bluuebunny/crossref_metadata_2025 huggingface dataset:
Ensemble feature ranking approach for software fault prediction
Authors: Agrawalla Bikash , Shukla Alok Kumar , Tripathi Diwakar , Singh Koushlendra Kumar , Ramachandra Reddy B.
Published: April 2024

Software fault prediction, which aims to find and fix probable flaws before they appear in real-world settings, is an essential component of software quality assurance. This article provides a thorough analysis of the use of feature ranking algorithms for successful software failure prediction. In order to choose and prioritise the software metrics or qualities most important to fault prediction models, feature ranking approaches are essential. The proposed focus on applying an ensemble feature ranking algorithm to a specific software fault dataset, addressing the challenge posed by the dataset’s high dimensionality. In this extensive study, we examined the effectiveness of multiple machine learning classifiers on six different software projects: jedit, ivy, prop, xerces, tomcat, and poi, utilising feature selection strategies. In order to evaluate classifier performance under two scenarios—one with the top 10 features and another with the top 15 features—our study sought to determine the most relevant features for each project. SVM consistently performed well across the six datasets, achieving noteworthy results like 98.74% accuracy on “jedit” (top 10 features) and 91.88% on “tomcat” (top 10 features). Random Forest achieving 89.20% accuracy on the top 15 features, on “ivy.” In contrast, NB repeatedly recording the lowest accuracy rates, such as 51.58% on “poi” and 50.45% on “xerces” (the top 15 features). These findings highlight SVM and RF as the top performers, whereas NB was consistently the least successful classifier. The findings suggest that the choice of feature ranking algorithm has a substantial impact on the fault prediction models’ predictive accuracy and effectiveness. When using various ranking systems, the research also analyses the trade-offs between computing complexity and forecast accuracy.
RETRACTED: WIFI based human activity recognition using multi-head adaptive attention mechanism
Authors: Abuhoureyah Fahd , Yan Chiew Wong , Zitouni M. Sami
Published: April 2024

This article has been retracted. A retraction notice can be found at https://doi.org/10.3233/JIFS-219433.
A selective LVQ algorithm for improving instance reduction techniques and its application for text classification
Authors: Hayel Rafa , El Hindi Khalil , Hosny Manar , Alharbi Rawan
Published: April 2024

Instance-Based Learning, such as the k Nearest Neighbor (kNN), offers a straightforward and effective solution for text classification. However, as a lazy learner, kNN’s performance heavily relies on the quality and quantity of training instances, often leading to time and space inefficiencies. This challenge has spurred the development of instance-reduction techniques aimed at retaining essential instances and discarding redundant ones. While such trimming optimizes computational demands, it might adversely affect classification accuracy. This study introduces the novel Selective Learning Vector Quantization (SLVQ) algorithm, specifically designed to enhance the performance of datasets reduced through such techniques. Unlike traditional LVQ algorithms that employ random vector weights (codebook vectors), SLVQ utilizes instances selected by the reduction algorithm as the initial weight vectors. Importantly, as these instances often contain nominal values, SLVQ modifies the distances between these nominal values, rather than modifying the values themselves, aiming to improve their representation of the training set. This approach is crucial because nominal attributes are common in real-world datasets and require effective distance measures, such as the Value Difference Measure (VDM), to handle them properly. Therefore, SLVQ adjusts the VDM distances between nominal values, instead of altering the attribute values of the codebook vectors. Hence, the innovation of the SLVQ approach lies in its integration of instance reduction techniques for selecting initial codebook vectors and its effective handling of nominal attributes. Our experiments, conducted on 17 text classification datasets with four different instance reduction algorithms, confirm SLVQ’s effectiveness. It significantly enhances the kNN’s classification accuracy of reduced datasets. In our empirical study, the SLVQ method improved the performance of these datasets, achieving average classification accuracies of 82.55%, 84.07%, 78.54%, and 83.18%, compared to the average accuracies of 76.25%, 79.62%, 66.54%, and 78.19% achieved by non-fine-tuned datasets, respectively.
Revolutionizing power grid loss prediction with advanced hybrid time series deep learning model
Authors: Senthamil Selvi M. , Senthamizh Selvi R. , Subbaiyan Saranya , Murshitha Shajahan M.S.
Published: April 2024

Accurate prediction of grid loss in power distribution networks is pivotal for efficient energy management and pricing strategies. Traditional forecasting approaches often struggle to capture the complex temporal dynamics and external influences inherent in grid loss data. In response, this research presents a novel hybrid time-series deep learning model: Gated Recurrent Units with Temporal Convolutional Networks (GRU-TCN), designed to enhance grid loss prediction accuracy. The proposed model integrates the temporal sensitivity of GRU with the local context awareness of TCN, exploiting their complementary strengths. A learnable attention mechanism fuses the outputs of both architectures, enabling the model to discern significant features for accurate prediction. The model is evaluated using well-established metrics across distinct temporal phases: training, testing, and future projection. Results showcase Resulting in encouraging Figures for mean absolute error, root mean squared error, and mean absolute percentage error, the model’s capacity to capture both long-term trends and transitory patterns. The GRU-TCN hybrid model represents a pioneering approach to power grid loss prediction, offering a flexible and precise tool for energy management. This research not only advances predictive accuracy but also lays the foundation for a smarter and more sustainable energy ecosystem, poised to transform the landscape of energy forecasting.
An optimization of the fully fuzzy transportation problem with triangular type-2 fuzzy parameters using novel ranking technique
Authors: Jansi Rani J. , Manivannan A.
Published: April 2024

This paper focuses on solving the fully fuzzy transportation problem in which the parameters are triangular Type-2 fuzzy numbers due to the instinctive of human imprecision. To deal with uncertainty more precisely, a triangular Type-1 fuzzy transportation problem is reformed as a transportation problem with triangular Type-2 fuzzy parameters in this paper. In order to compare triangular Type-2 fuzzy numbers, a new ranking(ordering) technique is proposed by extending the Yager’s function. However, two efficient algorithmic approaches namely, triangular Type-2 fuzzy zero suffix method (TT2FZSM) and triangular Type-2 fuzzy zero average method (TT2FZAM) are proposed to generate the initial transportation cost of the fully triangular Type-2 fuzzy transportation problem. Both TT2FZSM and TT2FZAM are converging towards an optimal solution. In addition to TT2FZSM and TT2FZAM, the modified distribution method is applied to ensure optimality. Subsequently, we carry out a comprehensive discussion of the obtained results to establish the validation of the proposed approach.
Relatedness in zero-determinant strategies
Authors: Atef Shimaa , El-Seidy Essam , Abd El-Salam Salsabeel M.
Published: April 2024

Relatedness is necessary and causal in the development of social life. Interlayer relatedness is a measure of how one player’s decisions affect the decisions of other players in the game. The relatedness can be positive or negative. We had to determine how effective each strategy was under specific conditions, and how the correlation between players affected their payoffs. In this paper, we analytically study the strategies that enforce linear payoff relationships in the Iterated Prisoner’s Dilemma (IPD) game considering both a relatedness factor. As a result, we first reveal that the payoffs of two players and three players can be represented by the form of determinants as shown by Press and Dyson even with the factor.
The degree of importance for partially labeled incomplete real-valued data with application to semi-supervised attribute reduction
Authors: Su Xue , Chen Lijun
Published: April 2024

Incomplete real-valued data often misses some labels due to the high cost of labeling data. This paper investigates for partially labeled incomplete real-valued data and considers its application in semi-supervised attribute reduction. There are two decision information systems (DISs) in a partially labeled incomplete real-valued data DIS (p-IRVDIS): a labeled incomplete real-valued data DIS (l-IRVDIS) and a unlabeled incomplete real-valued data DIS (u-IRVDIS). The degree of importance on an attribute subset in a p-IRVDIS are defined using an indistinguishable relation and conditional information entropy. It is the weighted sum of l-IRVDIS and u-IRVDIS using the missing rate of label to measure p-IRVDIS uncertainty. Based on the degree of importance, an adaptive semi-supervised attribute reduction algorithm in a p-IRVDIS is proposed. This algorithm can automatically adapt to various missing rates of label. The experimental results on 8 datasets reveal that the proposed algorithm performs statistically better than some state-of-the-art algorithms.
GraphECC: Enhancing GraphSMOTE with enhanced complementary classifier
Authors: Xu Liwen , Chen Jiali
Published: April 2024

Node classification in graph learning faces significant challenges due to imbalanced data, particularly for under-represented samples from minority classes. To address this issue, existing methods often rely on synthetic minority over-sampling techniques, introducing additional complexity during model training. In light of the challenges faced, we introduce GraphECC, an innovative approach that addresses numerical anomalies in large-scale datasets by supplanting the traditional CE loss function with an Enhanced Complementary Classifier (ECC) loss function’a novel modification to the CCE loss. This alteration ensures computational stability and mitigates potential numerical anomalies by incorporating a slight offset in the denominator during the computation of the complementary probability distribution. In this paper, we present a novel training paradigm, the Enhanced Complementary Classifier (ECC), which offers “imbalance defense for free” without the need for extra procedures to improve node classification accuracy.The ECC approach optimizes model probabilities for the ground-truth class, akin to the cross-entropy method. Additionally, it effectively neutralizes probabilities associated with incorrect classes through a “guided” term, achieving a balanced trade-off between the two aspects. Experimental results demonstrate that our proposed method not only enhances model robustness but also surpasses the widely used cross-entropy training objective.Moreover, we demonstrate the versatility of our method by seamlessly integrating it with various well-known adversarial training techniques, resulting in significant gains in robustness. Notably, our approach represents a breakthrough, as it enhances model robustness without compromising performance, distinguishing it from previous attempts.The code for GraphECC can be accessed from the following link:https://github.com/12chen20/GraphECC.
Heating load prediction in buildings using decision tree machine learning method
Authors: Yan Huiming , Yan Zilin , Wang Weiling , Liu Shuyue
Published: April 2024

In recent years, the burgeoning imperative of energy-efficient building management practices has surged dramatically, underscoring an urgent mandate for comprehensive studies that integrate cutting-edge optimization algorithms with precise heating load forecasting techniques. These studies are not merely endeavors; they represent concerted efforts to increase building energy efficiency and address mounting concerns regarding sustainability and resource utilization. In the intricate domain of heating, ventilation, and air conditioning (HVAC) systems, energy optimization challenges are being meticulously confronted through rigorous exploration and the application of innovative problem-solving methodologies. This pioneering study introduces groundbreaking methodologies by seamlessly integrating two state-of-the-art optimization algorithms— the Red Fox Optimization and the Golden Eagle Optimizer— with the Decision Tree model. This fusion is aimed at enhancing the accuracy of heating load predictions and streamlining HVAC system optimization processes, marking a significant leap toward achieving heightened energy efficiency and operational efficacy in building management practices. The study emphasizes the significance of precise heating load prediction in advancing energy efficiency, realizing cost savings, and fostering environmental sustainability in building management. Furthermore, it delves into the multifaceted impact of various building features on heating load, encompassing variables such as glazing area, orientation, height, relative compactness, roof area, surface area, and wall area. These insights furnish actionable intelligence for refined decision-making processes in both building design and operation. Based on the results, the DT single model experienced the weakest performance among the three models, with R2 = 0.975 and RMSE = 1.608. The model DTFO (DT + FOX) achieves an extraordinary R2 value of 0.996 and RMSE value of 0.961 for heating load prediction, surpassing the performance benchmarks set by other models. This achievement holds considerable promise for aiding engineers in crafting energy-efficient buildings, particularly within the swiftly evolving landscape of smart home technologies.
Development of nanosized graphene material for neutron intensity enhancement below cold neutron energy
Authors: Teshigawara Makoto , Ikeda Yujiro , Muramatsu Kazuo , Sutani Koichi , Fukuzumi Masafumi , Noda Yohei , Koizumi Satoshi , Saruta Koichi , Otake Yoshie
Published: April 2024

We have been developing nanosized graphene, called graphene flower, as a material that induces coherent scattering very cold neutrons. Previous experiments have found that the seed part of the graphene flower is more effective than the petal part in increasing the coherent scattering. Based on these results, we found that further modification of the graphene flower to increase the seed portion increased the total cross-section, although it did not reach the level of nanodiamonds.
Association of Misfolded α-Synuclein Derived from Neuronal Exosomes in Blood with Parkinson’s Disease Diagnosis and Duration
Authors: Schaeffer Eva , Kluge Annika , Schulte Claudia , Deuschle Christian , Bunk Josina , Welzel Julius , Maetzler Walter , Berg Daniela
Published: April 2024

Background:

Misfolded α-synuclein can be detected in blood samples of Parkinson’s disease (PD) patients by a seed amplification assay (SAA), but the association with disease duration is not clear, yet.

Objective:

In the present study we aimed to elucidate whether seeding activity of misfolded α-synuclein derived from neuronal exosomes in blood is associated with PD diagnosis and disease duration.

Methods:

Cross-sectional samples of PD patients were analyzed and compared to samples of age- and gender-matched healthy controls using a blood-based SAA. Presence of α-synuclein seeding activity and differences in seeding parameters, including fluorescence response (in arbitrary units) at the end of the amplification assay (F60) were analyzed. Additionally, available PD samples collected longitudinally over 5–9 years were included.

Results:

In the cross-sectional dataset, 79 of 80 PD patients (mean age 69 years, SD = 8; 56% male) and none of the healthy controls ( n = 20, mean age 70 years, SD = 10; 55% male) showed seeding activity (sensitivity 98.8%). When comparing subgroups divided by disease duration, longer disease duration was associated with lower α-synuclein seeding activity (F60: p < 0.001). In the longitudinal analysis 10/11 patients showed a gradual decrease of α-synuclein seeding activity over time.

Conclusions:

This study confirms the high sensitivity of the blood-based α-synuclein SAA applied here. The negative association of α-synuclein seeding activity in blood with disease duration makes this parameter potentially interesting as biomarker for future studies on the pathophysiology of disease progression in PD, and for biologically oriented trials in this field.
Preterm infant with necrotizing enterocolitis and arteritis secondary to streptococcus gallolyticus subspecies pasteurianus
Authors: Stewart J.A. , Culverhouse E. , Harrell Mathew
Published: April 2024

Streptococcus gallolyticus subspecies pasteurianus is a subtype of Streptococcus bovis ( S. bovis) that has become increasingly recognized as a sepsis-causing pathogen in neonates. It is well documented that S. bovis species have a predilection to both cardiac and gastrointestinal tissue, and in adult populations, isolating these organisms in the bloodstream often triggers further evaluation for co-morbid complications such as colon cancer or endocarditis. However, no such guidance currently exists in neonatal literature. We present a case of a preterm infant with S. gallolyticus subsp. pasteurianus bacteremia presenting as necrotizing enterocolitis (NEC) not previously described in the literature. Furthermore, through a complete diagnostic evaluation, including an echocardiogram, our patient was found to have the rare complication of endocarditis.
Overview of randomized controlled trials of moderate to severe traumatic brain injury: A systematic review
Authors: Teasell Robert , Flores-Sandoval Cecilia , Bateman Emma A. , MacKenzie Heather M. , Sequeira Keith , Bayley Mark , Janzen Shannon
Published: April 2024

BACKGROUND: Given the complexity of post-TBI medical, surgical, and rehabilitative care, research is critical to optimize interventions across the continuum of care and improve outcomes for persons with moderate to severe TBI. OBJECTIVE: To characterize randomized controlled trials (RCTs) of moderate to severe traumatic brain injury (TBI) in the literature. METHOD: Systematic searches of MEDLINE, PubMed, Scopus, CINAHL, EMBASE and PsycINFO for RCTs up to December 2022 inclusive were conducted in accordance with PRISMA guidelines. RESULTS: 662 RCTs of 91,946 participants published from 1978 to 2022 met inclusion criteria. The number of RCTs published annually has increased steadily. The most reported indicator of TBI severity was the Glasgow Coma Scale (545 RCTs, 82.3%). 432 (65.3%) RCTs focused on medical/surgical interventions while 230 (34.7%) addressed rehabilitation. Medical/surgical RCTs had larger sample sizes compared to rehabilitation RCTs. Rehabilitation RCTs accounted for only one third of moderate to severe TBI RCTs and were primarily conducted in the chronic phase post-injury relying on smaller sample sizes. CONCLUSION: Further research in the subacute and chronic phases as well as increasing rehabilitation focused TBI RCTs will be important to optimizing the long-term outcomes and quality of life for persons living with TBI.
Diverse ideas about the value of Official Statistics systems
Authors: Roy Ken
Published: April 2024

This paper offers an initial overview of different ideas about the value of Official Statistics systems. For the purposes of this paper, an Official Statistics ‘system’ is taken to cover the set of organisations (within a country) that are involved in the production, communication, use and governance of Official Statistics. The paper seeks to analyse the stated ambitions for, or the claims about, these systems that are contained within a sample of formal corporate documents mainly produced by different national statistical organisations.

These sources offer a range of diverse ideas about different types of value that societies may secure from having well-functioning Official Statistics systems. There are some foundational ideas of value that are often referenced – including the ambition that good Official Statistics will enable good (or better) decision-making by governments and others, which in turn will generate positive outcomes for society.

The analysis also flags a range of ambitions for wider outcomes that might be secured by well-functioning Official Statistics systems – for example, outcomes for citizens (enabling them to be better informed, represented and empowered), outcomes for governments (contributing to a better more effective governmental process) or outcomes in terms of having better informed public debate.

Collectively these concepts could inform any wider framework developed to communicate the potential value of Official Statistics systems, complementing the more specific expressions of value that might emerge directly from the views and judgements of users.
Measuring absorbed energy in the human auditory system using finite element models: A comparison with experimental results
Authors: Castro-Egler Cristina , Garcia-Gonzalez Antonio , Aguilera Jose A. , Cerezo Pablo M. , Lopez-Crespo Pablo , González-Herrera Antonio
Published: April 2024

BACKGROUND: There are different ways to analyze energy absorbance (EA) in the human auditory system. In previous research, we developed a complete finite element model (FEM) of the human auditory system. OBJECTIVE: In this current work, the external auditory canal (EAC), middle ear, and inner ear (spiral cochlea, vestibule, and semi-circular canals) were modelled based on human temporal bone histological sections. METHODS: Multiple acoustic, structure, and fluid-coupled analyses were conducted using the FEM to perform harmonic analyses in the 0.1–10 kHz range. Once the FEM had been validated with published experimental data, its numerical results were used to calculate the EA or energy reflected (ER) by the tympanic membrane. This EA was also measured in clinical audiology tests which were used as a diagnostic parameter. RESULTS: A mathematical approach was developed to calculate the EA and ER, with numerical and experimental results showing adequate correlation up to 1 kHz. Another published FEM had adapted its boundary conditions to replicate experimental results. Here, we recalculated those numerical results by applying the natural boundary conditions of human hearing and found that the results almost totally agreed with our FEM. CONCLUSION: This boundary problem is frequent and problematic in experimental hearing test protocols: the more invasive they are, the more the results are affected. One of the main objectives of using FEMs is to explore how the experimental test conditions influence the results. Further work will still be required to uncover the relationship between middle ear structures and EA to clarify how to best use FEMs. Moreover, the FEM boundary conditions must be more representative in future work to ensure their adequate interpretation.
Deep learning-based anatomical position recognition for gastroscopic examination
Authors: Su Xiufeng , Liu Weiyu , Jiang Suyi , Gao Xiaozhong , Chu Yanliu , Ma Liyong
Published: April 2024

BACKGROUND: The gastroscopic examination is a preferred method for the detection of upper gastrointestinal lesions. However, gastroscopic examination has high requirements for doctors, especially for the strict position and quantity of the archived images. These requirements are challenging for the education and training of junior doctors. OBJECTIVE: The purpose of this study is to use deep learning to develop automatic position recognition technology for gastroscopic examination. METHODS: A total of 17182 gastroscopic images in eight anatomical position categories are collected. Convolutional neural network model MogaNet is used to identify all the anatomical positions of the stomach for gastroscopic examination The performance of four models is evaluated by sensitivity, precision, and F1 score. RESULTS: The average sensitivity of the method proposed is 0.963, which is 0.074, 0.066 and 0.065 higher than ResNet, GoogleNet and SqueezeNet, respectively. The average precision of the method proposed is 0.964, which is 0.072, 0.067 and 0.068 higher than ResNet, GoogleNet, and SqueezeNet, respectively. And the average F1-Score of the method proposed is 0.964, which is 0.074, 0.067 and 0.067 higher than ResNet, GoogleNet, and SqueezeNet, respectively. The results of the t-test show that the method proposed is significantly different from other methods (p< 0.05). CONCLUSION: The method proposed exhibits the best performance for anatomical positions recognition. And the method proposed can help junior doctors meet the requirements of completeness of gastroscopic examination and the number and position of archived images quickly.
Hyperspectral crop image classification via ensemble of classification model with optimal training
Authors: Lavanya P Venkata , Tripathi Mukesh Kumar , E P Hemand , K Sangeetha , Ramesh Janjhyam Venkata Naga
Published: April 2024

Agriculture is a significant source of income, and categorizing the crop has turned into vital factor that aids more in the crop production sector. Traditionally, crop development stage determination is done manually by eye inspection. However, producing high-quality crop type maps using modern approaches remains difficult. In this paper, the hyperspectral crop image classification model is proposed that includes four stages, they are (a) preprocessing, (b) segmentation, (c) feature extraction and (d) classification. In the preprocessing step, the hyperspectral image is provided as input, where the filtering process will carried out using median filtering. The filtered image is then used as the segmentation’s input. The image is segmented in the segmentation step using the enhanced entropy-based fuzzy c-means technique. Subsequently, spectral spatial features and vegetation index-based features are derived from segmented images. The final step is the classification, where the ensemble of classification model will be used that includes models like Convolutional Neural Networks (CNN), Deep Maxout (DMO), Recurrent Neural Networks (RNN), and Bidirectional Gated Recurrent Unit (Bi-GRU), respectively. The proposed Self Improved Tasmanian devil Optimization (SI-TDO) approach has optimally adjusted the Bi-GRU model’s training weights to enhance ensemble classification performance. Finally, the effectiveness of the proposed SI-TDO method compared to the traditional algorithm is examined for several metrics. The SI-TDO obtained the greatest accuracy of 94.68% in training rate 80, while other existing models have the lowest ratings.
The relationship between received and perceived social support with ways of coping in nurses
Authors: Nazari Shima , Zamani Aida , Farokhnezhad Afshar Pouya
Published: April 2024

BACKGROUND: Stress is inevitable in the nursing profession, and ways of coping are one of the solutions to reduce stress. Social support is one of the influencing factors on coping strategies, but there is probably a difference between the effects of received or perceived social support on coping strategies. OBJECTIVE: The aim was to investigate the relationship between received and perceived social support with ways of coping in nurses. METHODS: This is a cross-sectional descriptive-correlational study. The sample consisted of 292 nurses by simple random sampling. The data collection tool included Zimmet’s multidimensional perceived social support scale, McCain and Marklin’s perceived social support questionnaire, and Lazarus’ ways of coping questionnaire. Data were analyzed using Pearson correlation and multiple regression tests in SPSS v.22. RESULTS: The results showed that received social support (β= 0.20, p < 0.001) and perceived social support (β= 0.35, p < 0.001) are able to explain changes in the dimension of seeking social support. Received social support (β= 0.16, p = 0.005) and perceived social support (β= 0.22, p < 0.001) are able to explain changes after positive reappraisal, but only received social support could explain changes in accepting responsibility (β= 0.15, p = 0.01). CONCLUSION: This study showed that received and perceived social support have a significant correlation with ways of coping, but only able to explain the changes in social support seeking dimensions, positive reappraisal, accepting responsibility. Both types of social support are necessary, but that received social support covered more adaptive coping strategies.
The effect of the COVID-19 pandemic on the caregiving burden of individuals caring for patients with chronic neurological diseases
Authors: Baytak Sevgi , Dogru-Huzmeli Esra , Uysal Halil Hakan
Published: April 2024

BACKGROUND: Caring for neurological patients poses a serious burden on caregivers, even in normal times. It is likely that this burden has become even more painful post-COVID-19 pandemic. OBJECTIVE: This study evaluates the effect of the COVID-19 pandemic on the care burden of those who care for patients with chronic neurological diseases. METHODS: The study included 50 patients and 50 caregivers. All the tests were conducted twice, it was answered firstly, according to the pre-COVID-19 pandemic, and secondly, during the COVID-19 pandemic period. The depression levels of the patients and caregivers were assessed with the Beck Depression Scale, their sleep quality with the Pittsburgh Sleep Quality Index, their quality of life was assessed with the RAND 36-Item Health Survey, and caregivers’ burden of care was evaluated with the Zarit Caregiver Burden Scale. RESULTS: It was observed at the end of the study that the depression levels of patients (p = 0.00), and caregivers (p = 0.00) increased post-COVID-19 pandemic, the sleep quality of caregivers (p = 0.00), and patients (p = 0.02) decreased post-COVID-19 pandemic. On the other hand, it was determined that caregivers’ burden of care increased (p = 0.00). Sub-parameters of the RAND 36-Item Health Survey, physical functioning (p = 0.02), role limitations due to physical health (p = 0.00), and role limitations due to emotional problems (p = 0.03) decreased for caregivers. CONCLUSION: In summary, we concluded that the well-being and psychological status of both patients and their caregivers were adversely affected by the COVID-19 pandemic.
Feature shared multi-decoder network using complementary learning for Photon counting CT ring artifact suppression
Authors: Cui Wei , Lv Haipeng , Wang Jiping , Zheng Yanyan , Wu Zhongyi , Zhao Hui , Zheng Jian , Li Ming
Published: April 2024

BACKGROUND:

Photon-counting computed tomography (Photon counting CT) utilizes photon-counting detectors to precisely count incident photons and measure their energy. These detectors, compared to traditional energy integration detectors, provide better image contrast and material differentiation. However, Photon counting CT tends to show more noticeable ring artifacts due to limited photon counts and detector response variations, unlike conventional spiral CT.

OBJECTIVE:

To comprehensively address this issue, we propose a novel feature shared multi-decoder network (FSMDN) that utilizes complementary learning to suppress ring artifacts in Photon counting CT images.

METHODS:

Specifically, we employ a feature-sharing encoder to extract context and ring artifact features, facilitating effective feature sharing. These shared features are also independently processed by separate decoders dedicated to the context and ring artifact channels, working in parallel. Through complementary learning, this approach achieves superior performance in terms of artifact suppression while preserving tissue details.

RESULTS:

We conducted numerous experiments on Photon counting CT images with three-intensity ring artifacts. Both qualitative and quantitative results demonstrate that our network model performs exceptionally well in correcting ring artifacts at different levels while exhibiting superior stability and robustness compared to the comparison methods.

CONCLUSIONS:

In this paper, we have introduced a novel deep learning network designed to mitigate ring artifacts in Photon counting CT images. The results illustrate the viability and efficacy of our proposed network model as a new deep learning-based method for suppressing ring artifacts.
Effect of Almond and Thyme Oils on the Nutritional Profile of Broiler and Quail Meat
Authors: Samar  , Yasmeen Roheela , Gillani Syeda Shaista , Iqbal Syeda Saira , Usman Shumaila
Published: April 2024

Broiler and quail farming have experienced significant growth in Pakistan. Broiler chickens are primarily raised for their meat, whereas quails are raised for both their meat and eggs. This study aims to assess the quality and shelf life of broiler and quail meat when treated with thyme and almond essential oils and stored under refrigeration at 4℃. Proximate analysis was conducted on the quail and broiler meat samples, assessing moisture, ash, protein, and fat content to evaluate their nutritional value. Nanoemulsions of thyme and almond oil were prepared at two different concentrations (15% and 25%) and applied to the meat samples, which were then divided into five groups: a control group and four treatment groups. The four treatment groups were labelled as 15% thyme, 25% thyme, 15% almond, and 25% almond. The meat samples were analyzed for their proximate composition (moisture, ash, protein, and fat) and pH levels on the first, seventh, and twenty-first days of the experiment. Fresh samples of chicken and quail meat exhibited significant differences in moisture, ash, proteins, and fats, while pH levels remained relatively stable. On the 7th and 21st days, there was an observable increase in ash, protein, and fat values across all experimental groups, while pH levels remained relatively unchanged. Broiler meat had the highest protein content, while quail meat contained the highest fat levels. Among the treatments, it was observed that thyme oil at a 25% concentration yielded the best results for preserving nutritional values close to their initial levels when compared to the other experimental groups. Based on the study's findings, it is reasonable to conclude that thyme oil, in comparison to almond oil, maybe a more effective option for preserving meat and extending the shelf life of stored meat.
Blockchain for Healthcare Management Systems : A Survey on Interoperability and Security
Authors: Ch Gopi  , D Saikumar  , G Premsagar  , MD Noouman
Published: April 2024

In recent years it has been shown that the secure exchange of medical information significantly benefits people’s life quality, improving their care and treatment. The interoperability of the entire healthcare ecosystem is a constant challenge, and even more, with all the risks posed to the security of healthcare information. Blockchain technology is emerging as one of the main alternatives when it comes to finding a balance in the healthcare ecosystem. However, the constant development of new Blockchain technologies and the evolution of healthcare systems make it difficult to find established proposals. From an architectural point of view, the design of blockchain-based solutions requires trade-offs e.g., security and interoperability. This paper focuses on two main objectives, in the first one, it was carried out a Systematic Literature Review for exploring architectural mechanisms used to support the interoperability and security of Blockchain-based Health Management Systems. Taking into account of results, a series of scenarios were generated where these mechanisms can be used along with their context, issues, and various architectural concerns (interoperability and security). In the second objective, a high-level architecture and its validation were proposed through an experiment for the whole process of developing a Domain Specific Language, using the Model Driven Engineering methodology for specific Smart Contracts.
Utilizing “E-Cooperative” Financial Technology For Business Sustainability
Authors: Elya Dasuki Rima , Khairil Bustaman Muhammad
Published: April 2024

The COVID-19 pandemic has had an impact on cooperative business activities. Business management models and mass marketing and direct selling are difficult to implement because of the high risk of spreading COVID-19 through direct interaction. Therefore, innovative efforts are needed so that the cooperative's business is running well so that the sustainability of the cooperative's business is maintained. Utilization of information technology is an alternative to solve the problem   impact of the COVID-19 pandemic through the development of e-cooperatives, which are information technology-based media to facilitate virtual cooperative business management. E-cooperative is an alternative that allows the management of cooperatives with less contact. The stages of development are carried out through; identification of requirements/requirements definition, system and software design, implementation and unit testing, integration and system testing, operation and maintenance. The implementation of the e_cooperative application is expected to help manage cooperatives effectively and efficiently and facilitate cooperative management so that it has a positive impact on the business development of members, so that cooperatives and their members can remain productive and carry out economic activities during the COVID-19 pandemic by running a less contact economy. This paper is an introductory paper that forms the basis for research preparation so that research is carried out systematically
ОРГАНІЗАЦІЯ ЛОГІСТИЧНИХ ПРОЦЕСІВ ПІДПРИЄМСТВА В УМОВАХ ВІЙНИ: ПРОБЛЕМИ ТА РІШЕННЯ
Authors: Кирилюк Ірина , Сокур Анастасія
Published: April 2024

Стаття присвячена питанню організації логістичних процесів у воєнних умовах. Виокремлено ключові проблеми з якими стикаються підприємства під час здійснення логістичних процесів, визначені збитки логістичної інфраструктури. Проаналізовано їх вплив на вартість поставок, адаптивність української логістичної інфраструктури до непередбачуваних подій, а також важливість логістики в забезпеченні життєво необхідними ресурсами суспільства. Розглядаються шляхи оптимізації логістичної системи з метою підвищення її ефективності в умовах війни. Аналізується ефективність застосування цих підходів у воєнний період та їх вплив на логістичні процесів. Стаття має на меті сприяти розумінню важливості логістичної діяльності під час воєнного стану та надати практичні рекомендації для підприємств у цих умовах.
PROSPECTS FOR THE DEVELOPMENT OF THE OIL REFINING INDUSTRY IN THE CONDITIONS OF THE GLOBAL MARKET
Authors:   The National University of Water and Environmental Engineering, Habrylevych Oleksii , Nikytenko Dmytro ,   The National University of Water and Environmental Engineering
Published: April 2024

This paper presents a comprehensive analysis of the present condition of the oil refining industry, delving into its intricacies and nuances. It particularly concentrates on dissecting the dynamics of the global market for petroleum products and elucidating its ramifications on the broader energy landscape. Through meticulous analysis and scrutiny, this study navigates through the multifaceted dimensions of the industry, shedding light on its various facets and complexities. Additionally, it elucidates the intricate interplay between the oil refining sector and the larger energy sector, highlighting the symbiotic relationship and the consequential effects on global energy markets. This paper offers a nuanced exploration of the current state of the oil refining industry, with a robust focus on the global market for petroleum products and its profound influence on the energy sector. Utilizing a multifaceted approach, the study integrates both quantitative and qualitative research methodologies to comprehensively analyze the subject matter. Through a blend of rigorous statistical analysis, extensive literature review, and expert assessments, the research uncovers intricate patterns and trends within the industry. By synthesizing diverse sources of data and insights, the study provides a holistic understanding of the challenges and opportunities facing the oil refining sector in today's dynamic economic landscape. The results of this research unveil a plethora of formidable challenges confronting the oil refining industry, ranging from the subdued competitiveness of producers to the pervasive reliance on external factors and the imperative for technological advancement. Furthermore, the analysis illuminates promising avenues for industry advancement, encompassing initiatives to diminish import dependency and integrate novel energy-efficient technologies. Leveraging these insights, the paper advocates for the adoption of strategic management approaches within the oil refining sector to foster sustainability and bolster competitiveness amidst the ever-evolving landscape of contemporary economic paradigms.
Evaluating Road Crash Severity Prediction with Balanced Ensemble Models
Authors: Roudnitski Alexei
Published: April 2024

This study evaluates the performance of an ensemble of five ML models (Random Forest, XGBoost, AdaBoost, LightGBM and CatBoost) on crash data from New South Wales, Australia. The model is evaluated based on ROC-AUC score, with a result of 0.68, indicating a moderate level of predictive accuracy. Feature importance analysis reveals the key predictors being the vehicle type involved, with sedans/hatchbacks and motorcycles being the most common in fatal crashes, and the collision type, with vehicle-to-object impacts often leading to fatalities. Furthermore, fatal crashes occur more on Saturdays, in country non-urban LGAs and speed limits of 100 km/h as the most usual settings for fatal accidents.
2023 Outstanding Young Researcher Award Recipient: A Review Study on Interactions Between the Deep-Sea Benthonic Mining Vehicle and the Marine Sediment
Authors: Chen Xuguang , Xu Tianyi , Li Gen , Luan Lubao
Published: April 2024

Developments of modern industrialization heavily depend on energy resources, leading to the rapid decrease of land-mineral resources. As a result, the exploitation of marine resources, including the coastwise oil and gas resources, and the benthonic metallic mineral resources in the abysmal sea, has gained its popularity in the industrial community. This paper mainly focuses on developments of exploiting the deep-sea benthonic metallic mineral resources and presents a review on recent studies about interactions between the deep-sea benthonic mining vehicle and the marine sediment. For that, the recently reported benthonic crawler-mounted vehicles are summarized firstly. Following that, responses of soils subjected to movements of the crawler-mounted vehicle, characteristics of pressure-sinkage relationship and of shear stress-shear displacement relationship reported in the literature are presented. Finally, some existing problems that existed in the current studies and future prospects about the deep-sea mining vehicle are presented in the conclusion. This study is capable of providing a basic understanding of the current researching status of the deep-sea benthonic mining vehicle.
Future Translators’ Linguistic and Non-linguistic Competencies and Skills in The Age of Neural Machine Translation and Artificial Intelligence: A Content Analysis
Authors: Alharbi Wael
Published: April 2024

Artificial Intelligence (AI) and machine learning (ML) are disrupting the way millions of people work. According to the Future of Jobs Report published by the World Economic Forum (WEF 2022), the Fourth Industrial Revolution will result in the disappearance of more than 75 million jobs by 2025. While this is considered bad news to many, the report also predicts the creation of 133 million jobs for the same reason. Among the at-risk jobs are translation and language services jobs which have witnessed a sharp drop in demand due to the adoption of AI and ML technologies in machine translation systems making them capable of producing translations that match the translations produced by expert human translators. Using content analysis method of online translation job advertisements in Saudi Arabia, this study sheds light on the current in-demand translator linguistic and non-linguistic competences and skills from the perspective of employers and matches those with the well-established translator competence models in literature. Through a content analysis of 213 online job advertisements, the study identifies five key competency domains: Language and Culture, Personal and Interpersonal, Service Provision, Technology, and Translation Knowledge. While language proficiency remains crucial, there is a growing emphasis on "soft skills," project management, technological adaptability, and ethical practice. The findings reveal a partial alignment with existing competency models but also highlight potential gaps, particularly regarding strategic planning and document analysis skills. The study underscores the need for translator education to adapt to the evolving demands of the industry, integrating traditional skills with emerging technologies and fostering a culture of lifelong learning. By bridging the gap between academic training and industry expectations, we can better equip future translators to navigate the complexities of the profession and thrive in the age of Neural Machine Translation and AI.
The EU’s Sustainable Product Initiative: Enhancing the Readiness of the Furniture Business in Ukraine
Authors:   Ivan Franko National University of Lviv, Vrublevska Olena
Published: April 2024

This study has been inspired by a set of new rules which are expected to be implemented within the EU’s Ecodesign for Sustainable Products Regulation (ESPR). Considering the possible market risks for the Ukrainian furniture industry, and that includes both exporters and non-exporters (within the context of the harmonisation of the rules), this research sought to capture and understand the readiness of companies as regards the changes on the EU market, along with those companies’ sentiments and ability to cope with the upcoming inherent challenges. The survey of furniture producers presented in this paper is one of the ﬁrst devoted to the problem of Ukrainian producers’ adaptability to the ESPR, made all the more challenging due to the burden of the ongoing war. This study has brought to light rather positive industry sentiments regarding furniture production volume and the competitive position of companies within the furniture industry, but there is also a lack of company awareness of the full range of ESPR measures and tools, hence there is a risk of underestimating those measures and tools’ possible impact along with the risk of a delayed response. Environmental sustainability values should be given higher priority than they are currently in companies’ current marketing strategies, and the effectiveness of supply chain management practices will inevitably have to be questioned by company leaders. Possible adaptation measures aimed at circularity are not sufﬁciently perceived, while attention is focused more on the use of materials, energy, and environmental pollution.