type of analysis runs into several problems, such as accurately handling high-dimensional data because of the abundance of extracted information that hampers the prediction process. This paper presents a new approach that integrates Principal Component Analysis (PCA) and feature selection techniques to improve the prediction performance of CHD models, especially in light of dimensionality consideration. Feature selection is identified as one of the contributors to enhance model performance. Reducing the input space and identifying important attributes related to heart disease offers a refined approach to CHD prediction. Then four classifiers were used, namely PCA, Random Forest (RF), Decision Trees (DT), and AdaBoost, and an accuracy of approximately 96% was achieved, which is quite satisfactory. The experimentations demonstrated the effectiveness of this approach, as the proposed model was more effective than the other traditional models including the RF and LR in aspects of precision, recall, and AUC values. This study proposes an approach to reduce data dimensionality and select important features, leading to improved CHD prediction and patient outcomes.
A Machine Learning Approach to Predict Time Delays in Marine Construction Projects
Authors: Nassar Aymen H. , Elbisy Ahmed M.
Published: October 2024

The estimation of time delays in construction projects represents a challenging undertaking, frequently constrained by insufficient data, inherent uncertainties, and potential risks. Nevertheless, it remains a crucial element in ensuring the success of a construction project. Marine construction projects represent a highly specialized subcategory of the construction sector, characterized by a considerable degree of risk and significant financial outlays. Despite the extensive application of Machine Learning (ML) techniques across a range of domains, there is a notable absence of studies evaluating their efficacy, particularly in the context of marine construction project assessment. In light of the above, the present study examines the potential of ML techniques for estimating time delays in marine construction projects. A total of 43 factors that affect marine construction projects in terms of time delay were identified and categorized into nine major groups through a detailed analysis of interviews with experts from the marine construction industry. The relative importance index method was employed to ascertain the relative importance of the factors affecting delays. The factors and groups were then ordered according to their level of impact on time delay. Considering the advancements in ML, this study utilizes General Regression Neural Networks (GRNN), Support Vector Machines (SVM), and Tree Boost functionality to estimate the time delay of marine construction projects. To evaluate the predictive capacity of each model, they were assessed using five statistical features and Taylor diagram visualization. With regard to predicting time delay, the overall performance of the GRNN was found to be more accurate than that of the other models, while the SVM model exhibited the least predictive capabilities. The GRNN model was found to be both efficient and precise and, therefore, may serve as a practical tool for predicting the time delay of marine construction projects.
Optimizing Data Availability and Scalability with RP*-SD2DS Architecture for Distributed Systems
Authors: Maabed Mohammed , Dennouni Nassim , Aridj Mohamed
Published: October 2024

This work introduces Range Partitioning Scalable Distributed Two-Layer Data Structures (RP*-SD2DS), an innovative data storage architecture with the objective of enhancing data availability and scalability in distributed systems. By employing SD2DS and preorder-preserving RP*, this design avoids the need for a router or coordinator, ensuring dynamic adaptability. The main goal is to minimize system downtime by efficiently distributing data across two layers and increasing availability during partitioning operations in traditional SDDSs, thereby avoiding the bottlenecks associated with master- or coordinator-based systems. The proposed solution offers significant improvements over MongoDB, a well-known and robust system, with a single or three Mongos instances.
The Impact of Enhancing the Damping in Lead Rubber Bearings on the Seismic Behavior of Base-isolated Steel Buildings
Authors: Athamnia Brahim , Kaab Mohamed Zohaïr , Boufarh Rafik
Published: October 2024

This study investigates the seismic behavior of a five-story steel base-isolated building equipped with Lead Rubber Bearings (LRBs). Focus is given to enhancing the damping of LRBs, from 10% to 30%, and its impact on seismic response. We specifically examine the story drift and acceleration under seismic excitations applied through 21 different time histories. The findings reveal that increasing the LRB damping to a range of 15-20% significantly improves the seismic performance of the building, effectively reducing both story drift and acceleration. These results underscore the importance of optimal damping levels in LRBs for enhancing the seismic resilience of base-isolated structures.
A Hybrid RNN-based Deep Learning Model for Lung Cancer and COPD Detection
Authors: Karla Raghuram , Yalavarthi Radhika
Published: October 2024

In the last ten years, lung cancer and chronic pulmonary diseases have become prominent respiratory diseases that require significant attention. This increase in prominence underscores their widespread impact on public health and the urgent need for better understanding, detection, and management strategies. Accurate identification of lung cancer and Chronic Obstructive Pulmonary Disease (COPD) is crucial for preserving human life. Accurate differentiation between the two disorders and the administration of the necessary treatment are very important. This study focuses on effectively discriminating between two of the deadliest chest diseases using chest X-ray images. Recurrent neural networks help to classify diseases accurately by improving feature extraction from radiographs. The proposed algorithm performs more effectively when analyzing chest X-ray image datasets showing alterations in a patient's chest, including the development of tiny lobes or thicker capillaries in the respiratory system among other details, compared to standard lung imaging.
Development of a Climate Equipment Parameter Acquisition System using PID and Fuzzy Logic Controllers to Improve Energy Efficiency
Authors: Moseva Marina , Simonov Sergey , Gorodnichev Mikhail
Published: October 2024

Today, energy-efficient resource management is an important task. This study aims to improve the energy efficiency of the cooling system of a technical room by developing a transparent and explainable temperature adaptation tuning algorithm based on the combination of PID control and fuzzy logic methods. This work focuses on the design and development of a hardware and software system consisting of a microcontroller and a temperature sensor. This paper analyzes temperature control based on PID and fuzzy controllers and proposes a combined method to allow for more accurate temperature control tuning. The experimental results show that the combined method reduces the rise time by at least 5%, the stabilization time by at least 17%, and the power consumption by at least 21%.
Numerical Analysis of Three-Dimensional Magneto hybridized Nanofluid (Al2O3-Cu/H2O) Radiative Stretchable rotating Flow with Suction
Authors: Lakshmi Bhavanam Naga , Bhagavan V. S. , Ramana Ravuri Mohana , Maheswari Chundru
Published: October 2024

The current study aims to investigate the heat generation/absorption, radiation, and chemical reaction impacts on a laminar flow, with an upward half-space three-dimensional, incompressible permeable, stretchable, rotating hybrid nanofluid (Al2O3-Cu/H2O). Using appropriate similarity transformations, leading nonlinear governing equations were taken into account and were converted into ordinary differential equations. These equations were evaluated by deploying MATLAB's bvp5c feature. The resulting graphs assess velocity, temperature, and concentration for a range of effects. Additionally, Sherwood and Nusselt numbers and skin friction results were obtained. The findings were compared with previously published research.
Experimental Evaluation of Diesel Engine Performance using Producer Gas and Conventional Fuel: A Comparative Study
Authors: Sookramoon Krissadang , Patummakason Chumpon , Pangsri Prapawan
Published: October 2024

This study empirically evaluated the performance of a downdraft biomass gasifier integrated with a diesel engine using a combination of biomass and diesel fuel. The employed downdraft gasifier consisted of two interconnected oil drums measuring 60×151 cm. A combustion chamber was installed in the conical steel drum with a central axis having a throat diameter of 31 cm. Rubberwood, tamarind wood, and sawdust were used as fuel sources. These woods were combusted to produce gas, which was subsequently channeled through a filtering and temperature-reduction apparatus before being blended with diesel fuel. A Nissan RD28 94 hp diesel engine was used for the experiments, with an airflow rate set at 0.226 m3/s. The experimental results showed that burning a mixture of rubberwood and sawdust (7.5 kg each) produced gas for 61 minutes. Tamarind wood combustion (15 kg) resulted in 58 minutes of gas production. Burning pure rubberwood (15 kg) produced gas for 37 minutes. Combusting a blend of tamarind wood and sawdust (7.5 kg each) produced gas for 45 minutes. The gooseberry wood (15 kg) was burned for 57 minutes with gas formation lasting 48 minutes. Adjusting the air input to the gasifier combustion chamber to 100%, the rubberwood and sawdust mixture exhibited the highest gas production. Furthermore, engaging the RD28 engine at 978 RPM in gear L resulted in the gear halting at 99 RPM after gradually adding steel blocks. The torque required to cease the gear was measured at 10.68 N.m, highlighting the potential for stopping gear L within the low-speed range due to misalignment between the engine's end flywheel and the gear pulley spline.
Effective Human Activity Recognition through Accelerometer Data
Authors: Thuong Vu Thi , Tran Duc-Nghia , Tran Duc-Tan , Thu Bui Thi , Tung Vu Duong , Phuong Nguyen Thi Anh , Khanh Phung Cοng Phi , Tung Pham Khanh , Vi Manh-Tuyen
Published: October 2024

In recent years, the field of Human Activity Recognition (HAR) has emerged as a prominent area of research. A plethora of methodologies have been documented in the literature, all with the objective of identifying and analyzing human activities. Among these, the use of a body-worn accelerometer to collect motion data and the subsequent application of a supervised machine learning approach represents a highly promising solution, offering numerous benefits. These include affordability, comfort, ease of use, and high accuracy in recognizing activities. However, a significant challenge associated with this approach is the necessity for performing activity recognition directly on a low-cost, low-performance microcontroller. This research presents the development of a real-time human activity recognition system. The system employs optimized time windows for each activity, a comprehensive set of differentiating features, and a straightforward machine learning model. The efficacy of the proposed system was evaluated using both publicly available datasets and data collected in experiments, achieving an exceptional activity recognition rate of over 95.06%. The system is capable of recognizing six fundamental daily human activities: standing, sitting, jogging, walking, going downstairs, and going upstairs.
Impact of Shear Strength Degradation on Raft Foundation Performance in Clay Shale
Authors: Suhendra Andryan , Suwondo Riza Ainul Hakim , Vincensia Natalia
Published: October 2024

The construction of structures on clay shale soils presents significant challenges due to the soils' propensity for water absorption and associated volumetric changes. These problematic soils are characterized by weak bond structures and expansive clay minerals that can lead to severe structural damage and foundation instability. This study investigates the performance of raft foundations under varying conditions of shear strength degradation and different thicknesses of degraded clay shale layers. The study employs numerical simulations using the finite element software PLAXIS 3D, which allows the detailed modeling of soil-structure interactions. Various scenarios were analyzed, considering shear strength degradation and clay shale thickness variations ranging from 0.5 m to 2.5 m. The findings indicate a clear trend of decreasing safety factors and increasing settlement with both the degradation of shear strength and the increased thickness of the degraded clay shale layer. These results emphasize the critical impact of soil degradation on foundation performance and highlight the necessity for rigorous soil assessments and the implementation of mitigation strategies to ensure long-term structural stability. The study's insights and recommendations contribute to advancing geotechnical engineering practices, thus promoting safer and more resilient foundations in challenging soil conditions.
An Analytical Solution of Piezoelectric Energy Harvesting from Vibrations in Steel-Concrete Composite Beams subjected to Moving Harmonic Load
Authors: Dan Dao Sy , Diem Nguyen Dang , Lam Nguyen Ngoc , Hung Le Quang
Published: October 2024

Steel–concrete composite beams are ubiquitous in construction, especially in bridge building. This paper addresses the harvesting of energy from a beam subjected to a moving harmonic load using analytical methods. The harvesting is performed by attaching a thin piezoelectric patch directly to the bottom surface of the steel beam. Based on the assumptions of the Euler–Bernoulli beam theory for the relationship between displacement and deformation, the differential equation for the vibration of a beam is derived using Hamiltonian principles. A theoretical formulation is presented for the problem of harvesting energy from a harmonic moving load on a simply supported beam. The dynamic responses are determined in exact form using analytical methods, and the energy harvested from the piezoelectric material layer is calculated. The influence of the speed of the load on the energy harvesting of the piezoelectric material layer is investigated in detail.
Clay and Cement Shielding Behavior from Gamma Sources
Authors: Eisa Mohamed E. M. , Ali Mamed D. M. , Abualreish Mustafa J.
Published: October 2024

Clay is a native building material in Sudan and is utilized by most low-income people. The radiation shielding characteristics of clay and cement were tested with a specific thickness to explore the behavior of gamma radiation through these materials. The results were compared and estimated by the Phy-X, XCOM, and Py-MLBUF software packages. Mass Attenuation Coefficient (MAC), Linear Attenuation Coefficient (LAC), Half-Value Layer (HVL), Tenth-Value Layer (TVL), Mean Free Path (MFP), and equivalent atomic number (Zeq), which describe the shielding properties of the examined materials, were all determined and compared. The comparison of calculations by software and experimental data of all selected samples showed a high degree of agreement, with discrepancies ranging between 0.01 and 5%. The experiments were carried out in a chamber close to 137Cs and 60Co sources at energies of 662, 1173, and 1332 keV.
Displacement Analysis of a Hydrostatic Spindle: An Experimental Investigation
Authors: Nguyen Manh-Toan , Pham Van-Hung , Tran Van-Thuc , Bui Tuan-Anh
Published: October 2024

This paper presents an experimental investigation of the displacement characteristics of a hydrostatic spindle used in a medium-sized circular grinding machine. The study focuses on the influence of lubricant pressure and oil viscosity on spindle displacement under varying load conditions. Experiments were conducted using a specially designed testing bench, which allowed precise control of pump pressure and oil viscosity. The results demonstrate a significant impact of both parameters on spindle displacement. An increase in oil viscosity leads to a decrease in displacement, with the optimal viscosity identified as 2 mPa.s. Similarly, higher pump pressure results in reduced displacement, with 5 MPa being the optimal pressure for the system studied. These findings highlight the importance of carefully selecting lubricant properties and operating conditions to achieve optimal machining accuracy and stability. This research provides valuable insights into the understanding and optimization of hydrostatic bearing systems in precision machining applications.
Talki: A Mobile Application to Improve English Learning of High School Students in Peru utilizing Virtual Reality and Gamification
Authors: San Martin Joaquin , Romero William's , Castillo-Sequera Jose Luis , Wong Lenis
Published: October 2024

Despite the widespread necessity of students to learn English, Peruvian students face challenges that include a lack of motivation, overwhelmed academic expectations, and low socioeconomic status. Therefore, in this paper, a mobile application that employs virtual reality and gamification is proposed to improve English language learning among Peruvian high school students. This research consists of three stages: (1) review, (2) proposal, and (3) validation. In (1), an exhaustive review of teaching techniques, gamification strategies, and virtual reality models applied to English language learning is carried out. In (2), specific guidelines are selected for the implementation of teaching techniques, gamification, and type of virtual reality for the construction of the mobile application, using Google Cardboard virtual reality glasses. Finally, in (3) the validation of the mobile application is reported, in which 58 high school students participated. Data were collected on student performance before and after using the application. The results showed that students increased their performance in the English language by 22%. This percentage was measured by the average of the grades taken at the beginning and at the end of the experiment.
Efficient Route Optimization for Ice Distribution: Enhanced VRPTW with Customer Retention Strategies
Authors: Thammasang Supitcha , Arunyanart Sirawadee
Published: October 2024

This study addresses the Vehicle Routing Problem with Time Windows (VRPTW) in the context of ice distribution by introducing a novel mathematical model that incorporates practical constraints essential for real-world applications. These constraints include customer retention strategies and quality preservation methods, which are important for maintaining customer satisfaction and product integrity. The objective is to minimize the total costs, including fuel expenses, standard and bonus driver wages, missed delivery penalties, and costs related to a quality preservation strategy. Given the NP-hard nature of this problem, this study proposes a hierarchical cluster-first-route-second approach and a Differential Evolution (DE) algorithm to solve large-scale problems. The effectiveness of these methods was examined and compared through test cases involving various problem sizes using real-world data from an ice distribution company in Thailand. The results show that the hierarchical cluster-first-route-second approach is more effective for the practical problem. Using capacitated K-means clustering, this hierarchical approach groups customers, enabling the solution of manageable subproblems through Mixed-Integer Linear Programming (MILP). The proposed method not only provides cost-effective and scalable solutions, but also outperforms traditional methods in terms of computation time and feasibility for large-scale applications. This study offers significant theoretical contributions by extending VRPTW models and providing practical implications for optimizing distribution strategies in competitive market environments, leading to substantial cost reductions and enhanced operational efficiency.
Performance of DVB-T2 Application in High-Speed Train Transportation System
Authors: Tarigan Nicolas Yonara , Pamungkas Wahyu , Isnawati Anggun Fitrian
Published: October 2024

The terrestrial television system in Indonesia has evolved from the first-generation Digital Terrestrial Video Broadcasting (DVB-T) to the second generation (DVB-T2), enhancing broadcast quality and spectral efficiency. This paper focuses on the application of DVB-T2 within High-Speed Train (HST) communication systems, where the unique challenges faced include high Doppler shifts and multipath effects due to the high train speed which exceeds 300 km/h. Multicarrier Orthogonal Frequency Division Multiplexing (OFDM) technology was employed along with 64-QAM modulation for the investigation of the performance under various conditions. The simulations were structured around varying speeds, scatterer counts, and angles, and 15 iterations were conducted for train speeds of 10 m/s, 50 m/s, and 100 m/s. The findings indicate that the error rates varied with speed and environmental complexity. At a lower speed (10 m/s), the system performance showed significant improvement, with a reduction of 322 bits in the error rate. However, as the speed increased to 100 m/s, the performance declined, demonstrating an increase of errors by 414 bits owing to exacerbated Doppler effects. Different scatterer counts also influenced the results. For instance, with four scatterers at 10 m/s speed, the error improved by 245 bits, but at 100 m/s speed and under the same scatterer conditions, the performance worsened, increasing the error by 361 bits.
A Greedy Simulated Annealing-based Multiobjective Algorithm for the Minimum Weight Minimum Connected Dominating Set Problem
Authors: Dahmri Hayet , Bouamama Salim , Balbal Samir
Published: October 2024

The minimum connected dominating set problem is a well-known NP-hard combinatorial optimization problem in graph theory, with various fields of application including wireless sensor networks, optical networks, and systems biology. This paper presents an adaptive multiobjective simulated annealing approach to address a variant of the minimum connected dominating set problem known as the Minimum Weight Minimum Connected Dominating Set Problem. This approach combines a multiobjective Pareto set with a greedy simulated annealing algorithm to tackle the problem by simultaneously optimizing two objectives, namely the cardinality and the total weight of the connected dominating set. Experimental results compared to those obtained by current state-of-the-art approaches show the superiority of our method.
Enhancing Arrhythmia Prediction using the Naked Mole Rat Algorithm and Machine Learning
Authors: Sureja Nitesh , Upadhyay Rocky , Chaudhari Nandini , Upadhyay Shivam , Panesar Sonia , Patel Hemant , Sureja Heli
Published: October 2024

An irregular heartbeat is known as cardiac arrhythmia. Cardiac arrhythmia occurs when electrical impulses that instruct the heart to beat malfunction. To save lives, arrhythmia must be detected early. Machine Learning (ML) is one of the fastest, cheapest, and easiest disease detection approaches. This study introduces an improved Naked Mole Rat Algorithm (RLNMRA) to select highly influential features in diagnosis and then uses various ML classifiers to train the model. The experimental results showed accuracies of 95.0%, 99.0%, 97.0%, 98.0%, 98.0%, 92.0%, and 99.0% with KNN, RF, GB, SVM, LR, DT, and GNB, classifiers, respectively. These results were obtained after removing invalid features from the UCI Arrhythmia dataset. The proposed method was also evaluated using recall, precision, and F score. The performance of the proposed model indicates that it can be used in various domains.
Design and Development of the Fermented Fish Chopper Machine using the Design of Experiments Method
Authors: Chanpahol Arawan , Rattanawai Narat , Nadondu Boonsin
Published: October 2024

The objective of this research is to design and develop a fermented fish chopper machine to provide support to community enterprises in Phetchabun province in Thailand. A full factorial design of experiments was employed for the purpose of evaluating and optimizing manufacturing parameters. The experiment involved varying the knife chopping speed, the chopping block speed, and the press distance of the knife from 250 to 280 rpm, 50 to 70 rpm, and 1 to 3 mm, respectively. The Analysis of Variance (ANOVA) was employed to analyze the variation in the experimental data. The main effects and interaction effects were found to be significant (p < 0.05), indicating their influence on chopping time. The results demonstrated the optimal operating conditions for minimizing chopping time and improving machine performance.
Prediction and Estimation of Highway Construction Cost using Machine Learning
Authors: Abd Abbas M. , Kareem Yassir A. , Zehawi Raquim N.
Published: October 2024

Cost estimation and prediction are crucial processes for the success of construction projects, especially for infrastructure development. This study analyzes historical data collected between 2011 and 2023 and investigates the relationship between construction elements and the final cost of highway construction projects in Iraq. Different cost analysis approaches, including statistical assessment and machine learning techniques, were applied to a dataset of 291 highway projects. Cost estimation is a time-consuming and risky process that requires many qualitative and quantitative parameters to be well analyzed. However, machine learning provides a comprehensive assessment tool to predict future costs. Four ANN-based models were investigated and precision was improved by combining RMSE and the correlation coefficient (R) as a controller. The results showed improvements in performance metrics, such as error reduction rate and correlation coefficient, for the models developed. The best performance was achieved at an R of 0.989. The proposed model can be effectively adapted to predict road construction costs. Despite the need for more data, the implication of the proposed model can ensure a sustainable application, saving the time and resources required by construction professionals to predict road project costs during the planning phase.
The Effect of Thermo-Mechanical Properties of Concrete on the Temperature Field in Mass Concrete
Authors: Pham Duc-Phong , Nguyen Trong Chuc , Vu Kim-Dien
Published: October 2024

The current study explores the effect of three basic thermal properties of concrete on the mass concrete temperature field. It investigates the relationship between thermal conductivity, specific heat capacity, and density with the maximum mass concrete temperature. The results revealed that all three thermal properties have a linear relationship with the maximum mass concrete temperature since the latter decreases while these properties increase.
Reliable High Impedance Fault Detection with Experimental Investigation in Distribution Systems
Authors: Satea Mostafa , Elsadd Mahmoud , Zaky Mohamed , Elgamasy Mahmoud
Published: October 2024

An approach for high-impedance fault detection is introduced in this paper. This technique discriminates between high-impedance faults and switching conditions by utilizing changes in the third harmonic current/voltage magnitude in conjunction with the conventional wavelet algorithm. The concept of discrimination is based on the observation that switching conditions typically do not involve changes in the zero-sequence third harmonic magnitude. However, in the case of high-impedance arcing faults, a noticeable change in the third harmonic current or voltage magnitude occurs. The performance of the proposed technique is examined through a detailed simulation of an actual medium voltage radial distribution feeder. In this simulation, high-impedance faults are represented by an arc model. The simulation, conducted in MATLAB for different fault cases, reveals that all fault cases are detected using the proposed technique. Furthermore, the experimental validation of the reliability of the proposed technique is accomplished using the same actual distribution feeder.