Life Technologies Research Articles

Scientific interest in rehabilitation: a bibliometric analysis

Purpose: It is aimed to conduct bibliometric analysis to explore scientific interest in rehabilitation. The date of bibliometric analysis was 17.01.2023, and a total of 410.000 documents from all times to this date were analyzed. Materials and Methods: The data for this study was obtained from Scopus, a multidisciplinary database, for the period of sixty-two years (1960-2022) using the keyword ‘rehabilitation’. Our bibliometric analysis includes articles, authors, subject area, document types, countries, languages, affiliations, publication years, citations, and number of citations. Since Scopus allows the download of 2000 data at a time, citation reports after 2021 were examined. Results: 109,138 of 410.000 articles were in all open Access and 342.918 were in English. When we look at the distribution of the number of articles by year, there were 10,095 articles in 2023, 27,615 articles in 2022, 25,594 articles in 2021, and 22,718 articles in 2020. Documents types were; article (72.5%), conference paper (9.9%), review (9.8%), book chapter (2.3%), and editorial (1.7%). Documents according to subject areas were as follows; Medicine (N=282.301), Health Professions (N=66,434), Engineering (N=53,704), Neuroscience (N=26,016), Computer Science (N=25,326), Social Sciences (N=23,080), Psychology (N=21,384), Nursing (N=19.481). A total of 199 cited documents have been reached. Sixty-two in 2023; 132 in 2022; 5 in 2021. The h index was 4. Conclusion: We see that 'Rehabilitation' is increasingly becoming an area of interest for many different disciplines, as developing technology and quality of life in the field of health are at the forefront of treatment approaches.

KEMAMPUAN LITERASI BAHASA INDONESIA PELAKU UMKM PRODUK LOKAL BADUI PADA TOKO ONLINE DAN E-COMMERCE TOKOPEDIA

The indigenous Badui community is known for its strict customs; among other things, prohibitions on schools and the use of technology in daily life so it is not surprising that the majority of Badui people cannot write, especially the Badui Dalam indigenous community and the older generation of the Badui Luar community. As time goes by, the Badui Luar indigenous people are starting to openup using technology in the form of cell phones and the internet. The use of cell phones and the internet among the Badui Luar indigenous community is not only for communication, but also for trading. Initially, the use of social media by the Badui indigenous community was Facebook and Instagram for trading. In the last year, some of them have started using e-commerce. The use of social media indirectly becomes a learning medium for the indigenous Badui Luar community to write because like it or not they have to communicate in writing with consumers in the form of chat texts and create product descriptions in the online shops they own. However, due to their limited ability to write and speak Indonesian well, they often experience communication barriers with consumers which ultimately impacts the success of buying and selling transactions. The objectives to be achieved in this research include: (1) knowing the ability of small business owner local Badui to write sentences, (2) knowing the ability of small business owner in understanding sentences or short conversation texts and responding to chats in writing. This research is a development of the research results of Saleh Abas (2006) which states that a person's ability to write is determined by accuracy in using languageelements, organizing discourse in the form of an essay, accuracy in using language, and choosing the words used to write. The method used in this research is descriptive qualitative. The research subjects were local Badui product who already had online shops on Tokopedia e-commerce. The sample obtained by purposive sampling consisted of 10 small business owner who already had an online shop on Tokopedia e-commerce with the status still active. Research data was obtained based on 26 texts describing local Badui products; including woven cloth, koja bags, palm sugar and tutu coffee. This data is to determine sentence structure, diction, punctuation, and creativity in creating sentences. The data analysis technique uses descriptive analysis based on product description text. The results of the research show that 6 out of 10 small business owner who are local Badui products have quite good writing skills, but are not good in terms of grammar, diction, punctuation and creativity in creating sentences. The conclusions from this research show that the Indonesian language literacy skills of small business owner local Badui product are quite good even though they have not received formal education.

OPTIMALISASI KINERJA TRUK QUESTER CWE 370 MELALUI MODIFIKASI SISTEM VESSEL

The Quester CWE 370 truck is one of the most widely used transport vehicles in the industry, where reliability and operational efficiency are critical to ensuring a smooth supply chain. This research aims to optimize truck performance through vessel system modification, which focuses on improving fuel strength and efficiency. The modification process includes checking the raw material, cutting, welding, and smoothing, which is followed by testing to ensure fuel flow stability and better engine response. The results show that vessel system modifications not only improve fuel efficiency, but also extend engine life and reduce the need for routine maintenance. Thus, these modifications provide long-term benefits for the company in terms of operational cost savings and increased productivity.

Assessing the Dynamics of Ecological Footprint in Relation to Economic and Energy Factors: A Comparative Analysis of Finland and Japan

AbstractThe ecological footprint (EF) has a complex relationship with several key factors, including economic growth, nuclear energy, urban population, and renewables. The scope of this paper is to analyze the effect of renewables (REN), GDP per capita, urbanization (URB), and nuclear energy (NUK) on EF by means of the ARDL model for Finland and Japan. The ARDL long-run estimated coefficients are validated by supplementary tests, such as FMOLS, DOLS, and CCR. While Finland and Japan share similarities in education, technology, and quality of life, they differ significantly in geography, population density, cultural practices, economic structure, and historical backgrounds. The main results indicate that in the long term, REN and NUK exhibit a negative impact on EF, while GDP and URB exhibit a positive impact on EF for Finland. Additionally, REN, URB, and NUK exert a negative impact on EF, while GDP exerts a positive impact on EF for Japan. The ECT of − 1.96 for Finland indicates a very fast and strong correction towards equilibrium in the EF following any short-term disturbances. Furthermore, an ECT of − 1.01 in the ARDL model for Japan indicates a very fast adjustment of the EF to its long-term equilibrium following any short-term disturbances caused by changes in REN, GDP, URB, or NUK. Our research proposes some policy implications for both economies. Its main contributions enhance the understanding of the complex relationships between economic growth, energy sources, urbanization, and environmental impact, providing guidance for sustainable development policies.

Exploring the influence of online restaurant promotions on consumer behavioral intentions

PurposeConsumer behavior is evolving rapidly due to the increasing role of technology in daily life. Online food ordering has emerged as a key channel in this changing landscape. This paper investigates the relationships between online promotions, consumer skepticism, information sharing on social media and the intention to purchase food and beverages through online delivery services.Design/methodology/approachMeasures were developed based on a review of existing literature. Data from 402 participants were analyzed using Structural Equation Modeling (SEM).FindingsThe study reveals that online promotions significantly impact consumers' sharing of restaurant posts. Additionally, consumer skepticism about online food sales affects both their sharing behavior and their intention to purchase online. Engagement in sharing restaurant posts online is a strong predictor of online food purchasing intentions.Practical implicationsThe findings offer valuable insights for restaurant operators, policymakers and technology developers in the competitive online food delivery sector. They emphasize the importance of implementing innovative promotions and crafting appealing food presentations. These strategies can accelerate customer decision-making, attract new customers and contribute to market expansion and customer base sustainability.Originality/valueThis research provides significant insights for restaurant owners and contributes to the limited literature on online promotions, consumer skepticism and information sharing in the restaurant industry. It also lays the groundwork for future studies aimed at deepening understanding in this field.

Ranking guidance actions to support engineers in fulfilling process constraints

AbstractIn safety‐critical systems engineering, regulations such as Automotive SPICE, ISO26262, or ED‐109A mandate software quality assurance measures to provide evidence that the developed system is high quality. The constraints that define quality assurance conditions during the engineering life cycle are often non‐trivial. This paper addresses the challenges, engineers face who are unfamiliar with the precise constraints of various projects (e.g., when newly joining a company or switching between departments). Understanding how to fulfill a constraint is a time‐consuming and challenging task as an engineer needs to determine the most suitable option (out of potentially many) to fulfill a constraint violation. To this end, we propose a guidance action ranking framework to provide engineers with the most relevant guidance actions. Our primary ranking algorithm analyzes in the background the actions that engineers have made in the past to resolve a constraint violation without requiring explicit feedback from them. We evaluated our framework on two real‐world data sets: an open‐source drone management and an industrial air traffic control software system. Concretely, we replay past engineering activities and measured whether, in the case of a constraint violation, our suggested guidance actions were indeed selected by the engineer. The evaluation results revealed that learning from prior guidance actions effectively identifies the most appropriate guidance actions (ranked top 1 or 2) when compared to ranking algorithms based on action simplicity and artifact property change frequency. Specifically, we achieve a median MRR of 0.95 for the first case study and 0.94 for the second case study: an improvement of 80% and 100% over the baseline. Additionally, we observed that the simplicity of a guidance action does not reliably indicate its suitability for fulfilling a constraint, whereas learning from prior change operation property out‐performed simplicity‐based ranking but did not surpass guidance frequency‐based ranking.

Artificial intelligence in the Russian regions

Objective: to provide a comparative assessment of the use of artificial intelligence technologies by organizations in the context of Russian regions and to identify determinants of their dynamics. Methods: descriptive statistics, histogram, grouping, principal component method, panel data models. Results: an absolute trend of recent years is to study and implement artificial intelligence technologies in many economic, industrial processes and social life. The article analyzes the trends in the application of artificial intelligence technologies in the Russian regions. The comparative analysis of regions by the level and growth rate of artificial intelligence technologiesuse by organizations showed that the regions were heterogenous by the dynamics of this indicator in 2020-2022. The regions were divided into four groups: above average and below average level in Russia. Econometric modeling based on the method of principal components gave grounds to unite the determinants of the use of artificial intelligence technologies into four components. Panel data fixed-effects models showed a significant impact of the component, characterizing the state of human capital, the level of economic development, and innovation activity of organizations in the region. Scientific novelty: for the first time an attempt was made to provide a comparative analysis of Russian regions by the level of artificial intelligence technologies use by organizations and to find the determinants of its change. Practical significance: the heterogeneity of regions in terms of the artificial intelligence technologies use by organizations was substantiated, as well as a great impact of the specific characteristics of regions, which should be taken into account when building a national policy of artificial intelligence development.

Prediction of Remaining Useful Life of Aero-engines Based on CNN-LSTM-Attention

Accurately predicting the remaining useful life (RUL) of aircraft engines is crucial for maintaining financial stability and aviation safety. To further enhance the prediction accuracy of aircraft engine RUL, a deep learning-based RUL prediction method is proposed. This method possesses the potential to strengthen the recognition of data features, thereby improving the prediction accuracy of the model. First, the input features are normalized and the CMAPSS (Commercial Modular Aero-Propulsion System Simulation) dataset is utilized to calculate the RUL for aircraft engines. After extracting attributes from the input data using a convolutional neural network (CNN), the extracted data are input into a long short-term memory (LSTM) network model, with the addition of attention mechanisms to predict the RUL of aircraft engines. Finally, the proposed aircraft engine model is evaluated and compared through ablation studies and comparative model experiments. The results indicate that the CNN-LSTM-Attention model exhibits superior prediction performance for datasets FD001, FD002, FD003, and FD004, with RMSEs of 15.977, 14.452, 13.907, and 16.637, respectively. Compared with CNN, LSTM, and CNN-LSTM models, the CNN-LSTM model demonstrates better prediction performance across datasets. In comparison with other models, this model achieves the highest prediction accuracy on the CMAPSS dataset, showcasing strong reliability and accuracy.

AUTOMATION OF MEASUREMENTS OF ENGINE HEAT CONSUMPTION IN THE LUBRICATION AND COOLING SYSTEMS

The paper presents the results of an intermediate stage of the study of heat transfer processes in high-speed diesel engines of the automotive tractor type at transient load discharge-load-injection modes, which are characteristic of this type of diesel engines. In such modes, as evidenced by the results of calculations of the thermal stress state of combustion chamber parts, in particular pistons, exhaust valves of the gas distribution mechanism, significant tensile and compressive stresses are observed compared to operation at steady-state conditions. This is the reason for the occurrence of defects in the form of cracks on the heat exchange surfaces of these parts, which limits the service life of automotive tractor engines. For a more detailed study of transient processes, as the main approach, it is proposed to use the well-known method of heat balance tests of a high-speed diesel engine, but with the involvement of automated processing of measurement results in digital form at steady-state and transient modes. It is proposed to focus on heat consumption, which is a component of the heat balance in the lubrication and cooling system. Without the use of automated information processing directly at the time of transient load shedding and load shedding, it is impossible to obtain such information on cooling and lubrication systems. Recording of transient heat transfer processes makes it possible to track the influence of engine operating parameters, the dynamics of the transient process depending on its duration, the initial and final steady-state modes between which the transition is being studied. In general, this way, it is possible to assess the adaptability of cooling and lubrication systems to heat dissipation during sudden changes in load, to avoid, or at least limit, the growth of thermal stresses. The publication presents a possible variant of the schematic solution of such a system for automated processing of the results of heat balance tests with the maximum use of equipment that is mass-produced and has already been used previously in heat balance tests.

Multiscale Modeling Approach to Understand Mechanism of Deposit Control by Sulfonate-Based Lubricant Detergents.

Protecting the material surfaces from deposits and insoluble sludge particles extends the engine life and reduces waste. Lubricant detergents in engine oils are essential additive technologies that prevent deposit formation in internal combustion engines. In this study, the effect of sulfonate detergent on deposit formation in a passenger car engine is investigated with experimental and multiscale molecular modeling methods to present a unified approach. First principles density-functional theory calculations, statistical sampling methods, all-atom molecular dynamics simulations, and coarse-grained simulations are examined to elucidate deposit control mechanism of sulfonate detergents. Analysis of the results reveals that sludge particles in the drain oil are similar in structure to piston deposits, and they might be the precursors of the piston deposits. Main factor for controlling the sludge particle deposition is the prevention of their colloidal aggregation at the microscale in the base oil matrix. Aggregation can be mitigated by the intercalation of detergent polar groups between the particles. This is followed by the extension of hydrophobic tails into the oil phase, which decreases further aggregation via formation of a repulsive layer.

Metode Weight Product Untuk Pemilihan Pestisida Yang Sesuai Dengan Jenis Hamanya

Technological developments are now growing rapidly in various fields, including the implementation of technology in everyday life, including the world of business and organizations. Digital technology includes information, computing, communications and connectivity, becoming a key factor for achieving sustainable competitive advantage in various industries. The use of technology also provides benefits such as sales monitoring, business recommendations and better decision making. Decision Support System (DSS) is a system that is deliberately created to assist certain parties in making decisions with quality and objective results, in an effective and efficient way of working, and does not depend solely on subjective decisions or based on wrong parameters. . The Weight Product (WP) method is one of the solution methods in Decision Support Systems. Choosing the best product is an important decision in various aspects of life, from purchasing consumer goods to choosing technology in business. UD. Anugrah Jaya Tani is one of the kiosks in Sayur Mahincat village, Kec. Barumun Selatan, Padang Lawas Regency, which operates in the agricultural sector and sells various types of pesticides, herbicides and fertilizers. Pesticides are chemical substances and other materials used to control various pests. For farmers, the types of pests are mites, pest plants, plant diseases caused by fungi (fungi), bacteria and viruses, nematodes (worms that damage roots), snails, mice, birds and other animals that are considered detrimental. This research aims to determine pesticides that are suitable for the type of pest on plants. The method used is a Decision Support System (DDS), namely Weight Product (WP). The data used is 10 data with 6 predetermined criteria. The results of this research are based on the final value obtained from each alternative, the pesticide with the highest value is considered the best alternative and the result is that Alternative A5 is the best pesticide with the highest value of 0.1396, namely Sidabas. This means that the pesticide is considered to best meet the specified criteria with the weight given.

Integrating imprecise data in generative models using interval-valued Variational Autoencoders

Variational Autoencoders (VAEs) enable the integration of diverse data sources into a unified latent representation, facilitating the fusion of information from various inputs and the creation of disentangled representations that separate different factors of variation in the data. Traditional VAEs, however, are limited by assuming a single prior distribution for latent variables, which restricts their ability to handle epistemic uncertainty from imprecise measurements and incomplete data. This paper introduces the Interval-Valued Variational Autoencoder (iVAE), which employs a family of prior distributions and incorporates specialized neurons and redefined objective functions for handling interval-valued data. This architecture maintains computational efficiency while extending the model’s applicability to scenarios with pronounced epistemic uncertainty. The iVAE’s efficacy is demonstrated in managing two types of data: intrinsically interval-valued and noisy data preprocessed into interval formats. The first category is exemplified by a graphical analysis of questionnaires, while the second involves case studies focused on estimating the remaining useful life of aviation engines, where the iVAE outperforms traditional methods, thereby providing more accurate diagnostics and robust predictions.

The impact of digital technologies on public life: a critical examination of perspectives from spatial design experts

ABSTRACT This study explores the influence of digital technologies on public life and evaluates spatial design professionals’ perceptions of these impacts. The research involved surveying architects, urban planners, and landscape architects to comprehend the ramifications of digital technology on public life. In total, 356 spatial design experts participated in the study. The analysis utilised descriptive statistical methods, t-tests and ANOVA tests for independent samples. The findings reveal that social isolation was rated highest among the spatial design experts’ scale, whereas social anxiety scored lowest among the elements comprising the spatial design experts’ scale. A comparison between participants’ socio-demographic characteristics and their scores on the impact of digital technologies in public life showed variances in gender, employment status, profession, and daily usage of digital technology, but found no significant differences in marital status, level of education, or professional experience. Consequently, this research sheds light on the repercussions of digital technology usage in public life, offering invaluable insights for all stakeholders, particularly spatial design professionals and social scientists. By understanding these impacts, the study highlights the importance of adopting more informed and conscious approaches to fostering positive social interactions and enhancing community well-being in public life.

Combined improved tuna swarm optimization with graph convolutional neural network for remaining useful life of engine

AbstractAccurate prediction of the engine's remaining useful life (RUL) is essential to ensure the safe operation of the aircraft because. However, traditional deep‐learning based methods for RUL prediction has been limited by interpretability and adjustment for hyperparameters in practical applications due to the intricate potential relations during the degradation process. To address these dilemmas, an improved multi‐strategy tuna swarm optimization‐assisted graph convolutional neural network (IMTSO‐GCN) is developed to achieve RUL prediction in this work. Specifically, mutual information is used to describe potential relationships among measured parameters so that they could be utilized in building edges for these parameters. Besides, considering that not all relational nodes will positively affect the RUL prediction and the inherent hyperparameters of the GCN are high‐dimensional. Inspired by “No Free Lunch (NFL)”, IMTSO is proposed to reduce the optimization cost of hyperparameters, in which cycle chaotic mapping is employed to achieve initialization of the population for improving the uniformity of the initial population distribution. Besides, a novel adaptive approach is proposed to enhance the exploration and exploitation of tuna swarm optimization (TSO). The CMAPSS dataset was used to validate the effectiveness and advancedness of IMTSO‐GCN, and the experimental results show that the R2 of the IMTSO‐GCN is greater than 0.99, RMSE is less than 3, the Score error is within 1.

Deep residual ensemble model for predicting remaining useful life of turbo fan engines

Abstract Capturing degradation trends from the Condition monitored signals is a proven technique for predicting the Remining Useful Life (RUL) of the equipment, which has gained more prominence in Prognostics and Health Management (PHM) in Industry 4.0. However, this process is tiresome and comprehending all the physical parameters of the system to construct a Health Index that characterize the health state is a complex process, especially if multiple sensors are involved. This work proposes a Deep residual ensemble model which constructs Fused Health Index (FHI) by harnessing temporal property of signals. The proposed Residual network integrates Bi-directional Long Short Term Memory (Bi-LSTM) and Deep Neural Network (DNN) which absorbs individual residuals of both the forward and reverse LSTMs that acts as an important feature to improve the overall prediction process. The work validated using CMAPPS dataset using various unique performance metrics to portray the effectiveness of the model.

Engineering characteristics, techno-economic feasibility and life cycle assessment of bio-fiber based green engineered cementitious composites

The objective of the present work is to understand the microstructural, and life cycle analysis of novel engineered cementitious composites (ECC) where the conventional synthetic micro-fibers are replaced using bio-based fibers. A detailed mechanical and microstructural characteristics of the developed ECC mix are presented first to understand the feasibility of introducing natural fibers without compromising the ductility and strength of the mix. To prove the cost-effectiveness and sustainable balance, a detailed life cycle analysis (LCA) was performed using different sustainability indices such as cost, embodied energy, and carbon emissions. The natural fiber ECC mixes developed in this work are compared with similar artificial fiber ECC mixes for different LCA parameters. Results from the tensile tests revealed that using natural fibers in an ECC mix aided in achieving significant tensile strength (4.3–10.2 MPa) and failure strain levels (2.5 %–3.5 %). Specifically, using flax fibers in the ECC mix aided in achieving a high tensile strength of 10.2 MPa corresponding to an ultimate strain of 2.5 %. From the outcomes of LCA, the use of kenaf and pineapple can be recommended for sustainable ECC production without compromising its mechanical characteristics.

Maturing the design: challenges in maturing a first of a kind fusion power plant.

The design, delivery and operation of a large-scale infrastructure project are challenging at best. For the Spherical Tokamak for Energy Production (STEP) prototype powerplant (SPP), the challenges increased dramatically. In addition to being a large-scale infrastructure project, it is a cutting edge, first of a kind (FOAK) technology demonstrator. The design teams are working in a volatile, uncertain, complex and ambiguous environment, where technology is constantly emerging, maturing and changing. STEP will be unlike any power plant ever built and requires the development of new technologies and capabilities, but also a novel approach to planning and maturing the design. By taking a holistic view of the engineering life cycle from the start, the programme will be better positioned to achieve an SPP that is fit for purpose and can be used to show a path to ultimate commercial viability for subsequent power plants. This paper will review the key challenges in maturing a FOAK fusion power plant and look in depth at how the STEP team are maturing the required capabilities and planning to ensure successful delivery of the SPP. This article is part of the theme issue 'Delivering Fusion Energy - The Spherical Tokamak for Energy Production (STEP)'.

Full Life Cycle Research Based on the Contradiction of Engineering General Objective

In the management of the entire life cycle of an engineering project, the contradiction of the overall engineering objectives is a complex and important issue. The study of the engineering life cycle based on the contradiction of overall engineering objectives aims to comprehensively analyze each stage of the project life cycle, identify the points of conflict among various objectives, and propose corresponding solutions to optimize the overall engineering objectives. This research on the engineering life cycle based on the contradiction of overall engineering objectives is a complex and significant topic. Through in-depth research and practice, it can promote the continuous improvement and development of engineering life cycle management, providing strong support for the successful implementation of engineering projects.

Membangun Inovasi di Era Digital: Pengaruh Pembelajaran Digital terhadap Perilaku Kerja Karyawan Magang melalui Kesiapan untuk Berubah

Technological developments have made the use of digital technology in everyday life, including the implementation of tasks in organizations, making competition for companies to innovate even tighter. Apprentices as prospective workers in the future are expected to have innovative work behavior so that innovation in the company becomes easier. This research aims to determine the influence of digital learning orientation on innovative work behavior which is mediated by readiness to change. Data were obtained with convinience sampling, from interns who live in Indonesia (N = 285). The participants have aged between 18-23 years (M =21. 35; SD =3.40). There are three measuring tools used in this research, the nine-item innovative work behavior scale, the fifteen-item digital learning orientation scale and the readiness for change scale consists of fourteen items. Analysis using Hayes' simple mediation analysis technique using PROCESS version 4.0. The research results show that readiness to change is partial mediated the influence of digital learning orientation on apprentices' innovative work behavior. This study can provide insights for interns to better prepare themselves for the challenges they will face when entering the workforce. Additionally, for educational institutions or campuses, the findings of this study can serve as a consideration in designing curricula that equip students to face these challenges.

Adoption of assistive technologies in long-term care homes: What the pandemic has taught us.

COVID-19 increased the use of technology in everyday life and highlighted critical applications for assistive technologies. This comparative research study explores how assistive technologies, including socially assistive robots, can be adopted by long-term care homes to mitigate the lasting effects of the pandemic. In particular, we investigated the types of assistive technologies used by long-term care facilities to help with the care of older residents and for what tasks. Furthermore, we identified barriers to adoption and proposed policy measures that encourage technology adoption.