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In this study, adaptive neuro-fuzzy inference system, and feed forward neural network as two artificial intelligence-based models along with conventional multiple linear regression model were used to predict the multi-station modelling of dissolve oxygen concentration at the downstream of Mathura City in India. The data used are dissolved oxygen, pH, biological oxygen demand and water temperature at upper, middle and downstream of the river. To predict outlet of dissolved oxygen of the river in each station, considering different input combinations as i) 11 inputs parameters for all three locations except, dissolved oxygenat the downstream ii) 7 inputs for middle and downstream except dissolved oxygen, at the target location and lastly iii) 3 inputs for downstream location. To determine the accuracy of the model, root mean square error and determination coefficient were employed. The simulated results of dissolved oxygen at three stations indicated that, multi-linear regression is found not to be efficient for predicting dissolved oxygen. In addition, both artificial intelligence models were found to be more capable and satisfactory for the prediction. Adaptive neuro fuzzy inference system model demonstrated high prediction ability as compared to feed forward neural network model. The results indicated that adaptive neuro fuzzy inference system model has a slight increment in performance than feed forward neural network model in validation step. Adaptive neuro fuzzy inference system proved high improvement in efficiency performance over multilinear regression modeling up to 18% in calibration phase and 27% in validation phase for the best models. [ABSTRACT FROM AUTHOR]

The number of news produced every day is as much as 3 million per day, making readers have many choices in choosing news according to each reader's topic and category preferences. The recommendation system can make it easier for users to choose the news to read. The method that can be used in providing recommendations from the same user is collaborative filtering. Neural collaborative filtering is usually being used for recommendation systems by combining collaborative filtering with neural networks. However, this method has the disadvantage of recommending the similarity of news content such as news titles and content to users. This research wants to develop neural collaborative filtering using sentences BERT. Sentence BERT is applied to news titles and news contents that are converted into sentence embedding. The results of this sentence embedding are used in neural collaboration with item id, user id, and news category. We use a Microsoft news dataset of 50,000 users and 51,282 news, with 5,475,542 interactions between users and news. The evaluation carried out in this study uses precision, recall, and ROC curves to predict news clicks by the user. Another evaluation uses a hit ratio with the leave one out method. The evaluation results obtained a precision value of 99.14%, recall of 92.48%, f1-score of 95.69%, and ROC score of 98%. Evaluation measurement using the hit ratio@10 produces a hit ratio of 74% at fiftieth epochs for neural collaborative with sentence BERT which is better than neural collaborative filtering (NCF) and NCF with news category.

Hospital is one of the most complex organization with highly intensive interaction between stakeholders (patients, nurses, doctors, staff, etc.). In the operation of a hospital, the use of Information technology has been proven to improve effectiveness and efficiency. However, in the majority of cases, the processes to achieve the Strategic Objectives through implementation of Information Technology are full of challenges. Based on the case study in Dharmais Cancer Hospital, there are many symptoms that are identified by this study and lead to 4 issues, namely: lack of ownership from Business users, lack of alignment between business strategy and IT strategy, lack of awareness to use IT as a tool for competitive advantage, and low quality of IT operation performances. In order to solve the issues and support the achievement of Strategic Business Objective through IT, an Enterprise Architecture approach can be used to develop baseline architecture, identify the target architecture, finding the gap, and use the gap as recommendation to solve those issues. The methodology chosen is TOGAF ADM, based on its focus on processes and its flexibility to combine artifacts and approaches that are most suitable for the case. This study develops 7 recommendations to Strengthen Business area of organization, 5 recommendations to Align IT plan with Business Strategy, 16 recommendations to Implement several IT solutions as Competitive Advantage for organization, and 8 recommendations to provide higher performances by enabling Service Management approach for IT Operation. This study also shows how TOGAF ADM can improve the awareness of the business users to the business itself. Doi: 10.28991/esj-2021-01278 Full Text: PDF

Enriching visitors'experiences at religious,historical sites (RHS) is getting more attention than before. This research aims to identify and investigatethe influential factors enriching the visitors'experience at such a site, using Al-Khandaq battle site as the case study. Forty-tworespondents were interviewed at the site and NVivo 12 pro software was used to code and analyze theresponses. The study explored tourist motivations, expectations, and perceptions as the guiding constructs in detailed qualitative analysis. Foremost, learning, reliving the experience of the events of the battle, and Islamic values were the primarymotivation to the historic site visitation. Next, findings reveal the extent to which tourists' expectations can be met through the availability of an open museum, accessibility, and the spatial and experiential simulation of the ancient Al-Khandaq path. Lastly, the need to reconstruct the historical site in spatial planning and experiential dimensions formed the main thrust of theelement of visitors'perception. Based on the foregoing, the study recommends that Al-Khandaq site should be developed traditionally as an open museum, displaying both religious and historical elements of the battle with emphasis on the visual connection to the Prophet's mosque.

Computer bug elimination is an important phase in the software development process. A ticketing system is usually used to classify the identified bug type and to assign a suitable developer. This system is handled manually and error prone. This paper proposes a new bug classification method using the fast string search algorithm. The method searches the error string and compares it to the full text. The approach is deployed to the software development process at PT. Selaras Anugerah Lestari and it results in a significant reduction in the average value of the time required to handle the bugs.

This article discusses the analysis of customer loyalty using three data mining methods: C4.5,Naive Bayes, and Nearest Neighbor Algorithms and real-world empirical data. The data contain ten attributes related to the customer loyalty and are obtained from a national multimedia company in Indonesia. The dataset contains 2269 records. The study also evaluates the effects of the size of the training data to the accuracy of the classification. The results suggest that C4.5 algorithm produces highest classification accuracy at the order of 81% followed by the methods of Naive Bayes 76% and Nearest Neighbor 55%. In addition, the numerical evaluation also suggests that the proportion of 80% is optimal for the training set.

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

This paper presents a method using multiobjective particle swarm optimization (PSO) approach to improve the consistency matrix in analytic hierarchy process (AHP), called PSOMOF. The purpose of this method is to optimize two objectives which conflict each other, while improving the consistency matrix. They are minimizing consistent ratio (CR) and deviation matrix. This study focuses on fuzzy preference matrix as one model comparison matrix in AHP. Some inconsistent matrices are repaired successfully to be consistent by this method. This proposed method offers some alternative consistent matrices as solutions.

Abstract Background Translational and post-translational protein modifications play a key role in the response of plants to pathogen infection. Among the latter, phosphorylation is critical in modulating protein structure, localization and interaction with other partners. In this work, we used a multiplex staining approach with 2D gels to study quantitative changes in the proteome and phosphoproteome of Flavescence dorée-affected and recovered ‘Barbera’ grapevines, compared to healthy plants. Results We identified 48 proteins that differentially changed in abundance, phosphorylation, or both in response to Flavescence dorée phytoplasma infection. Most of them did not show any significant difference in recovered plants, which, by contrast, were characterized by changes in abundance, phosphorylation, or both for 17 proteins not detected in infected plants. Some enzymes involved in the antioxidant response that were up-regulated in infected plants, such as isocitrate dehydrogenase and glutathione S-transferase, returned to healthy-state levels in recovered plants. Others belonging to the same functional category were even down-regulated in recovered plants (oxidoreductase GLYR1 and ascorbate peroxidase). Our proteomic approach thus agreed with previously published biochemical and RT-qPCR data which reported down-regulation of scavenging enzymes and accumulation of H2O2 in recovered plants, possibly suggesting a role for this molecule in remission from infection. Fifteen differentially phosphorylated proteins (| ratio | > 2, p Conclusions Proteomic data were integrated into biological networks and their interactions were represented through a hypothetical model, showing the effects of protein modulation on primary metabolic ways and related secondary pathways. By following a multiplex-staining approach, we obtained new data on grapevine proteome pathways that specifically change at the phosphorylation level during phytoplasma infection and following recovery, focusing for the first time on phosphoproteome changes during pathogen infection in this host.

This study presents optimization and prediction of tribological behaviour of filled polytetrafluoroethylene (PTFE) composites using hybrid Taguchi and support vector regression (SVR) models. To achieve the optimization, Taguchi Deng was employed considering multiple responses and process parameters relevant to the tribological behaviour. Coefficient of friction (µ) and specific wear rate (Ks) were measured using pin-on-disc tribometer. In this study, load, grit size, distance and speed were the process parameters. An L27 orthogonal array was applied for the Taguchi experimental design. A set of optimal parameters were obtained using the Deng approach for multiple responses of µ and KS. Analysis of variance was performed to study the effect of individual parameters on the multiple responses. To predict µ and Ks, SVR was coupled with novel Harris Hawks' optimization (HHO) and swarm particle optimization (PSO) forming SVR-HHO and SVR-PSO models respectively, were employed. Four model evaluation metrics were used to appraise the prediction accuracy of the models. Validation results revealed enhancement under optimal test conditions. Hybrid SVR models indicated superior prediction accuracy to single SVR model. Furthermore, SVR-HHO outperformed SVR-PSO model. It was found that Taguchi Deng, SVR-PSO and SVR-HHO models led to optimization and prediction with low cost and superior accuracy. [ABSTRACT FROM AUTHOR]

The increases in extreme rainfall could increase the probable maximum flood (PMF) and pose a severe threat to the critical hydraulic infrastructure such as dams and flood protection structures. This study is conducted to assess the impact of climate change on PMF in a tropical catchment. Climate and inflow data of the Tenmengor reservoir, located in the state of Perak in Malaysia, have been used to calibrate and validate the hydrological model. The projected rainfall from regional climate model is used to generate probable maximum precipitation (PMP) for future periods. A hydrological model was used to simulate PMF from PMP estimated for the historical and two future periods, early (2031 − 2045) and late (2060 − 2075). The results revealed good performance of the hydrological model with Nash–Sutcliffe efficiency, 0.74, and the relative standard error, 0.51, during validation. The estimated rainfall depths were 89.5 mm, 106.3 mm, and 143.3 mm, respectively, for 5, 10, and 50 years of the return period. The study indicated an increase in PMP by 162% to 507% and 259% to 487% during early and late periods for different return periods ranging from 5 to 1000 years. This would cause an increase in PMF by 48.9% and 122.6% during early and late periods. A large increase in PMF indicates the possibility of devastating floods in the future in his tropical catchment due to climate change. [ABSTRACT FROM AUTHOR]

Background: Hormone production by the thyroid gland is a prime aspect of maintaining body homeostasis. In this study, the ability of single artificial intelligence (AI)-based models, namely multi-layer perceptron (MLP), support vector machine (SVM), and Hammerstein–Weiner (HW) models, were used in the simulation of thyroidism status. The study's primary aim is to unveil the best performing model for the simulation of thyroidism status using hepatic enzymes and hormones as the independent variables. Three statistical metrics were used in evaluating the performance of the models, namely determination coefficient (R2), correlation coefficient (R), and mean squared error (MSE). Results: Considering the quantitative and visual presentation of the results obtained, it has been observed that the MLP model showed higher performance skills than SVM and HW, which improved their performances up to 3.77% and 12.54%, respectively, in the testing stages. Furthermore, to boost the performance of the single AI-based models, three different ensemble approaches were employed, including neural network ensemble (NNE), weighted average ensemble (WAE), and simple average ensemble (SAE). The quantitative predictive performance of the NNE technique boosts the performance of SAE and WAE approaches up to 2.85% and 1.22%, respectively, in the testing stage. Conclusions: Comparative performance of the ensemble techniques over the single models showed that NNE outperformed all the three AI-based models (MLP, SVM, and HW) and boosted their performance accuracy up to 7.44%, 11.212%, and 19.98%, respectively, in the testing stages. [ABSTRACT FROM AUTHOR]

The concept of security is becoming a global challenge, and governments, stakeholders, corporate societies, and individuals must urgently create a reasonable protection mechanism for good. Therefore, a real-time surveillance system is essential for detection, tracking, and monitoring. Many studies have attempted to provide better solutions but more research and better approaches are essential. This study presents a real-time framework for object detection and tracking for security surveillance systems. The system has been designed based on approximate median filtering, component labeling, background subtraction, and deep learning approaches. The new algorithms for object detection, tracking, and recognition have been implemented using Python and integrated with C# programming languages for ease of use. A software application framework is designed, implemented, and evaluated. The experimental results based on MOT-Challenge performance metrics show that the proposed algorithms have much better performance in terms of accuracy and precision on the MOT15, MOT16, and MOT17 datasets compared to state-of-the-art approaches. This framework also provides an accurate and effective means of monitoring and recognizing moving objects. The software development, including the design of the framework user interfaces, is coded in the C# programming language and integrated with Python using Microsoft Visual Studio (2019 edition). The integration is performed to provide a convenient user interface and to enable the execution of the framework as a standard and standalone software application. Future studies will consider the dynamic scalability of the framework to accommodate different surveillance application areas in overcrowded scenarios. Multiple data sources are integrated to enhance the performance for different scene times, locations, and weather conditions. Furthermore, other object-detection techniques such as You Only Look Once (YOLO) and its variants shall be considered in future studies. These techniques allow the framework to adapt to complex situations in which security surveillance is challenging., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

In a global context, trace element pollution assessment in complex multi-aquifer groundwater systems is important, considering the growing concerns about water resource quality and sustainability worldwide. This research addresses multiple objectives by integrating spatial, chemometric, and indexical study approaches, for assessing trace element pollution in the multi-aquifer groundwater system of the Al-Hassa Oasis, Saudi Arabia. Groundwater sampling and analysis followed standard methods. For this purpose, the research employed internationally recognized protocols for groundwater sampling and analysis, including standardized techniques outlined by regulatory bodies such as the United States Environmental Protection Agency (USEPA) and the World Health Organization (WHO). Average values revealed that Cr (0.041) and Fe (2.312) concentrations surpassed the recommended limits for drinking water quality, posing serious threats to groundwater usability by humans. The trace elemental concentrations were ranked as: Li < Mn < Co < As < Mo < Zn < Al < Ba < Se < V < Ni < Cr < Cu < B < Fe < Sr. Various metal(loid) pollution indices, including degree of contamination, heavy metal evaluation index, heavy metal pollution index, and modified heavy metal index, indicated low levels of groundwater pollution. Similarly, low values of water pollution index and weighted arithmetic water quality index were observed for all groundwater points, signifying excellent groundwater quality for drinking and domestic purposes. Spatial distribution analysis showed diverse groundwater quality across the study area, with the eastern and western parts displaying a less desirable quality, while the northern has the best, making water users in the former more vulnerable to potential pollution effects. Thus, the zonation maps hinted the necessity for groundwater quality enhancement from the western to the northern parts. Chemometric analysis identified both human activities and geogenic factors as contributors to groundwater pollution, with human activities found to have more significant impacts. This research provides the scientific basis and insights for protecting the groundwater system and ensuring efficient water management., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Introduction: In spite of the substantial impact of pediatric asthma on healthcare resources, there is a notable dearth of research focusing on allergen sensitization specifically in children and adolescents with asthma in Cameroon, with existing studies primarily concentrating on adult populations. We, therefore, set out to determine factors associated with sensitization to selected aero-allergens in children and adolescents with asthma followed up at Laquintinie Hospital Douala, Cameroon., Methodology: We conducted a cross-sectional analytical study on patients aged 2-17 years followed up for pediatric asthma at Laquintinie Hospital Douala for 8 months (January to August 2023). Skin prick tests were performed using selected standardized aeroallergens extracts. Factors associated with sensitization were determined using multivariable logistic regression analysis. The threshold for significance was set at p < .05., Results: In total, 126 children and adolescents with asthma were recruited, 50.79% of whom were male with an average age of 8.00 ± 3.99 years. Eight out of 10 patients (83.52%) were sensitized to common aero-allergens (20.59% monosensitized and 79.41% polysensitized). The most common aero-allergens found were the dust mites Dermatophagoïdes pteronyssinus (81.37%), Blomia tropicalis (67.64%) and Dermatophagoïdes farinae (52.94%) followed by Blatella species (25.49%). The factors independently associated with sensitization in our population were the personal history of allergic rhinitis in the patient [adjusted odds ratio, aOR: 4.07 (95% confidence interval, CI: 1.41-11.76), b = 0.54, p = .009], chest tightness at rest [aOR: 5.92 (95% CI: 1.08-32.38), b = 0.87, p = .040), and age above 5 years [aOR: 4.65 (95% CI: 1.47-14.69), b = 0.59, p = .009)., Conclusion: Sensitization to multiple aeroallergens is common in children and adolescents with asthma, especially for dust mites and cockroaches. Factors associated with sensitization are patients aged >5 years, chest tightness at rest, and a personal history of allergic rhinitis., (© 2024 Wiley Periodicals LLC.)

Purpose: Genital lymphedema is a chronic debilitating condition associated with highly impaired health-related quality of life (QoL). This prospective multicenter study evaluated the use of a new compressive garment in patients with secondary and primary genital lymphedema. Methods: Thirty-two patients prospectively enrolled were advised to wear the compressive garment for 12 weeks (day and night). The primary endpoint was change in patient-reported QoL at 12 weeks via the patient global impression of change (PGI-C) instrument. Secondary outcomes included change in other QoL measures at 12 weeks (visual analog scale, Lymphedema Quality of Life Inventory [LyQLI], and EQ-5D questionnaires), lymphedema severity (genital lymphedema score [GLS]), and physician assessment (Clinical Global Impression-Improvement [CGI-I]). Safety and tolerability were also assessed. Results: After 12 weeks, improvement was reported in 78.6% of patients (PGI-C). Physician assessment (CGI-I) indicated clinical improvement in 82.8% of patients. Patient assessment of lymphedema symptoms showed a significant decrease in discomfort ( p  = 0.02) and swelling ( p  = 0.01). Significant declines in the mean global GLS ( p  < 0.0001), and in the proportion of patients reporting heaviness, tightness, swelling, or urinary dysfunction ( p  < 0.05 for all), were also observed. LyQLI scores decreased (indicating improved QoL) in each of the physical, psychosocial ( p  = 0.05), and practical domains. The compressive garment was well tolerated with high compliance, and adverse events (due to swelling or discomfort) led to permanent discontinuation in only three patients. Conclusion: The use of a new genital compression garment over 12 weeks improves the QoL and clinical measures in patients with genital lymphedema (ClinicalTrials.gov ID: NCT04602559; Registration: October 20, 2020).

Introduction: In Cameroon, acute bronchiolitis has been reported as the third commonest lower respiratory infection and is usually associated with low mortality. Nonetheless, respiratory distress associated with non-adherence to management guidelines can prolong hospital stay. This study aimed to explore predictors of prolonged hospital stay (≥ 5 days) and mortality in patients aged < 2years hospitalised for acute bronchiolitis., Methodology: We conducted a retrospective cohort study at three paediatric units in the city of Douala, Cameroon. Factors associated with prolonged hospital stay and mortality were determined using multivariable linear regression model. Threshold for significance was set at p ≤ 0.05., Results: A total of 215 patients with bronchiolitis were included with mean age of 6.94 ± 5.71 months and M/F sex ratio of 1.39/1. Prolonged hospital stay was reported in 46.98% and mortality in 10.70% of patients hospitalised for bronchiolitis. Factors independently associated with prolonged hospital duration were oxygen administration [b = 0.36, OR = 2.35 (95% CI:1.16-4.74), p = 0.017], abnormal respiratory rate [b = 0.38, OR = 2.13 (1.00-4.55), p = 0.050] and patients presenting with cough [b = 0.33, OR = 2.35 (95% CI: 1.22-4.51), p = 0.011], and diarrhoea [b = 0.71, OR = 6.44 (95% CI: 1.6-25.86), p = 0.009] on admission. On the other hand, factors independently associated with mortality were age of the patient [b= -0.07, OR = 0.84 (95% CI: 0.74-0.97), p = 0.014] and oxygen administration [b = 1.08, OR = 9.64 (95% CI:1.16-79.85), p = 0.036] CONCLUSION: Acute bronchiolitis represented 1.24% of admissions and was common in the rainy season, in males and 3-11-month-old patients. Management guidelines were poorly respected. Prolonged length of stay was reported in half of the patients hospitalized and mortality was high, especially in younger patients and in patients receiving oxygen., (© 2024. The Author(s).)

Introduction: Pretibial myxedema is a rare manifestation of Graves' disease, and pseudotumoral forms may be confused with lower limb lymphedema., Observations: We reported 3 cases of pretibial myxedema in 2 women and 1 man, aged 72, 66, and 49 years, treated for Graves' disease 3, 25 and 32 years previously. Two patients were active smokers. Lymphedema diagnosis of the lower limbs was suspected in the presence of bilateral pseudotumoral lesions of the feet, toes and ankles and the presence of a Stemmer's sign (skin thickening at the base of the 2nd toe, pathognomonic of lymphedema). Lymphoscintigraphy in one case was normal, not confirming lymphedema., Conclusion: Pretibial pseudotumoral myxedema is a differential diagnosis of lower limb lymphedema. This diagnosis is confirmed by questioning the patient about preexisting Graves' disease, the underlying etiology, to decide the appropriate treatment and to encourage cessation of smoking, which is a risk factor for pretibial myxedema., (Copyright © 2023 Société Nationale Française de Médecine Interne (SNFMI). Published by Elsevier Masson SAS. All rights reserved.)

The intrusion of seawater (SWI) into coastal aquifers is a major concern worldwide, affecting the quantity and quality of groundwater resources. The region of Saudi Arabia that lies along the eastern coast has been affected by SWI, making it crucial to accurately identify and monitor the affected areas. This investigation aimed to map the degree of seawater intrusion in a complex aquifer system in the study area using an integrated clustering analysis approach. The study collected 41 groundwater samples from wells penetrating multi-layered aquifers, and the samples were analyzed for physicochemical properties and major ions. Clustering analysis methods, including Hierarchical Clustering Analysis (double-clustering) (HCA-DC), K-mean (KMC), and fuzzy k-mean clustering (FKM), were employed to evaluate the spatial distribution and association of the groundwater properties. The results revealed that the analyzed GW samples were divided into four clusters with varying degrees of SWI. Clusters A, B, C, and D contained GW samples with very low ( f sea of 1.9%), high ( f sea of 5.2%) degrees of SWI, respectively. FKM clustering exhibited superior performance with a silhouette score of 0.83. Additionally, the study found a direct correlation between the degree of SWI and increased concentrations of boron, strontium, and iron, demonstrating SWI's impact on heavy metal levels. Notably, the boron concentration in cluster B, which endured high SWI, exceeded WHO guidelines. The study demonstrates the value of clustering analysis for accurately monitoring SWI and associated heavy metals. The findings can guide policies to mitigate SWI impacts and benefit groundwater-dependent communities. Further research can help develop effective strategies to mitigate SWI effects on groundwater quality and availability.f sea of 7.9%), and low ( f sea of 5.2%) degrees of SWI, respectively. FKM clustering exhibited superior performance with a silhouette score of 0.83. Additionally, the study found a direct correlation between the degree of SWI and increased concentrations of boron, strontium, and iron, demonstrating SWI's impact on heavy metal levels. Notably, the boron concentration in cluster B, which endured high SWI, exceeded WHO guidelines. The study demonstrates the value of clustering analysis for accurately monitoring SWI and associated heavy metals. The findings can guide policies to mitigate SWI impacts and benefit groundwater-dependent communities. Further research can help develop effective strategies to mitigate SWI effects on groundwater quality and availability., Competing Interests: The authors declare no conflict of interest.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

Fluoride and nitrate contamination of groundwater is a major environmental issue in the world's arid and semiarid regions. This issue is severe in both developed and developing countries. This study aimed at assessing the concentration levels, contamination mechanisms, toxicity, and human health risks of NO 3 - and F - in the groundwater within the coastal aquifers of the eastern part of Saudi Arabia using a standard integrated approach. Most of the tested physicochemical properties of the groundwater exceeded their standard limits. The water quality index and synthetic pollution index evaluated the suitability of the groundwater and showed that all the samples have poor and unsuitable quality for drinking. The toxicity of F - was estimated to be higher than NO 3 - . Also, the health risk assessment revealed higher risks due to F - than NO 3 - . Younger populations had higher risks than elderly populations. For both F - and NO 3 . Na-Cl and Ca-Mg-Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks.- , the order of health risk was Infants > Children > Adults. Most of the samples posed medium to high chronic risks due to F - and NO 3 - ingestion. However, negligible health risks were obtained for potential dermal absorption of NO 3 - . Na-Cl and Ca-Mg-Cl water types predominate in the area. Pearson's correlation analysis, principal component analysis, regression models, and graphical plots were used to determine the possible sources of the water contaminants and their enrichment mechanisms. Geogenic and geochemical processes had greater impact he groundwater chemistry than anthropogenic activities. For the first time, these findings provide public knowledge on the overall water quality of the coastal aquifers and could help the inhabitants, water management authorities, and researchers to identify the groundwater sources that are most desirable for consumption and the human populations that are vulnerable to non-carcinogenic health risks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper, (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Due to the significant energy and economic losses brought on by the global oil spill, there has been an increased interest in oil-water separation. This study presents strong non-linear machine learning models (support vector regression (SVR) and Gaussian process regression (GPR)) with the Response surface method (RSM) to predict the oil flux and oil-water separation efficiency of wastewater using ceramic membrane technology. For the model development and prediction of oil flux (OF) and oil-water separation efficiency (OSE), oil concentration (mg/L), feed flow rate (mL/min), and pH were considered as input variables. The input variables are combined in three combinations to study the most contributing input features to the models' performance. Mean square error (MSE) and Nash-Sutcliffe coefficient efficiency (NSE) were used to assess the prediction performances of the developed models with the different number of input combinations considered in the study. For the two target variables (OF and OSE), GPR and SVR models were used to separately predict them. For OF, the SVR-2 [Combo-2] model (MSE = 0.9255 and NSE = 2.7976) performed better with higher prediction accuracy compared to GPR-2 [Combo-2] model (MSE = 0.763 and NSE = 6.437). In addition, for OSE, the GPR-3 [Combo-3] model (MSE = 0.995 and NSE = 0.5544) performed slightly better than SVR-3 [Combo-3] model (MSE = 0.992 and NSE = 0.8066). The results showed that the SVR model with the combo-2 and GPR-3 models for OF and OSE variables are the proposed models with the best performance and accuracy. This machine learning study will aid in better evaluating the function of materials such as ceramic in membrane performance features such as oil flux and rejection prediction, separation efficiency, water recovery, membrane fouling, and so on. As for academics and manufacturers, this machine learning (ML) strategy will boost performance and allow a better understanding of system governance., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Human health and the sustainability of the socioeconomic system are directly related to water quality. As anthropogenic activity becomes more intense, pollutants, particularly potentially harmful elements (PHEs), penetrate water systems and degrade water quality. The purpose of this study was to evaluate the safety of using groundwater for domestic and drinking purposes through oral and dermal exposure routes, as well as the potential health risks posed to humans in the Nnewi and Awka regions of Nigeria. The research involved the application of a combination of the National Sanitation Foundation Water Quality Index (NSFWQI), HERisk code, and hierarchical dendrograms. Additionally, we utilized the regulatory guidelines established by the World Health Organization and the Standard Organization of Nigeria to compare the elemental compositions of the samples. The physicochemical parameters and NSFWQI evaluation revealed that the majority of the samples were PHE-polluted. Based on the HERisk code, it was discovered that in both the Nnewi and Awka regions, risk levels are higher for people aged 1 to <11 and >65 than for people aged 16 to <65. Overall, it was shown that all age categories appeared to be more vulnerable to risks due to the consumption than absorption of PHEs, with Cd > Pb > Cu > Fe for Nnewi and Pb > Cd > Cu > Fe for water samples from Awka. Summarily, groups of middle age are less susceptible to possible health issues than children and elderly individuals. Hierarchical dendrograms and correlation analysis showed the spatio-temporal implications of the drinking groundwater quality and human health risks in the area. This research could help local government agencies make informed decisions on how to effectively safeguard the groundwater environment while also utilizing the groundwater resources sustainably., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2023 The Authors.)

The novel coronavirus (COVID-19), also known as SARS-CoV-2, is a highly contagious respiratory disease that first emerged in Wuhan, China in 2019 and has since become a global pandemic. The virus is spread through respiratory droplets produced when an infected person coughs or sneezes, and it can lead to a range of symptoms, from mild to severe. Some people may not have any symptoms at all and can still spread the virus to others. The best way to prevent the spread of COVID-19 is to practice good hygiene. It is also important to follow the guidelines set by local health authorities, such as physical distancing and quarantine measures. The World Health Organization (WHO), on the other hand, has classified this virus as a pandemic, and as a result, all nations are attempting to exert control and secure all public spaces. The current study aimed to (I) compare the weekly COVID-19 cases between Israel and Greece, (II) compare the monthly COVID-19 mortality cases between Israel and Greece, (III) evaluate and report the influence of the vaccination rate on COVID-19 mortality cases in Israel, and (IV) predict the number of COVID-19 cases in Israel. The advantage of completing these tasks is the minimization of the spread of the virus by deploying different mitigations. To attain our objective, a correlation analysis was carried out, and two distinct artificial intelligence (AI)-based models-specifically, an artificial neural network (ANN) and a classical multiple linear regression (MLR)-were developed for the prediction of COVID-19 cases in Greece and Israel by utilizing related variables as the input variables for the models. For the evaluation of the models, four evaluation metrics (determination coefficient (R2), mean square error (MSE), root mean square error (RMSE), and correlation coefficient (R)) were considered in order to determine the performance of the deployed models. From a variety of perspectives, the corresponding determination coefficient (R2) demonstrated the statistical advantages of MLR over the ANN model by following a linear pattern. The MLR predictive model was both efficient and accurate, with 98% accuracy, while ANN showed 94% accuracy in the effective prediction of COVID-19 cases.

The growing increase in groundwater (GW) salinization in the coastal aquifers has reached an alarming socio-economic menace in Saudi Arabia and various places globally due to several natural and anthropogenic activities. Hence, evaluating the GW salinization is paramount to safeguarding the water resources planning and management. This study presents three different scenarios viz.: real field investigation, experimental laboratory analysis (using ion chromatography (IC) and inductively coupled plasma mass spectrometry (ICP-MS), etc.), and artificial intelligence (AI) based metaheuristic optimization (MO) algorithms in Saudi Arabia. The main purpose of this study is to validate the obtained experimental-based analysis using hybrid MO techniques comprising of adaptive neuro-fuzzy inference system (ANFIS) hybridized with genetic algorithm (GA), particle swarm optimization (PSO), and biogeography-based optimization (BBO) for identification of GW salinization in the coastal region of eastern Saudi Arabia. Additionally, ArcGIS 10.3 software generates the prediction map based on ANFIS-GA, ANFIS-PSO, and ANFIS-BBO. Feature selection was assessed using the PSO algorithm, and four indices evaluated the estimated models, namely, root mean square error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and standard deviation (SD). The simulated results are based on three variable input combinations, which showed that the ANFIS-PSO (MAE = 0.00439) algorithm had the highest accuracy (99 %), followed by the ANFIS-GA (MAE = 0.00767) and ANFIS-BBO (MAE = 0.0132) algorithms. Besides, Ca 2+ , Na + , Mg 2+ , and Cl - were the most influential parameters. The accuracy also demonstrated the potential reliability of MO algorithms based on spatial distribution mapping. The employed approach proved to be merit and reliable tool for water resources decision-makers in the coastal aquifer of Saudi Arabia. This approach is believed to improve water scarcity as one of the essential targets for Goal 6 of Sustainable Development Vision 2030 and the Kingdom in general., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

It is difficult to predict and model with an accurate model the floods, that are one of the most destructive risks across the earth's surface. The main objective of this research is to show the prediction power of three ensemble algorithms with respect to flood susceptibility estimation. These algorithms are: Iterative Classifier Optimizer - Alternating Decision Tree - Frequency Ratio (ICO-ADT-FR), Iterative Classifier Optimizer - Deep Learning Neural Network - Frequency Ratio (ICO-DLNN-FR) and Iterative Classifier Optimizer - Multilayer Perceptron - Frequency Ratio (ICO-MLP-FR). The first stage of the manuscript consisted of the collection and processing of the geodatabase needed in the present study. The geodatabase comprises a number of 14 flood predictors and 132 known flood locations. The Correlation-based Feature Selection (CFS) method was used in order to assess the prediction capacity of the 14 predictors in terms of flood susceptibility estimation. The training and validation of the three ensemble models constitute the next stage of the scientific workflow. Several statistical metrics and ROC curve method were involved in the evaluation of the model's performance and accuracy. According to ROC curves all the models achieved high performances since their AUC had values above 0.89. ICO-DLNN-FR proved to be the most accurate model (AUC = 0.959). The outcomes of the study can be used to guide future flood risk management and sustainable land-use planning in the designated area., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Natural streams longitudinal dispersion coefficient (Kx) is an essential indicator for pollutants transport and its determination is very important. Kx is influenced by several parameters, including river hydraulic geometry, sediment properties, and other morphological characteristics, and thus its calculation is a highly complex engineering problem. In this research, three relatively explored machine learning (ML) models, including Random Forest (RF), Gradient Boosting Decision Tree (GTB), and XGboost-Grid, were proposed for the Kx determination. The modeling scheme on building the prediction matrix was adopted from the well-established literature. Several input combinations were tested for better predictability performance for the Kx. The modeling performance was tested based on the data division for the training and testing (70-30% and 80-20%). Based on the attained modeling results, XGboost-Grid reported the best prediction results over the training and testing phase compared to RF and GTB models. The development of the newly established machine learning model revealed an excellent computed-aided technology for the Kx simulation., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Spirulina is a microalga and its phenolic compound is affected by growth mediums. In this study, Artificial intelligence (AI) based models, namely the Adaptive-Neuro Fuzzy Inference System (ANFIS) and Multilayer perceptron (MLP) models, and Step-Wise-Linear Regression (SWLR) were used to predict total phenolic compounds (TPC) of the spirulina algae. Spirulina productivity (P), extraction yield (EY), total flavonoids (TF), percent of flavonoid (%F) and percent of phenols (%P) are considered as input variables with the corresponding TPC as an output variable. From the result, TPC has a high positive correlation with the input variables with R = 0.99999. Also, the models showed that the ANFIS and SWLR gives superior result in the testing phase and increased its accuracy by 2% compared to MLP model in the prediction of TPC., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 Published by Elsevier B.V. on behalf of King Saud University.)

Conflict has a negative effect on immunization outcomes leading to epidemics of measles and other vaccine preventable diseases. Borno state, located in the North-Eastern parts of the country, started experiencing insurgency since 2009 by a militant group commonly referred to as "Boko Haram", leading to displacement of numerous persons from their homes. The state government created internally displaced persons (IDPs) camps with an estimated 600,000 persons and over 100,000 children aged 9 - 59 months. We explored the challenges in implementing the measles campaign in 2017 and its effect on measles elimination and control efforts in Borno State., Methods: We conducted a retrospective review of the measles surveillance data from the Integrated disease surveillance and response (IDSR), the early warning alert and response system (EWARS), Measles SIA implementation strategy reports and the Post campaign evaluation survey data., Result: Seven (26%) of the 27 LGAs had security challenges in Borno state in November 2017. Following the measles campaign, 30 EAs were surveyed as part of the post campaign evaluation with a coverage validation of 72%, the lowest reported state level coverage in the country. Reported measles cases in the age cohort <1Year increased in 2018 by 14.3% compared to 2017., Conclusion: Despite the security challenges in Borno state, the 2017 measles SIA was conducted with the post campaign evaluation highlighting some areas of suboptimal performance. There remains a need to review the required strategies for successful implementation in Borno and other states affected by security challenges. The reestablishment of holding camps and vaccination posts at all entry points into IDP camps and host communities to ensure new entrants are screened and vaccinated with the measles vaccines, as has been successful in the polio programme in Nigeria will promotes efforts towards Measles control in the state., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Irrigation systems are becoming increasingly important, owing to the increase in human population, global warming, and food demand. This study aims to design a low-cost autonomous sensor interface to automate the monitoring and control of irrigation systems in remote locations, and to optimize water use for irrigation farming. An internet of things-based irrigation monitoring and control system, employing sensors and actuators, is designed to facilitate the autonomous supply of adequate water from a reservoir to domestic crops in a smart irrigation systems. System development lifecycle and waterfall model design methodologies have been employed in the development paradigm. The Proteus 8.5 design suite, Arduino integrated design environment, and embedded C programming language are commonly used to develop and implement a real working prototype. A pumping mechanism has been used to supply the water required by the soil. The prototype provides power supply, sensing, monitoring and control, and internet connectivity capabilities. Experimental and simulation results demonstrate the flexibility and practical applicability of the proposed system, and are of paramount importance, not only to farmers, but also for the expansion of economic activity. Furthermore, this system reduces the high level of supervision required to supply irrigation water, enabling remote monitoring and control.

We propose an autonomous self-aware and adaptive fault-tolerant routing technique (ASAART) for wireless sensor networks. We address the limitations of self-healing routing (SHR) and self-selective routing (SSR) techniques for routing sensor data. We also examine the integration of autonomic self-aware and adaptive fault detection and resiliency techniques for route formation and route repair to provide resilience to errors and failures. We achieved this by using a combined continuous and slotted prioritized transmission back-off delay to obtain local and global network state information, as well as multiple random functions for attaining faster routing convergence and reliable route repair despite transient and permanent node failure rates and efficient adaptation to instantaneous network topology changes. The results of simulations based on a comparison of the ASAART with the SHR and SSR protocols for five different simulated scenarios in the presence of transient and permanent node failure rates exhibit a greater resiliency to errors and failure and better routing performance in terms of the number of successfully delivered network packets, end-to-end delay, delivered MAC layer packets, packet error rate, as well as efficient energy conservation in a highly congested, faulty, and scalable sensor network.

Background: Sickle cell disorder is the most important genetic hematological disease that affects people of black African descent. The years of young adulthood present a good opportunity for screening and counseling for this genetic blood disorder., Objectives: To assess effect of health education and provision of free sickle cell haemoglobin screening on knowledge of sickle cell disorder, attitude towards sickle cell haemoglobin screening, and uptake of sickle cell haemoglobin screening among students of a School of Nursing., Methods: Study design was a quasi-experimental noncontrolled study. Self-administered questionnaire was used for pre- and post-intervention data collection. Implemented interventions were seminar on sickle cell disorder combined with free sickle cell haemoglobin screening. The data was analyzed with Epi-info version 3.5.1 statistical software package., Results: Respondents who participated in all the study phases were 104. Mean knowledge score (%) was high (80.9 +/- 22.8%) at baseline and improved significantly to 91.8 +/- 9.4% (p < 0.001) at post intervention. 91.3% were willing to screen fiancée before getting married while 72.1% were willing not to go ahead with marriage if self and fiancée are carriers of sickle cell haemoglobin. Phenotype of the respondents that volunteered to be screened for sickle cell haemoglobin were: A (70.5%), AC (6.8%) and AS (22.7%)., Conclusions: Implemented interventions, seminar on sickle cell disorder combined with free sickle cell haemoglobin screening service yielded significant impact on respondents' knowledge, attitude and uptake of sickle cell haemoglobin screening.

Expression profiles of isocitrate lyase (TbICL), malate synthase (TbMLS) and fructose-1,6-bisphosphatase (TbFBP) from the mycorrhizal ascomycete Tuber borchii were investigated by real-time RT-PCR in fruiting bodies at different stages of maturation. In addition, a time course experiment was set up to determine how the transcription profile of TbICL, TbMLS and TbFBP in axenic-grown mycelia is affected by different carbon sources. The transcript levels of the three genes in the fruiting bodies were all much higher than those measured in the vegetative stage. The investigation on axenic-grown mycelia revealed that the main positive regulator of TbICL and TbMLS gene expression is the availability of acetate and ethanol, while oleic acid is a too complex substrate for the limited degradative capacities of T. borchii. Immunolabelling on axenic-grown mycelia showed a co-localization of TbICL and the peroxisomal marker protein FOX2. This result demonstrated that in T. borchii ICL is compartmentalized in peroxisomes. The high induction of TbICL, TbMLS and TbFBP transcription and the translocation of lipids in fruiting bodies let us hypothesize that glyoxylate cycle and gluconeogenesis are key metabolic pathways in the recycling of existing cell material and the channelling towards the biosynthesis of new cell components during the maturation of fruiting bodies.

Background: The expressed sequence tag M6G10 was originally isolated from a screening for differentially expressed transcripts during the reproductive stage of the white truffle Tuber borchii. mRNA levels for M6G10 increased dramatically during fruiting body maturation compared to the vegetative mycelial stage., Results: Bioinformatics tools, phylogenetic analysis and expression studies were used to support the hypothesis that this sequence, named TbDHN1, is the first dehydrin (DHN)-like coding gene isolated in fungi. Homologs of this gene, all defined as "coding for hypothetical proteins" in public databases, were exclusively found in ascomycetous fungi and in plants. Although complete (or almost complete) fungal genomes and EST collections of some Basidiomycota and Glomeromycota are already available, DHN-like proteins appear to be represented only in Ascomycota. A new and previously uncharacterized conserved signature pattern was identified and proposed to Uniprot database as the main distinguishing feature of this new group of DHNs. Expression studies provide experimental evidence of a transcript induction of TbDHN1 during cellular dehydration., Conclusion: Expression pattern and sequence similarities to known plant DHNs indicate that TbDHN1 is the first characterized DHN-like protein in fungi. The high similarity of TbDHN1 with homolog coding sequences implies the existence of a novel fungal/plant group of LEA Class II proteins characterized by a previously undescribed signature pattern.

A heavy metal tolerant strain of the ericoid mycorrhizal species Oidiodendron maius, isolated from roots of Vaccinium myrtillus growing in soil heavily contaminated with zinc, was previously shown to tolerate high concentrations of zinc and cadmium ions in the growth medium. We have investigated the genetic basis of this fungal strain tolerance to high zinc concentrations by using an untargeted approach. From a cDNA library constructed by using mRNA from Zn-treated O. maius mycelia, 444 clones were randomly selected and 318 were sequenced. Sequence analysis identified 219 unique clones: 117 showed homology to previously identified genes, 26 matched unknown protein coding regions found in other organisms, and 76 were novel. Variation in the gene expression level after a 20-day treatment with high concentrations of Zn was monitored on 130 unigenes by reverse northern blot hybridisation. Sixteen unigenes were shown to be either up- (9) or down- (7) regulated. The putative function of these genes and their involvement in stress tolerance is discussed.