Life Applications Research Articles

Strong, healable materials with bio‐like ordered architectures and versatile functionality

AbstractThe capacity of biological tissues to undergo self‐healing is crucial for the performance of functions and the continuation of life. Conventional intrinsic self‐healing materials demonstrate analogous functionality depending on the dissociation‐recombination of reversible bonds with no need of extra repair agents. However, the trade‐off relationship between mechanical strength and self‐healing kinetics in intrinsic self‐healing systems, coupled with the lack of additional functionality, restricts their service life and practical applications. Diversified highly ordered structures in organisms significantly affect the energy dissipation mechanism, signal transmission efficiency, and molecular network reconstruction capability due to their multi‐dimensional differentiated macroscopic composite constructions, microscopic orientation textures, and topologies/bonding types at molecular level. These architectures exhibit distinctive strengthening mechanisms and functionalities, which provide valuable references. This review aims at providing the current status of advanced intrinsic self‐healing materials with biomimetic highly ordered internal micro/nanostructures. Through highlighting specific examples, the classifications, design inspirations, and fabrication strategies of these newly developed materials based on integrating dynamic interactions with ordered nano/microstructures are outlined. Furthermore, the strengthening and self‐healing balance mechanisms, structure–functionalization relationships, and potential application values are discussed. The review concludes with a perspective on the challenges, opportunities, and prospects for the development, application, and promotion of self‐healable materials with bio‐like ordered architectures.

Artificial Intelligence-Assisted Automatic Raman-Activated Cell Sorting (AI-RACS) System for Mining Specific Functional Microorganisms in the Microbiome.

The microbiome represents the natural presence of microorganisms, and exploring, understanding, and leveraging its functions will bring about significant breakthroughs in life sciences and applications. Raman-activated cell sorting (RACS) enables the correlation of phenotype and genotype at the single-cell level, offering a solution to the bottleneck in microbial community functional analysis caused by challenges in cultivating diverse microorganisms. However, current labor-intensive manual procedures fall short in catering to the demands of single-cell functional analysis in microbial communities. To address this issue, we developed an artificial intelligence-assisted Raman-activated cell sorting system (AI-RACS) that integrates precise single-cell positioning, automated data collection, optical tweezers capture, and single-cell printing to elevate microbial single-cell RACS from manual to automated, validating the efficacy of the system by isolating aluminum-tolerant microbes from acidic soil microbiota. Leveraging the AI-RACS framework, we sorted 13 strains from red soil samples under near-in situ conditions, with all demonstrating strong aluminum tolerance. AI-RACS efficiently segregates microbial cells from intricate environmental samples, investigating their functional attributes and presenting a novel tool for microbial research and applications.

Cu−Bi Bimetallic Catalysts Derived from Metal–Organic Framework Arrays on Copper Foam for Efficient Glycine Electrosynthesis

AbstractGlycine as one of the most abundant amino acids in human proteins, with extensive applications in both life and industry, is conventionally synthesized through complex procedures or toxic feedstocks. In this study, we present a facile and benign electrochemical pathway for synthesis of glycine through reductive coupling of glyoxylic acid and nitrate over a copper‐bismuth bimetal catalyst derived from a metal–organic framework (MOF) array on copper foam (Cu/Bi−C@CF). Remarkably, Cu/Bi−C@CF achieves a fantastic selectivity of 89 %, corresponding a high Faraday efficiency of 65.9 %. From control experiments, the introduction of Bi caused the binding energy of Cu shift to a lower state, which leads to a high selectivity towards the formation of key intermediate hydroxylamine rather than ammonia product, facilitating the formation of oxime and providing additional sites for subsequent hydrogenation reaction on the way to glycine. Moreover, the derivation of MOF arrays ensures the effective dispersion of Bi and enhances the stability of Cu/Bi−C@CF. This innovative approach not only presents sustainable pathways for the production of value‐added organonitrogen compounds utilizing readily available carbon and nitrogen sources, but also provides novel insights into the design of multistage structural catalysts for sequential reactions.

Digital Literacy Skills among Students in Technical Education and Training (TET) Institutions, Tanzania

Digital literacy skills (DLS) are one of the essential factors which facilitate academic and non-academic life. The TET institutions remain relevant and attractive if they apply DLS in this era of the changing world. One of the major beneficiaries of DLS in TET are students. However, little is known about the extent to which the students are equipped with DLS. The particular angle of life in which such students apply DLS is as well not yet established. Therefore, this study evaluated the extent to which the TET students are equipped with DLS; and the angle of life from which the DLS are applied by the given students. The DLS of this study are theoretically grounded from digital literacy indicators for undergraduate students by Techataweewan and Prasertsin (2017). This study used a quantitative approach with a descriptive research design. The data were collected using questionnaires from 500 final-year students of four TET institutions sampled through a stratified random sampling technique. The collected data were analysed using descriptive statistics. The findings indicate that the students acquired operation skills (cognition, invention and presentation) and collaboration skills (teamwork, networking and sharing) to a large extent. On the other hand, the students acquired thinking skills (analysis, evaluation and creativity) and awareness skills (ethics, legal literacy and safeguarding self) to a small extent. Furthermore. The TET institutions should advocate more DLS to their students for them to balance the application in academic and non-academic life.

A Comprehensive Approach to Fault Classification of Helicopter Engines with Adaboost Ensemble Model

This work is based on the PHM North America 2024 Conference Data Challenge’s datasets of Helicopter turbine engine performance measurements. These datasets were large and moderately imbalanced. For dealing with these challenges, we demonstrate significant tools covering feature engineering, augmentation and selection, model exploration, visualizations, model explainability and confidence margin estimation. This work was performed in its entirety using MATLAB. All these tools will be generally applicable to data-driven modeling and prediction of health to real life applications. Initially, we explored the 742k observations in the training set, noting a 60-40 split between healthy and faulty labels, and identified two major operational clusters within the data. We enhanced the dataset by removing duplicates and engineered new features based on domain knowledge, expanding the feature set to 242 dimensions. However for the torque margin estimation, we trained a regression model on a limited subset (18 features), which includes engineered features using domain knowledge, quadratic terms and linear interaction between all the terms. For the final submission, we utilized a stepwise linear regression model to optimize feature selection. This approach achieved a perfect regression score on test data, validated by a consistent torque margin residual range of +/- 0.5%. The model's RMSE and MAE metrics were optimal for employing a normal distribution probability density function. For , we reduced the feature set to 58 using dimensionality reduction techniques and balanced the data with upsampling and down-weighing the minority class. We employed ASHA (Asynchronous Successive Halving Algorithm) in conjunction with AutoML to efficiently determine the most suitable model family, significantly saving compute time. Subsequently, we trained ensemble models, including bagged tree and AdaBoost (Adaptive Boosting), which minimized false negatives and positives, achieving robust classification performance. This was particularly critical given the high penalty for false negatives in the data challenge. The MathWorks team score on Testing Data was 0.9686 at the close of competition. This was further improved to 0.9867. Our approach demonstrates the effectiveness of combining strategic data processing, feature engineering, and model selection to enhance predictive accuracy in complex operational datasets.

Improving mechanical properties of 3D printed products through honeycomb structure and deep learning optimization

The use of three-dimensional (3D) Printing has rapidly grown due to its various applications in daily life. However, products made with this technology are not widely adopted because their quality is not yet seen as equal to that of products made with traditional materials. To address this issue, a honeycomb structure model was developed to improve the mechanical properties of the items created using 3D printing technology. This model not only saves materials, but also reduces printing time compared to previous models. The honeycomb structure was designed using a theoretical model, and its components were modified to ensure compatibility with the 3D printing extrusion method. Moreover, deep learning was used to optimise the honeycomb structure model’s boundary conditions by generating contour curves through linear interpolation. This process significantly improved the mechanical strength of the honeycomb structure model compared to structures made using 3D printing technology. The theoretical findings were validated through various material tensile tests conducted under different scenarios. As a result, this model can be used in various industries and products that use 3D printing technology, thanks to the critical role that deep learning in improving its mechanical properties.

ATPAM: Adversarial Temporal Pattern Attention Mechanism

Abstract. Time series forecasting has a large number of applications in daily life, for instance the prediction of stock prices, electricity consumption, exchange rate changes etc. However, the existing time series prediction methods have limitations. The most significant one is that when all the prediction models get the predicted value, a new round of iteration starts after the loss function is calculated with the corresponding real data. This may cause the accumulation of errors, since there is only one loss function to measure the difference between the predicted value and the ground truth, it will make the connection weak and mainly depend on the accuracy of the prediction model. Unfortunately, there are few time series prediction models with high accuracy in reality. To solve the issue, in this paper, we propose a new time series forecasting model Adversarial Temporal Pattern Attention Mechanism (ATPAM), which based on Generative Adversarial Nets (GANs). ATPAM adopts a Temporal Pattern Attention model as the generator to learn time-invariant temporal patterns, and use a discriminator to improve the prediction performance and do auxiliary adversarial training. Extensive experiments on several real-world datasets show the effectiveness of our method.

An Overview of the Basic Theory of Probability Theory and its Commercial Application

Abstract. Probability theory is a key field of mathematics that focus on developing theoretical models and mathematical frameworks to describe and analyze random events or processes. As a part of basic science, probability theory holds a central place in pure mathematics while also serving a crucial function across various domains, including natural science, social science and engineering technology. This paper summarizes the main concepts, basic theories and commercial applications of probability theory. Probability theory encompasses ideas such as random experiment, sample space, event, probability, conditional probability, independence, Bayes Theorem, random variable, probability distribution, expected value and variance, law of large numbers and central limit theorem. This paper provides an overview of key concepts and theorems from probability theory, along with their practical applications in everyday life, in order to make people deeply recognize the broad and profound contents and research fields of probability theory and its wide application in various fields as an academic tool, which has a great relation with people.

A critical review on spatially explicit life cycle assessment methodologies and applications

A critical review on spatially explicit life cycle assessment methodologies and applications

Analyzing tube arrangements of a finned-tube heat exchanger to optimize overall efficiency using artificial neural network and response surface methodology

Temperature is a critical factor in numerous equipment, industries, and daily life applications. Heat exchangers are essential devices that help regulate and optimize this factor. The finned-tube heat exchanger (FTHE) is widely favored due to its high efficiency in facilitating heat transfer between liquids and gases. Improving the performance of FTHE can significantly provide thermal requirements in industrial and engineering processes and reduce energy consumption. The present research includes a numerical study of an FTHE and the optimization of the performances based on the input variables by response surface methodology (RSM) and artificial neural network (ANN) methods. The tubes' transverse and longitudinal pitches and inlet Reynolds number were selected as input variables. Also, the examined responses were the Colburn and friction factors. Changing tubes' pitches makes it possible to significantly affect the thermo-hydraulic performance without incurring additional costs and processes. The acquired results showed that the responses predicted by the models are very close to the numerical results, which indicates the high accuracy and validity of these models. According to the results, the optimum heat exchanger's efficiency index was obtained at Pt=26.128mm, Pl=28mm, and Re=800. It was also observed that the overall performance of the optimal design is 185% higher than the weakest FTHE.

음악과 개념기반 탐구학습 수업모형 개발 및 수업설계 방안 탐색

This study begins with the issue that existing models of concept-based inquiry processes generally fail to capture the distinctive characteristics of music education, which involves creation, practice, immersion, and expression. The aim of this research is to develop a music class model applicable to concept-based inquiry learning. To achieve this, we examined the features of concept-based curricula and the unique characteristics of music education, analyzed existing related instructional models to derive their features, and then developed a seven-stage instructional model. This model, which incorporates feedback from a music education research group and expert reviews, was applied to music classes centered around the concept of "change". The seven stages include: 'Observing', 'Exploring Contextual Information', 'Refining', 'Immersing and Expressing', 'Meaning Constructing', 'Transferring', and 'Reflecting'. The focus on 'Immersing and Expressing', a core characteristic of music education, is central to this model, with the structure allowing for flexible transitions to 'Immersing and Expressing' from other stages as needed. This model is suitable for forming music concepts and transferring them to other musical areas, thus promoting enriched musical experiences and practical applications in life. It is expected that this model will contribute to discussions on various applications of concept-based inquiry learning in music and practical approaches to instructional methods in the future.

High-performance supercapacitor of ZnO-incorporated structurally modified g-C3N4 with circular mesoporous channels attained via a template-free approach

Abstract Here, the superior structural features of graphitic carbon nitride (g-C3N4) in combination with integrated mesoporous channels have been explored for its use as a supercapacitor electrode material. A facile template-free strategy is adopted for the preparation of ZnO-incorporated modified g-C3N4 nanocomposite, where material characterization via x-ray difraction, Fourier transfrom infrared spectroscopy, field-emission scanning electron microscopy, transmission electron microscopy and x-ray photoelectron spectroscopy analysis revealed the presence of structurally modified g-C3N4 having uniform circular mesoporous channels with well-dispersed ZnO with strong Zn–C and Zn–N interactions. The electrical double-layer capacitance together with the pseudocapacitance of the ZnO/g-C3N4 electrode material resulted in improved performance, leading to a specific capacitance of 146.3 F g−1 at a current density of 0.5 A g−1; an increased capacitance is observed in 5000 repeated charge–discharge cycles. A symmetric coin cell supercapacitor fabricated from the material displayed an energy density of 38.8 mWh kg−1 at a power density of 4259 mW kg−1. Additionally, the long life of 6000 cycles (retaining 100% specific capacitance) exhibited by the coin cell supercapacitor further indicates the promising energy storage nature of the ZnO-incorporated modified g-C3N4 mesoporous nanoarchitecture. Real life application of the ZnO/g-C3N4-derived supercapacitor is illustrated by lighting up a green LED with a series connection of four coin cells.

Profile of Green Chemistry on Chemistry Education Students: Study on Developing Green Chemistry Practical Module to Support Sustainable Development Goals (SDGs)

The Society 5.0 era emphasizes production efficiency by transitioning from conventional to renewable energy sources. Thus, the Society 5.0 Era aligns with one of the goals of the SDGs, which is that industrial processes become more environmentally friendly and more efficient in using energy resources while increasing their value. Green chemistry is a comprehensive approach that designs safe chemicals ranging from environmentally friendly chemical products and processes to reducing the formation of harmful chemical substances. The type of research is descriptive and aimed at describing the student's early understanding of green chemistry. The subject of this study is a student of Prodi Education Chemistry at the University of Muhammadiyah Cirebon. The test instrument consists of 4 description questions compiled to measure the student's initial understanding of green chemistry. The analysis results show that the students understood the terminology for green chemistry. However, students still need to understand the 12 principles of green chemistry and their applications in everyday life.

Advancing quinoxaline chemistry: Sustainable and green synthesis using L-arabinose as a catalyst

In the present study, an innovative and eco-friendly methodology for synthesizing quinoxaline derivatives has been introduced, leveraging L-arabinose as a novel catalyst. This approach not only underscores the feasibility of green chemistry in synthesizing high-purity quinoxaline derivatives but also highlights the enhanced sustainability of the process. Detailed mechanistic investigations revealed the synergistic interactions between L-arabinose and the reactants, providing a deeper understanding of the catalytic process. The synthesized compounds, due to their unique structural and electronic properties, may find promising applications in life and material sciences such as optoelectronics. Furthermore, our environmental impact assessment demonstrated significant reduction in generated waste, solvent use, and overall carbon footprint, aligning with the global agenda for sustainable development. This research marks a significant step forward in quinoxaline chemistry, promoting the adoption of green synthesis pathways while reinforcing the principles of sustainable and environment-friendly chemical practices.

A weighted hybrid indoor positioning method based on path loss exponent estimation

With the rapid development of the Internet of Things (IoT), location-based services (LBS) have gained significant attention due to their widespread applications in daily life. This paper addresses the indoor target positioning problem in wireless sensor networks (WSNs). A weighted constrained linear least squares algorithm based on path loss exponent estimation (PLE-WCLLS) with received signal strength (RSS) and angle of arrival (AoA) hybrid measurements is proposed. To address the challenges of unknown transmission power and path loss exponent (PLE), the proposed method employs a linear least squares (LLS) estimation approach based on the ranging maximum likelihood (ML) estimation model to estimate both parameters. Subsequently, a confidence weight adjustment strategy is designed to reduce positioning errors. To handle the highly non-convex and nonlinear nature of the RSS/AoA hybrid optimization model, a linearization method based on Taylor series expansion is presented. Accurate target position estimation is achieved by solving a constrained quadratic programming problem. The effectiveness of the proposed algorithm is validated through numerical simulations and experimental evaluation in a real indoor environment. Compared to traditional positioning methods, the PLE-WCLLS algorithm improves positioning accuracy by 13.2%, and it performs exceptionally well even in scenarios with fewer sensor nodes. This gives it broad application prospects in areas such as IoT device management, personnel tracking in smart buildings, and asset localization in industrial automation.

Prediction of Broadband and Highly-Efficient Electromagnetic Wave-Absorbing SiC@SiO2 Nanowire Aerogel by Genetic Algorithm.

Searching for lightweight and high-temperature stable electromagnetic wave-absorbing materials with broad absorbing bandwidth and high efficiency is of significance for applications in daily life and industry. Optimizing the dielectric properties of SiC nanowire aerogel by both compositional and structural designs is an efficient way to obtain simultaneous efficient wave-dissipation ability and good impendence matching and thus the desired properties. However, due to the complex effects of dielectric parameters on the wave-absorbing properties, rational design of high-performance electromagnetic wave-absorbing materials remains challenging. Herein, we propose a genetic algorithm-based approach to predict broadband and highly efficient electromagnetic wave-absorbing materials in a SiC@SiO2 nanowire aerogel-based system. The obtained SiC@SiO2 nanowire aerogels exhibit a gradient multilayered structure with a low dielectric outer layer, a medium layer with alternatively distributed electromagnetic wave transparent and attenuation layers, and an inner high attenuation layer, giving it a broadband electromagnetic wave-absorbing performance covering almost all the 2-18 GHz bandwidth and simultaneous high efficiency. The results show that the genetic algorithm-based approach is efficient in predicting high-performance electromagnetic wave-absorbing ceramic aerogels.

Quantile-based robust Kibria–Lukman estimator for linear regression model to combat multicollinearity and outliers: Real life applications using T20 cricket sports and anthropometric data

The performance of ordinary least squares (OLS) and ridge regression (RR) are influenced when outliers are present in y-direction with multicollinearity among independent variables. The robust RR with ridge parameters provides a biased estimator that has a smaller variance than conventional OLS and RR estimators. The optimal value of the ridge parameter has a vital role in bias-variance tradeoff. This study proposes quantile-based estimation of ridge parameter in robust RR to deal with the joint issue of y-direction outliers and multicollinearity. The effectiveness of the proposed estimators is evaluated using intensive Monte Carlo simulation and two real data sets in terms of mean square error (MSE) and predictions sum of squared error (PSSE) criterion. Simulation findings reveal that the newly developed estimators of ridge parameter in robust RR have better performance than OLS, RR, and existing robust RR estimators when errors are normally and non-normally distributed. The results from two numerical examples of T20 Cricket sports and anthropometric data show that the new estimator with quantile probability 0.50 and 0.99 respectively has winning performance among all competing and proposed estimators.

A New Lifetime Distribution and its Application to Cancer Data

Introduction: Recently, researchers have introduced new generated families of univariate lifetime distributions. These new generators are obtained by adding one or more extra shape parameters to the underlying distribution or compounding two distributions to get more flexibility in fitting data in different areas such as medical sciences, environmental sciences, and engineering. The addition of parameter(s) has been proven useful in exploring tail properties and for improving the goodness-of-fit of the family of the proposed distributions. Methods:A new Three-Parameter Weibull-Generalized Gamma distribution which provides more flexibility in modeling lifetime data is developed using a two-component mixture of Weibull distribution (with parameters θ and λ) and Generalised Gamma distribution (with parameters α=4,θ and λ). Some of its mathematical properties such as the density function, cumulative distribution function, survival function, hazard rate function, moment generating function, Renyi entropy and order statistics are obtained. The maximum likelihood estimation method was used in estimating the parameters of the proposed distribution and a simulation study is performed to examine the performance of the maximum likelihood estimators of the parameters. Results: Real life applications of the proposed distribution to two cancer datasets are presented and its fit was compared with the fit attained by some existing lifetime distributions to show how the Three-Parameter Weibull-Generalized Gamma distribution works in practice Conclusion: The results suggest that the proposed model performed better than its competitors and it’s a useful alternative to the existing models.

Using 3D Hand Pose Data in Recognizing Human–Object Interaction and User Identification for Extended Reality Systems

Object detection and action/gesture recognition have become imperative in security and surveillance fields, finding extensive applications in everyday life. Advancement in such technologies will help in furthering cybersecurity and extended reality systems through the accurate identification of users and their interactions, which plays a pivotal role in the security management of an entity and providing an immersive experience. Essentially, it enables the identification of human–object interaction to track actions and behaviors along with user identification. Yet, it is performed by traditional camera-based methods with high difficulties and challenges since occlusion, different camera viewpoints, and background noise lead to significant appearance variation. Deep learning techniques also demand large and labeled datasets and a large amount of computational power. In this paper, a novel approach to the recognition of human–object interactions and the identification of interacting users is proposed, based on three-dimensional hand pose data from an egocentric camera view. A multistage approach that integrates object detection with interaction recognition and user identification using the data from hand joints and vertices is proposed. Our approach uses a statistical attribute-based model for feature extraction and representation. The proposed technique is tested on the HOI4D dataset using the XGBoost classifier, achieving an average F1-score of 81% for human–object interaction and an average F1-score of 80% for user identification, hence proving to be effective. This technique is mostly targeted for extended reality systems, as proper interaction recognition and users identification are the keys to keeping systems secure and personalized. Its relevance extends into cybersecurity, augmented reality, virtual reality, and human–robot interactions, offering a potent solution for security enhancement along with enhancing interactivity in such systems.

Structure and Dynamics of ATP and the ATP-Zn2+ Complex in Solution.

Despite the crucial role of ATP in life and artificial life-like applications, fundamental aspects relevant to its function, such as its conformational properties and its interaction with water and ions, remain unclear. Here, by integrating linear and two-dimensional infrared spectroscopy with ab initio molecular dynamics, we provide a detailed characterization of the vibrational spectra of the phosphate groups in ATP and in its complex with Zn2+ in water. Our study highlights the role of conformational disorder and solvation dynamics, beyond the harmonic normal-mode analysis, and reveals a complex scenario in which electronic and environmental effects tune the coupling between phosphate vibrations. We identify βγ-bidentate and αβγ-tridentate modes as the preferential coordination modes of Zn2+, as was proposed in the literature for Mg2+, although this conclusion is reached by a different spectral interpretation.