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In classification and identification of plant species, palyno morphological character has remain helpful for plant taxonomist. The aim to investigation this study was to identify the characteristic features of Asteraceae and Poaceae family member collected from lower Himalayas of Pakistan. Light microscope and Scanning electron microscope were used for feature study of allergenic pollen. Our study includes 10 species of Asteraceae and Poaceae family. The most dominant shape of pollen was Oblate-spheroidal, Prolate-Spheroidal, Prolate and Suboblate shape of pollen. Quantitative character of pollen includes maximum pollen diameter found in Dichanthium annulatum (46.0 μm) and minimum polar axis pollen diameter found in Parthenium hysterophorous (16.0 μm). Similarly highest value of P/E ratio was noted in Cenchrus echinatus (1.05) and smallest value of P/E ratio was observed in Erigeron bonariensis (0.88). Poaceae family member were monoporate. Character feature of exine sculpturing include Microechinate, granulate, echinate, conical base, lacunae Nanogammate, Aerolate-Scabratre, Verrucate, like variation found in exine of both family member. Highest value of pollen fertility was observed in P. hysterophorous 93%, Similarly maximum value of pollen sterility was noted in E. bonariensis and Brachiaria reptans 24%. This investigation provides the characteristic feature of allergenic pollen species and help to arrange them in define class. RESEARCH HIGHLIGHTS: Pollen has affected many people around the world. Using microscopic techniques, morpho-palynological analysis of Asteraceae and Poaceae families were recognized. LM and SEM study show both qualitative and quantitative character of pollen. Identification of allergenic pollen and microscopic identified character play important role for proper guideline of identification of pollen having allergenic potential.

Utilization of renewable and alternative energy feedstocks such as nonedible seeds oil to deal with the increasing energy crises and related ecological concerns have gained the attention of researchers. Biodiesel is an efficient and renewable substitute for diesel engine. This work investigates the potential of inexpensive nonedible seed oil of Linum usitatissimum to synthesize biodiesel using iron sulfate green nanocatalyst through the process of transesterification. Flax seed contains about 37.5% oil content estimated through Soxhlet apparatus. Light microscopy revealed that seed size varies from 3.0 to 6.0 cm in length, 2.0 to 3.3 cm in width, and 0.7 to 1.0 mm in diameter. Color of seed varied from yellow to brown. Characterization of biodiesel is performed through GC-MS and FTIR. Scanning electron microscopy was carried out to study the morphological features of seed coat. Catalyst was characterized by scanning electron microscopy, energy diffraction X-ray, and X-ray diffraction. The diffraction peaks of Fe

The establishment of new communities and land reclamation projects in the Egyptian desert areas is one of the most important national targets. One of these areas is the 10th of Ramadan city. The main goal of the study is concerned with delineation of groundwater contaminants plume in the future. The results show that; the ions concentration of most samples from the oxidation ponds resources contain concentrations of Al3+, Cr2+, Fe2+, Ni2+ and Sr2+ exceeding the acceptable limit of WHO (1996) standards. Also, most of the groundwater samples are polluted with Al3+ and Fe2+ and Sr2+ ions. The infiltration from the oxidation ponds and expansion of reclaimed lands irrigated with wastewater reflect critical environmental hazards. The results of the groundwater flow and transport simulation reveal contamination plume expansion. It will travel 1.8, 2.1 and 2.3 km due NE direction after for Al3+, Fe2+ and Sr2+contaminants respectively. It is strongly recommended that wastes from oxidation ponds must not be used in irrigation without tertiary treatment. The oxidation ponds to minimize the pollution of groundwater with heavy metals is highly recommended.

Hydrological modelling in karst environments is a difficult task due to the inherent complexity of the karst system and the usual lack of information about its geometrical description. The hydrological processes of karst systems in Mediterranean semiarid environments are particularly difficult to simulate mathematically due to the pattern of long dry periods and short wet periods. In this study, we tested the ability of the open-source SWAT hydrologic modelling code to simulate the behaviour and hydrologic output of a karstic watershed in a Mediterranean semiarid environment (SE Spain). Calibration and first validation were accomplished using a 20-year and 10-year record of stream water discharge, respectively, at the catchment outlet. Additional testing of the model was accomplished using groundwater discharge data from four natural springs in the watershed and by comparing the results of the SWAT model with the SIMPA model (a hydrological model used by the Spanish national water authority to simulate the water balance among others). Likewise, an adapted form of the Nash-Sutcliffe Efficiency (NSE) index for arid environments, ANSE, is presented. Based on results, the simulated behaviour was good and very good (with ANSE values of 0.96 and 0.78 for the calibration and validation periods respectively). SWAT and SIMPA results provide spatial distributions of the main hydrological processes of the watershed, such as aquifer recharge, actual evapotranspiration, and surface runoff, being verified as useful tools for water policy managers in karstic environments.Keywords:Water balance; karst aquifer springs; SWAT; SIMPA; ANSE index; Mediterranean karstic catchment.

The phenomenon of aeolian sand deposit constitutes a major problem in the region of Ksour Mountains in Algeria, which causes serious social and economic problems. To assess the textural properties of sands, and to investigate the origin of aeolian sand deposit in the study area, eleven (11) samples were collected from the sands of Ain Sefra. Then the granulometry, calcimetry and morphoscopy of the samples were studied. Granulometric testing of samples from 11 locations showed that sand size was very fine to fine grained, with a mean grain value of 226.5 μm. It sort ranged from 1.22 to 1.90, indicating well sorting the sands and skewness values ranging from-0.4 to 0.66, being mainly negative with an average of -0.06, indicating symmetrical to fine skewed values. The values of kurtosis ranges from 0.79 to 1.5, with an average of 0.93, where 64 percent of samples are Mesokurtotic and 36 percent of samples are Platikurtotic. Calcimetric results show a very low percentage of carbonate, which ranges from 1.85 % to 7.4 % and illustrate a very low content of fine fraction and organic matter. The preliminary examinations of the sands in our study area by morphoscopy showed that 73 percent of sand are in RM category, 19 percent in the rounded and shiny (EL) category. In comparison, 08 percent are represented by the fresh and angular (NU) category. The outcome of various experimentation led to the conclusion that the aeolian sand deposit in the study area, are linked to the wind transport from the Moroccan territory such as Chott Tigri, Oued Maader, the corridor between Figuig, Bouarfa and Massa Daraa, which are the dominant sources of sands in the region of the study area. Therefore, the study better understands the origin of sands and their characteristics in this part of the Ksour Mountains.

Recycled aggregate is a good option to be used in concrete production as a coarse aggregate that results in environmental benefits as well as sustainable development. However, recycled aggregate causes a reduction in the mechanical and durability performance of concrete. On the other hand, the removal of industrial waste would be considerably decreased if it could be incorporated into concrete production. One of these possibilities is the substitution of the cement by slag, which enhances the concrete poor properties of recycled aggregate concrete as well as provides a decrease in cement consumption, reducing carbon dioxide production, while resolving a waste management challenge. Furthermore, steel fiber was also added to enhance the tensile capacity of recycled aggregate concrete. The main goal of this study was to investigate the characteristics of concrete using ground granulated blast-furnace slag (GGBS) as a binding material on recycled aggregate fibers reinforced concrete (RAFRC). Mechanical performance was assessed through compressive strength and split tensile strength, while durability aspects were studied through water absorption, acid resistance, and dry shrinkage. The results detected from the different experiments depict that, at an optimum dose (40% RCA, 20%GGBS, and 2.0%), compressive and split tensile strength were 39% and 120% more than the reference concrete, respectively. Furthermore, acid resistance at the optimum dose was 36% more than the reference concrete. Furthermore, decreased water absorption and dry shrinkage cracks were observed with the substitution of GGBS into RAFRC.

Sediment transport in rivers is a nonlinear natural phenomenon, which can harm the environment and hydraulic structures and is one of the main reasons for the dams’ siltation. In this paper, the following artificial intelligence approaches were used to simulate the suspended sediment load (SSL) during periods of flood events in the northeastern Algerian river basins: artificial neural network combined with particle swarm optimization (ANN-PSO), adaptive neuro-fuzzy inference system combined with particle swarm optimization (ANFIS-PSO), random forest (RF), and long short-term memory (LSTM). The comparison of the prediction accuracies of such different intelligent system approaches revealed that ANN-PSO, RF, and LSTM satisfactorily simulated the nonlinear process of SSL. Carefully comparing the results, the ANN-PSO model showed a slight superiority over the RF and LSTM models, with RMSE = 67.2990 kg/s in the Chemourah basin and RMSE = 55.8737 kg/s in the Gareat el tarf basin.

Nano graphite platelets (NGPs) belong to the carbon family and have a huge impact on the construction industry. NGPs are used as multi-functional fillers and have the potential to develop reinforcing within cementitious composites. In this paper, NGPs were incorporated in cementitious composites to investigate the effects of NGPs on the fresh, mechanical, durability, and microstructural properties of concrete. Five mixes were prepared with intrusion of NGPs (0%, 0.5%, 1.5%, 3%, and 5% by weight of cement). The properties studied involved workability, air content, hardened density, compressive strength, tensile strength, flexural strength, sorptivity, ultrasonic pulse velocity (UPV), water absorption, and external sulfate attack. The workability and percent air content decrease by 22.5% and 33.8%, respectively, for concrete with 5% NGPs compared to the control mix. The specimens containing 5% of NGPs revealed the hardened density, compressive, tensile, and flexural strength to increase by 11.4%, 38.5%, 31.6%, and 44.34%, respectively, compared to the control mix. The results revealed that the incorporation of 5%NGPs in cementitious composites reduces the sorptivity and water absorption by 32.2% and 73.9%, respectively, whereas, it increases the UPV value by 7.5% compared to the control mix. Furthermore, the incorporation of NGPs provided better resistance against external sulfate attacks. SEM–EDX spectroscopy was carried out to investigate its microstructural analysis.

For shallow groundwater, hydrogeochemical processes and quality assessment must be addressed because shallow groundwater is freely available in many parts of the globe. Due to recent anthropogenic activities and environmental changes in Sakrand, Sindh, Pakistan, the groundwater is extremely vulnerable. To provide safe drinking and agricultural water, hydrogeochemical analysis is required. Ninety-five groundwater samples were analyzed using agricultural and drinking indices to determine the hydrogeochemical parameters using multivariate analysis such as Pearson correlations, principal component cluster analysis, as well as Piper diagrams and Gibbs plot for drinking and agricultural indices. An abundance of ions was observed through the statistical summary; however, cations and anions were recorded in the orders Na+ > Ca2+ > Mg2+ > K+ and HCO3− > Cl− > SO42− > NO3− > F−. The hydrogeochemical process used to quantify the major reactions occurring in the groundwater system showed rock dominance; the Piper diagrams evaluated the water type. A mixed pattern of calcium, magnesium, and chloride ions (Ca2+−Mg2+−Cl− type) was observed. Additionally, the ion exchange method showed an excess of bicarbonate ions due to carbonic acid weathering. The water quality index (WQI) resulted 32.6% of groundwater being unsuitable for human consumption; however, the United States Salinity Laboratory (USSL) diagram showed 60% of samples were unsuitable for irrigation due to high salinity and the Wilcox diagram depicted 5% of samples lying in the unsuitable region. Most of the water samples were suitable for drinking; only a few samples were unsafe for drinking purposes for children due to the high hazard index.

Groundwater contamination along with anthropogenic actions and land use forms are increasing threats in urbanized zones around the world. Additionally, water quality and quantity are declining due to urbanization development. DRASTIC parameters (depth to the water table, net recharge, aquifer media, soil media, topography, impact of the vadose zone, hydraulic conductivity) were considered to investigate hydrological characteristics for assessment of contamination. Having a major effect of anthropogenic activities, various susceptibility zones were produced by modifying the DRASTIC model into DRASTICA, integrating anthropogenic effects as the “A” parameter in an alphabetic system. After the assessment, the research exposes that from the total area, 14% is under very high susceptibility, 44% is of high susceptibility, 39% is of moderate susceptibility, and 3% is of low susceptibility to groundwater pollution. The results in the built-up areas and based on the parameter of nitrate in quality of water show that the altered DRASTIC model or DRASTICA model proved to give better outcomes compared with the usual DRASTIC model. The policy advisers and management authorities must use the analysis data as precaution measures so that future calamities can be avoided.

This study presents the performance of quarry rock dust (QRD) incorporated fly ash (FA) and slag (SG) based geopolymer concretes (QFS-GPC) exposed to ambient and elevated temperatures. A total of five QFS-GPC mix types were prepared. The quantity of FA (50%) was kept constant in all the mixes, and SG was replaced by 5%, 10%, 15%, and 20% of QRD. The fresh, hardened properties of the QFS-GPC mixes, viz., workability, compressive strength, splitting tensile strength, and flexural strengths, and XRD for identification of reaction phases were evaluated. The prepared mixes were also heated up to 800 °C to evaluate the residual compressive strength and weight loss. The workability of the QFS-GPC mixes was observed to be reduced by increasing the dosage (0 to 20%) of QRD. Superplasticizer (SP) was used to maintain the medium standard of workability. The compressive, tensile, and flexural strengths were increased by replacing SG with QRD up to 15%, whereas a further higher dosage (20%) of QRD reduced the mechanical strengths of the QFS-GPC mixes. The strength of the QFS-GPC specimens, heated to elevated temperatures up to 800 °C, was reduced persistently with the increased contents of QRD from 0 to 20%. It was concluded from the study that QFS-GPC can be used to achieve 30 MPa strength of concrete.

Understanding of behavior, variability, and links between hydrological series is a key element for successful long-term water resources planning and management. In this study, various time-scale-based methods such as correlation and spectral analysis (CSA), cross wavelet (XWT), and wavelet coherence transform (WCT) were applied to assess the response of daily rainfall and karst spring discharge for the Sebaou River basin, which is located on Mediterranean basin in northern Algeria. The CSA revealed that the hydrogeological systems under study are characterized by various memory effect (small, poor, reduced, and extensive) with regularization times ranging from 5 to 50 day. XWT between rainfall and discharge time series indicates few marked disruptions in the spectra between the 1980s and 1990s corresponding to the dry period. The annual process is visible, dominant, and more amplified compared to the multi-annual fluctuations that characterize the 1-3- and 3–6-year modes, which explained the multi-annual regulation. The nonlinear relationship of the short-term components seems to be linked to the periods of storage (infiltration). Compared to the WCT components of 2–5, 26, and 52 weeks, there is a strong coherence for 102 weeks, which explains the long-term component, indicating a quasi-linearity of the rainfall-runoff relationship. According to the obtained results, the construction of more water resources structures is recommended to increase the water storage and improve the water supply due to the richness of the hydrographic network. On the other hand, the impacts of human activities on streamflow due to the looting of rocks and sands in the Sebaou River valleys have reached alarmingly high levels that require urgent intervention for the protection of water and ecological resources and their better rational use.

The adsorption of dyes using 39 adsorbents (16 kinds of agro-wastes) were modeled using random forest (RF), decision tree (DT), and gradient boosting (GB) models based on 350 sets of adsorption experimental data. In addition, the correlation between variables and their importance was applied. After comprehensive feature selection analysis, five important variables were selected from nine variables. The RF with the highest accuracy (R2 = 0.9) was selected as the best model for prediction of adsorption capacity of agro-waste using the five selected variables. The results suggested that agro-waste characteristics (pore volume, surface area, agro-waste pH, and particle size) accounted for 50.7% contribution for adsorption efficiency. The pore volume and surface area are the most important influencing variables among the agro-waste characteristics, while the role of particle size was inconspicuous. The accurate ability of the developed models’ prediction could significantly reduce experimental screening efforts, such as predicting the dye removal efficiency of agro-waste activated carbon according to agro-waste characteristics. The relative importance of variables could provide a right direction for better treatments of dyes in the real wastewater.

Owing to the extensive global dependency on groundwater and associated increasing water demand, the global groundwater level is declining rapidly. In the case of Islamabad, Pakistan, the groundwater level has lowered five times over the past five years due to extensive pumping by various departments and residents to meet the local water requirements. To address this, water reservoirs and sources need to be delineated, and potential recharge zones are highlighted to assess the recharge potential. Therefore, the current study utilizes an integrated approach based on remote sensing (RS) and GIS using the influence factor (IF) technique to delineate potential groundwater recharge zones in Islamabad, Pakistan. Soil map of Pakistan, Landsat 8TM satellite data, digital elevation model (ASTER DEM), and local geological map were used in the study for the preparation of thematic maps of 15 key contributing factors considered in this study. To generate a combined groundwater recharge map, rate and weightage values were assigned to each factor representing their mutual influence and recharge capabilities. To analyze the final combined recharge map, five different assessment analogies were used in the study: poor, low, medium, high, and best. The final recharge potential map for Islamabad classifies 15% (136.8 km2) of the region as the “best” zone for extracting groundwater. Furthermore, high, medium, low, and poor ranks were assigned to 21%, 24%, 27%, and 13% of the region with respective areas of 191.52 km2, 218.88 km2, 246.24 km2, and 118.56 km2. Overall, this research outlines the best to least favorable zones in Islamabad regarding groundwater recharge potentials. This can help the authorities devise mitigation strategies and preserve the natural terrain in the regions with the best groundwater recharge potential. This is aligned with the aims of the interior ministry of Pakistan for constructing small reservoirs and ponds in the existing natural streams and installing recharging wells to maintain the groundwater level in cities. Other countries can expand upon and adapt this study to delineate local groundwater recharge potentials.

More than 8000 pedestrians were killed due to road crashes in Australia over the last 30 years. Pedestrians are assumed to be the most vulnerable users of roads. This susceptibility of pedestrians to road crashes conflicts with sustainable transportation objectives. It is critical to know the causes of pedestrian injuries in order to enhance the safety of these vulnerable road users. To achieve this, traditional statistical models are used frequently. However, they have been criticized for their inflexibility in handling outliers and missing or noisy data, and their strict pre-assumptions. This study applied an advanced machine learning algorithm, a Bayesian neural network, which has the characters of both Bayesian theory and neural networks. Several structures of this model were built, and the best structure was selected, which included three hidden neuron layers—sixteen hidden nodes in the first layer and eight hidden nodes in the second and third layers. The performance of this model was compared with the performances of some other machine learning techniques, including standard Bayesian networks, a standard neural network, and a random forest model. The Bayesian neural network model outperformed the other models. In addition, a study on the importance of the features showed that the individuals’ characteristics, time, and circumstantial factors were essential. They greatly increased model performance if the model used them. This research lays the groundwork for using machine learning approaches to alleviate pedestrian deaths caused by road accidents.

Due to the rapid increase in population, the use of automobile vehicles increases day by day, which causes a considerable increase in the waste tires produced worldwide. Research studies are in progress to utilize scrap tires and waste rubber material in several fields to cater the pollution problems in a sustainable and environmentally friendly manner. In this research, the shredded waste tires were used in concrete to replace fine aggregates in different percentages. The fine aggregates in the rubberized concrete were replaced 10%, 15%, and 20% by rubber. The stress–strain behavior of the concrete models is then determined and compared with the already established analytical models, i.e., Modified Kent and Park Model, Mander’s model, and Razvi and Saatcioglu Model. A total of 12 standard concrete cylinders and 18 models of each type of concrete, i.e., normal concrete, reinforced rubberized concrete with 10%, 15%, and 20% addition of rubber, were fabricated. Specimens fabricated in each replacement of rubber were laterally confined, employing 3 in (76 mm) and 6 in (152 mm) c/c tie spacing. The model and cylinders were subjected to uni-axial compression tests using Universal Testing Machine (UTM). The drop in compressive strength, stress–strain constitutive law, strain limits, and overall behavior of the rubberized reinforced concrete were explored experimentally. The results were then compared with the analytical results of the established models. The research can help explore the possible future for the use of rubberized concrete for the potential application as a structural material.

Worker productivity is critical within construction projects as it is the measure of the rate at which work is performed and, more importantly, helps to know how to motivate them to perform at high levels. This research aimed to examine the impact of employee age and industry experience on the intrinsic workforce diversity factors influencing construction worker productivity. Sieving through the previous research and models and theories of analysis, the intrinsic workforce diversity was modeled into the following set of factors, i.e., income, motivation, psychosocial factors, and technical skills. The data were collected by means of a questionnaire survey and examined for the employees having different ages and experiences using the Mann–Whitney U test through SPSS. The results show that employees of varied ages do not concur over motivation-, psychosocial, and technical skills-related workforce diversity factors, whereas employees of varied industrial experiences are in disagreement over some income and motivation related workforce diversity factors. In order to overcome intrinsic workforce diversity, firm support is direly needed for old and mature employees in terms of financial incentives leading to motivation, less supervised scheduling, opportunities for firm advancement, and reporting back every time work is completed. Furthermore, support is required for young employees who are more susceptible due to psychosocial stresses like unevenly distributed work, communication gaps, and technical skills like knowledge of technological equipment and advancement in construction technology which has reduced the skills of workers.

The reduction of chemical oxygen demand (COD) from palm oil mill effluent (POME) is very significant to ensure aquatic protection and the environment. Continuous adsorption of COD in a fixed bed column can be an effective treatment process for its reduction prior to discharge. Adsorption capacity of bone derived biocomposite synthesized from fresh cow bones, zeolite, and coconut shells for the reduction in the organic pollutant parameter was investigated in this study in a fixed bed column. The effect of influent flow rate (1.4, 2.0, and 2.6 mL/min) was determined at an influent pH 7. The optimum bed capacity on the fabricated composite of surface area of 251.9669 m2/g was obtained at 1.4 mL/min at breakthrough time of 5.15 h influent POME concentration. The experimental data were fitted to Thomas, Adams–Bohart, and Yoon–Nelson models fixed bed adsorption models. It was revealed that the results fitted well to the Adams Bohart model with a correlation coefficient of R2 > 0.96 at different influent concentration. Adsorption rate constant was observed to increase at lower flow rate influent concentration, resulting in longer empty bed contact time (EBCT) for the mass transfer zone of the column to reach the outlet of the effluent concentration. In general, the overall kinetics of adsorption indicated that the reduction in COD from POME using a bone-biocomposite was effective at the initial stage of adsorption. The pore diffusion model better described the breakthrough characteristics for COD reduction with high correlation coefficient. Shorter breakthrough time compared to EBCT before regeneration indicated that the bone composite was suitable and effective for the reduction in COD from POME using fixed bed column adsorption.

This research aims to assess the efficiency of the synthesized corncob as a cost-effective and eco-friendly adsorbent for the removal of heavy metals. Therefore, to carry out the intended research project, initially, the corncob was doped with nanoparticles to increase its efficiency or adsorption capacity. The prepared adsorbent was evaluated for the adsorption of cadmium (Cd) and chromium (Cr) from aqueous media with the batch experiment method. Factors that affect the adsorption process are pH, initial concentration, contact time and adsorbent dose. The analysis of Cd and Cr was performed by using atomic absorption spectrometry (AAS), while the characterization of the adsorbent was performed using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM). The results showed that there is a significant difference before and after corncob activation and doping with CeO2 nanoparticles. The maximum removal for both Cd and Cr was at a basic pH with a contact time of 60 min at 120 rpm, which is 95% for Cd and 88% for Cr, respectively. To analyze the experimental data, a pseudo-first-order kinetic model, pseudo-second-order kinetic model, and intra-particle diffusion model were used. The kinetic adsorption studies confirmed that the experimental data were best fitted with the pseudo-second-order kinetic model (R2 = 0.989) and intra-particle diffusion model (R2 = 0.979). This work demonstrates that the cerium oxide/corncob nanocomposite is an inexpensive and environmentally friendly adsorbent for the removal of Cd and Cr from wastewater.

In this study, bentonite (a naturally occurring pozzolana) was incorporated as a partial replacement (up to 20%) for high-calcium fly ash (HCFA)-based geopolymeric natural aggregate concrete (GNAC) and geopolymeric recycled aggregate concrete (GRAC). The mechanical (compressive strength and splitting tensile strength), durability (chloride migration coefficient, water absorption, and acid attack resistance), and rheological properties (slump test, fresh density, and workability) were investigated. The results revealed that incorporation of bentonite (10 wt % with ordinary Portland cement) showed appreciable improvement in the strength and durability of both the GNAC and GRAC, though its effect is more significant for GRAC than the GNAC.

For nuclear power plants construction, each country has to make use of its own available raw materials. Therefore, through the current research we had to search for the relevant local materials suitable for usage as concrete components and satisfy the needed requirements. The target of this research is to investigate the effect of some type’s heavy aggregates on the physico-mechanical properties and attenuation coefficient of the high performance heavy concrete used as radiation shielding of nuclear facilities. For this purpose radiation shielding properties of high performance heavy concrete mixture prepared using different types of heavy aggregates such as magnetite in addition to industrial waste of lead slag as a coarse aggregate and fine aggregate of local sand or replacing sand by fine magnetite or lead slag, were investigated comparatively with hardened dolomite concrete. The physico-mechanical properties of type aggregates and hardened concretes were examined. Gamma transmission parameters for the studied concrete for Cs137 and Co60 as gamma source were measured, also the effective removal cross-section (ΣR) of fast neutrons spectra for a wide range of neutron energies of [2–12 MeV] with thicknesses 4, 6, 8, 10 and 12 cm of heavy concrete mixes for Plutonium-Beryllium as neutrons source. While the thermal neutrons tests were performed using californium radioisotope. An innovative material that the current research has introduced to be used as a heavy coarse aggregate in HPC is “lead slag”, lead slag has been converted from a subsidiary industrial and environment polluting waste to a very valuable material as one of the ingredients of heavy concrete. It was found that, the magnetite and lead slag aggregates improved the physico-mechanical properties of heavy concrete compared to dolomite concrete. Replacing fine sand by the fine aggregates of magnetite or lead slag enhances the efficiency of attenuation by the concrete mixes against these radiations and the compressive strength values increase by 12 and 27% respectively, of heavy concrete after 28 days. The concrete mix containing lead slag aggregate has higher mechanical properties and enhances the attenuation properties for fast neutrons, gamma rays and thermal neutrons than the other concrete mixes.

Dye solution temperature influences the elimination efficiency of water-soluble and anionic acid dye. Acid Blue 25 dye, using a gas–liquid electrical discharge system, was successfully investigated. The results showed an increase in the percentage of dye decolorization from 91.16% to 96.12% when the dye solution temperature was increased from 278 K to 308 K. However, the initial dye decolorization percentage was decreased with the further increase in dye solution temperature from 318 K to 358 K. The 2D simulation model was introduced to consider the influence of temperature and the electric field generated by corona discharge plasma in air and water. Results also showed a great match between the experimental and the simulation results. The reaction rates of dye degradation were analyzed using the Arrhenius equation. Furthermore, pseudo-zero-, pseudo-first-, and pseudo-second-order models were used to determine the reaction kinetics. The best fit for the experimental data would follow the pseudo-first-order model. Finally, electrical energy per order, energy yield, and experimental degradation data were calculated to investigate the cost analysis.

The boiling heat transfer performance of porous surfaces greatly depends on the morphological parameters, liquid thermophysical properties, and pool boiling conditions. Hence, to develop a predictive model valid for diverse working fluids, it is necessary to incorporate the effects of the most influential parameters into the architecture of the model. In this regard, two Bayesian optimization algorithms including Gaussian process regression (GPR) and gradient boosting regression trees (GBRT) are used for tuning the hyper-parameters (number of input and dense nodes, number of dense layers, activation function, batch size, Adam decay, and learning rate) of the deep neural network. The optimized model is then employed to perform sensitivity analysis for finding the most influential parameters in the boiling heat transfer assessment of sintered coated porous surfaces on copper substrate subjected to a variety of high- and low-wetting working fluids, including water, dielectric fluids, and refrigerants, under saturated pool boiling conditions and different surface inclination angles of the heater surface. The model with all the surface morphological features, liquid thermophysical properties, and pool boiling testing parameters demonstrates the highest correlation coefficient, R2 = 0.985, for HTC prediction. The superheated wall is noted to have the maximum effect on the predictive accuracy of the boiling heat transfer coefficient. For example, if the wall superheat is dropped from the modeling parameters, the lowest prediction of R2 (0.893) is achieved. The surface morphological features show relatively less influence compared to the liquid thermophysical properties. The proposed methodology is effective in determining the highly influencing surface and liquid parameters for the boiling heat transfer assessment of porous surfaces.