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Aizaz Ali Farman,Muhammad Irfan,Noor Ul Amin,Zaib Jahan,Xiangju Song,Heqing Jiang,Saeed Gul 한국화학공학회 2022 Korean Journal of Chemical Engineering Vol.39 No.11
The selection of an appropriate draw solution is decisive to augment the performance of the forward osmosis (FO) process with maximal permeate flux and minimal reverse solute flux. Inorganic salts provide higher permeate flux in FO, but high reverse salt flux (RSF) associated with greater diffusivity limits their potential application. Herein, the incorporation of organic compounds including sodium acetate (NaAce) and glucose in CaCl2 provides draw solutions with constant osmotic pressure for boosting FO performance. The PRO mode (active layer facing draw solution) is examined with 0.18m/s of cross-flow velocity using a polyamide membrane associated with a polyethyleneimine (PEI) interlayer on a NaOH treated polyacrylonitrile substrate. The 5% NaAce with CaCl2 as a draw solution (DS) delivers better performance against deionized water compared to glucose, resulting in lower RSF (6.64 g/m2 h) and higher water flux (23.9 L/m2 h). The NaAce with CaCl2 suppresses RSF up to 41% without a significant reduction in permeate flux compared to single salt DS. The optimized draw solution concentrates the orange juice, resulting in 2.13 L/m2 h of average water flux and 3.6 g/m2 h of RSF for 72 h, thus concentrating the orange juice from the initial concentration of 13 oBrix to 24 oBrix.
Morphological characterization and biological control of Alitropus typus (Isopoda: Aegidae)
Syed Aizaz Ali SHAH,Asma ASHRAF,Naveeda-Akhtar Qureshi 한국곤충학회 2017 Entomological Research Vol.47 No.6
The Alitropus typus infestation has a serious influence on fish farming in Pakistan. The present study focuses on the external morphology and control of highly infested ectoparasitic isopod Alitropus typus. Sixteen morphological parameters of the whole body were measured and statistically analyzed for mean, standard error and coefficient of variation by using the student t test. Non‐significant variations were observed in the size of pereopods which depict the significance of swimming and attached to its host. For biological control, different concentrations (1000, 5000 and 10 000 ppm) of a leaf extract of four plants (i.e. Euphorbia helioscopia, Ajuga bracteosa, Cannabis sativa and Callistemon citrinus) were tested against A. typus, and the mortality rates were recorded after 12, 24 and 36 h. One way Anova and Tukey tests were used to analyze the results. The recorded percentage mortality was in the order of E. helioscopia > A. bracteosa > C. sativa, whereas C. citrinus did not showed any toxicity. Concentrations of biochemical components like carbohydrates (mg/g), proteins (mg/g), and lipids (mg/dL) of treated isopods were estimated by the phenol‐sulfuric acid process, Lowry's method and biochemistry analyzer, respectively. The protein contents of the insects tested had decreased markedly as compared to control rates, and this might be due to insecticidal stress caused by the extracts. In another biocontrol experiment, crabs were introduced along with isopods into an aquarium. It was then noted that one crab consumes an average of seven isopods/week and can act as a scavenger for dead organisms.
Artificial Intelligence (AI)-based Deep Excavation Designed Program
Yoo, Chungsik,Aizaz, Haider Syed,Abbas, Qaisar,Yang, Jaewon Korean Geosynthetics Society 2018 한국지반신소재학회 논문집 Vol.17 No.4
This paper presents the development and implementation of an artificial intelligence (AI)-based deep excavation induced wall and ground displacements and wall support member forces prediction program (ANN-EXCAV). The program has been developed in a C# environment by using the well-known AI technique artificial neural network (ANN). Program used ANN to predict the induced displacement, groundwater drawdown and wall and support member forces parameters for deep excavation project and run the stability check by comparing predict values to the calculated allowable values. Generalised ANNs were trained to predict the said parameters through databases generated by numerical analysis for cases that represented real field conditions. A practical example to run the ANN-EXCAV is illustrated in this paper. Results indicate that the program efficiently performed the calculations with a considerable accuracy, so it can be handy and robust tool for preliminary design of wall and support members for deep excavation project.
Development of AI-based Prediction and Assessment Program for Tunnelling Impact
유충식,SYED AIZAZ HAIDER2,양재원,TABISH ALI 한국지반신소재학회 2019 한국지반신소재학회 논문집 Vol.18 No.4
In this paper the development and implementation of an artificial intelligence (AI)-based Tunnelling Impact prediction and assessment program (SKKU-iTunnel) is presented. Program predicts tunnelling induced surface settlement and groundwater drawdown by utilizing well trained ANNs and uses these predicted values to perform the damage assessment likely to occur in nearby structures and pipelines/utilities for a given tunnel problem. Generalised artificial neural networks (ANNs) were trained, to predict the induced parameters, through databases generated by combining real field data and numerical analysis for cases that represented real field conditions. It is shown that program equipped with carefully trained ANN can predict tunnel impact assessments and perform damage assessments quiet efficiently and comparable accuracy to that of numerical analysis. This paper describes the idea and implementation details of the SKKU-iTunnel with an example for demonstration.
Development of AI-based Prediction and Assessment Program for Tunnelling Impact
Yoo, Chungsik,HAIDER, SYED AIZAZ,Yang, Jaewon,ALI, TABISH Korean Geosynthetics Society 2019 한국지반신소재학회 논문집 Vol.18 No.4
In this paper the development and implementation of an artificial intelligence (AI)-based Tunnelling Impact prediction and assessment program (SKKU-iTunnel) is presented. Program predicts tunnelling induced surface settlement and groundwater drawdown by utilizing well trained ANNs and uses these predicted values to perform the damage assessment likely to occur in nearby structures and pipelines/utilities for a given tunnel problem. Generalised artificial neural networks (ANNs) were trained, to predict the induced parameters, through databases generated by combining real field data and numerical analysis for cases that represented real field conditions. It is shown that program equipped with carefully trained ANN can predict tunnel impact assessments and perform damage assessments quiet efficiently and comparable accuracy to that of numerical analysis. This paper describes the idea and implementation details of the SKKU-iTunnel with an example for demonstration.
Muhammad ASAD,Mueen Aizaz ZAFAR,Aymen SAJJAD 한국유통과학회 2022 The Journal of Asian Finance, Economics and Busine Vol.9 No.2
The impact of supervisory communication apprehension (SCA) on subordinates’ job performance was investigated in this study. We also examined the impact of task-related uncertainty in mediating the relationship between SCA and subordinate work performance, as well as the role of information-seeking behavior in moderating the relationship between task-related uncertainty and subordinate job performance. A sample of subordinates and their supervisors from public and private sector enterprises in Pakistan were used in the study. The concept of communication apprehension is not limited to a single organization or industry, and the conditions suggest that apprehensive supervisors are likely to exist in different organizations and industries in Pakistan, including banks, telecommunications, and development sector organizations. Company directors and leaders of human resources departments were contacted to reach out to possible respondents. SmartPLS software was used to evaluate the data using a structural equation modeling technique which is commonly used in explanatory studies (Atta et al., 2021). We found evidence to support ideas predicting the association between SCA and subordinate job performance, as well as the mediating role of task-related ambiguity in the relationship. Furthermore, the findings show that information-seeking activity has a moderating effect on the link between task-related ambiguity and subordinate job performance. This is one of the first studies to look at major mediating and moderating mechanisms in the link between SCA and subordinate job performance.
Artificial Intelligence (AI)-based Deep Excavation Designed Program
유충식,HAIDER SYED AIZAZ,Qaisar Abbas,양재원 한국지반신소재학회 2018 한국지반신소재학회 논문집 Vol.17 No.4
This paper presents the development and implementation of an artificial intelligence (AI)-based deep excavation induced wall and ground displacements and wall support member forces prediction program (ANN-EXCAV). The program has been developed in a C# environment by using the well-known AI technique artificial neural network (ANN). Program used ANN to predict the induced displacement, groundwater drawdown and wall and support member forces parameters for deep excavation project and run the stability check by comparing predict values to the calculated allowable values. Generalised ANNs were trained to predict the said parameters through databases generated by numerical analysis for cases that represented real field conditions. A practical example to run the ANN-EXCAV is illustrated in this paper. Results indicate that the program efficiently performed the calculations with a considerable accuracy, so it can be handy and robust tool for preliminary design of wall and support members for deep excavation project.