Innovative approach for predicting daily reference evapotranspiration using improved shallow and deep learning models in a coastal region: A comparative study.

Accurate and reliable estimation of Reference Evapotranspiration (ETo) is crucial for water resources management, hydrological processes, and agricultural production. The FAO-56 Penman-Monteith (FAO-56PM) approach is recommended as the standard model for ETo estimation; nevertheless, the absence of comprehensive meteorological variables at many global locations frequently restricts its implementation. This study compares shallow learning (SL) and deep learning (DL) models for estimating daily ETo against the FAO-56PM approach based on various statistic metrics and graphic tool over a coastal Red Sea region, Sudan. A novel approach of the SL model, the Catboost Regressor (CBR) and three DL models: 1D-Convolutional Neural Networks (1D-CNN), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU) were adopted and coupled with a semi-supervised pseudo-labeling (PL) technique. Six scenarios were developed regarding different input combinations of meteorological variables such as air temperature (Tmin, Tmax, and Tmean), wind speed (U2), relative humidity (RH), sunshine hours duration (SSH), net radiation (Rn), and saturation vapor pressure deficit (es-ea). The results showed that the PL technique reduced the systematic error of SL and DL models during training for all the scenarios. The input combination of Tmin, Tmax, Tmean, and RH reflected higher performance than other combinations for all employed models. The CBR-PL model demonstrated good generalization abilities to predict daily ETo and was the overall superior model in the testing phase according to prediction accuracy, stability analysis, and less computation cost compared to DL models. Thus, the relatively simple CBR-PL model is highly recommended as a promising tool for predicting daily ETo in coastal regions worldwide which have limited climate data.

An innovative approach for predicting groundwater TDS using optimized ensemble machine learning algorithms at two levels of modeling strategy.

Groundwater salinization in coastal aquifers is a major socioeconomic challenge in Oman and many other regions worldwide due to several anthropogenic activities and natural drivers. Therefore, assessing the salinization of groundwater resources is crucial to ensure the protection of water resources and sustainable management. The aim of this study is to apply a novel approach using predictive optimized ensemble trees-based (ETB) machine learning models, namely Catboost regression (CBR), Extra trees regression (ETR), and Bagging regression (BA), at two levels of modeling strategy for predicting groundwater TDS as an indicator for seawater intrusion in a coastal aquifer, Oman. At level 1, ETR and CBR models were used as base models or inputs for BA in level 2. The results show that the models at level 1 (i.e., ETR and CBR) yielded satisfactory results using a limited number of inputs (Cl, K, and Sr) from a few sets of 40 groundwater wells. The BA model at level 2 improved the overall performance of the modeling by extracting more information from ETR and CBR models at level 1 models. At level 2, the BA model achieved a significant improvement in accuracy (MSE = 0.0002, RSR = 0.062, R2 = 0.995 and NSE = 0.996) compared to each individual model of ETR (MSE = 0.0007, RSR = 0.245, R2 = 0.98 and NSE = 0.94), and CBR (MSE = 0.0035, RSR = 0.258, R2 = 0.933 and NSE = 0.934) at level 1 models in the testing dataset. BA model at level 2 outperformed all models regarding predictive accuracy, best generalization of new data, and matching the locations of the polluted and unpolluted wells. Our approach predicts groundwater TDS with high accuracy and thus provides early warnings of water quality deterioration along coastal aquifers which will improve water resources sustainability.

Recycling of gas-to-liquid sludge as a potential organic amendment: Effect on soil and cotton properties under hyperarid conditions.

Gas-to-liquid (GTL) sludge is a specific wastewater treatment by-product, which is generated during the industrial process of natural gas conversion to transportation fuels. This least studied sludge is pathogen-free and rich in organic carbon and plant nutrients. Therefore, it can be reused for soil enhancement as a sustainable management strategy to mitigate landfill gas emissions. In this field study, we compared the performance of soil treatments with GTL sludge to the more conventional chemical fertilizers and cow manure compost for the cultivation of cotton under hyperarid conditions. After a complete growing season, GTL sludge application resulted in the enhancement of soil properties and plant growth compared to conventional inputs. As such, there was a significant dose-dependent increase of soil organic matter (4.01% and 4.54%), phosphorus (534 and 1090 mg kg-1), and cumulative lint yield (4.68 and 5.67 t ha-1) for GTL sludge application rates of 1.5% and 3%, respectively. The produced fiber quality was adequate for an upland cotton variety (Gossypium hirsutum var. MAY 344) and appeared more dependent on the prevailing climate conditions than soil treatments. On the other hand, the adverse effects generally related to industrial sludge reuse were not significant and did not affect the designed agro-environmental system. Accordingly, plants grown on GTL sludge-amended soils showed lower antioxidant activity despite significant salinity increase. In addition, the concentrations of detected heavy metals in soil were within the standards' limits, which did not pose environmental issues under the described experimental conditions. Leachate analysis revealed no risks for groundwater contamination with phytotoxic metals, which were mostly retained by the soil matrix. Therefore, recycling GTL sludge as an organic amendment can be a sustainable solution to improve soil quality and lower carbon footprint. To reduce any environmental concerns, an application rate of 1.5% could be provisionally recommended since a two-fold increase in sludge dose did not result in a significant yield improvement.

Plasmodium falciparum resistance and malaria presentation in children at Dongola specialist hospital: A prospective cohort study.

OBJECTIVE: To provide evidence about the susceptibility of anti-malarial drugs, and to identify the clinical features of the disease in children. Methods: The prospective observational comparative study was conducted at the Dongola Specialist Hospital, Dunqulah, Sudan, from February 2016 to February 2017, and comprised children aged <16 years with bodyweight >5kg who had malaria. The subjects were enrolled into group 1, which received treatment based on physician's discretion, and group 2, which received treatment in accordance with the national guidelines. The follow-up was conducted on days 3, 7 and 14 to identify cases as early treatment failure, late treatment failure, or treatment success. Data were analysed in terms of frequencies and percentages using statistical analysis software R version 3.1.2. RESULTS: Of the 120 children, 60(50%) were in each of the two groups. Overall, 63(52.5%) were aged 1-6 years, 66(55%) were males, and 42(35%) were exposed to malaria for the first time. Post-treatment test was negative for all 120(100%) the subjects in both the groups. showing no inter-group difference. Conclusion: Although resistance to combination therapy was not detected, it remains extremely important to remain vigilant for the emergence of resistance in the future.

An integrated approach for assessing surface water quality: Case of Beni Haroun dam (Northeast Algeria).

In this paper, we use an integrated approach to carry out a comprehensive evaluation of water quality in the Beni Haroun (BH) dam, the largest surface water resource in Algeria. Several techniques have been employed under the same framework, including the Canadian Council Ministers Environment Water Quality Index (CCME-WQI), principal component analysis and factor analysis (PCA/FA), the K-means clustering, and the ordinary least square (OLS) analysis. A data set of 22 physicochemical parameters has been collected, over a period of 11 years, from three sampling stations: Ain Smara (ST1) and Menia (ST2), both located upstream of "Wadi Rhumel," and BH dam station (ST3), located at the dam site. The PCA/FA enables the identification of seven key factors that influence significantly BH dam water quality. The average values of CCME indices at the BH dam were 17, 40, 42, and 32 for drinking, irrigation, industry, and aquatic life purposes, respectively, which indicate poor water quality, according to the CCME categorization scheme. Besides, the K-means algorithm has been proven to be a very useful machine learning tool to detect that the major source of BH dam pollution is "Wadi Rhumel." Finally, OLS analysis, along with the Mann-Kendall test, highlighted the positive trend of BH dam's water quality.

Middle East Respiratory Syndrome Coronavirus, Saudi Arabia, 2017-2018.

We characterized exposures and demographics of Middle East respiratory syndrome coronavirus cases reported to the Saudi Arabia Ministry of Health during July 1-October 31, 2017, and June 1-September 16, 2018. Molecular characterization of available specimens showed that circulating viruses during these periods continued to cluster within lineage 5.

Middle East Respiratory Syndrome Coronavirus Infection Dynamics and Antibody Responses among Clinically Diverse Patients, Saudi Arabia.

Middle East respiratory syndrome coronavirus (MERS-CoV) shedding and antibody responses are not fully understood, particularly in relation to underlying medical conditions, clinical manifestations, and mortality. We enrolled MERS-CoV-positive patients at a hospital in Saudi Arabia and periodically collected specimens from multiple sites for real-time reverse transcription PCR and serologic testing. We conducted interviews and chart abstractions to collect clinical, epidemiologic, and laboratory information. We found that diabetes mellitus among survivors was associated with prolonged MERS-CoV RNA detection in the respiratory tract. Among case-patients who died, development of robust neutralizing serum antibody responses during the second and third week of illness was not sufficient for patient recovery or virus clearance. Fever and cough among mildly ill patients typically aligned with RNA detection in the upper respiratory tract; RNA levels peaked during the first week of illness. These findings should be considered in the development of infection control policies, vaccines, and antibody therapeutics.

Epidemiology of a Novel Recombinant Middle East Respiratory Syndrome Coronavirus in Humans in Saudi Arabia.

BACKGROUND: Middle East respiratory syndrome coronavirus (MERS-CoV) causes severe respiratory illness in humans. Fundamental questions about circulating viruses and transmission routes remain. METHODS: We assessed routinely collected epidemiologic data for MERS-CoV cases reported in Saudi Arabia during 1 January-30 June 2015 and conducted a more detailed investigation of cases reported during February 2015. Available respiratory specimens were obtained for sequencing. RESULTS: During the study period, 216 MERS-CoV cases were reported. Full genome (n = 17) or spike gene sequences (n = 82) were obtained from 99 individuals. Most sequences (72 of 99 [73%]) formed a discrete, novel recombinant subclade (NRC-2015), which was detected in 6 regions and became predominant by June 2015. No clinical differences were noted between clades. Among 87 cases reported during February 2015, 13 had no recognized risks for secondary acquisition; 12 of these 13 also denied camel contact. Most viruses (8 of 9) from these 13 individuals belonged to NRC-2015. DISCUSSIONS: Our findings document the spread and eventual predominance of NRC-2015 in humans in Saudi Arabia during the first half of 2015. Our identification of cases without recognized risk factors but with similar virus sequences indicates the need for better understanding of risk factors for MERS-CoV transmission.

Genome-wide DArT and SNP scan for QTL associated with resistance to stripe rust (Puccinia striiformis f. sp. tritici) in elite ICARDA wheat (Triticum aestivum L.) germplasm.

KEY MESSAGE: Identified DArT and SNP markers including a first reported QTL on 3AS, validated large effect APR on 3BS. The different genes can be used to incorporate stripe resistance in cultivated varieties. Stripe rust [yellow rust, caused by Puccinia striiformis f. sp. tritici (Pst)] is a serious disease in wheat (Triticum aestivum). This study employed genome-wide association mapping (GWAM) to identify markers linked to stripe rust resistance genes using Diversity Arrays Technology (DArT(®)) and single-nucleotide polymorphism (SNP) Infinium 9K assays in 200 ICARDA wheat genotypes, phenotyped for seedling and adult plant resistance in two sites over two growing seasons in Syria. Only 25.8 % of the genotypes showed resistance at seedling stage while about 33 and 44 % showed moderate resistance and resistance response, respectively. Mixed-linear model adjusted for false discovery rate at p < 0.05 identified 12 DArT and 29 SNP markers on chromosome arms 3AS, 3AL, 1AL, 2AL, 2BS, 2BL, 3BS, 3BL, 5BL, 6AL, and 7DS significantly linked to Pst resistance genes. Of these, the locus on 3AS has not been previously reported to confer resistance to stripe rust in wheat. The QTL on 3AS, 3AL, 1AL, 2AL, and 2BS were effective at seedling and adult plant growth stages while those on 3BS, 3BL, 5BL, 6AL and 7DS were effective at adult plant stage. The 3BS QTL was validated in Cham-6 × Cham-8 recombinant inbred line population; composite interval analysis identified a stripe resistance QTL flanked by the DArT marker, wPt-798970, contributed by Cham-6 parent which accounted for 31.2 % of the phenotypic variation. The DArT marker "wPt-798970" lies 1.6 cM away from the 3BS QTL detected within GWAM. Epistatic interactions were also investigated; only the QTL on 1AL, 3AS and 6AL exhibited interactions with other loci. These results suggest that GWAM can be an effective approach for identifying and improving resistance to stripe rust in wheat.

A Surrogate Water Quality Index to assess groundwater using a unified DEA-OWA framework.

In this paper, we introduce a new approach, based on a unified framework incorporating Data Envelopment Analysis (DEA) and Ordered Weighted Averaging (OWA), for assessing water quality in contextual settings that involve a large number of hydrochemical parameters. In order to enhance discrimination among water sources, the DEA model is adopted with data-driven input variables, called "surrogate optimistic closeness values," computed through an aggregation procedure that includes the observed values of the hydrochemical parameters with OWA weights. The proposed DEA-OWA methodology has been employed to assess the quality of 51 water samples, collected from irrigation wells in Sereflikochisar Basin, Turkey, by means of 19 hydrochemical parameters. Using different subjectivity levels, the Surrogate Water Quality Indices (SWQIs) that are produced are proven effective in enhancing discrimination among the water sources while enabling a more robust water quality-based ranking. The k-means analysis has been used for clustering the water quality of the wells into Excellent, Good, Permissible, and Unsuitable rather than using pre-set boundaries. Only one water source has been identified as Excellent, whereas 17.65%, 45.10%, and 35.29% of the sampled wells, respectively, are categorized with Good, Permissible, and Unsuitable water quality. Inferred from wells' location, the results suggest that the groundwater might be drastically affected by saline water intrusion from Lake Tuz. The latter conclusion has been corroborated through a Tobit regression analysis.

Reversible hypoxic-ischemic encephalopathy post prolonged out-of-hospital cardiac arrest: A case series.

This article highlights the possibility of positive outcomes associated with prolonged CPR and anoxic brain injury contesting the idea that such patients will invariably end up in a persistent vegetative state.

Hospital-based surveillance of influenza A(H1N1)pdm09 virus in Saudi Arabia, 2010-2016.

BACKGROUND: Influenza is a highly contagious acute viral respiratory tract infection. The emergence of influenza A(H1N1)pdm09 in 2009 caused a pandemic. Since then it has become a seasonal influenza virus. It causes symptoms ranging from mild to severe illness, which might be fatal, particularly in people with underlying chronic medical conditions, immunocompromised people, the elderly, and pregnant women. OBJECTIVE: Describe the data generated by the influenza A(H1N1) pdm09 surveillance in Saudi Arabia from 2010 to 2016. DESIGN: Retrospective, descriptive. SETTING: Hospitals reporting to the Ministry of Health. MATERIALS AND METHODS: We studied aggregate data on hospitalized cases of influenza A(H1N1)pdm09 in Saudi Arabia between 2010 and 2016. The surveillance system used the case definition proposed by the WHO. The cases were confirmed by performing the real-time PCR (polymerase chain reaction) on upper respiratory samples. MAIN OUTCOME MEASURES: Suspected and confirmed influenza A(H1N1)pdm09 cases. SAMPLE SIZE: 113 502 suspected H1N1 cases and 17 094 (15.1%) confirmed cases. RESULTS: Most of the reported cases were registered in the Riyadh region. During the period of the study, the highest number of confirmed cases, 9262 (54.2 %), was in 2015. The case fatality rate for confirmed cases was 3.6%. CONCLUSION: Influenza A(H1N1)pdm09 showed seasonal trends. The number of suspected influenza cases each year was proportionate to the number of confirmed cases for that year. Riyadh, Jeddah and the Eastern areas (regions with the highest population) reported most of the cases. LIMITATION: Only one strain of H1N1 was tested. CONFLICT OF INTEREST: None.

Dataset comparing the growth of fodder crops and soil structure dynamics in an industrial biosludge amended arid soil.

The dataset in this work compares the response of two fodder crops, alfalfa (Medicago sativa) and buffel grass (Cenchrus ciliaris), to industrial biosludge amendment of an arid soil in the State of Qatar. It also evaluates the response of soil structure parameters in the biosludge-amended soils containing the different fodder crops. The dataset relates to our previously published works detailed subsequently. The underlying data comparing the water storage capacity and pore structure evolution of the planted soils treated with 0.75, 1.5, and 3% biosludge contents, which showed good outcomes in the companion articles, alongside soil only and soil-fertilizer controls, are presented. These are shown in terms of the percentage of irrigation water leached, and variations in the logarithmic mean T2 (i.e., T2LM - a proxy for mean pore size) and cumulative porosity, respectively. Data on plant growth parameters such as the number of days to flowering, plant height, and aboveground fresh biomass weight in individual replicates of the different treatments as a percentage of the soil-fertilizer control are also shown. The dataset shows the different responses of both plants and the planted soils to amendments with industrial biosludge from the wastewater treatment plant of a gas-to-liquid (GTL) plant.

Dataset on the influence of gas-to-liquid biosludge on arid soil properties and growth performance of alfalfa.

The dataset presented here is related to our research article entitled "Effect of gas-to-liquid biosludge on soil properties and alfalfa yields in an arid soil" [1]. It relates to selected performance parameters of alfalfa grown in an arid soil amended with five different (0.75-12%) gas-to-liquid biosludge contents, and selected properties of the soil determined using several material characterization techniques. A detailed description of the raw data relating to figures on alfalfa performance parameters such as fresh biomass weight, plant height, the number of tillers, and biomass elemental content in the companion article is provided alongside additional data on the number of days to flowering. The underlying data for leachate from the soil and underlying spectra and diffractograms for the proton nuclear magnetic resonance (1H-NMR) and X-ray diffraction (XRD) data, respectively, shown in the companion article are presented. These show changes in the pore structure characteristics and the mineralogical composition of the soil, soil-fertilizer, soil-biosludge, and soil-compost mixtures tested over time. Additional data showing the effect of the amendments on the bulk and particle densities of the soil is presented. The dataset demonstrates the influence of the industrial biosludge on arid soil properties and alfalfa yields (Kogbara et al., [1]).

Recycling industrial biosludge for buffel grass production in Qatar: Impact on soil, leachate and plant characteristics.

The agricultural industry in Qatar is highly dependent on using soil enhancing materials due to challenging soil and climatic conditions. Hence, this work investigated the potential of industrial biosludge from the wastewater treatment plant (WWTP) of a Gas-to-Liquids (GTL) plant to enhance an arid soil compared to fertilizer and compost. A fodder crop, buffel grass (Cenchrus ciliaris), was grown in semi-controlled pots containing a typical Qatari agricultural soil and admixtures over a 12-month period. The treatments included soil plus five biosludge percentage contents: 0.75, 1.5, 3, 6 and 12%. These were compared with soil only, soil plus 20-20-20 NPK fertilizer and soil plus 3% compost controls. Analyses of soil physical and chemical properties, the resulting leachate, and plant growth characteristics were conducted at set periods. The results indicate that up to 3% biosludge content led to better plant growth compared to the controls, with the optimum at 1.5% biosludge content for all growth characteristics studied. Biosludge addition to soil increased the volume of different pore types, especially micropores, which enhanced water retention and influenced plant growth. Regression modelling identified leachate Si and Fe concentrations, and biomass K content as the most influential variables for fresh biomass weight, plant height and the number of tillers, respectively. Biosludge addition to the soil around the optimum level did not cause detrimental changes to the resulting leachate and plant biomass. The findings of this work could lead to minimization of biosludge landfilling and allow for savings in fertilizers and irrigation water in arid regions.

Scope and extent of healthcare-associated Middle East respiratory syndrome coronavirus transmission during two contemporaneous outbreaks in Riyadh, Saudi Arabia, 2017.

OBJECTIVE: To investigate a Middle East respiratory syndrome coronavirus (MERS-CoV) outbreak event involving multiple healthcare facilities in Riyadh, Saudi Arabia; to characterize transmission; and to explore infection control implications. DESIGN: Outbreak investigation. SETTING: Cases presented in 4 healthcare facilities in Riyadh, Saudi Arabia: a tertiary-care hospital, a specialty pulmonary hospital, an outpatient clinic, and an outpatient dialysis unit. METHODS: Contact tracing and testing were performed following reports of cases at 2 hospitals. Laboratory results were confirmed by real-time reverse transcription polymerase chain reaction (rRT-PCR) and/or genome sequencing. We assessed exposures and determined seropositivity among available healthcare personnel (HCP) cases and HCP contacts of cases. RESULTS: In total, 48 cases were identified, involving patients, HCP, and family members across 2 hospitals, an outpatient clinic, and a dialysis clinic. At each hospital, transmission was linked to a unique index case. Moreover, 4 cases were associated with superspreading events (any interaction where a case patient transmitted to ≥5 subsequent case patients). All 4 of these patients were severely ill, were initially not recognized as MERS-CoV cases, and subsequently died. Genomic sequences clustered separately, suggesting 2 distinct outbreaks. Overall, 4 (24%) of 17 HCP cases and 3 (3%) of 114 HCP contacts of cases were seropositive. CONCLUSIONS: We describe 2 distinct healthcare-associated outbreaks, each initiated by a unique index case and characterized by multiple superspreading events. Delays in recognition and in subsequent implementation of control measures contributed to secondary transmission. Prompt contact tracing, repeated testing, HCP furloughing, and implementation of recommended transmission-based precautions for suspected cases ultimately halted transmission.

Energy Analysis of a Complementary Heating System Combining Solar Energy and Coal for a Rural Residential Building in Northwest China.

In order to utilize solar energy to meet the heating demands of a rural residential building during the winter in the northwestern region of China, a hybrid heating system combining solar energy and coal was built. Multiple experiments to monitor its performance were conducted during the winter in 2014 and 2015. In this paper, we analyze the efficiency of the energy utilization of the system and describe a prototype model to determine the thermal efficiency of the coal stove in use. Multiple linear regression was adopted to present the dual function of multiple factors on the daily heat-collecting capacity of the solar water heater; the heat-loss coefficient of the storage tank was detected as well. The prototype model shows that the average thermal efficiency of the stove is 38%, which means that the energy input for the building is divided between the coal and solar energy, 39.5% and 60.5% energy, respectively. Additionally, the allocation of the radiation of solar energy projecting into the collecting area of the solar water heater was obtained which showed 49% loss with optics and 23% with the dissipation of heat, with only 28% being utilized effectively.

Infectious MERS-CoV Isolated From a Mildly Ill Patient, Saudi Arabia.

Middle East respiratory syndrome coronavirus (MERS-CoV) is associated with a wide range of clinical presentations, from asymptomatic or mildly ill to severe respiratory illness including death. We describe isolation of infectious MERS-CoV from the upper respiratory tract of a mildly ill 27-year-old female in Saudi Arabia 15 days after illness onset.

Outcome of strict implementation of infection prevention control measures during an outbreak of Middle East respiratory syndrome.

BACKGROUND: The objective of this retrospective cohort study was to assess the impact of implementation of different levels of infection prevention and control (IPC) measures during an outbreak of Middle East respiratory syndrome (MERS) in a large tertiary hospital in Saudi Arabia. The setting was an emergency room (ER) in a large tertiary hospital and included primary and secondary MERS patients. METHODS: Rapid response teams conducted repeated assessments of IPC and monitored implementation of corrective measures using a detailed structured checklist. We ascertained the epidemiologic link between patients and calculated the secondary attack rate per 10,000 patients visiting the ER (SAR/10,000) in 3 phases of the outbreak. RESULTS: In phase I, 6 primary cases gave rise to 48 secondary cases over 4 generations, including a case that resulted in 9 cases in the first generation of secondary cases and 21 cases over a chain of 4 generations. During the second and third phases, the number of secondary cases sharply dropped to 18 cases and 1 case, respectively, from a comparable number of primary cases. The SAR/10,000 dropped from 75 (95% confidence interval [CI], 55-99) in phase I to 29 (95% CI, 17-46) and 3 (95% CI, 0-17) in phases II and III, respectively. CONCLUSIONS: The study demonstrated salient evidence that proper institution of IPC measures during management of an outbreak of MERS could remarkably change the course of the outbreak.