Search Results (3,046)

Energy measurement in dynamic penetration tests is key to correctly interpreting test results and ensuring comparable geotechnical data. Although commercial devices are widely used, their high cost limits adoption in developing regions such as Peru, affecting the accuracy of soil evaluation in many geotechnical studies. In this context, this research presents an energy measurement system called Kallpa, which uses low-cost electronic components to digitize sensor signals during Standard Penetration Tests (SPTs). Kallpa employs high-resolution analog-to-digital converters (ADCs) with an advanced sampling frequency, processing and storing data via a Raspberry Pi 4 microcomputer. The sensors, including accelerometers and strain gauges, were calibrated and compared with the Pile Driving Analyzer (PDA) to validate their accuracy in the Kallpa system. This study involved sixteen Standard Penetration Tests (SPTs) conducted in various regions of Peru using donut hammers and two tests involving automatic hammers. The results demonstrate that the Kallpa system is comparable to other energy measurement devices on the market, such as the Dynamic Penetration Test (DPT), which provides accurate SPT energy measurements. The Kallpa Processor (Version 1.0) software was developed to perform data acquisition and calibration, analyzing approximately 500 hammer blows and comparing peak values with those of the Pile Driving Analyzer. The data collected by Kallpa’s sensors strongly agreed with the PDA data, validating the reliability of the device. The Energy Transfer Ratio (ETR) for manual hammers ranged from 43.5% to 68.4%, with an average of 58.9%, whereas automatic hammers presented ETR values between 82% and 87%. The correction of the N60 blow count allowed for the estimation of the relative density of soils evaluated at different depths and locations across Peru. Full article

This study explores how digital technologies and data analytics can transform urban waste management in smart cities by addressing systemic inefficiencies. Integrating perspectives from the Resource-Based View, Socio-Technical Systems Theory, Circular Economy Theory, and Institutional Theory, the research examines sustainability, operational efficiency, and resilience in extended supply chains. A case study of Company A and its demand-side supply chain with Retailer B highlights key drivers of waste, including overstocking, inventory mismanagement, and inefficiencies in transportation and promotional activities. Using a mixed-methods approach, the study combines quantitative analysis of operational data with advanced statistical techniques and machine learning models. Key data sources include inventory records, sales forecasts, promotional activities, waste logs, and IoT sensor data collected over a two-year period. Machine learning techniques were employed to uncover complex, non-linear relationships between waste drivers and waste generation. A waste-type-specific emissions framework was used to assess environmental impacts, while IoT-enabled optimization algorithms helped improve logistics efficiency and reduce waste collection costs. Our findings indicate that the adoption of IoT and AI technologies significantly reduced waste by enhancing inventory control, optimizing transportation, and improving supply chain coordination. These digital innovations also align with circular economy principles by minimizing resource consumption and emissions, contributing to broader sustainability and resilience goals in urban environments. The study underscores the importance of integrating digital solutions into waste management strategies to foster more sustainable and efficient urban supply chains. While the research is particularly relevant to the food production and retail sectors, it also provides valuable insights for policymakers, urban planners, and supply chain stakeholders. By bridging theoretical frameworks with practical applications, this study demonstrates the potential of digital technologies to drive sustainability and resilience in smart cities. Full article

Predicting meat quality, especially dark, firm and dry meat, as well as muscle fat prior to slaughter, presents a challenge in practice. Medical as well as high-frequency ultrasound applications can be utilized to predict body composition and meat quality aspects. Ultrasounds are non-invasive, rapid-to-operate in vivo and show high correlations to the animal production traits being estimated. Farm animal ultrasounds are used to predict intramuscular fat content in the beef cattle industry. Challenges are identified in applying ultrasound technology to detect glycogen content in farm animals due to a wide range of fat, muscle and water composition. Other technologies and methods are reported in this literature review to overcome issues in the practicability and accuracy of ultrasound technology when estimating muscle glycogen levels in cattle. The discussion of other tools such as hyperspectral imaging, microwave sensor technology and digital infrared thermal imaging were addressed because of their superior accuracy in estimating moisture and fat components. Full article

The aim of this research was to prepare pH sensor films based on litmus using the sol–gel method with tetraethoxysilane (TEOS) and phenyltrimethoxysilane (PTMS) as precursors. The pH sensor film was then applied to millireactors to evaluate its performance on the intricate geometries of static mixers commonly found in millireactor designs. Millireactors were made from Formlabs High Temp resin using stereolithography (SLA) and from Anycubic Basic resin using digital light processing (DLP) technology. The performance of the pH sensor films was evaluated by tracking color changes in the pH sensor films and analyzing RGB (red, green, blue) and hue values through a smartphone application. The experiment involved mixing solutions with different pH values at varying flow rates within the millireactor channels. Furthermore, along with analyzing the hue values, characterization techniques involved measuring contact angles with water and diiodomethane. A film combining a litmus indicator with titanium dioxide (TiO₂) displayed a color change within one minute and maintained this color throughout the study, confirming its reusability. Sensor films exhibited excellent reversibility (RSD = 2.4–3.3%) and stability. The findings demonstrate that the pH-sensitive films perform robustly across varying geometries, paving the way for their integration into advanced millireactor systems with static mixers and continuous chemical monitoring within Industry 4.0. Full article

Ensuring synchronization between real-world sensor data and industrial robotic simulations remains a critical challenge in digital twin and virtual commissioning applications. This study proposes an innovative method for integrating real sensor signals into RoboDK simulations, bridging the gap between virtual models and real-world dynamics. The proposed system utilizes an Arduino-based data acquisition module and a custom Python script to establish real-time communication between physical sensors and RoboDK’s simulation environment. Unlike traditional simulations that rely on predefined simulated signals or manually triggered virtual inputs, our approach enables dynamic real-time interactions based on live sensor data. The system supports both analog and digital signals and is validated through latency measurements, demonstrating an average end-to-end delay of 23.97 ms. These results confirm the feasibility of real sensor integration into RoboDK, making the system adaptable to various industrial applications. This framework provides a scalable foundation for researchers and engineers to develop enhanced simulation environments that more accurately reflect real industrial conditions. Full article

Wound healing is a complicated biological process that is important for restoring tissue integrity and function after injury. Infection, usually due to bacterial colonization, significantly complicates this process by hindering the course of healing and enhancing the chances of systemic complications. Recent advances in wearable biosensors have transformed wound care by making real-time monitoring of biomarkers such as pH, temperature, moisture, and infection-related metabolites like trimethylamine and uric acid. This review focuses on recent advances in biosensor technologies designed for wound management. Novel sensor architectures, such as flexible and stretchable electronics, colorimetric patches, and electrochemical platforms, enable the non-invasive detection of changes associated with wounds with high specificity and sensitivity. These are increasingly combined with AI and analytics based on smartphones that can enable timely and personalized interventions. Examples are the PETAL patch sensor that applies multiple sensing mechanisms for wide-ranging views on wound status and closed-loop systems that connect biosensors to therapeutic devices to automate infection control. Additionally, self-powered biosensors that tap into body heat or energy from the biofluids themselves avoid any external batteries and are thus more effective in field use or with limited resources. Internet of Things connectivity allows further support for remote sharing and monitoring of data, thus supporting telemedicine applications. Although wearable biosensors have developed relatively rapidly and their prospects continue to expand, regular clinical application is stalled by significant challenges such as regulatory, cost, patient compliance, and technical problems related to sensor accuracy, biofouling, and power, among others, that need to be addressed by innovative solutions. The goal of this review is to synthesize current trends, challenges, and future directions in wound healing and infection monitoring, with emphasis on the potential for wearable biosensors to improve patient outcomes and reduce healthcare burdens. These innovations are leading the way toward next-generation wound care by bridging advanced materials science, biotechnology, and digital health. Full article

Digital Holographic Microscopy (DHM) is a method of converting hologram images into three-dimensional (3D) images by image processing, which enables us to obtain the detailed shapes of the objects to be observed. Three-dimensional imaging of the microscopic objects by DHM can contribute to the early diagnosis and the detection of the diseases in the medical field by observing the shape of the cells. DHM requires several experimental components. One of them is the laser, which is a problem because its high power may cause the deformation and the destruction of the cells and the death of the microorganisms. Since the greatest advantage of DHM is the detailed geometrical information of the object by 3D measurement, the loss of such information is a serious problem. To solve this problem, a Neutral Density (ND) filter has been used to reduce power after the laser irradiation. However, the image acquired by the image sensor becomes too dark to obtain sufficient information, and the effect of noise increased due to the decrease in the amount of light. Therefore, in this paper, we propose the Frame-Stacking Method (FSM) for dark DHM for reproducing 3D profiles that enable us to observe the shape of the objects from the images taken in low-power environments when the power is reduced. The proposed method realizes highly accurate 3D profiles by the frame decomposition of the low-power videos into images and superimposing and rescaling the obtained low-power images. On the other hand, the continuous irradiation of the laser beam for a long period may destroy the shape of the cells and the death of the microorganisms. Therefore, we conducted experiments to investigate the relationship between the number of superimposed images corresponding to the irradiation time and the 3D profile, as well as the characteristics of the power and the 3D profile. Full article

Background: The integration of artificial intelligence (AI) with the internet of things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges the gap between digital and physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement in the production process and quality control for better overall efficiency. This integration facilitates enabling machine learning and deep learning for real-time analysis, predictive maintenance, and automation—continuously monitoring key manufacturing parameters. Objective: This paper reviews the current applications and potential impacts of integrating AI and the IoTs in concert with key enabling technologies like cloud computing and data analytics, within the pharmaceutical sector. Results: Applications discussed herein focus on industrial predictive analytics and quality, underpinned by case studies showing improvements in product quality and reductions in downtime. Yet, many challenges remain, including data integration and the ethical implications of AI-driven decisions, and most of all, regulatory compliance. This review also discusses recent trends, such as AI in drug discovery and blockchain for data traceability, with the intent to outline the future of autonomous pharmaceutical manufacturing. Conclusions: In the end, this review points to basic frameworks and applications that illustrate ways to overcome existing barriers to production with increased efficiency, personalization, and sustainability. Full article

Flexible pressure sensors play an extremely important role in the fields of intelligent medical treatment, humanoid robots, and so on. However, the low sensitivity and the small initial capacitance still limit its application and development. At present, the method of constructing the microstructure of the dielectric layer is commonly used to improve the sensitivity of the sensor, but there are some problems, such as the complex process and inaccurate control of the microstructure. In this work, an ion composite photosensitive resin based on polyurethane acrylate and ionic liquids (ILs) was prepared. The high compatibility of the photosensitive resin and ILs was achieved by adding a chitooligosaccharide (COS) chain extender. The microstructure of the dielectric layer was optimized by digital light processing (DLP) 3D-printing. Due to the introduction of ILs to construct an electric double layer (EDL), the flexible pressure sensor exhibits a high sensitivity of 32.62 kPa⁻¹, which is 12.2 times higher than that without ILs. It also has a wide range of 100 kPa and a fast response time of 51 ms. It has a good pressure response under different pressures and can realize the demonstration application of human health. Full article

Skin physiopathological conditions have a strong influence on its biomechanical properties. However, it remains difficult to accurately assess the surface stiffness of soft tissues. The aim of this study was to evaluate the performances of an impact-based analysis method (IBAM) and to compare them with those of an existing digital palpation device, MyotonPro^®. The IBAM is based on the impact of an instrumented hammer equipped with a force sensor on a cylindrical punch in contact with agar-based phantoms mimicking soft tissues. The indicator $\mathsf{\Delta }t$ is estimated by analyzing the force signal obtained from the instrumented hammer. Various phantom geometries, stiffnesses and structures (homogeneous and bilayer) were used to estimate the performances of both methods. Measurements show that the IBAM is sensitive to a volume of interest equivalent to a sphere approximately twice the punch diameter. The sensitivity of the IBAM to changes in Young’s modulus is similar to that of dynamic mechanical analysis (DMA) and significantly better compared to MyotonPro. The axial (respectively, lateral) resolution is two (respectively, five) times lower with the IBAM than with MyotonPro. The present study paves the way for the development of a simple, quantitative and non-invasive method to measure skin biomechanical properties. Full article

Mega-facility management has long been inefficient due to manual, reactive approaches. Current facility management systems face challenges such as fragmented data integration, limited predictive systems, use of traditional methods, and lack of knowledge of new technologies, such as Building Information Modeling and Artificial Intelligence. This study examines the transformative integration of Artificial Intelligence and Digital Twin technologies into Building Information Modeling (BIM) frameworks using IoT sensors for real-time data collection and predictive analytics. Unlike previous research, this study uses case studies and simulation models for dynamic data integration and scenario-based analyses. Key findings show a significant reduction in maintenance costs (25%) and energy consumption (20%), as well as increased asset utilization and operational efficiency. With an F1-score of more than 90%, the system shows excellent predictive accuracy for equipment failures and energy forecasting. Practical applications in hospitals and airports demonstrate the developed ability of the platform to integrate the Internet of Things and Building Information Modeling technologies, shifting facilities management from being reactive to proactive. This paper presents a demo platform that integrates BIM with Digital Twins to improve the predictive maintenance of HVAC systems, equipment, security systems, etc., by recording data from different assets, which helps streamline asset management, enhance energy efficiency, and support decision-making for the buildings’ critical systems. Full article

The structural health monitoring (SHM) of bridge infrastructure has become essential for ensuring safety, serviceability, and long-term functionality amid aging structures and increasing load demands. SHM leverages sensor networks to enable real-time data acquisition, damage detection, and predictive maintenance, offering a more reliable alternative to traditional visual inspection methods. A key challenge in SHM is optimal sensor placement (OSP), which directly impacts monitoring accuracy, cost-efficiency, and overall system performance. This review explores recent advancements in SHM techniques, sensor technologies, and OSP methodologies, with a primary focus on bridge infrastructure. It evaluates sensor configuration strategies based on criteria such as the modal assurance criterion (MAC) and mean square error (MSE) while examining optimisation approaches like the Effective Independence (EI) method, Kinetic Energy Optimisation (KEO), and their advanced variants. Despite these advancements, several research gaps remain. Future studies should focus on scalable OSP strategies for large-scale bridge networks, integrating machine learning (ML) and artificial intelligence (AI) for adaptive sensor deployment. The implementation of digital twin (DT) technology in SHM can enhance predictive maintenance and real-time decision-making, improving long-term infrastructure resilience. Additionally, research on sensor robustness against environmental noise and external disturbances, as well as the integration of edge computing and wireless sensor networks (WSNs) for efficient data transmission, will be critical in advancing SHM applications. This review provides critical insights and recommendations to bridge the gap between theoretical innovations and real-world implementation, ensuring the effective monitoring and maintenance of bridge infrastructure in modern civil engineering. Full article

The integration of artificial intelligence (AI) with additive manufacturing (AM) is driving breakthroughs in personalized rehabilitation and physical therapy solutions, enabling precise customization to individual patient needs. This article presents the current state of knowledge and perspectives of using personalized solutions for rehabilitation and physiotherapy thanks to the introduction of AI to AM. Advanced AI algorithms analyze patient-specific data such as body scans, movement patterns, and medical history to design customized assistive devices, orthoses, and prosthetics. This synergy enables the rapid prototyping and production of highly optimized solutions, improving comfort, functionality, and therapeutic outcomes. Machine learning (ML) models further streamline the process by anticipating biomechanical needs and adapting designs based on feedback, providing iterative refinement. Cutting-edge techniques leverage generative design and topology optimization to create lightweight yet durable structures that are ideally suited to the patient’s anatomy and rehabilitation goals .AI-based AM also facilitates the production of multi-material devices that combine flexibility, strength, and sensory capabilities, enabling improved monitoring and support during physical therapy. New perspectives include integrating smart sensors with printed devices, enabling real-time data collection and feedback loops for adaptive therapy. Additionally, these solutions are becoming increasingly accessible as AM technology lowers costs and improves, democratizing personalized healthcare. Future advances could lead to the widespread use of digital twins for the real-time simulation and customization of rehabilitation devices before production. AI-based virtual reality (VR) and augmented reality (AR) tools are also expected to combine with AM to provide immersive, patient-specific training environments along with physical aids. Collaborative platforms based on federated learning can enable healthcare providers and researchers to securely share AI insights, accelerating innovation. However, challenges such as regulatory approval, data security, and ensuring equity in access to these technologies must be addressed to fully realize their potential. One of the major gaps is the lack of large, diverse datasets to train AI models, which limits their ability to design solutions that span different demographics and conditions. Integration of AI–AM systems into personalized rehabilitation and physical therapy should focus on improving data collection and processing techniques. Full article

This article presents research on the possibilities of using information and communication technologies in monitoring systems for electrical networks with isolated neutral, aimed at improving and automating production functions in the energy sector. This aligns with the digitalization policy of Kazakhstan’s economy and is part of similar programs in the field of the electric power industry. This article explores an approach to organizing a digital monitoring system for emergency conditions, specifically single-phase ground faults in medium-voltage lines within the range of 6–35 kV, including the new voltage class of 20 kV. A version of such a system is proposed, based on a combination of a server, a wireless information network, and remote digital voltage measurement nodes. This wireless information and communication network is designed to detect the locations of single-phase ground faults (SPGF) using specialized zero-sequence voltage sensors installed at various points along the power transmission lines, along with wireless signal transmission channels to the dispatcher’s server. To ensure protection against industrial interference, based on the results of practical environment modeling, a transmission technology most resistant to external noise is selected. This article proposes the selection of equipment necessary for implementing wireless transmission technology and develops two versions of a digital voltmeter design based on low-power programmable microcontrollers. The proposed technical solutions require further experimental validation, and therefore, the authors plan to conduct additional research and practical experiments in the future. Full article

This paper examines the essential role of artificial lighting in protected agriculture, a crucial sector in addressing the increasing global food demand and the challenges posed by climate change. It explores how advanced lighting technologies, particularly LED systems, have revolutionized productivity and sustainability in greenhouses and indoor or urban farming systems. These technologies enable precise control over key factors influencing crop growth, optimizing both yield and resource efficiency. The methodology was based on a bibliometric analysis developed in four phases: collection of information in the scientific database Scopus, filtering and selection of relevant documents, quantitative and qualitative analysis of trends, and visualization of the results using tools such as VOSviewer. The study included scientific publications between 1974 and 2024, focusing on keywords related to greenhouse lighting technologies and protected agriculture systems. Key findings identified a significant increase in research over the last two decades, with countries such as the United States, Canada, the Netherlands, and China leading the way in scientific output. The main trends in artificial lighting for protected agriculture include the use of specific light spectra (particularly red and blue) to optimize photosynthesis and morphogenesis, as well as the integration of LED systems with digital sensors and controllers for enhanced precision. However, in developing countries such as Colombia, the adoption of these technologies remains in its early stages, presenting significant opportunities for implementation and expansion. Additionally, this bibliometric analysis provides a robust foundation for identifying key areas for improvement and guiding future research toward more sustainable and efficient agricultural practices. Full article