bluuebunny/crossref_metadata_2025 huggingface dataset:
MEDIJSKA KONSTRUKCIJA MENTALITETA ŽRTVE U SVJETLU TEORIJE KULTIVACIJE
Authors: Bogdanić Aleksandar , Karanović Bojana , Ćurguz Jelena
Published: December 2024

Rad istražuje vezu između medijskih sadržaja o žrtvama nasilja iz prethodnih ratova i kultivacije mentaliteta žrtve na temelju teorije kultivacije. Teorija kultivacije pretpostavlja da osobe koje često i mnogo gledaju televiziju, odnosno koje su značajno dugotrajno izložene nasilju u medijima doživljavaju svijet nasilnim. Mentalitet žrtve je psihološko stanje osobe koja sebe vidi kao žrtvu djelovanja osoba ili okoline u kojoj živi – takva osoba osjeća se i ponaša kao žrtva čak i kad nije direktno doživjela fizičko ili mentalno nasilje. Mentalitet žrtve stiče se u porodici ili u društvenom, odnosno medijskom okruženju. Česte posljedice osjećaja žrtve su i odbrambenost, odnosno optuživanje drugih za svoje nedaće i negativan odnos prema drugima i društvenom okruženju. Glavna pretpostavka ovog rada, koja je dijelom potvrđena, jeste da osobe koje mnogo gledaju televizijske programe u RS-u, u kojima ima značajan broj sadržaja o sjećanjima na žrtve nasilja iz prethodnih ratova, smatraju svijet opasnim i pokazuju osobine žrtve. U radu se iznose rezultati istraživanja analize sadržaja informativnih programa RTRS i RTVBN i anketnog istraživanja punoljetnih građana RS-a na području grada Banjaluke u periodu od maja do jula 2015. godine. Rad razmatra i okolnosti i posljedice ove svojevrsne vikariozne viktimizacije.
Ključne riječi: teorija kultivacije, komemoracije ratnih tragedija, vikariozna viktimizacija, mentalitet žrtve, nasilje na televiziji
STORYTELLING U ULOZI NOSITELJA IDEOLOGIJE PROMJENE
Authors: Putica Marija
Published: December 2024

U radu se analizira i kroz praktične primjere predstavlja primjena storytellinga kao najpogodnijeg nositelja ideologije uvjeravanja i promjene. Tijek primjene predstavljen je od njegova nastanka u filmskoj industriji, preko masovnih medija do virtualne stvarnosti. Pojašnjeno je kako storytelling postaje dio panoptičkog pogleda i sudjelovanja u hegemoniji moći na način da osigurava kako pravila ponašanja, tako i pedagogiju promjene. U ovom je radu storytelling predstavljen kao čuvar pamćenja i nositelj strateškoga projekta jer posjeduje moć koja se ne ogleda u kontroli i disciplini, već u kolektivnoj priči i pamćenju.
Ključne riječi: storytelling, ideologija, pamćenje, promjena, moć, filmska industrija, masovni mediji, računalne igre
SUOČAVANJE S PUTEM KROZ PJESNIŠTVO
Authors: Lučić Antun
Published: December 2024

Iskustvo otvorenog čitanja pjesništva, klasičnog i starijeg kao i razvedena suvremenog, iznosi bitnu zaokupljenost putem u biranim tekstovima. Upućivanje u probleme puta te njegova razmjena s drugim motivima produbljuje individualna i kolektivna, povijesna i civilizacijska određenja. Premda iskazuje i odlučne i relativizirajuće odnose, iskazivost puta svjedočna je u sudbinskome svijetu, na biologističkom području, ali je i potragom za metafizičkim osloncem kao izdašno referiranje prema univerzumu. Istraživanje se provodi kroz imanentno tumačenje i postavlja binarnosti: ostanak – putovanje, domaja – izbjeglištvo, zavežljaj – teret, zavičaj – tuđina, podjednako i ušire realno – nadrealno, sigurnost – pogubnost, neposredno – posredno. U prosudbe su upletene značajke i fenomeni kretanja, ali i preobrazbe stvarnoga puta u situacijske pojmove, evidencije selidbenih iščašenja i nosivih esencija života.
Ključne riječi: put, pjesništvo, imanentnost, kretanje, binarnost, teret, fenomeni
REPUBLIKA IRSKA KAO SLAVONSKA „OBEĆANA ZEMLJA“: DRUŠTVENOJEZIČNI I GOSPODARSTVENI ČIMBENICI
Authors: Živić Tihomir , Vranješ Antonija
Published: December 2024

Rad se bavi tekstološkom analizom iskazâ Hrvata koji su emigrirali ili uskoro namjeravaju emigrirati u Republiku Irsku te društvenojezičnim i gospodarstvenim čimbenicima koji su utjecali ili još uvijek utječu na takvu odluku. Posebna pozornost pritom je posvećena njihovu znanju engleskoga jezika kao jednom od bitnih uvjeta lakšega snalaženja i zapošljivosti u inozemstvu općenito te motivaciji za useljenje upravo u tu državu. Za potrebe rada provedeno je i istraživanje među državljanima Republike Hrvatske kako bi bile razmotrene ključne implikacije sve intenzivnije hrvatske emigracije, s naglaskom na slavonsku populaciju. Pretpostavka je da većina hrvatskih emigranata napušta domovinu zbog potrage za stalnim zaposlenjem, odnosno ekonomskom sigurnošću, što navodi na zaključak da je pojedinac trenutačno spremniji na radnu žrtvu u nekoj od zemalja zapadne Europe nego li u domovini.
Ključne riječi: Republika Hrvatska, Slavonija, emigracija, Republika Irska, tekstološka analiza, engleski jezik
Disminución y Recuperación: Impacto del COVID-19 en la Cobertura de Tamizaje en Bolivia
Authors: Chavez Andrade Livia Khana
Published: December 2024

Objetivos: Evaluar la cobertura de tamizaje de cáncer de cuello uterino en Bolivia durante 2018, 2020 y 2022, considerando el impacto de la pandemia de COVID-19 en los servicios de salud.
Métodos: Se utilizó el SNIS-VE y estimaciones poblacionales para calcular la cobertura en cada año y provincia. Se emplearon herramientas de visualización de datos en R Studio para analizar los resultados.
Resultados: Se observa una marcada disminución en la cobertura en 2020, seguida de una recuperación en 2022 en la mayoría de los departamentos. Algunas provincias muestran aumentos significativos, mientras que otras presentan reducciones preocupantes en la cobertura.
Conclusiones: El estudio resalta la importancia de analizar las causas detrás de los cambios en la cobertura y tomar medidas para garantizar una detección temprana adecuada del cáncer cervical en Bolivia, especialmente en el contexto de la pandemia.
Palabras claves:
Prueba de Papanicolaou, Cobertura de condiciones preexistentes, Bolivia,Pandemia de COVID-19.
Sick Leaves, Smart Solutions: Deep Learning in Plant Care
Authors: Shaankari S. , Rohini D. , Nikesh M. , Bharathi M. , Srinivas T. Aditya Sai
Published: December 2024

Agriculture is often referred to as the backbone of the Indian economy, with around 70% of the population working in farming and related industries. This deep connection underscores the importance of supporting plant health for sustainable agricultural practices. However, one of the significant challenges we face is the prevalence of plant diseases, which can lead to diminished crop yields and financial losses for farmers. Diagnosing these diseases through traditional visual inspection can be difficult and often unreliable. This project aims to improve early disease detection in plants by leveraging the power of convolutional neural networks (CNNs). In this paper, we will explore various classification techniques used to identify plant diseases, highlighting how deep learning can revolutionize our approach to agriculture and contribute to healthier, more robust crops.
Facile Synthesis of Thin Film Cobalt-doped Zinc Oxide Nanorods Photoanode for Efficient Methylene Blue Degradation
Authors: Suhartana Suhartana , Novia Maria Christina , Sriatun Sriatun , Sriyanti Sriyanti , Pardoyo Pardoyo
Published: December 2024

ZnO nanorod thin films doped with cobalt were successfully synthesized on Indium Thin Oxide (ITO) glass substrates using the chemical bath deposition method to assess their characteristics and methylene blue degradation capabilities. ZnO nanorod thin films doped with cobalt were successfully synthesized on Indium ThinOxide (ITO) glass substrates using the chemical bath deposition method to evaluate their characteristics and methylene blue degradation capabilities. This study compared undoped and Co-doped ZnO on ITO glass substrates for photoelectrocatalytic (PEC) applications. XRD analysis confirmed that all samples exhibited a crystalline hexagonal wurtzite structure, with no additional phases detected in films with 5 % Co doping. The formation of nanorod structures in both undoped and Co-doped ZnO was revealed by SEM images where the presence of Co in the doped ZnO was confirmed through EDX analysis  UV-DRS analysis indicated changes in band gap values with the addition of 5 % Co doping, decreasing from 3.09 to 3.07 eV for the ZnO/ITO and Co-ZnO/ITO photoanode. Evaluation of the PEC performance of the photoanodes toward methylene blue in an aqueous solution showed that Co doping enhanced the PEC efficiency of the ZnO/ITO photoanode. The decolourization efficiency of methylene blue reached 93.72 % using Co-ZnO/ITO, which is higher than that of ZnO/ITO at 86.84 %. Furthermore, the kinetic study showed that Co-ZnO/ITO photoanode follows a first-order reaction kinetics model with a reaction rate constant of 0.0085 min−1. The result of our study suggests the potential of Co-ZnO/ITO photoanode in addressing dyecontamination issues effectively.
HIGHLIGHTS

Co-doped ZnO thin films synthesized via chemical bath deposition method.
Hexagonal structure retained; band gap reduced from 3.09 to 3.07 eV.
Co-doped ZnO showed 93.72 % methylene blue degradation vs. 86.84 % for ZnO.
First-order kinetics with a rate constant of 0.0085 min⁻¹ for Co-ZnO/ITO.
Co-ZnO/ITO thin films show promise for dye-contaminated water treatment.

GRAPHICAL ABSTRACT
Application of Artificial Intelligence in the Identification of Banana Bunch Top Virus (BBTV) in Mozambique
Authors: Simango Abel , Mananze Sosdito , Bila Joao
Published: December 2024

Agricultural production faces many challenges, such as disease and pest infestation, which can lead to severe crop loss and environmental impacts due to the excessive use of chemicals. Artificial intelligence has become a key technique to solve different agricultural-related challenges. The main objective of this study was to train and validate artificial intelligence algorithms for the detection of Banana Bunchy Top Virus (BBTV) in banana crops. Approximately 2,500 images of healthy and BBTV-infected leaves were collected, stratified according to the stage of plant development, and used to calibrate and validate an artificial intelligence algorithm for the detection of BBTV. Pre-trained models such as VGG 16, ResNet50, and InceptionV3 were tested. The ResNet50 model achieved a training accuracy of 99.56% and validation precision, recall, and F1 score of 96.53%, 94.94%, and 95.73%, respectively, outperforming the other models in detecting BBTV-infected plants.
A Framework for Smart City Traffic Management utilizing BDA and IoT
Authors: Nagalapuram Jayalakshmi , Samundeeswari S.
Published: December 2024

This paper explores a new approach to traffic flow optimization in smart cities, harnessing the combined power of Big Data Analytics (BDA) and the Internet of Things (IoT). The system utilizes a citywide network of connected sensors to acquire live traffic information, including vehicle speeds, density, and congestion points. These data are thereafter processed applying some top-notch BDA algorithms to identify traffic anomalies and forecast congestion levels, and generate actionable insights. By analyzing this information, the system can dynamically adjust traffic signals, recommend alternative routes, and improve traffic efficiency in real-time. The system's adaptive learning capabilities allow it to continuously enhance its predictions based on new data, ensuring its effectiveness in managing evolving traffic patterns. This intelligent traffic management solution promises to significantly reduce congestion, ameliorate overall mobility and road safety, and contribute to a more sustainable city environment.
Cloud-Cyber Physical Systems: Enhanced Metaheuristics with Hierarchical Deep Learning-based Cyberattack Detection
Authors: Azar Ahmad Taher , Amin Syed Umar , Majeed Mohammed Abdul , Ahmed Al-Khayyat  , Kasim Ibraheem
Published: December 2024

Cyber-Physical Systems (CPS) integrate several interconnected physical processes, networking units, and computing resources, along with monitoring the processes of the computing system. The connection between the cyber and physical world creates threatening security problems, particularly with the growing complexities of transmission networks. Despite efforts to overcome this challenge, it remains challenging to analyze and detect cyber-physical attacks in CPS. This study mainly focuses on the development of Enhanced Metaheuristics with Hierarchical Deep Learning-based Attack Detection (EMHDL-AD) method in a cloud-based CPS environment. The proposed EMHDL-AD method identifies various types of attacks to protect CPS. In the initial stage, data preprocessing is implemented to convert the input dataset into a useful format. Then, the Quantum Harris Hawks Optimization (QHHO) algorithm is used for feature selection. An Improved Salp Swarm Algorithm (ISSA) is used to optimize the hyperparameters of the HDL technique to recognize several attacks. The performance of the EMHDL-AD algorithm was examined using two benchmark intrusion datasets, and the experimental results indicated improvements over other existing approaches.
Enhancing Filter Assembly and Printing Efficiency through Automation and Virtual Reality Integration
Authors: Malega Peter , Daneshjo Naqib , Kovac Juraj , Korba Peter
Published: December 2024

This paper presents a comprehensive approach to improving the assembly and printing process of filters in a selected company. Currently, the company's process involves significant manual labor, which leads to inefficiencies and potential health risks for employees. The proposed solution integrates the use of pneumatic cylinders to automate the assembly and printing process, reducing manual effort and improving production efficiency. The fixture and filter were modeled using SOLIDWORKS 2022 and simulated in virtual reality with Pixyz Review software. The virtual reality simulation serves as an innovative training tool for new employees, enhancing understanding and accuracy in the assembly process. The study compares the current state, where manual handling results in a time-consuming process with a lower production rate, to the proposed automated system. The proposed solution eliminates unnecessary intermediate storage and manual fixture transfers, significantly reducing the total assembly and printing time from 24 s to 22 s per unit. This results in a 14.16 % increase in production efficiency, allowing the company to produce 1227.3 filters per shift compared to the current 1075 filters.
Trajectory Tracking Control for a Quadcopter under External Disturbances
Authors: Nguyen Cuong V. , Nguyen Minh Tuan , Tran Hoang T. , Trinh Mien L. , La Hung M. , Nguyen Hoa T. T.
Published: December 2024

This paper initially discusses the application of a Sliding Mode Controller (SMC) for drone control, encompassing vertical takeoff and landing. Subsequently, the dynamic model of the quadcopter is formulated using the Newton-Euler method. Despite the challenges posed by the nonlinear characteristics of Unmanned Aerial Vehicles (UAVs), empirical evidence from previous tests and simulation studies underscores the efficacy of the SMC in delivering satisfactory performance and robust resistance against interference. Moreover, this research endeavors to present a quadcopter model and simulation, leveraging the SMC alongside the Newton-Euler formula to enhance control precision in the face of external magnetic disturbances affecting the UAV. Both the position and attitude of the UAV are governed by the SMC. The dynamic and control models of the quadcopter are implemented and visualized in MATLAB, culminating in results that substantiate the efficacy of the proposed controller across diverse scenarios. Furthermore, the performance of the proposed control method is compared with alternative methodologies such as PID, particularly in scenarios involving disturbances. The simulation results indicate promising and practical implications.
A Scalability Enhancement Scheme for Ethereum Blockchains: A Graph-based Decentralized Approach
Authors: Khan Burhan Ul Islam , Goh Khang Wen , Zuhairi Megat F. , Putra Rusnardi Rahmat , Khan Abdul Raouf , Chaimanee Mesith
Published: December 2024

Amidst the rising demands for data security across expansive networks, blockchain technology is witnessing an upsurge in its adoption, particularly within Internet of Things (IoT) applications, services, and smart cities. Blockchains offer an immutable property that bolsters security and aids in the structured management of distributed ledgers. Nevertheless, ensuring scalability remains a formidable challenge, especially within decentralized Ethereum systems. Current methods often fall short of offering tangible solutions, and the scrutiny of Ethereum-based cases reveals persistent deficiencies in addressing scalability issues due to inherent system complexities, dependency on resource-intensive consensus algorithms, lack of optimized storage solutions, and challenges in ensuring synchronous transaction validation across a decentralized network. This paper proposes a foundational scheme underpinned by a unique graph-based topology and hash bindings for nodes that join the system. The proposed scheme establishes an innovative indexing mechanism for all transactions and blocks within the IoT framework, ensuring optimal node accessibility. Transaction and block replications occur over the joining nodes' graphical structure, ensuring efficient subsequent retrieval. A standout feature of the proposed scheme is its ability to enable participating nodes to forgo retaining a complete ledger, making it non-reliant on individual node capabilities. Consequently, this facilitates a broader spectrum of nodes to participate in the consensus system, irrespective of their operational prowess. This study also offers a novel empirical model for Proof-of-Validation (PoV), which reduces computational intricacy and expedites the validation process in stark contrast to prevailing blockchain systems.
Terminal Synergetic Control with the Dragonfly Algorithm for Zoonotic Visceral Leishmaniasis Eradication
Authors: Kumsaen Tinnakorn , Boonyaprapasorn Arsit , Chinviriyasit Settapat , Sa-Ngiamsunthorn Parinya , Sethaput Thunyaseth , Maneewarn Thavida , Pengwang Eakkachai
Published: December 2024

Visceral Leishmaniasis (VL) is a prevalent vector-borne disease that affects both human and animal populations in subtropical and tropical regions, contributing to a substantial mortality rate. Establishing efficient control policies is crucial to eradicating the VL epidemic. The VL epidemic system, containing reservoirs, vectors, and human populations, can be accurately modeled through differential equations. Managing the VL epidemic under multiple control policies can be considered a high-order nonlinear feedback control challenge. This study explores the application of Terminal Synergetic Control (TSC) to eradicate Zoonotic Visceral Leishmaniasis (ZVL). Notably, Synergetic Control (SC) is one of the suitable feedback control methods for manipulating high-order nonlinear systems, providing practical control inputs because of their chattering-free behavior. Additionally, the convergence properties of the control system can be enhanced through terminal attraction. Optimization of control parameters within the system is achieved through the integration of control mechanisms by the Dragonfly Algorithm (DA). The results demonstrate that the multiple control policies synthesized by the TSC method effectively regulate subpopulations in alignment with the specified control objectives. Furthermore, the enhanced convergence rate achieved by the TSC method, in comparison to the SC method, serves as evidence of TSC's effectiveness in guiding the dynamics of ZVL epidemic eradication. This research underscores the potential of the TSC method, utilizing optimal control parameters provided by the DA, to achieve targeted outcomes with improved convergence properties.
Increasing the Performance of Ring Foundation to Lateral Loads by using a Skirt Foundation
Authors: Ahmed Balqees A. , Saleh Husam M. , Jameel Mina M. , Al-Taie Asmaa
Published: December 2024

In numerous engineering foundation designs, the influence of lateral loads is frequently underestimated in calculations.  Consequently, recent studies have increasingly concentrated on comprehending the behavior of soil and foundations when subjected to lateral load influences. The present study aims to examine the performance of ring foundations, a common engineering solution employed in the construction of tall and slender structures that are vulnerable to lateral loads, such as those exerted by wind forces. The objective of this study is to enhance the lateral resistance of ring foundations by incorporating skirt foundations. The efficacy of skirt foundations was evaluated through a series of tests conducted on sandy soils of varying densities, ranging from dense to medium loose sand. Subsequently, lateral loads were applied to the ring foundations, both with and without skirt foundations.  The results demonstrated that the lateral resistance increased in proportion to the ratio between the inner and outer diameters. Furthermore, the improvement rate was enhanced by the addition of the skirt foundation. Additionally, the lateral resistance increased with increasing the skirt foundation depth, reaching a maximum of approximately 50-100%. Similarly, an increase in the skirt inclination ratio to 450 resulted in a lateral resistance increase of up to 650%.
Evaluating the Effectiveness of Wenner Mode Configurations for Resistivity-based Moisture Monitoring in Compressed Earth Bricks
Authors: Nguyen Tuan Anh , Pham Minh Dung , Angellier Nicolas , Ulmet Laurent , Dubois Frederic
Published: December 2024

Compressed Earth Bricks (CEBs) have emerged as an eco-friendly construction material, although their properties are highly moisture dependent. This study investigated the applicability of electrical resistivity techniques for non-destructive moisture assessment in CEBs and determined the optimal electrode configurations for small-scale CEB samples. Various Wenner array electrode configurations, including Wenner Alpha, Beta, and Gamma arrangements, were tested on CEB specimens across a wide range of relative humidity levels. Numerical modeling using the finite element method was employed to simulate the current diffusion process in CEB samples. A mathematical formulation was developed to calculate the true electrical resistivity of the specimens based on the measured resistance and the geometric factors obtained from the numerical model. The results show that the electrical resistivity of CEBs exhibited a logarithmic relationship with moisture content, and the Wenner Alpha and Gamma configurations were proved to be the most suitable for small-scale samples. The proposed approach demonstrates the feasibility of continuous non-destructive moisture monitoring of CEBs to improve quality control.
Wear Behavior of 2024 Aluminum Alloy Reinforced with SiC Particles
Authors: Salman Abdulhakeem Amer
Published: December 2024

The wear behavior of Aluminum Alloy (AA) 2024  reinforced with SiC particles were investigated at room conditions. Stir casting method was used to prepare the matrix composite of AA 2024 alloy reinforced with different weight percentages of SiC (3, 6, 9, 12 wt%). The wear behavior of both AA 2024 and AA 2024/SiC composite was studied using the reciprocating wear test. Loads of 2.5, 5, 7.5, 10, and 12.5 N were applied with sliding speeds of .0.55, 0.72, 0.88, 1.04 and 1.2 m/sec in dry sliding contact conditions. The microstructure and phase distribution were investigated using Optical Microscope (OM) and Scanning Electron Microscopy (SEM). The results demonstrate the presence of fine dendritic structure with semi-homogeneously distributed SiC particles in the alloy matrix. The micro-hardness increased with increasing SiC percentage. The highest value of hardness of 79.3 HV was found at 12 wt% SiC, from the initial 44 HV of the base alloy. The wear tests showed that the wear rate increased with increasing applied normal contact load at a contact sliding speed of 0.88 m/sec. The wear rate of the as cast material was 18.59 ×10-8 g/cm. Adding the SiC particles decreased the wear rate to 13.78, 9.76, 7.98, and 5.81 ×10-8 g/cm for 3, 6, 9, 12 wt% SiC addition at 12.5 N applied load, respectively. SEM examination of worn surfaces showed that severe and abrasive wear was exhibited at higher loads in the dry case while mild and adhesive wear were observed at lower loads in the lubricated condition.
Structural Behavior of Self-Compacting Bendable Mortar Beams reinforced by GFRP Bars under Monotonic Loads
Authors: Mohammed Tabark , Alsaraj Wissam , Zghair Luma
Published: December 2024

Flexible or bendable concrete is an Engineered Cementitious Composite (ECC) that exhibits ductile material properties, in contrast to the brittle nature of conventional concrete. The material composition of conventional concrete is modified in order to impart a flexible nature to the material. This research presents the findings of an experimental study examining the flexural response of self-compacting bendable concrete beams reinforced by Glass Fiber Reinforced Polymers (GFRP) bars under a symmetrical two-point load. The experimental work comprised the casting of eight reinforced beams. The dimensions of all beams were identical: an overall height of 150 mm, a width of 100 mm, and a total length of 1,000 mm. The beams were classified into three groups based on the type of variables adopted and the type of the strengthening method employed, the percentage of steel fibers (1%, 1.5%, and 2%), and the percentage of Polyvinyl Alcohol (PVA) (0.2%, 0.3%, and 0.4%) were also considered. The following measurements were taken: the first cracking load, midspan vertical deflections, concrete surface strains, and the ultimate load capacity. Additionally, the crack patterns were recorded and the failure mode was observed, in addition to the mechanical properties of self-mortar bendable concrete (both fresh and hardened). The results indicated that an increase in the PVA ratio from 0.2% to 0.3% and 0.4% resulted in a notable rise in the modulus of rupture and modulus of elasticity, by approximately 16% and 40%, respectively, and 0.09% and 2%, respectively. The ductility of the beams increased with the steel fiber ratio due to enhanced flexural and splitting properties, which is a positive outcome. This allows for more caution to be exercised before the beam reaches its limit of stability. Furthermore, the value of deflection at maximum load increases with the increase of steel fiber content due to the increase in load capacity.
AMPS-1D Simulation of P3HT Solar Cells: Impact of HOMO-LUMO Offset, Thickness, Temperature, and Optical Bandgap on Performance
Authors: Al-Otaibi Hadab , Eid Omer I. , Eisa M. E. M. , Alzahrani Amwaj N.
Published: December 2024

This study employed the AMPS-1D software to investigate the relationship between the open-circuit voltage (Voc) and the energy difference between the Highest Occupied Molecular Orbital (HOMO) of the donor and the Lowest Unoccupied Molecular Orbital (LUMO) of the acceptor in P3HT:PCBM bulk heterojunction organic solar cells. The findings indicate a correlation between Voc and the HOMO-LUMO offset up to 1.1 eV, after which Voc remains constant. This behavior is further elucidated using a theorem based on the quasi-Fermi level, which predicts a Voc of 0.64 V, in good agreement with our simulation result of 0.68 V. The Power Conversion Efficiency (PCE) of the solar cell was studied with respect to the active layer thickness, demonstrating an increase in PCE up to 0.40 μm followed by a decrease, yielding a maximum PCE of 5.023%, consistent with the literature. The effect of temperature on PCE was also examined, demonstrating an increase in PCE with decreasing temperature in the range of 150–320 K, with a performance of 6.371% at 150 K. Furthermore, the impact of the optical bandgap on PCE was explored, showing that the PCE increased with a decrease in the optical bandgap of the P3HT:PCBM solar cell, reaching 9.94% when the optical bandgap was 1.5 eV. These findings provide valuable insights into the optimization of the performance of organic solar cells by manipulating key parameters, such as the HOMO-LUMO offset, active layer thickness, temperature, and optical bandgap.
A Study of the Influence of Additives of Nanostructured Functional Ceramics in the Coating of Welding Electrodes on their Welding and Technological Properties
Authors: Saidov Rustam , Rakhimov Rustam , Touileb Kamel , Abduraimov Sanjar
Published: December 2024

The present work aims to study the influence of Nanostructured Functional Ceramics Photocatalysts (PNFC) under the brand name ZB-2, obtained using a synthesis method deploying pulsed radiation activation technology on welding and technological properties. This method of obtaining ceramic material allows the latter to be produced on an industrial scale. Therefore, it can replace the technology for producing PNFC under the influence of concentrated solar radiation at the Big Solar Furnace (BSF) of the Institute of Materials Science, Academy of Sciences of the Republic of Uzbekistan. The ZB-2 ceramic material has shown its effectiveness when used as an additive in the coating charge of the MR-3 welding electrode. Thus, the breaking arc length of the MR-3 welding electrode is increased up to 2% with the addition of ZB-2 to the coating charge. With additions of more than 2%, the breaking arc length decreases. The additive ZB-2 has the same effect on the diameter of the deposited point of the MP-3 welding electrode. When its content in the coating is up to 2%, the diameter of the deposited point increases, and a further increase in the additive content reduces this indicator. Adding up to 1%  ZB-2 into the coating composition has a beneficial effect on the size of the peak at the end of the electrode. When its content exceeds 1%, the latter decreases. Also, an increase in the content of the PNFC additive in the coating of the MR-3 electrode reduces the value of the melting coefficient and increases the value of the deposition coefficient, which contributes to a sharp reduction in losses due to waste and spattering up to 53% when the additive content in the coating mass is up to 8%.
Sensitivity Analysis of Multi-Parameter Magnetic Control Indicators for determining the Mechanical Properties of Steel
Authors: Zhakupov Alibek , Zhakupova Aray , Bogomolov Alexey
Published: December 2024

This paper presents the results of a steel magnetic property study using a non-destructive method of determination of the mechanical properties of products made from structural steels 20, 09Mn2Si, and 25CrMoV. The influence of tempering temperature on the change of magnetic properties was analyzed and a statistical analysis of the input parameter significance, including coercive force, residual magnetic induction, and maximum magnetic permeability, was carried out. As a result, it was found that the greatest sensitivity for steels at tempering temperatures up to 400 °C is the coercive force and, above 300 °C, the residual magnetic induction. At the same time, the maximum magnetic permeability provides a correlation over the whole range of tempering temperatures, justifying the use of magnetic properties in the determination of mechanical properties.
An Experimental and Analytical Study on the Compressive Behavior of Glass Fiber Reinforced Concrete (GFRC) confined with GFRP Composites
Authors: Zendaoui Abdelhakim , Saadi Mohamed , Yahiaoui Djarir , Amouri Chahinez
Published: December 2024

This study investigates the axial compression behavior of confined circular concrete columns through a combined experimental and analytical approach. It examines the influence of the concrete strength, 8.5, 16, and 25 MPa, internal glass fiber percentage, 0.3-1.2 %, and Glass Fiber Reinforced Polymer (GFRP) confinement thickness, 0.8, 1.6, and 2.4 mm. The Glass Fiber (GF) percentage and GFRP thickness have a significant impact on the results of the uniaxial compression tests exploring both the load-deformation behavior and crack propagation characteristics of the specimens, ranging from 90 to 110%. The proposed confinement model demonstrates excellent agreement with the experimental data for the ultimate axial strain and across the investigated range of concrete strengths.
An Ensemble Kernelized-based Approach for Precise Emotion Recognition in Depressed People
Authors: Sahoo Bidyutlata , Gupta Arpita
Published: December 2024

As the COVID-19 pandemic created serious challenges for mental health worldwide, with a noticeable increase in depression cases, it has become important to quickly and accurately assess emotional states. Facial expression recognition technology is a key tool for this task. To address this need, this study proposes a new approach to emotion recognition using the Ensemble Kernelized Learning System (EKLS). Nonverbal cues, such as facial expressions, are crucial in showing emotional states. This study uses the Extended Cohn-Kanade (CK+) dataset, which was enhanced with images and videos from the COVID-19 era related to depression. Each of these images and videos is manually labeled with the corresponding emotions, creating a strong dataset for training and testing the proposed model. Facial feature detection techniques were used along with key facial measurements to aid in emotion recognition. EKLS is a flexible machine-learning framework that combines different techniques, including Support Vector Machines (SVMs), Self-Organizing Maps (SOMs), kernel methods, Random Forest (RF), and Gradient Boosting (GB). The ensemble model was thoroughly trained and fine-tuned to ensure high accuracy and consistency. EKLS is a powerful tool for real-time emotion recognition in both images and videos, achieving an impressive accuracy of 99.82%. This study offers a practical and effective approach to emotion recognition and makes a significant contribution to the field.
Analysis of the Effects of Temperature and Treatment Duration on the Resistance of Expansive Soil Improved with Lime in Baghdad, Iraq
Authors: Al-Mohammedi Amenah Adnan Shakir
Published: December 2024

Multiple studies have revealed the challenge of constructing infrastructure on expansive soils, including pipelines, roads, or buildings. This predicament stems from the uneven moisture distribution inherent to the specific soil type. Numerous methods, involving the addition of chemicals, have been employed to enhance the properties of clayey soils. This study introduces lime as a cation exchange material and demonstrates its capacity to improve the load-bearing characteristics, rendering it a more favorable option for engineering construction purposes. Lime's reaction with clay minerals and water produces calcium hydroxide, which subsequently reacts with the silica and alumina in the clay to form new compounds that promote stability. Additionally, lime helps reduce the soil's water-holding ability, thereby decreasing its swelling potential. This research will focus on evaluating the influence of temperature and treatment duration on the osmotic pressure behavior of chemically treated expansive clayey soils using lime. The swell meter test was utilized to develop lime-clay samples containing 7% lime by dry weight. These samples were then subjected to compression at temperatures ranging from 20 °C to 40 °C over a period of up to 28 days. The findings indicate that the pozzolanic reaction results in higher compressive strengths when tested at the upper limits of the temperature range in laboratory experiments. Therefore, the combined effects of temperature and curing duration play a positive role in improving the compressive strength of expansive soils.
Comparison of Multiple Regression and Model Averaging Model-Building Approach for Missing Data with Multiple Imputation
Authors: Abdullah Mohd Asrul Affendi , Jesintha Lai , Khuneswari Gopal Pillay , Jamil Siti Afiqah Muhamad , Olaniran Oyebayo Ridwan
Published: December 2024

Model construction is of significant importance for the extraction of information from datasets and the prediction of responses based on predictor variables. The objective of this study is to compare the Multiple Regression (MR) and model averaging approaches in the context of missing data and to validate the effectiveness of the Multiple Imputation (MI) method used to address missing data issues. A comparison was performed between the results obtained from the multiple-imputed data and those derived from the Complete Case (CC) data, using a diabetes dataset from Hospital Besar Alor Setar. Prior to the application of MI and model building, k-fold cross-validation was employed to partition the dataset, resulting in 90% of the data lacking complete covariates for training and 10% of the data comprising complete covariates for testing. Subsequently, MI was applied to the 90% training dataset. Model M115, derived from the multiple-imputed data, was identified as the optimal model for MR. In the model averaging approach, two models were identified as optimal: Model 1 (without interaction variables) and Model 2 (with interaction variables). The first one, exhibited the lowest values of Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). These results indicate that model averaging, specifically Model 1, is the superior model-building approach for this study, demonstrating improved performance compared to MR and validating the effectiveness of the MI method.
A New Approach to the Quality Determination of Used Palm Cooking Oil using Supervised Learning based on Electronic Sensors
Authors: Anifah Lilik , Wikandari Prima Retno , Rusimamto Puput Wanarti , Haryanto . , Widayaka Parama Diptya
Published: December 2024

As discarding used palm oil in nature is very dangerous, a processing mechanism is needed to utilize it according to needs. This utilization depends on the palm oil used, so the sorting process becomes important. This study proposes a new classification approach for the quality of used palm oil using Self-Organizing Map (SOM), Linear Vector Quantization (LVQ), and K-means, based on electronic sensors. This study included hardware design, software development, data collection, and training and testing processes. Based on the experimental results, the proposed system performed well using 13 parameters consisting of e-nose data, color, viscosity, and turbidity. The accuracy of SOM was 91.11%, LVQ achieved 95.56%, and K-Means obtained an accuracy of 98.89%. This system can be used as a decision support system in the automatic recognition of used palm oil to classify its quality.
Neurospectral Computation for the Resonant Characteristics of an Equilateral Triangular Patch Antenna on Suspended Substrates
Authors: Mahamdi Ahmed , Aris Skander , Fortaki Tarek , Benkouda Siham , Bedra Sami
Published: December 2024

Modeling and design of an equilateral triangular patch antenna on suspended and single substrate are accomplished in this paper. The spectral domain approach is important due to its accuracy, but has a high computational cost. On the other hand, the Artificial Neural Networks (ANNs) have recently become a fast and flexible vehicle for modeling and designing microwave antennas. This paper introduces electromagnetic knowledge combined with ANNs to compute the resonant frequency of the fundamental and higher order modes and to eliminate the difficulties of handling the singularity points encountered in the numerical evaluation of integrals. The resonant frequency results obtained from the neural model are in very good agreement with the experimental and theoretical results available in the literature.
HCLNet: A Hybrid CNN-LSTM Model for Enhanced Healthcare Product Classification for Recommendation
Authors: Reddy B. Ramakantha , Kumar R. Lokesh
Published: December 2024

The rapid growth of healthcare commodities requires automated categorization. Effective categorization solutions save time and money and can provide precise recommendations. Poorly annotated data, inconsistency of product category, and imbalanced datasets impede machine learning algorithms in current systems. CNNs work well with large datasets but struggle with the classification of healthcare products due to class imbalance and lack of labeled data. This study presents a Hybrid CNN-LSTM (HCLNet) model to improve the classification of healthcare products and resolve these issues. This method enhances classification using CNN feature extraction and LSTM sequential pattern recognition. HCLNet can better handle class imbalance and limited labeled data with a comprehensive data preprocessing pipeline, including selection, transformation, and filtering. The hybrid design overcomes CNN constraints and captures product feature temporal connections using LSTM layers. HCLNet was compared with ResNet, GoogleNet, and AlexNet, surpassing them. HCLNet classified complex and imbalanced datasets with 96.25% accuracy, 96.60% precision, and 96.05% recall. The proposed method can improve the classification of healthcare products to obtain accurate automated product recommendations.
Optimizing Hepatitis C Virus Inhibitor Identification with LightGBM and Tree-structured Parzen Estimator Sampling
Authors: Noviandy Teuku Rizky , Idroes Ghifari Maulana , Maulana Aga , Afidh Razief Perucha Fauzie , Idroes Rinaldi
Published: December 2024

Identifying potent inhibitors against the Hepatitis C Virus (HCV) is crucial due to the continuous emergence of drug-resistant strains. Traditional drug discovery methods, including high-throughput screening, are often resource-intensive and time-consuming. Machine Learning (ML) approaches, particularly Quantitative Structure-Activity Relationship modeling, have been increasingly adopted to address this. This study utilized LightGBM, an efficient gradient-boosting framework, to predict the activity of potential HCV inhibitors. Additionally, the Tree-structured Parzen Estimator (TPE) was employed for hyperparameter optimization to enhance model performance. The optimized LightGBM-TPE model outperformed other ML models, including standard LightGBM, XGBoost, Random Forest, K-Nearest Neighbors, and Support Vector Machines, achieving an accuracy of 86.27%, a precision of 85.47%, a recall of 87.50%, a specificity of 85.03%, and an F1-score of 86.47%. Feature importance analysis identified critical molecular descriptors contributing to the model's predictive power. The results underscore the potential of advanced ML techniques and robust optimization methods to accelerate drug discovery, particularly for challenging targets such as HCV.
The Potential of Biomethane produced from Waste Landfill to Supplement Renewable Energy in Saudi Arabia
Authors: Rahman Muhammad Muhitur
Published: December 2024

This study investigates the potential of biomethane from waste landfills in five major cities of Saudi Arabia (Riyadh, Jeddah, Makkah, Madina, and Dammam) using the Landfill Gas Emissions (LandGEM) model to estimate methane emissions from 2015 to 2115. The research assesses the cumulative methane emissions, projected to reach nearly 25.5 billion m3 by 2115, and quantifies the electricity generation potential from this biomethane, peaking at 1,299 GWh annually. Sensitivity analysis of key parameters, including the methane producing rate (k) and potential methane producing capacity (L₀), indicates that L₀ has a more impact on methane output. These findings highlight the importance of methane capture and landfill management strategies to enhance the renewable energy capacity of Saudi Arabia. Policy implications are discussed, highlighting the opportunity for biomethane to supplement the country’s energy mix in alignment with its Vision 2030 goals and commitment to reducing greenhouse gas emissions through the Global Methane Pledge.
Categories and Factors of Cost Overrun in Construction Projects: A Systematic Review
Authors: Afana Omar , Al Zubaidi Radhi , Abu Dabous Saleh , Ibrahim Fakhariya
Published: December 2024

Cost overruns represent a significant challenge in construction project management and often compromise project success. This study addresses gaps in previous research, particularly the lack of a unified classification of cost overrun factors. The aim is to provide a comprehensive, unbiased, and structured synthesis of existing research on the factors contributing to cost overruns in construction projects. It involves identifying, evaluating, and categorizing studies to answer predefined research questions related to cost overruns across various geographical contexts, project types, stakeholder perspectives, and project lifecycle phases. Through a Systematic Literature Review (SLR), the current study identifies and categorizes 99 factors into 10 distinct categories: 1) Execution, Resource, and Project Management Factors, 2) Design Factors, 3) Contractor Factors, 4) Consultant Factors, 5) Client Factors, 6) Financial Management Factors, 7) Bidding and Cost Estimation Factors 8) Contracts, Legal, and Regulatory Factors, 9) External Risks, Technology, and Sustainability Factors, and 10) Defects Liability Period (DLP) Operations and Maintenance Factors. Additionally, the present research examines both advanced and traditional methodologies for mitigating these overruns, emphasizing accurate cost estimation, risk management, and the use of advanced technologies, like Building Information Modeling (BIM), alongside strong financial and contract management. This paper synthesizes results from different global contexts to establish a solid foundation for future academic research and industry practices aimed at alleviating cost overruns in construction projects. It also promotes the development of customized frameworks that are specific to a country, a lifecycle phase, or a combination of conditions.
Determining the Best Design Factors of a Two-stage Helical Gearbox with Two Gear Sets in the First Stage to Increase Efficiency and Reduce Volume using the SAW Method
Authors: Dinh Van Thanh , Vu Duc Binh , Nguyen Manh Cuong , Truong Thi Thu Huong , Nguyen Quoc Tuan
Published: December 2024

This study describes the outcomes of employing the Simple Additive Weighting (SAW) approach to address the Multi-Objective Optimization Problem (MOOP) of a two-stage helical gearbox with two gear sets at the first stage. Its objective is to determine the key design variables that can reduce the volume of the gearbox while simultaneously maximizing its efficiency. For this investigation, three key design parameters were selected, namely the coefficients of the wheel face width of the first and second stages (Xba1 and Xba2), and the gear ratio of the first stage u1. In addition, the SAW technique was deployed to deal with the problem of Multi-Criteria Decision Making (MCDM), while the Method based on the Removal Effects of Criteria (MEREC) was employed to determine the weight criterion for addressing the MOOP. The obtained results are valuable for defining the optimal values for three primary design factors, which are essential for the development of a two-stage helical gearbox with two gear sets at the first stage.
SWB I-Shaped Microstrip Patch Antenna with Extended Ground Plane Structure for 5G and beyond 5G Applications
Authors: Iriqat Sanaa
Published: December 2024

This study presents an I-shaped antenna with an extended ground plane structure, which plays a pivotal role in markedly enhancing the antenna's performance in terms of bandwidth, gain, and efficiency across a multitude of 5G mm-wave bands, including 28 GHz, 39 GHz, 41 GHz, 60 GHz, 73 GHz, and others. The proposed antenna exhibits Super Wideband (SWB) characteristics, with a frequency range extending from 25.5 GHz to beyond. It also demonstrates a peak gain of 10.75 dBi and a maximum radiation efficiency of 88%. The compact dimensions of the design, measuring 7 × 10.6 × 1.52 mm³, facilitate the attainment of high gain (10.75 dBi), SWB characteristics (25.5–80 GHz), and high radiation efficiency (<88%), rendering it a promising contender for prospective 5G and B5G applications.