Comprehensive Application Research Articles

Research Progress on Bioactive Substances of Beets and Their Functions

As a globally cultivated and economic crop, beets are particularly important in the cane sugar and feed industries. Beet pigments are among the most important natural pigments, while various chemical components in beets display beneficial biological functions. Phenolic substances and betalains, as the main bioactive compounds, determine the functional characteristics of beets. This review categorizes the basic types of beets by the chemical composition of bioactive substances in their leaves, stems, and roots and emphatically summarizes the research progress made on the functions of two major substances in different types of beets: phenolic compounds and betalain pigments. This study provides useful insights for the comprehensive and effective application of beets in the health food and pharmaceutical industries.

Antioxidant Function and Application of Plant-Derived Peptides.

With the development of society and the improvement of people's health consciousness, the demand for antioxidants is increasing. As a natural antioxidant with no toxic side effects, antioxidant peptides are widely used in food, cosmetics, medicine, and other fields because of their strong antioxidant capacity and easy absorption by the human body. Plant-derived antioxidant peptides have attracted more attention than animal-derived antioxidant peptides because plants are more diverse than animals and produce a large number of protein-rich by-products during the processing of their products, which are the main source of antioxidant peptides. In this review, we summarize the source, structure and activity, other biological functions, mechanism of action, and comprehensive applications of plant antioxidant peptides, and look forward to their future development trends, which will provide a reference for further research and development of plant antioxidant peptides.

Comprehensive Analysis of Security Requirements Engineering Approaches with Assurance Perspective

Given the wide distribution of software and its current growth, security is inevitable, and software systems have become more complex. To avoid the complexity and protect valuable assets of software systems, researchers, and practitioners in the fields of security requirement engineering (SRE) and security assurance advise incorporating security requirements early in software development life cycle (SDLC). Nowadays, security requirements specification and identification are an active research area for ensuring the effectiveness of software systems. However, security requirement identification is a challenging task due to sheer numbers of threats and attacks to a software system. Previous researchers have identified different SRE techniques, each looking at the same problem from a different perspective. Therefore, a literature review conducted on SRE techniques. We compared and analyzed different SRE techniques to find the most suitable SRE techniques for researchers and practitioners. Selected SRE techniques compared based on various parameters, such as different attributes, requirement elicitation, requirement analysis, project size, and integration of standards. The literature also indicates that among SRE approaches, security quality requirements engineering (SQAURE), secure tropos, security requirements engineering process (SREP), and comprehensive lightweight application security process (CLASP) are the most used SRE approaches that focus on activities such as threats, security requirements identification, and security objectives identification. Unfortunately, several SRE approaches fail to explicitly integrate the security standard and security assurance perspective. Three questions developed for this study: Question 1 is based on comparison of existing SRE approaches, Question 2 is based on SRE with security requirements elicitation and analysis, and Question 3 highlights the incorporation of security requirements assurance by SRE approaches implicitly or explicitly. The researcher must work with the developer to easily adopt SRE approaches to elicit and ensure security requirements for the software system.

Research and Empirical Evidence of Machine Learning based Financial Statement Analysis Methods

This study presents a novel approach called FinAnalytix which merging machine learning’s prowess in pattern recognition with financial statement analysis. This integrated algorithm combines deep neural networks and recurrent neural networks for predictive accuracy in stock return analysis, alongside logistic regression and random forest models for robust fraud detection in financial statements. The empirical evidence demonstrates FinAnalytix effectiveness in identifying abnormal financial patterns and predicting market reactions to earnings announcements. The study utilizes extensive data from listed companies, ensuring a comprehensive and practical application. FinAnalytix represents a significant advancement in the field, providing a dual approach to financial analysis for enhancing investment strategies through accurate stock return forecasts and bolstering financial integrity by detecting fraudulent activities. The simulation of the study based on the financial data of 100 sample listed companies. This research not only bridges the gap between traditional financial analysis and modern machine learning techniques but also offers a powerful tool for investors and regulatory bodies in navigating the complex financial landscape.

First principles design of two dimensional TiSSe Janus drug delivery system for nitrosourea.

In this paper, we delved into the exploration of a novel drug delivery platform for nitrosourea, leveraging a Janus-structured two-dimensional material, TiSSe, as the carrier. Our approach was grounded in a comprehensive application of first-principles computational methods. By evaluating the adsorption energies across a spectrum of potential configurations, we demonstrated the favorable attributes of TiSSe as a carrier for nitrosourea. Our in-depth examination of the electronic structure unveiled intriguing insights. The Janus nature of TiSSe imparts distinct adsorption profiles to nitrosourea molecules at the sulfur (S) and selenium (Se) terminated surfaces. This disparity in electronic properties not only facilitates precise detection but also helps the design of more intriguing two-dimensional materials.

OPTIMIZATION OF HEAT CONSUMPTION IN CENTRAL HEATING SYSTEMS AT COMMERCIAL FACILITIES

Rising energy prices and stricter environmental regulations are exacerbating the problem of inefficient heat consumption in the commercial sector. Central heating systems of office buildings, shopping malls, and industrial complexes are often characterized by excessive thermal energy consumption, which leads to significant economic losses and a negative impact on the natural environment. The purpose of the study is to study the directions and trends of optimization in the field under consideration (taking into account modern technological capabilities, and economic factors). There are disagreements in the professional community about the priority of measures to improve energy efficiency: some experts prefer improving the thermal insulation of buildings, others — the introduction of intelligent control systems, and others — the integration of renewable energy sources. The present study demonstrates that the greatest effect is achieved with a systematic approach combining various initiatives. The conclusion is formulated that in the current conditions and the future, a comprehensive application of modern thermal insulation materials, highly efficient heating equipment, intelligent management mechanisms, and alternative energy sources is required. The article is of interest to engineers-designers of heating systems, energy managers of commercial facilities, specialists in energy efficiency of buildings, and heads of companies interested in optimizing operating costs and improving the environmental friendliness of their real estate.

Hierarchical deep compartment modeling: A workflow to leverage machine learning and Bayesian inference for hierarchical pharmacometric modeling.

Population pharmacokinetic (PK) modeling serves as the cornerstone for understanding drug behavior within a specific population. It integrates subject covariates to elucidate the variability in PK parameters, thus enhancing predictive accuracy. However, covariate modeling within this framework can be intricate and time-consuming due to the often obscure structural relationship between covariates and PK parameters. Previous attempts, such as deep compartment modeling (DCM), aimed to streamline this process using machine learning techniques. Nonetheless, DCM fell short in assessing residual errors and interindividual variability (IIV), potentially leading to model misspecification and overfitting. Furthermore, DCM lacked the ability to quantify model uncertainty. To address these limitations, we introduce hierarchical deep compartment modeling (HDCM) as an advancement of DCM. HDCM harnesses machine learning to discern the interplay between covariates and PK parameters while simultaneously evaluating diverse levels of random effects and quantifying uncertainty through Bayesian inference. This tutorial provides a comprehensive application of the HDCM workflow using open-source Julia tools.

Progressive failure characteristics of coal under uniaxial compression: A comprehensive application of micro-ct and digital volume correlation

Progressive failure characteristics of coal under uniaxial compression: A comprehensive application of micro-ct and digital volume correlation

Research on Blended Teaching in Integrated English Course Based on BOPPPS Model

The BOPPPS model has been widely used in information-based teaching reform, which emphasizes student-centered principles and student participation and interaction, including six steps: bridge-in, objective, pre-assessment, participatory learning, post-assessment, and summary. This paper applies the BOPPPS model in the Integrated English course at the three stages of teaching practice—before class, during class, and after class—to achieve blended teaching. It shows that this teaching mode provides students with a more abundant learning experience, helps to stimulate students’ learning interests, and improves their comprehensive language application skills.

Liberation Theology in Indonesia: The Role of Cak Nun and Maiyah Community in Social Transformation

This article aims to examine the application of liberation theology in Indonesian Islam, focusing on the efforts of Muhammad Ainun Nadjib (Cak Nun) and the Maiyah Community Assembly, analyzed through Ali Shari’ati's concept of rausyanfikr. The study aims to explore how Cak Nun contextualizes Islam to address contemporary issues of injustice, oppression, and social alienation. Utilizing qualitative library research methods, it investigates various literary sources to evaluate the role of Islamic teachings in cultural and social emancipation. Findings indicate that Cak Nun and the Maiyah Community Assembly have played crucial roles in heightening community awareness about prevalent oppression, employing the Quran and Islamic thought as tools for liberation. The study concludes that Islam, as interpreted by Cak Nun, serves as an effective means for societal change, promoting a comprehensive application of Islamic teachings to address modern societal challenges. This research holds significant implications for scholars and practitioners interested in the intersections of religion and social transformation.

How to Enhance Functional Adequacy in Senior High School Students’ Writing

With the reform of the new college entrance examination, senior high school English writing has received great attention. The English Curriculum Standards for Senior High Schools (2017 Edition Revised in 2020) proposes that English teaching in senior high schools should focus on cultivating students’ core competence in the English discipline. In particular, English writing, which can fully reflect students’ comprehensive English application skills, plays a significant role in English learning. However, according to current learning situation, the writing performance among senior high school students is not satisfactory. In the recent years, the importance of functional adequacy as an essential component of L2 proficiency has been observed by some scholars. Based on this, this study highlights the effectiveness of functional adequacy in facilitating students’ writing performance and emphasizes the importance of developing students’ pragmatic writing ability. Also, this study explores how to develop functional adequacy in senior high school students’ writing and puts forward some pedagogical implications of improving pragmatic performance in writing for English teachers.

Research on the Cultivation of Critical Thinking Ability in College English Teaching under the Background of New Liberal Arts

Under the background of new liberal arts, the traditional college English teaching model which is mainly based on knowledge input can no longer meet the goal of talent cultivation. In the new liberal arts pattern, taking the cultivation of students' critical thinking ability as the entry point and exploring a three-dimensional and comprehensive teaching system for college English critical thinking ability not only conforms to the teaching laws of language classes but also can cultivate students' comprehensive English application ability, effectively promote the construction of a new college English curriculum system, and improve students' comprehensive qualities and abilities.

ELLA: Modeling Subcellular Spatial Variation of Gene Expression within Cells in High-Resolution Spatial Transcriptomics.

Spatial transcriptomic technologies are becoming increasingly high-resolution, enabling precise measurement of gene expression at the subcellular level. Here, we introduce a computational method called subcellular expression localization analysis (ELLA), for modeling the subcellular localization of mRNAs and detecting genes that display spatial variation within cells in high-resolution spatial transcriptomics. ELLA creates a unified cellular coordinate system to anchor diverse cell shapes and morphologies, utilizes a nonhomogeneous Poisson process to model spatial count data, leverages an expression gradient function to characterize subcellular expression patterns, and produces effective control of type I error and high statistical power. We illustrate the benefits of ELLA through comprehensive simulations and applications to four spatial transcriptomics datasets from distinct technologies, where ELLA not only identifies genes with distinct subcellular localization patterns but also associates these patterns with unique mRNA characteristics. Specifically, ELLA shows that genes enriched in the nucleus exhibit an abundance of long noncoding RNAs or protein-coding mRNAs, often characterized by longer gene lengths. Conversely, genes containing signal recognition peptides, encoding ribosomal proteins, or involved in membrane related activities tend to enrich in the cytoplasm or near the cellular membrane. Furthermore, ELLA reveals dynamic subcellular localization patterns during the cell cycle, with certain genes showing decreased nuclear enrichment in the G1 phase while others maintain their enrichment patterns throughout the cell cycle. Overall, ELLA represents a calibrated, powerful, robust, scalable, and versatile tool for modeling subcellular spatial expression variation across diverse high-resolution spatial transcriptomic platforms.

TimeNorm: a novel normalization method for time course microbiome data.

Metagenomic time-course studies provide valuable insights into the dynamics of microbial systems and have become increasingly popular alongside the reduction in costs of next-generation sequencing technologies. Normalization is a common but critical preprocessing step before proceeding with downstream analysis. To the best of our knowledge, currently there is no reported method to appropriately normalize microbial time-series data. We propose TimeNorm, a novel normalization method that considers the compositional property and time dependency in time-course microbiome data. It is the first method designed for normalizing time-series data within the same time point (intra-time normalization) and across time points (bridge normalization), separately. Intra-time normalization normalizes microbial samples under the same condition based on common dominant features. Bridge normalization detects and utilizes a group of most stable features across two adjacent time points for normalization. Through comprehensive simulation studies and application to a real study, we demonstrate that TimeNorm outperforms existing normalization methods and boosts the power of downstream differential abundance analysis.

Characterization of contractual relations in digital turnover

The article is devoted to identifying the peculiarities of contractual relations in digital circulation. Digital turnover is becoming increasingly widespread, which leads to the emergence of new forms of contractual relations characterized by significant differences. The purpose of the article is to reveal the characteristics and peculiarities of contractual relations arising within the framework of digital civil turnover. The study of contractual relations in digital circulation is necessitated by the lack of sustainable legal regulation of this area which requires further improvement. The peculiarity of building relations in digital circulation is associated with its technical properties, primarily the need to gain access to digital platforms for the exercise of subjective rights by users. The author emphasizes that the exercise of subjective rights in digital circulation can be realized only through the use of complex intellectual property objects, in particular, those combining computer programs, databases, music, animation objects, photographs, etc. The author proposes to distinguish the following types of relations in the field of digital circulation: 1) regarding the creation and maintenance of digital circulation (legal regime of intellectual property rights); 2) organizational relations in the field of business administration of the digital environment (corporate, labor relations with the appropriate legal regime); 3) between the user and the administrator of the relevant digital environment (contractual regulation, with elements of the legal regime of personal data protection); 4) between users of the digital space regarding digital assets (a complex regime of legal regulation which may combine the legal regimes of. The author emphasizes that contractual relations in digital circulation are based on both civil law and rules and standards used in electronic systems. It is proved that different types of relations require a comprehensive application of different legal norms and are mediated by different agreements, such as: license agreements; agreements for the provision of digital services (cloud storage, marketplaces); sale and purchase of goods; exchange and pledge of digital assets; token agreements (regulating the use of cryptocurrencies and other tokens in digital ecosystems), etc.

Stainless Steel Mesh in Electrochemistry: Comprehensive Applications and Future Prospects.

Stainless steel mesh (SSM) has emerged as a cornerstone in electrochemical applications owing to its exemplary versatility, electrical conductivity, mechanical robustness, and corrosion resistance. This state-of-the-art review delves into the diverse roles of SSM across a spectrum of electrochemical domains, including energy conversion and storage devices, water treatment technologies, electrochemical sensors, and catalysis. We meticulously explore its deployment in supercapacitors, batteries, and fuel cells, highlighting its utility as a current collector, electrode, and separator. The review further discusses the critical significance of SSM in water treatment processes, emphasizing its efficacy in supporting membranes and facilitating electrocoagulation, as well as its novel uses in electrochemical sensing and catalysis, which include electrosynthesis and bioelectrochemistry. Each section delineates the recent advancements, identifies the inherent challenges, and suggests future directions for leveraging SSM in electrochemical technologies. This comprehensive review showcases the current state of knowledge and articulates the novel integration of SSM with emerging materials and technologies, thereby establishing a new paradigm for sustainable and efficient electrochemical applications. Through critical analysis and insightful recommendations, this review positions itself as a seminal contribution, paving the way for researchers and practitioners to harness the full potential of SSM in advancing the electrochemistry frontiers.

Technical evaluation of supercritical fluid impregnation scaling-up of olive leaf extract: From lab to pilot scale

Supercritical fluid technology is an advanced field that offers remarkable versatility and it applies to a broad range of processes. However, the scaling-up of the supercritical fluid impregnation process is still scarcely explored. This research investigates the technical analysis of the supercritical fluid impregnation scaling-up of olive (Olea europaea L.) leaf extract into polypropylene (PP) films. The impregnation scale was increased up to 20-fold while maintaining constant ratios among the extract volume, film area, CO2 amount, and vessel geometry. The study compares homogeneity, total extract loading, and ABTS antioxidant activity of impregnated films at both scales. Homogeneity was assessed using CIELAB coordinates and desorption electrospray ionization mass spectroscopy (DESI-MS) imaging. Films impregnated at 250 bar and 55 °C for 1 h in 100 ml and 500 ml vessels were uniform in colour, whereas those from the larger vessel were less homogeneous. To evaluate the effect of time on impregnation at the largest scale, impregnation was conducted for 1, 6, 24, and 48 h. Homogeneity and ABTS antioxidant activity improved over time, and DESI-MS images showed a gradual decrease in oleuropein signal intensity over time. After 6 h, PP films achieved a total extract loading of 14.02 mg g−1 of polymer, 23 % ABTS antioxidant activity, with a global diffusion coefficient of the extract compounds in PP films of 3.42 × 10−13 m2 s−1. While this study offers valuable insights into scaling up the supercritical fluid impregnation of olive leaf extract into PP films, several limitations remain. Future research is required for a more comprehensive understanding and practical application of the process.

Sustainable synthesis of ZnO and FexOy nanoparticles and their nanocomposite ZnFe2O4: Comprehensive characterization and applications in antioxidant activity and antibiotics degradation efficiency

This study focuses on developing and evaluating eco-friendly nanoparticles, specifically FexOy NPs, ZnO NPs, and a ZnFe2O4 nanocomposite (NC), for potential applications in environmental remediation and biomedicine. The nanoparticles were synthesized and characterized using X-ray diffraction (XRD), which revealed their crystalline structures with sizes of 20.3 nm for FexOy NPs, 22.1 nm for ZnO NPs, and 10.9 nm for ZnFe2O4 NC. Fourier-transform infrared (FTIR) spectroscopy identified functional groups, while UV–visible spectroscopy determined band gap energies of 2.35 eV, 3.38 eV, and 2.68 eV for FexOy NPs, ZnO NPs, and ZnFe2O4 NC, respectively. Morphological analysis via scanning electron microscopy (SEM) and transmission electron microscopy (TEM) showed that FexOy NPs have cubic, hexagonal, and tetragonal forms, ZnO NPs are hexagonal nanorods, and ZnFe2O4 NC has a hexagonal-faced cubic structure. Antioxidant activity, assessed through the DPPH assay, revealed that ZnFe2O4 NC had the highest potency. Additionally, under sunlight irradiation, ZnFe2O4 NC demonstrated superior degradation of the antibiotic cephalexin (96 % within 30 min) compared to FexOy NPs (58.2 %) and ZnO NPs (52 %), with respective kinetic rate constants of 0.109 min−1, 0.029 min−1, and 0.025 min−1. These results highlight the nanoparticles’ potential for environmental and biomedical applications.

Exploring the pathogenesis of insomnia and acupuncture intervention strategies based on the microbiota-gut-brain axis.

Insomnia is a common sleep disorder observed in clinical settings, with a globally rising prevalence rate. It not only impairs sleep quality and daytime functioning but also contributes to a range of physiological and psychological conditions, often co-occurring with somatic and mental disorders. Currently, the pathophysiology of this condition is not fully understood. Treatment primarily involves symptomatic management with benzodiazepine receptor agonists, melatonin and its receptor agonists, sedative antidepressants, atypical antipsychotics, and orexin receptor antagonists. However, due to the adverse side effects of these drugs, including dependency, addiction, and tolerance, there is an urgent need for safer, more effective, and environmentally friendly treatment methods. In recent years, research on the microbiota-gut-brain axis has received significant attention and is expected to be key in uncovering the pathogenesis of insomnia. Acupuncture stimulates acupoints, activating the body's intrinsic regulatory abilities and exerting multi-pathway, multi-target regulatory effects. A substantial body of evidence-based research indicates that acupuncture is effective in treating insomnia. However, the unclear mechanisms of its action have limited its further clinical application in insomnia treatment. Therefore, this study aims to elucidate the pathogenesis of insomnia from the perspective of the microbiota-gut-brain axis by examining metabolic, neuro-endocrine, autonomic nervous, and immune pathways. Additionally, this study discusses the comprehensive application of acupuncture in treating insomnia, aiming to provide new strategies for its treatment.

Effects of cadmium stress on the growth and physiological characteristics of sweet potato

This study evaluated the responses of sweet potatoes to Cadmium (Cd) stress through pot experiments to theoretically substantiate their comprehensive applications in Cd-polluted agricultural land. The experiments included a CK treatment and three Cd stress treatments with 3, 30, and 150 mg/kg concentrations, respectively. We analyzed specified indicators of sweet potato at different growth periods, such as the individual plant growth, photosynthesis, antioxidant capacity, and carbohydrate Cd accumulation distribution. On this basis, the characteristics of the plant carbon metabolism in response to Cd stress throughout the growth cycle were explored. The results showed that T2 and T3 treatments inhibited the vine growth, leaf area expansion, stem diameter elongation, and tuberous root growth of sweet potato; notably, T3 treatment significantly increased the number of sweet potato branches. Under Cd stress, the synthesis of chlorophyll in sweet potato was significantly suppressed, and the Rubisco activity experienced significant reductions. With the increasing Cd concentration, the function of PS II was also affected. The soluble sugar content underwent no significant change in low Cd concentration treatments. In contrast, it decreased significantly under high Cd concentrations. Additionally, the tuberous root starch content decreased significantly with the increase in Cd concentration. Throughout the plant growth, the activity levels of catalase, peroxidase, and superoxide dismutase increased significantly in T2 and T3 treatments. By comparison, the superoxide dismutase activity in T1 treatment was significantly lower than that of CK. With the increasing application of Cd, its accumulation accordingly increased in various sweet potato organs. The the highest bioconcentration factor was detected in absorbing roots, while the tuberous roots had a lower bioconcentration factor and Cd accumulation. Moreover, the transfer factor from stem to petiole was the highest of the potato organs. These results demonstrated that sweet potatoes had a high Cd tolerance and a restoration potential for Cd-contaminated farmland.