Application Area Research Articles

Bidirectional rotational antagonistic shape memory alloy actuators for high-frequency artificial muscles

Shape memory alloys (SMA) are commonly utilized in compact actuators due to their high energy density, meaning possible work output in relation to their weight and volume. Their application area is limited by their poor dynamic behavior, caused by the thermal activation characteristics of SMA materials. Typical actuation frequencies of SMA-based actuators range from 1 Hz to 10 Hz. In this work, we introduce an actuator system architecture, termed bidirectional rotational antagonistic (BIRAN) SMA actuator, which uses bundles of thin SMA wires to generate repeated rotational movement at frequencies up to 200 Hz. This marks a new frequency record for electrically activated SMA wire-based actuator systems. The high frequency reported results from the combination of mechanical design, electronics, and control strategy. We describe the fabrication techniques and the power electronics development and demonstrate the system performance through a systematic experimental study. A bio-inspired robotic wing-flapping joint illustrates the expansion of possible SMA-based application areas, pushing the dynamic limitations of this actuator technology.

The production of ready to drink terebinth coffee and the changes occurring during storage

The purpose of this study was to bring a new product to the food industry and investigate a different application area for ready-to-drink terebinth (Pistacia terebinthus) coffee made with water and milk. Accordingly, formulation was determined based on preliminary trials conducted with terebinth coffees. Coffees prepared according to this formulation were processed and preserved using two different methods. In the first method, the products were filled into amber-colored glass bottles using hot filling technique and sterilized, while in the other method, potassium sorbate was added to the products and preserved in amber-colored glass bottles. Samples were stored at room temperature (24°C) and in the refrigerator (+4°C) for 180 days. Dry matter, ash, pH, free fatty acids, fatty acid compositions, color characteristics, antioxidant activity, total phenolic content, and phenolic fractions were investigated in the prepared ready-to-drink menengic coffees. As a result of the analyses, while the fat content remained constant in the coffee samples, an increase in free fatty acids and a decrease in pH were detected. Although there was a slight decrease in antioxidant capacities and phenolic content during the storage period, it was determined that this decrease was not significant. Luteolin, ellagic acid, gallic acid, fumaric acid, and vanillic acid were predominantly found in menengic coffees in terms of phenolic compounds. In milk coffees, it was observed that flocculation occurred due to heat treatment.

The Impact of Statistical Process Control on Industrial Sustainability: A Torque and Angle Control-Based Approach for Threaded Connections

Objective: To investigate the application of statistical process control (SPC) in defining accurate acceptance limits for tightening angles in threaded connections. The aim is to enhance process reliability and product quality by addressing gaps in existing standards, such as ISO 22514 and VDI 2645, through the integration of torque and angle data into a unified control framework. This research seeks to optimize manufacturing efficiency, reduce rework and production downtime, and contribute to sustainable industrial practices by minimizing waste and ensuring safer, more reliable products. Theoretical Framework: This research is grounded in the principles of Statistical Process Control (SPC), which provides the foundational methodology for analyzing and improving process variability. SPC focuses on monitoring and controlling manufacturing processes to ensure they operate at optimal efficiency and within defined limits, aligning with the standards outlined in ISO 22514 for process capability and performance. The study also draws upon the concepts presented in VDI/VDE 2645, which emphasize the role of torque and angle as auxiliary quantities for ensuring proper preload in bolted joints. These standards highlight the complexities of managing tightening parameters, such as torque and angle, and the need for precise assessment of their interrelation to guarantee joint integrity. The application of Gaussian distribution models and quantile-based methods provides statistical tools to identify outliers and define normal process behavior, particularly in non-normal data distributions. By integrating these theoretical perspectives, this study establishes a robust framework for enhancing the reliability and sustainability of fastening processes in industrial applications. Method: This research comprises a quantitative approach, focusing on the statistical analysis of torque and angle data collected from an automotive assembly line based in international deployed standards, to define an application method for fastening control. The study design included monitoring fastening events under controlled conditions, with torque as the primary controlled variable and angle as the monitored metric. Data collection was conducted through real-time observations using fastening controllers equipped with integrated torque and angle measurement systems. The data were analyzed using statistical tools, including Gaussian distribution models and quantile-based methods, to define control limits and identify outliers. The methodology ensured a comprehensive understanding of process variability and facilitated the development of a robust framework for evaluating joint quality. Results and Discussion: The results obtained revealed a significant improvement in the quality and reliability of threaded connections, with reduction in rework and minimized production downtime. The statistical analysis demonstrated that integrating torque and angle metrics effectively identifies process anomalies, allowing for precise control limit definition. Quantile-based methods proved superior in handling non-normal data distributions, enhancing the robustness of the process evaluation. In the discussion, these findings are contextualized within the theoretical framework, reinforcing the importance of statistical process control in industrial applications. The relationship between torque and angle as indicators of joint behavior aligns with prior studies, emphasizing their predictive value in ensuring connection integrity. However, limitations such as data dependency on equipment precision and the need for large sample sizes were identified. These factors highlight areas for future refinement and broader application in diverse industrial settings. Research Implications: The practical and theoretical implications of this research provide valuable insights into the application of statistical process control in fastening processes. The findings can significantly influence practices in the fields of industrial manufacturing and quality management, particularly in sectors such as automotive, aerospace, and construction. Practical implications include the enhancement of production efficiency, reduction in rework and downtime, and improved product safety and reliability. Theoretically, the study reinforces the importance of integrating torque and angle metrics in quality control, offering a robust framework for future research on non-normal data distributions and their implications for process optimization. Originality/Value: This study contributes to the literature by introducing an innovative approach to statistical process control in fastening processes, emphasizing the integration of torque and angle metrics for defining precise control limits. By addressing gaps in the practical application of statistical methods for non-normal data distributions, the research offers new insights into optimizing manufacturing efficiency and quality. The relevance and value of this study are evidenced by its potential to reduce rework, minimize downtime, and enhance product reliability in critical sectors such as automotive and aerospace. These contributions provide a robust foundation for advancing both academic understanding and industrial practices in quality management.

Evolution of Mass Spectrometers for High m/z Biological Ion Formation, Transmission, Analysis and Detection: A Personal Perspective.

Mass spectrometry (MS) has become an essential tool in virtually all academic, pharmaceutical, and biopharmaceutical analytical laboratories. The specialized and bespoke area of MS research and application of high m/z ion (>m/z 6000 and high mass, >150 kDa) formation, transmission, analysis, and detection is a relatively new area of focus for MS that has seen dramatic acceleration in interest over the last two decades. Herein we delve into this exciting aspect of MS, discussing how MS instrumentation has been refined and evolved for native-MS analysis. We cover the early groundbreaking experiments showing high m/z ion formation, transmission, and preservation of protein structure in the gas phase. Additionally, we discuss specific instrument optimizations and modifications that have advanced high m/z ion generation, transmission, analysis, and detection, contributing to the research area known as gas-phase structural biology. Native-MS sample introduction methods, emerging technologies, and future perspectives are also examined. Finally, we share personal opinions, observations, and experiences that are new to the community or previously unpublished.

Building a sustainable future: the role of digitalization in Nigerian heavy engineering projects

PurposeDigital technologies (DTs) have the potential to revolutionize various aspects of heavy engineering projects by providing innovative solutions to traditional challenges. Therefore, this study explores and identifies critical areas for implementing DTs in heavy engineering projects in Lagos State, Nigeria, to increase productivity, reduce costs and enhance project delivery.Design/methodology/approachA quantitative research approach informed the formulation of a structured questionnaire, which was then disseminated to construction professionals. The data obtained were analyzed using the Statistical Package for the Social Sciences (SPSS). The relative importance index (RII) was employed to rank the significance of each application area. Exploratory factor analysis (EFA) then grouped the various application areas for DTs before fuzzy synthetic evaluation (FSE) was used to evaluate the significance of the identified key categories.FindingsBased on the RII, the top five significant application areas identified were project management, cost estimation and budgeting, quality assurance and control, risk management and mitigation and construction site management. These areas were further grouped into three clusters: pre-construction phase, construction phase and post-construction phase.Originality/valueA majority of existing studies have been focused on regular construction projects, with limited research addressing the unique challenges and opportunities within heavy engineering projects. This study fills this gap by specifically examining the implementation of DTs in heavy engineering projects, offering sector-specific insights that are distinct from those in regular construction.

Bayesian modeling with locally adaptive prior parameters in small animal imaging.

Medical images are hampered by noise and relatively low resolution, which create a bottleneck in obtaining accurate and precise measurements of living organisms. Noise suppression and resolution enhancement are two examples of inverse problems. The aim of this study is to develop novel and robust estimation approaches rooted in fundamental statistical concepts that could be utilized in solving several inverse problems in image processing and potentially in image reconstruction. In this study, we have implemented Bayesian methods that have been identified to be particularly useful when there is only limited data but a large number of unknowns. Specifically, we implemented a locally adaptive Markov chain Monte Carlo algorithm and analyzed its robustness by varying its parameters and exposing it to different experimental setups. As an application area, we selected radionuclide imaging using a prototype gamma camera. The results using simulated data compare estimates using the proposed method over the current non-locally adaptive approach in terms of edge recovery, uncertainty, and bias. The locally adaptive Markov chain Monte Carlo algorithm is more flexible, which allows better edge recovery while reducing estimation uncertainty and bias. This results in more robust and reliable outputs for medical imaging applications, leading to improved interpretation and quantification. We have shown that the use of locally adaptive smoothing improves estimation accuracy compared to the homogeneous Bayesian model.

Indoor Air Quality Prediction in Sick Building Using Machine and Deep Learning: Comparative Analysis

Air pollution is a significant global concern that is continually increasing and threatening both the environment and human health. Air pollution is the principal factor leading to the deterioration of Indoor Air Quality (IAQ) in buildings. Carbon dioxide (CO2) significantly contributes to indoor pollution intensifying, primarily from human activities. The demand for effective IAQ systems has increased due to the necessity for sustainable building development. The artificial intelligence (AI) models presented in this work utilized Machine Learning (ML) and Deep Learning (DL) methodologies to train the available dataset. This dataset was collected by the indoor sensors in Shanghai from November 2016 to March 2017 to predict CO2 concentration and obtain pertinent information. The accuracy and the result of Ml and DL algorithms may differ depending on the datasets used and the algorithms' suitability for the specific data and application domain. Therefore, a significant benefit would be achieved by finding the best-fitted ML and DL models concerning the actual datasets and the application area. This necessity was fulfilled through an intensive review of the already existing DL and ML models. This analysis aims to implement the specified models and assess the efficiency of their prediction by computing several performance metrics like Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Median Absolute Error (Median AE), and Coefficient of determination (R2). Among implemented models, the Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) have performed better results in forecasting IAQ.

The Use of Multi-Criteria Decision-Making Techniques in The Domain of Ergonomics: A Literature Review

Ergonomics briefly aims to provide work and human harmony. In providing it, there are different kinds of problems need to be solved by various methods. To generate reasonable solutions for these problems the Multi-Criteria Decision Making (MCDM) techniques are utilized in the literature. A systematic review of literature was carried out at the intersection of ergonomics and MCDM to figure out the answers to the questions about how MCDM techniques are employed in ergonomics problems, on which subjects the application area is concentrated, which MCDM technique is frequently utilized. Electronic databases were investigated in the years between 2010 to 2024. It was determined that MCDM techniques are utilized to solve a wide range of ergonomic problems from design to ergonomic risk score calculation. It was specified that the AHP and TOPSIS techniques are frequently employed and the fuzzy extensions of these two methods are frequently preferred by the authors.

A Review of Industrial Digital Twin Technology Research: Progress, Challenges and Future Directions

With the rapid development of industrial Internet, Internet of Things (IoT), and big data technologies, digital twin technology has emerged as a pivotal tool in enhancing industrial productivity, optimizing resource allocation, and driving the evolution of smart manufacturing systems. This paper provides a comprehensive review of the current state of research on industrial digital twins, exploring their applications across various sectors such as production processes, equipment management, and quality control. The review highlights the technological advances that have enabled the widespread adoption of digital twins, including virtual modeling, real-time data transmission and processing, and data fusion. It also discusses the key challenges faced by industries in implementing digital twins, such as data accuracy and integration, the complexity of virtual model construction, and the need for standardization and cross-platform integration. Additionally, the paper addresses the ongoing development trends in the field, including the increasing integration of digital twins with artificial intelligence, machine learning, and cloud computing. Finally, the paper provides an outlook on the future directions of digital twin research, identifying areas for further innovation and application, and offers recommendations for overcoming current limitations to support wider industrial adoption. By summarizing the progress and challenges, this review aims to provide theoretical insights and practical guidance for advancing industrial digital twin technology.

Sodium-Glucose Transporter-2 Inhibitors (SGLT2i) and Myocardial Ischemia: Another Compelling Reason to Consider These Agents Regardless of Diabetes.

In recent years, the introduction of sodium-glucose transporter-2 inhibitors (SGLT2is) marked a significant advancement in the treatment of cardiovascular disease (CVD). Beyond their known effects on glycemic control and lipid profile, SGLT2is demonstrate notable benefits for cardiovascular morbidity and mortality, regardless of diabetic status. These agents are currently recommended as first-line therapies in patients with heart failure, both with reduced and preserved ejection fraction, as they improve symptoms and reduce the risk of hospitalization. While several studies have demonstrated that SGLT2is can reduce the incidence of major adverse cardiovascular events (MACEs), the true impact of these agents on atherosclerosis progression and myocardial ischemia remains to be fully understood. A global beneficial effect related to improved glycemic and lipid control could be hypothesized, even though substantial evidence shows a direct impact on molecular pathways that enhance endothelial function, exhibit anti-inflammatory properties, and provide myocardial protection. In this context, this narrative review summarizes the current knowledge regarding these novel anti-diabetic drugs in preventing and treating myocardial ischemia, aiming to define an additional area of application beyond glycemic control and heart failure.

Harnessing Photon Density Wave Spectroscopy for the Inline Monitoring of up to 100 L Vinyl Acetate-Versa® 10 Polymerization: Insights into Dispersion Dynamics and Mixing.

Photon Density Wave (PDW) spectroscopy is used as process analytical technology (PAT) in three batch sizes, 1 L, 10 L and 100 L, of polyvinyl acetate-neodecanoic acid vinyl ester (Versa® 10) copolymerization. The effects on particle formation and growth are comparably analyzed. The data show comparability across scales up to a polymer volume fraction of around 0.15. Deviations beyond this suggest differences in particle growth dynamics. A detailed analysis of the dispersion dynamics and mixing properties provides an enhanced understanding compared to previous studies. Furthermore, the PDW spectroscopy data suggest inhomogeneity due to insufficient mixing at the beginning of the syntheses, despite very low feed-rates of the monomer mixture. PDW spectroscopy is thus capable of monitoring deviations in syntheses at different reaction volumes in real-time. These findings underline the potential of PDW spectroscopy not only for monitoring synthesis but also for enabling inhomogeneity analysis as a new application area. The integration of offline conversion and particle size measurements emphasizes the critical role of mixing efficiency in achieving optimal polymer dispersion properties and final product quality.

The Role of Virtual Reality on Parkinson's Disease Management: A Bibliometric and Content Analysis.

The management of Parkinson's disease (PD) has increasingly focused on innovative technologies, particularly virtual reality (VR), which has emerged as a significant tool for addressing neurological disorders. This bibliometric analysis summarizes current research trends and hotspots regarding VR applications in PD management. A comprehensive search of the Science Citation Index Expanded (SCIE) within the Web of Science Core Collection (WoSCC) identified 475 publications from 2000 to 2024. Key findings indicate a substantial increase in publication output, especially after 2013, driven by technological advancements and investments from major IT companies. Prominent research institutions and scholars from Australia, Israel, Italy, and Spain have led this field, exploring various VR applications for PD patients. The focus of VR therapy research has evolved from primarily addressing freezing of gait (FOG) to a broader range of functional impairments, including balance, postural control, upper limb motor, and cognitive function. This study provides valuable insights into the evolving landscape of clinical research on VR in PD management, highlighting global trends and potential areas for future investigation and application of VR therapies.

Assessing AgERA5 and MERRA-2 Global Climate Datasets for Small-Scale Agricultural Applications

AgERA5 (ECMWF) is a relatively new climate dataset specifically designed for agricultural applications. MERRA-2 (NASA) is also used in agricultural applications; however, it was not specifically designed for this purpose. Despite the proven value of these datasets in assessing global climate patterns, their effectiveness in small-scale agricultural contexts remains unclear. This research aims to fill this gap by assessing the suitability and performance of AgERA5 and MERRA-2 in precision irrigation management, which is crucial for regions with limited ground data availability. The wine-making region of Nemea, Greece, with its complex and challenging terrain is used as a characteristic case study. The datasets are assessed for key weather variables and for irrigation planning, using detailed local meteorological station data as a reference. The results reveal that both products have serious limitations in small scale irrigation scheduling applications in contrast to what was reported in previous studies for other regions. The uneven performance of global datasets in different regions due to lack of sufficient observation data for reanalysis data calibration was also indicated. Comparing the two datasets, AgERA5 outperforms MERRA-2, especially in precipitation and reference evapotranspiration. MERRA-2 shows comparable potential in irrigation planning, as it occasionally matches or exceeds AgERA5’s performance. The study findings underscore the importance of evaluating metanalysis datasets in the application area before their use for precision agriculture, particularly in regions with complex topography.

Exploring antecedents of built environment for walkability of older adults: A systematic literature review

Background This systematic literature review (SLR) explores how urban design influences walking access in the built environment for walking of older adults, summarizing existing studies. Examining various theories, it analyses themes like safety, security, comfort, convenience, aesthetics, governance, and policy frameworks. By critically reviewing the literature, it seeks to understand current research trends and suggest future directions. Methods The study employs an evidence-based systematic approach, following the standard procedure of SLR to identify relevant studies and extract information. Results It was found that five prominent theories dominated research literature: Active Aging Framework (AAF) (35.4%), Social-Ecological Model of Health Promotion (SEMHP) (25%), Community-Engaged Urban Design Theory (CEUDT) (18%), Neighbourhood Social Environment Theory (NSET) (11%), and the fifth Ecological Model of Aging (EMA) (8%). Empirical research dominated (37.5%), followed by grounded theory (20.8%), case study (18.8%), comparative study (12.5%), document analysis (8.3%), and analytical research (2.1%) in terms of the research type. The SLR revealed that most research uses empirical methods and focuses on applying existing theories. Conclusions It identifies opportunities for future research, suggesting areas for model integration, advancement, and application. Notably, it highlights the potential of combining the two most relevant theories for interventions promoting older adults’ health through walkable environments. This review can aid practitioners and researchers in identifying the theoretical underpinning of key factors for designing walkable cities that cater to older adults’ needs. A potential limitation is its focus on dominant theories, excluding others that might offer valuable insights.

Comprehensive Review of Robotics Operating System-Based Reinforcement Learning in Robotics

Common challenges in the area of robotics include issues such as sensor modeling, dynamic operating environments, and limited on-broad computational resources. To improve decision making, robots need a dependable framework to facilitate communication between different modules and the optimal action for real-world applications. The Robotics Operating System (ROS) and Reinforcement Learning (RL) are two promising approaches that help accomplish precise control, seamless integration of sensors-actuators, and exhibit learned behavior. The ROS enables seamless communication between heterogeneous components, while RL focuses on learning optimal behaviors through trial-and-error scenarios. Combining the ROS and RL offers superior decision making, improved perception, enhanced automation, and reliability. This work focuses on investigating ROS-based RL applications across various domains, aiming to enhance understanding through comprehensive discussion, analysis, and summarization. We base our evaluation on the application area, type of RL algorithm used, and degree of ROS–RL integration. Additionally, we provide summary of seminal works that define the current state of the art, along with GitHub repositories and resources for research purposes. Based on the review of successfully implemented projects, we make recommendations highlighting the advantages and limitations of RL techniques for specific applications and environments. The ultimate goal of this work is to advance the robotics field by providing a comprehensive overview of the recent important works that incorporate both the ROS and RL, thereby improving the adaptability of these emerging techniques.

Steering conservation biocontrol at the frontlines: A fuzzy logic approach unleashing potentials of climate-smart intercropping as a component within the integrated management of fall armyworm in Africa.

Steering conservation biocontrol at the frontlines: A fuzzy logic approach unleashing potentials of climate-smart intercropping as a component within the integrated management of fall armyworm in Africa.

ZIF-8 derived porous carbon material with improved specific surface area for supercapacitor application

ZIF-8 derived porous carbon material with improved specific surface area for supercapacitor application

High-efficiency application area in China of evaporative cooling garments: Effects of solar radiation and wind speed

High-efficiency application area in China of evaporative cooling garments: Effects of solar radiation and wind speed

MechBERT: Language Models for Extracting Chemical and Property Relationships about Mechanical Stress and Strain.

Language models are transforming materials-aware natural-language processing by enabling the extraction of dynamic, context-rich information from unstructured text, thus, moving beyond the limitations of traditional information-extraction methods. Moreover, small language models are on the rise because some of them can perform better than large language models (LLMs) when given domain-specific question-answer tasks, especially about an application area that relies on a highly specialized vernacular, such as materials science. We therefore present a new class of MechBERT language models for understanding mechanical stress and strain in materials. These employ Bidirectional Encoder Representations for transformer (BERT) architectures. We showcase four MechBERT models, all of which were pretrained on a corpus of documents that are textually rich in chemicals and their stress-strain properties and were fine-tuned on question-answering tasks. We evaluated the level of performance of our models on domain-specific as well as general English-language question-answer tasks and also explored the influence of the size and type of BERT architectures on model performance. We find that our MechBERT models outperform BERT-based models of the same size and maintain relevancy better than much larger BERT-based models when tasked with domain-specific question-answering tasks within the stress-strain engineering sector. These small language models also enable much faster processing and require a much smaller fraction of data to pretrain them, affording them greater operational efficiency and energy sustainability than LLMs.

Lime and marble deposits mapping and estimation through deep neural layers-random forest merger and remote sensing imagery

Abstract Lime and marble are sedimentary (carbonate) rocks, mainly composed of calcite and dolomite as major ingredients. Calcite (CaCO3) and dolomite (MgCO3) offer excellent reflection in visible and short-wave infrared bands (0.4 to 2.5 μm), which distinguish them from other rock types and makes them a solid application area for multispectral sentinel-2 sensors. In this study, the monumental deposits of carbonated rocks of the intended study region are mapped and estimated with better accuracy by using a novel Deep Neural Layers-Random Forest merger model and Sentinel-2 imagery. We used ArcGIS 10.5 to prepare a high-quality classified map of the study regions, which can be used further to effectively and efficiently reach out and extract those deposits. The proposed model, trained on a locally generated dataset, containing 134,897 pixels of size 10 m, mapped the deposits with 0.95 accuracy (kappa coefficient 0.94), which is 70% of the total mineral deposits of the study regions. The proposed model is the improved version of a prior experimented model consisting of convolution layers and dense layers only, whose accuracy was 0.93. The experimental outcomes of the study stood successful in finding the optimum combination of hyperparameters for the AI models used, to delineate mineralization zones with precision, focusing on carbonate in particular.