Search Results (19)

Under the “Healthy China” strategy, the demand for home fitness equipment is increasing, but existing solutions face challenges such as large size, limited functionality, and lack of personalization. This study proposes an innovative integrated design framework for multifunctional home fitness equipment, combining modular design, space optimization, and intelligent health monitoring. The design integrates an exercise bike, rowing machine, and spring tensioner into a single unit, reducing equipment footprint by 30% while enabling seamless transitions between exercise modes. Multimodal sensors collect real-time physiological data, processed via Kalman filtering and adaptive algorithms to generate personalized fitness recommendations. The system achieves 95% monitoring accuracy for key metrics (heart rate: 97–147 bpm, energy consumption: 216–550 kcal) and improves user satisfaction by 40% compared to conventional equipment. This research demonstrates a scalable and intelligent solution that bridges the gap between multifunctional integration and user-centric health management, offering significant advancements over previous designs. Full article

Background/Objectives: This study aimed to evaluate the effects of artificial intelligence (AI)-assisted virtual reality (VR) applications on preoperative anxiety levels and vital signs in children undergoing endoscopy. Methods: A randomized controlled trial design was employed, including a total of 80 children aged 8–17 years (40 in the intervention group and 40 in the control group). Children in the intervention group were exposed to VR applications featuring space and underwater themes, while the control group received standard procedures. Anxiety levels were assessed using the “State-Trait Anxiety Inventory for Children (STAIC)”, and vital signs were evaluated through measurements of systolic and diastolic blood pressure, heart rate, temperature, and SpO₂. Results: VR applications significantly reduced anxiety scores in the intervention group (36.3 ± 1.9), while no significant changes were observed in the control group (45.4 ± 2.74) (p < 0.001). Regarding vital signs, more favorable outcomes were observed in the intervention group. Systolic blood pressure was measured as 89 ± 6.7 mmHg in the intervention group and 96.5 ± 10.5 mmHg in the control group (p < 0.001). Diastolic blood pressure was 60.8 ± 4.7 mmHg in the intervention group and 63.8 ± 6 mmHg in the control group (p < 0.05). Heart rate was recorded as 88.7 ± 10.1 bpm in the intervention group and 94.5 ± 14.8 bpm in the control group (p < 0.05). SpO₂ levels were 98 ± 1 in the intervention group and 96.2 ± 1.3 in the control group (p < 0.001). Conclusions: AI-assisted VR applications emerge as an effective non-pharmacological method for reducing preoperative anxiety and promoting physiological stability in children. This approach holds the potential to enhance pediatric experiences during invasive procedures such as endoscopy. Full article

Early SOC balancing techniques primarily centered on simple hardware circuit designs. Passive balancing circuits utilize resistors to consume energy, aiming to balance the SOC among batteries; however, this approach leads to considerable energy wastage. As research progresses, active balancing circuits have garnered widespread attention. Successively, active balancing circuits utilizing capacitors, inductors, and transformers have been proposed, enhancing balancing efficiency to some extent. Nevertheless, challenges persist, including energy wastage during transfers between non-adjacent batteries and the complexity of circuit designs. In recent years, SOC balancing methods based on software algorithms have gained popularity. For instance, intelligent control algorithms are being integrated into battery management systems to optimize control strategies for SOC balancing. However, these methods may encounter issues such as high algorithmic complexity and stringent hardware requirements in practical applications. This paper proposes a fast state-of-charge (SOC) balance control strategy that incorporates a weighting factor within a modular battery energy storage system architecture. The modular distributed battery system consists of battery power modules (BPMs) connected in series, with each BPM comprising a battery cell and a bidirectional buck–boost DC-DC converter. By controlling the output voltage of each BPM, SOC balance can be achieved while ensuring stable regulation of the DC bus voltage without the need for external equalization circuits. Building on these BPMs, a sliding mode control strategy with adaptive acceleration coefficient weighting factors is designed to increase the output voltage difference of each BPM, thereby reducing the balancing time. Simulation and experimental results demonstrate that the proposed control strategy effectively increases the output voltage difference among the BPMs, facilitating SOC balance in a short time. Full article

This study explores the application of the BPM lifecycle to optimize the market analysis process within the market intelligence department of a major energy company. The semi-structured, virtual nature of the process necessitated careful adaptation of BPM methodology, starting with process discovery through data collection, modeling, and validation. Qualitative analysis, including value-added and root-cause analysis, revealed inefficiencies. The redesign strategy focused on selective automation using Python 3.10 scripts and Power BI dashboards, incorporating techniques such as linear programming and forecasting to improve process efficiency and quality while maintaining flexibility. Post-implementation, monitoring through a questionnaire showed positive results, though ongoing interviews were recommended for sustained performance evaluation. This study highlights the value of BPM methodology in enhancing decision-critical processes and offers a model for adaptable, value-driven process improvements in complex organizational environments. Full article

During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded backpropagation neural network (CBPNN) that is informed by the physical structure. This system serves as an environmental map for reinforcement learning, aiding beam current correction. The CBPNN was implemented on the acceleration node to hasten the forward inference process, leveraging sparsification, quantization algorithms, and pipelining techniques. Our experimental results demonstrated that the simulated inference speed reached 28 μs with FPGA hardware as the edge acceleration node, achieving forward inference speeds 35.66 and 12.66 times faster than those of the CPU and GPU. The energy efficiency ratio was 10.582 MOPS/W, which was 989 and 410 times that of the CPU and GPU, respectively. This confirms the designed architecture’s energy efficiency and low latency attributes. Full article

Backgrounds: Sleep disturbances are prevalent among elderly individuals. While polysomnography (PSG) serves as the gold standard for sleep monitoring, its extensive setup and data analysis procedures impose significant costs and time constraints, thereby restricting the long-term application within the general public. Our laboratory introduced an innovative biomarker, utilizing artificial intelligence algorithms applied to PSG data to estimate brain age (BA), a metric validated in cohorts with cognitive impairments. Nevertheless, the potential of exercise, which has been a recognized means of enhancing sleep quality in middle-aged and older adults to reduce BA, remains undetermined. Methods: We conducted an exploratory study to evaluate whether 12 weeks of moderate-intensity exercise can improve cognitive function, sleep quality, and the brain age index (BAI), a biomarker computed from overnight sleep electroencephalogram (EEG), in physically inactive middle-aged and older adults. Home wearable devices were used to monitor heart rate and overnight sleep EEG over this period. The NIH Toolbox Cognition Battery, in-lab overnight polysomnography, cardiopulmonary exercise testing, and a multiplex cytokines assay were employed to compare pre- and post-exercise brain health, exercise capacity, and plasma proteins. Results: In total, 26 participants completed the initial assessment and exercise program, and 24 completed all procedures. Data are presented as mean [lower 95% CI of mean, upper 95% CI of mean]. Participants significantly increased maximal oxygen consumption (Pre: 21.11 [18.98, 23.23], Post 22.39 [20.09, 24.68], mL/kg/min; effect size: −0.33) and decreased resting heart rate (Pre: 66.66 [63.62, 67.38], Post: 65.13 [64.25, 66.93], bpm; effect size: −0.02) and sleeping heart rate (Pre: 64.55 [61.87, 667.23], Post: 62.93 [60.78, 65.09], bpm; effect size: −0.15). Total cognitive performance (Pre: 111.1 [107.6, 114.6], Post: 115.2 [111.9, 118.5]; effect size: 0.49) was significantly improved. No significant differences were seen in BAI or measures of sleep macro- and micro-architecture. Plasma IL-4 (Pre: 0.24 [0.18, 0.3], Post: 0.33 [0.24, 0.42], pg/mL; effect size: 0.49) was elevated, while IL-8 (Pre: 5.5 [4.45, 6.55], Post: 4.3 [3.66, 5], pg/mL; effect size: −0.57) was reduced. Conclusions: Cognitive function was improved by a 12-week moderate-intensity exercise program in physically inactive middle-aged and older adults, as were aerobic fitness (VO₂max) and plasma cytokine profiles. However, we found no measurable effects on sleep architecture or BAI. It remains to be seen whether a study with a larger sample size and more intensive or more prolonged exercise exposure can demonstrate a beneficial effect on sleep quality and brain age. Full article

In recent years, research on digital transformation (DT) and business process management (BPM) has gained significant attention in the field of business and management. This paper aims to conduct a comprehensive bibliometric analysis of global research on DT and BPM from 2007 to 2022. A total of 326 papers were selected from Web of Science and Scopus for analysis. Using bibliometric methods, we evaluated the current state and future research trends of DT and BPM. Our analysis reveals that the number of publications on DT and BPM has grown significantly over time, with the Business Process Management Journal being the most active. The countries that have contributed the most to this field are Germany (with four universities in the top 10) and the USA. The Business Process Management Journal is the most active in publishing research on digital transformation and business process management. The analysis showed that “artificial intelligence” is a technology that has been studied extensively and is increasingly asserted to influence companies’ business processes. Additionally, the study provides valuable insights from the co-citation network analysis. Based on our findings, we provide recommendations for future research directions on DT and BPM. This study contributes to a better understanding of the current state of research on DT and BPM and provides insights for future research. Full article

As the primary means of modern enterprise management, business process management (BPM) technology has become the mainstream development trend of modern enterprise management. The efficient and accurate establishment of business processes is essential for effective BPM. However, the traditional manual-based modeling approach is time-consuming and error-prone. To overcome this, process recommendation technology can improve the intelligence and efficiency of modeling to a certain extent. However, existing process modeling recommendation methods suffer from the problem of low accuracy and neglecting short-process models. Therefore, a novel process modeling recommendation method that integrates disjoint paths and sequential patterns was proposed. This method uses edge-disjoint paths for the first time to represent the behavioral semantics of processes, and an improved contiguous sequential pattern mining algorithm was proposed to mine the contiguous path sequential patterns (CPSPs) of edge-disjoint paths. In the process modeling recommendation stage, the k CPSPs with the highest matching degree with the current reference model process were calculated, and the last node in these CPSPs was used as the set of recommendation nodes. In cases with CPSPs with the same matching degree, the one with the higher value was recommended according to their corresponding lift, confidence, and support degrees. Through experimental evaluation and comparison, it was shown that the proposed method effectively improved the accuracy of the recommendation of both short-process and long-process models while ensuring effectiveness and time efficiency. Full article

Recent studies show that decision making in Business Process Management (BPM) and incorporating sustainability in business is vital for service innovation within a company. Likewise, it is also possible to save time and money in an automated, intelligent and sustainable way. Robotic Process Automation (RPA) is one solution that can help businesses improve their BPM and sustainability practices through digital transformation. However, deciding which processes to automate with RPA technology can be complex. Consequently, this paper presents a model for selecting indicators to determine the profitability of shifting to RPA in selected business processes. The method used in this work is the Performance Analysis Method, which allows for predicting which processes could be replaced by RPA to save time and money in a service workflow. The Performance Analysis Method consists of collecting data on the speed and efficiency of a business process and then using that data to develop discrete event simulations to estimate the cost of automating parts of that process. A case study using this model is presented, using business process data from an international utility company as input to the discrete event simulation. The model used in this study predicts that this Electric Utility Company (EUC) will save a substantial amount of money if it implements RPA in its call center. Full article

Algorithms have evolved from machine code to low-code-no-code (LCNC) in the past 20 years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub mentioned that the future of coding is no coding at all. This paper systematically reviewed several of the recent studies using mainstream LCNC platforms to understand the area of research, the LCNC platforms used within these studies, and the features of LCNC used for solving individual research questions. We identified 23 research works using LCNC platforms, such as SetXRM, the vf-OS platform, Aure-BPM, CRISP-DM, and Microsoft Power Platform (MPP). About 61% of these existing studies resorted to MPP as their primary choice. The critical research problems solved by these research works were within the area of global news analysis, social media analysis, landslides, tornadoes, COVID-19, digitization of process, manufacturing, logistics, and software/app development. The main reasons identified for solving research problems with LCNC algorithms were as follows: (1) obtaining research data from multiple sources in complete automation; (2) generating artificial intelligence-driven insights without having to manually code them. In the course of describing this review, this paper also demonstrates a practical approach to implement a cyber-attack monitoring algorithm with the most popular LCNC platform. Full article

We conducted a two-phase study to test the reliability and usability of an all-in-one artificial intelligence-based device (ButterfLife), which allows simultaneous monitoring of five vital signs. The first phase of the study aimed to test the agreement between measurements performed with ButterfLife vs. standard of care (SoC) in 42 hospitalized patients affected by acute respiratory failure. In this setting, the greatest discordance between ButterfLife and SoC was in respiratory rate (mean difference −4.69 bpm). Significantly close correlations were observed for all parameters except diastolic blood pressure and oxygen saturation (Spearman’s Rho −0.18 mmHg; p = 0.33 and 0.20%; p = 0.24, respectively). The second phase of the study was conducted on eight poly-comorbid patients using ButterfLife at home, to evaluate the number of clinical conditions detected, as well as the patients’ compliance and satisfaction. The average proportion of performed tests compared with the scheduled number was 67.4%, and no patients reported difficulties with use. Seven conditions requiring medical attention were identified, with a sensitivity of 100% and specificity of 88.9%. The median patient satisfaction was 9.5/10. In conclusion, ButterfLife proved to be a reliable and easy-to-use device, capable of simultaneously assessing five vital signs in both hospital and home settings. Full article

Key analysts are emphasizing the importance of the digitalization especially of the supply chain. This work aims to improve maritime shipping companies by introducing digitalization in their operations. This objective is achieved analyzing the impact of maritime container shipping companies’ digitalization. This analysis requires as input the Business Process Model (BPMo) and an inventory of digital applications to verify how the BPMo changes when deploying the applications, define the prerequisites necessary for this deployment, and identify the key performance indicators (KPIs) to track it. The impact of the deployment of the applications has been quantified by using four performance dimensions: Costs, Time, Quality, and Flexibility. The results show that the impacts are different per application, with changes in the processes, the addition of new ones, and the decommissioning of others. The impact of digitalization is high when trying to deploy all the applications at the same time. Companies can leverage this work, which requires reviewing the documented impacts in their processes and the applications’ prerequisites as well as updating their existing balanced scorecard, incorporating the application’s KPIs. A list of 10 applications has been identified as “quick wins”; then, applications can be the starting point for digitalizing a company. Full article

Physiological measures, such as heart rate variability (HRV) and beats per minute (BPM), can be powerful health indicators of respiratory infections. HRV and BPM can be acquired through widely available wrist-worn biometric wearables and smartphones. Successive abnormal changes in these indicators could potentially be an early sign of respiratory infections such as COVID-19. Thus, wearables and smartphones should play a significant role in combating COVID-19 through the early detection supported by other contextual data and artificial intelligence (AI) techniques. In this paper, we investigate the role of the heart measurements (i.e., HRV and BPM) collected from wearables and smartphones in demonstrating early onsets of the inflammatory response to the COVID-19. The AI framework consists of two blocks: an interpretable prediction model to classify the HRV measurements status (as normal or affected by inflammation) and a recurrent neural network (RNN) to analyze users’ daily status (i.e., textual logs in a mobile application). Both classification decisions are integrated to generate the final decision as either “potentially COVID-19 infected” or “no evident signs of infection”. We used a publicly available dataset, which comprises 186 patients with more than 3200 HRV readings and numerous user textual logs. The first evaluation of the approach showed an accuracy of 83.34 ± 1.68% with 0.91, 0.88, 0.89 precision, recall, and F1-Score, respectively, in predicting the infection two days before the onset of the symptoms supported by a model interpretation using the local interpretable model-agnostic explanations (LIME). Full article

The increasing role of emerging technologies, such as big data, the Internet of Things, artificial intelligence (AI), cognitive technologies, cloud computing, and mobile technologies, is essential to the business process manager profession’s sustainable development. Nevertheless, these technologies could involve new challenges in labor markets. The era of intelligent business process management (BPM) has begun, but how does it look in real labor markets? This paper examines the hypothesis that the transformation of the business process manager profession has been caused by certain determinants that involve the need for an improvement in BPM skills. The main contribution is a model of the dimensions of the impact of digital technologies on business process management supplemented with skills that influence the business process manager profession. The paper fills the gap in research on perspectives of the impact of digital technologies on business process management, considering both a literature analysis and labor market research. The purpose of the literature review was to identify the core dimensions that drive the use of emerging technologies in business process management. The labor market study was conducted in order to analyze the current demand for core skills of business process managers in the Polish labor market with a particular emphasis on the intelligent BPM concept. Additionally, to study the determinants that slow down the iBPM concept’s development, the digital intensity level of the enterprises and public administration units in Poland was studied. Finally, a fuzzy cognitive map presenting the core determinants of the business process manager profession’s transformation is described. Full article

eSports is a rapidly growing industry with increasing investment and large-scale international tournaments offering significant prizes. This has led to an increased focus on individual and team performance with factors such as communication, concentration, and team intelligence identified as important to success. Over a similar period of time, personal physiological monitoring technologies have become commonplace with clinical grade assessment available across a range of parameters that have evidenced utility. The use of physiological data to assess concentration is an area of growing interest in eSports. However, body-worn devices, typically used for physiological data collection, may constitute a distraction and/or discomfort for the subjects. To this end, in this work we devise a novel “invisible” sensing approach, exploring new materials, and proposing a proof-of-concept data collection system in the form of a keyboard armrest and mouse. These enable measurements as an extension of the interaction with the computer. In order to evaluate the proposed approach, measurements were performed using our system and a gold standard device, involving 7 healthy subjects. A particularly advantageous characteristic of our setup is the use of conductive nappa leather, as it preserves the standard look and feel of the keyboard and mouse. According to the results obtained, this approach shows 3–15% signal loss, with a mean difference in heart rate between the reference and experimental device of −1.778 ± 4.654 beats per minute (BPM); in terms of ECG waveform morphology, the best cases show a Pearson correlation coefficient above 0.99. Full article