Development Of System Research Articles

Dance Creation Based on the Development and Application of a Computer Three-Dimensional Auxiliary System

A three-dimensional auxiliary system serves as a foundational framework for spatial analysis and modeling in various fields. This system serves as a fundamental tool for visualizing and manipulating three-dimensional data, allowing researchers, designers, and engineers to accurately represent and analyze complex structures and environments. Dance creation is a multifaceted artistic process that involves choreographing movements, sequences, and gestures to convey ideas, emotions, and narratives through bodily expression. This paper uses the advanced automated application model for the dance creation with the 3D-auxiliary system for the choreography. The constructed model incorporates statistically integrated Principal Component Analysis (PCA) for the computation of features in the dance creation movement prediction. Finally, the estimation of the statistically integrated PCA model is applied over neural network modeling for the classification of features in the dance creation. With the estimated PCA model values statistical correlation between the PCA features are estimated and classified for the different dance types. The examination is based on the classification of dance movement dynamics, patterns, and stylistic elements for the dance creation. Simulation estimation demonstrated that a constructed statistical 3D auxiliary system was effectively involved in the dance movement prediction with the classification of features through a neural network for the dance movement prediction. The PCA model uses the 5 features to evaluate the auxiliary points of the dance movement in the reference human video. Through the analysis of the PCA features with the statistical values the outline sketch of the dance is framed and dance movement are created.

Optimization of Interfacial Properties Improved the Stability and Activity of the Catalase Enzyme Immobilized on Plastic Nanobeads.

The immobilization of catalase (CAT), a crucial oxidoreductase enzyme involved in quenching reactive oxygen species, on colloids and nanoparticles presents a promising strategy to improve dispersion and storage stability while maintaining its activity. Here, the immobilization of CAT onto polymeric nanoparticles (positively (AL) or negatively (SL) charged) was implemented directly (AL) or via surface functionalization (SL) with water-soluble chitosan derivatives (glycol chitosan (GC) and methyl glycol chitosan (MGC)). The interfacial properties were optimized to obtain highly stable AL-CAT, SL-GC-CAT, and SL-MGC-CAT dispersions, and confocal microscopy confirmed the presence of CAT in the composites. Assessment of hydrogen peroxide decomposition ability revealed that applying chitosan derivatives in the immobilization process not only enhanced colloidal stability but also augmented the activity and reusability of CAT. In particular, the use of MGC has led to significant advances, indicating its potential for industrial and biomedical applications. Overall, the findings highlight the advantages of using chitosan derivatives in CAT immobilization processes to maintain the stability and activity of the enzyme as well as provide important data for the development of processable enzyme-based nanoparticle systems to combat reactive oxygen species.

Recycled Concrete Fines as a Supplementary Cementitious Material: Mechanical Performances, Hydration, and Microstructures in Cementitious Systems

Using recycled concrete fines (RFs) as a supplementary cementitious material in blended cement systems offers potential benefits for the construction industry. This study investigated the mechanical properties, hydration development, and microstructural evolutions of blended Portland cement systems incorporating different particle-size distributions of untreated RFs. The results highlighted that finer RFs improved the compressive strength of mortar and influenced hydration by promoting monocarbonate formation. The incorporation of RFs optimizes the pore structure in the early stage but increases porosity. RFs affected the distribution of capillary pores and created a rougher and more complex surface compared to inert mineral admixtures. Furthermore, the particle size of RFs impacted the fractal dimension of multi-scale pore sizes.

A Low-Cost Redundant Attitude System for Small Satellites, Based on Strap-Down Inertial Techniques and Gyro Sensors Linear Clustering

The significant technological changes related to the manufacturing of the miniaturized sensors produced a higher impact at the level of the detection units equipping the strap-down inertial navigation systems (INSs). Together with miniaturization, many more advantages are brought by these technologies, related to low costs, low necessary energy, high robustness and high potential for adapting the design solutions. However, reducing the dimensions and weight of the sensors is reflected by a decrease in their performance in terms of sensitivity, noise and the possibility of controlling sensitive elements. On the other hand, there is a permanent increase in the need to have in-space applications of miniaturized systems with a high degree of redundancy and to equip miniaturized satellites, miniaturized space robots or space rovers. The paper proposes a new methodology to increase the quality of the signals received from the miniaturized inertial measurement units (IMUs), but also to increase the degree of redundancy, by using low-cost sensors arranged in redundant linear configurations. The presentation is focused on the development of an attitude system based on strap-down inertial techniques which uses a redundant IMU equipped with three linear clusters of miniaturized gyros. For each of the three clusters, a data fusion mechanism based on the maximal ratio combining method is applied. This fusion mechanism reduces the noise power and bias of the signal delivered to the navigation processor. Shown are the theory, software modeling and experimentation results for the attitude algorithm, for the data fusion method, and for the integrated system.

Transport characteristics of heavy metals in the soil-atmosphere-wheat system in farming areas and development of multiple linear regression predictive model

Heavy metal accumulation in agricultural products has become a major concern. Previous studies have focused on the transport of heavy metals from the soil and their accumulation in crops. However, recent studies revealed that wheat leaves, ears, and awns can also transport and accumulate heavy metals. Wheat grains can be influenced by two sources of heavy metals: soil contamination and atmospheric deposition. To comprehend the transport characteristics of heavy metals in soil, atmospheric deposition, and wheat, 37 samples each for wheat rhizosphere soil, wheat roots, stems, leaves, and grains were collected. Fifteen samples of atmospheric dry deposition and atmospheric wet deposition were collected from Linshu County (northern area), China. Based on the test data, the characteristics of heavy metals and their distribution in the study area were analyzed. Migration patterns of heavy metals in crops from different sources were investigated using Pearson correlation and redundancy analysis. Finally, a predictive model for heavy metals in wheat grains was developed using multiple linear regression analysis. Significant disparities in the distribution of heavy metals existed among wheat roots, stems, leaves, and grains. The coefficient of variation of heavy metals in atmospheric deposition was relatively high, indicating discernible spatial patterns influenced by human activities. Notably, a positive correlation was observed between the concentration of heavy metals in wheat grains and atmospheric deposition of Hg, Cd, and Pb. Conversely, Zn and Ni levels in wheat grains were significantly negatively associated with soil Zn, Ni, pH, and OM content. The contribution of heavy metal elements from different sources varied in their impact on the grain's heavy metal content. Specifically, atmospheric deposition was the primary source of Hg and Pb in wheat grains, while Cd, Ni, Cu, and Zn were predominantly derived from soil. Using a multiple linear regression model, we could accurately predict Hg, Pb, Cd, Ni, Zn, and As concentrations in crop grains. This model can facilitate quantitative evaluation of ecological risk of heavy metals accumulation in crops in the study area.

Two‐stage algorithm for traffic signal optimization and web‐service system development

Two‐stage algorithm for traffic signal optimization and web‐service system development

Catalytic Asymmetric Aza-Electrophilic Additions of 1,1-Disubstituted Styrenes.

Electrophilic addition of alkenes is a textbook reaction that plays a pivotal role in organic chemistry. In the past decades, catalytic asymmetric variants of this important type of reaction have witnessed great achievements by the development of novel catalytic systems. However, enantioselective aza-electrophilic additions of unactivated alkenes, which could provide a transformative strategy for the preparation of synthetically significant nitrogen-containing compounds, still remain a formidable challenge. Herein, we have developed unprecedented Au(I)/NHC-catalyzed asymmetric aza-electrophilic additions of unactivated 1,1-disubstituted styrenes by the utilization of readily available dialkyl azodicarboxylates as electrophilic nitrogen sources. Based on this approach, a series of transformations, including [2 + 2] cycloaddition, intermolecular 1,2-oxyamination, and several types of intramolecular hydrazination-induced cyclizations, have been realized. These transformations provide a previously unattainable platform for the divergent synthesis of hydrazine derivatives, which could also be converted to other nitrogen-containing chiral synthons. Experimental and computational studies support the idea that carbocation intermediates are involved in reaction pathways.

The Effects of Rearing Environment on Organization of the Olfactory System and Brain of Juvenile Sockeye Salmon, Oncorhynchus nerka.

Pacific salmon (Oncorhynchus spp.) hatch and feed in freshwater habitats, migrate to sea to mature, and return to spawn at natal sites. The final, riverine stages of the return migrations are mediated by chemical properties of the natal stream that they learned as juveniles. Like some other fish, salmon growth is asymptotic; they grow continuously throughout life toward a maximum size. The continued growth of the nervous system may be plastic in response to environmental variables. Due to the ecological, cultural, and economic importance of Pacific salmon, individuals are often reared in hatcheries and released into the wild as juveniles to supplement natural populations. However, hatchery-reared individuals display lower survivorship and may also stray (i.e., spawn in a non-natal stream) at higher rates than their wild counterparts. Hatchery environments may lack stimuli needed to promote normal development of the nervous system, thus leading to behavioral deficits and a higher incidence of straying. This study compared the peripheral olfactory system and brain organization of hatchery-reared and wild-origin sockeye salmon fry (Oncorhynchus nerka). Surface area of the olfactory rosette, diameter of the olfactory nerve, total brain size, and size of major brain regions were measured from histological sections and compared between wild and hatchery-origin individuals. Hatchery-origin fish had significantly larger optic tecta, and marginally insignificant, yet noteworthy trends, existed in the valvula cerebelli (hatchery>wild) and olfactory bulbs (hatchery<wild). We also found a putative difference in olfactory nerve diameter (dmin) (hatchery>wild), but the validity of this finding needs further analyses with higher resolution methods. Overall, these results provide insight into the potential effects of hatchery rearing on nervous system development in salmonids, and may explain behavioral deficits displayed by hatchery-origin individuals post-release.

Potential field mechanisms and distributed learning for enhancing the navigation of micro/nanorobot in biomedical environments

In complex systems, single micro/nanorobots encounter challenges related to limited loading capacity and navigation, hindering their effective utilization in targeted therapy and drug delivery. To solve these challenges, this paper explores potential field mechanisms as a means to simulate natural collective behavior. This approach aims to enhance the navigation and efficiency of micro/nanorobots in high-demand therapeutic areas. The mechanism enables micro/nanorobots to dynamically adapt to environmental gradients, minimizing off-target effects while maximizing therapeutic efficacy and enhancing robustness through redundancy. Additionally, this study introduces innovative distributed learning and cooperative control strategies. Each micro/nanorobot updates its navigation strategy through local interactions and influences with the dynamic environment. This allows micro/nanorobots to share information and improve their navigation toward therapeutic targets. The simulation results demonstrate that collective behavior and potential field mechanisms can enhance the precision and efficiency of targeted therapy and drug delivery in dynamically changing environments. In conclusion, the proposed approach can improve the limitations of single micro/nanobot, offering new possibilities for the development of advanced therapeutics and drug delivery systems.

Comparative study on the prediction of the unconfined compressive strength of the one-part geopolymer stabilized soil by using different hybrid machine learning models

With the development of green, low-carbon, and sustainable economic systems, the issues of high pollution and energy consumption in construction materials have become increasingly prominent. This study focuses on adopting one-part geopolymer (OPG) in soil stabilization for underground engineering, which exhibits environmental and low-carbon advantages. The unconfined compressive strength (UCS) serves as a crucial parameter for assessing stabilized soil’s performance. However, it is necessary to conduct a large number of experiments, inducing high costs and time consumption. In this study, one multiple linear regression model, one Decision Tree (DT) model, five ensemble machine learning (ML) models (i.e. Random Forest [RF], Extra Tree [ET], Gradient Boosting [GB], Gradient Boosting Decision Tree [GBDT], and Extreme Gradient Boosting [XGBoost]), and hybrid models of those single ensemble models with Particle Swarm Optimization (PSO) (i.e. PSO-RF, PSO-ET, PSO-GB, PSO-GBDT, and PSO-XGBoost) were adopted and compared to achieve better prediction on the UCS of the OPG-stabilized soil. Furthermore, the interpretable method including SHAP and PDP (1D and 2D), was employed to investigate the precise mechanisms by which input parameters influenced the output label. The results revealed that the multiple linear regression model delivered the lowest accuracy, and PSO-XGBoost and PSO-ET exhibited the best performance on the prediction of the UCS with R2 value of 0.9964 and 0.9928, respectively. In addition, Curing time exerted the most significant impact on the UCS, followed by FA/GGBFS, Molarity, Water/Binder, and NaOH/Precursor. Compared to the SHAP method, the PDP offered a more intuitive approach to reveal the relationship between the inputs and output. The outcome of the study shed new light on the application of ML models in the prediction of the OPG-stabilized soil’s performance in underground engineering.

Development of Multi-Motor Servo Control System Based on Heterogeneous Embedded Platforms

Development of Multi-Motor Servo Control System Based on Heterogeneous Embedded Platforms

Critical care nurses' competence in mentoring students in intensive care units—A cross-sectional study

BackgroundMentors play an important role in the practical education of critical care nursing students in intensive care units, yet little is known about the mentoring competencies of critical care nurses. AimThe aim of this study was to assess Norwegian critical care nurses' competence in mentoring students in intensive care units. DesignThis study has a descriptive, cross-sectional design, utilising a self-administered online survey. SettingsThe study population consisted of critical care nurses who mentor students in Norwegian intensive care units. Participants178 critical care nurses participated in the study. The participants were recruited by contacting the units directly, through social media, and at a national critical care nursing conference. MethodsThe study utilised the Mentors' Competence Instrument, a self-evaluation tool for evaluating mentoring competence. ResultsThe Norwegian critical care nurses generally evaluated their mentoring competence as middle to high level. The “reflection during mentoring” dimension was rated as the highest and “student-centered evaluation” as the lowest competence dimension. The critical care nurses who had formal mentoring education reported significantly higher mentoring competences, but the other demographic characteristics were not related to mentoring competence. Regardless of previous mentoring education, most participants reported a need to further develop their mentoring competencies. ConclusionsEmployers should collaborate with educational institutions to establish a system for continuous competence development for critical care nurse mentors.

Açai seed biochar improves soil quality and black pepper seedling development in the Amazon region

Sustainable management of the Amazon rainforest is fundamental for supporting life on earth because of its crucial role in sequestering carbon. One of the species grown in the forest is açaí (Euterpe oleracea), which is an important food and income source for its inhabitant. The acai seed, resulting from the processing of the fruit, is a solid organic residue, which has been an agent of undesirable environmental impacts such as natural landscape modifications, clogging sewers and water courses, eutrophication of surface waters. In this research, we evaluated the use of wood chips as a source of energy in a rustic oven to produce acai biochar so that family farmers carry out sustainable management of the residue and use biochar to improve soil quality and produce seedlings of native plants to regenerate degraded forests. The experiment was conducted in Pará, Brazil, Amazon region, using a randomized complete block design. A factorial treatment structure was implemented consisting of four biochar particle sizes (3, 5, 7, and 12 mm), 4 application rates (4, 8, 16, and 32 t ha−1), and a biochar-free control, with 5 replications. The results showed that the methodology for biochar production was easy to apply and low cost, allowing its use by family farmers. The combination of biochar rate and particle size affected soil properties and the development of black pepper seedlings in different ways. The soil properties affected were water retention capacity, moisture, fluorescein diacetate hydrolysis and arylsulphatase activity. The growth parameters of the affected black pepper seedlings were height and root system development.

Systematic characterization of the expression, prognosis and immune characteristics of PLOD family genes in breast cancer.

The expression patterns and prognostic value of Procollagen-lysine, 2-oxoglutarate 5-dioxygenase (PLOD) family genes in breast cancer remain to be elucidated. The expression levels, prognostic value, and biological function of PLODs were determined using Oncomine, cBioPortal, GEPIA, Timer, UALCAN, PrognoScan, GeneMANIA, Metascape, and breast cancer tissue microarrays. The expressions of PLOD1 and PLOD3 were upregulated in breast cancer tissues, indicating worse clinical stages. High expression levels of PLOD family genes were associated with worse disease-free survival and distant metastasis-free survival, while high expression levels of PLOD1 and PLOD3 were related to worse overall survival in all breast cancer patients. The levels of PLOD family genes were all significantly higher in the age ≤51 y group, HR-negative patients, and triple negative breast cancer (TNBC) patients. They are associated with tumor-infiltrating immune cells (TIICs), including CD4+ T cells, CD8+ T cells, B cells, macrophages, neutrophils, and dendritic cells. According to co-expression gene analysis and functional enrichment, they are associated with protein hydroxylation, collagen biosynthesis and modifying enzymes, collagen metabolism, RNA splicing, extracellular matrix organization, VEGFA-VEGFR2 signaling pathway, and skeletal system development. Immunohistochemistry showed that the expressions of all PLOD family genes were significantly elevated in breast cancer tissues. PLOD1 expression was positively correlated with ER, TNBC status, and tumor grade. PLOD2 expression was positively connected with Ki-67 status. PLOD3 expression was positively related with age and tumor grade. PLOD family genes are novel potential prognostic biomarkers for breast cancer, and targeting PLOD inhibitors might be an effective strategy for breast cancer therapy.

Design and Development of a Portable Neutron Detection System Based on a 6Li-Doped ZnS(Ag) Scintillator

In this study, the design and development of a portable neutron detection system based on a 6LiF + ZnS(Ag) scintillator was carried out. The detector system was first modeled using the Monte Carlo simulation code MCNP. As a result of the detector simulation, the materials and photomultiplier tubes (PMTs) were supplied for the system, and the material properties and thicknesses were optimized for the best scintillator, with the EJ-420 (Eljen Technology), consisting of 95% 6Li dispersed in ZnS(Ag), chosen because it has a distinctive feature with regard to high thermal neutron detection efficiency. Since this material has been shown to offer a significant advantage in favor of neutron detection in mixed radiation fields with gammas and neutrons, the pulse shape discrimination method was employed. To achieve this, a proper electronic circuit was developed to discriminate the pulses from neutrons and gammas. In the experimental step, the EJ-420 scintillator with a 50-mm diameter was optically coupled with a special fast PMT (Hamamatsu H1949-51 model). In conclusion, this study, which underlines the performed simulations for a neutron detector configuration, gives the obtained experimental results showing discrimination capability in neutron/gamma detection using a 241Am-Be neutron source. The results show that the EJ-420 is a good scintillator due to its highly enriched 6Li transmator, which results in more effective neutron measurements when a portable neutron detector design is chosen.

Bayesian networks for the development of an interdisciplinary decision support system in screening for the risk of laryngotracheal aspiration

Screening for the risk of laryngotracheal aspiration in hospitalized adults is a complex and interdisciplinary task that involves multiple variables and causes. The objective of this study was to develop and assess the applicability of a clinical decision support system based on Bayesian Network to assist the interdisciplinary hospital team. Methods: A Bayesian network was constructed using a systematic literature review to identify and categorize the main variables required for assessing the risk of laryngotracheal aspiration. These variables were grouped into the patient’s current diagnosis, history of swallowing disorders, swallowing-related aspects, and basic epidemiological characteristics. An application was developed and subsequently evaluated by nine specialists from an interdisciplinary hospital team. The evaluation focused on both the internal and external quality of the application. Results: The system was developed for Android mobile devices and was evaluated by a group of specialists (n=9) from various disciplines including physicians, nurses, nutritionists, and speech pathologists. The specialists considered the software product to be functional (86.1%), reliable (83.3%), usable (84.4%), efficient (92.6%), maintainable (90.4%), and portable (88.9%). Conclusions: The interdisciplinary screening system developed in this study was found to be valuable and feasible for use by specialists. Study registration: The validation of this study involved specialists from the fields of medicine, nursing, nutrition, and speech pathology. As a result, it was necessary to obtain evaluation and approval from the Ethics Committee in Research with Human Beings of the Federal University of Health Sciences of Porto Alegre – UFCSPA (# 44920121.0.0000.5345).

A study of appropriate wave energy technology for sustainable development in Australia

The deployment and development of wave energy systems to increase energy sector efficiency is essential for governments. To develop and maximize the exploitation of wave energy sources, applying appropriate technologies is extremely important. The design of decision-making tools to identify the best technology for developing energy resources optimally is one of the primary decision-making issues in the energy sector. In this article, we have proposed a Multi-Criteria Decision Making (MCDM) model to select suitable technologies among eleven wave energy harvesting technologies: OPT PowerBuoy, AquaBuoy, Archimedes Wave Swing, Salter’s Duck, Aquamarine PowerOyster, Bio Wave, SEAREV, Weptos, Mighty Whale, Wave dragon. To handle conflicting objectives during evaluation, the Fuzzy AHP method is used to calculate the weight of the criteria. Then, wave energy harvesting technology is ranked using the Fuzzy TOPSIS approach. An actual case study from Australia was examined in order to show the viability of the suggested decision-making methodology. This study applies a valuable reference to the issue of wave technology selection; Therefore, managers involved in wave energy can use the problem-solving approach in this study to identify the most suitable wave energy technology based on their criteria. Research results show that the most suitable technologies for optimal development of wave energy sources in Australia are WET-05 and WET-03 with coefficients of 0.852 and 0.806, respectively. Meanwhile, two technologies considered unsuitable for developing wave energy sources in this study are WET-06 and WET-11 with scores of 0.375 and 0.381, respectively. In this study, the combined application of the two FAHP-FTOPSIS methods is considered more appropriate because of its theoretical ease of understanding as well as the simplicity of application and robustness of the results.

Moisture Power Generation: From Material Selection to Device Structure Optimization.

Moisture power generation (MPG) technology, producing clean and sustainable energy from a humid environment, has drawn significant attention and research efforts in recent years as a means of easing the energy crisis. Despite the rapid progress, MPG technology still faces numerous challenges with the most significant one being the low power-generating performance of individual MPG devices. In this review, we introduce the background and underlying principles of MPG technology while thoroughly explaining how the selection of suitable materials (carbons, polymers, inorganic salts, etc.) and the optimization of the device structure (pore structure, moisture gradient structure, functional group gradient structure, and electrode structure) can address the existing and anticipated challenges. Furthermore, this review highlights the major scientific and engineering hurdles on the way to advancing MPG technology and offers potential insights for the development of high-performance MPG systems.

A study on the effects of informal care on the health of older adults--Evidence from China.

This study aimed to explore the impact of informal care on the health of older adults, providing data to inform the development of informal care systems and social support policies for long-term care services in developing countries. Based on data from the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS), this study employed the Propensity Score Matching (PSM) method to empirically analyze the effects of informal care on older adults' self-rated health, depression, and anxiety. A total of 4790 older adults were included in the study sample, with 3194 receiving informal care and 1596 not receiving informal care. The results indicated that older adults who received informal care reported better self-rated health and had lower depression and anxiety scores compared to those who did not receive informal care. Among different groups, the positive effects of informal care on physical and mental health were more significant for very old adults, rural residents, and those without a spouse. Informal care had a greater positive impact on the physical health of women and the mental health of men. Government agencies should promote effective coordination between informal and formal care through financial support, awareness campaigns, and policy incentives. This will help to accelerate the improvement of the long-term care system and effectively meet the long-term care needs of older adults.

Explorer: efficient DNA coding by De Bruijn graph toward arbitrary local and global biochemical constraints.

With the exponential growth of digital data, there is a pressing need for innovative storage media and techniques. DNA molecules, due to their stability, storage capacity, and density, offer a promising solution for information storage. However, DNA storage also faces numerous challenges, such as complex biochemical constraints and encoding efficiency. This paper presents Explorer, a high-efficiency DNA coding algorithm based on the De Bruijn graph, which leverages its capability to characterize local sequences. Explorer enables coding under various biochemical constraints, such as homopolymers, GC content, and undesired motifs. This paper also introduces Codeformer, a fast decoding algorithm based on the transformer architecture, to further enhance decoding efficiency. Numerical experiments indicate that, compared with other advanced algorithms, Explorer not only achieves stable encoding and decoding under various biochemical constraints but also increases the encoding efficiency and bit rate by ¿10%. Additionally, Codeformer demonstrates the ability to efficiently decode large quantities of DNA sequences. Under different parameter settings, its decoding efficiency exceeds that of traditional algorithms by more than two-fold. When Codeformer is combined with Reed-Solomon code, its decoding accuracy exceeds 99%, making it a good choice for high-speed decoding applications. These advancements are expected to contribute to the development of DNA-based data storage systems and the broader exploration of DNA as a novel information storage medium.