Database Technology Research Articles

Improvement of biodegradability prediction QSAR system through optimal combination of algorithms

Abstract This paper introduces a novel quantitative structure-activity relationship (QSAR) system for predicting the biodegradability of chemicals. The system focuses on evaluating the ratio of biochemical oxygen demand (BOD) to theoretical oxygen demand (ThOD) (%BOD/ThOD) and the presence of degradation products to categorize chemicals as “readily biodegradable” (RB) or “not readily biodegradable” (NB). The conventional laboratory testing method for biodegradability assessment is time-consuming and expensive, thus highlighting the need for a quantitative structure-activity relationship (QSAR) system that can predict biodegradability without the need for extensive testing. The authors acquired a dataset of 3948 chemicals from the National Institute of Technology and Evaluation (NITE) database, which included information on molecular structure, %BOD/ThOD values, and the presence of degradation products. The QSAR system consists of two prediction models: a %BOD/ThOD prediction model and a discrimination prediction model. Multiple algorithms were employed in each model to enhance prediction accuracy. The %BOD/ThOD prediction model utilizes eight algorithms, including linear, nonlinear, and tree-structured models. The discrimination prediction model employs ten algorithms, and the outcomes are combined using majority voting. Explanatory variables for both models include molecular descriptors and spatial structure derived from the chemical’s molecular representation. The performance of the QSAR system was evaluated based on training and validation datasets. Results showed that the combination of three %BOD/ThOD prediction algorithms improved the average correction rate compared to using all algorithms. Similarly, selecting the discrimination prediction algorithm further enhanced the average correction rate.

Perinatal outcomes of women with recurrent pregnancy loss undergoing frozen embryo transfer from the Society of Assisted Reproductive Technology database

To assess whether infants born to women with a history of recurrent pregnancy loss (RPL) have an increased risk of adverse perinatal outcomes after frozen embryo transfer (FET) compared with women without a history of infertility or RPL. Retrospective cohort study utilizing the Society for Assisted Reproductive Technology Clinic Outcomes Reporting System database between 2014 and2020. Not applicable. Patients in the Society for Assisted Reproductive Technology Clinic Outcomes Reporting System database who underwent their first FET resulting in live birth either with a diagnosis of RPL (cases n = 3,299) or without a history of RPL or infertility, the comparison population being tubal ligation (TL) only (n = 1,408). Not applicable. Low birthweight (<2,500 g), additional outcomes included gestational age (continuous), birthweight (continuous), preterm delivery (<37 weeks), mode of delivery, and neonatal death, defined as death before the completion of the 28th day of life. We observed no statistically significant difference in low birthweight, birthweight overall, mode of delivery, or risk of neonatal death between patients with RPL compared with women with TL who underwent their first FET, resulting in live birth. Patients with history of RPL compared with TL utilizing FET were more likely to have a later gestational age at delivery. Patients with RPL were also less likely to have a preterm delivery (imputed adjusted odds ratio, 0.75; 0.64-0.89) than the patients with TL. Furthermore, performing preimplantation genetic testing for aneuploidy (PGT-A) in both patients with RPL or TL did not impact perinatal outcomes compared with patients who did not undergo PGT-A. Patients with history of recurrent pregnancy loss do not have an increased risk of adverse perinatal outcomes when they undergo FET compared with patients without infertility or RPL. In addition, performing invitro fertilization/PGT-A in patients with RPL does not adversely impact birth outcomes of their infants.

How does digital technology affect export in services?

Based on the comprehensive measures of digital technology and cross-country services trade database, our study empirically examines the impacts of digital technology on export in services. We find that digital technology significantly promotes services export. However, the impact varies in different modes of supply. Digital technology significantly promotes service export in the mode of cross border delivery, consumption abroad, and commercial presence rather than the mode of the natural person movement. Heterogeneous analysis shows that the trade impact of digital technology is more pronounced in developed countries and the countries with higher levels of digital openness. Based on both the empirical analysis and the case studies, three main channels of the enhancement of tradability, the function of digital platforms, and innovation of new trade models are discussed for the influencing mechanisms of digital technology.

MHealth Service Effects for Negative Emotions Among Parents of Preterm Infants: A Systematic Review and Meta-Analysis.

This review aims to ascertain the effects of mHealth on negative emotions among parents of preterm infants and analyze the current state of mHealth services in this context. Caring for preterm infants often engenders negative emotions in parents, such as anxiety, depression, and stress. Mobile Health (mHealth) services offer potential benefits for preterm infant care. However, there are several uncertainties regarding the impact of mHealth services on parental emotions. Randomized controlled trial (RCT) and quasi-RCT were published in English and Chinese until 2023 and are available in the electronic databases of MEDLINE, EMBASE, the Cochrane Central Register of Controlled Trials, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINHAHL), Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Wanfang Database, and China Science and Technology Journal Database were reviewed. Review Manager 5.4 was employed for random-effects meta-analysis. Seven RCTs and 6 quasi-RCTs with high risks were included, most of them were based on small sample size and single-centered. Meta-analysis demonstrated that mHealth interventions mitigated negative emotions, including anxiety (standard mean difference [SMD]=-1.07; 95% confidence interval [CI], -1.47 to -0.66; P<0.0001), depression (SMD=-1.94; 95% CI, -3.01 to -0.86; P<0.0001), and stress (SMD=-1.24; 95% CI, -2.07 to -0.40; P<0.0001), among parents of preterm infants. It is recommended to develop independent specialized software for mHealth, enhance theoretical underpinnings, and standardize practical protocols. Incorporating early recognition and screening of negative emotions within mHealth could offer comprehensive support for the mental health of parents with preterm infants. The mHealth-based interventions are needed to focus on the parents' emotional needs and minimize the impacts associated with mental stress.

Challenges to Public Administration Related to the Implementation of the Scientific and Technological Development Strategy of the Russian Federation

The author considers the requirements of the Decree of the President of Russia “On the Scientific and Technological Development Strategy of the Russian Federation” to the characteristics and principles of functioning of the public administration system in Russia. It is shown that the list of these characteristics and requirements is incomplete and insufficient for the implementation of the Decree. The main gaps in the requirements formed by the Decree relate to limiting the reconfiguration of the management system to only one subsystem, management in the field of scientific and technological development. The article argues that the complex nature of grand scientific and technological challenges implies profound changes in the public administration system as a whole, the transition from a structural and functional management system (a combination of meritocratic and client-oriented models) to a project management system (indicative and public-state models). The changes should affect the nomenclature and the scheme of interaction between structural divisions of state bodies, increase the level of project intra- and interdepartmental coordination. In addition, significant changes must be made in the public service system. We should organize training of specialists capable of using data analytics tools and data-based decision-making technologies as well as of carrying out an indicative assessment of project implementation indicators. The structure of the civil service should be associated with project offices and temporary performance of duties in these offices, and not only with permanent functions, taking into account the specifics of project service activities and project teams within government agencies. Civil servants should actively interact with independent expert structures and with civil society, which is the ultimate beneficiary of science and technology projects.

A Secure and Verifiable Blockchain-Based Framework for Personal Data Validation

The online services provided by the Service Provider (SP) have brought significant convenience to people’s lives. Nowadays, people have grown accustomed to obtaining diverse services via the Internet. However, some SP utilize or even tamper with personal data without the awareness or authorization of the Data Provider (DP), a practice that seriously undermines the authenticity of the DP’s authorization and the integrity of personal data. To address this issue, we propose a Verifiable Authorization Information Management Scheme (VAIMS). During the authorization process, the authorization information and personal data fingerprints will be uploaded to the blockchain for permanent record, and then the SP will store such authorization information and personal data. The DP generates corresponding authorization fingerprints based on the authorization information and stores them independently. Through the authorization information and authorization fingerprints on the chain, the DP can verify the authenticity of the authorization information stored by the SP at any time. Meanwhile, by leveraging the personal data fingerprint on the blockchain, the DP can check whether the personal data stored by the SP has been tampered with. Additionally, the scheme incorporates database technology to accelerate data query. We implemented a VAIMS prototype on Etherum, and experiments demonstrate that the scheme is effective.

Effect of prehabilitation for patients undergoing gastric cancer surgery: a protocol of a systematic review and meta-analysis

IntroductionGastric cancer is a high-risk cancer with surgical treatments often leading to significant postoperative complications and mortality. Prehabilitation, involving exercise, nutrition and psychological support before surgery, aims to boost patients’...

Cost‐Effective and Fully Hardware‐Oriented Reservoir Computing Based on IGZO/HZO Ferroelectric Thin‐Film Transistor with Electrically and Optically Distinguishable States

AbstractHardware‐based reservoir computing (RC) systems provide benefits like energy efficiency and effective predictability. The implementation of different switching characteristics for the reservoir and readout layers requires the use of different types of devices or additional processing. However, an RC system with distinguishable switching characteristics obtained by changing stimulation on a single device is not identified yet, but it is appealing in terms of process simplicity and efficient processing costs. This study develops an RC system that uses ferroelectric thin‐film transistor (FeTFT) devices with an indium gallium zinc oxide channel and Hf0.5Zr0.5O2 ferroelectric layer for both networks. The nonvolatile FeTFT utilizes the remnant polarization properties of the ferroelectric layer through electrical stimulation, showing stable retention characteristics (104 s) and long‐term potentiation/depression. By using optical stimulation, the volatile FeTFT demonstrates short‐term characteristics, such as paired‐pulse facilitation, and a 4‐bit RC system. This proves that it is possible to meet the functional requirements of both the reservoir and readout networks by simply varying the type of stimulation applied to a single FeTFT. Finally, the fully FeTFT‐based RC system can recognize digit patterns from the Modified National Institute of Standards and Technology database with a high accuracy of 90.5%.

Averrhoa carambola Leaf Extract Induces Apoptosis-Like Death with Increased ROS Generation in Leishmania donovani.

The parasitic disease leishmaniasis is responsible for high mortality and morbidity rates worldwide. The visceral form is the most severe form of leishmaniasis (or leishmaniosis), which is caused predominantly by Leishmania donovani. Currently, clinically recommended antileishmanial drugs are not convenient because of several medical complications and resistance issues. Phytocompounds are the best candidates in this regard. The present study aimed to evaluate the antileishmanial activity of Averrhoa carambola leaf extract. The antipromastigote activity and cytotoxicity were assessed using the MTT assay. Morphological distortions were determined using phase contrast microscopy and scanning electron microscopy (SEM). Reactive oxygen species (ROS) production, nonprotein thiol depletion and apoptotic death in promastigotes were determined via flow cytometry. UV-visible spectroscopy and energy dispersive X-ray (EDX) spectroscopy was performed for elemental analysis. Fourier-transform infrared spectroscopy (FTIR) and liquid chromatography‒mass spectrometry (LCMS) were used to characterize the phytocomponent(s) present in the extract. The chloroform extract of Averrhoa carambola leaf (ACCEX) (IC50 = 50.76 ± 1.7µg/mL) exhibited the highest activity, followed by the ethyl acetate, hexane, and methanol extracts. ACCEX has also exhibited lower toxicity towards host macrophages. ACCEX also induced morphological distortions in promastigotes, with significant generation of ROS and the concomitant apoptosis initiation followed by a decrease in the nonprotein thiol level. The major phytometabolites present in ACCEX were identified from the National Institute of Standards and Technology (NIST) database and from a literature review. This study suggested that Averrhoa carambola leaf extracts are rich in some classes of biologically active phytocompounds and exhibit good antileishmanial activity.

GenAI-Assisted Database Deployment for Heterogeneous Indigenous–Native Ethnographic Research Data

In ethnographic research, data collected through surveys, interviews, or questionnaires in the fields of sociology and anthropology often appear in diverse forms and languages. Building a powerful database system to store and process such data, as well as making good and efficient queries, is very challenging. This paper extensively investigates modern database technology to find out what the best technologies to store these varied and heterogeneous datasets are. The study examines several database categories: traditional relational databases, the NoSQL family of key-value databases, graph databases, document databases, object-oriented databases and vector databases, crucial for the latest artificial intelligence solutions. The research proves that when it comes to field data, the NoSQL lineup is the most appropriate, especially document and graph databases. Simplicity and flexibility found in document databases and advanced ability to deal with complex queries and rich data relationships attainable with graph databases make these two types of NoSQL databases the ideal choice if a large amount of data has to be processed. Advancements in vector databases that embed custom metadata offer new possibilities for detailed analysis and retrieval. However, converting contents into vector data remains challenging, especially in regions with unique oral traditions and languages. Constructing such databases is labor-intensive and requires domain experts to define metadata and relationships, posing a significant burden for research teams with extensive data collections. To this end, this paper proposes using Generative AI (GenAI) to help in the data-transformation process, a recommendation that is supported by testing where GenAI has proven itself a strong supplement to document and graph databases. It also discusses two methods of vector database support that are currently viable, although each has drawbacks and benefits.

The Role of Blockchain Technology in Ideological and Political Education in Higher Education

Blockchain technology, as an emerging distributed database technology, is changing the way various fields develop. This article focuses on ideological and political education in higher education institutions and explores the role of blockchain technology in this context. Firstly, it introduces the background and significance of ideological and political education in the online realm. Then, it discusses the basic principles of blockchain technology and its applications in ideological and political education. Furthermore, it analyzes the advantages and challenges of using blockchain technology in ideological and political education in higher education institutions. Finally, it summarizes the role of blockchain technology in ideological and political education and suggests directions for further research.

User Acceptance of Blockchain Technology in Financial Applications: Information Security, Technology Awareness and Privacy Aspects

Blockchain technology is now an advanced and innovative database technology and the attributes of blockchains are apparent in a variety of industries, especially in the financial industry. One of the most famous blockchain use cases, cryptocurrencies, has provoked much interest in social network users and customers. According to CoinMarketCap’s information, the global crypto market capitalization has reached around USD 2.37 T and there are around 9975 different cryptocurrencies available in the market. Despite the fact that academia and industry have paid much attention towards the blockchain direction, there is not much research on the factors that influence customer acceptability. This paper studies blockchains from a different angle, probing the factors prompting customers to use financial applications that utilize blockchain technology. We established the model and sorted the individual factors of perceived information security, technology awareness and privacy and found that users’ acceptance is significantly affected by information security and technology awareness, while privacy does not significantly influence users. According to the findings, we provide useful insights for application developers, conclude by presenting the limitations of the research and provide guidelines for future research.

Prospects for the database development in electrical engineering materials

With the proposal and development of the Material Genome Engineering program, artificial intelligence has played a significant role in accelerating the research and development of new materials. In the field of electrical engineering materials, high-throughput experimental and computational methods provide a huge amount of data. It also poses new challenges to how to manage material data scientifically and efficiently. Database technology has become a hot topic for material scientists and engineers. This paper makes a comprehensive overview of the development, demand analysis and application of database technology in the electrical engineering materials, and discusses the existing problems and the future development trend of the database. Compared with many materials, such as energy materials, catalytic materials, biomedical materials, etc., the electrical material database still has a long way to go in the process of database platform construction, management and operation, and practical application. However, driven by governmental support and market demand, the construction of electrical material database will gradually improve and play an important role in the data-driven new materials researches.

Building Performance Strategy to Achieve Thermal Comfort on Post-Disaster Design

Purpose: This study is a development of previous studies that focused on testing building performance from the aspect of building comfort. A house is a place to live that is used daily that must meet the needs of the occupants' thermal comfort which is greatly influenced by environmental and building conditions, such as natural lighting, air flow, and thermal performance as well as the conditions of the building's orientation layout and space openings. To determine the thermal performance of a building, it is necessary to conduct several analyses of several parameters of air temperature, air circulation and thermal conditions that occur in buildings in several conditions of building layout or orientation and openings made. Theoretical reference: Adaptive buildings are buildings that have adaptation to external environmental conditions, occupant needs and building operational conditions with the aim of increasing energy efficiency, comfort and sustainability. These adaptive buildings are able to provide a more comfortable environment, flexible and efficient space by maximizing natural ventilation and using building materials that function as insulation against temperature and solar radiation. Method: To determine the thermal comfort conditions of the building, it is necessary to conduct several tests on several parameters, such as natural lighting, air flow, and thermal performance using several analyzes such as solar analysis, air movement analysis and thermal analysis of the planned building prototype with several alternative building orientations. To test the thermal performance of the building, solar analysis and thermal analysis are carried out using Revit-based computational fluid dynamics (CFD). Results and Conclusions: The results of the study showed that the influence of building orientation and opening placement patterns will produce several different thermal performances. And the optimal results obtained from this adaptive strategy were that the optimal heat generated caused heat generation in the envelope of 0.76 kW/m2, meaning that by considering the heat transfer value of the building envelope not exceeding 35 W/m2 (SNI 6389:2020), a wall envelope made of 10 cm thick calciboard with air holes was used to withstand the rate of heat flow into the room. Research implications: As part of the adaptive building context that supports the field of knowledge and practice in the fields of architecture, engineering and the environment, it has positive implications related to the flexibility of spatial arrangement to adjust to the needs of occupants' space, the use of more efficient and environmentally friendly building technology and materials according to the needs of adaptive buildings, and the adjustment of natural ventilation strategies according to local climate potential. Originality/value: The components that distinguish this research from previous research can be seen from the various approaches and innovations carried out by the author, namely: Adaptive Design Method: developing the functionality of spatial arrangements in post-disaster residential buildings that apply the principles of spatial efficiency and arrangement to improve more dynamic relationships between spaces and their relationships with the surrounding environment. Use of New and Data-based Technology: applying new technology in ways to monitor and control the quality of space with the surrounding environment that affects it in real-time. Using technology applications for data analysis, modeling and predicting the performance of post-disaster residential buildings according to environmental and building parameters that affect them, and using big data analysis to understand the thermal behavior of buildings to optimize building design and operations.

Energy-efficient synthetic antiferromagnetic skyrmion-based artificial neuronal device

Magnetic skyrmions offer unique characteristics such as nanoscale size, particle-like behavior, topological stability, and low depinning current density. These properties make them promising candidates for next-generation spintronics-based memory and neuromorphic computing. However, one of their distinctive features is their tendency to deviate from the direction of the applied driving force that may lead to the skyrmion annihilation at the edge of nanotrack during skyrmion motion, known as the skyrmion Hall effect (SkHE). To overcome this problem, synthetic antiferromagnetic (SAF) skyrmions that having bilayer coupling effect allows them to follow a straight path by nullifying SkHE making them alternative for ferromagnetic (FM) counterpart. This study proposes an integrate-and-fire (IF) artificial neuron model based on SAF skyrmions with asymmetric wedge-shaped nanotrack having self-sustainability of skyrmion numbers at the device window. The model leverages inter-skyrmion repulsion to replicate the IF mechanism of biological neuron. The device threshold, determined by the maximum number of pinned skyrmions at the device window, can be adjusted by tuning the current density applied to the nanotrack. Neuronal spikes occur when initial skyrmion reaches the detection unit after surpassing the device window by the accumulation of repulsive force that result in reduction of the device’s contriving current results to design of high energy efficient for neuromorphic computing. Furthermore, work implements a binarized neuronal network accelerator using proposed IF neuron and SAF-SOT-MRAM based synaptic devices for national institute of standards and technology database image classification. The presented approach achieves significantly higher energy efficiency compared to existing technologies like SRAM and STT-MRAM, with improvements of 2.31x and 1.36x, respectively. The presented accelerator achieves 1.42x and 1.07x higher throughput efficiency per Watt as compared to conventional SRAM and STT-MRAM based designs.

Accelerated Exploration of Li-Rich Cathode Materials with High Energy Density for Li-Ion Batteries

Li-rich cathode materials have been widely researched as promising cathode materials for Li-ion batteries due to the abundant numbers of Li+ in their formula units. There are, however, still some problems, such as the fading discharge voltage during cycling, to be overcome before its commercial application can be realized. In recent years, there has been a global expansion of efforts to innovate processes in material development by utilizing data science and materials informatics (hereafter referred to as MI), moving away from the traditional trial-and-error approach based on experiments. These initiatives have been surpassing the limitations of human-designed patterns and the number of feasible experiments, and examples of such endeavors are starting to be reported even in the field of battery materials[1]. In this study, we challenged a MI technology combined with first-principles calculation and database technology to widely search for suitable compositions of Li-rich cathode materials at high speed.Li1.4(Mn0.667Ni0.333)1-x Mx O2.35F0.05 (hereafter referred to as LMNOF, M: a dopant element, 0 ≤ x ≤ 0.01) was fabricated by a conventional method. The cathode was fabricated by mixing powder of the active material, acetylene black as a conducting powder and PTFE as a binder at the ratio of 7:2:1 by weight. The electrochemical performance was investigated using 2032 coin type half cells. The models for predicting battery performance were constructed by machine learning techniques. Composition formula information, synthesis conditions, and cell weight were used as candidate explanatory variables. XenonPy[2] was used for elemental-derived information. Principal component analysis was performed on the candidate explanatory variables, and only the variables determined to be important were used to construct the predictive model. The number of candidate M was narrowed down from approximately 100 to 30 based on the values of the enthalpy change between LMNOF with and without M. The enthalpy was calculated to determine the dopant elements for LMNOF by first-principles calculation. Specific M elements will be reported at the time of the presentation. The oxygen desorption energies of LMNOF with the 30 extracted elements were calculated to evaluate the stability of the LMNOF structure against the reaction in the fully charged state. Ten elements with negative values of the energy were selected as the dopant ones (Ms) for experimental demonstration of this study and the rest were used to predict the performance by using the database of the Materials Project[3]. The battery performance of M-doped LMNOF were predicted by the constructed model based on machine learning techniques with experimental data. This model indicated that the performances of LMNOF with a certain element M1 or M2 tended to be higher than ones with another M dopant elements at the stage where there had been no experimental data for M1 and M2. When the experiment was conducted for M1 and M2, the obtained data was turned out to be close to the predicted values and higher than ones with another M. These experimental demonstrations revealed that the constructed model at the initial stage in this study was suitable to predict the reasonable values for the composition without adequate experimental data. The loop of accumulating the experimental data and constructing the prediction model have been continuously carried out to improve the accuracy of predictions. As the number of experimental trials increased, the performance was gradually improved and the composition including M1 alone, M2 alone, M1 and other multiple Ms together, or M2 and other multiple Ms together, showed relatively higher performance, which, we considered, surpassed the maximum values obtained by the conventional trial-and-error experimental method. As a result, our new MI technology was demonstrated as an effective method to search for an unknown and unexplored composition with high performance due to the prediction accuracy of machine learning. In future, we are going to further improve prediction accuracy to find multiple Ms for even higher performance.

STDNet: Rethinking Disentanglement Learning With Information Theory.

Disentangled representation learning is typically achieved by a generative model, variational encoder (VAE). Existing VAE-based methods try to disentangle all the attributes simultaneously in a single hidden space, while the separation of the attribute from irrelevant information varies in complexity. Thus, it should be conducted in different hidden spaces. Therefore, we propose to disentangle the disentanglement itself by assigning the disentanglement of each attribute to different layers. To achieve this, we present a stair disentanglement net (STDNet), a stair-like structure network with each step corresponding to the disentanglement of an attribute. An information separation principle is employed to peel off the irrelevant information to form a compact representation of the targeted attribute within each step. Compact representations, thus, obtained together form the final disentangled representation. To ensure the final disentangled representation is compressed as well as complete with respect to the input data, we propose a variant of the information bottleneck (IB) principle, the stair IB (SIB) principle, to optimize a tradeoff between compression and expressiveness. In particular, for the assignment to the network steps, we define an attribute complexity metric to assign the attributes by the complexity ascending rule (CAR) that dictates a sequencing of the attribute disentanglement in ascending order of complexity. Experimentally, STDNet achieves state-of-the-art results in representation learning and image generation on multiple benchmarks, including Mixed National Institute of Standards and Technology database (MNIST), dSprites, and CelebA. Furthermore, we conduct thorough ablation experiments to show how the strategies employed here contribute to the performance, including neurons block, CAR, hierarchical structure, and variational form of SIB.

Post-independence health research productivity in Portuguese-speaking African countries: A bibliometric analysis of 43 years of research and higher education in Mozambique

BackgroundAfrica has a high double burden of infectious and non-communicable diseases underscoring the critical need for robust scientific research. However, it is also associated with low scientific research productivity. Mozambique, which gained independence in 1975, serves as a poignant example. However, there remains a notable scarcity of evidence evaluating the country's trajectory in scientific and academic development. This study aims to evaluate 43 years of health-related scientific knowledge production through bibliometric analysis, focusing on key indicators. Additionally, it seeks to characterize the higher education institutions within the country. MethodsThe data was retrieved from the Web of Science Core Collection using an advanced search tool with Boolean research strategies, covering the period from 1976 to 2022 for all PALOP and 1976 to 2019 for Mozambique. To map Mozambican higher institutions, information was sourced from the Ministry of Science Technology and Higher Education database. Descriptive statistics were employed to summarize the findings, while the VOSviewer program version 1.6.19 was utilized to visualize distance-based bibliometric networks, focusing on co-authorship among institutions and keyword co-occurrence. ResultsPortuguese-speaking African countries (PALOP) contribute 2.5 % (10,933 out of 442,309) to Africa's total scientific output, with Mozambique leading at 63.6 % (6,951 publications) followed by Angola at 16.6 % (1,811 publications). All PALOP countries experienced decreased scientific productivity during the third year of the COVID-19 pandemic. In Mozambique, over 70 % (1,710 out of 2,380) of health-related publications from 2011 to 2019 were concentrated in this period. Key journals for health sciences include PLOS ONE, Malaria Journal, and Tropical Medicine & International Health, focusing on HIV, malaria, and tuberculosis. Higher education institutions in Mozambique show regional disparities, with 67.9 % in the South and only 8.9 % in the North, indicating significant inequality in their distribution across the country. ConclusionThis study highlights Mozambique's significant progress in health research productivity over 43 years, establishing it as a leader among PALOP countries. The substantial increase in publications, particularly after 2008, underscores the nation's growing research capacity and commitment to addressing critical health challenges such as HIV, malaria, and tuberculosis. However, regional disparities in higher education access and limited research contributions from private universities remain high in Mozambique.

Analysis of Internal Factors Affecting Tax Avoidance in the 4.0 Era in Digital Technology Sector

This study aims to analyze internal factors that influence tax avoidance in the annual financial reports of digital technology sector companies listed on the Indonesia Stock Exchange for the 2018-2022 research period in the economy 4.0 era. The independent variables in this study are profitability, fixed asset intensity, and leverage. The approach used in this study is a secondary data quantitative approach. The population of the study was 73 digital technology sector companies listed on the Indonesia Stock Exchange in the 2018-2022 research period, and the sample was taken using the purposive sampling method with a total of 25 companies so that the number of observations in the study was 125 observation data. The data analysis technique uses panel data regression analysis using statistical calculations. The results of the research analysis prove that profitability and leverage have a partial and positive and negative influence on tax avoidance. In contrast, fixed asset intensity does not have a partial and negative influence on tax avoidance. The managerial implications are that the implementation of digital technology can help create a more transparent and accountable system, and the use of data-based audit technology can detect anomalies and potential tax avoidance earlier.

Effects of structure parameter and material property on thermal performance of on-board hydrogen storage tanks during fast refueling

As a power generation fuel, high-pressure hydrogen gas is widely used in transportation, and significantly contributed to development of hydrogen fuel cell electric vehicles. However, obvious temperature rise generated during fast refueling causes serious safety risk to reliable operation of fuel cell stacks. Therefore, it is necessary to conduct in-depth research on fast filling process. Based on safety reasons, the SAE J2601/ISO 15869 strictly regulates the maximum temperature limit of 85 °C in specifications for refueling hydrogen gas tanks. In this study, a 70 MPa, Type IV on-board hydrogen storage tank is adopted as the research object, and a 2-dimensional axisymmetric numerical model is established to study the temperature rise of the vapor within storage tanks during fast refueling. The Realizable k-ε turbulence model is employed to simulate high intensity turbulent fluctuation during fast filling. Considering the compressibility of vapor, the state of equation for real hydrogen gas is used by obtaining fluid thermodynamic properties from the National Institute of Standards and Technology database. Three groups of fast filling experiments are selected to validate the developed numerical model. The comparison result shows that the numerical model has good prediction ability on hydrogen fast refueling. The final mass average temperature differences between numerical results and experimental data are 2.6 K, 4.3 K, and 0.03 K, respectively. Based on the developed numerical model, the influences of structure parameters and material properties on the temperature rise of hydrogen storage tanks are investigated. The results indicate that the inlet diameter and wall thickness of the inner and outer layers of the storage tank have weaken impacts on the temperature rise during rapid charging, with the maximum temperature differences of 1.8 K, 0.74 K, and 0.54 K, respectively. Due to the influence of heat capacity, the material properties of different layers exhibit a similar variation trend. This work can deepen researchers' understanding of the temperature rise of on-board hydrogen storage tanks during fast filling, and provide technical references for the structural optimal design of tanks and rapid refueling at hydrogen stations.