bluuebunny/crossref_metadata_2025 huggingface dataset:
Influential Time and Cost Factors for Commercial Projects in the Malaysian Construction Industry
Authors: Sohu Samiullah , Ramadzan Tahara , Khan Omar Shahid , Bhatti Sajjad Ahmed , Sohoo Arslan Ahmed
Published: June 2024

Time and cost play an important role in project completion for both developing and developed countries. This study aims to identify the critical factors that influence the time and cost of commercial projects in Malaysia. A detailed questionnaire survey was conducted with industry professionals, and the results were analyzed based on the survey responses. The average index method was used to determine critical factors based on responses from professionals. The critical factors that affect time are poor contract management, client fund shortages, late drawing submissions, land acquisition problems, and inadequate surveying before construction. The critical factors identified for cost are delayed client payments, shortage of skilled workers, design changes, errors in the construction process, and changes in top management. This study raises awareness by identifying critical factors to minimize their impact so that construction can be completed on time and according to the defined budget, and maximize the benefits of future projects.
Prioritizing Road Maintenance: A Framework integrating Fuzzy Best-Worst Method and VIKOR for Multi-Criteria Decision Making
Authors: Hasan Ali Ezat , Jaber Firas K.
Published: June 2024

A nation’s development depends on its transport networks, particularly the road network, which plays a crucial role in the country’s economic and social advancement and well-being. However, roads are subject to deterioration due to weather conditions, traffic loading, and construction quality. If they are not maintained properly, they will quickly worsen over time, resulting in reduced mobility and accessibility. To develop and maintain a good road network, careful planning is needed, which covers all aspects of road maintenance, funding, construction, quality, and other criteria. However, due to limited budgets, not all roads can be maintained and rehabilitated at the same time. Road maintenance priority and optimal use of insufficient funding are the most challenging problems the authorities face. The development of a systematic approach is essential to formulate appropriate maintenance policies. This is why the concept of road maintenance prioritization is essential. Additionally, industry experts have also identified a lack of a Multi-Criteria Decision Making (MCDM) technique that can incorporate the views of all Decision Makers (DMs) in the road maintenance prioritization process. This study aims to propose a framework for prioritizing road maintenance using MCDM techniques in a fuzzy environment. A case study that considers 20 criteria was conducted. The study integrated two MCDM techniques, namely the Fuzzy Best-Worst Method (BWM) and VIKOR, to help DMs evaluate and rank the alternatives, on the basis of the selected maintenance criteria. The aim of this framework is to enhance the decision-making process with impartiality and reliability and to assist in reaching an optimal decision. By comparing the Q values for each alternative, A5 was revealed to have higher priority over the other roads in terms of maintenance and rehabilitation activities.
Energy-Efficient and Reliable Routing for Real-time Communication in Wireless Sensor Networks
Authors: El-Fouly Fatma H. , Kachout Mnaouer , Ramadan Rabie A. , Alzahrani Abdullah J. , Alshudukhi Jalawi Sulaiman , Alseadoon Ibrahim Mohammed
Published: June 2024

Wireless Sensor Networks (WSN) can be part of a tremendous number of applications. Many WSN applications require real-time communication where the sensed data have to be delivered to the sink node within a predetermined deadline decided by the application. In WSNs, the sensor nodes' constrained resources (e.g. memory and power) and the lossy wireless links, give rise to significant difficulties in supporting real-time applications. In addition, many WSN routing algorithms strongly emphasize energy efficiency, while delay is not the primary concern. Thus, WSNs desperately need new routing protocols that are reliable, energy-efficient, and appropriate for real-time applications. The proposed algorithm is a real-time routing algorithm appropriate for delay-sensitive applications in WSNs. It has the ability to deliver data on time while also enabling communications that are reliable and energy-efficient. It achieves this by deciding which candidate neighbors are eligible to participate in the routing process and can deliver the packet before its deadline. In order to lessen the delay of the chosen paths, it also computes the relaying speed for each eligible candidate. Moreover, it takes into account link quality, hop count, and available buffer size of the selected relays, which leads to end-to-end delay reduction while also minimizing energy consumption. Finally, it considers the node's energy consumption rate when selecting the next forwarder to extend the network lifetime. Through simulation experiments, the proposed algorithm has shown improved performance in terms of packet delivery ratio, network lifetime packets miss ratio, average end-to-end delay, and energy imbalance factor.
Digital Shadow-based Control of Temperature and Fault Classification in Shell and Tube Heat Exchanger using Fuzzy Logic Technique
Authors: Jeyarajah Surendran T. , Kumar G. Joselin Retna
Published: June 2024

In this study, the Digital Shadow (DS) of the Shell and Tube Heat Exchanger (STHE) is designed and analyzed for numerous disturbances that occur when the system is in a running condition. The disruptive segregation of the heat exchanger is related to the DS for its operation, and thus a realistic DS was developed for the STHE. Fuzzy Logic (FL) was used to identify and segregate the disturbance signals from the process output. The Response Optimization (RO) algorithm was adopted and modified to work on the STHE. The observer-based residual generator design was implemented to prevent system failure and defective conditions. Model Predictive Controller (MPC), Transposed System Controller (TSC), and a looping-based control technique called Unity Response Loop (URL) were also implemented, and the results are discussed. The findings of this study contribute to the improvement of the overall performance of non-linear systems in industrial processes and the avoidance of defects.
The Effect of Different Curing Methods on the Properties of Reactive Powder Concrete Reinforced with Various Fibers
Authors: Luti Ahmed A. , Abbas Zena K.
Published: June 2024

The current study explores the effects of four curing methods on the strength of Reactive Powder Concrete (RPC) reinforced with different fibers. Four mixtures of RPC, reference (RM-RPC), wavy fiber reinforced (WF-RPC), carbon fiber reinforced (CF-RPC), and micro steel fiber reinforced (MF-RPC) mixes were prepared and cured following four curing methods (normal, autogenous, coating, and warm water). The results revealed that warm water curing achieved the highest values of compressive, flexural, and splitting strength, attaining 138.9 MPa 22.4 MPa, and 20.89 MPa, respectively. The results of using different fiber reinforcement displayed that the compressive strength of fiber-reinforced RPC mixes was notably higher than that of the RM-RPC. The compressive strength increase results were 9.04% for WF-RPC, 24% for CF-RPC, and 27.96% for MF-RPC regardless of the curing method adopted. Flexural strength increased by 21.2%, 38.47%, and 55.86% for WF-RPC, CF-RPC, and MF-RPC, accordingly in autogenous curing, whereas the change in flexural strength was 30.65%, 39.14%, and 36.59%, correspondingly in coating curing and 21.27%, 29.22%, and 39.55%, respectively, for warm water curing. The optimum flexural values were mainly obtained for MF-RPC regardless of the kind of curing used. CF-RPC almost achieved the same results as MF-RPC with slightly lower values. It can be concluded that fiber reinforcement had a more positive influence on the flexural and splitting strength of RPC than on the compressive strength.
Α Chaotic Map-based Approach to Reduce Black Hole Attacks and Authentication Computational Time in MANETs
Authors: Syed Ahsan Saud Qadri , Atheeq C. , Ali Layak , Quasim Mohammad Tabrez
Published: June 2024

The need for Mobile Ad hoc Networks (MANETs) has expanded with the development of mobile computing and wireless sensor network technologies. However, this increase has also led to a rise of the attacks on these networks. In order to ensure high Quality of Service (QoS) and maintain connectivity, MANETs require careful consideration of factors, such as power, connectivity, secure transmissions, authentication, and handovers. Handovers are necessary for seamless network connectivity and require quick authentication to ensure uninterrupted service. Although RSA (Rivest Shamir Adleman) and ECC (Elliptic Curve Cryptography) algorithms are commonly used for authentication due to their fast asymmetric key encryption-decryption and exchange, they are less effective against black hole attacks. Chaos algorithms provide a faster authentication process and are efficient against false behavior black hole attacks. This study demonstrates that the chaos algorithm is a viable option for providing fast authentication and preventing malicious nodes from disrupting the network.
Experimental and Analytical Investigation of the Ceep Drawing Process for producing Pentacle Cups
Authors: Mohsein Zainab H. , Jawad Waleed K. , Abed Aseel H.
Published: June 2024

Sheet metal forming is a critical process in modern manufacturing, used to create both finished and semi-finished products. In this industry, there is an increasing demand for fast and cost-effective manufacturing and modification of dies. Therefore, improving theoretical and experimental engineering approaches to reduce manufacturing costs and lead-time between design and production is essential. The development of numerical methods has made Finite Element Analysis (FEA) a valuable tool for predicting product deformation. This study used three forming methods to create a pentacle cup from a low-carbon steel sheet (1008-AISI) with a thickness of 0.7 mm and a diameter of 80 mm. ANSYS Workbench 3-D modeling software was utilized to simulate the drawing procedures. The resulting product's wall thickness and strain were measured and graphed to demonstrate the impact of the different forming methods. The first method involved direct formation by drawing a circular blank metal into a pentacle shape. The second method involved redrawing a cylindrical cup into a pentagonal cup, while the third method entailed converting a pentagonal cup into a pentacle cup. The results showed that the second forming method produced the highest maximum punch load reaching approximately 42.24 kN in experimental testing and 36.66 kN in Finite Element Modeling (FEM), exceeding that of the third forming method. The maximum thinning at cup curvature was observed in the pentacle cup created by the second method, particularly in the major and minor areas, and was more pronounced than in the pentacle cups produced by the third forming method. Ultimately, the third forming method was identified as the optimal technique for producing a pentacle cup with less thinning at the cup curvature and a more uniform distribution of thickness and strain. Overall, this study highlights the importance of advancements in theoretical and experimental engineering approaches to reduce manufacturing costs and improve the efficiency of the sheet metal forming process. The findings from this study can lead to the development of optimal forming techniques for creating high-quality products.
Investigation of Influencing Factors on Surface Quality during Low-Speed Cutting of Steels with a Hardness exceeding 50 HRC for forging Dies
Authors: Sukkam Chaiyakron , Chaijit Seksan
Published: June 2024

This study investigates the factors that affect surface quality in low-speed milling of steel with a hardness greater than 50 HRC, specifically for forming molds. The material used in the experiment was SKT4 mold steel with a hardness of 50 HRC, which is commonly employed to form molds, with dimensions of 100×100×50 mm. The cutting tools put into service were carbide ball end mills of the HARD Series 5R×10×60L. This study examines changes in the surface roughness values of the milled workpiece material based on the feed rate and cutting depth. The constant spindle speed deployed was 1,200 rpm, and heat dissipation was achieved by air cooling. The results revealed that the feed rate and the interaction between the feed rate and the cutting depth had a p-value of 0.000. This considerably influences the average surface roughness (Ra) value at the 0.05 significance level. However, the cutting depth had a p-value of 0.061, which is greater than the significance level of 0.05 and thus does not substantially affect the average surface roughness.
Association between the Analytical Technique and Finite Element Method for designing SPMSMs with Inner Rotor Type for Electric Vehicle Applications
Authors: Nguyen Duc-Quang , Thai Thanh Nguyen , Thi Cuc Le , Minh Dinh Bui , Quoc Vuong Dang
Published: June 2024

This paper focuses on computing the electromagnetic parameters of inner rotor-configuration Surface-mounted Permanent Magnet Synchronous Motors (SPMSMs) via the combination of analytical and finite element methods with an application to electric bicycles. The analytical method is, first, developed in detail to calculate the essential parameters of the inner rotor-configuration SPMSM. Then, a Finite Element Method (FEM) is introduced to verify the analytical parameters found. Further, a simulation of magnetic flux density, torque, cogging torque, torque ripple, back Electromagnetic Force (EMF), linkage flux, and temperature is performed. The end result is validated with a real 2.2 kW inner rotor-configuration SPMSM.
Detection and Classification of Urea Adulteration in Milk with Deep Neural Networks
Authors: Ghodinde Ketaki , Chaskar Uttam
Published: June 2024

Milk is a major food constituent. However, the existing discrepancy between milk demand and supply leads to adulteration, which can be dangerous since it causes detrimental effects on health implicating lethal diseases. Although classical methods for adulteration detection are very accurate, their implementation requires skilled technicians as well as expensive and sophisticated instruments. These reasons trigger the need for improved techniques in uncovering adulteration. Urea is a natural component in milk and accounts for a substantial share of adulteration in the non-protein content of milk. The current research proposes and employs a sensor system utilizing the Electrical Impedance Spectroscopy (EIS) method to determine the presence of urea. The classification system was developed using different machine learning algorithms. Three classifiers, Extreme Gradient Boosting (XGBoost), Extreme Learning Machines (ELM), and Deep Neural Networks (DNN) were considered for various levels of urea adulteration. Milk samples were assessed by deploying the developed EIS sensor assembly and the results derived were employed in the training of the machine learning algorithms. The estimated classifiers displayed promising outcomes, involving up to 98.33% classification accuracies, outshining frequently used existing learning approaches like logistic regression.
Modified Equivalent Compression Stress Block for Normal-Strength Concrete Flexural Design using Energy Modeling
Authors: El-Gohary Hamdy
Published: June 2024

The equivalent stress block is recommended for use in the design of reinforced concrete sections to simplify the analysis of the composite behavior of concrete and steel reinforcement. In most current codes, a rectangular equivalent stress block is provided. The design parameters of the equivalent block were recommended many years ago. Due to the importance of the equivalent stress block concept, numerous investigations have been performed to increase its accuracy. In the current paper, an exploration of the rectangular equivalent stress block has been carried out using the energy modeling approach. Energy modeling is a new general approach for studying the behavior of concrete elements. In this method, the energy consumed (work done) can be determined by integrating the force-displacement diagram (in the current study this will be the concrete stress-strain curve in compression). Schematic and equivalent stress-strain curves for concrete in uniaxial compression provided in most current codes and relevant textbooks were considered in this research. The codes taken into account in the current study are ACI-318-19, Canadian Code CSA A23.3-04, Eurocode EC-2, and Chinese standard GB 500 10 – 2002. The energy consumed by these curves for different values of concrete strength has been compared with numerous experimental results. This comparison shows that the results of the equivalent stress block provided in most of the considered current codes are conservative. Applying the energy modeling for the considered experimental stress-strain curves a modified equivalent stress block is recommended for practical use. The results of the proposed equivalent stress block are in good agreement with the experimental ones. The ratio between the predicted total energy engaging the proposed model and the total energy calculated for the experimental results ranges between 0.95 and 1.08 with a mean value equal to unity.
Forecasting of Cryptocurrency Price and Financial Stability: Fresh Insights based on Big Data Analytics and Deep Learning Artificial Intelligence Techniques
Authors: Bouslimi Jihen , Boubaker Sahbi , Tissaoui Kais
Published: June 2024

This paper evaluates the performance of the Long Short-Term Memory (LSTM) deep learning algorithm in forecasting Bitcoin and Ethereum prices during the COVID-19 epidemic, using their high-frequency price information, ranging from December 31, 2019, to December 31, 2020. Deep learning (DL) techniques, which can withstand stylized facts, such as non-linearity and long-term memory in high-frequency data, were utilized in this paper. The LSTM algorithm was employed due to its ability to perform well with time series data by reducing fading gradients and reliance over time. The obtained empirical results demonstrate that the LSTM technique can predict both Ethereum and Bitcoin prices. However, the performance of this algorithm decreases as the number of hidden units and epochs grows, with 100 hidden units and 200 epochs delivering maximum forecast accuracy. Furthermore, the performance study demonstrates that the LSTM approach gives more accurate forecasts for Ethereum than for Bitcoin prices, indicating that Ethereum is more prominent than Bitcoin. Moreover, the increased accuracy of forecasting the Ethereum price made it more reliable than Bitcoin during the COVID-19 coronavirus crisis. As a result, cryptocurrency traders might focus on trading Ethereum to increase their earnings during a crisis.
Enhancing Roadway Efficiency through Comprehensive Studies on Travel Time, Delays, and Spot Speeds
Authors: Islam Md. Kamrul
Published: June 2024

This study integrates travel time and delay with spot speed study, utilizing the floating car method. The correlation between increased traffic flow and elevated delay times is underscored. Notably, a significant portion of vehicles operating below anticipated speeds emerges as a pivotal factor contributing to congestion. The average journey time is approximately 4 minutes, with an average stopped delay ranging from 0.17 to 0.11 minutes. Spot speed study in four directions reveals nuanced speed dynamics. In the East-West direction, the 15th, 85th, and 98th percentile speeds are 39.14 km/h, 62.27 km/h, and 85.00 km/h and similar patterns are observed in the other directions. These results transcend routine traffic studies, offering actionable insights for urban planning, traffic regulation, and economic assessments. The study delves beyond statistical compilations, providing concrete evidence of the intricate interplay between traffic flow, delay times, and speed dynamics. It aspires to guide traffic engineers, urban planners, and decision-makers into the realms of future urban development. The comprehensive understanding derived ensures informed decision-making in transportation management and infrastructure planning.
Evaluation of the Impact of Rooftop Solar Power on the Power Quality of Vietnam Urban Distribution Networks according to Relevant Vietnamese and IEEE and IEC Standards
Authors: Vu Hoang-Giang , Huu Duc Nguyen
Published: June 2024

The goal of reducing greenhouse gas emissions and energy transition has created many favorable conditions to promote solar power generation technology. However, from a technical perspective, integrating solar power into the power grid poses many challenges in grid operation. This study investigates the impact of rooftop solar power in terms of power quality in the urban distribution grid in Vietnam. The current study simulated a typical low-voltage distribution grid with single-phase and three-phase loads to evaluate important power quality issues, such as Total Harmonic Distortion (THD) of voltage and current, voltage unbalance, and voltage rises. A grid simulation grid was developed in Matlab/Simulink under different conditions for the penetration level of solar power and the daily variation of loads. These indexes were compared with the limits specified in Vietnamese standards and the relevant international standards of IEEE and IEC. Compliance with the provisions of these standards was provided, even in the case of solar power systems with a high level of penetration into Vietnam's urban distribution grid.
BlockEstate: Revolutionizing Real Estate Transactions through Hyperledger-based Blockchain Technology
Authors: Naz Laviza Falak , Qamar Rohail , Asif Raheela , Ahmed Saad , Imran Muhammad
Published: June 2024

This study introduces BlockEstate, an innovative platform to revolutionize real estate transactions through the application of Hyperledger blockchain technology. BlockEstate presents novel contributions in the form of a pioneering compensation request mechanism and a sophisticated chaincode for real estate transaction management. These advancements address long-standing challenges in traditional real estate transactions by leveraging the decentralization, immutability, and transparency of blockchain technology. By ensuring secure and transparent financial transactions and automating property ownership conveyances, BlockEstate sets a new standard for efficiency and safety in the real estate industry. This study comprehensively investigates the design, functionality, and impact of BlockEstate, highlighting its unique contributions and potential to transform the real estate market.
Chronic Obstructive Pulmonary Disease Diagnosis with Bagging Ensemble Learning and ANN Classifiers
Authors: Siddiqui Taskeena , Latif Mustafa , Farooq Muhammad Umer , Baig Mirza Adnan , Hassan Yusuf Sharif
Published: June 2024

Chronic Obstructive Pulmonary Disease (COPD) is a persistent respiratory disease that poses a significant threat to global human health with elevated incidence and mortality rates. Timely recognition and diagnosis of COPD play a pivotal role in efficiently managing and treating the condition. The incorporation of deep learning technologies into healthcare has significant potential to enhance diagnostics and treatment outcomes. This study proposes an innovative deep-learning approach along with an ensemble technique to address the imperative need for an effective predictive model in COPD disease classification, particularly in situations with limited available data. This was achieved by leveraging the ensemble bagging technique and incorporating ANN as a classifier within this framework. Training and evaluation of the proposed ensemble ANN model were performed on a dataset comprising a variety of attributes, including demographic information, medical history, diagnostic measurements, and pollution exposures. Data were collected from people aged 18 to 60 originating from Pakistan, encompassing patients, attendants, hospital staff, faculty, and students. The effectiveness of the model in classifying COPD was measured using F1 score, recall, precision, and accuracy. The evaluation of the model produced notable results, as it achieved a 90% F1 score, 96% recall, 84% precision, and 89% accuracy in identifying the presence of COPD in individuals. Furthermore, this study carried out a comparative analysis between a standalone ANN model and the proposed ensemble ANN model which revealed that the proposed Ensemble ANN model outperforms existing methods, particularly in scenarios with limited sample size. This research provides substantial contributions to healthcare technology, as it presents an efficient tool for COPD prediction, facilitates early intervention, and significantly increases the overall standard of patient care.
Efficient On-Board Charger to Improve the Life Time of Electric Vehicle Battery
Authors: Karike Swathi , Raju Kuthuri Narasimha , Donepudi Sudha Rani
Published: June 2024

Internal combustion engines produce about 10% of the world’s greenhouse gas emissions. Electric vehicles generate 17-30% lower emissions than the internal combustion engines. However, the formers entail certain drawbacks, namely the few available charging stations, the high charging cost, and the limited battery life. The purpose of this paper is to propose the best suitable converter for the on-board charger, which will be able to decrease the charging cost by improving the power factor and the battery life span. This enhancement will be accomplished through the reduction of the charging current either at a very high or very low State of Charge (SOC). Isolated and non-isolated converter topologies were studied to identify the most suitable converter for the on-board charger that will be able to ameliorate the efficiency and the input power factor as well as control the charging current limits. A non-isolated buck converter with switched inductors is used for the power factor adjustment along with the current control approach to achieve a highly efficient on-board charger. Compared to the isolated converter with transformers, the non-isolated hybrid switched inductor buck converter has a wider current control range. MATLAB/Simulink output results were analyzed to validate the performance of the designed on-board charger with a non-isolated converter.
Accelerated testing of the Wear Behavior of 3D-printed Spur Gears
Authors: Portoaca Alexandra Ileana , Zisopol Dragos Gabriel , Ripeanu Razvan George , Nae Ion , Tanase Maria
Published: June 2024

This paper presents the results of an in-depth investigation of 3D-printed plastic gears made of ABS, PLA, and annealed PLA. Wear tests performed on a specialized rig underscore the superior wear resistance of ABS gears, while the annealing process shows a modest improvement in PLA gear durability. The novelty of this study is a comprehensive evaluation of the wear behavior of different 3D printed materials under different loading conditions. This study introduces an innovative accelerated testing method, emphasizing efficiency in product development through reduced testing durations and adaptability to various scenarios.
Robust and Secure Routing Protocol Based on Group Key Management for Internet of Things Systems
Authors: Othmen Salwa , Mansouri Wahida , Asklany Somia
Published: June 2024

The Internet of Things (IoT) has significantly altered our way of life, being integrated into many application types. These applications require a certain level of security, which is always a top priority when offering various services. It is particularly difficult to protect the information produced by IoT devices from security threats and protect the exchanged data as they pass through various nodes and gateways. Group Key Management (GKM) is an essential method for controlling the deployment of keys for network access and safe data delivery in such dynamic situations. However, the huge volume of IoT devices and the growing subscriber base present a scalability difficulty that is not addressed by the current IoT authentication techniques based on GKM. Moreover, all GKM models currently in use enable the independence of participants. They only concentrate on dependent symmetrical group keys for each subgroup, which is ineffective for subscriptions with very dynamic behavior. To address these issues, this study proposes a unique Decentralized Lightweight Group Key Management (DLGKM) framework integrated with a Reliable and Secure Multicast Routing Protocol (REMI-DLGKM), which is a reliable and efficient multicast routing system for IoT networks. REMI-DLGKM is a cluster-based routing protocol that qualifies for faster multiplex message distribution within the system. According to simulation results, this protocol is more effective than cutting-edge protocols in terms of end-to-end delay, energy consumption, and packet delivery ratio. The packet delivery ratio of REMI-DLGKM was 99.21%, which is 4.395 higher than other methods, such as SRPL, QMR, and MAODV. The proposed routing protocol can reduce energy consumption in IoT devices by employing effective key management strategies.
Dominant Gray Level-based Genetic K-means Clustering Algorithm for MRI Image Segmentation
Authors: Khaleel Maha Ibrahim , Mohammed Musab Ahmed , Qays Maryam
Published: June 2024

In this paper, a method and fresh results associated with medical image segmentation of brain Magnetic Resonance Imaging (MRI) scans are presented. Gray-converted segmentation and Genetic Algorithm (GA) are utilized along with unsupervised k-means classification. The image segmentation employed indicates the tissue type or the anatomical structure of each pixel. The cluster centroid initialization is performed by GA. GA offers efficient search processes (selection, crossover, and mutation), suited to determine global optima regarding centroid problems. As a result, this research offers more accurate, reliable, and efficient image segmentation for MRI, by improving the k-means algorithm with GA. The results indicate that the accuracy obtained from the proposed method is at least 3.5% higher than the PSO algorithm in this matter.
A Comprehensive Literature Review on the Elastic Modulus of Rock-filled Concrete
Authors: Ihteshaam Muhammad Ibrar , Jin Feng
Published: June 2024

Rock-Filled Concrete (RFC) is formed by pouring High-performance Self-Compacting Concrete (HSCC) into gaps between pre-placed rocks (that form a strong rock skeleton) in the formwork. An in-depth analysis of RFC's elastic modulus must focus on its static and elastic modulus behavior, strength characteristics, and sustainability aspects. Mesoscopic finite element modeling effectively incorporates pre-positioned rocks, Self-Compacting Concrete (SCC), and the Interfacial Transition Zone (ITZ) to correctly predict performance. RFC is a promising alternative to traditional construction methods, offering combined advantages for masonry and concrete techniques while reducing cement usage. Studies continue to examine the creep properties of reinforced fiber composites, with promising signs of their effectiveness in reducing hydration heat and concrete shrinkage. Subaquatic conservation agents enhance environmental stewardship in wet situations. The elastic modulus of rock-filled concrete increases logarithmically, mostly influenced by the rock-fill composition. It is crucial to study the shape, size, and rock-fill ratio of rocks in RFC that impact its stability, strength, and resistance to static and dynamic loads. Irregularly shaped rocks can enhance interlocking and mechanical properties, while a well-graded mix of sizes improves compaction and uniformity. Studying these properties enables engineers to optimize design and construction for durability and performance.
IoT Security Model for Smart Cities based on a Metamodeling Approach
Authors: Alotaibe Daifallah Zaid
Published: June 2024

Security solutions for the Internet of Things (IoT) in smart cities are complex and require a comprehensive approach to success. Several models and frameworks have been developed focusing on IoT security. Some deal with access controls and security and some with authentication and authorization in various forms. Literature still lacks a comprehensive IoT security model for smart cities, which can support the implementation of IoT. Accordingly, this study has set two objectives: to explore the present studies in IoT security for smart cities and to develop an IoT security model for smart cities based on the metamodeling approach. According to the findings of the study, the existing IoT security models for smart cities consider seven security aspects: authentication and authorization, device management, intrusion detection and prevention, device integrity, secure communication, secure data storage, and response to security incidents. The model developed in this study, called IoT Security Metamodel (IoTSM), combines these aspects. IoTSM captures the main qualities of IoT security practices in smart cities through domain security processes.
The Impact of Motorcycle Positioning on Start-Up Lost Time: The Empirical Case Study of Signalized Intersections in Marrakech using VISSIM
Authors: Charef Ayoub , Jarir Zahi , Quafafou Mohamed
Published: June 2024

This study explores the influence of a high percentage of motorcycles on the traffic flow and congestion in Marrakech by examining the impact of motorcycle positioning in shaping urban traffic dynamics, in particular, the start-up lost time at signalized intersections. Different motorcycle positioning strategies are analyzed to improve intersection efficiency and safety. A twofold approach was followed to achieve this objective. First, empirical data were collected using computer vision techniques. Second, different strategies were simulated in VISSIM based on the collected data. The approach adopted for data collection was based on mobile phone video recording at a representative signalized intersection in Marrakech, capturing traffic behaviors during four distinct time periods. Then the YOLOv8 algorithm was employed for real-time object detection and analysis, allowing precise monitoring of motorcycle positioning and examining its influence on the start-up lost time. Afterwards, VISSIM simulations were implemented, on the basis of the collected data, to explore various scenarios, such as motorcycles sharing lanes with cars or dedicated motorcycle lanes. The results reveal a compelling correlation between motorcycle proximity to cars and traffic congestion, with closer positioning leading to increased congestion, longer travel times, reduced average vehicle speeds, and extended queue lengths at intersections. On the contrary, scenarios with dedicated motorcycle lanes consistently show reduced congestion and smoother traffic flow.
A Forensic Framework for gathering and analyzing Database Systems using Blockchain Technology
Authors: Alzahrani Ahmed Omar , Al-Khasawneh Mahmoud Ahmad , Alarood Ala Abdulsalam , Alsolami Eesa
Published: June 2024

A blockchain is a distributed database that contains the records of transactions that are shared among all members of a community. Most members must confirm each and every transaction in order for a fraudulent transaction to fail to occur. As a rule, once a record is created and accepted by the blockchain, it cannot be altered or deleted by anyone. This study focuses on improving the investigation task in the database forensics field by utilizing blockchain technology. To this end, a novel conceptual framework is proposed for the forensic analysis of data from database systems engaging blockchain technology. This is the first time that blockchain technology is followed in database forensics for the purpose of tracing digital evidence. The design science research method was adopted to accomplish the objectives of the present study. The findings displayed that with the developed forensics framework, the data regarding database incidents could be gathered and analyzed in a more efficient manner.
An sEMG Signal-based Robotic Arm for Rehabilitation applying Fuzzy Logic
Authors: Nguyen Ngoc-Khoat , Dao Thi-Mai-Phuong , Nguyen Tien-Dung , Nguyen Duy-Trung , Nguyen Huu-Thang , Nguyen Van-Kien
Published: June 2024

The recent surge in biosignal-based control signifies a profound paradigm shift in biomedical engineering. This innovative approach has injected new life into control theory, ushering in advancements in human-body interaction and control. Surface Electromyography (sEMG) emerges as a pivotal biosignal, attracting considerable attention for its wide-ranging applications across medicine, science, and engineering, particularly in the domain of functional rehabilitation. This study delves into the use of sEMG signals for controlling a robotic arm, with the overarching aim of improving the quality of life for people with disabilities in Vietnam. Raw sEMG signals are acquired via appropriate sensors and subjected to a robust processing methodology involving analog-to-digital conversion, band-pass and low-pass filtering, and envelope detection. To demonstrate the efficacy of the processed sEMG signals, this study introduces a robotic arm model capable of mimicking intricate human finger movements. Employing a fuzzy logic control strategy, the robotic arm demonstrates successful operation in experimental trials, characterized by swift response times, thereby positioning it as a valuable assistive device for people with disabilities. This investigation not only validates the feasibility of sEMG-based control for robotic arms, but also underscores its potential to significantly improve the lives of individuals with disabilities, a demographic that represents a substantial portion (approximately 8%) of the Vietnamese population.
Fuzzy TOPSIS and Fuzzy AHP-based MCDA for selecting the Optimal Location for a Solar PV-powered RO Desalination Unit in Visakhapatnam, India
Authors: Somasi Anantha Sai , Kondamudi Srichandan
Published: June 2024

This feasibility study explores the viability of solar PV-powered Reverse Osmosis (RO) desalination in five locations in Visakhapatnam, India. The assessment integrates technical, economic, environmental, social, and political considerations using Multi-Criteria Decision Analysis (MCDA) with Fuzzy Technique for Order of Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) and Fuzzy Analytic Hierarchy Process (Fuzzy AHP) methods to handle uncertainties in decision-making. The study evaluates the technical feasibility of the integration, conducts economic analysis, examines environmental impacts, investigates social benefits, and challenges, and analyzes the political landscape. The former emphasizes the significance of understanding challenges and potential solutions associated with RO desalination, aiming for sustainable development aligned with local and global goals. Yarada and Bheemili were the most suitable locations selected based on Fuzzy TOPSIS and Fuzzy AHP, respectively. The study also highlighted the need for public awareness and government support for desalination projects.
Comparative Analysis of Bipolar and Unipolar SPWM Techniques in PIC-Based Pure Sine Wave Single-Phase Inverters
Authors: Chaaira Aymen , Kraiem Habib , Gamoudi Rabiaa , Sbita Lassaad
Published: June 2024

This paper provides a comparative analysis of bipolar versus unipolar Sinusoidal Pulse Width Modulation (SPWM) in DC-AC inverters, focusing on Total Harmonic Distortion (THD) across modulation indices and the latter’s effects on the R-L loads. Using the PIC18F2431 microcontroller for its efficiency, a single-phase inverter accomplished to deliver a high-fidelity sine wave. This study discovered that while both SPWM methods reduce harmonics, the unipolar approach yields more uniform THD reduction and superior performance, particularly noticeable in RL load conditions, where minimal harmonic distortion is crucial. The bipolar inverter, despite a higher initial THD, shows a considerable improvement at higher indices, significantly enhanced by an LC low-pass filter. This filter is a key component in achieving sub-1% THD levels at full modulation, ensuring optimal sine wave quality. The findings highlight the operational differences between the SPWM techniques and the importance of the LC filters in ameliorating the inverter output for various power applications.
Evaluation of the Hydrodynamic Properties and Performance Efficiency of a Three-Row Permeable Vertical Breakwater
Authors: Eldamaty Tarek , Helal Medhat
Published: June 2024

Coastal protection structures reduce risks and economic losses by eliminating coastal erosion, wave damage, and flooding. Fixed breakwaters are used along the coast but are often inappropriate due to their negative environmental impact. Permeable breakwaters resemble a row of breakwaters with continuous walls and are proposed as a more environmentally friendly alternative. The wave-structure interaction and flow behavior of this type of breakwater are more complex but must be analyzed before designing it. This study develops a mathematical model of wave interaction with a permeable three-row vertical breakwater based on the least squares method. Comparison with experimental measurements of the reflection, transmission, and dissipation coefficients shows that the mathematical model adequately reproduces most of the important features of the results. This study provides a deeper understanding of the hydrodynamic performance of a permeable three-row vertical continuous wall breakwater.
CFD Analysis for Improving Forced Convection Heat Transfer from Newly Designed Perforated Heat Sinks
Authors: Al-Zahrani Ahmed
Published: June 2024

This study develops a 3D-CFD model to analyze the thermal performance of perforated fin heat sinks and evaluates four perforated continuous and interrupted fin heat sinks with distinct geometric patterns. Using the Finite-Volume Method (FVM) to discretize the governing equations, the SolidWorks 2019 flow simulation software was implemented to solve and validate the latter, demonstrating that the CFD simulation model employed in the current study is reliable. The performance parameters of the heat sink are presented in terms of Reynolds number and heater power. The results indicate that modules B and C achieved higher heat transfer rates, average heat transfer coefficient, and Nusselt number compared to the other modules. Module A had the highest fin efficiency and module D exhibited greater fin effectiveness than the other ones.
A Novel Architecture Design of a USB Module in Wireless Modem for IOT Applications
Authors: Praneeth Annavarapu , Immadi Govardhani , Prabhakar V. S. V. , Reddy Venkata Narayana Madhava
Published: June 2024

Embedded micro-electro-mechanical technologies and network connectivity allow for the integration of sensing, identification, and communication capabilities into a variety of smart devices. These intelligent devices can automatically link to create the Internet of Things (IoT). The greater power consumption of a scan-based test has been one of the biggest problems since Very Large-Scale Integration (VLSI) architecture was introduced. There are too many switches made during the scan shifting procedures due to the enormous number of the scan cells. The design and implementation of an IoT access point are presented in this paper using the Logic Vision tool. In the semiconductor sector, scan chains are frequently employed for structural testing following fabrication or production. In this paper, a new architecture was designed with USB protocol, which reduces dynamic power, and fault-free circuits were constructed. The proposed architecture can work with the current one without changing the decompression architecture. Experimental findings on commercial circuits demonstrate that this strategy minimizes the scan shifting power.
Behavior of Reactive Powder Concrete reinforced with Hybrid Fibers containing Sustainable Materials
Authors: Hendi Saif Ibrahim , Aljalawi Nada Mahdi Fawzi
Published: June 2024

This study investigates the behavior of recycled Reactive Powder Concrete (RPC), made from finely ground recycled raw materials and containing a certain percentage of recycled copper (electrical waste copper wire) and steel fibers. This concrete has a relatively low water-to-binder ratio and is composed of cement, fine aggregate, and ultrafine powders, such as quartz powder and silica fume. The properties of Fiber-Reinforced Reactive Powder Concrete (FR-RPC) containing micro-steel fibers, recycled copper fibers, and a mixture of steel-recycled and copper fibers were investigated. A micro-steel fiber RPC (MF1) was used as a reference mix, having 1% steel fibers by volume with 13 mm length and 0.2 mm diameter. Recycled copper fiber RPC (MF2) was prepared utilizing 1% recycled copper fibers by volume, with a diameter of 0.2 mm and a length of 10 mm. In addition, Hybrid FR-RPC (HFR-RPC) samples were prepared by mixing micro steel fibers and recycled copper fibers in proportions of 0.5-0.5% (MF3), 0.4-0.6% (MF4), and 0.3-0.7% (MF5), respectively. The compressive strength, flexural strength, and splitting tensile strength of these FR-RPC mixes were studied. The results displayed that MF3 achieved slightly lower compressive strength, flexural strength, and splitting tensile strength than MF1 and higher than MF2, MF4, and MF5. Although the mechanical strengths of MF3 were marginally lower than those of MF1, compressive strength, flexural strength, and splitting tensile strength were almost the same. Therefore, copper wire waste fibers can be employed along with steel fibers with excellent results.
Dermatological Decision Support Systems using CNN for Binary Classification
Authors: Dondapati Rajendra Dev , Sivaprakasam Thangaraju , Kumar Kollati Vijaya
Published: June 2024

Skin cancer diagnosis, particularly melanoma detection, is an important healthcare concern worldwide. This study uses the ISIC2017 dataset to evaluate the performance of three deep learning architectures, VGG16, ResNet50, and InceptionV3, for binary classification of skin lesions as benign or malignant. ResNet50 achieved the highest training-set accuracy of 81.1%, but InceptionV3 outperformed the other classifiers in generalization with a validation accuracy of 76.2%. The findings reveal the various strengths and trade-offs of alternative designs, providing important insights for the development of dermatological decision support systems. This study contributes to the progress of automated skin cancer diagnosis and establishes the framework for future studies aimed at improving classification accuracy.
Estimation οf Wave Overtopping Discharges at Coastal Structures with Combined Slopes using Machine Learning Techniques
Authors: Elbisy Moussa S.
Published: June 2024

Coastal defense structures are of paramount importance in protecting coastal communities from the adverse impacts of severe weather events and flooding. This study uses machine learning techniques, specifically Decision Tree (DT), Gradient Boosted Tree (GBT), and Support Vector Machine (SVM) models, to estimate wave overtopping discharge at coastal structures with combined slopes employing the recently built EurOtop database. The models were evaluated by deploying statistical metrics and Taylor diagram visualization. The GBT model demonstrated a high level of accuracy in predicting wave-overtopping discharge. Compared to the other models, the scatter index of GBT (0.392) was lower than that of DT (0.512) and SVM (0.823). In terms of the R-index, GBT (0.991) was superior to DT (0.977) and SVM (0.943). The GBT results were also compared with those of previous works. The findings showed that the GBT model significantly decreased the overall error and provided accurate estimations of the wave-overtopping discharge.