. Screening for lymphedema and accurate quantitative assessment of dermal backflow patterns on ICG represents a major shift in current clinical practice paradigms, putting an emphasis on early detection of lymphedema rather than palliative treatments and symptomatic relief. These findings set the stage for the development of a practical, universal, ICG-based quantification system for the staging of lymphedema, a significant advancement in the field of plastic surgery.

Purpose Lean Management (LM) is a process improvement approach with growing interest from healthcare organizations. Obtaining a culture of continuous improvement is a primary objective of LM, and a culture of continuous improvement indicates a mature LM approach, and here leadership plays a central role. However, a comprehensive overview of leadership activities influencing LM maturity is lacking. This study aims to identify leadership activities associated with continuous improvement and, thus, LM, maturity. Methods Following the PRISMA guidelines, a scoping literature review of peer-reviewed articles was conducted in twenty healthcare management journals. The search provided 466 articles published up until 2023. During the selection process, 23 studies were included in the review. The leadership activities related to continuous improvement maturity were identified using the grounded theory approach and data coding. Results The analysis highlighted a total of 58 leadership activities distributed across nine themes of LM leadership. Next, analysing leadership activities concerning the different maturity levels revealed three maturity stages: beginner, intermediate, and expert. Based on the findings, we propose a framework that guides suitable leadership activities at the various stages of LM maturity. The framework provides leaders in healthcare with a practical overview of actions to facilitate the growth of the LM approach, and the related propositions offer academics a theoretical basis for future studies. Conclusion This review presents the first comprehensive overview of LM leadership activities in relation to continuous improvement and LM maturity. To enhance LM maturity, leaders are encouraged to consider their leadership style, (clinical) stakeholder involvement, alignment with the organizational strategy, and their role in promoting employee autonomy.

The material extrusion fused deposition modeling (FDM) technique has become a widely used technique that enables the production of complex parts for various applications. To overcome limitations of PLA material such as low impact toughness, commercially available materials such as UltiMaker Tough PLA were produced to improve the parent PLA material that can be widely applied in many engineering applications. In this study, 3D-printed parts (test specimens) considering six different printing parameters (i.e., layer height, wall thickness, infill density, build plate temperature, printing speed, and printing temperature) are experimentally investigated to understand their impact on the mechanical properties of Tough PLA material. Three different standardized tests of tensile, flexural, and compressive properties were conducted to determine the maximum force and Young’s modulus. These six properties were used as responses in a design of experiment, definitive screening design (DSD), to build six regression models. Analysis of variance (ANOVA) is performed to evaluate the effects of each of the six printing parameters on Tough PLA mechanical properties. It is shown that all regression models are statistically significant (p<0.05) with high values of adjusted and predicted R2. Conducted confirmation tests resulted in low relative errors between experimental and predicted data, indicating that the developed models are adequately accurate and reliable for the prediction of tensile, flexural, and compressive properties of Tough PLA material.

Wi-Fi-based passive sensing is considered as one of the promising sensing techniques in advanced wireless communication systems due to its wide applications and low deployment cost. However, existing methods are faced with the challenges of low sensing accuracy, high computational complexity, and weak model robustness. To solve these problems, we first propose a robust channel state information (CSI)-based Wi-Fi passive sensing method using attention mechanism deep learning (DL). The proposed method is called as convolutional neural network (CNN)-ABLSTM, a combination of CNNs and attention-based bi-directional long short-term memory (LSTM). Specifically, CSI-based Wi-Fi passive sensing is devised to achieve the high precision of human activity recognition (HAR) due to the fine-grained characteristics of CSI. Second, CNN is adopted to solve the problems of computational redundancy and high algorithm complexity which are often occurred by machine learning (ML) algorithms. Third, we introduce an attention mechanism to deal with the weak robustness of CNN models. Finally, simulation results are provided to confirm the proposed method in three aspects, high recognition performance, computational complexity, and robustness. Compared with CNN, LSTM, and other networks, the proposed CNN-ABLSTM method improves the recognition accuracy by up to 4%, and significantly reduces the calculation rate. Moreover, it still retains 97% accuracy under the different scenes, reflecting a certain robustness.

The communications between vehicle-to-vehicle (V2V) with high frequency, group sending, group receiving and periodic lead to serious collision of wireless resources and limited system capacity, and the rapid channel changes in high mobility vehicular environments preclude the possibility of collecting accurate instantaneous channel state information at the base station for centralized resource management. For the Internet of Vehicles (IoV), it is a fundamental challenge to achieve low latency and high reliability communication for real-time data interaction over short distances in a complex wireless propagation environment, as well as to attenuate and avoid inter-vehicle interference in the region through a reasonable spectrum allocation. To solve the above problems, this paper proposes a resource allocation (RA) method using dueling double deep Q-network reinforcement learning (RL) with low-dimensional fingerprints and soft-update architecture (D3QN-LS) while constructing a multi-agent model based on a Manhattan grid layout urban virtual environment, with communication links between V2V links acting as agents to reuse vehicle-to-infrastructure (V2I) spectrum resources. In addition, we extend the amount of transmitted data in our work, while adding scenarios where spectrum resources are relatively scarce, i.e. the number of V2V links is significantly larger than the amount of spectrum, to compensate for some of the shortcomings in existing literature studies. We demonstrate that the proposed D3QN-LS algorithm leads to a further improvement in the total capacity of V2I links and the success rate of periodic secure message transmission in V2V links.

Radio-frequency fingerprint (RFF), which comes from the imperfect hardware, is a potential feature to ensure the security of communication. With the development of deep learning (DL), DL-based RFF identification methods have made excellent and promising achievements. However, on one hand, existing DL-based methods require a large amount of samples for model training. On the other hand, the RFF identification method is generally less effective with limited amount of samples, while the auxiliary data set and the target data set often needs to have similar data distribution. To address the data-hungry problems in the absence of auxiliary data sets, in this article, we propose a supervised contrastive learning (SCL)-based RFF identification method using data augmentation and virtual adversarial training (VAT), which is called “SCACNN.” First, we analyze the causes of RFF, and model the RFF identification problem with augmented data set. A nonauxiliary data augmentation method is proposed to acquire an extended data set, which consists of rotation, flipping, adding Gaussian noise, and shifting. Second, a novel similarity radio-frequency fingerprinting encoder (SimRFE) is used to map the RFF signal to the feature coding space, which is based on the convolution, long short-term-memory, and a fully connected deep neural network (CLDNN). Finally, several secondary classifiers are employed to identify the RFF feature coding. The simulation results show that the proposed SCACNN has a greater identification ratio than the other classical RFF identification methods. Moreover, the identification ratio of the proposed SCACNN achieves an accuracy of 92.68% with only 5% samples.

Alpine skiing is a sport and recreational physical activity which requires fine postural control to maintain balance in challenging conditions. Theoretically, balance dominates in alpine skiing, but coordinated action of the whole body of the skiers is equally important. The aim of this research was to determine the effects of experimental short-term program of intensive training of alpine skiing techniques to postural stability (on Biodex Balance System) of students. The sample is divided into an experimental (31 students, age 21.4±1.0 and body height 180.7±6.3 cm) and control group (34 students, age 20.6±0.8 and body height 180.3±6.8 cm). The results of ANCOVA within variables for the evaluation of postural stability show statistically significant effects of the applying experimental program in all applied variables at the level of significance p=.000. From the mean value results (M) it is obvious that the experimental group achieved better results compared to the identical tests applied to the control group. The results of this research show that learning to ski can improve the ability to maintain balance, especially if it is conducted under the expert supervision of a ski instructor, which can have the effect of reducing the risk of injury.

This paper presents an effective approach to enable performance improvement in human-robot collaboration scenarios. The problem is tackled from the perspective of speed and separation monitoring principle, which stems from the recently instituted safety standard. The proposed approach attempts to seek for performance gains, measured by the speed-up of the production cycle, without compromising the safety constraints consistent with the standard. The approach is based on the notion of braking surface - an abstraction of the swept volume described by the manipulator during braking motion. We address two types of braking behavior: general and path-consistent. In both cases, the braking surface can be evaluated in a receding horizon manner. The robot velocity is continuously scaled such that, in case of a controlled stop, the corresponding volume spanned by the robot (braking surface) does not interfere with the surrounding obstacles. The approach is entirely kinematic and does not require the knowledge of the robot's dynamic model. Simulation study indicates that the pro-posed approach offers performance improvements compared to other state of the art methods. Moreover, the experiments demonstrate the real-time applicability of the method with the real robot in human-shared environment.

The implementation of disruptive technologies of Industry 4.0 is carried out in all segments of society, but we still do not fully understand the breadth and speed of its application. We are currently witnessing major changes in all industries, so that new business methods are emerging, as well as transformation of production systems, new form of consumption, delivery and transport. All this is happening due to the implementation of disruptive technological discoveries that include: the Internet of Things (IoT), advanced robotics, smart sensors, Big Data, analytics, cloud computing, 3D printing, machine learning, virtual and augmented reality (AR), artificial intelligence, and productive maintenance. Advanced robotics is one of the most important technologies in Industry 4.0. The robotic application in the automation of production processes, with the support of information technology, leads us to ‘’smart automation’’, i.e., ‘’smart factory’’. The changes are so profound that, from the perspective of human history, there has never been a time of greater promise or potential danger. New generation robots have many advantages compared to the firstgeneration industrial robots such as: they work alongside with workers, workers perform their tasks in a safe environment, robots take up less space, robots do not need to be separated by fences, robots are easy to manipulate and cheaper to implement. The paper analyzes the trend of implementation of collaborative and service robots for logistics, which make the automation of production processes more flexible. Robotic technology is the basic technology of Industry 4.0, because without its application, the implementation of Industry 4.0 would not be possible. The trend of application of new generation robots will have an increasing character in the future, because the goals of the fourth industrial revolution cannot be achieved without collaborative robots. In other words, the objective is to achieve a ‘’smart production process’’ or ‘’smart factory’’.

Since the introduction of the concept of Industry 4.0 until today, the world is facing a series of changes resulting from intensive scientific, technical and technological innovations. Research, innovation and development changes are aimed at improving production, business and everyday life through the application of basic technologies of Industry 4.0. In order for individuals, organizations, communities and states to be able to use the benefits of these improvements, it is necessary to rapidly adapt to all innovative trends: developing the necessary skills of individuals and groups for the adoption and use of these technologies, the implementation of technologies in companies, organizations and institutions, and the development of appropriate strategies that these processes would be managed and directed. In the developed world, these I 4.0 implementation processes are already reaching their maturity: educational programs are adapted to the needs of monitoring technical-technological changes, companies deal with solving challenges related to these processes after the implementation of Industry 4.0 technologies, and states and communities are working on devising further directions of development and a strategy that will further accelerate changes. In Bosnia and Herzegovina, the processes are somewhat slower: educational programs partially follow the needs of education for Industry 4.0, companies struggle with the challenges of adopting and implementing Industry 4.0 without adequate institutional support, and strategies related to exploiting the opportunities of Industry 4.0 have not been developed, both due to the lack of initiative, as well as due to administrative restrictions related to the complex political system of Bosnia and Herzegovina. Considering that, this paper presents the results of research on the representation of Industry 4.0 technologies in the economy and education of Sarajevo Canton. The sample on which the research was conducted included 105 companies and 239 respondents from the general population. The results show that the highest level of application of Industry 4.0 technologies exists in the part related to the advanced management of company resources using planning and management support systems, and in communications. These findings, as well as the results related to the established level of knowledge of Industry 4.0 technologies in the general population, speak in favor of the need for the urgent development of various educational programs that will accelerate the learning of Industry 4.0 among all members of the community, as well as the establishment of state programs to support the implementation of technologies in companies, so that the economy of the Canton and the country as a whole would not fall behind in relation to the world driven by the fourth industrial revolution.

Digital twins represent a new paradigm that brings fundamental changes to business and asset management. The proliferation of connected devices and sensors has generated vast amounts of data from physical assets and processes. Digital twins leverage this data to create a virtual counterpart that reflects the behavior, performance, and characteristics of their physical counterparts in real-time. The definition of digital twins encompasses a wide range of applications and contexts. This paper provides an overview of existing literature on digital twins, including their definition, key characteristics, and classification. Additionally, it highlights potential challenges and limitations associated with digital twins and identifies the technologies that enable their implementation. By understanding the fundamental concepts and technological advancements in the field of digital twins, organizations can harness their potential to enhance their business, optimize resources, and foster innovation. Numerous examples of digital twin applications in various industries are highlighted in this paper, with a particular focus on the elevator industry. Therefore, this paper serves as a comprehensive source of information for researchers, practitioners, and decision-makers who wish to explore the application of digital twins in different industries and domains.

Pomegranate has shown a favorable effect on gingivitis/periodontitis, but the mechanisms involved are poorly understood. The aim of this study was to test the effect of pomegranate peel extract (PoPEx) on gingiva-derived mesenchymal stromal cells (GMSCs) under physiological and inflammatory conditions. GMSC lines from healthy (H) and periodontitis (P) gingiva (n = 3 of each) were established. The lines were treated with two non-toxic concentrations of PoPEX (low—10; high—40 µg/mL), with or without additional lipopolysaccharide (LPS) stimulation. Twenty-four genes in GMSCs involved in different functions were examined using real-time polymerase chain reaction (RT-PCR). PoPEx (mostly at higher concentrations) inhibited the basal expression of IL-6, MCP-1, GRO-α, RANTES, IP-10, HIF-1α, SDF-1, and HGF but increased the expression of IL-8, TLR3, TGF-β, TGF-β/LAP ratio, IDO-1, and IGFB4 genes in H-GMSCs. PoPEx increased IL-6, RANTES, MMP3, and BMP2 but inhibited TLR2 and GRO-α gene expression in P-GMSCs. LPS upregulated genes for proinflammatory cytokines and chemokines, tissue regeneration/repair (MMP3, IGFBP4, HGF), and immunomodulation (IP-10, RANTES, IDO-1, TLR3, COX-2), more strongly in P-GMSCs. PoPEx also potentiated most genes’ expression in LPS-stimulated P-GMSCs, including upregulation of osteoblastic genes (RUNX2, BMP2, COL1A1, and OPG), simultaneously inhibiting cell proliferation. In conclusion, the modulatory effects of PoPEx on gene expression in GMSCs are complex and dependent on applied concentrations, GMSC type, and LPS stimulation. Generally, the effect is more pronounced in inflammation-simulating conditions.