Computer Application Research Articles

Synthesis and characterization of quinoxaline-based rhenium(I) organometallic compounds: Biological and computational applications

Rhenium(I) organometallic compounds (C1-C6) with substituted quinoxaline Schiff base-based ligands (L1-L6) were synthesized and characterized using spectroscopic techniques such as 1HNMR , LC-MS, FT-IR spectroscopy, conductivity measurement, and elemental analysis. CT-DNA was tested under the impact of complexes using absorbance titration and viscosity measuring techniques to evaluate the binding capacities and manner. The compounds bind to the DNA by partial intercalation. The Kb values for all synthesized compounds are in the range of 0.44 to 1.72×105 M−1 in the absorption titration experiment. The evaluation of compounds binding to BSA was carried out using a fluorescence quenching study and the Ksv values were observed in the range of 0.93 to 1.67×104 M−1. The Ksv values for the fluorescence quenching-based DNA binding investigation were observed in the range of 0.75 to 1.87×104 M−1. The findings of the molecular docking investigations support the previously described point to an intercalation type of binding. The protein binding studies of all the synthesized compounds were examined using an absorption titration experiment, and the Kb values for all of the compounds range from 0.64 to 1.35×104 M−1. The structures of Re(I) complexes were optimized using DFT investigations. The antibacterial activity of organometallic compounds was tested against two Gram-positive and three Gram-negative pathogens, and the results showed that organometallic compounds outperform all ligands in this regard. The pharmacokinetic profile of each synthesized molecule was assessed as part of the ADME investigation using the online tools SwissADME and admetSAR. The MCF-7 cancer cell line was used to examine the anticancer effects of the compounds.

Computational application of internationally harmonized defined approaches to skin sensitization: DASS App.

Chemically induced skin sensitization, or allergic contact dermatitis, is a common occupational and public health issue. Regulatory authorities require an assessment of potential to cause skin sensitization for many chemical products. Defined approaches for skin sensitization (DASS) identify potential chemical skin sensitizers by integrating data from multiple non-animal tests based on human cells, molecular targets, and computational model predictions using standardized data interpretation procedures. While several DASS are internationally accepted by regulatory agencies, the data interpretation procedures vary in logical complexity, and manual application can be time-consuming or prone to error. We developed the DASS App, an open-source web application, to facilitate user application of three regulatory testing strategies for skin sensitization assessment: the Two-out-of-Three (2o3), the Integrated Testing Strategy (ITS), and the Key Event 3/1 Sequential Testing Strategy (KE 3/1 STS) without the need for software downloads or computational expertise. The application supports upload and analysis of user-provided data, includes steps to identify inconsistencies and formatting issues, and provides predictions in a downloadable format. This open-access web-based implementation of internationally harmonized regulatory guidelines for an important public health endpoint is designed to support broad user uptake and consistent, reproducible application. The DASS App is freely accessible via https://ntp.niehs.nih.gov/go/952311 and all scripts are available on GitHub ( https://github.com/NIEHS/DASS ).

ИСПОЛЬЗОВАНИЕ АЛГОРИТМА СЛУЧАЙНОГО ПОИСКА ДЛЯ ОПТИМИЗАЦИИ ПРОЦЕССА ВЫДЕЛЕНИЯ БАЗИСНОГО СТОКА И ОПРЕДЕЛЕНИЯ ТРЕНДОВ ДЛЯ Р. ЕШИЛЬЫРМАК (СЕВЕРНАЯ ТУРЦИЯ)

Estimation of baseflow is a complex hydrographic task. Baseflow techniques and coefficients vary from basin to basin, stream to stream, and year to year. In this study, meta-heuristic optimization is used to automatically identify baseflow. The Particle Swarm Optimization (PSO), a meta-heuristic optimization approach, is chosen. The constraint and cost functions were determined using the PSO algorithm, Lyne and Hollick techniques, and a computer application. Over the period 1980-2015, the data were collected at the Kale station in the Yesilırmak River basin to validate the study model. The results show that the hydrographs and baseflow dividing line were separated effectively. It has also been revealed that the PSO has a high speed as well as a high level of precision. In the research, in addition to the baseflow separation, the hydrograph, baseflow, and ratio of the baseflow to the streamflow at the station No. 1402 were assessed using the Mann-Kendall test and Innovative Trend Test (ITA), and as a result, their trends have been found. By the use of both of these methods, it has been shown that all parameters have an unfavorable trend. In addition, the research came to some other significant conclusions, such as the fact that the baseflow declines in tandem with the flow values and that the baseflow rates are low in years with high peak values of the hydrograph.

Interpreted social graph traversal algorithms for enhanced recommendation precision and efficiency in computational applications

Interpreted social graph traversal algorithms for enhanced recommendation precision and efficiency in computational applications

Computational applications in physics: Spectral representation of Green’s function with Python

Este artigo explora o uso do operador de Sturm-Liouville na representação espectral da função de Green, destacando sua aplicabilidade em física matemática. Focamos na representação espectral como uma técnica para resolver equações diferenciais não-homogêneas, utilizando as autofunções do operador de Sturm-Liouville. Demonstramos a aplicação desses conceitos em problemas fundamentais de eletromagnetismo e mecânica quântica, obtendo soluções analíticas e revisitando conceitos. Destacamos o uso da programação em Python para visualização gráfica das soluções, facilitando a compreensão de conceitos matemáticos complexos e oferecendo uma abordagem intuitiva para a análise de fenômenos físicos. Na seção final, enfatizamos como o trabalho proporciona insights valiosos para a solução de equações diferenciais e interpretação de fenômenos físicos. Concluímos apontando para trabalhos futuros, sugerindo uma integração ainda mais profunda entre programação e matemática na física contemporânea, com o objetivo de tornar o aprendizado mais intuitivo.

Navigating pathways to automated personality prediction: a comparative study of small and medium language models.

Recent advancements in Natural Language Processing (NLP) and widely available social media data have made it possible to predict human personalities in various computational applications. In this context, pre-trained Large Language Models (LLMs) have gained recognition for their exceptional performance in NLP benchmarks. However, these models require substantial computational resources, escalating their carbon and water footprint. Consequently, a shift toward more computationally efficient smaller models is observed. This study compares a small model ALBERT (11.8M parameters) with a larger model, RoBERTa (125M parameters) in predicting big five personality traits. It utilizes the PANDORA dataset comprising Reddit comments, processing them on a Tesla P100-PCIE-16GB GPU. The study customized both models to support multi-output regression and added two linear layers for fine-grained regression analysis. Results are evaluated on Mean Squared Error (MSE) and Root Mean Squared Error (RMSE), considering the computational resources consumed during training. While ALBERT consumed lower levels of system memory with lower heat emission, it took higher computation time compared to RoBERTa. The study produced comparable levels of MSE, RMSE, and training loss reduction. This highlights the influence of training data quality on the model's performance, outweighing the significance of model size. Theoretical and practical implications are also discussed.

Refining DIIS algorithms for Si and GaAs solar cells: incorporation of weight regularization, conjugate gradient, and reverse automatic differentiation techniques.

Pivotal enhancements in electronic minimization algorithms, which are vital for the advancement of computational materials science, are introduced in this research. Our research is dedicated to refining the DIIS algorithm specifically for electronic structure calculations of silicon (Si) and gallium arsenide (GaAs) solar cells, aiming to enhance their efficiency and stability. We have enriched DIIS by integrating a weight regularization factor, significantly bolstering its convergence stability. This modification enhances iteration robustness and curtails the average iteration duration, thus streamlining the convergence process. Furthermore, we have incorporated the conjugate gradient (CG) algorithm to proficiently resolve symmetric positive definite residual matrices. This inclusion substantially accelerates the solution-finding process within the DIIS framework. A novel aspect of our research is the application of reverse automatic differentiation (AD), deployed in two distinct methodologies: the construction of the Jacobian matrix and direct chain rule application for gradient computation. These approaches involve sophisticated mathematical techniques that enhance computational precision and efficiency specifically for Si and GaAs solar cell materials in determining the optimal weights for residual combinations during DIIS iterations. The integration of these advanced methods into the DIIS algorithm not only augments its convergence stability but also ensures a substantial reduction in total computational time. Our findings demonstrate that the enhanced DIIS, CG-enhanced DIIS, and AD-integrated DIIS methods collectively lead to a more efficient electronic minimization process. This balance of stability and efficiency is crucial in high-performance computational materials science, particularly for complex systems analysis. The findings of this research represent a notable advancement in computational strategies for Si and GaAs solar cell materials, providing enhanced methodologies and insights that significantly improve the efficiency and stability of electronic structure calculations in these critical components of renewable energy technologies.

Starter Article: Multi-Fluid and Fluid–Structure interactions in Biological systems: Computational Methods and Applications

Starter Article: Multi-Fluid and Fluid–Structure interactions in Biological systems: Computational Methods and Applications

Analysis of three-dimensional modeling technology for complex garment structure design with value of computational geometry application

Analysis of three-dimensional modeling technology for complex garment structure design with value of computational geometry application

SoUth Korean study to PrEvent cognitive impaiRment and protect BRAIN health through Multidomain interventions via facE-to-facE and video communication plaTforms in mild cognitive impairment (SUPERBRAIN-MEET): Protocol for a Multicenter Randomized Controlled Trial.

The SoUth Korea study to PrEvent cognitive impaiRment and protect BRAIN health through lifestyle intervention (SUPERBRAIN) proved the feasibility of multidomain intervention for elderly people. One-quarter of the Korean population over 65 years of age has mild cognitive impairment (MCI). Digital health interventions may be cost-effective and have fewer spatial constraints. We aim to examine the efficacy of a multidomain intervention through both face-to-face interactions and video communication platforms using a tablet personal computer (PC) application in MCI. Three hundred participants aged 60-85 years, with MCI and at least one modifiable dementia risk factor, will be recruited from 17 centers and randomly assigned in a 1:1 ratio to the multidomain intervention and the waiting-list control groups. Participants will receive the 24-week intervention through the tablet PC SUPERBRAIN application, which encompasses the following five elements: managing metabolic and vascular risk factors, cognitive training, physical exercise, nutritional guidance, and boosting motivation. Participants will attend the interventions at a facility every 1-2 weeks. They will also engage in one or two self-administered cognitive training sessions utilizing the tablet PC application at home each week. They will participate in twice or thrice weekly online exercise sessions at home via the ZOOM platform. The primary outcome will be the change in the total scale index score of the Repeatable Battery for the Assessment of Neuropsychological Status from baseline to study end. This study will inform the effectiveness of a comprehensive multidomain intervention utilizing digital technologies in MCI. ClinicalTrials.gov Identifier: NCT05023057.

Artificial Intelligence, Radiomics, and Computational Modeling in Skull Base Surgery.

This chapter explores current artificial intelligence (AI), radiomics, and computational modeling applications in skull base surgery. AI advancements are providing opportunities to improve diagnostic accuracy, surgical planning, and postoperative care. Currently, computational models can assist in diagnosis, simulate surgical scenarios, and improve safety during surgical procedures by identifying critical structures. AI-powered technologies, such as liquid biopsy, machine learning, radiomic analysis, computer vision, and label-free optical imaging, aim to revolutionize skull base surgery. AI-driven advancements promise safer, more precise, and effective surgeries, improving patient outcomes and preoperative assessment.

Guest Editorial: Multi-Fluid and Fluid–Structure Interactions in Biological Systems: Computational Methods and Applications

Guest Editorial: Multi-Fluid and Fluid–Structure Interactions in Biological Systems: Computational Methods and Applications

The Impact of Cooperative membership on Adoption of Modern Technology by Bee farmers in Kenya.

The main aim of this research is to examine the impact of cooperative membership on adoption of modern technology by bee farmers in Kenya. This study’s data comes from a bee farmers survey in Baringo County, Kenya using a sampling size of 201 households. Probit, logit and Instrumental variable models were incorporated to test for the estimation of relationships and the information generated imported into the SPSS computer application (SPSS 2015) and STATA MP 17 for statistical analysis. The results show that education has a negative significant predictor of cooperative membership at a 5% significance level. Attaining secondary levels did not contribute to farmers’ decision to join cooperative membership. Older farmers were more likely to join cooperatives, unlike the young farmers. Using neighbors in cooperatives as an instrumental variable, the findings indicate that farmers with neighbors who were already in cooperative membership were more likely to join cooperatives. In addition, the majority of adopters of modern bee farming technologies had access to both credit and markets and reported to have experienced an increase in income from bee farming. The adoption of modern beekeeping technologies, such as protective gears, and honey extractors, enabled the farmers to produce more and better-quality honey, which fetched higher prices in the market. Cooperative membership increased the probability of adopting modern beekeeping technology. As such, the study recommends that cooperatives should provide more incentives and support to their members, such as training, subsidies, quality assurance, and market linkages to promote the adoption of modern bee farming technologies.

Internal Control and Financial Performance of Accounting Firms in Calamba City

Internal control implementation in accounting firms ensures the integrity of financial information, safeguards assets, and mitigates risks, promoting operational efficiency and compliance with regulatory standards. The main thrust of this study was to describe and determine the relationship between the level of internal control implementation and the financial performance of accounting firms in Calamba City. This study employed a descriptive correlational design and utilized a simple random sampling technique to select the sample, enabling the researcher to obtain representatives from the population. With the aid of the Gpower sample size computation application, this study came up with 80 respondents based on a 0.35 effect size and 95% degrees of confidence. As the primary data collection tool, a validated researcher-made questionnaire was utilized. The data gathered were collected, tallied, analyzed, and interpreted using four-point Likert scales, mean, and Pearson Product-Moment Correlation Coefficient. Based on the results of the study, the internal control of accounting firms in Calamba City was fully implemented, and their financial performance was very high, with a general assessment of 3.43 and it explained that these accounting firms have a definite process and procedures for assessing financial information of the customers to secure and to maintain good financial performance among other competitors in the market. As an output of the study, a proposed action plan based on the COSO framework was proposed to improve the quality of the control environment, risk assessment, information and communication, control activities and monitoring procedures, and improve the financial performance of accounting firms in Calamba City.

Design and Practice of Ideological and Political Education in Computer Application Fundamentals Course for Vocational Undergraduate Students

Design and Practice of Ideological and Political Education in Computer Application Fundamentals Course for Vocational Undergraduate Students

Implementation of Omics Approaches in Unravelling the Potency of Cordyceps militaris in Drug Discovery

: Natural products have long garnered the interest of scientific communities as they have proven to be an effective therapeutic resource that paved the path for medicinal research and drug development. Among them, Cordyceps militaris is an entomopathogenic caterpillar fungus with a renowned history of being utilized as a medicinal remedy for centuries in Eastern civilizations. The number of pharmacological functions reported by this specific fungus resulted in continuous efforts to unravel new effective bioactive compounds and their corresponding mechanism of action. As time progresses, computational techniques become the forefront of genomic and proteomic analysis, besides acting as a platform for integrating various up-to-date multidisciplinary data sources. This review briefly introduces Cordyceps militaris alongside the latest known biologically active compounds and their respective therapeutic potential. The need to implement computational applications to cope with the continuous phytochemical evolution of Cordyceps militaris will be illustrated. Moreover, many databases, mathematical algorithms, or sourcing tools that could benefit data visualization, dissemination, and interpretation aligned to fungal-based research are enumerated, in addition to describing some of the broad discoveries relative to Cordyceps militaris in the past. In conclusion, using advanced computational technology may be the foundation to leverage natural product discovery about Cordyceps militaris and contribute to future mass production of this fungus for commercial purposes in the world pharmaceutical industry.

Innovation and practice of computer application professional curriculum system based on the integrated training mode of education + skills

Innovation and practice of computer application professional curriculum system based on the integrated training mode of education + skills

Deep learning combined with the teaching practice path of computer software application courses under the perspective of “five-education integration”

Abstract As a core course of educational technology, the teaching of application software in colleges and universities faces the problem of insufficient practical ability of learners. This paper constructs a suitable path of computer software application teaching with reinforcement learning by combining reinforcement learning algorithm with students’ cognitive diagnosis theory. Students’ practical level is diagnosed through students’ actual operation results combined with the factors of guessing and mistakes, and appropriate teaching practice paths are recommended based on students’ practical ability combined with Markov decision-making to model the environment of computer software application practice. The TOVE method was used to evaluate the computer application practice course, and the practice paths with individualization were reasoned based on the connection between students’ knowledge points. The results show that among the experimental results of different practice paths, the effectiveness of the deep learning practice path of S1 is the highest at 85%. Compared with the expert-recommended practice path, the deviation value of the path effectiveness of S5 is 1, which indicates that the deep learning practice path is similar to the expert path likewise with a certain degree of specialization. On the Analysis of classroom project practice, the deviation value of the control class is 0.5512, and the deviation value of the experimental class is 4.2662, which is significantly larger than that of the control class, indicating that there is a significant improvement in students’ practical ability under the deep learning teaching practice path.

Solution and gaseous phase sensing of formaldehyde with economical triphenylmethane based sensors: a tool to estimate formaldehyde content in stored fish samples.

The use of formalin to preserve raw food items such as fish, meat, vegetables etc. is very commonly practiced in the present day. Also, formaldehyde (FA), which is the main constituent of formalin solution, is known to cause serious health issues on exposure. Considering the ill effects of formaldehyde, herein we report synthesis of highly sensitive triphenylmethane based formaldehyde (FA) sensors from a single step reaction of inexpensive reagents namely 4-hydroxy benzaldehyde and 2,6-dimethyl phenol. The synthetic method also provides highly pure product in bulk quantity. The analytical activity of the triphenylmethane sensor 1 with a limit of detection (LOD) value of 2.31 × 10-6 M for FA was significantly enhanced through induced deprotonation and thereafter a LOD value of 1.82 × 10-8 M could be achieved. To the best of our knowledge, the LOD value of the deprotonated form (sensor 2) for FA was superior to those of all the FA optical sensors reported so far. The mechanism of sensing was demonstrated by 1H-NMR titration and recording mass spectra before and after addition of FA to a solution of sensor 2. Both sensor 1 and sensor 2 exhibit quenching in emission upon addition of FA. A fluorescence study also demonstrates enhancement in analytical activity of the sensor upon induced deprotonation. Then the sensor was effectively immobilized into a hydrophilic and biocompatible starch-PVA polymer matrix which enabled detection of FA in a 100% aqueous system reversibly. Again, quick and effective sensing of FA in real food samples (stored fish) with the help of a computational application was demonstrated. The sensors have significant practical applicability as they effectively detect FA in real food samples qualitatively and quantitatively.

ENHANCED PEST INFORMATICS SYSTEM

Pest Management System (PMS) is a computer application in Integrated Pest Management. The term encompasses computer-based storage, retrieval, sharing, and optimal use of pest management data, information, and knowledge for problem solving and decision-making. The pest control industry is continuously experiencing novel technologies and products that will improve the way to manage and prevent pests. The development of an intensive electronic crop pest informatics is highly essential to help bridge these gaps. The aim of this research is to design and develop a voice enhanced Pest Informatics System(PIS). Pest Informatics System was designed and implemented using VModel an extension of the Waterfall model, which offers a means of making the development process more feasible. The tools used for the development of the voice enhanced web-based pest management system are PHP/Laravel, Java Script, HTML and CSS. Result shows that PIS consists of over one hundred pests with their scientific names, descriptions and control measures. It is voice enhanced, therefore can assist visually impaired individuals and aid decision support on farm through listening and not necessarily reading. It is web based which makes it easily accessible, compact, educative and enhance decision making. Conclusively, The need to deliver a first hand information to farmers as at when due is critical to good yield. Access to compact information about pests is of great significance to farmers. Enhanced pest informatics system is a smart tool that creates mobile extension services to farmers using a stress free and friendly approach. User’s evaluation result confirmed that the tool is flexible, user friendly, reliable, efficient and robust.