Search

Introduction Peritoneal dialysis (PD) is an alternative modality to hemodialysis (HD) for end-stage Kidney disease (ESKD) treatment. However, PD is underutilized, and patients’ knowledge about PD is crucial in making an informed decision about the dialysis modality. This study aims to evaluate the knowledge about PD among HD patients and explore the reasons for its underutilization.Materials and methods A cross-sectional study of 108 HD patients was conducted from August to November 2023, at Al-Jaber Dialysis Center in Al-Ahsa. They interviewed to assess the knowledge about PD and explore the reasons for its underutilization.Results The patients’ ages ranged from 20 to 80 years, with a mean age of 44.2 years. Regarding knowledge about PD, 81.5% of the ESKD patients had heard about other renal replacement modalities, with kidney transplant being the most commonly known option. Only 11.1% were aware of both kidney transplants and PD. 60.2% of patients reported that their nephrologists had discussed other treatment options with them. However, overall knowledge about PD among patients was relatively low, with 43.5% having good knowledge and 56.5% having poor knowledge. Lack of information, considering HD as a better method, and fear of complications were the primary reasons for not considering PD as the first treatment option.Conclusion This study highlights the inadequate knowledge about PD among HD patients and identifies factors contributing to the underutilization of PD. Lack of information, misconceptions about the effectiveness and safety of PD, and preference for HD were significant barriers. Enhancing patient education and addressing misconceptions are crucial in promoting PD as a viable treatment option for ESKD, thereby improving patients’ quality of life.

This study addresses the challenges of flash flood susceptibility mapping in Yemen’s Qaa’Jahran Basin, characterized by complex terrain and limited hydro-meteorological data. To enhance predictive accuracy, we integrate metaheuristic feature selection with ensemble learning models. Initially, fifteen flash flood variables were retrieved using Geographic Information System (GIS) based remote sensing, setting the stage for a novel feature selection algorithm. Then, the Memo Search Algorithm (MSA), a metaheuristic approach is proposed to efficiently reduce feature space. Through comprehensive comparisons with established algorithms such as the Artificial Bee Colony (ABC) and Gray Wolf Optimizer (GWO), MSA refined the selection, identifying 'elevation’ and 'distance to streams’ as optimal factors. Statistical validations using the Friedman and Wilcoxon signed-rank tests confirmed the significant superiority of MSA over competing algorithms. Ensemble classifiers (bagging, boosting, stacking) were then applied to the reduced feature space. Comprehensive evaluation revealed the boosting ensemble with MSA outperformed traditional techniques reaching 98.75% accuracy, 0.9896 Area Under the Curve (AUC), and 98.95% the harmonic mean of the precision and recall (F1-score). Precision in identifying high-risk flash flood zones was underlined via spatial prediction, confirming the integrated framework’s ability to significantly improve forecast accuracy. The findings aid disaster management with powerful geographic mapping in data-poor regions. The proposed framework is adaptable globally for flash flood-prone areas with similar constraints. As climate change is expected to increase extreme rainfall events, communities globally will need robust data-driven methodologies for flash flood susceptibility mapping. The Key recommendations of the current study include investigating hybrid feature selection methods to better enhance predictive inputs and analyzing transferability across hydro-climatic zones.

This paper presents the challenges and options for reducing GHG emissions in the cement sector in the Kingdom of Saudi Arabia (KSA) and analyses short- and long-run causal relationships between emissions from cement production and their key factors through developing a Vector Error Correction Model (VECM). The cement industry in the KSA has been expanding rapidly due to the annual rise in the number of tourists, the development of residential and commercial sectors, and the government’s strong drive towards economic diversification under Vision 2030. With this growth of the sector, however, arises an increase in Greenhouse Gas (GHG) emissions. The growing population coupled with an objective to grow economically subsequently demands further jobs and infrastructure. The local production of cement depends on many factors including demography, urbanization, tourism, GDP and interactions. The unit root test results prove no variables have unit roots “at first differences”. Equilibrium relationship results displays cement emissions are positively correlated with population, number of tourists, GDP, energy consumption and negatively correlated with urban population. Causality test results demonstrates cement production have a long-run causal relationship with population, urbanization, GDP, and energy consumption and negative causal relationship with urban population. Therefore, GDP and energy consumption must be clean, and policymakers must accelerate the transition to low- or zero-carbon economies and energy sources to cut cement industry emissions. Future population growth must be accommodated in metropolitan areas to limit cement emissions. The results also show a unidirectional causal relationship between tourists and emissions. Therefore, public and private sectors should offer services with low carbon footprints and support initiatives to reduce tourist-induced emissions. A set of recommendations were also provided to mitigate GHG emissions in cement industries.

Owing to the recent global emergence of COVID-19 and its effects on education, particularly in Saudi Arabia, schools in the Kingdom have adopted online virtual learning styles. This is a quick reaction to the government's decision to close all schools to control the spread of the coronavirus. This study examines the effects of virtual learning on architectural engineering and architecture education in the context of Saudi Arabia's Pandemic Lockdown. The study collected population-representative survey data from 146 eligible respondents via a five-scale Likert questionnaire survey distributed among Saudi university students. The statistical package SPSS 28.0 was used to analyze the results, which included simple descriptive statistics and Spearman's correlation. The study discovered that the pandemic had affected architecture and architectural engineering teaching and learning pedagogy, online assessment and examination, the environment and resources for teaching and learning, and the future application of online teaching and learning. The study reveals a strong reluctance to recommend online learning in the future for both studio- and lab-type lessons, because the available online learning hardware tools for studio-type instruction are insufficient. The findings of this study will help handle various architecture and architectural engineering courses in times of emergency that necessitates switching to online classes. [ABSTRACT FROM AUTHOR]

Context: When the epidemic first broke out, no specific treatment was available for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The urgent need to end this unusual situation has resulted in many attempts to deal with SARS-CoV-2. In addition to several types of vaccinations that have been created, anti-SARS-CoV-2 monoclonal antibodies (mAbs) have added a new dimension to preventative and treatment efforts. This therapy also helps prevent severe symptoms for those at a high risk. Therefore, this is one of the most promising treatments for mild to moderate SARS-CoV-2 cases. However, the availability of anti-SARS-CoV-2 mAb therapy is limited and leads to two main challenges. The first is the privacy challenge of selecting eligible patients from the distribution hospital networking, which requires data sharing, and the second is the prioritization of all eligible patients amongst the distribution hospitals according to dose availability. To our knowledge, no research combined the federated fundamental approach with multicriteria decision-making methods for the treatment of SARS-COV-2, indicating a research gap. Objective: This paper presents a unique sequence processing methodology that distributes anti-SARS-CoV-2 mAbs to eligible high-risk patients with SARS-CoV-2 based on medical requirements by using a novel federated decision-making distributor. Method: This paper proposes a novel federated decision-making distributor (FDMD) of anti-SARS-CoV-2 mAbs for eligible high-risk patients. FDMD is implemented on augmented data of 49,152 cases of patients with SARS-CoV-2 with mild and moderate symptoms. For proof of concept, three hospitals with 16 patients each are enrolled. The proposed FDMD is constructed from the two sides of claim sequencing: central federated server (CFS) and local machine (LM). The CFS includes five sequential phases synchronised with the LMs, namely, the preliminary criteria setting phase that determines the high-risk criteria, calculates their weights using the newly formulated interval-valued spherical fuzzy and hesitant 2-tuple fuzzy-weighted zero-inconsistency (IVSH2-FWZIC), and allocates their values. The subsequent phases are federation, dose availability confirmation, global prioritization of eligible patients and alerting the hospitals with the patients most eligible for receiving the anti-SARS-CoV-2 mAbs according to dose availability. The LM independently performs all local prioritization processes without sharing patients' data using the provided criteria settings and federated parameters from the CFS via the proposed Federated TOPSIS (F-TOPSIS). The sequential processing steps are coherently performed at both sides. Results and Discussion: (1) The proposed FDMD efficiently and independently identifies the high-risk patients most eligible for receiving anti-SARS-CoV-2 mAbs at each local distribution hospital. The final decision at the CFS relies on the indexed patients' score and dose availability without sharing the patients' data. (2) The IVSH2-FWZIC effectively weighs the high-risk criteria of patients with SARS-CoV-2. (3) The local and global prioritization ranks of the F-TOPSIS for eligible patients are subjected to a systematic ranking validated by high correlation results across nine scenarios by altering the weights of the criteria. (4) A comparative analysis of the experimental results with a prior study confirms the effectiveness of the proposed FDMD. Conclusion: The proposed FDMD has the benefits of centrally distributing anti-SARS-CoV-2 mAbs to high-risk patients prioritized based on their eligibility and dose availability, and simultaneously protecting their privacy and offering an effective cure to prevent progression to severe SARS-CoV-2 hospitalization or death. [ABSTRACT FROM AUTHOR]

In the fuzzy multicriteria decision-making approach, a committee of decision-makers is usually involved in the assessment of the suitability of different alternatives based on the evaluation criteria by using linguistic terms and their equivalent fuzzy numbers. In this context, researchers have developed the Pythagorean fuzzy set (PFS) to overcome the limitation of intuitionistic fuzzy set in the description of decision-maker information such as imposing restrictions on the representation of membership and nonmembership grades. On the one hand, PFS still does not have sufficient ability and flexibility to deal with such issues. On the other hand, multipolar technology is used to operate large-scale systems in real-life situations, especially in dealing with dissatisfaction and indeterminacy grades for the alternatives of the reference set. Thus, m-polar fuzzy set is utilized and applied with other fuzzy sets because of its remarkable ability as a tool for depicting fuzziness and uncertainty under multipolar information in many circumstances. With the practical features of m-polar fuzzy set in combination with PFS, this paper employs it to extend two considerable MCDM methods, namely, fuzzy decision by opinion score method and fuzzy-weighted zero inconsistency. Such extensions, called Pythagorean m-polar fuzzy-weighted zero-inconsistency (Pm-PFWZIC) method and Pythagorean m-polar fuzzy decision by opinion score method (Pm-PFDOSM), are formulated to weight the evaluation criteria followed by alternative ranking progressively. The research methodology is presented as follows. Firstly, the mechanisms of Pm-PFWZIC and Pm-PFDOSM are formulated and integrated into the development phase. Secondly, the description of the real-world case study of the evaluation and benchmarking of the sign language recognition systems is adapted and presented. The result of Pm-PFWZIC shows that the criterion of 'finger movements' has the highest weight amongst the rest of the criteria, whereas 'misclassification error' has the lowest weight. In the ranking results, a variation of ranking is scored by each expert, and group decision-making is applied to solve the individual ranking variety. The robustness of the formulated methods is evaluated using systematic ranking, sensitivity analysis and comparison analysis. [ABSTRACT FROM AUTHOR]

Intrusion detection systems (IDSs) employ sophisticated security techniques to detect malicious activities on hosts and/or networks. IDSs have been utilized to ensure the security of computer and network systems. However, numerous evaluation and selection issues related to several cybersecurity aspects of IDSs were solved using a decision support approach. The approach most often utilized for decision support in this regard is multi-attribute decision-making (MADM). MADM can aid in selecting the most optimal solution from a huge pool of available alternatives when the appropriate evaluation attributes are provided. The openness of the MADM methods in solving numerous cybersecurity issues makes it largely efficient for IDS applications. We must first understand the available solutions and gaps in this area of research to provide an insightful analysis of the combination of MADM techniques with IDS and support researchers. Therefore, this study conducts a systematic review to organize the research landscape into a consistent taxonomy. A total of 28 articles were considered for this taxonomy and were classified into three main categories: data analysis and detection (n = 4), response selection (n = 7) and IDS evaluation (n = 1 7). Each category was thoroughly analyzed in terms of a variety of aspects, including the issues and challenges confronted, as well as the contributions of each study. Furthermore, the datasets, evaluation attributes, MADM methods, evaluation and validation and bibliography analysis used by the selected articles are discussed. In this study, we highlighted the existing perspective and opportunities for MADM in the IDS literature through a systematic review, providing researchers with a valuable reference. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown as reflected in the large number of latent topics generated during this period. The overall sentiment for each lockdown was mostly positive, followed by neutral and then negative. Topic modelling results identified staying at home, quarantine and lockdown as the main aspects of discussion for the first lockdown, whilst importance of health measures and government efforts were the main aspects for the second and third lockdowns. Governments may utilise these findings to understand public sentiment and to formulate precautionary measures that can assure the safety of their citizens and tend to their most pressing problems. These results also highlight the importance of positive messaging during difficult times, establishing digital interventions and formulating new policies to improve the reaction of the public to emergency situations. [ABSTRACT FROM AUTHOR]

Approximately 15% of couples fail to have a child birth despite of regular intercourse for more than 12 month duration without the use of contraceptives. So that we evaluated some parameters that may affect the fertilization. To find the relation of TEX101, vitamin D and Zinc with fertility condition of men and to evaluate how their change affect the men fertility. In case-control study the 90 serum and seminal plasma sample which collected from fertile (n = 45) and infertile (n = 45) men were estimated for TEX101 and vitamin D by the use of ELISA technique but the element zinc was estimated by colorimetric method. Whole blood was collected via vein puncture. We were collect 90 sample of seminal plasma and serum from the fertility center which divided into two groups 45 fertile and 45 infertile men. In present study showed significantly (P<0.001) decrease in infertile group compare to fertile group in serum Vitamin D and seminal plasma TEX101protein and Zinc. This study was showed a significant (P <0.001) positive correlation between TEX101 protein, vitamin D and zinc. From this study can be concluded that zinc element and vitamin D changes affects the semen quality and as a result decrease fertilization rate in couples with male factor infertility. And can be concluded that TEX101testicular protein can be regarded as a good marker of male fertility. [ABSTRACT FROM AUTHOR]

Introduction: Peritoneal dialysis (PD) is an alternative modality to hemodialysis (HD) for end-stage Kidney disease (ESKD) treatment. However, PD is underutilized, and patients' knowledge about PD is crucial in making an informed decision about the dialysis modality. This study aims to evaluate the knowledge about PD among HD patients and explore the reasons for its underutilization., Materials and Methods: A cross-sectional study of 108 HD patients was conducted from August to November 2023, at Al-Jaber Dialysis Center in Al-Ahsa. They interviewed to assess the knowledge about PD and explore the reasons for its underutilization., Results: The patients' ages ranged from 20 to 80 years, with a mean age of 44.2 years. Regarding knowledge about PD, 81.5% of the ESKD patients had heard about other renal replacement modalities, with kidney transplant being the most commonly known option. Only 11.1% were aware of both kidney transplants and PD. 60.2% of patients reported that their nephrologists had discussed other treatment options with them. However, overall knowledge about PD among patients was relatively low, with 43.5% having good knowledge and 56.5% having poor knowledge. Lack of information, considering HD as a better method, and fear of complications were the primary reasons for not considering PD as the first treatment option., Conclusion: This study highlights the inadequate knowledge about PD among HD patients and identifies factors contributing to the underutilization of PD. Lack of information, misconceptions about the effectiveness and safety of PD, and preference for HD were significant barriers. Enhancing patient education and addressing misconceptions are crucial in promoting PD as a viable treatment option for ESKD, thereby improving patients' quality of life.