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.     

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.     

Population dynamics of Salmonella spp. and Shigella spp. in ready-to-eat Mediterranean vegetable salads

This study evaluated the behavior of Salmonella and Shigella (5–6 log CFU/g) in tomato–cucumber (TC) salad without additives (control), TC with 1.0% lemon juice and 0.5% salt, TC with 10% wt/wt tahini, coleslaw, and toum sauce at 4, 10, or 24°C for 5 days. At 4°C, both pathogens survived well in all salads, with a 0.2–1.6 log CFU/g reduction after 5 days (except for toum sauce with >3.5 log CFU/g reduction after 4 days). At 10°C, Salmonella in the different TC salads remained constant, whereas Shigella numbers significantly increased by 1.0–1.7 log CFU/g after 5 days. Yet, both pathogens significantly decreased by 1.2–1.4 log CFU/g in coleslaw after 5 days and by >3.5 log CFU/g in toum sauce after 3 days. At 24°C, Salmonella significantly increased in TC salad without additives by 1.4 log CFU/g after 5 days and were below the detection level in the other types of salad after 5 days. However, Shigella numbers significantly increased by 1.0 log CFU/g in TC with tahini, but they significantly declined by 1.9–2.9 log CFU/g in TC salads after 5 days, and the pathogen was not detected in coleslaw and toum sauce after 4 days.     

Effects of blade tenderization,aging method and aging time on meat quality characteristics of Longissimus lumborum steaks from cull Holstein cows

The effects of blade tenderization (BT), two aging methods (dry (D) and wet (W)), and aging time (2 and 23 d) on tenderness, color, and sensory properties of Longissimus lumborum muscles from 12 cull Holstein cows were evaluated. Dry-aged loins had higher combined trim and aging losses than control (C) for both D- and W-aging, mostly because of excess trim losses. BT steaks had WBSF of 33.13 N while C steaks had WBSF of 41.46 N (P = 0.09). Aging decreased WBSF. Blade tenderized steaks had higher cook loss than C steaks. Aging, W-aging, and BT × W-aging improved myofibrillar (sensory) tenderness scores. Aging and/or BT improves sensory panel tenderness cull cow Longissimus lumborum steaks. Aging and blade tenderization combined can increase tenderness and value of Longissimus steaks from cull Holstein cows.     

Relationship among fecal coliforms and <Emphasis Type="Italic"> Escherichia coli </Emphasis>in various foods

For much of the twentieth century, coliform bacteria and especially Escherichia coli have been used as indicators of possible post-processing contamination and the presence of E. coli as an indicator of fecal contamination in foods. In this study, 500 foods in 10 different groups, mainly dairy products, delicatessen products, salads, spices, cream cakes and fresh fruit and vegetable samples, were analyzed for the natural contamination of fecal coliforms and E. coli by the standard most probable number (MPN) method. The difference between weighted means of fecal coliforms and E. coli counts were only 0.246 log₁₀ MPN/g-ml (MPN/gram for solid samples, and MPN/milliliter for liquids). Enumeration results were also evaluated by Pearson's correlation coefficient ( r), Cronbach's alpha (f) and determination coefficient ( r ²) analysis. According to results, although 33 samples contained only non- E. coli fecal coliforms, the results of reliability analyses indicated that fecal coliform counts and E. coli counts may be used interchangeably ( P <0.0001). It can be said that fecal coliform or, preferably E. coli analysis is sufficient for rapid routine determination of fecal contamination, at least for those food groups analyzed in this research.     

Effects of heavy metals and polyelectrolytes in humic substance coagulation under saline conditions

Charge neutralisation plays a major role in heavy metal and humic substance removal in water treatment. Humic substances have no readily identifiable structure and they consist of anionic macromolecules of low to moderate molecular weight. Humic substances are easily coagulated using cationic metals and polyelectrolytes. Different concentrations of humic substances have been coagulated with different concentrations of heavy metals and/or polyelectrolytes. The charge neutralisation was determined using U.V. spectrophotometer. Humic substance removal increased with increasing salinity level until reaching a point where HS destabilization is considered complete and salinity no longer play a role in HS removal. Humic substance removal increased with increasing heavy metals concentration and precipitation was experienced at high concentrations of heavy metals (15–20 mg/L) and low concentration of humic substances (10 mg/L). In addition, HS removal also increased with increasing polyelectrolyte concentration. Diallydimethylammonium chloride (PDADMAC) polyelectrolyte was more effective in humic substance coagulation compared to copolymer of dimethyl aminoethyl acrylate (CoAA). The addition of heavy metals in polyelectrolyte coagulation increased humic substance removal due to the combined charge neutralization of the metals and polyelectrolytes.     

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.