Towards a comprehensive methodology for KBS development

Abstract: The vitality of effective methods and processes for developing knowledge-based systems (KBS) is examined. The paper discusses what should be expected from a methodology. Have their advocates been getting it all wrong all the way? That is certainly a suggested view put forward. The criticism includes an articulation of the major schools of thought in developing KBS; their implicit assumptions and philosophies, especially the prototyping options, are presented in the light of this paper. After the criticism, the philosophical basis of a new comprehensive KBS development methodology is presented. This breeds the line model of development, which in turn creates what is suspected to be an essential concept of thought in the developer's mind.     

Heat transfer and fluid flow characteristics in flash desalination units

The present work investigates the effects of different operating and geometrical parameters on the flashing efficiency of water in a single stage flashing chamber. This chamber uses a submerged rectangular orifice as flow control area. Experiments were performed to investigate the effects of flow rate, liquid level in the flashing chamber, liquid inlet temperature and flashing range, on the flashing efficiency under various design conditions. These comprise flow with baffle placed in the flashing chamber, and wire meshes placed at the orifice to increase the turbulence level.It was found that the efficiency increased with the increase of the turbulence levels, flashing range and liquid inlet temperature; the efficiency deceased with the increase of the flow rate and liquid level in the flashing chamber. The application of numerical procedure to predict flow pattern and heat transfer in the flashing chamber is introduced as a powerful design tool.     

Non-Newtonian Fluids in Spout-Fluid Beds: Mass Transfer Coefficient at Low Reynolds Numbers

Following on from the work of Anabtawi et al. (2003), this study examined how the volumetric liquid-phase mass transfer coefficient, k_La, of oxygen in air in three-phase spout-fluid beds was affected by varying the system parameters of bed height, bed diameter, gas velocity, and liquid velocity. The liquid used was 0.1% CMC solution, displaying a pseudo-plastic rheology, with 1.75 mm glass spheres as packing. The values of the Sherwood number were lower than in previous studies (Anabtawi et al., 2003), in the range 9,000-186,000. Gas velocity had a similar effect on k_La as in a bubble column, with results also giving good agreement with previous work on two-phase and three-phase spouted bed systems. The correlation obtained for the effect of liquid velocity on k_La compared well with that of Schumpe et al. (1989). An increase in the height of packing increased k_La to the power of 0.319, with an increase in column diameter also causing an increase in k_La, which is in agreement with the results of Akita and Yoshida (1973).     

CSatDTA: Prediction of Drug–Target Binding Affinity Using Convolution Model with Self-Attention

Drug discovery, which aids to identify potential novel treatments, entails a broad range of fields of science, including chemistry, pharmacology, and biology. In the early stages of drug development, predicting drug–target affinity is crucial. The proposed model, the prediction of drug–target affinity using a convolution model with self-attention (CSatDTA), applies convolution-based self-attention mechanisms to the molecular drug and target sequences to predict drug–target affinity (DTA) effectively, unlike previous convolution methods, which exhibit significant limitations related to this aspect. The convolutional neural network (CNN) only works on a particular region of information, excluding comprehensive details. Self-attention, on the other hand, is a relatively recent technique for capturing long-range interactions that has been used primarily in sequence modeling tasks. The results of comparative experiments show that CSatDTA surpasses previous sequence-based or other approaches and has outstanding retention abilities.     

ENet-6mA: Identification of 6mA Modification Sites in Plant Genomes Using ElasticNet and Neural Networks

N6-methyladenine (6mA) has been recognized as a key epigenetic alteration that affects a variety of biological activities. Precise prediction of 6mA modification sites is essential for understanding the logical consistency of biological activity. There are various experimental methods for identifying 6mA modification sites, but in silico prediction has emerged as a potential option due to the very high cost and labor-intensive nature of experimental procedures. Taking this into consideration, developing an efficient and accurate model for identifying N6-methyladenine is one of the top objectives in the field of bioinformatics. Therefore, we have created an in silico model for the classification of 6mA modifications in plant genomes. ENet-6mA uses three encoding methods, including one-hot, nucleotide chemical properties (NCP), and electron–ion interaction potential (EIIP), which are concatenated and fed as input to ElasticNet for feature reduction, and then the optimized features are given directly to the neural network to get classified. We used a benchmark dataset of rice for five-fold cross-validation testing and three other datasets from plant genomes for cross-species testing purposes. The results show that the model can predict the N6-methyladenine sites very well, even cross-species. Additionally, we separated the datasets into different ratios and calculated the performance using the area under the precision–recall curve (AUPRC), achieving 0.81, 0.79, and 0.50 with 1:10 (positive:negative) samples for F. vesca, R. chinensis, and A. thaliana, respectively.     

CdS-sensitized TiO2 in phenazopyridine photo-degradation: Catalyst efficiency,stability and feasibility assessment

Mineralization of phenazopyridine, 1, in water, under solar-simulator radiation was efficiently achieved using nanoparticle CdS-sensitized rutile TiO₂, TiO₂/CdS, 2, as photo-catalysts. Despite that, 2 showed two main drawbacks. Firstly, the system was difficult to recover by simple filtration, and demanded centrifugation. Secondly, the sensitizer CdS showed relatively high tendency to leach out hazardous Cd²⁺ ions under photo-degradation reaction conditions. In an attempt to solve out such difficulties, 2 was supported onto sand surface. The sand/TiO₂/CdS system, 3, was easier to recover but showed slightly lower catalytic activity compared to 2. On the other hand, the support failed to prevent leaching of Cd²⁺. This indicates limited future applicability of CdS-sensitized TiO₂ photo-catalyst systems, in solar-based water purification strategies, unless leaching out tendency is completely prevented.     

Effect of anti-scale agents on the solubility of CO2 in seawater at temperatures of 60 to 90ºC and pressures of 1–2 bar

The objective of this work is to present experimental data that would show the effect of temperature, salinity, pressure and the presence of anti-scale additives on CO₂ solubility in seawater. The paper examines the solubility of CO₂ in real seawater and real seawater dosed with two different anti-scale additives. The study has been performed at temperatures between 60ºC and 90ºC and at pressures of 1 and 2 bar. To assess the effect of each anti-scale additive on CO₂ gas solubility varying doses, 2–10 ppm of anti-scale additive have been used. For the purpose of the experimentation an experimental rig has been designed to ensure adequate contact between the gas phase and the aqueous phase. The continuous quantitative analysis of CO₂ concentration flowing from the experimental rig was measured using a CM-5011 Carbon Coulometer. A mass balance was carried out to calculate the amount of CO₂ absorbed into solution; Henry’s law constant was then calculated.     

Malware Detection Using Decision Tree Based SVM Classifier for IoT

The development in Information and Communication Technology has led to the evolution of new computing and communication environment. Technological revolution with Internet of Things (IoTs) has developed various applications in almost all domains from health care, education to entertainment with sensors and smart devices. One of the subsets of IoT is Internet of Medical things (IoMT) which connects medical devices, hardware and software applications through internet. IoMT enables secure wireless communication over the Internet to allow efficient analysis of medical data. With these smart advancements and exploitation of smart IoT devices in health care technology there increases threat and malware attacks during transmission of highly confidential medical data. This work proposes a scheme by integrating machine learning approach and block chain technology to detect malware during data transmission in IoMT. The proposed Machine Learning based Block Chain Technology malware detection scheme (MLBCT-Mdetect) is implemented in three steps namely: feature extraction, Classification and blockchain. Feature extraction is performed by calculating the weight of each feature and reduces the features with less weight. Support Vector Machine classifier is employed in the second step to classify the malware and benign nodes. Furthermore, third step uses blockchain to store details of the selected features which eventually improves the detection of malware with significant improvement in speed and accuracy. ML-BCT-Mdetect achieves higher accuracy with low false positive rate and higher True positive rate.     

Towards more active and stable PdAgCr electrocatalysts for formic acid electrooxidation: The role of optimization via response surface methodology

In this study, multiwall carbon nanotube (MCNT)‐supported Pd (Pd/MWCNT) catalysts are prepared by using NaBH₄ reduction method. In order to maximize the oxidation and reduction of H₂SO₄, synthesis conditions (Pd ratio, molar ratio of NaBH₄/K₂PdCl₄, volume of deionized water, and duration of agitation) are optimized by using response surface methodology (RSM). The optimum synthesis conditions are determined as 58.2% of Pd by weight, 154.6 molar ratio of NaBH₄ to K₂PdCl₄, 19.48 mL of deionized water, and 186.16 min of agitation duration. The effect of electrochemical measurement conditions on the oxidation kinetics of Pd/MWCNT is also investigated by RSM. The optimum electrochemical measurement conditions are found as 10 μL of catalyst mixture, 90°C of H₂SO₄ solution, and 5.5 M H₂SO₄. The Pd/MWCNT, Pd₅₀Ag₅₀/MWCNT, and Pd_65.6Ag_33.6Cr_0.80/MWCNT catalysts prepared under optimized conditions are characterized by using X‐ray diffraction, transmission electron microscopy, N₂ adsorption‐desorption, and inductively coupled plasma mass spectrometry. The crystallite sizes of these catalysts are found as 4.85, 5.66, and 5.26 nm for Pd/MWCNT, Pd₅₀Ag₅₀/MWCNT, and Pd_65.6Ag_33.6Cr_0.80/MWCNT catalysts, respectively. Isotherms of all these catalysts are found to be similar to Type V isotherms with H3 hysteresis loop. The average particle size of Pd₅₀Ag₅₀/MWCNT and Pd_65.6Ag_33.6Cr_0.80/MWCNT catalysts are determined as 5.2 and 9.2 nm, respectively. Electrochemical performance of as‐prepared catalysts is evaluated by using cyclic voltammetry and chronoamperometry. The formic acid electrooxidation (FAEO) activities are found as 18.9, 27.8, and 51.6 mA/cm² for Pd/MWCNT, Pd₅₀Ag₅₀/MWCNT, and Pd_65.6Ag_33.6Cr_0.80/MWCNT, respectively. Pd_65.6Ag_33.6Cr_0.80/MWCNT shows the highest activity and stability. Optimization of synthesis conditions and electrochemical measurement parameters allow us to obtain very good electrochemical activity and stability for FAEO reaction compared with anode catalysts in the literature.