Digital Approach Research Articles

Digital twin-driven architecture for AIoT-based energy service provision and optimal energy trading between smart nanogrids

This article explores integrating digital twin technology and blockchain within smart grids to optimize energy trading among prosumers and consumers in smart nanogrids. Our platform employs a multi-objective optimization strategy, including Particle Swarm Optimization (PSO), to delineate energy trading routes between nanogrids, optimizing parameters such as route distance, surplus renewable energy, and energy power loss. Our platform ensures efficient and effective energy trading services by meticulously considering factors such as surplus energy amount, energy price, route distance, and time. The proposed digital twin-based architecture comprises seven layers, each tailored to address specific functionalities and services for energy management within smart nanogrids. At the apex lies the application layer (digital twin services), leveraging the digital twin's capabilities to optimize energy trading, manage surplus energy, and efficiently meet energy demand. This layer facilitates informed decision-making and resource optimization. Integrating a digital twin-driven architecture with a blockchain-based platform tackles challenges inherent in decentralized energy trading. The digital twin offers real-time energy resource monitoring and optimisation, ensuring efficient utilisation and autonomous decision-making. Concurrently, leveraging blockchain technology ensures secure and transparent transactions, fostering trust among participants and facilitating peer-to-peer energy exchange. Task generation, device virtualization, task mapping, scheduling on edge devices, and task assignment layers further streamline task execution and resource utilization, enhancing the efficiency of energy management processes. The predictive optimal energy control layer also orchestrates the entire architecture, enabling predictive and optimized energy control within smart nanogrids. Furthermore, the Security as a Service (SECaaS) layer enhances security and trustworthiness using blockchain technology, incorporating components such as consensus management, real-time distributed ledgers, and identity management. This layer enhances the security and transparency of energy-related transactions and data within the digital twin framework. The results showcase a remarkable 53% reduction in peak load, emphasizing the optimized energy consumption and demand achieved. Furthermore, our platform has significantly increased the utilization of renewable energy resources by 24%, highlighting its contribution to sustainable energy resource management. Rigorous assessment of the prediction and optimization modules reveals their high accuracy and precision, with mean absolute percentage error (MAPE) values of 15.125 and 14.369, respectively. These findings underscore the efficacy and reliability of our digital twin-based approach, surpassing existing solutions and benchmarks.

Creating a digital approach for promoting physical activity in pediatric pulmonary hypertension: A framework for future interventions.

Children with pulmonary hypertension (PH) often demonstrate limited exercise capacity. Data support exercise as an effective nonpharmacologic intervention among adults with PH. However, data on exercise training in children and adolescents are limited, and characteristics of the optimal exercise program in pediatric PH have not been identified. Exercise programs may have multiple targets, including muscle deficits which are associated with exercise limitations in both adult and pediatric PH. Wearable accelerometer sensors measure physical activity volume and intensity in the naturalistic setting and can facilitate near continuous data transfer and bidirectional communication between patients and the study team when paired with informatics tools during exercise interventions. To address the knowledge gaps in exercise training in pediatric PH, we designed a prospective, single arm, nonrandomized pilot study to determine feasibility and preliminary estimates of efficacy of a 16-week home exercise intervention, targeting lower extremity muscle mass and enriched by wearable mobile health technology. The exercIse Training in pulmONary hypertEnsion (iTONE) trial includes (1) semistructured exercise prescriptions tailored to the participant's baseline level of activity and access to resources; (2) interval goal setting fostering self-efficacy; (3) real time monitoring of activity via wearable devices; (4) a digital platform enabling communication and feedback between participant and study team; (5) multiple avenues to assess participant safety. This pilot intervention will provide information on the digital infrastructure needed to conduct home-based exercise interventions in PH and will generate important preliminary data on the effect of exercise interventions in youth with chronic cardiorespiratory conditions to power larger studies in the future.

A Digital Approach to Improve Infection Screening Among Solid Organ Transplant Candidates.

Pretransplantinfection screening (IS) of potential organ recipientsis essential to optimal outcome of solid organ transplantation (SOT). A pre-post study was performed during 2020-2023 to investigate the impact of the STREAM (Solid organ TRansplant stEwArdship and Multidisciplinary approach) intervention to improve IS in SOT. The intervention, performed in 2022, included the implementation of IS through educational meetings, local guidelines, and the availability of a digital screening tool. The objective of the study was the assessment of IS completion, including a list of 17 laboratory tests and the investigation of vaccination status. The reduction of unnecessary tests was also analyzed. The test of proportions and a multilevel multivariate Poisson regression model were used to compare IS completion before and after STREAM. infectious diseases (ID) consultation and urgent evaluation were investigated as predictors of IS completion. A total of 171 patients were enrolled, including liver (44%), heart (32%), and kidney (24%) transplant candidates. Mean age was 56 ± 11 years, and most patients (77%) were males. Ninety-five (56%) patients were included before the intervention and 76 (44%) after STREAM. IS completion increased after STREAM (IRR 1.41, p < 0.001) with significant improvement recorded for seven (39%) IS items. Unnecessary tests decreased by 43% after the intervention. ID consultation (IRR 1.13, p = 0.02) and urgent evaluation (p = 0.68, p < 0.001) were predictors of IS improvement. STREAM was successful in improving IS completion. Further research is needed to investigate the impact of this intervention on posttransplant infections.

Robust digital voltage mode control of buck converter with enhanced tracking performance

The digital voltage controller of power converters is used because it has many advantages over its analog counterparts. Thus, in this paper, a controller of digital voltage mode is designed and analyzed for a non-isolated DC-DC buck converter in continuous conduction mode (CCM). First, an analog controller is designed using a Bode plot to achieve the desired stability margins in the frequency domain. Next, the analog controller is discretized using bilinear transformation with the pre-warping method. The characteristics of the discretized compensator can be maintained close to the corresponding analog compensator as long as a proper sampling time is selected. The digital control scheme is simulated with a nonlinear DC-DC buck converter model in MATLAB/Simulink to investigate the controller performance. The simulation results demonstrated the validation of the proposed digital voltage-mode control design approach. The developed digital controller eliminates DC error by tracking the reference voltage, achieving a fast transient response, maintaining specified stability margins, and effectively rejecting large disturbances.

A compensator design and stability margin monitor for digitally controlled DC-DC converters

ABSTRACT In this paper, an automatic direct digital design approach for compensators is presented to achieve a lookup table proportional – integral – derivative controller. Additionally, a correlation-based stability margin monitor with adaptive sliding window smoothing is proposed to increase the identifiable range for bandwidth detection. The proposed automatic compensator design approach meets the expected phase margin and crossover frequency and considers limit-cycle oscillation conditions throughout the design process. The digital controller with the direct digital design was implemented on a field programmable gate array to validate the approach and alternative design capabilities. Experimental results show that the direct digital design yields a higher crossover frequency than digital redesign. The best transient response performance can be obtained if a proper integral gain is designed. The digital controller with ASWS was fabricated using the TSMC 1P6M 0.18-μm standard CMOS process. Further experimental results show that the proposed ASWS provides a significantly more accurate identified frequency response by using narrow windows at low frequencies and expansive windows at high frequencies.

A Semantic Digital Twinning Approach for the Management of Road Distress Data

A Semantic Digital Twinning Approach for the Management of Road Distress Data

Comparison of a conventional image processing approach with an artificial neural network approach to three‐dimensionally trace multiple particles in dynamic x‐ray microtomography experiments under laboratory conditions

AbstractThis paper compares a classic digital image processing approach to trace particles in laboratory x‐ray micro‐computed tomography (µCT), which is based on a combination of random sample consensus (RANSAC) algorithm and least squares ellipse fitting (LSF), with an approach based on artificial neural networks (ANNs). In order to be able to perform the comparison, dynamic experiments were carried out in a laboratory microtomography facility. During active scans with a duration of 30–75 s several sedimentation experiments have been carried out with an exposure time of 0.13 s/projection. Through the movement of the particles during the scan curved motion artefacts in the reconstructed data occur, where the vertex marks the coordinate of the particle. It could be shown that both approaches enable the tracing of particles in laboratory x‐ray µCT with deviations from a manually evaluated result of exemplarily 1.25% for the conventional digital image processing (CDIP) and 0.48% for the ANN. It was found that ANNs are able to identify particle positions in non‐symmetrical motion artefacts, appearing around the first and last position of the particles in the scan, allowing an extension of the motion range of the particles that can be evaluated. Both methods have their advantages and disadvantages. Due to the high complexity and size as well as partly black box structures of neural networks, they are not 100% comprehensible whereas conventional image processing is 100% transparent and understandable. But because of the complexity of the tracing of particles, the CDIP code offers many parameters that can be set, which is why the application is therefore slightly more complex.

Digital Biohacking Approach to Dietary Interventions: A Comprehensive Strategy for Healthy and Sustainable Weight Loss.

The rising obesity epidemic requires effective and sustainable weight loss intervention strategies that take into account both of individual preferences and environmental impact. This study aims to develop and evaluate the effectiveness of an innovative digital biohacking approach for dietary modifications in promoting sustainable weight loss and reducing carbon footprint impact. A pilot study was conducted involving four participants who monitored their weight, diet, and activities over the course of a year. Data on food consumption, carbon footprint impact, calorie intake, macronutrient composition, weight, and energy expenditure were collected. A digital replica of the metabolism based on nutritional information, the Personalized Metabolic Avatar (PMA), was used to simulate weight changes, plan, and execute the digital biohacking approach to dietary interventions. The dietary modifications suggested by the digital biohacking approach resulted in an average daily calorie reduction of 236.78 kcal (14.24%) and a 15.12% reduction in carbon footprint impact (-736.48 gCO2eq) per participant. Digital biohacking simulations using PMA showed significant differences in weight change compared to actual recorded data, indicating effective weight reduction with the digital biohacking diet. Additionally, linear regression analysis on real data revealed a significant correlation between adherence to the suggested diet and weight loss. In conclusion, the digital biohacking recommendations provide a personalized and sustainable approach to weight loss, simultaneously reducing calorie intake and minimizing the carbon footprint impact. This approach shows promise in combating obesity while considering both individual preferences and environmental sustainability.

Digital neutron-gamma discrimination algorithm using adaptive noise filter

Pulse shape discrimination of a liquid scintillator-based detector (BC501A) has been studied using a digital approach. The objective of this work is to develop a new neutron-gamma discrimination algorithm to improve the discrimination efficiency. The new approach is a combination of two parts. The first part uses an adaptive Sallen-Key filter to reduce noise in the pulse followed by traditional approaches of either charge comparison or time over threshold. The approach of adaptive Sallen-Key combined with time over threshold was found to have a much better capability to discriminate neutron events from that of gamma. The pulse shape discrimination performance obtained for a 5 inches by 5 inches (length by diameter) liquid scintillator detector using the combination of the adaptive Sallen-Key filter with time over threshold is found to be significantly higher compared to that obtained using traditional methods. Moreover, this combination has the potential for real-time hardware implementation.

3D Printing of Customizable Transient Bioelectronics and Sensors

AbstractTransient electronics have emerged as a new category of devices that can degrade after their functional lifetime, offering tremendous potential as disposable sensors, actuators, wearables, and implants. Additive manufacturing methods represent a promising approach for patterning transient materials, yet examples of fully printed bioelectronic devices are scarce. This study introduces a fully digital 3D printing approach enabling the prototyping and customization of soft bioelectronics made of transient materials. The direct ink writing of poly(octamethylene maleate (anhydride) citrate) (POMaC) as an elastomeric matrix and of a shellac‐carbon ink as a conductor is investigated. Precise and repeatable deposition of both structural and conductive features is achieved by optimizing printing parameters, i.e., the dispense gap, printing speed, and inlet pressure. Multi‐material 3D printing enables the fabrication of functional transient devices. Notably, pressure and strain sensors are shown to operate in ranges relevant to implanted biomechanical monitoring. 3D‐printed transient electrodes are demonstrated to be comparable to state‐of‐the‐art devices in terms of impedance behavior. Finally, physical degradation of the materials is confirmed at physiological conditions. These fully digital additive manufacturing processes enable the monolithic fabrication of customizable transient bioelectronics with adaptable functions and geometries.

Individualized Digital Nasoalveolar Molding: A Proof of Concept.

Nasoalveolar molding (NAM) is a recognized adjunctive therapy to primary cheiloplasty in patients with complete cleft lip and palate (CLP). The conventional technique can be time-consuming, costly, and requires experienced clinicians. Over the past decade, CAD/CAM technology has been employed to facilitate NAM therapy, showing promising results. This study introduces an individualized Digital NAM (iDNAM), a digital approach revolutionizing NAM treatment using CAD/CAM technology, and evaluates treatment outcomes. A 57-day-old infant with complete UCLP underwent intraoral and nasolabial scans, along with 3D photogrammetry. Open-source software was utilized for virtual alveolar segmentation and NAM plate design. iDNAM incorporates a nasal molding extension, introducing an innovative nasal stent activation protocol. Changes in alveolar and nasal morphology were compared between the initial and presurgery stages. After treatment with iDNAM, the alveolar cleft gap was reduced to 4.22mm. Nostril height on the cleft side, columellar length, and nose projection increased by 0.98, 2.40, and 1.90mm, respectively. The nasal base-columellar angle improved by 29.50 degrees. The results demonstrate a significant reduction in the alveolar cleft gap and improved nasal morphology, making iDNAM as an alternative to NAM therapy.

ЭФФЕКТИВНОЕ ИСПОЛЬЗОВАНИЕ ОБРАЗОВАТЕЛЬНЫХ РЕСУРСОВ В УСЛОВИЯХ ОГРАНИЧЕННОГО БЮДЖЕТА: ОПЫТ ЭКОНОМИЧЕСКОГО ПЛАНИРОВАНИЯ В ОБРАЗОВАНИИ

The paper considers the problem of effective use of educational resources in conditions of a limited budget, with an emphasis on the experience of economic planning in education. In modern conditions, many educational institutions face financial difficulties, which requires the development of strategies for the optimal allocation and use of available resources. The article analyzes the key principles and methods of economic planning aimed at improving the effectiveness of the educational process without increasing costs. Special attention is paid to practical examples and successful cases from various educational systems demonstrating the possibilities of improving the quality of education through rational resource management. Recommendations are also offered on the implementation of these methods at the school and university levels, as well as considering the potential of digital technologies and innovative approaches to achieve these goals.

Developing Sustainable Muslim-Friendly Tourism Village Model Through Digital Tourism: Perspective of Shari’ah Economy

Using digital technology and social media is important in advancing sustainable tourism and expanding its appeal internationally. In this regard, the digitalization program for tourist villages needs to be encouraged because it has significant prospects in increasing the number of visitors while supporting the sustainability of tourism. This study aims to discuss the development of a sustainable tourist village model through a digital tourism approach carried out in the Provinces of West Java, the Special Region of Yogyakarta, and Lampung from an Islamic economic perspective. Data were obtained through observation and questionnaire methods from primary and secondary sources. The collected data were then processed and analyzed using triangulation and SWOT techniques. The study results show that the three provinces above have good internet access, making it possible to promote tourism potential through digital platforms. In terms of opportunities, adopting the digital tourism concept can open up great potential in increasing tourist engagement and market expansion. On the other hand, the lack of information and limited Human Resources must be addressed because they can hinder marketing potential and operational effectiveness. In addition, tight competition with other tourist areas must be anticipated because it can reduce investment interest and tourist visits. The sustainable tourism village model development through an environmentally friendly tourism approach has a strong foundation from a shari’ah perspective because it is supported by the Qur’an, Hadith, and the fatwa of the DSN MUI.

A digital twin approach for weld penetration prediction of tig welding with dual ellipsoid heat source

A digital twin approach for weld penetration prediction of tig welding with dual ellipsoid heat source

Hebrew Manuscripts as a Source of Knowledge

Abstract The Hebrew Manuscripts Catalogue of the National Library of Israel holds a wealth of information about Jewish history, literature, and culture. However, its complex structure and limited accessibility have resulted in underutilized data. This study aims to harness new digital approaches to unlock the potential of this valuable resource. Specifically, we focus on 275 Yemenite Jewish manuscripts related to ‘sciences’ to explore complex relationships within the data. We began by consolidating metadata from the Hebrew Manuscripts Catalogue, often found scattered within the ‘notes’ field of the records. We supplemented this data with information from linked open data (LOD) entities as well as previous research. This comprehensive approach reveals details about copyists, authors, and owners, even identifying lesser-known figures. Our methodology involved using Nodegoat for data management and visualization. This platform allowed us to create a comprehensive data model for manuscript provenance and explore relationships between objects, transactions, persons, and organizations, along with geographical data.

Character Building in Students through “Rumi Sang Pemimpi”: A Digital Book Approach

Character Building in Students through “Rumi Sang Pemimpi”: A Digital Book Approach

A digital image analysis approach to understand the microscopic and macroscopic phenomena in dissolved air flotation

Dissolved air flotation (DAF) is an effective method for separating suspended oil and solid particles from wastewater by utilizing small air bubbles. This study aims to investigate the impact of key factors, such as saturating pressure and water flow rate, on the separation of fine oil droplets from a water stream. The macroscopic flow patterns within the cell were analyzed using particle image velocimetry (PIV), while Digital Image Analysis (DIA) was employed to study microscopic phenomena, including oil droplet rising velocity and oil-bubble contact mechanisms. Our findings propose a safe operating window (specifically, water flow rate and saturation pressure) for the effective separation of oil droplets without any oil escaping into the clean water stream. It was found that the oil droplet rising velocity increases with the saturation pressure up to 200 kPa. However, a further increase in the pressure of the air saturating chamber leads to a decrease in oil droplet rising velocity. Additionally, we identified a peak in rising velocity at an oil droplet size of approximately 200 µm. Below this threshold, the rising velocity increases with droplet size, while for droplet sizes exceeding 200 µm, the rising velocity decreases with size. This behavior can be explained by the conflicting effects of droplet size increment according to the Stokes law for the rising velocity of oil droplets. As the droplet size increases, the average density of the bubbles/droplet aggregate rises, reducing the ∆ρ in the Stokes law and subsequently lowering the aggregate rising rate. However, as per the Stokes law, the oil droplet rising velocity increases proportionally to the square of its size.

Highly Sensitive, Stretchable, and Adjustable Parallel Microgates‐Based Strain Sensors

AbstractThe demand for stretchable strain sensors with customizable sensitivities has increased across a spectrum of applications, spanning from human motion detection to plant growth monitoring. Nevertheless, a major challenge remains in the digital fabrication of scalable and cost‐efficient strain sensors with tailored sensitivity to diverse demands. Currently, there is a lack of simple digital fabrication approaches capable of adjusting strain sensitivity in a controlled way with no changes to the material and without affecting the linearity. In this study, parallel microgates‐based strain sensors whose strain sensitivity can be adjusted systematically throughout an all‐laser‐based fabrication process without any material replacement are presented. The technique employs a two‐step direct laser writing method that combines the well‐established capabilities of laser ablation and laser marking, boasting a varying gauge factor of up to 433% (GF = 168), while paving the way for the mass production of nanocomposite strain sensors. Parallel microgates‐based strain sensors exhibit a remarkable signal‐to‐noise ratio at ultralow strains (ɛ = 0.001), rendering them ideal for monitoring the gradual growth of plants. As an application demonstration, the proposed sensors are deployed on tomato plants to capture their growth under varying planting conditions including hydroponic and soil mediums, as well as diverse irrigation regimens.

A Digital Mental Health Approach for Supporting Suicide Prevention: A Qualitative Study

A Digital Mental Health Approach for Supporting Suicide Prevention: A Qualitative Study

Facilitating interprofessional learning: experiences of using a digital activity for training handover of critically ill patients between a primary health care centre and ambulance services – a qualitative study

ObjectiveTo explore students’ and facilitators’ experiences of using a developed digital activity for interprofessional learning (IPL) focusing on critically ill patient handovers from a primary healthcare (PHC) centre to the...