A comprehensive review for removal of non-steroidal anti-inflammatory drugs attained from wastewater observations using carbon-based anodic oxidation process

Non-steroidal anti-inflammatory drugs (NSAIDs) (concentration <µg/L) are globally acknowledged as hazardous emerging pollutants that pass via various routes in the environment and ultimately enter aquatic food chains. In this context, the article reviews the occurrence, transport, fate, and electrochemical removal of some selected NSAIDs (diclofenac (DIC), ketoprofen (KTP), ibuprofen (IBU), and naproxen (NPX)) using carbon-based anodes in the aquatic environment. However, no specific protocol has been developed to date, and various approaches have been adopted for the sampling and elimination processes of NSAIDs from wastewater samples. The mean concentration of selected NSAIDs from different countries varies considerably, ranging between 3992–27,061 µg/L (influent wastewater) and 1208–7943 µg/L (effluent wastewater). An assessment of NSAIDs removal efficiency across different treatment stages in various wastewater treatment plants (WWTPs) has been performed. Overall, NSAIDs removal efficiency in wastewater treatment plants has been reported to be around 4–89%, 8–100%, 16–100%, and 17–98% for DIC, KTP, NPX, and IBU, respectively. A microbiological reactor (MBR) has been proclaimed to be the most reliable treatment technique for NSAIDs removal (complete removal). Chlorination (81–95%) followed by conventional mechanical biological treatment (CMBT) (94–98%) treatment has been demonstrated to be the most efficient in removing NSAIDs. Further, the present review explains that the electrochemical oxidation process is an alternative process for the treatment of NSAIDs using a carbon-based anode. Different carbon-based carbon anodes have been searched for electrochemical removal of selected NSAIDs. However, boron-doped diamond and graphite have presented reliable applications for the complete removal of NSAIDs from wastewater samples or their aqueous solution

The Impact of Molar Proportion of Sodium Hydroxide and Water Amount on the Compressive Strength of Slag/Metakaolin (Waste Materials) Geopolymer Mortar

This investigation aimed to improve great early geopolymer mortar strengths under various parameters with various binder proportions to reduce the use of cement since the OPC production process leads to high emissions of CO2. Hence, to solve this problem, alternative materials were used. In this research, metakaolin (MK) and ground-granulated blast-furnace slag (GGBFS) waste materials were utilized and mixed together with the sodium hydroxide and alkaline activator sodium silicate (NaOH and Na2SiO3). The performance of the various mixtures was assessed via compressive strength testing based on British standards. The compressive strength was found to be highly affected by molar proportion and water amount. The optimum strength was 77.8 MPa for a mix design of 95% GGBFS +5% MK and a 2.5 mass proportion between Na2SiO3 and NaOH (12 Molar), together with a 0.2 water/binder proportion

Graph attention neural network for water network partitioning

Partitioning a water distribution network into several district metered areas is beneficial for its management. Partitioning a network according to its node features and connections remains a challenge. A recent study has realized water network partitioning based on node features or pipe connections individually. This study proposes an unsupervised clustering method for nodes based on a graph neural network, which uses graph attention technology to update node features based on the connections and a neural network to cluster nodes. The similarity between nodes located in each area and the balance of the total water demand between areas are optimized, and the importance of the boundary pipes is calculated to determine the installation position of flowmeters and valves. Three water distribution networks with different structures and sizes are used to verify the proposed model. The results show that the average location differences (LocDiffs) within the areas of the three networks completed by partitioning are 0.12, 0.07, and 0.06, and the total demand differences (DemDiffs) between areas are 0.13, 0.27, and 0.29, respectively. The LocDiff and DemDiff of the proposed method decreased by 6% and 55%, respectively, when compared to the traditional clustering method. Additionally, the proposed method for calculating the importance of boundaries provides an objective basis for boundary closure. When the same number of boundaries are closed, the comprehensive impact of the proposed method on the pipe network decreases by 17.1%. The proposed method can be used in practical applications because it ensures a highly reliable and interpretive water distribution network partitioning method

Corrosion reduction in steam turbine blades using nano-composite coating

The current study aims to reduce the hot corrosion issues in steam turbines for Al-Mussaib thermal power stations. To gain the aim of the study, many experimental tests were conducted by taking a sample from an existing broken steam turbine blade to identify the alloy composition and preparing samples with exact composition by powder metallurgy method, then using the electro-deposition method to coat the prepared samples by three different coating composite materials consists of TiO2 in different ratios (5, 10 and 15) g/l and 5 g/l SiO2 added to Watt's solution. To verify the efficiency of coating, several tests were conducted (surface roughness, hardness, wear, and oxidation test). The obtained results indicated that increasing the Ni-5%SiO2-TiO2 (5, 10 and 15) g/l caused an increase in the coating thickness, which is compatible with increasing the surface roughness. Also, the sample hardness increased after coating, which returned to increasing TiO2 amount (5, 10 and 15) g/l. However, wear resistance for the samples after coating by selected coating composite and 10 g/l TiO2 amount records the highest reduction in the wear of the sample

Assessment of urban green space dynamics influencing the surface urban heat stress using advanced geospatial techniques

Urban areas are mostly heterogeneous due to settlements and vegetation including forests, water bodies and many other land use and land cover (LULC) classes. Due to the overwhelming population pressure, urbanization, industrial works and transportation systems, urban areas have been suffering from a deficiency of green spaces, which leads to an increase in the variation of temperature in urban areas. This study investigates the conceptual framework design towards urban green space (UGS) and thermal variability over Kolkata and Howrah city using advanced remote sensing (RS) and geospatial methods. The low green space is located in the highly built-up area, which is influenced by thermal variations. Therefore, the heat stress index showed a high area located within the central, north, northwestern and some parts of the southern areas. The vegetated areas decreased by 8.62% during the ten years studied and the other land uses increased by 11.23%. The relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) showed significant changes with R2 values between 0.48 (2010) and 0.23 (2020), respectively. The correlation among the LST and the normalized difference built-up index (NDBI) showed a notable level of change with R2 values between 0.38 (2010) and 0.61 (2020), respectively. The results are expected to contribute significantly towards urban development and planning, policymaking and support for key stakeholders responsible for the sustainable urban planning procedures and processes

Evaluation and prediction of groundwater quality for irrigation using an integrated water quality indices, machine learning models and GIS approaches: a representative case study

Agriculture has significantly aided in meeting the food needs of growing population. In addition, it has boosted economic development in irrigated regions. In this study, an assessment of the groundwater (GW) quality for agricultural land was carried out in El Kharga Oasis, Western Desert of Egypt. Several irrigation water quality indices (IWQIs) and geographic information systems (GIS) were used for the modeling development. Two machine learning (ML) models (i.e., adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM)) were developed for the prediction of eight IWQIs, including the irrigation water quality index (IWQI), sodium adsorption ratio (SAR), soluble sodium percentage (SSP), potential salinity (PS), residual sodium carbonate index (RSC), and Kelley index (KI). The physicochemical parameters included T°, pH, EC, TDS, K+, Na+, Mg2+, Ca2+, Cl−, SO42−, HCO3−, CO32−, and NO3−, and they were measured in 140 GW wells. The hydrochemical facies of the GW resources were of Ca-Mg-SO4, mixed Ca-Mg-Cl-SO4, Na-Cl, Ca-Mg-HCO3, and mixed Na-Ca-HCO3 types, which revealed silicate weathering, dissolution of gypsum/calcite/dolomite/ halite, rock–water interactions, and reverse ion exchange processes. The IWQI, SAR, KI, and PS showed that the majority of the GW samples were categorized for irrigation purposes into no restriction (67.85%), excellent (100%), good (57.85%), and excellent to good (65.71%), respectively. Moreover, the majority of the selected samples were categorized as excellent to good and safe for irrigation according to the SSP and RSC. The performance of the simulation models was evaluated based on several prediction skills criteria, which revealed that the ANFIS model and SVM model were capable of simulating the IWQIs with reasonable accuracy for both training “determination coefficient (R2)” (R2 = 0.99 and 0.97) and testing (R2 = 0.97 and 0.76). The presented models’ promising accuracy illustrates their potential for use in IWQI prediction. The findings indicate the potential for ML methods of geographically dispersed hydrogeochemical data, such as ANFIS and SVM, to be used for assessing the GW quality for irrigation. The proposed methodological approach offers a useful tool for identifying the crucial hydrogeochemical components for GW evolution assessment and mitigation measures related to GW management in arid and semi-arid environments

Self-organizing maps of typhoon tracks allow for flood forecasts up to two days in advance

[[abstract]]Typhoons are among the greatest natural hazards along East Asian coasts. Typhoon-related precipitation can produce flooding that is often only predictable a few hours in advance. Here, we present a machine-learning method comparing projected typhoon tracks with past trajectories, then using the information to predict flood hydrographs for a watershed on Taiwan. The hydrographs provide early warning of possible flooding prior to typhoon landfall, and then real-time updates of expected flooding along the typhoon’s path. The method associates different types of typhoon tracks with landscape topography and runoff data to estimate the water inflow into a reservoir, allowing prediction of flood hydrographs up to two days in advance with continual updates. Modelling involves identifying typhoon track vectors, clustering vectors using a self-organizing map, extracting flow characteristic curves, and predicting flood hydrographs. This machine learning approach can significantly improve existing flood warning systems and provide early warnings to reservoir management.[[notice]]補正完

Laparoscopy in management of appendicitis in high-, middle-, and low-income countries: a multicenter, prospective, cohort study.

BACKGROUND: Appendicitis is the most common abdominal surgical emergency worldwide. Differences between high- and low-income settings in the availability of laparoscopic appendectomy, alternative management choices, and outcomes are poorly described. The aim was to identify variation in surgical management and outcomes of appendicitis within low-, middle-, and high-Human Development Index (HDI) countries worldwide. METHODS: This is a multicenter, international prospective cohort study. Consecutive sampling of patients undergoing emergency appendectomy over 6 months was conducted. Follow-up lasted 30 days. RESULTS: 4546 patients from 52 countries underwent appendectomy (2499 high-, 1540 middle-, and 507 low-HDI groups). Surgical site infection (SSI) rates were higher in low-HDI (OR 2.57, 95% CI 1.33-4.99, p = 0.005) but not middle-HDI countries (OR 1.38, 95% CI 0.76-2.52, p = 0.291), compared with high-HDI countries after adjustment. A laparoscopic approach was common in high-HDI countries (1693/2499, 67.7%), but infrequent in low-HDI (41/507, 8.1%) and middle-HDI (132/1540, 8.6%) groups. After accounting for case-mix, laparoscopy was still associated with fewer overall complications (OR 0.55, 95% CI 0.42-0.71, p < 0.001) and SSIs (OR 0.22, 95% CI 0.14-0.33, p < 0.001). In propensity-score matched groups within low-/middle-HDI countries, laparoscopy was still associated with fewer overall complications (OR 0.23 95% CI 0.11-0.44) and SSI (OR 0.21 95% CI 0.09-0.45). CONCLUSION: A laparoscopic approach is associated with better outcomes and availability appears to differ by country HDI. Despite the profound clinical, operational, and financial barriers to its widespread introduction, laparoscopy could significantly improve outcomes for patients in low-resource environments. TRIAL REGISTRATION: NCT02179112

Pooled analysis of WHO Surgical Safety Checklist use and mortality after emergency laparotomy

Background The World Health Organization (WHO) Surgical Safety Checklist has fostered safe practice for 10 years, yet its place in emergency surgery has not been assessed on a global scale. The aim of this study was to evaluate reported checklist use in emergency settings and examine the relationship with perioperative mortality in patients who had emergency laparotomy. Methods In two multinational cohort studies, adults undergoing emergency laparotomy were compared with those having elective gastrointestinal surgery. Relationships between reported checklist use and mortality were determined using multivariable logistic regression and bootstrapped simulation. Results Of 12 296 patients included from 76 countries, 4843 underwent emergency laparotomy. After adjusting for patient and disease factors, checklist use before emergency laparotomy was more common in countries with a high Human Development Index (HDI) (2455 of 2741, 89.6 per cent) compared with that in countries with a middle (753 of 1242, 60.6 per cent; odds ratio (OR) 0.17, 95 per cent c.i. 0.14 to 0.21, P <0001) or low (363 of 860, 422 per cent; OR 008, 007 to 010, P <0.001) HDI. Checklist use was less common in elective surgery than for emergency laparotomy in high-HDI countries (risk difference -94 (95 per cent c.i. -11.9 to -6.9) per cent; P <0001), but the relationship was reversed in low-HDI countries (+121 (+7.0 to +173) per cent; P <0001). In multivariable models, checklist use was associated with a lower 30-day perioperative mortality (OR 0.60, 0.50 to 073; P <0.001). The greatest absolute benefit was seen for emergency surgery in low- and middle-HDI countries. Conclusion Checklist use in emergency laparotomy was associated with a significantly lower perioperative mortality rate. Checklist use in low-HDI countries was half that in high-HDI countries.Peer reviewe