bluuebunny/crossref_metadata_2025 huggingface dataset:
Evaluation of Specimen Rejection Rates in the Preanalytical Phase and Nurses' Experiences: A Mixed Design Study
Authors: GÜNER Yasemin , GÜNER Elif KILIÇ , ÜÇÜNCÜOĞLU Melek , YÜKSEL Harun
Published: January 2025

Background
Aim of the study was to examine the rejection rates of specimens in the preanalytical phase and to determine the experiences of nurses.
Methods
A mixed design was used in the study, combining retrospective data analysis and individual in-depth interviews. First, data on specimens rejection reported by microbiology and biochemistry laboratories were collected for one year. Then, individual in-depth interviews were conducted with nurses working in the clinics with the highest specimens rejection rates.
Results
As a result of the study, three main themes were determined: the functioning of the preanalytical phase, the causes of medical errors in the preanalytical phase, and the impact of the preanalytical phase on the well-being of nurses.
Conclusion
These results, institutional factors, patient profiles, stakeholders involved in the phase, and the clinical environment all affect the preanalytical phase, potentially leading to medical errors; this phase also affects the attitudes and behaviors of patients and their relatives, and the well-being of nurses, causing emotional fatigue. Despite these difficulties, nurses strive to protect their well-being and provide emotional support to patients and their families. Based on the findings of our study, there is a need for strategies focused on ensuring the well-being of nurses, and reducing emotional fatigue.
Clinical trial number:
Not applicaple.
Information Literacy and Entrepreneurial Innovation in Medical Students: An Empirical Study
Authors: Lixin Gong , Xinming Lv , Zhen Tian , Ya You , Youyou Zhang , Jianzhou Wu , Xiaoyin Niu
Published: January 2025

Background Training in innovation and entrepreneurship (I&E) in medical education has become increasingly prevalentamong medical schools. The information literacy equips innovators with crucial information, facilitates resource integration, and has emerged as a powerful catalyst for driving innovation. Our study explores the current participation of the medical students in innovation and entrepreneurship and provides forwards the suggestions for integrating information literacy into innovation education.
Methods A cross-sectional study was conducted among medical students at Shanghai Jiao Tong University School of Medicine, using a questionnaire to explore the relationship between information literacy and entrepreneurial innovation. The questionnaire comprises five sections, participant background, current involvement in innovation and entrepreneurship activities, satisfaction levels, and perceived needs and suggestions for support in I&E activities.
Results A total of 178 medical students participated in this survey. The results indicate that the medical students primarily face challenges related to time constraints, insufficient energy and inadequate professional knowledge during their engagement in innovation and entrepreneurship activities. Peer-to-peer communication serves as the primary source of information in the initial stage of these activities, while expert consultation is the least used by students. Additionally, the students have a low demand for library resources. Students reported a moderately high level of satisfaction with the innovation and entrepreneurship resources provided by the school, with an average score of 3.63 on a 5-point scale.
Conclusion This study highlights the vital role of information literacy in enhancing the medical students' innovation and entrepreneurship capabilities. By integrating information literacy into education, we can improve students' ability to access, analyze, and utilize information effectively, thus promoting innovation and ethical entrepreneurship. These findings provide valuable insights and a foundation for further research, while emphasizing the necessity of targeted educational strategies to support students' entrepreneurial development.
Overcoming Challenges in Implementation of Competency- Based Medical Education in India: Leveraging Postgraduates for Multiple Choice Item Design for Undergraduate Assessments in Pathology
Authors: Kini Reshma G. , Marla Nisha J , Moras Christol Blanch
Published: January 2025

Background
The implementation of Competency-Based Medical Education (CBME) in India has highlighted significant challenges, including faculty shortages and the need for standardized assessments. Pathology postgraduates, often involved in undergraduate teaching, represent an underutilized resource. Training them in Multiple Choice Item (MCI) development and validation can improve assessment quality while addressing gaps in CBME implementation.
Method
A structured training program was conducted for nine first-year pathology postgraduates at a single institution. The program included nine interactive sessions on CBME principles, Bloom’s taxonomy, MCI construction, identification of technical flaws, validation processes, and blueprinting. Participants’ reactions and self-perceived learning were evaluated using Kirkpatrick Levels 1 and 2 through pre- and post-program surveys and structured feedback forms. Product evaluation analyzed the MCIs for difficulty and discrimination indices, and the need for revalidation by subject matter experts (SMEs). Open-ended feedback was thematically analyzed.
Results
Participants showed significant improvements in knowledge and confidence in MCI development (p < 0.05). Product evaluation revealed that 43.2% of MCIs required revalidation, but most met acceptable thresholds for difficulty (62.5%) and discrimination (75%). Feedback highlighted the program’s structured and practical focus, while suggesting improvements in session pacing and content depth. Multiple choice items predominantly targeted lower order thinking skills, reflecting participants’ novice status and the need for longitudinal support to improve question complexity.
Conclusion
This study demonstrates the feasibility of leveraging pathology postgraduates to address CBME implementation challenges. By equipping postgraduates with assessment design skills, the program improved undergraduate assessment quality while addressing faculty shortages. Future efforts should focus on expanding such programs across institutions, incorporating longitudinal mentorship, and evaluating their long-term impact on medical education outcomes.
Left tracheal diverticulum showing a central airway obstruction pattern in pulmonary function: A case report
Authors: Kodaka Norio , Watanabe Kayo , Nakano Chihiro , Imaizumi Chisato , Niitsuma Kumiko , Shinozawa Saeko , Hirouchi Takatomo , Yoshida Yuto , Takei Hiroaki , Yamada Yuka , Yoshizawa Go , Matsuse Hiroto
Published: January 2025

Background:
Central airway obstruction patterns on the flow–volume curves of pulmonary function tests are commonly caused by upper respiratory tract disease and intratracheal tumors. Here, we describe a rare case in which the central airway obstruction patterns on the flow–volume curves were attributed to the slow progression of tracheal lumen narrowing caused by chronic inflammation in the surrounding area, rather than tracheal diverticulum enlargement.
Case presentation:
A 73-year-old woman diagnosed with a tracheal diverticulum was followed as an outpatient. Although her subjective symptoms did not worsen, her flow–volume curves in the pulmonary function test indicated a central airway obstruction pattern. Annual chest computed tomography imaging showed no significant changes; however, tracheal narrowing due to inflammation around the tracheal diverticulum was suspected. Surgical treatment, including laser therapy, was considered. We report a rare case of a tracheal diverticulum diagnosed >10 years ago that exhibited central airway obstruction patterns on flow–volume curves of pulmonary function tests.
Conclusion:
Pulmonary function tests and flow–volume curves are valuable in monitoring airway obstruction.
Decoding Gender: A Machine Learning Approach for Classifying Indian Names with Advanced Feature Extraction
Authors: Ghate Sudeep D. , H Saishma , D Dhanush Ghate , M Adithya , Alex Anjusha , D’Souza Neevan , Patil Prakash
Published: January 2025

Classifying gender based on Indian names poses a unique challenge due to the nation's immense cultural, linguistic, and regional diversity. Existing methods often struggle to address the complexities of naming conventions shaped by religious, familial, and linguistic influences, resulting in inconsistent and inaccurate classifications. To address these challenges, this study developed a culturally diverse dataset of 31.3 lakh male and female names and leveraged advanced machine learning (ML) and deep learning (DL) techniques for gender classification. These names were sourced from Indian electoral data, synthetic names generated using custom scripts, and publicly available names from websites to ensure diversity. Twelve ML models were evaluated, with the top four - Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Units (GRU), and XGBoost—prioritized for detailed analysis. CNN emerged as the best-performing model, achieving the highest accuracy (96%) and the fastest prediction time (5.61 seconds), highlighting its efficiency and ability to generalize across diverse naming conventions. LSTM and GRU also demonstrated strong performance, achieving accuracies of 95% and 93% respectively, with LSTM offering higher precision but significantly longer prediction times (50 seconds). XGBoost, a traditional ML model, achieved an accuracy of 86% but struggled with female name classification, indicating potential biases in feature representation. All models effectively captured complex naming patterns, though challenges such as the misclassification of unisex names and the underrepresentation of North-East Indian names in the dataset highlighted areas for improvement. This study underscores the advantages of deep learning models, particularly CNN, in leveraging hierarchical and sequential patterns in names for robust gender classification. However, limitations in dataset diversity and model generalizability indicate the need for further refinement. These findings contribute to advancing automated gender classification systems, offering practical applications in healthcare, marketing, and social sciences. Future work should focus on enhancing computational efficiency, expanding datasets to improve cultural inclusivity, and addressing biases to ensure equitable ML innovations.
Optimizing financial success: The synergistic impact of artificial intelligence and R&D investments in U.S. firms
Authors: Kumari Sonia , Shaikh Raja , Bhayo Mujeeb-u-Rehman , Devi Sharmila , Cao Shengjie
Published: January 2025

The use of artificial intelligence (AI) and intellectual machines can support businesses in performing various activities. Therefore, it is necessary to examine the performance outcomes by assessing the concentration of AI technologies. To create a quantifiable score of AI concentration, AI-related terms are identified in the annual reports of all listed firms in the U.S. For analysis purposes, a fixed effects model is employed, using firms’ panel data from 2003 to 2022. The analysis reveals that AI concentration is beneficial for a company’s financial success. Additional analysis examines the moderating role of research and development (R&D). Firms with higher R&D spending experience increased financial benefits from concentrating on AI technologies. The uniqueness of this study lies in analyzing the financial success through the AI and R&D parameters. The findings support a higher concentration on AI, combined with higher R&D spending, to attain greater financial success. The main insights suggest that management must evaluate their existing focus on AI and R&D spending to improve their financial position.
JEL Classification: F65; G30; O32; P33
Investigation of concrete properties using recycled waste concrete aggregate
Authors: Mohammed Suha , Naimi Sepanta
Published: January 2025

There are several environmental, economic, and energy reasons why RC is gaining popularity around the world. Key environmental problems include the potential for groundwater contamination due to the washing out of fresh concrete brought back from project sites, as well as the loss of natural sources of high-quality aggregate materials. The RC concrete (RCA) impact on the fundamental characteristics of traditional con-crete is investigated besides the effect of different ratio effects of water-cement (w/c). The concrete as crushed rubble obtained from several Iraqi demolition sites and landfills is utilized to examine the properties of (RCA). This work utilized sand as natural, and concrete as crushed from various sources as aggregates around Baghdad city. A total of forty-five concrete mixtures were cast into nine groups. Groups were created to examine the impact of recycled coarse aggregate (CA) quality/content, dosage of cement, and w/c ratio. Strengths of compressing and splitting, and modulus of elastic tests were conducted. The findings demon-strated that the concrete rubble (CR) may be turned into recycled aggregate (RC) and utilized in the concrete manufacturing with qualities adequate for the vast majority of structural concrete applications in Iraq. Fur-thermore, the concrete strength has decreased by 6% to 30%, depending on the proportion of RC utilized to replace natural aggregate and the w/c ratio.
Quantum fuzzy genetic algorithm with Turing to solve DE
Authors: Hamid, et. al. Ahmed
Published: January 2025

In this study, we create the quantum fuzzy Turing machine (QFTM) approach for solving fuzzy differen-tial equations under Seikkala differentiability by combining it with a differential equation and a genetic algorithm. A theoretical model of computation called a quantum fuzzy Turing machine (QFTM) incor-porates aspects of fuzzy logic and quantum physics.
Monthly drought monitoring of the surface water area of Sawa Lake, Iraq during 2016-2022 using remote sensing data
Authors: Alshammari, et. al. Lubna
Published: January 2025

Drought is a common phenomenon in Iraq's environment, and the country has experienced severe drought events exacerbated by the threat of climate change (low rainfall and high temperatures) over the past two decades. Iraq is located in a semi-arid region whose water resources have been restricted and mostly shared with its neighbours. To investigate the effect of drought on the surface water area of Sawa lake, we analysed 52 Sentinel-2 images from May 2016 to July 2022 using an open-source SNAP toolbox to map the boundary of the surface-water body of the lake. The results indicate that the surface water area of Sawa lake has de-creased significantly over the last six years with the most extreme decline beginning in May 2021, when the area of the lake lost about 51% of its initial size (May 2016). By March 2022, the lake had disappeared and about 96% of the water's surface area had been lost. To better understand the potential causes of droughts, further analysis has been conducted on the effects of precipitation and human activities (vegetation cover and Al-Samawah saltpan for salt production) on the lake. Investigations revealed that the rapid expansion of agricultural areas around the lake by 254% and the increase in salt production from the Al-Samawah saltpan by about 121% are among the direct causes of the drought. In addition, the results of the statistical test anal-ysis between the estimated surface water area of Sawa lake and human activities were significant at a 95% level of confidence. The findings of this study can assist decision-makers to understand the interaction be-tween human activities and the lake's environment to design a strategic plan for lake recovery and a sustain-able water resource management system in southern Iraq.
Technical-economic evaluation of a portable solar machine of potential use in pumping systems and water purification, through the use of photovoltaic solar technology for non-interconnected areas in Colombia
Authors: Calderón, et. al. Diego Armando Cartagena
Published: January 2025

The purpose of this article is to present the dimensioning of a portable, autonomous solar machine, of potential utility in pumping systems and water purification, through the use of photovoltaic solar technology and inno-vation to an unsatisfied basic need, such as service drinking water, in areas of the country, where there are inconveniences for access to drinking water and electricity service. The solar machine is innovative, modular, portable, easy to transport and install, sustainable and environmentally friendly, with zero greenhouse gas emissions as it is powered by photovoltaic technology. The solar machine is innovative, modular, portable, easy to transport and install, sustainable and environmentally friendly, with zero greenhouse gas emissions as it is powered by photovoltaic technology and does not use batteries. This portable solar machine substantially reduces the cost of investment, operation and maintenance, by using photovoltaic solar panels and working with direct current, eliminating the use of the conventional electrical power network, inverters, for its opera-tion and can be dimensioned for water. surface water from rivers or reservoirs to the reservoir or pond, the same as for groundwater from wells, for the supply of drinking water, through the improvement, disinfection and sterilization of drinking water, to improve the quality of life of rural communities and secondly measure for use in the agricultural sector, for irrigation, domestic use, livestock (livestock pasture) and other services, for the rural development of the regions in Colombia.
Studying the effect of concentration on spectral and optical properties of chitosan polymer
Authors: Abdulrazzaq Shaymaa Jabbar
Published: January 2025

Due to its noteworthy physico-chemical behaviors, the chitosan (CS) biopolymer presents a compelling alter-native to conventional biomaterials. By measuring the emission spectra at a spectrofluorophotometer in differ-ent concentrates (1x10-4, 1x10-5, and 1x10-6) M, the spectral characteristics of the CS polymer were obtained. The crystallinity of the CS polymer was determined by X-ray diffraction (XRD) analysis, which shows only one dominant peak indexed to the orthorhombic structure. With a drop in solution concentration, there was a shift toward the shorter wavelength and a decline in quantum yield. Using a UV-Vis spectrophotometer, the optical properties of the CS polymer, including its refractive index, absorption coefficient, energy band gap, extinction coefficient, and dielectric constant, have been studied.  It was observed through the results that the optical parameters are linearly dependent with concentration. In contrast to the optical energy gap, which de-creases with increasing concentration.
A comparison between the hierarchical clustering methods for postgraduate students in Iraqi universities for the year 2019-2020 using the cophenetic and delta correlation coefficients
Authors: Essa, et. al. Ameena Kareem
Published: January 2025

The educational sector is one of the important sectors in the world, and it is considered one of the means of community development. In addition, it is one of the means of making the country’s renaissance and devel-opment because it represents the factory of thinking minds that make change. There is no doubt that this sector is the same as any other sector. The deficit in the studied scientific planning has been prolonged, which led to its deterioration, and the problems of education remain diverse and inherited from previous time periods, where the hierarchical cluster analysis was used on postgraduate students in universities in Iraq, except for Kurdistan region, and the number of universities that were included in the study was (30) universities. In the whole of Iraq for the year 2020, when using the comparison measures the Cophenetic Correlation Coefficient (CCC) and the Coefficient Delta (DC), it was found that the Complete Linkage Method is the best among the hierarchical methods, as the value of (CCC) is 0.952061, and the value of (DC(0.1)) it is 0.288973, and in the case (DC(0.5)) it is 0.26877, then followed by Median method, Ward's method and finally Single Linage Method.
EEG-based image classification using an efficient geometric deep network based on functional connectivity
Authors: Hasan, et. al. Haitham S.
Published: January 2025

To ensure that the FC-GDN is properly calibrated for the EEG-ImageNet dataset, we subject it to extensive training and gather all of the relevant weights for its parameters. Making use of the FC-GDN pseudo-code. The dataset is split into a "train" and "test" section in Kfold cross-validation. Ten-fold recommends using ten folds, with one fold being selected as the test split at each iteration. This divides the dataset into 90% training data and 10% test data. In order to train all 10 folds without overfitting, it is necessary to apply this procedure repeatedly throughout the whole dataset. Each training fold is arrived at after several iterations. After training all ten folds, results are analyzed. For each iteration, the FC-GDN weights are optimized by the SGD and ADAM optimizers. The ideal network design parameters are based on the convergence of the trains and the precision of the tests. This study offers a novel geometric deep learning-based network architecture for classifying visual stimulation categories using electroencephalogram (EEG) data from human participants while they watched various sorts of images. The primary goals of this study are to (1) eliminate feature extraction from GDL-based approaches and (2) extract brain states via functional connectivity. Tests with the EEG-ImageNet database validate the suggested method's efficacy. FC-GDN is more efficient than other cutting-edge approaches for boosting classification accuracy, requiring fewer iterations. In computational neuroscience, neural decoding addresses the problem of mind-reading. Because of its simplicity of use and temporal precision, Electroencephalographys (EEG) are commonly employed to monitor brain activity. Deep neural networks provide a variety of ways to detecting brain activity. Using a Function Connectivity (FC) - Geometric Deep Network (GDN) and EEG channel functional connectivity, this work directly recovers hidden states from high-resolution temporal data.  The time samples taken from each channel are utilized to represent graph signals on a topological connection network based on EEG channel functional connectivity. A novel graph neural network architecture evaluates users' visual perception state utilizing extracted EEG patterns associated to various picture categories using graphically rendered EEG recordings as training data. The efficient graph representation of EEG signals serves as the foundation for this design. Proposal for an FC-GDN EEG-ImageNet test. Each category has a maximum of 50 samples. Nine separate EEG recorders were used to obtain these images. The FC-GDN approach yields 99.4% accuracy, which is 0.1% higher than the most sophisticated method presently available
Characterization and analysis of N2 plasma DC glow discharge at different voltage
Authors: Haji Suzan M.
Published: January 2025

In this work, the plasma glow discharge characteristics of nitrogen gas will be studied and analyzed at dif-ferent pressures, and we will study the effect of these pressures on the I-V curve, the Panchen curve, the I-P curve, the current-voltage curve, and their effect on the electrical conductivity of the generated plasma. The distance between the glow electrodes was (15.5) cm. The pressures that were used in this work were (0.025, 0.05, 1.5, 5) T. The results indicated that the discharge was operating in the atypical glow region. On the other hand, the discharge current decreased as the gas pressure increased. On the other hand. Also, the con-ductivity decreased in its values with the increase in applied pressure as well as the voltage of the plasma focus.
A novel approach for coordinated design of TCSC controller and PSS for improving dynamic stability in power systems
Authors: Alwan Hayder Okab
Published: January 2025

The purpose of this article is to present a novel strategy for the coordinated design of the Thyristor Con-trolled Series Compensator (TCSC) controller and the Power System Stabilizer (PSS). A time domain objec-tive function that is based on an optimization problem has been defined. This objective function takes into account not only the influence that disturbances have on the mechanical power, but also, and this is more accurately the case, the impact that disturbances have on the reference voltage. When the objective function is minimized, potential disturbances are quickly mitigated, and the deviation of the speed of the generator's rotor is limited; as a result, the system's stability is ultimately improved. Particle Swarm Optimization (PSO) and the Shuffled Frog Leaping Algorithm are both components of a composite strategy that is utilized in the process of determining the optimal controller parameters. (SFLA). An independent controller design as well as a collaborative controller design utilizing PSS and TCSC are developed, which enables a direct evalua-tion of the functions performed by each. The presentation of the eigenvalue analysis and the findings of the nonlinear simulation can help to provide a better understanding of the efficacy of the outcomes. The find-ings indicate that the coordinated design is able to successfully damp low-frequency oscillations that are caused by a variety of disturbances, such as changes in the mechanical power input and the setting of the reference voltage, and significantly enhance system stability in power systems that are connected weekly.
Preparation, investigation and studying the (physical, spectral and biological activity) of new monomers and heterocyclic compounds containing nitrogen and oxygen
Authors: Jaffer, et. al. Noor Dia
Published: January 2025

This study involves the preparation of seven new compounds, some of them contain two functional groups that are considered condensation monomers. On the other hand, other compounds contain double bonds that are considered addition monomers. They started from 2- amino Thiazole with 2-hydroxy benzaldehyde (sa-licylaldehyde) in ethanol and hydrochloric acid to prepare azo compound (A), which reacted with acetophe-none derivatives (p-bromo, p-chloro and p-hydroxy acetophenone) to prepare chalcone monomers deriva-tives (H1-H3). Also, the work includes preparing seven-membered heterocyclic rings through two consecu-tive steps; the first step includes the preparation of Schiff base compound (S). The second step includes the preparation of the oxazepine derivatives (Z1, Z2) through the cyclic closure of compound (S) with the (ma-leic, phthalic) anhydrides, respectively. All compounds were investigated with FT-IR, H1NMR, and 13CNMR techniques. The physical properties and the antibacterial activity were also studied for all the pre-pared compounds and showed good results.
Simulation of flow over stepped and traditional spillways
Authors: Ahmed, et. al. Abdulnaser A.
Published: January 2025

There are several challenges that are faced by these structures, such as cavitation and high speed of flow during the dissipation of flow energy. The spillway is one of the key basic elements of dams that are utilized to pass huge volumes of discharge. The current work used computational fluid dynamics (CFD) to numeri-cally estimate the water surface flow and pressure distribution over stepped and traditional spillways. The numerical findings were compared with experimental data using the Renormalization group k- model. The numerical and experimental data have been compared, and it has been determined that there is an acceptable agreement between the two sets of findings.
Corbels strengthened with CFRP under deferent loading
Authors: Joudah, et. al. Ali
Published: January 2025

Short cantilevers under loads are known as corbels, and they generally have a detrimental impact on the strength of concrete structural components by reducing their resistance to external loads. Strengthening with CFRP strips as an externally bounded approach is employed to raise that. The behavior and durability of strengthened corbels under (constant and incremental) loads are examined in this research. Twelve double corbels were cast and tested as part of the experimental program, with the dimensions of the corbels on all specimens remaining constant. While the other nine are left un-fortified as control corbels, nine of them are strengthened using CFRP strips in various patterns. The results indicate that the maximum load capacity of corbels is affected by both constant and incremental loads. In addition, the ultimate load and ultimate deflection of the legs subjected to five load cycles were less than those subjected to constant stress. The bull legs rein-forced with externally bonded CFRP strips demonstrated a significant increase in ultimate load under any ap-plied load but a reduction in ultimate deflection relative to the unreinforced bull legs under the same applied load. All failures of the bull limb were ascribed to the unbounded CFRP strips.
Three-dimensional final element analysis of composite steel - concrete aches
Authors: Al-Brees, at. al. Roaa H.
Published: January 2025

Latest structure construction crafts frequent use of composite steel-concrete arches for building and bridge applications. The leading goal of this research is to describe a three-dimensional finite element model capa-ble of describing the response of a composite steel-concrete arch. The commercial software Abaqus was implemented to develop the proposed approach, where a certain experimental test results in the literature on a scaled steel-concrete composite arch have been compared to validate the accuracy and adequacy of the proposed finite element model.  Furthermore, the current research developed a finite element model to study the fundamental behavior of a composite steel box-concrete arch with a specific parameter. These parame-ters including the degree of curvature, the number of the shear connector, and the support conditions have been considered the main study parameters. The proposed model has presented an excellent prediction of the initial stiffness, the ultimate strength, and the ductility of the composite arch. The Representative behavior was affected significantly by the designated variable for the steel box section with composite deck. There-fore, these findings may be crafted with the aim of structural design rules.
Development of a monitoring system for COVID-19 monitoring in early stages
Authors: Maradey – Lázaro, et. al. J. G.
Published: January 2025

Covid-19 is considered the most infectious virus today. Likewise, the struggle to mitigate the effects of the variants, the flexibility in some measures such as the use of face masks, the advancement of vaccination and prevention and self-care campaigns continue to be topics of research and of global interest. The world health authorities published that the disease was characterized by presenting the same symptoms as the flu along with a complex picture where in the most serious cases they lead to difficulty breathing due to pneumonia, sepsis and septic shock that can lead to death. Some systems implemented for taking body temperature such as thermographic cameras, digital thermometers, for the description of symptoms in the people they analyze at the time of carrying out the epidemiological fences are not enough, since they handle low precision, are taken in isolation, individually or randomly and is not suitable for characterizing interest groups. Then, establishing risk levels by measuring non-invasive variables can be considered inputs into prevention campaigns and a low-cost way of monitoring the community. This article shows the design of a non-invasive embedded device for the measurement of 5 priority variables for the detection of the risk of covid-19 infection. The proposed device was duly calibrated and synchronized for the acquisition of data from 594 people in the city of Bucaramanga, Colombia, who authorize the monitoring of the symptoms. The people must be in a state of rest to be able to acquire the data with great accuracy, in this way the data is entered into the system in charge of doing the monitoring analysis. Additionally, the implementation of an interface that allows the visualization of results, laying the foundations for the development of automatic learning techniques or models for the risk classification in future work.
Reliability of the Tigris River water treatment center in al-Rashidiyah-Baghdad by using Weibull distribution
Authors: Samih, et. al. Mohammed Jumaa
Published: January 2025

The research includes a study of analyzing and evaluating the reliability of turbidity removal and the amount of residual chlorine in the water in the Rashidiya water center located in Rashidiya-Baghdad-Iraq according to the permissible criteria for making decisions based on solid scientific foundations. Thus, water treatment centers in general need a reliable evaluation that helps in evaluating the output Water treatment centers according to the standards set for each center. The research aims to evaluate the reliability of the Rashidiya water center using the two-parameter Weibull distribution, where the distribution parameters were estimated using the least squares method using the (Matlab 2022) program. The chi-square test was used for Goodness of Fit at a significant level of 0.05, through the Easy Fit 5.5 professional program. It was found to be dis-tributed according to the Weibull distribution, and descriptive statistics were calculated for each parameter, and it was found that the mean (and median) of the turbidity values (9.379), which is within the permissible limit, The mean (and median) chlorine values are (0.0279), which is also much less than the permissible limit. The efficiency scale was used to remove turbidity. It was found that the average efficiency of turbidity removal is 71.35%, which is a relatively acceptable level. However, it is necessary to increase the turbidity treatment to raise the level of efficiency. It was found through the Weibull distribution analysis that the value of reliability for the remov-al of turbidity is (0.99), and for the remaining amount of chlorine is (0.99). The research con-cluded that the Rashidiya water center is reliable during the research period. The research rec-ommends evaluating all water treatment centers in Iraq in general, and in Baghdad in particular, periodically and at certain time periods. The research also recommends using the natural loga-rithm distribution as it is suitable for evaluation.
Solving the multi-criteria: total completion time, total late work, and maximum earliness problem
Authors: Neamah, et. al. Nagham Muosa
Published: January 2025

Within this research, The problem of scheduling jobs on a single machine is the subject of study to minimize the multi-criteria and multi-objective functions. The first problem, minimizing the multi-criteria, which include Total Completion Time, Total Late Work, and Maximum Earliness Time (∑C_j,∑V_j,E_max ), and the second problem, minimizing the multi-objective functions ∑C_j+ ∑V_j+E_max are the problems at hand in this paper. In this study, a mathematical model is created to address the research problems, and some rules provide efficient (optimal) solutions to these problems. It has also been proven that each optimal solution for ∑C_j+ ∑V_j+E_max is an efficient solution to the problem (∑C_j,∑V_j,E_max ). Because these problems are NP-hard problems so it is difficult to determine the efficient (optimal) solution set for these problems so some special cases are shown and proven which find some efficient (optimal) solutions suitable for the discussed problem, and highlight the significance of the Dominance Rule (DR), which can be applied to this problem to enhance efficient solutions.
Exploring the structural behavior of recycled aggregates concrete pipe under dynamic loads through experimental and numerical investigation
Authors: Mohamed, et. al. Shaymaa Abdelghany
Published: January 2025

The most used material in construction is concrete. This study aimed to investigate the impact of recycled aggregate on compressive and tensile strength in concrete. To address the issue of accumulating tons of re-cycled aggregates, a new method was developed using chemical additive fractions to convert these aggre-gates into valuable products. The overconsumption of materials is a significant problem that leads to route overflow, pollution, and road fragmentation. The mechanical properties of corroded concrete pipes were also examined through a series of tests and the development of an ABAQUS-based 3D finite element model. It was discovered that the inside of the pipe wall was more vulnerable to damage than the outside, with cor-roded areas experiencing large stresses and strains. As corrosion depth increased, so did maximum principal stress and hoop strain in the affected region. Fear increased rapidly at first (0-10°), then decreased rapidly (10-45°), before gradually decreasing with increasing erosion latitude (45-180°). Additionally, both circum-ferential strain and maximum principal stress increased rapidly with greater depth of coverage or increasing moving loads.
Enhancing smart home energy efficiency through accurate load prediction using deep convolutional neural networks
Authors: Saber, et. al. Suaad M.
Published: January 2025

The method of predicting the electricity load of a home using deep learning techniques is called intelligent home load prediction based on deep convolutional neural networks. This method uses convolutional neural networks to analyze data from various sources such as weather, time of day, and other factors to accurately predict the electricity load of a home. The purpose of this method is to help optimize energy usage and reduce energy costs. The article proposes a deep learning-based approach for nonpermanent residential electrical ener-gy load forecasting that employs temporal convolutional networks (TCN) to model historic load collection with timeseries traits and to study notably dynamic patterns of variants amongst attribute parameters of electrical energy consumption. The method considers the timeseries homes of the information and offers parallelization of large-scale facts processing with magnificent operational efficiency, considering the timeseries aspects of the information and the problematic inherent correlations between variables. The exams have been done using the UCI public dataset, and the experimental findings validate the method's efficacy, which has clear, sensible implications for setting up intelligent strength grid dispatching.
Enhancing cloud security through the integration of deep learning and data mining techniques: A comprehensive review
Authors: Salem, et. al. Israa Ezzat
Published: January 2025

Cloud computing is crucial in all areas of data storage and online service delivery. It adds various benefits to the conventional storage and sharing system, such as simple access, on-demand storage, scalability, and cost savings. The employment of its rapidly expanding technologies may give several benefits in protecting the Internet of Things (IoT) and physical cyber systems (CPS) from various cyber threats, with IoT and CPS providing facilities for people in their everyday lives. Because malware (malware) is on the rise and there is no well-known strategy for malware detection, leveraging the cloud environment to identify malware might be a viable way forward. To avoid detection, a new kind of malware employs complex jamming and packing meth-ods. Because of this, it is very hard to identify sophisticated malware using typical detection methods. The article presents a detailed assessment of cloud-based malware detection technologies, as well as insight into understanding the cloud's use in protecting the Internet of Things and critical infrastructure from intrusions. This study examines the benefits and drawbacks of cloud environments in malware detection, as well as pre-sents a methodology for detecting cloud-based malware using deep learning and data extraction and highlights new research on the issues of propagating existing malware. Finally, similarities and variations across detection approaches will be exposed, as well as detection technique flaws. The findings of this work may be utilized to highlight the current issue being tackled in malware research in the future.
Enhancing child safety with accurate fingerprint identification using deep learning technology
Authors: Sahy, et. al. Seba Aziz
Published: January 2025

Utilizing deep learning algorithms to differentiate the fingerprints of children can greatly enhance their safety. This advanced technology enables precise identification of individual children, facilitating improved monitoring and tracking of their activities and movements. This can effectively prevent abductions and other forms of harm, while also providing a valuable resource for law enforcement and other organizations responsible for safeguarding children. Furthermore, the use of deep learning algorithms minimizes the potential for errors and enhances the overall accuracy of fingerprint recognition. Overall, implementing this technology has immense potential to significantly improve the safety of children in various settings. Our experiments have demonstrated that deep learning significantly enhances the accuracy of fingerprint recognition for children. The model accu-rately classified fingerprints with an overall accuracy rate of 93%, surpassing traditional fingerprint recognition techniques by a significant margin. Additionally, it correctly identified individual children's fingerprints with an accuracy rate of 89%, showcasing its ability to distinguish between different sets of fingerprints belonging to different children.
Optimization of the urban green area in Erbil territory for sustainable development
Authors: Wahab, et. al. Azad Arshad Abdul
Published: January 2025

This research paper studies the status and condition of the green areas in the city of Erbil, for this purpose all green areas in the city (763 plots) and all population number according to 12 sectors are collected ac-cording to their locations and are analyzed spatially by GIS program (Moran I). Researchers have proved that distribution of green areas is random. Moreover, this distribution is not based on the urban planning basics and its criteria: green area per person (GAPP) and green area to the city ratio (GAR) also not based on the basics of urban planning for two criteria  , GAPP is optimized from 9.3 to 14 and  GAR optimized from 0.06 to 0.09 while the equilateral tringle  adopted as optimum distribution  for green area units  GAU, for 12 sectors adopted combined standards together  and the solution was the population density ratio must be 0.01 or less, to obtain criteria and this must preserved and adhered to the planning and laws and regula-tions strictly. This method can be applied to the study of the spatial distribution in order to compare it with the distribution of schools, health centers and other services or infrastructures.
Profile of digital literacy of mathematics education students in online learning and its relationship with learning motivation
Authors: Rusli, et. al. Rusli
Published: January 2025

This study aims to determine the digital literacy profile of Mathematics Education students at Universitas Negeri Makassar in online learning and its relationship with learning motivation. The research was designed with a quantitative approach ex-post facto model with the main method of survey. The research was con-ducted on Mathematics Education students at Universitas Negeri Makassar. The result of the studies: (1) mathematics education students have a high level of digital literacy, (2) mathematics education students have a high level of learning motivation, and (3) the relationship between digital literacy and learning moti-vation has a linear relationship and strong correlation between digital literacy and learning motivation.
The study of deep learning for automotive logo recognition and classification
Authors: Saeed, et. al. Saeed R.
Published: January 2025

Most vehicle manufacturer recognition (VMR) techniques are established in vehicle logo recog-nition because a vehicle's logo is the most obvious sign from the vehicle's manufacturer. Howev-er, due to the difficulty in accurately segmenting a vehicle logo on the picture with demand in resilience against many imaging scenarios, logo recognition can still be challenging. After sub-jective overview about this scope, a convolutional neural network (CNN) method for VMR is investigated in this research, which does away with the need for exact logo detection and seg-mentation. A powerful pertaining approach has also been developed to improve real-world appli-cations to lower the high computational cost for kernel training on CNN-based systems. The con-tribution of this paper is to study the multiclass logo employing random forest ensemble learn-ing and convolution mapping in nonlinear space. To boost accuracy by roughly 35%, 800 images from 15 types of car classes were investigated in the paper.
A low-cost podoscope for extracting morphological features of the foot
Authors: Musaid, et. al. Ruba
Published: January 2025

Foot morphology evaluation techniques are commonly used to evaluate foot abnormalities. The foot is es-sential for keeping the biomechanical performance of the lower extremities. It is responsible for the support of body balance and stability during gait. For the qualitative examination of foot morphology, many podo-scopes have been designed. However, these podoscopes are mainly expensive, complex to operate, and have low accessibility. As a consequence the goal of this research was to create a simple, low-cost podoscope to assess the plantar footprint. In addition to providing a visual representation of the supporting regions. The proposed system enables accurate estimation of clinical indexes of foot deformities.  With bare feet, bipedal support, and orthostatic posture, the participant was placed on the podoscope. Doom camera was used to capture the image of the plantar of the foot, and the data was sent to a computer via LAN connection. Based on image processing techniques, the proposed podoscope can automatically estimate the Arch Index, the Chippaux-Smirak Index, the Staheli Index, and Wejsflog's index.Ten samples were tested by the proposed podoscope, and the calculated parameters defining foot deformities are presented. The proposed podoscope appears to have the advantages of simplicity, being portable, inexpensive enabling rapid and precise foot-print assessment with correct diagnoses, as well as a record for future assessments, and being economically viable in countries with people having low socio-economic status.