All projects require a structure to meet project requirements and achieve established goals. This framework is called project management. Therefore, project management plays an important role in national development and economic growth. Project management includes various knowledge areas such as project integration management, project scope management, project schedule management, etc. The article focuses on the resource-constrained project scheduling known as problem so- called the resource-constrained project scheduling problem (RCPSP). The RCPSP is a part of schedule management. The standard RCPSP has two important constraints, resource constraints and precedence relationships of activities during project scheduling. The objective of the problem is to optimize and minimize the project duration, subject to the above constraints. In this paper, we develop a convolutional neural network approach to solve the standard single mode RCPSP. The advantage of this algorithm over conventional methods such as metaheuristics is that it does not need to generate many solutions or populations. In this paper, the serial schedule generation scheme (SSGS) is used to schedule the project activities using an evolved convolutional neural network (CNN) as a tool to select an appropriate priority rule to filter out a candidate activity. The evolved CNN learns according to the eight project parameters, namely network complexity, resource factor, resource strength, average work per activity, etc. The above parameters are the inputs of the network and are recalculated at each step of the project planning. Moreover, the developed network has priority rules which are the outputs of the developed neural network. Therefore, after the learning process, the network can automatically select an appropriate priority rule to filter an activity from the eligible activities. In this way, the algorithm is able to schedule all project activities according to the given project constraints. Finally, the performance of the Convolutional Neural Network (CNN) approach is investigated using standard benchmark problems from PSPLIB in comparison to the MLFNN approach and standard metaheuristics.

Today, improving machine availability is vital for industries to compete in the global market. Spare parts play an essential role in the maintenance and repair of equipment, but planning an extensive network in strategic industries with various spare parts can be very challenging due to the existence of different decision factors. The spare parts supply chain deals with inventory management issues, which necessitates considering the related decisions such as determining the stock level and order quantity. Moreover, demand uncertainty and long supply time make decision-making more complex. This paper presents a repair and supply planning model for repairable spare parts while considering a modified formulation of demand uncertainty to minimize costs. The model determines the optimal stock level, lateral transshipment, assignment of spare part orders to suppliers, equipment to repair centers, and the number of intervals over the planning horizon used in demand estimation. This research contributes to the literature by integrating recent decisions, using demand approximation by piecewise linearization, and considering backorder in warehouses evaluated by queuing models. A hybrid approach, including heuristic and genetic algorithms, is used to optimize the model using data from an oil company. The results show that using piecewise linearization and integrated repair and supply planning decisions optimizes costs and improves performance. Also, the availability is affected by the demand estimation, which necessitates precision prediction.

In the present work, synthesis of phthalimide and naphthalimide derived Biginelli compounds was performed. Allylation of phthalic & naphthalic anhydride, followed by ozonolysis resulted in the formation of N-phathalimido/naphpthalimido acetaldehyde (2 and 7). These aldehydes were subjected to Biginelli reaction using urea/thiourea and divergent β-keto esters in the presence of sulfated tin oxide (5 mol%) as catalyst in ethanol reflux to produce the corresponding dihydropyrimidinone compounds (5a-j and 8a-h). Additionally, both their antioxidant and anti-inflammatory functions were carried out. Compounds 5e, 5f, 5i, and 5j have shown potent to moderate potent activity for both antioxidant and anti-inflamamtory activities when compared to standard. Compounds 8c and 8g have shown potent antioxidant and anti-inflamamtory activities when compared to other compounds.

This paper examines the role of vision as a mediating variable of the relationship between organizational factors and IoT adoption in audit firms in the US. Using a combination of analyses based on structural equation modeling (SEM) and artificial neural network (ANN) technology as the primary research methodology. Seven hypotheses were accepted, including one related to the impact of vision on IoT adoption. In general, all accepted hypotheses had a positive effect on IoT adoption. In addition to the direct positive impact of vision on IoT technology adoption, the magnitude of that effect varied depending on the context of each hypothesis. Drawing evidence from the results, this study demonstrates that vision was a partial mediating variable in the relationship between the organizational factor and IoT adoption. As a result, the model can help audit firms adopt IoT technology successfully. On the other hand, it makes essential recommendations for implementing IoT technology in light of the role that vision plays as a mediating variable in this model. The Technology-Organization-Environment (TOE) framework and Diffusion of Innovation theory (DOI) are combined with the vision to improve model predictive power.

Plant growth regulators (PGR), also known as plant hormones, are a variety of chemical compounds that have a significant impact on the development and differentiation of plant cells. This work aimed to study the effect of various combinations of Plant Growth Regulators (PGRs) for example [6-Benzyladenine (BA), 2,4-dichlorophenoxyaceticg acid (2,4-D), Naphthalene acetic acid (NAA) and indol-3-ylbutyric acid (IBA)] on callus induction and shoot regeneration from anther culture of S1 progenies resulted from Manfalouty cultivar open pollinated. The results showed that highest value of percentage of callus induction was 10.6% on MS medium supplemented with 1 mg / l of NAA and 2 mg / l of BA also highest value of percentage of shoot formation 60 % on MS medium supplemented with 3 mg/ l of NAA and 2 mg /l of BA.

The current technique was developed to estimate the amount of alectinib present in spiked rabbit plasma using liquid chromatographic mass spectrometry. The liquid-liquid extraction method was used, and chromatographic separation was carried out on a C18 (4.6mm id x 50mm) analytical column with a mobile phase consisting of acetonitrile and water with 0.1% formic acid at a volume ratio of 75:25. Alectinib's product m/z +483.2 (parent) 396.1 (product) and the internal standard m/z +447.5 (parent) 380.3 (product) were both obtained using positive ion mode. The calibration curve was linear from 0.5 to 600 ng/ml. The percentage extraction recovery (98.15% → 98.86%), demonstrated excellent matrix and analyte selectivity (% interference = 0), and satisfactory stability study results in all types (% nominal 94.94% → 99.63%). The intra and interday accuracy with % nominal 97 → 98.8%, precision % CV ≤ 2% in all quality control levels. The rabbit model's pharmacokinetic parameters were examined, and alectinib's area under the curve (AUC 0—∞) was 4269 ± 8.13 hr.ng/ml. The half-life of elimination (t1/2) is 8.52 ± 6.66 hours. The currently established approach was used in rabbit blood samples for pharmacokinetic investigations of commercial formulations since it was thought to be a novel, verified bioanalytical method based on experimental results.

Giving credit to debtors can pose a default risk. This risk arises because of an error in analyzing the credit risk rate of the debtor. Therefore, this study aims to design a framework for analyzing the credit risk rate of debtors so that the default risk can be reduced. This framework is created using the integration of factor analysis and Tsukamoto’s fuzzy logic method. This integration method can group many credit assessment variables into several decisive factors. In addition, the integration method can estimate credit risk rate firmly based on the α-predicate of each basic rule. This analytical framework is simulated on credit application data at a Rural Bank, in Indonesia. The simulation results show that there are three factors and one variable to measure the credit risk rate, namely: factor 1 represents repayment capacity, business length, working capital, and liquidity value; factor 2 represents the age and the difference between the granted and the proposed loan amount; factor 3 represents the stay length, character, and credit history; and one variable represents a dependent number. This research is expected to help credit institutions measure the credit risk rate in making credit decisions for prospective debtors.

The processes involved to extract chitosan biopolymers from natural resources often employ hazardous chemicals and long processing time. This work provides a sustainable direct extraction method of chitosan from snail shells. Previous attempts using acetogenin in graviola extracts succeeded in the extraction of chitosan from marine shells. However, the slow reaction has prompted the addition of hydrogen bond acceptor solution into the graviola extract. Choline chloride is an excellent hydrogen bond acceptor mixed with acetogenin as hydrogen bond donor to form natural deep eutectic solvent (NADES) for the direct extraction of chitosan. Chitosan obtained from this method has a degree of deacetylation of 91% and a molecular weight of 481 kDa with fiber-like morphology. The direct extraction of chitosan from NADES consisting of choline chloride and acetogenin has proven to extract chitosan with comparable properties to commercial chitosan.

The website of higher educational institutes is considered a vital communication channel to provide main resources to their stakeholders. It plays an important role in disseminating information about an institute to a variety of visitors at a time. Thus, the quality of an academic website requires special attention to respond to the users’ demands. This study aims to explore the quality of the PSAU website based on data mining techniques. The first step: was collecting opinions about the PSAU website using a survey. After that, data mining processes were used as descriptive and predictive models. The descriptive model was applied to describe and extract the major indicators of website quality. Besides, the predictive model was applied to create models for predicting the website quality level. More than one classification algorithm was used. Naive Bayes and Support Vector Machine have given the best results in all performance indicators, and the achieved accuracy rate for both algorithms was 86% and 84% respectively. The results revealed that the overall quality level of the PSAU website is very good. The usability quality and content quality were very good. The service quality needs more attention. which indicated that the service level is inadequate and needs to be further enhanced. The results of the study should be useful to the deanship of Information Technology at PSAU, and website developers, in redesigning with high quality in terms of its usability, content, and service.

This study used 30 cm long packed by 3 kg soil columns to explore the effects of peat, compost, and charcoal at 5% on the movement of fipronil (99.9 % purity) in clay loam soil and sandy clay loam soil. The soil columns were preconditioned with calcium chloride solution (0.01 M) before applying the pesticide and potassium iodide. Potassium iodide solution (0.2 M) was added at a rate of 10 mL as a water tracer and a quantity of fipronil solution (10 µg g^-1soil) was applied on the soil surface of each column. Next, fipronil was leached from the soil columns with a calcium chloride solution (0.1 M) and the leachates were collected and analyzed. The results show that water tracer I^- leached fast in soil columns. The breakthrough curve of fipronil appeared from leachates of soil columns after iodide for a while, indicating that fipronil is a moderate or lower mobile compound depending on type of soil and type of soil amendments. In general, adding soil amendments (peat, compost, and charcoal) to clay loam soil and sandy clay loam soil resulted in improved fipronil adsorption and decreased the amount of fipronil that leached into groundwater. This study is very useful for preserving groundwater from pollution, especially in the Middle East due to the lack of water sources.