Cervical cancer remains a major public health problem. Despite the screening programs, cervical cancer is the second most commonly diagnosed cancer. Most of the cases occur in less developed countries, and it is the third leading cause of cancer death among females. In 2000–2007, the five-year relative survival rate for European women diagnosed with cervical cancer was 62%.[1] Standard treatment for locally advanced cervical cancer reveals external beam radiotherapy (EBRT) with concurrent chemotherapy followed by image-guided adaptive brachytherapy (IGABT) boost [2, 3]. In the last years, the Gynaecological (GYN) working group composed of Group Européen de Curiethérapie-European Society for Therapeutic Radiology and Oncology (GEC-ESTRO) has developed new recommendations for the treatment of cervical cancer based on IGABT. The treatment plan includes 3-dimensional (3D) imaging based on magnetic resonance imaging (MRI) or computed tomography (CT), adaptive to the target volume and organ at risk [4–6]. The brachytherapy insertion is made under anaesthesia. At the time of the application insertion, the ultrasound is used to guide the dilatation of the uterine canal and assess the depth of the uterine cavity. In this paper, we review the data available in international literature about the use of ultrasound (US) in brachytherapy treatment of cervical cancer.

In industry 4.0 era, video applications such as surveillance visual systems, video conferencing, or video broadcasting have been playing a vital role. In these applications, for manipulating and tracking objects in decoded video, the quality of decoded video should be consistent because it largely affects to the performance of the machine analysis. To cope with this problem, we propose a novel perceptual video coding (PVC) solution in which a full reference quality metric named Video Multimethod Assessment Fusion (VMAF) is employed together with a deep convolutional neural network (CNN) to obtain the consistent quality while still achieving the high compression performance. First of all, to achieve the consistent quality requirement, we propose a CNN model with an expected VMAF as input to adaptively adjust the quantization parameters (QP) for each coding block. Afterwards, to increase the compression performance, a Lagrange coefficient of Rate-Distortion optimization (RDO) mechanism is adaptively computed under Rate-QP and Quality-QP models. Experimental results show that the proposed PVC has achieved two targets simultaneously: the quality of video sequence is kept consistently with an expected quality level and the bit rate saving of the proposed method is higher than traditional video coding standards and relevant benchmark, notably with around 10% bitrate saving in average.

Studies evaluating candidate tick-derived proteins as anti-tick vaccines in natural hosts have been limited due to high costs. To overcome this problem, animal models are used in immunization tests. The aim of this article was to review the use of rabbits as an experimental model for the evaluation of tick-derived proteins as vaccines. A total of 57 tick proteins have been tested for their immunogenic potential using rabbit as model for vaccination. The most commonly used rabbit breeds were New Zealand (73.8%), Japanese white (19%), Californians (4.8%) and Flemish lop-eared rabbit (2.4%). Anti-tick vaccines efficacy resulted in up to 99.9%. Haemaphysalis longicornis (17.9%) and Ornithodoros moubata (12.8%) were the most common tick model in vaccination trials. Experiments in rabbits have revealed that some proteins (CoAQP, OeAQP, OeAQP1, Bm86, GST-Hl, 64TRP, serpins and voraxin) can induce immune responses against various tick species. In addition, in some cases it was possible to determine that the vaccine efficacy in rabbits was similar to experiments performed in natural hosts (e.g. Bm86, IrFER2, RmFER2, serpins and serine protease inhibitor). In conclusion, results have shown that prior to performing anti-tick vaccination trials using natural hosts, rabbits can be used as suitable experimental models for these studies

3D object detection is essential for an accurate and reliable autonomous driving system. Currently, the methods used by the state-of-the-art two-stage detectors are not flexible enough and their fea-ture extraction capabilities are very limited to cope effectively with the disorder and irregularity of point clouds. In this paper, we combine the advantages of both PV-RCNN and PAConv (Position Adaptive Convolution) to create a completely new network, FANet, in order to overcome the ir-regularity and disorder of point clouds. The convolution in our network builds convolutional ker-nels from a basic weight matrix, whose combined coefficients are learned adaptively by LearnNet from relative points. This network allows for flexible modeling of complex spatial variations and geometric structures in the 3D point cloud, enabling better extraction of point cloud features and producing high-quality 3D proposal boxes. Compared to other methods, FANet is superior in terms of 3D object detection accuracy. Extensive experiments on the KITTI dataset have shown a signif-icant improvement in our approach.

In this research work, an adaptive scheme for the coordinated protection of AC Microgrids using directional overcurrent (DOCR) relays is presented. Protection of AC MGs is a complex and challenging issue due to the dynamic nature of the network including, a) its capability to reconfigure the modes of operation ranging from grid-connected to the islanded mode, c) bidirectional-power flow capability, and c) integration of intermittent renewable energy resources with real-time variations in the resource availability. Consequently, the fault current contributions may largely vary depending upon the incident conditions on the network. Conventional protection schemes, generally designed for radial networks, and unidirectional power flow from the source end to the load may either mal-operate or exhibit very poor performance, if not adapted according to the dynamic conditions of the network. To address this issue, a communication-based adaptive protection scheme capable to adapt its settings according to the generation resource availability and network configuration is presented in this work. The proposed scheme consists of an intelligent central protection unit (ICPU) capable to update the settings and communicate it to the individual relays based on the pre-calculated offline settings. The directional overcurrent relays employed in the scheme use two-stage settings, i.e. definite time and inverse definite minimum time characteristics for the effective coordination among the downstream and upstream relays. An adaptive algorithm for ICPU operation is presented and a case study is implemented for a modified IEEE 9-bus system using DigSilent Power factory. The results for various scenarios including, a) grid-connected mode of operation, b) islanded mode of operation, and c) variable distributed generation mode are obtained and compared to the static scheme, which validates the effectiveness of the proposed scheme.

Weight gain affects about 10-20% of patients after bariatric surgery. It is a phenomenon that is difficult to understand and to intervene due to its complexity and etiological heterogeneity. In the present study, we investigated, from a network analysis perspective, the associations between weight regain, psychological, sociodemographic factors and physical activity in patients undergoing bariatric surgery. The sample consisted of 124 patients, of both sexes, aged 39 ± 9.1 years, who had undergone surgical intervention for more than 18 months. After voluntary consent, respondents answered questionnaires and instruments directly on the Google Forms platform. The results indicated that weight gain was negatively associated with the items of depression, anxiety and stress, binge eating and with the dimensions of the personality questionnaire (negative affectivity -0.182; detachment -0.078; antagonism -0.107; disinterest - 0.198 and psychoticism -0.158). The centrality indicators revealed that the characteristics of disinterest and negative affectivity and most of the items on the depression, anxiety and stress scale had a greater expected influence (values ​​from 1,043 to 1,502), indicating that these are the most sensitive variables to intervention and who need more attention from health professionals.

Progressive technologies and practices are shifting the possibilities of building design and improving work efficiency. Constantly changing site conditions require different procedures and designs that take into account these changing conditions, whether it is a design solution, a change in environmental conditions, or just sustainability factors. Adaptive building design offers opportunities to cope with changing factors to achieve the highest possible level of building quality. This case study deals with the topic of adaptive formwork design for building renovation, taking into account sustainability. Aim of the article is an investigation and demonstration of the building information modelling (BIM) environment used for the adaptive design of formwork elements for the building renovation in the context of sustainability. The object of the case study is a building in the center of Kosice, Slovakia. BIM environment allows prompt and correct adaptation of the formwork design to changing conditions of lighting, ventilation, heating and temperature during the design of the building.

Although Mast cells are known as key drivers of type I allergic reactions, there is increasing evidence for their critical role in host defense. MCs do not only play an important role in initiating innate immune responses, but also influence the onset, kinetic and amplitude of the adaptive arm of immunity, or fine-tune the mode of the adaptive reaction. Intriguingly, MCs have been shown to affect T cell activation by direct interaction or indirectly by modifying properties of antigen-presenting cells, and can even modulate lymph node-borne adaptive responses remotely from the periphery. In this review, we provide a summary of recent findings that explain how MCs act as a link between the innate and the adaptive immunity, all the way from sensing inflammatory insult to orchestrating the final outcome of the immune response.

Recently, various relations and criteria have been presented to establish a proper relationship between control systems and control Global Positioning System (GPS)-intelligent buoy system. Given the importance of controlling the position of buoys and the construction of intelligent systems, in this paper, dynamic system modeling is applied to position marine buoys through the improved neural network with a backstepping technique. This study aims at developing a novel controller based on adaptive fuzzy neural network to optimally track the dynamically positioned vehicle on water with unavailable velocities and unidentified control parameters. In order to model the network with the proposed technique, uncertainties and the unwanted disturbances are studied in the neural network. The presented study aims at developing a neural controlling which applies the vectorial back-stepping technique to the surface ships, which have been dynamically positioned with undetermined disturbances and ambivalences. Moreover, the objective function is to minimize the output error for the neural network (NN) based on closed-loop system. The most important feature of the proposed model for the positioning buoys is its independence from comparative knowledge or information on the dynamics and the unwanted disturbances of ships. The numerical and obtained consequences demonstrate that the controller system can adjust the routes and the position of the buoys to the desired objective with relatively few position errors.

Early childhood is considered to be a period of rapid development, with the acquisition of abilities predicting future positive school competences. Motor, cognitive and social difficulties related to cancer therapies heavily impact the development of children with cancer. This study focused on two main aims: to assess the developmental pathways of preschool children with acute lymphoblastic leukaemia one year post-treatment and to compare these abilities both with those of a control group of healthy peers and with Italian norms. Forty-four children and their families, recruited through the Haematology-Oncologic Clinic of the Department of Child and Woman Health (University of Padua), agreed to participate to this study. The children’s mean age was 4.52 years (SD = 0.94, range = 2.5-6 years), equally distributed by gender, all diagnosed with Acute Lymphoblastic Leukaemia. Matched healthy peers were recruited through paediatricians’ ambulatories. Each family was interviewed adopting the Vineland Adaptive Behaviour Scales. Paired sample Wilcoxon tests revealed that children were reported to have significantly more developmental difficulties than their healthy peers. When compared with Italian norms they scored particularly low in verbal competence, social and coping skills. No significant association were found between treatment variables and developmental abilities. These findings suggest that the creation of specialized interventions both for parents and children may fill the possible delays in children’s development probably due to stress, lack of adequate stimulation or difficult adaptation.

Early childhood is considered to be a period of rapid development, with the acquisition of abilities predicting future positive school competences. Motor, cognitive and social difficulties related to cancer therapies heavily impact the development of children with cancer. This study focused on two main aims: to assess the developmental pathways in preschool children with leukaemia one year post-treatment; and to compare these abilities with those of a control group of healthy peers. Forty-eight children and their families, recruited through the Haematology-Oncologic Clinic of the Department of Child and Woman Health (University of Padua), agreed to participate in this study. The children’s mean age was 4.36 years (SD = 1.07, range = 1.91–6 years), equally distributed by gender, most of whom were diagnosed with Acute Lymphoblastic Leukaemia (N = 44). Matched healthy peers were recruited through paediatricians’ ambulatories. Each family was interviewed adopting the Vineland Adaptive Behaviour Scales. Paired sample t-tests revealed that children, especially aged 42–72 months, were reported to have significantly more developmental difficulties than their healthy peers, particularly in verbal competence, social and coping skills and gross motor abilities. These findings suggest that the creation of specialized interventions for both parents and children may fill the possible delays in children’s development due to toxic therapies and their associated hospitalisation.

LncRNA are involved in multiple regulatory pathways, its versatile mode of action has disclosed a new layer in gene regulation. They are reportedly modulated during plant development, with specific tissue functions and in response to stresses. In this study, we analyzed LncRNA from leave samples collected from the legume Copaifera langsdorffii (copaiba) from two divergent ecosystems: Cerrado (CER) and Atlantic Rain Forest (ARF). We identified 8020 novel lncRNAs, from which 2893 transcripts were regulated above 2-fold and 566 above 5-folds in either condition. This putative lncRNA set was compared with seven Fabaceae genomes, of which 1747 and 1879 transcripts (from ARF and CER, respectively) aligned to at least two genomes. Further, 2194 copaiba lncRNAs were successfully mapped to at least one of six Fabaceae transcriptomes. The secondary structures of the lncRNAs that were conserved and differentially expressed between the populations were predicted using in silico methods. Our results indicate the potential involvement of lncRNAs in the adaptation of C. langsdorffii to two different biomes.

Symmetry-breaking is a phenomenon that is observed in various contexts, from the early universe to complex organisms, and is considered a key puzzle in understanding the emergence of life. The presence of enantiomeric amino acids and proteins highlights its critical role. However, the origin of symmetry-breaking has yet to be comprehensively explained, particularly from an energetic standpoint. This article explores a novel approach by considering energy dissipation, specifically lost free energy, as a crucial factor in elucidating symmetry-breaking. Through a thorough thermodynamic analysis applicable to enantiomeric systems at various scales, from elementary particles to aggregated matter like crystals, we present experimental findings that establish a direct correlation between non-equilibrium free energy and energy dissipation during formation processes. Results emphasize the pivotal role of energy dissipation not only as an outcome but as the trigger for symmetry-breaking. This insight suggests that understanding the origins of complex systems, from cells to living beings and the universe itself, requires a lens focused on non-equilibrium processes, ultimately facilitating the formation of non-racemic systems.

Only by understanding the ability to take third-person perspective can we begin to elucidate the neural processes responsible for one’s inimitable conscious experience. The current study examined differences in hemispheric laterality during a first-person perspective (1PP) and third-person perspective (3PP) taking task, when using Transcranial Magnetic Stimulation (TMS). Participants were asked to take either the 1PP or 3PP when identifying the number of spheres in a virtual scene. During this task, single-pulse TMS was delivered to the motor cortex of both the left and right hemispheres of 10 healthy volunteers. Measures of TMS-induced motor-evoked potentials (MEPs) of the contralateral abductor pollicis brevis (APB) were employed as an indicator of lateralized cortical activation. The data suggest that the right hemisphere is more important in discriminating between 1PP and 3PP. These data add a novel method for determining perspective taking and add to the literature supporting the role of the right hemisphere in meta representation.

This study is a synthesis of 159 articles that were selected for their relevance to comprehend key aspects of the Massive Open Online Courses (MOOCs) phenomenon, from a discourse analysis perspective. Since 2011, MOOCs are expanding worldwide so that the number of subscribers outpointed 101 million at the end of 2018. This paper explores the question whether the MOOCs are the embodiment of the global one-world classroom or whether, instead, they represent a low-cost alternative tailored to a segment that doesn´t have enough time or resources to attend a brick-and-mortar college. In addition, the review tackles the link between motivation and low completion rates. Finally, we discuss the need to devise better methods to assess the pedagogical value of MOOCs.

Semantic segmentation of high-resolution remote sensing images provides detailed and precise feature information, playing a pivotal role in decision-making and analysis in sectors such as water management, agriculture, military, and environmental protection. However, most methods merely combine features from various branches directly, without a mechanism for spatial location feature screening, and treat all extracted features as equally important. To overcome these limitations, we introduce a novel spatially adaptive interaction network (SAINet) for dynamic interaction across different features in remote sensing semantic segmentation task. Specifically, we propose a spatial refined module that leverages local context information to filter spatial locations and extract salient regions. Following this, we introduce an innovative spatial interaction module that uses a spatial adaptive modulation mechanism to dynamically select and allocate spatial position weights. This facilitates interaction between local salient areas and global information, allowing the network to focus more on relevant regions. This flexible approach enables the network to capture more informative features, thereby enhancing segmentation accuracy. Experiments conducted on the DeepGlobe, Vaihingen, and Potsdam public datasets confirm the effectiveness and capability of SAINet. Our code and models will be publicly available

Discrete-time architectures offer a distinct advantage over their continuous counterparts, as they can be seamlessly implemented on embedded hardware without the necessity for discretization processes. Yet, because of the difficulty of ensuring Lyapunov difference expressions, their designs, which are based on quadratic Lyapunov-based frameworks, are highly complex. As a result, various existing continuous-time results using adaptive control methods to deal with system uncertainties and coupled dynamics in agents of a multiagent system cannot be directly applied to the discrete-time context. Furthermore, compared to their continuous-time equivalent, discrete-time information exchange based on periodic time intervals is more practical in the control of multiagent systems. In this paper, we first introduce a discrete-time adaptive control architecture designed for uncertain scalar multiagent systems without coupled dynamics as a preliminary result. We then introduce another discrete-time adaptive control approach for uncertain multiagent systems in the presence of coupled dynamics. Our approach incorporates observer dynamics to manage unmeasurable coupled dynamics, along with a user-assigned Laplacian matrix to induce cooperative behaviors among multiple agents. Our solution includes Lyapunov analysis with logarithmic and quadratic Lyapunov functions for guaranteeing asymptotic stability with both controllers. To demonstrate the effectiveness of the proposed control architectures, we provide an illustrative example.

This article focuses on artificial intelligence in educational technology, starting with an introduction to educational technology, an interdisciplinary field of study that covers the design, development, utilization, and evaluation of technology and digital tools in educational Settings. A detailed description of its definition in an academic context - a multidisciplinary field of computer science and cognitive science that deals with the development of computational systems that exhibit intelligent behaviour, describing its areas of coverage and scope of application. It then introduces the benefits of AI in education technology, specifically addressing personalized learning, adaptive learning systems, automated scoring and feedback, virtual tutors and chatbots, data analytics, as well as content recommendations and natural language processing, accessibility and inclusion. Then it introduces the main concepts of AI personalized learning: AI personalized learning uses the power of artificial intelligence to meet the unique needs and preferences of individual learners, as well as its key principles and main characteristics. Adaptive learning systems harness the power of artificial intelligence and data analytics to tailor the learning experience to each student's needs and abilities, as well as its core principles and strengths. It also introduces the main operating principles of automated grading and feedback: it uses artificial intelligence and computer algorithms to evaluate and evaluate student assignments, tests and exams without the direct involvement of human graders, and its associated benefits. Secondly, the main concepts of virtual tutor are introduced: Virtual tutor is a computer-based system that uses artificial intelligence and machine learning algorithms to provide students with personalized and interactive educational support and its key characteristics and main advantages, and then the nature and benefits of chatbots and help in the field of education. The final conclusion summarizes the benefits and future challenges of integrating AI into educational technology.

Since 2019, notable global viral outbreaks have occurred necessitating further research and healthcare system investigations. Following the COVID—19 pandemic, an unexpected duality has occurred of SARS–CoV–2 and monkeypox virus (MPXV) infections. Monkeypox virus is of the Orthopoxviridae genus, belonging to the family Poxviridae. Zoonotic transmission (animal to human transmission) may occur. The Orthopoxviridae genus includes other Orthopoxviruses (OPXV) present in animal host reservoirs that include cowpox viruses (CPXV), vaccinia virus (VACV) and variola virus (VARV), with the latter being causal agent of smallpox and excessive mortality. The aim in this review is to present facts about MPXV specific pathogenesis, epidemiology, and immunology alongside historical perspectives. Monkeypox virus was rarely reported outside Africa before April 2000. Early research since 1796 contributed towards eradication of VARV leading to immunisation strategies. The World Health Organisation (WHO) announcement that VARV had been eradicated was confirmed in 1980. On the 23rd of July 2022, the WHO announced MPXV as a health emergency. Therefore, concern due to propagation of MPXV causing MPOX disease requires clarity. Infected hosts display symptoms like extensive cellular initiated rashes and lesions. Infection with MPXV makes it difficult to differentiate from other diseases or skin conditions. Anti–viral therapeutic drugs were typically prescribed for smallpox and MPOX disease; however, the molecular and immunological mechanisms with cellular changes remain of interest. Furthermore, no official authorised treatment exists for MPOX disease. Some humans across the globe may be considered at risk. Historically, presenting symptoms of MPOX resemble other viral diseases. Symptoms include rashes or lesions like Streptococcus, but also human herpes viruses (HHV) including Varicella zoster (VZV).

Due to the complex underground environment, pumping machines are prone to produce numerous failures. The indicator diagrams of faults are similar in a certain degree, which produces indistinguishable samples. As the samples increases, manual diagnosis becomes difficult, which decreases the accuracy of fault diagnosis. For accurately and quickly judging the fault type, we propose an improved adaptive activation function and apply it to five types of neural networks. The adaptive activation function improves the negative semi-axis slope of the ReLU activation function by combining the gated channel conversion unit to improve the performance of the deep learning model. The proposed adaptive activation function is compared with the traditional activation function through the fault diagnosis data set and the public data set. The results show that the activation function has better nonlinearity, can improve the generalization performance of deep learning model, the accuracy of fault diagnosis. In addition, the proposed adaptive activation function can also be well embedded in other neural networks.

CD4+ T cells orchestrate and regulate immunity within jawed vertebrates, yet our understanding of their evolution, development, and cellular physiology has only begun to be unearthed in the past few decades. Discoveries of genetic diseases that ablate this cellular population have provided insight into their critical functions while transcriptomics, proteomics, and highresolution microscopy have recently revealed new insights into CD4+ T cell anatomy and physiology. This article compiles historical, microscopic, and multi omics data which can be used as a reference atlas to dissect cellular physiology within these influential cells and further understand pathologies like HIV infection that inflict human CD4+ T cells.

The development of anti-pain technologies in the complex treatment of pain syndromes is one of the most urgent tasks of modern medicine. We have undertaken a placebo-controlled experimental study of the therapeutic potential of low-intensity laser radiation when applied to acupuncture points, which are directly related to the autonomic nervous system. The adaptation effect of puncture photobiomodulation on the induction of stress-mediated autonomic reactions, oxidative metabolism and microcirculation in animals during the acute phase of pain stress was revealed. The data obtained are of interest for use in the complex rehabilitation of patients with pain syndrome.

In recent years, gamification has captured the attention of researchers and educators, particularly in science education, where students often express negative emotions. Gamification methods aim to motivate learners to participate in learning by incorporating intrinsic and extrinsic motivational factors. However, the effectiveness of gamification has yielded varying outcomes, prompting researchers to explore adaptive gamification as an alternative approach. Nevertheless, there needs to be more research on adaptive gamification approaches, particularly concerning motivation, which is the primary objective of gamification. In this study, we developed and tested an adaptive gamification environment based on specific motivational and psychological frameworks. This environment incorporated adaptive criteria, learning strategies, gaming elements, and all crucial aspects of science education for six classes of 3rd-grade students in primary school. We employed a quantitative approach to gain insights into the motivational impact on students and their perception of the adaptive gamification application. We aimed to understand how each game element experienced by students influenced their motivation. The findings of our study revealed encouraging results in terms of increased motivation and engagement among students, as well as the influence of different game elements when connected with an individual's profile based on a multidimensional adaptive framework.

Routing a person through a traffic network presents a tension between selecting a fixed route that is easy to navigate and selecting an aggressively adaptive route that minimizes the expected travel time. We propose to create non-aggressive adaptive routes in the middle-ground seeking the best of both these extremes. Specifically, these routes still adapt to changing traffic conditions, however we limit the number of adjustments made in the route. This improves the user experience, by providing a continuum of options between saving travel time and minimizing navigation. We design strategies to model single and multiple route adjustments, and investigate enumerative techniques to solve these models. To alleviate the intractability with handling real-life traffic data, we develop efficient algorithms with easily computable lower and upper bounds. We finally present computational experiments highlighting the benefits of limited adaptability in terms of reducing the expected travel time.

Wireless cellular communication technology has developed into a very resourceful commodity worldwide. Today, people of all races can hardly live without means of voice and data cellular communication technology. Imprecise propagation loss estimation leads to high power waste, high co-channel interference and poor service quality in cellular communication system networks. This paper proposes a realistic adaptive fine-tuning method for distinctive propagation loss estimation over a microcellular communication radio links based on signal power measurements from Long Term Evolution radio broadband networks, taking non-line of sight (NLOS) and line of sight (LOS) environments into consideration. The methodology is verified by measurements taken in non-line of sight and line of sight signal propagation scenarios. The results showed that the estimated propagation losses using the proposed realistic adaptive tuning models were more accurate than the existing Cost -231 modelling estimation approach

Wireless cellular communication technology has developed into a very resourceful commodity worldwide. Today, people of all races can hardly live without means of voice and data cellular communication technology. Imprecise propagation loss estimation leads to high power waste, high co-channel interference and poor service quality in cellular communication system networks. This paper proposes a realistic adaptive fine-tuning method for distinctive propagation loss estimation over a microcellular communication radio links based on signal power measurements from Long Term Evolution radio broadband networks, taking non-line of sight (NLOS) and line of sight (LOS) environments into consideration. The methodology is verified by measurements taken in non-line of sight and line of sight signal propagation scenarios. The results showed that the estimated propagation losses using the proposed realistic adaptive tuning models were more accurate than the existing Cost -231 modelling estimation approach.

Multiple sclerosis (MS) is a chronic autoimmune disease that affects the central nervous system (CNS) characterized by varying degrees of demyelination of uncertain etiology, and is associated with specific environmental and genetic factors. Upon recognition of CNS antigens, the immune cells initiate an inflammatory process which leads to destruction and deterioration of the neurons. Innate immune cells such as macrophages, dendritic cells and natural killer cells are known to play critical roles in the pathogenesis of MS. Also, the activation of peripheral CD4+ T cells by CNS antigens leads to their extravasation into the CNS causing damages that exacerbates the disease. This could be accompanied by dysregulation of T regulatory cells and other cell types functions. Experimental autoimmune encephalomyelitis (EAE) is a mouse model used to study the pathophysiology of MS disease. In this review, we highlight the roles of innate and adaptive immune players in the pathogenesis of MS and EAE.

The paper deals with the navigation data exchange between two satellites moving in a swarm. It is focused on reduction of the inter-satellite demanded communication channel capacity taking into account dynamics of the satellites relative motion and possible erasing in the channel the navigation data. The feedback control law is designed ensuring regulation of the relative satellites motion. The adaptive binary coding/decoding procedure for the satellites navigation data transmission over the limited capacity communication channel is proposed and studied for the cases of ideal and erasure channels. Dependence of the regulation time on the data transmission rate is numerically evaluated. Results of the numerical study of the closed-loop system performance and accuracy of the data transmission algorithm on the communication channel bitrate and erasure probability are obtained based on the extensive simulations. These results provide dependence of the required load of the communication channel on the desired quality of the regulation process. It is shown that both data transmission error and regulation time depend inversely proportional on the communication rate, and that for the significantly high data transmission rate erasure of data in the channel with probability up to 0.3 does not make an effect on the regulation time.

Standard evolutionary theory, supported by mathematical modelling of outbred, dispersed populations predicts that ageing is not an adaptation. We recently argued that in clonal, viscous populations, programmed organismal death could promote fitness through social benefits and has, in some organisms (e.g. Caenorhabditis elegans), evolved to shorten lifespan. Here we review previous adaptive death theory, including consumer sacrifice, biomass sacrifice, and defensive sacrifice types of altruistic adaptive death. In addition we discuss possible adaptive death in semelparous fish, coevolution of reproductive and adaptive death, and adaptive reproductive senescence in C. elegans. We also describe findings from recent tests for the existence of adaptive death in C. elegans using computer modelling. Such models have provided new insights into how trade-offs between fitness at the individual and colony levels mean that senescent changes can be selected traits. Exploring further the relationship between adaptive death and social interactions, we consider examples where adaptive death results more from action of kin than from self-destructive mechanisms and, to describe this, introduce the term adaptive killing of kin.

We applied a framework to assess climate change vulnerability of 52 major vegetation types in the western United States to provide spatially-explicit input to adaptive management decisions. The framework addressed climate exposure and ecosystem resilience; the latter derived from analyses of ecosystem sensitivity and adaptive capacity. Measures of climate change exposure used observed climate change (1981-2014) and then climate projections for the mid-21st century (2040-2069 RCP 4.5). Measures of resilience included (under ecosystem sensitivity) landscape intactness, invasive species, fire regime alteration, and forest insect & disease risk, and (under adaptive capacity), measures for topo-climate variability, diversity with functional species groups, and vulnerability of any keystone species. Outputs are generated per 100km² hexagonal area for each type. As of 2014, moderate climate change vulnerability was indicated for >50% of the area of 50 of 52 types. By the mid-21^st century, all but 19 types face high or very high vulnerability with >50% of the area scoring in these categories. Measures for resilience explain most components of vulnerability as of 2014, with most targeted vegetation scoring low in adaptive capacity measures and variably for specific sensitivity measures. Elevated climate exposure explains increases in vulnerability between the current and mid-century time periods.

Using the sustainable livelihoods analytical framework, adaptability of cattle raising to multiple stressors (e.g. climate change and market conditions) in the dry tropics of Chiapas, Mexico was evaluated. Three case studies located in the Frailesca region of Chiapas were analyzed: (I) peasant cattle raising in a rural village in the Frailesca Valley; (II) peasant cattle raising in a rural village in a natural protected area in the Frailesca Highlands; and (III) holistic cattle raising by farmers with private land ownership in the Frailesca Valley. Adaptability was evaluated using an index on a scale of one to a hundred; average values were: case I = 20.9 ± 1.4; case II = 32.1 ± 1.8; and case III = 63.6 ± 3.5. In order to increase farms adaptability and reduce the vulnerability of cattle raising families, there is a need to modify public policy to take into account the conditions of the most vulnerable farmers (cases I and II). Given the economic, environmental, and social context of Mexico´s dry tropics, establishing ecological or organic cattle raising and silvopastoral systems may reduce the vulnerability of farm families and increase their level of adaptability of their farms to multiple stressors.

When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

The scientific fields of complexity, artificial life (ALife), and artificial intelligence (A.I.) share several commonalities: historic, conceptual, methodological, and philosophical. It was possible to develop them only because of information technology, while their origins can be traced back to cybernetics. In this perspective, I'll revise the expectations and limitations of these fields, some of which have their roots in the limits of formal systems. I will use interactions, self-organization, emergence, and balance to compare different aspects of complexity, ALife, and A.I. The paper poses more questions than answers, but hopefully it will be useful to align efforts in these fields towards overcoming --- or accepting --- their limits.

This study addresses the critical challenge of Speech Enhancement (SE) in noisy environments, where the deployment of Deep Neural Networksolutions on microcontrollers is hindered by their extensive computational demands. Focusing on this gap, our research introduces a novel SE method optimized for MCUs, employing a 2-layer GRU model that capitalizes on perceptual speech properties and innovative training methodologies. By incorporating self-reference signals and a dual strategy of compression and recovery based on the Mel scale, we develop an efficient model tailored for low-latency applications. Our GRU-2L-128 model demonstrates a significant reduction in size and computational requirements, achieving a 14.2× decrease in model size and a 409.1× reduction in operations compared to traditional DNN methods like DCCRN, without compromising performance. This advancement offers a promising solution for enhancing speech intelligibility in resource-constrained devices, marking a pivotal step in SE research and application.

The main consequence of radiation damage on a Silicon-Photomultiplier (SiPM) is a significant increase in the dark current. If the SiPM is not adequately cooled, the power dissipation causes it to heat up, which in turn affects its performance based on the temperature. In order to investigate this heating effect, experiments were carried out using a KETEK SiPM glued on an Al2O3 substrate, as well as HPK SiPMs that were either glued onto an Al2O3 substrate or a flexible PCB. The assemblies were connected either directly to a temperature-controlled chuck on a probe station or through layers of materials with well-defined thermal resistance. A DC-mode LED was used to illuminate the SiPM, and the resulting SiPM current was measured to determine the steady-state temperature. This temperature depended on the power dissipated in the multiplication region of the SiPM and the thermal resistance. This information can be utilized to adjust the SiPMs working point, taking into account the effects of self-heating. Similarly, this approach can be applied to packaged SiPMs that have an unknown thermal contact to a heat sink.

The evolution of sentence representation learning, especially with parse tree encoders, has shown remarkable progress. Traditional approaches predominantly rely on recursive encoding of tree structures, which impedes parallel processing capabilities. Additionally, these methods often overlook the significance of dependency tree arc labels. To overcome these limitations, we introduce the Syntax-Enhanced Transformer (SET), incorporating a novel dual-attention mechanism that integrates relation-focused attention alongside traditional self-attention. This design effectively encodes both dependency and spatial positional relationships within sentence dependency trees. Our approach innovatively incorporates syntactic information into the Transformer framework without compromising its inherent parallelizability. The SET demonstrates superior or comparable performance to contemporary methods across various sentence representation tasks, significantly enhancing computational efficiency.

This article is a scoping review analyzing advances in experimental studies on bacterial self-healing of concrete. From Scopus, ScienceDirect, Web of Science and Google Scholar search engine databases, 54 studies were selected for analysis, and an increasing trend of publications since 2015 was found, with India as the country with the largest research presence. Different ma-terials are used as replacement or admixture in concrete, such as fibers, particles and recycled material from construction waste. It was found that the most commonly used bacteria are Bacillus subtilis and Bacillus sphaericus, which can improve the strength of concrete by generating calcite and sealing cracks. In addition, it was found that the presence of materials such as fibers, ashes and construction recyclates can protect bacteria in an alkaline environment. The designs usually in-clude more than two experimental groups and a control. In summary, this study highlights the importance of bacterial self-healing in concrete and provides information on advances in this field of research.

This review article delves into the intricate psychology behind galivanting, absenteeism, and self-denial among college students. By conducting a meticulous analysis of relevant books, journals, and articles, key psychological factors come to light. The review examines the influence of exploration and identity formation, social motivation, coping mechanisms, lack of motivation or engagement, time management, and self-denial on these behaviors. By addressing gaps in the literature, it provides a deeper understanding of the phenomena at hand. The implications of this study extend to the development of interventions and strategies that address self-denial tendencies, promote effective time management, and foster a supportive learning environment. By shedding light on the intricate interplay of these factors, this paper opens doors for future research and targeted interventions, ultimately aiming to enhance academic engagement, reduce absenteeism, and support the overall well-being and success of college students.

Background: Nursing services provided by nurses are based on knowledge and competence in the field of nursing science developed in accordance with Client needs, scientific developments, and the demands of globalization. These health services include nursing services that are carried out responsibly, accountably, with quality, and safely by nurses who have obtained registration and permission to practice. Nursepreneur is innovation and courage to take risks and is ready to work hard to achieve goals with optimism. Which has important components namely: 1) quality, 2) role, 3) option and success. The post-pandemic new world, especially in the health care system, continues to grapple with the demands of costs to provide patient care and safety. By using the application of the nursing theory model. Purpose :. Assessing scientific studies on the application of evidence-based intervention in wound care using nursing theory, identifying the important role of telenursing in improving nursing services in Nerspreneurship, Method: literature study Conclusion: Telenursing in nursing focuses on providing nursing care through information technology, such as telephone, computers, and the Internet that can be used to improve treatment outcomes among older people, especially those with low literacy skills and low financial status (Esmaeilpour-BandBoni et al., 2020). The statement from the results of the study refutes that Telenursing is intended for patients who have higher education and middle and upper economic states.

Background: Indonesia is an archipelago with various cultured, will bee affects all aspects of people's lives, including beliefs related to health behavior and health care. One problem that needs serious attention is the elderly group because the elderly are a vulnerable group who have a high risk of experiencing health problems, such as anemia with hypertension, so they need good care management. The research objective was to determine self-management intervention for hypertensive elderly. Methods: This research is a literature review with five databases (Scopus, CINAHL, ScienceDirect, PubMed, Proquest), the study design used is an experimental, descriptive, cross-sectional, and review systematic, in March 2021. Guidelines for using JBI to measure quality and listing check out Prisma in this review guide. A feasibility study based on title, abstract, full text, and research methodology. The data analysis used narrative analysis based on the research findings. Results: Twelve articles met the predefined review inclusion criteria. The study found eight interventions for self-management in elderly hypertension, namely: 1) Knowledge of hypertension, 2) Regulation of physical activity, 3) Limitation of alcohol and cigarette consumption, 4) Sleep rest and stress management, 5) Diet and body weight management, 6) Blood pressure control, 7) Family and social support, 8) Adherence to therapy regimens. Conclusion: The conclusion of the study results obtained eight elderly self-management interventions, this done properly will affect compliance in optimizing the health of the elderly.

Background: Since the beginning of the 20th century, the importance of creating healthy work environments and promoting the health of workers in the healthcare sector to create Healthy and Resilient Organizations has been emphasized. In this context, self-esteem is an essential construct which influences health and healthy life styles, and therefore, the general wellbeing of nurses. The objective of this study was to analyze the mediating role of reasons for exercising in the effect that self-esteem has on uncontrolled eating by nursing professionals. Methods: The sample was made up of 1094 nurses who were administered the Rosenberg General Self-esteem Scale, the Goal Content for Exercise Questionnaire and the Three-Factor Eating Questionnaire-R18. Results: Bivariate correlation analysis and multiple mediation analysis showed that self-esteem has direct and indirect effects on uncontrolled eating. More so, self-esteem predicts doing physical exercise to improve one’s image, recognition and social affiliation, although the effects on uncontrolled eating were only significant through image. Conclusions: The results have important practical implications in the framework of Positive Occupational Health Psychology (POHP) as it emphasizes self-esteem, physical exercise and eating as essential aspects of the health and wellbeing of employees in the healthcare sector, highlighting the importance of creating organizations committed to promoting the psychosocial health of their workers.

Partial differential representation of self-similarity feature has been derived from notion of the homogenous function in general sense. This representation allows consideration of stochastic self-similar systems. As well as the partial differential representation allows consideration of Stochastic Partial Differential Equation.

Neurodevelopmental disorders are characterized by an amalgam of genetic and epigenetic faults implicating neuronal protein-related loss-of-function and gain-of-function respectively, hence their inclusion into the category of complex diseases. Neurodevelopmental disorders are regarded as autism spectrum disorder due to this reason; that the risk factors and phenotype constitute a spectrum of faults and protein overactivity. One example of an epigenetic factor leading to the onset of neurodevelopmental delays constitutes a set of immune response-based dysregulations, as well as the excessive growth of the adaptive immune memory of the fetus or the baby following significant infectious disease during pregnancy, as well as in babies aged 0-2, given the existence of profound links between developmental immunity and developmental neurology. Given that genetic information very likely represents a form of energy, the intake of excessive pathogen-related information may transiently overload the adaptive immune memory similarly to the manner functional pathogens do whilst inducing significant illness, and if events as such occur during important stages of neuro-immunological development, then there could often be irreversible effects upon the rate of development in neurological networks that normally have a major importance in the modulation of the related cognitive and behavioral phenotype. The full extent of evidence may only be obtained via a thorough study of the applications of physical laws into biology and medicine, given the foundational role of the physical matter in the development and maintenance of life. A common misconception is that neurodevelopmental delays are characterized solely by an accumulation of protein loss-of-function incidences in the central nervous system area, rather than a disruption of neuronal region developmental rates. For example, an overexpression of neuronal proteins related to cognition factors that is accompanied by an underexpression of proteins related to social behavior is phenotypically manifested in people with sharper rational thinking and poorer communication skills, with an impact on the abilities of decision-making as well, and this tends to be regarded as a classical example of the clinical manifestation of neurodevelopmental delays. More severe forms of autism spectrum disorder tend to implicate a more generalistic impact upon the developmental rates of key human behaviors, including those related with the development and maintenance of social and intimate relationships. Likewise, more severe implications upon regular developmental rates tend to bring a major impact upon the healthy function of related functions. As a result, lower functioning forms of neurodevelopmental delays bring fresh concerns with regards to a possible uncanny increase of incidences of deviated sexuality during years of adulthood, and in the worst cases, such deviations may lead to the planning and development of criminal activity, particularly in individuals who are or become prone to breaking the moral code that established the civilized society. What holds the concerns valid is the exponential increase in the number of lower functioning autism cases throughout the world and the major signs that such an increase will continue with a full force. One major solution to this potential future problem would be a much higher investment into awareness of the exact genetic and environmental factors that directly or indirectly result in the onset of lower-functioning autism and of the current research efforts to develop the matching support for people found in situations as such. The most important aim of future research with regards to such a scenario would be a separation of disease and disability with choices made to step into more severe forms of disease and into areas that are outside of the established moral circle.

Neuromuscular symptoms in long COVID patients are common. Since adequate diagnostics are still missing, investigating muscle function might be beneficial. The holding capacity (maximal isometric Adaptive Force; AFisomax) was previously suggested to be especially vulnerable for impairments. This longitudinal, non-clinical study aimed to investigate the AF in long COVID patients in recovery process. AF parameters of elbow/hip flexors were assessed in 17 patients at three timepoints (pre: long COVID state, post: immediately after first treatment, end: recovery) by an objectified manual muscle test. The tester applied an increasing force on the limb of the patient, who had to resist isometrically for as long as possible. The intensity of 13 common symptoms were queried. At pre, patients started to lengthen their muscles at ~50% of the maximal AF (AFmax), which was then reached during eccentric motion, indicating unstable adaptation. At post and end, AFisomax increased significantly to ~99% and 100% of AFmax, respectively, reflecting stable adaptation. AFmax was statistically similar for all three timepoints. Symptoms intensity decreased significantly from pre to end. In conclusion, maximal holding capacity seems to be impaired in long COVID patients and increases with substantial health improvement. AFisomax might be a suitable sensitive functional parameter to assess long COVID patients and to support therapy process.

Though Cyber Physical Systems (CPS) become very popular in last the decade, dependability of CPS is still a critical issue and related survey is rare. We try to spell out the jigsaw of technologies and figure out the technical trends of dependable self-managing CPS. This survey first recalls the motivation and the similar concepts. By analyzing four generic architectures, we summarize the common characteristics and related assurance technologies, and propose a more generic environment-in-loop processing flow of CPS and a formal interaction flow between physical space and cyber space. Further, the similarity between correctness and dependability is formally analyzed and the new five research questions of dependable self-managing CPS are presented. Then we review the critical technologies and related correctness verification & validation (V&V) methods, the architectures for dependable self-managing CPS. Further, the detail dependability management and V&V technologies are surveyed, which covers the areas of running-time fault management methods and whole life cycle V&V technologies, maintenance and available tool sets. For holistic CPS development, Modeling techniques and MDE (model driven engineering) based V&V methods are analyzed in detail. Then we complete the jigsaw of technologies and figure out the missing part. Further, we propose the technical challenges and the further direction. To our best knowledge, this is the first comprehensive survey on dependable self-managing CPS development and evaluation.

Postpartum depression is a significant health issue during the postpartum period and not only affects the woman but also has important repercussions for the newborn. Group support interventions during the postpartum period have proven effective in helping women cope with depression and improving breastfeeding rates. The aim of this study was to assess the effectiveness of a midwife-led breastfeeding support group intervention on breastfeeding rates, postpartum depression and general self-efficacy. This was a multicentric cluster randomized controlled trial with control and intervention groups and was not blinded. It was conducted in Andalusia (southern Spain) from October 2021 to May 2023. A total of 382 women participated in the study. Results showed a significant difference in exclusive breastfeeding rates at 4 months postpartum between the groups (p < 0.001). Additionally, there was a lower mean score on the Edinburgh Postnatal Depression Scale in the intervention group (12.49 ± 3.6 vs 13.39 ± 4.0; p = 0.044). Similarly, higher scores of general self-efficacy were observed among breastfeeding women at 2 and 4 months postpartum (p = 0.002 and p < 0.001, respectively). In conclusion, midwife-led breastfeeding support groups enhanced self-efficacy, prolonged breastfeeding and reduced postpartum depression 4 months after giving birth.

Background and Objective: Intimate relationships hold significant importance during emerging adulthood. This study aimed to investigate the critical determinants of perceived well-being in friendship relationships during emerging adulthood.Method: Using a quantitative approach, a sample of 726 undergraduate students from the Ferdowsi University of Mashhad were selected through convenience sampling. They completed the Interpersonal Behaviors Questionnaire (IBQ), the Compassionate and self-image goals scale (CSIGS), the Basic Need Satisfaction in Relationships Scale (BNS-RS), the Interpersonal Needs Questionnaire (INQ-10), Friendship Self-Regulation Questionnaire (SRQ-F), Interpersonal Behaviors Questionnaire-Self (IBQ-self), State Level Subjective Vitality Scale (SVS-SL), the Perceived Relationship Quality Component (PRQC), and the Basic Empathy Scale (BES). Data analysis was conducted using Partial Least Squares Structural Equation Modeling (PLS-SEM).Results: The results indicated that the critical determinants in predicting perceived well-being in friendship relationships were, in order, goals in friendship relationships, friendship self-regulation, perceived support of basic psychological needs by friends, satisfied basic psychological needs, frustrated psychological needs, and supporting the basic psychological needs of friends.Conclusion: The findings demonstrate that self-determination theory (SDT) provides a coherent framework for explaining perceived well-being in friendships.

The current article examined stoma self-care and health-related quality of life in patients with drainage enterostomy, describe clinical and sociodemographic variables and analyze the relations between all of them. Trained interviewers collected data using a standardized form that queried sociodemographic and clinical variables, collected from the electronic medical record, in addition to the Specific Self-Care for Ostomized Patients Questionnaire (CAESPO) and Stoma Quality of life (S-QoL) from January 2016 to January 2017. This was a multicenter, cross sectional study conducted in four hospitals of the province of Castellon (Spain) where 120 participants were studied. Significant differences were found in Self Care according to sex (p = .043); married participants show higher score (p < .01); also, significant differences according to work activity were observed (p < .01). Regarding the clinical variables, differences were observed according to the autonomy of care, the presence of complications in the stoma, the use of irrigation and the type of effluent (p < .01). We can highlight the importance of the skills related to self-care by ostomized patients has for a good level of quality of life related to health. In this learning process, the figure of the stoma therapist plays a very important role.

In the last years, advances in materials engineering based on adaptive electronics have found a new paradigm to optimize drawbacks in signal processing. A two-layer MnO/ZnO:Zn heterostructure envisioned for frequency adaptive electronic signal processing is synthesized by sputtering where the use of internal states allows reconfigurability to obtain new operating modes at different frequency input signals. An X-Ray Diffraction (XRD) is performed on each layer, MnO:Mn had a cubic and ZnO:Zn a hexagonal structure with preferential growth in [111] and [002] directions respectively. It is determined that the coupling in this heterojunction is compatible with a mismatch less 1 % due texture in each layer which implies a low number of defects. An electrical characterisation with an oscilloscope and signal generator was carried out to obtain the time-response signals and current-voltage (I-V) curves where no degradation is detected when changing from a range of frequencies (100 Hz – 1 MHz). An equivalent circuit is proposed to explain the effects in the interface. Finally, the MnO/ZnO:Zn heterojunction delivers states that are stable, repeatable, and reproducible and it demonstrates how the interaction of the materials can be used in adaptive device applications applying frequencies and internal states as to create new and innovative design schematics, reducing the numbers of components/connections in a system for future sustainable electronics.

Climate change is unpredictable and can change rapidly or over time; anthropogenic stressors work synergistically to strengthen their impact on vulnerable ecosystems and the fisheries sector. This study documented the past occurrence of extreme climate change impacts, such as supertyphoon Pablo (ST: Bopha), which severely affected Baganga, Davao Oriental, about ten years ago. A mixed-methods data collection was used, combining semi-structured interviews with small-scale fishers (N=120) in four fishing villages and focus group discussions (N=4, participated by about 4 focus groups) on assessing the impacts and their adaptation after climatic events occurred. Our findings indicate that the four selected fishing villages were exposed to common factors of climate change events that destroyed many lives and their livelihood. Including a decrease in catch per trip and the fishing operations disruption (98%). However, due to the impact of the supertyphoon, small-scale fishers in Baganga developed psychological distress (emotional 44% and physical 24%) due to the extreme event or disaster and economic losses (32%). In order to survive, their adaptation strategies relied on government and non-government-provided provisions and projects, e.g., planting trees, constructing a sea wall with wave breakers, cash for work, and tourism as an alternative livelihood. Despite this, our study revealed that the community had limited knowledge on the impacts of climate change, lack clear management goals especially for fishers, and the widespread use of illegal fishing gears. The local government must strongly implement rules, policies, and adaptation measures that will contribute to better and more resilient communities. This can help them to withstand future shocks and provide sustainable fisheries resources that will benefit all the fishers. The study will be useful to policymakers, disaster management group and organization that will help assist affected communities highly affected by climatic events.

To describe the hysteresis nonlinearities in smart actuators, numerous models have been presented in the literature, among which the Preisach operator would be the most effective one due to its capability in capturing multi-loop or sophisticated hysteresis curves. When such an operator is coupled with uncertain nonlinear dynamics, especially in noncanonical form, it is a challenging problem to develop techniques to cancel out the hysteresis effects, and at the same time achieve asymptotic tracking performance. To resolve this problem, in this paper, we investigate the problem of iterative inverse-based adaptive control for an uncertain noncanonical nonlinear systems with unknown input Preiasch hysteresis, and a new adaptive version of the closest match algorithm is proposed to compensate for the Preisach hysteresis. With our scheme, the stability and convergence of the closed-loop system can be established. The effectiveness of the proposed control scheme is illustrated by simulation and experiment results.

The present paper provides a new definition for dual spline curves in a geometric intuitive way based on adaptive curve refinement techniques. The dual spline is an implementation of the interpolatory subdivision scheme for curve modeling, which is comprised of polynomial segments of different degrees. Specially, the dual spline curves are mainly aims to solve the difficult geometry defeaturing problems in the existing computer-aided technology. Dual spline curves maintain various desirable properties of conventional curve modeling methods, such as local adaptive subdivision, high interpolation accuracy and convergence, continuous and discontinuous boundary approximation. By adding fictitious and intrinsic nodes inside or at the vertices of interpolation elements, the dual spline is flexible and convenient for approximating a set of ordered points or discrete segments. Combined with the Lagrange interpolation polynomial and meshless method, the proposed approach is capable of approximating the non-smooth boundary for geometry defeaturing. Experimental results are given to verify the validity, robustness, and accuracy of the proposed method.

The work focuses on the possibility of application of tunable magnetic devices in electrical power systems with adaptive features. The idea underlying this type of operation, which depends on interaction with magnetic fluxes in a quassi-linear range of magnetic core characteristics, is a new approach to the design of magnetic elements. The suitable examples of adaptive electrical power systems can be devices for improving the quality of electricity. When used in compensators of reactive power or both reactive and distortion power, tunable magnetic devices offer much wider possibilities for the compensation, compared to “standard“ solutions, involving compensators based on fixed inductors. Nevertheless, the application of the proposed device in electrical systems such as is only an example of its possible implementations in the area of electrical power. In this work the following issues are covered: exemplary solution of an adaptive ‘passive’ compensator, rules for the operation of tunable magnetic device, and results investigation of the laboratory model of an electrical system based on such device.

Deep convolutional neural networks (CNN) are favored methods widely used in medical image processing due to their assured shown performance. Recently, the emergence of new lung diseases and the possibility of early detection of their symptoms has attracted many researchers to classify diseases by training deep CNNs on lung CT images. The trained networks are expected to distinguish between lung indications in different diseases, especially at the early stages of them. With the hope of achieving this purpose, we proposed an efficient deep CNN called AFEX-Net with adaptive feature extraction layers that successfully extract distinguishing features and classify chest CT images. The efficiency of the proposed network has two aspects: it is a lightweight network with low number of parameters and fast training and it has adaptive pooling layers and adaptive activation functions to increase its level of compatibility to the input data. The proposed network has been evaluated on a dataset with more than 10K chest CT slices, while an efficient pre-processing method is developed to remove any bias from the images. Additionally, we evaluated the performance of the proposed model on the public COVID-CTset dataset to prove the generalisability of our model. The obtained results confirm the competence of the proposed network in confronting medical images, where prompt and accurate learning is required.

The impacts of climate change are already felt across the globe, and (SIDS) are at the forefront. Small Islands Developing States (SIDS) are extremely vulnerable to climate change and adaptation is crucial, however they often lack funding or the fiscal capacity to make the necessary invest-ments and require support from climate finance instruments. The Green Climate Fund (GCF) was designed with the objective of achieving a “paradigm shift” towards low-carbon and climate resilient country-driven development pathway. Despite the amounts invested, assessing the impacts of climate finance on adaptation and adaptive capacity, particularly at the institutional level remains a challenge. Researchers identified two key components for more efficient adapta-tion policies at the national level: the degree of adaptation mainstreaming and institutional adaptive capacity. In SIDS, institutional capacity at the national level is seen as a key component to achieve the objectives of climate change strategies, and is supported by several programmes, including the Green Climate Fund Readiness Preparatory Support Programmes. However, to date few studies have analysed the linkages between climate finance, adaptation mainstreaming and adaptive institutional capacity. Through the review of the Readiness Grants and semi-structured interviews in three Caribbean SIDS, this research assess how climate finance may promote in-stitutional change through the mainstreaming of adaptation policies at the national level and contribute to more institutional adaptive capacity. It shows that the grants had a positive impact, which can be limited to by the strength of the institutions in place. These results demonstrate that access to climate finance can create a window of opportunity for countries to accelerate institu-tional change and allow to make recommendations on how to maximise the impacts adaptation funds. More in-depth studies would be needed to examine the complementary influence of the different climate finance flows (multilateral or bilateral) and their interplay with national institu-tional mechanisms.

Recently, two pig-to-human kidney transplants and a pig-to-human heart transplant were completed. The kidney trials involved a patient who was deceased and a patient who was brain dead. They seemed to indicate that pig kidneys can be at least somewhat functional in humans. However, patients still have to be under severe immunosuppression - and the first patient to receive a porcine heart passed away after two months. It is difficult to know exactly which proteins we need to overexpress or underexpress/knockout in a porcine organ to negate the human recipient’s immunological response to it. And testing different porcine organ genetic modifications in baboons can cost around $500,000 per transplant. But there might be a way to decrease immunogenicity where we don’t have to worry so much about modifying the animal’s organs genetically. First, however, we would have to prevent complement factor-mediated lysis of the porcine vascular endothelial cells, which we have made much progress on with triple knockout animals. Then, we could modify the porcine organ so that the cells of said organ secrete a small molecule or peptide that acts as a chemorepellent for the host immune cells. The host immune cells can be modified via bone marrow transplant or vector delivery to express the chemorepulsion receptor.

The compelling effects of climate change on forests may have been underestimated in the past few decades in practical forestry. Although the first attempts to draw attention to this complex problem appeared almost half a century ago, the debate has been conceptual rather than experimental and applicative. At first glance, the con-cerns were mainly related to sustainable forest management (SFM) issues, which obviously needed attention. Over time, the effects of climate change have been mainly considered in the context of the SFM; they started from various and somewhat different scales and goals. Over time, more research and awareness of the im-portance of SFM under the pressure of climate change have led to the development of a clearer field that can be defined as ‘adaptive forest management’ - to climate change. One of the characteristics of this discipline is to be featured by the absence of univocal methods and / or objectives to be pursued but to identify, verify, and adapt methods to the various climatic and forest types and conditions found in the field. Therefore, this work shows some phases of forest planning and management concepts and criteria over time and recalls some innovative and / or adaptive methods related to the approach to forest planning and management under climate change

In the past two decades, metaheuristic optimization algorithms (MOAs) have been increasingly popular, particularly in logistic, science, and engineering problems. The fundamental characteristics of such algorithms are that they are dependent on a parameter or a strategy. Some online and offline strategies are employed in order to obtain optimal configurations of the algorithms. Adaptive operator selection is one of them, and it determines whether or not to update a strategy from the strategy pool during the search process. In the filed of machine learning, Reinforcement Learning (RL) refers to goal-oriented algorithms, which learn from the environment how to achieve a goal. On MOAs, reinforcement learning has been utilised to control the operator selection process. Existing research, however, fails to show that learned information may be transferred from one problem-solving procedure to another. The primary goal of the proposed research is to determine the impact of transfer learning on RL and MOAs. As a test problem, a set union knapsack problem with 30 separate benchmark problem instances is used. The results are statistically compared in depth. The learning process, according to the findings, improved the convergence speed while significantly reducing the CPU time.

The colossal depths of the deep neural network sometimes suffer from ineffective backpropagation of the gradients through all its depths. Whereas, The strong performance of shallower multilayer neural structures prove their ability to increase the gradient signals in the early stages of training which easily gets backpropagated for global loss corrections. Shallow neural structures are always a good starting point for encouraging the sturdy feature characteristics of the input. In this research, a shallow, deep neural structure called PrimeNet is proposed. PrimeNet is aimed to dynamically identify and encourage the quality visual indicators from the input to be used by the subsequent deep network layers and increase the gradient signals in the lower stages of the training pipeline. In addition to this, the layerwise training is performed with the help of locally generated errors which means the gradient is not backpropagated to previous layers, and the hidden layer weights are updated during the forward pass, making this structure a backpropagation free variant. PrimeNet has obtained state-of-the-art results on various image datasets, attaining the dual objective of (1) compact dynamic deep neural structure, which (2) eliminates the problem of backwards-locking. The PrimeNet unit is proposed as an alternative to traditional convolution and dense blocks for faster and memory-efficient training, outperforming previously reported results aimed at adaptive methods for parallel and multilayer deep neural systems.

With the introduction of HTTP/3, whose transport is no longer the traditional TCP protocol but the novel QUIC protocol, research for solutions to the unfairness of Adaptive Streaming over HTTP (HAS) has become more challenging. That is, because of different transport layers, the HTTP/3 may not be available for some networks and the clients have to use HTTP/2 for their HAS applications instead. Therefore, the scenario that HAS over HTTP/3 (HAS/3) compete against HTTP/2 (HAS/2) must be considered seriously. However, there have been a shortage of investigations on the performance and the origin of the unfairness in such a cross-protocol scenario in order to produce proper solutions. Therefore, this paper provides a performance evaluation and root-cause analysis of the cross-protocol unfairness between HAS/3 and HAS/2. It is concluded that, due to differences in the congestion control mechanisms of QUIC and TCP, HAS/3 clients obtain larger congestion windows, thus requesting higher video bitrates than HAS/2. As the problem lies in the transport layer, existing client-side ABR-based solutions for the unfairness from the application layer may perform suboptimally for the cross-protocol case.

This paper presents a comprehensive and adaptive framework for orchid conservation programs illustrated with data from published and unpublished case studies. There is a specific focus on West Australian terrestrial orchids, but many of the approaches have universal relevance. Aspects of the framework include (1) setting appropriate objectives, (2) establishing effective collaborations between scientists, volunteers and regulators to fill knowledge and funding gaps, (3) use of survey and demographics data to determine extinction risks and management requirements for species, (4) effective habitat management to overcome threats such as grazing, (5) finding potential new habitats by modelling climate and site data, (6) investigating the effectiveness of pollinators and (7) using seed baiting to detect mycorrhizal fungi. The relative cost and effectiveness of different methods used to propagate orchids for translocation are compared. Methods known to be successful, in order of complexity, include placement of seed in situ, vegetative propagation, symbiotic germination in non-sterile organic matter, symbiotic germination in sterile culture, asymbiotic sterile germination and clonal division in tissue culture. These form a continuum of complexity, cost, time required, faculties needed, as well as the capacity to maintain genetic diversity and produce seedlings preadapted to survive in situ. They all start with seed collection and lead to seed storage, living collections used as tuber banks and seed orchards, as well as translocation for conservation. They could also lead to commercial availability and sustainable ecotourism, both of which are needed to reduce pressure on wild plants. Overall, there has been a strong preference to use relatively complex, expensive and time-consuming methods for orchid conservation, despite evidence that simpler approaches have also been successful. These simpler methods, which include in situ seed placement and non-sterile germination on inorganic substrates, should be trialled in combination with more complex orchid propagation methods as part of an adaptive management framework. It is essential that orchid conservation projects harness the unique biological features of orchids, such as abundant seed production and mycorrhizal fungi which are far more widespread than their hosts. This is necessary to increase the efficiency and coverage of recovery actions for the largest and most threatened plant family.

HTTP/2 video streaming has caught a lot of attentions in the development of multimedia technologies over the last few years. In HTTP/2, the server push mechanism allows the server to deliver more video segments to the client within a single request in order to deal with the requests explosion problem. As a result, recent research efforts have been focusing on utilizing such a feature to enhance the streaming experience while reducing the request-related overhead. However, current works only optimize the performance of a single client, without necessary concerns of possible influences on other clients in the same network. When multiple streaming clients compete for a shared bandwidth in HTTP/1.1, they are likely to suffer from unfairness, which is defined as the inequality in their bitrate selections. For HTTP/1.1, existing works have proven that the network-assisted solutions are effective in solving the unfairness problem. However, the feasibility of utilizing such an approach for the HTTP/2 server push has not been investigated. Therefore, in this paper, a novel proxy-based framework is proposed to overcome the unfairness problem in adaptive streaming over HTTP/2 with the server push. Experimental results confirm the outperformance of the proposed framework in ensuring the fairness, assisting the clients to avoid rebuffering events and lower bitrate degradation amplitude, while maintaining the mechanism of the server push feature.

In this paper, the fixed-time stabilization problem for a class of uncertain chained system is addressed by using a novel nonsingular recursive terminal sliding mode control approach. A fixed-time controller and an adaptive law are designed to guarantee the uncertain chained form system both Lyapunov stable and fixed-time convergent within the settling time. The advantage of the controller based on the sliding mode is that the settling time does not depend on the system initial state. Furthermore, we use RBF neural network to estimate the uncertainty of the system. Finally, the simulation results demonstrate the performance of the control laws.

Molenaar’s manifesto on psychology as idiographic science brought the N = 1 times series perspective firmly to the attention of developmental scientists. The rich intraindividual variation in complex developmental processes requires the study of these processes at the level of the individual. Yet, the idiographic approach is all but easy in practical research. One major limitation is the collection of short interval times series of high quality data on developmental processes. In this paper we present a novel measurement approach to this problem. We developed an online practice and monitoring system which is now used by thousands of Dutch primary school children on a daily or weekly basis, providing a new window on cognitive development. We will introduce the origin of this new instrument, called Math Garden, explain its setup, and present and discuss ways to analyze children’s individual developmental pathways.

This study presents a French translation and validation of the Durand Adaptive Psychopathic Traits Questionnaire (DAPTQ), an instrument for assessing adaptive traits known to correlate with the psychopathic personality. Bilingual (French and English) individuals from France and Canada (N = 141, 52% in France, M_age = 29.73, SD = 9.09) completed both versions of the DAPTQ (French and English), alongside measurements of perceived stress, trait anxiety, authentic leadership and creativity. Correlation between the DAPTQ total and subscales across versions showed strong associations (r = .84 to .96). The DAPTQ – French version also demonstrated good internal consistency (α = .87), convergent validity, and concurrent validity. These findings support the cross-cultural equivalence of the DAPTQ and therefore its effectiveness as a valid assessment method of adaptive traits.

The concept of smart morphing blades, which can control themselves to reduce or eliminate the need for active control systems, is a highly attractive solution in blade technology. In this paper an innovative passive control system based on Shape Memory Alloys (SMAs) is proposed. On the basis of previous thermal and shape characterization of a single morphing blade for a heavy-duty automotive cooling axial fan, this study deals with the numerical analysis of the aerodynamic loads acting on the fan. By coupling CFD and FEM approaches it is possible to analyze the actual blade shape resulting from both the aerodynamic and centrifugal loads. The numerical results indicate that the polymeric blade structure ensures proper resistance and enables shape variation due to the action of the SMA strips.

Background: Low-level physical activity (PA) among Chronic Low Back Pain (cLBP) is associated with various biopsychosocial factors. This research aimed to study the predictors of PA behavior among cLBP patients. Methods: In the present study 300 eligible patients with cLBP who referred to comprehensive health service centers in the Shahid Beheshti University of Medical Sciences (SBUMS) in Tehran, Iran were random selected.To diagnose the predictors of PA behavior, all the Theory of Reasoned Action (TRA) constructs were examined as risk factors to see if they influence on the probability of PA behavior occurrence and were interpreted through odds ratio (OR). SPSS version 19 was used to analyze the data. Results: Totally 280 cLBP patients with mean age of 57.07 ±13.09 years old participated in the study. This study showed that motivation to comply significant predictor the cLBP patients for subjective norm OR (%95CI): 2.095(0.116-2.792), p-value<0.001), intention was significant predictor for perform the PA behavior OR (%95CI): 1.431(0.138-1.538), p-value <0.001), behavior beliefs could predictor for attitude OR (%95CI): 1.276(0.106-1.355), p-value= 0.002). attitude, normative beliefs, subjective norm ,and evaluation outcome behavior could predictors the cLBP patients for intention to perform the PA behavior OR (%95CI): 1.188(0.032-1.312), p-value<0.001)., OR (%95CI): 1.158(0.076-2.208), p-value=0.003) ., OR (%95CI): 1.104(0.076-1.128), p-value<0.001) ., OR (%95CI): 0.814(0.301-1.440), p-value=0.007). Conclusions: This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm). This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm). This study showed that the cLBP patients who were normative beliefs and evaluation regarding PA behavior could effect on the intention to engage in greater PA than those via other constructs (attitude and subjective norm).

Background: The practice of self-medication is common worldwide and is an important component of medical self-care. However, the practice can be dangerous and is linked to various risks for the patients and the entire community. This study aimed to estimate the prevalence and factors associated with self-medication among Bangladeshi adults. Methods: A cross-sectional survey was conducted between April and June 2021 among Bangladeshi adults (aged &gt;19 years) using convenient sampling. A total of 1320 data were collected through face-to-face interviews using a standardized questionnaire. Multivariable logistic regression analysis was used to identify factors associated with self-medication practice (SMP). Results: Overall, 41% of adults in our survey reported SMP. The most common illnesses that prompted SMP were common cold/flu (66.4%), gastric problems (65%), and headache (64.4%). The most frequent reasons for SMP were to get better services (30.59%), perceive SMP without side effects (23.28%), and save time with effectiveness (14.56%). Potential risk factors included 10 years (AOR = 1.91; 95% CI: 1.04 - 3.50) and &gt;12 years of schooling (AOR = 5.03; 95% CI: 2.27 - 11.15), being a businessman (AOR = 4.64; 95% CI: 1.74 - 12.37), having ≤6 family members (AOR = 2.13; 95% CI: 1.40 - 3.24), being a member of a social group (AOR = 1.53; 95% CI: 1.10 - 2.12), a health status check after every six months (AOR = 1.52; 95% CI: 1.08 - 2.13), and current ill-health (AOR = 1.41; 95% CI: 1.06 - 1.87). Protective factors identified included ≤30 years of age (AOR = 0.40; 95% CI: 0.17 - 0.93), and practice of modern (AOR = 0.39; 95% CI: 0.22- 0.69) and herbal (AOR = 0.45; 95% CI: 0.21- 0.97) healing types. Conclusion: More than one-third of the study participants reported practicing self-medication. Increasing the community’s awareness of the extreme consequences of SMP and not just the average experience might sway individuals away from SMP, and implementing strict jurisdiction could be a way to minimize the SMP.

When examining the crucial role of civic competence in fostering sustainable societies, economies, and ecosystems, its significance for cultivating responsible and engaged citizens in our ever more interconnected global landscape must be recognized. Notably, education influences the development of civic competence throughout the learning experience at all levels of education. This research aims to explore the evolution of students’ self-assessment in terms of civic competence and its three sub-competences. The objective is to comprehend the present status of students’ self-assessments and explore their potential impact on the formation of a sustainable society. Data were collected for this study using an assessment tool elaborated in the ESF project “Development and Implementation of the Education Quality Monitoring System” for students’ generic competences. The tool, presented in a digital form using the QuestionPro platform, measures six generic competences, including civic competence. A total of 1,166 bachelor students and 354 Master’s students participated in the study, representing 22 Latvian higher education institutions. The study’s findings indicate that civic competence in the sample is considerably lower than the other generic competences measured, with a greater dispersion of results. The results also highlight specific areas within universities that require improvement during the learning process to cultivate individuals who are sustainable and socially responsible members of society.

Self-esteem, the evaluation of one's own worth or value, is a critical aspect of psychological well-being and mental health. In this paper, we propose an active inference account of self-esteem, casting it as a sociometer, or an inferential capacity to interpret one's standing within a social group. This approach allows us to explore the interaction between an individual's self-perception and the expectations of their social environment.When there's a mismatch between these perceptions and expectations, the individual needs to adjust their actions or update their self-perception to better align with their current experiences. We also consider this hypothesis in relation to recent research on affective inference, suggesting that self-esteem enables the individual to track and respond to this discrepancy through affective states such as anxiety or positive affect. By acting as an inferential sociometer, self-esteem allows individuals to navigate and adapt to their social environment, ultimately impacting their psychological well-being and mental health.

The purpose of this study is to analyze the self-regulated learning and the ability to write scientific papers of students after learning with the Project-Based Digital Science Module (DSM). This research is a mixed methods by analyzing quantitatively and qualitatively. The research instrument used to obtain data is using a product assessment sheet and a self-regulated learning questionnaire. Data analysis uses N-Gain to determine the increase in self-regulated learning scores and student scientific writing skills scores, and uses linear regression analysis to determine the effect of self-regulated learning on student scientific writing skills. The results obtained showed N-Gain score of learning independent is 0,65 and scientific writing ability is 0,94, then significant’s score from linear regression by using SPSS is 0,22 < 0,05. This result showed an increase in self-regulated learning and the ability to write scientific papers of students on each indicator after learning using the Project-Based Digital Science Module (DSM). In addition, based on the results of data analysis, it can be seen that there is an influence of self-regulated learning on the ability to write scientific papers of students after learning using the Project-Based Digital Science Module.

X-rays production through betatron radiation emission from electron bunches is a valuable resource for several research fields. The EuAPS (EuPRAXIA Advanced Photon Sources) project, within the framework of the EuPRAXIA project, aims to provide 1-10 keV photons (soft X-rays) developing a compact plasma-based system designed to exploit self-injection processes that occur in highly nonlinear laser-plasma interaction (LWFA) to drive electron betatron oscillations. Since the emitted radiation spectrum, intensity, angular divergence, and possible coherence strongly depend on the properties of the self-injected beam, accurate preliminary simulations of the process are necessary to evaluate the optimal diagnostic device specifications. Dedicated tools for these tasks are under development: electron trajectories from particle-in-cell (PIC) simulations are currently undergoing numerical analysis through the calculation of retarded fields and spectra for various plasma and laser parameter combinations. At the same time, an analytical approach is being examined to obtain an expression for the emitted spectrum, assuming linear or quadratic electron energy variation. Code structure and some trajectories analysis results are presented.

Self-selecting the intensity of aerobic training is a perspective that allows practitioners to develop autonomy (choice of pace), improve physical fitness and achieve weight loss. To compare the effect of four aerobic training protocols on cardiometabolic and psycho-affective responses in physically inactive adult men and women. The sample consisted of 11 individuals aged 56 ± 10 years and BMI 29.2 ± 2.4 kg/m2 performed four different treadmill aerobic training protocols, including: 1-individual with self-selected intensity (ISS), 2-individual with prescribed intensity (IPI) (64-76% of HR max), 3-small group with self-select. selected intensity (SGS) and 4-small group prescribed intensity (SGP) (64-76% of HR max). Heart rate (HR), lactate (LACT), perceived exertion (RPE), affective response (FS), activation scale (FAS), enjoyment (PACES) and intention to repeat the session (INT) were analyzed. Statistical analysis used ANOVA with Tukey's post hoc test (p&lt;0.05). HR, LACT and RPE analyzes did not show significant differences between protocols. The FS showed a smaller decline in the SI protocol (p&lt;0.05), and the intention to repeat the training session was greater in the SI protocol (p&lt;0.05). Conclusion: The data allow us to conclude that the SI training protocol produced superior results in affective response, pleasure and intention to repeat the session.

Background Coronavirus disease 2019 (COVID-19) almost ceased to spread across the globe, but post Covid psychological experiences are rarely documented. It is essential to understand the individual psychological needs and challenges regarding working during the covid-19 pandemic and resilience and psychological health to continuing work in the aftermath of this crisis. Objective To explore the psychological transformation of staff nurses after COVID-19 Methods A phenomenological and purposive sampling approach was employed. Registered nurses working at the largest health organization in Qatar includes 14 health facilities were invited to participate in this study. 17 nurses who were working in the facilities and were deployed during the first, second and third wave of Covid 19 participated in the interview. Data were collected through semi-structured face-to-face interviews using quarantine measures. The interview data were analyzed using inductive thematic analysis. Results The psychological transformation of participants was summarized into three themes fear zone, Learning zone and growth zone. These themes were classified into 10 sub-themes Fear and anxiety, Influence of media and news, Frequent change in policies and guidelines were the fear zone factors .Secondly Coping mechanism, teamwork, establishing compassionate care, social acceptance, personal and professional benefits helps them learn psychologically. Thirdly growth factors such as confident & resilient, Sense of purpose and Change life perception and meaning Conclusion Working during COVID-19 imposed many challenges on nurses such as workload and stress. However, the different waves of the pandemic may increase the resilience, confidence and life meaning in nurses. Their previous experiences in handling difficult situations during the pandemic may make the nurses become more confident in dealing with stressful situations and working under pressure.

The production associated with geopolymers is more environmentally friendly because it does not emit as much carbon dioxide as when OPCs are used. In addition, research has been initiated to produce a geopolymer with very good mechanical properties without the need to cure it at high temperatures. Bio-additives such as honey, cotton or molasses were found to have a good effect on the properties of the geopolymer. The purpose of this study was to develop a composition of geopolymer modified with bioaddi-tives (introduction of plant-derived materials containing sugars) in order to improve the strength para-meters of geopolymers cured at ambient temperature or to accelerate their setting process, and to identify whether it is possible to produce geopolymers with good strength parameters without the need for annealing. In this study, fly ash (FA)-based materials were made and addi-tionally modified with cane molasses and beet molasses in varying amounts. The made samples were subjected to surface morphology analysis (SEM), mechanical properties analysis - compres-sive and flexural strengths, phase composition analysis (XRD) and absorption analysis.

In response to escalating environmental concerns and the surge in eco-friendly sports activities, this study investigates the determinants of sustained intention to participate in plogging, a prac-tice that combines jogging and litter collection. The study seeks to discern the effects of autonomy, competence, and relatedness experiences, as suggested by Self-Determination Theory, on the intent to persist in plogging. Additionally, the study probes the moderating role of eco-friendly attitudes, thereby propelling the confluence of sports and environmental research. The research methodology encompassed surveying plogging participants and analyzing their responses. The validation of these relationships was executed using multi-group structural equation modeling. The results revealed that autonomy and competence did not significantly sway extrinsic motivation, but relatedness surfaced as the most influential factor. This suggests that plogging, primarily viewed as a volunteering activity, serves more as a prosocial behavior that bolsters relationships rather than a means to enhance physical competence. The study also found that the value participants derived from plogging and their intention to continue varied based on their eco-friendly attitudes. The study concludes that voluntary participation and socializing are the core values of plogging, and understanding these values can foster healthier and more sustainable behaviors. The findings offer valuable insights for academic, practical, and policy perspectives in promoting eco-friendly sports activities.

In the study, it was aimed to develop a valid and reliable scale in order to determine the self-efficacy perceptions of classroom teachers in the field of mathematics learning difficulties. In the development process of the scale, the basic resources and the scale development studies in the literature were examined. First, a pool of substances was created. Content validity was ensured by taking the opinions of 21 expert participants in the 24 question areas determined in the item pool. Content validity index was used in content validity. In this analysis, two questions were removed from the scale. Preliminary application was made with 44 participating teachers who are experts in their fields and exploratory factor analysis was started. For exploratory factor analysis, 359 people consisting of classroom teachers in Sakarya province were studied. In the explanatory factor analysis, 5 items were removed from the scale by looking at the factor load. The factor loadings for the 17 items in the scale vary between 0.508 and 0.976. Among the comparative fit indices obtained as a result of confirmatory factor analysis, RMSEA=0.067 and Cmin/DF=1.568 were calculated. The Cronbach alpha reliability coefficient of the measurements is 0.970. The data obtained were analyzed with statistical methods using SPSS 25, AMOS 24 and Microsoft Excel programs. The validity and reliability criteria of the scale developed within the scope of the study were met.

Research for novel vaccine adjuvants remains a critical need to improve the immune responses to a recombinant vaccine antigen. Emulsion adjuvants such as AS03 and MF59, particularly in the area of Influenza vaccines, have shown antigen dose sparing and allowed reduced immunizations. It was previously demonstrated that these emulsion adjuvants can be formulated using a simpler, low shear process of self-emulsification. The role of alpha tocopherol as an immunomodulator in emulsion adjuvants is evident from the success of AS03 in the on-going covid-19 pandemic. Although, it was a challenge to formulate alpha tocopherol with a low shear process to get closer to the AS03 composition, the self-emulsified version referred as self-emulsifying adjuvant systems (SE-AS) showed comparable immune responses to AS03 when co-administered with a Quadrivalent influenza virus (QIV) vaccine. In this paper, we first optimized the SE-AS with alpha tocopherol referred to as SE-AS44 to allow sterile filtration. We compared the in vitro cytokine profile with self-emulsifying adjuvant 160 (SEA160), a squalene-only self-emulsified adjuvant with composition similar to MF59. We compared SE-AS44 and SEA160 co-administered with a recombinant cytomegalovirus (CMV) pentamer antigen, which is a less immunogenic antigen, in vivo to compare the antibody and T-cell responses, in different adjuvanted groups, in C57BL/6 mice.

Self-healing is the capability of materials to repair themselves after damage has occurred, usually by interaction between molecules or chains. Physical and chemical processes are applied for the preparation of self-healing systems. There are different approaches for these systems such as heterogeneous systems, shape memory effects, hydrogen bonding or covalent-bond interaction, diffusion and flow dynamics. Self-healing mechanisms can occur in particular by heat and light exposure or by reconnection without direct effect. The applications of these systems display an increasing trend in both R&D and industry sectors. Moreover, self-healing systems and their energy storage applications are currently getting great importance. This review aims to provide general information on recent developments in self-healing materials and their energy applications in view of the critical importance of self-healing systems for lithium-ion batteries (LIBs). In the first part of the review, an introduction about self-healing mechanisms and design strategies of self-healing materials is given. Then, selected important healing materials in the literature for the anodes of LIBs are mentioned in the second part. The results and future perspectives are stated in the conclusion section.

Background: HIV self-testing (HIVST) is one of the recommended approaches for HIV testing services, particularly for helping reach populations who would not normally access facility-based HIV testing. HIVST must be tailored to different populations to ensure uptake. Objective: The main objective of this study was to develop an acceptable HIVST delivery strategy to help improve urban men’s engagement with HIV services. Methods: We invited key stakeholders for urban men’s HIV services to participate in a co-creation workshop aimed at developing HIVST delivery approaches for urban men, using eThekwini municipality as a study setting. We conducted purposive sampling to include health care users and health care providers, representing a range of views across the public sector and voluntary sector. We employed the Nominal Group Technique (NGT) method for data collection. The NGT workshop was conducted in two consecutive phases: phase one was focused on determining barriers for men’s engagement with the current/facility-based HIV testing services; phase two was aimed at determining HIVST delivery strategies. We used the results of the NGT to design a tailored HIVST strategy for urban men in eThekwini District. Results: Participants identified the following psychological factors as the most important barriers to uptake of HIV testing services by urban men: stigma, ignorance about the importance of testing and testing process as well as fear of positive test results. Key stakeholders suggested internal motivation strategies as a potentially effective approach to support HIVST delivery strategy. Guided by the NGT results, we designed a HIVST delivery strategy that is supported by a risk communication approach Conclusion: We designed an evidence-based risk communication mobile health (mHealth) strategy coupled with SARS COV-2 self-testing tailored to improve men’s uptake of HIVST. A follow-up study to evaluate the feasibility of implementing these approaches is recommended.

Our brains process information using a layered hierarchical network architecture, with abundant connections within each layer and sparse long-range connections between layers. As these long-range connections are mostly unchanged after development, each layer has to locally self-organize in response to new inputs to enable information routing between the sparse in- and output connections. Here we demonstrate that this can be achieved by a well-established model of cortical self-organization based on a well-orchestrated interplay between several plasticity processes. After this self-organization, stimuli conveyed by sparse inputs can be rapidly read out from a layer using only very few long-range connections. To achieve this information routing, the neurons that are stimulated form feed-forward projections into the unstimulated parts of the same layer and get more neurons to represent the stimulus. Hereby, the plasticity processes ensure that each neuron only receives projections from and responds to only one stimulus such that the network is partitioned into parts with different preferred stimuli. Along this line, we show that the relation between the network activity and connectivity self-organizes to a biologically plausible regime. Finally, we argue how the emerging connectivity may minimize the metabolic cost for maintaining a network structure under the above described constraints.

This proposal describes the design and development of an interoperable application that supports green open access with long-term sustainability and improved user experience of article deposit. Introduction: The lack of library resources and unfriendly repository user interface are two significant barriers that hinder green open access. Tasked to implement the open access mandate, librarians at an American research university developed a comprehensive system called Easy Deposit 2 to automate the support workflow of green open access. Implementation: Easy Deposit 2 is a web application that is able to harvest newly publications, outreach for manuscript on behalf of the library, and facilitate self-archiving to IR. It is developed and maintained by the library and integrated with the IR. Results and Discussion: The article deposit rate is about 25% with Easy Deposit 2, which increases significantly comparing to the previous period. It also serves as a local database for faculty publications with open access status. The lesson learned is that library cannot rely on a single commercial provider for publication data due to mismatched priorities. Conclusion: Recent IT developments provides new opportunities of innovation like Easy Deposit 2 in supporting open access. Academic librarians are vital in promoting "openness" in scholarly communication such as transparency and diversity in the sharing of publication data.

The study aimed to understand the experiences of adolescent smokers who participated in a smoking cessation program based on self-reflection, and to develop the theoretical basis for constructing similar programs. The participants, ten students from middle and high schools located in cities G and S, were interviewed after the program ended. Data were collected from August to November 2019 and analyzed using a phenomenological approach to understand participant experiences in depth. The analysis revealed five major themes: ‘Uniqueness of the Program,’ ‘Perception of Smoking Cessation,’ ‘Positive Reflection on Life,’ ‘Understanding Others,’ and ‘A Search for Hope and Vision in Life.’ The findings revealed that the smoking cessation program facilitated the development of self-identity and vision, which may indirectly strengthen the motivation for adolescent smokers to quit smoking. These findings suggest the need for a life-changing smoking cessation program that enhances self-concept and self-esteem. Moreover, it highlights the importance of follow-up research to ensure effectiveness, and the need to develop programs with creative content.

While some researchers have suggested that the self-employment (SE) sector is a haven during a financial Crisis, others believe that SE is not necessarily the desired outcome, but an indicator that the labor market is tightening for some groups. Few researchers have compared the SE sector before and after the occurrence of a significant financial Crisis, especially in developed countries. This paper analyzes the determinants of entry into self-employment during the 2008 Spanish Crisis. Using data from the Encuesta de Presupuesto Familiar (EPF), results show that although the rate of SE did not experience a significant change during this time the Crisis affected people differently based on gender, with being females more affected than males. Results also suggest differences between Comunidades Autonomas in how the self-employment sector behaved during the Crisis.

Due to limitation of the binding energy of a self-gravitating matter, the radius of a body is at least twice larger than the Schwarzschild radius. The total energy is adsorbed at the body surface, giving rise of a size-dependent surface tension. Since the Hawking temperature appears to be the critical one, the black holes possess zero surface tension. Microscopic neutrino stars are also introduced.

This study aimed to know the conditioning factors of the transition process to the self-care of women diagnosed with HIV/AIDS. This qualitative study was carried out from June to September 2015 with seven seropositive women, users of a specialized service in sexually transmitted diseases in the municipality of Imperatriz, Maranhão State, Brazil. For the data collection, an individual interview was used, and data analysis was performed by content analysis delineated by Hsieh and Shannon (2005). The resources that influence the self-care in the transition process of women with HIV/AIDS are represented by personal conditioning factors, such as the meaning they attribute to the living with the disease, personal attitudes and cultural beliefs, socioeconomic status, preparation and knowledge about the disease, and by conditioning factors found in the community and society. The transition theory can provide important insights about the resources present in the adaptation process of women diagnosed with HIV so that they can perform their self-care satisfactorily.

To better understand the shape memory materials and self-healing materials, a new series of liquid-crystalline shape memory polyurethane (LC-SMPU) composites, named SMPU-OOBAm, were successfully prepared by incorporating 4-octyldecyloxybenzoic acid (OOBA) into the PEG-based SMPU. The effect of OOBA on the structure, morphology and properties has been carefully investigated. The results demonstrate that SMPU-OOBAm have liquid crystalline properties, triple-shape memory properties and self-healing properties. The incorporated OOBA promotes the crystallizability of both soft and hard segments of SMPU, and the crystallization rate of the hard segment of SMPU decreases when the OOBA-content increases. Additionally, the SMPU-OOBAm forms a two-phase separated structure (SMPU phase and OOBA phase), and it shows two-step modulus changes upon heating. Therefore, the SMPU-OOBAm shows triple-shape memory behavior, and the shape recovery ratio decreases with an increase in the OOBA content. Finally, SMPU-OOBAm shows self-healing properties. The new mechanism can be ascribed to the heating-induced “bleeding” of OOBA in the liquid crystalline state and the subsequent re-crystallization upon cooling. This successful combination of liquid crystalline properties, triple-shape memory properties and self-healing properties make the SMPU-OOBAm composites with many promise applications in smart optical devices, smart electronic devices and smart sensors.

Two polyurethanes (PUs) were similarly synthesized by reacting a cycloaliphatic isocyanate with 1,4 butanediol and two polyols of different nature (polyester, polycarbonate diol) with molecular weights of 1000 Da. The PU made with polycarbonate diol polyol (YCD) was the only showing intrinsic self-healing at 20 °C. For assessing the mechanism of intrinsic self-healing of YCD, a structural characterization by molecular weights determination, infrared and X-ray photoelectronic spectroscopies, differential scanning calorimetry, X-ray diffraction, thermal gravimetric analysis and dynamic mechanical thermal analysis was carried out. It was concluded that the self-healing at 20 °C of YCD was due to non-covalent dynamic exchange interactions among the carbonate groups of the soft segments. Therefore, the chemical nature of the polyol played a key role in developing PUs with intrinsic self-healing at 20 °C.

This work is the first part of a series of papers on a comprehensive analysis of the processes of prebiotic self-organization and protophotosynthesis on the surface of semiconductor minerals and in systems of natural dispersed semiconductors. Comprehensive analysis within the framework of the "semiconductor world" concept allows integrating a variety of models on a single physical basis - from ZnS world and FeS world (based on inorganic semiconductors) to the PAH world and aromatic world (including organic semiconductors). Thus, we do not put forward a new alternative hypothesis of chemical evolution - a new "chemical world", but only integrate the evolutionary and geochemical criteria of different "chemical worlds" into a single "physical world", which gives one the opportunity to reconstruct and predict the directions of chemical evolution according to the uniform principles of physical chemistry. In the first part of this series we consider photoinduced self-organization and "photo-controlled" evolution of the early protobiological systems performing solar energy conversion on the surface of dispersed semiconductor minerals capable of (photo)catalyzing and photosensitizing prebiological processes. The latter include a transition from the elementary cycles of photocatalytic reactions on the surface of semiconductors to protophotometabolic cycles and protophotosynthesis, from photophysical processes on the surface of mineral semiconductors to photoinduced membrane potentials, from photocontrolled sorption on the surface of such minerals to the formation of photosensitive protomembranes and selection of photosynthetic structures. The evolutionary approach to the analysis of protobiological mechanisms and protobioenergetics within the framework of the "semiconductor world" concept provides a new approach to the study of the last common point of divergence of protobiological systems, where the emergence principles of different energy supply schemes (like the energy source-specific photoautotrophy and substrate-specific chemoautotrophy) merge at the physicochemical level. The proposed integrating scheme is consistent with most biological, geological, and physicochemical concepts, which ensures its complete cross-checking and internal consistency.

In 2015, the Self-Action Leadership Theory—a qualitative, nomological expansion of self-leadership theory rooted in atmospheric and astronomical metaphor aimed at expanding the personal freedom of individuals, organizations, and nations by bolstering the existential growth of individuals through a series of Maslow-esque stages of holistic, personal development. This article introduces an accompanying, practitioner-based Model of Self-Action Leadership (SAL) aimed at the implicit enhancement of a holistic range of administrative processes through explicit training, mentoring, and coaching in the model’s general and universally-applicable principles and practices. The SAL model produces an original construct of personal leadership practice that builds upon the extant self-leadership academic canon, which dates back to 1983 (Manz, 1983). It also provides an analogue to four of the five core processes of Project Management by positioning a self-action leader (an individual) as the ongoing “project” at hand. The SAL Model is rooted in action research and was developed through a variety of self-oriented, action research projects in conjunction with a comprehensive, qualitative, analytical autoethnographic study of a scholar’s life experiences.

Nanometer-curved surfaces are abundant in biological systems as well as in nano-sized technologies. Properly functionalized polymer-grafted nanoparticles (PGNs) adhere to surfaces with different geometries and curvatures. This work explores some of the energetic and mechanical characteristics of the adhesion of PGNs to surfaces with positive, negative and zero curvatures using Coarse-Grained Molecular Dynamics (CGMD) simulations. Our calculated free energies of binding of the PGN to the curved and flat surfaces as a function of separation distance show that curvature of the surfaces critically impacts the adhesion strength. We find that the flat surface is the most adhesive, and the concave surface is the least adhesive surface. This somewhat counterintuitive finding suggests that while a bare nanoparticle is more likely to adhere to a positively curved surface than a flat surface, grafting polymer chains to the nanoparticle surface inverts this behavior. Moreover, we studied the rheological behavior of PGN upon separation from the flat and curved surfaces under external pulling force. The results presented herein can be exploited in drug delivery and self-assembly applications.

The goal of this work is to show that the underlying symmetries of effective field theory can be traced to the onset of self-similarity. In particular, we argue that the scale-free structure of fractal geometry lies at the heart of invariance principles in classical and Quantum Field Theory.

COVID-19, caused by the SARS-CoV-2 virus, has spread around the world and killed around 6.9 million people. Rapid and accurate diagnosis is essential for preventing and controlling the disease, reducing transmission and consequently saving lives. RT-PCR is the gold standard test used to detect the disease. However, the test is expensive and the result is time-consuming, which makes mass testing difficult, especially in countries with limited resources. In addition, the test has high analytical specificity and low diagnostic sensitivity, which leads to false-negative results. Several studies in the literature report the presence of hematological and biochemical alterations in infected patients and use these alterations with machine learning algorithms to help diagnose the disease. Therefore, this article presents the results obtained by different neural network architectures based on Adaptive Resonance Theory (ART) for the diagnosis of COVID-19. The study was conducted in two distinct stages: the first consisted of selecting the best ART network among several, using three open-access datasets and comparing the results with the literature. In the second stage, the chosen model was tested on a dataset containing patients from various hospitals in four countries. In addition, the model was subjected to external validation, including data from a country not present during the training and adjustment of the model, in order to validate the robustness and generalization capacity of the model. The results obtained by the ART networks in this study are promising, outperforming not only classical models, but also the deep learning models often used in the literature. Validation on data from different countries strengthens the model’s reliability and effectiveness.

This paper proposed an optimal tracking control scheme through adaptive dynamic programming(ADP) for a class of partially unknown discrete-time nonlinear systems based on radial basis function neural network(RBF-NN). In order to acquire the unknown system dynamics, we use two RBF-NNs, the one is used to construct the identifier, and the another is used to directly approximate the steady-state control input, where a novel adaptive law is proposed to update neural network weights. While the optimal feedback control and the cost function are derived via feedforward neural networks approximating, it is proposed to regulate the tracking error, the critic network and the actor network are then trained online to obtain the solution of the associated Hamilton–Jacobi–Bellman (HJB) equation being built under the ADP framework. Simulations verify the effectiveness of the optimal tracking control technique using the neural networks.

In this paper, an adaptive proportional and integral (PI) controller for speed control of electric drives is developed. The adaptive PI controller is designed based on model reference adaptive identification and the Lyapunov function so that the system stability can be proved. To identify the inertia, the viscous friction torque coefficient, and the load torque simultaneously and smoothly, sinusoidal speed reference is usually employed. A modified motion equation considering the low-pass filter for feedback speed and the Coulomb friction torque is introduced and the relationship between the identified parameters and their actual values are clarified. A high-precision digital simulation model is built based on MATLAB/Simulink. The effectiveness of the proposed method is verified by simulations under various conditions.

In this paper, we present findings from a qualitative interview study, which highlights the difficulties and challenges with quantifying carbon emissions, and discusses how to move productively through these challenges by drawing insights from studies of deep uncertainty. Our research study focuses on the digital sector and was governed by the research question: how do practitioners researching, working or immersed in the broad area of sustainable digitisation (researchers, industry, NGOs, and policy representatives) understand and engage with quantifying carbon? Our findings show how stakeholders struggled to measure carbon emissions across complex systems, the lack of standardisation to assist with this, and how these challenges led stakeholders to call for more data to address this uncertainty. We argue that calls for more data to address this uncertainty obscure the fact that there will always be uncertainty, and that we must learn to govern from within it.

The implementation of neural networks (NN) on edge devices enables local processing of wireless data but faces challenges such as high computational complexity and memory requirements when deep neural networks (DNN) are used. Shallow neural networks customized for specific problems are more efficient, requiring fewer resources, and resulting in a lower latency solution. An additional benefit of the smaller network size is that it is suitable for real-time processing on edge devices. The main concern with shallow neural networks is their accuracy performance compared to DNNs. In this paper, we demonstrate that a customized adaptive activation function (AAF) can meet the accuracy of a DNN. We designed an efficient FPGA implementation for a customized segmented spline curve neural network (SSCNN) structure to replace the traditional fixed activation function with an AAF. We compared our SSCNN with different neural network structures such as real-valued time delay neural network (RVTDNN), augmented real-valued time delay neural network (ARVTDNN), and deep neural networks with different parameters. Our proposed SSCNN implementation uses 40% fewer hardware resources and no Block RAMS compared to the DNN with similar accuracy. We experimentally validate this computationally efficient and memory-saving FPGA implementation of SSCNN for digital predistortion of RF power amplifiers using the AMD/Xilinx RFSoC ZCU111. The implemented solution uses less than 3% of the available resources. The solution also enables an increase of the clock frequency to 221.12 MHz, allowing the transmission of wide bandwidth signals.

In high-traffic harbor waters, marine radar frequently encounters signal interference stemming from various obstructive elements, thereby presenting formidable obstacles in the precise identification of ships. To achieve precise pixel-level ship identification in the complex environments, a customized neural network-based ship segmentation algorithm named MrisNet is proposed. MrisNet employs a lightweight and efficient FasterYOLO network to extract features from radar images at different levels, capturing fine-grained edge information and deep semantic features of ship pixels. To address the limitation of deep features in the backbone network lacking detailed shape and structured information, an adaptive attention mechanism is introduced after the FasterYOLO network to enhance crucial ship features. To fully utilize the multi-dimensional feature outputs, MrisNet incorporates a Transformer structure to reconstruct the PANet feature fusion network, allowing for the fusion of contextual information and capturing more essential ship information and semantic correlations. In the prediction stage, MrisNet optimizes the target position loss using the EIoU function, enabling the algorithm to adapt to ship position deviations and size variations, thereby improving segmentation accuracy and convergence speed. Experimental results demonstrate that MrisNet achieves high recall and precision rates of 94.8% and 95.2%, respectively, in ship instance segmentation, outperforming various YOLO and other single-stage algorithms. Moreover, MrisNet has a model parameter size of 13.8M and real-time computational cost of 23.5G, demonstrating notable advantages in terms of convolutional efficiency. In conclusion, MrisNet accurately segments ships with different spot features and under diverse environmental conditions in marine radar images. It exhibits outstanding performance, particularly in extreme scenarios and challenging interference conditions, showcasing robustness and applicability.

Climate change impacts threaten sustainable development efforts. The magnitude of the impacts, however, varies with socio-ecological characteristics of locations. This is the reason there is con-sensus on the necessity for climate change adaptive capacity building that is country driven, based on and responsive to local needs. However, information on context specific capacity building needs in developing countries is not readily available. The objective of this study was to establish location specific awareness, training, educational, research and technology capacity building needs for climate change adaptation among small-holder farmers in Uganda. Structured interviews were undertaken with 465 households from five agro-ecological zones selected based on the level of vulnerability of agricultural systems to the main climate variation and change hazards. Results reveal substantial capacity building needs in all the zones. Majority of the farmers needed capacity building for interventions on soil water conservation practices for adapting to drought and un-predictable rainfall. For all zones, education, research, and technology were perceived as key needs. However, the needs varied among zones. These results demonstrate the importance of context specificity in adaptation efforts. The study provides agro-ecological and social system specific in-formation for climate change adaptation planning and policy interventions for effective capacity building

In this paper, the filtering method of biomedical image denoising is described comprehensively. Firstly, it introduces the biomedical image denoising, describes the relationship between biomedical image denoising and medical care, introduces the filtering methods, the filtering methods of biomedical image denoising, the challenges encountered by the current filtering methods, and other application fields of filtering methods. Firstly, the background of biomedical image denoising is introduced. Biomedical image denoising is a challenge. Different imaging modes have different noise characteristics, and noise levels can vary greatly depending on the specific application. Secondly, it describes that biomedical image denoising plays an important role in medical care, and the biomedical image directly affects the patient's diagnosis, treatment plan and the overall quality of medical care service. Then the filtering method is introduced in detail, describing the core concepts and related features of linear filtering, nonlinear filtering and frequency domain filtering, and then focusing on the adaptive filtering method, describing the characteristics, conditions of use, common algorithms and advantages of adaptive filtering method. Then the filter methods of biomedical image denoising are introduced, and the core concepts of Gaussian filter, median filter, total variation denoising and Wiener filter are introduced respectively. Then, the challenges encountered by filtering methods are described, such as the accurate selection of filters, the balance between noise reduction and image detail preservation are introduced. Finally, the application of filtering method in other fields is mentioned, such as audio processing, speech recognition and so on. In summary, this paper comprehensively expounds the denoising and filtering methods of biomedical images, the filtering methods of medical image denoising, the relationship between medical image denoising and medical care, and the challenges encountered by filtering methods.