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.

The runtime environment is an important concern for self-adaptive systems (SASs). Although researchers have proposed many approaches for developing SASs that address the issue of uncertain runtime environments, the understanding of these environments varies depending on the objectives, perspectives, and assumptions of the research. Thus, the current understanding of the environment in SAS development is ambiguous and abstract. To make this understanding more concrete, we describe the landscape in this area through a systematic literature review (SLR). We examined 128 primary studies and 14 unique environment models. We investigated concepts of the environment depicted in the primary studies and the proposed environment models based on their ability to aid in understanding. This illustrates the characteristics of the SAS environment, the associated emerging environmental uncertainties, and what is expressed in the existing environment models. This paper makes explicit the implicit understanding about the environment made by the SAS research community and organizes and visualizes them.

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.

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.

Research has shown a protective association between social support and depression, depression among stroke patients, and health impacts of depression. Yet not much is known on the effect of social support on depression among stroke patients. This review aims to summarize the current research examining the association between social support and depression among stroke patients. A literature search was performed in PubMed to find original peer-reviewed journal articles from 2016 to Mar. 12, 2023 that examined the association between social support and depression among stroke patients. The search terms were depression and "social support" and stroke, which lead to 172 articles. After abstract review, seven observational studies that studied the target association among stroke patients were selected. One additional study was found using PsycINFO as a complementary source with the same search strategy and criteria. Overall, a negative association was found between social support and depression among stroke patients in seven studies, with more social support leading to lower rates of depression post-stroke. One study found that social support was positively related with depression, but the result was nonsignificant. Overall, the results of recent studies suggest that social support is negatively associated with depression among stroke patients. In most studies, this association was statistically significant. The findings suggest the importance of improving social support perceived by stroke patients in the prevention of depression after the occurrence of stroke.

BACKGROUND: Social determinants of health are conditions in the environments that significantly influence the health of individuals and communities. Various factors like income, socio-economic status, stress, customs, culture, healthcare services largely affect the health status of individuals and communities. OBJECTIVE: This study was carried out to identify the factors responsible for the social determinants of health among residents of Buari Ishola Isibo in Ede North Local Government Area, Osun State, Nigeria. METHODOLOGY: A descriptive cross – sectional study was utilized, and multi-stage random sampling method was employed in selecting 60 households of Buari Ishola Isibo in Ede North Local Government Area, Osun State, Nigeria. Questionnaire was used for data collection and data was analyzed using SPSS version 21. RESULTS: The respondents’ ages were between 15 – 60 years and above. 29 (48.3%) were between 31 – 60 years old, 31 (52.0%) were males, 40 (66.7%) were married, 57 (95.0%) were Islam and Yoruba. 29 (48.3%) earned 10,000 – 30,000 monthly, 23 (38.3%) had secondary school education, 39 (65.0%) were traders and 27 (45.0%) have 6 – 10 households respectively. Of the 60 respondents, 34 (56.7%) visit government health facility when they are sick, 21 (35.0%) make use of hand dug well in their compound as the source of their water supply, 27 (45.0%) uses flush water closet toilet as the means of disposing off their excreta, 36 (60.0%) lives in concrete with zinc roof house, 27 (45.0%) have between 6 – 10 rooms in their house, 55 (91.7%) sleep in a room and 21 (35.0%) make use of charcoal as source of fuel for their cooking. CONCLUSION: Poverty or lower income affects the health of individuals through a person’s health throughout their life span. Issues that affect social determinants of health of individuals and community in a negative way should be accosted with all seriousness.

Although adults and children differ in self-versus-other perception, the developmental perspective on this discriminative ability is missing. We compared neural activation of self-vs-others in 39 participants of 4 different age groups (4 yo. to adulthood). Two brain regions, the MCC and right postcentral gyrus, exhibited respectively non-linear and gradual gains in discriminative abilities for self-vs-others stimuli. These regions play critical roles in self-referential processing, empathy, and social cognition. Nine brain regions showed linear increase for others-vs-self stimuli. These regions are associated with multisensory processing, somatosensory skills, complex visual stimuli, self-awareness, empathy, theory of mind, and social recognition. Understanding the neural basis of self-vs-others discrimination during development can inform when and how social contexts support learning processes during childhood and adolescence.

COVID-19 may not be a ‘youth disease’ but nevertheless impacts the life of young people dramatically, loneliness and negative mood being an unexpected additional pandemic. Many young people rely on social media for their feeling of connectedness with others. However, social media are suggested to have many negative effects on people’s anxiety. Instead of self-disclosing to others, design may develop alternatives to employ social robots for self-disclosure. In a follow-up on Duan et al. (2021), we report on a lab experiment of self-disclosing negative emotions to a social-media group as compared to writing a conventional diary journal or to talk to an AI-driven social robot after negative mood induction (i.e. viewing shocking earthquake footage). Participants benefitted the most from talking to a robot rather than from writing a journal page or sharing their feelings on social media. Self-disclosure on social media or writing a journal page did not differ significantly. In the design of interventions for mental well-being, human helpers thus far took center stage. Based on our results, we propose design alternatives for an empathic smart-home, featuring social robots and chatbots for alleviating stress and anxiety: a social-media interference chatbot, smart watch plus speaker, and a mirror for self-reflection.

Exisitng studies argue life chances are, in part, vertically reproduced. Such a statement is applicable to the Chinese contexts as, but not limited to, parental hukou status, to some extent, determines the life chances their children receive. In this essay, the author would like to introduce human capital theory (HCT) and educational signalling theory (EST), and assess how the applications of each of these two models can enrich the understanding of vertical reproduction of individuals’ social mobility opportunities. The author would also present the limitations of each of these two models when addressing relevant Chinese contexts.

The aim of this study is to investigate “resource portfolios” and total capital, as well as the degree of those resources capitalization, which representatives of different social classes in the modern Latvia have at their disposal. The amount and structure of “resource portfolio” and total capital of different social classes studied using the resource-asset-capital approach. The article presents results of the sociological survey of social stratification in modern Latvia on the example of its one region – Latgale (2019, n = 798, representative sample of the adult population), identifying social classes based on two objective (income and education) and one subjective (self-identification of respondents) criteria. Based on the example of the lower working class and the middle class, the authors proved that representatives of these polar social classes have a total capital of different amount, which is determined by two main reasons: 1) the lower working class has statistically significantly smaller “resource portfolio” than the middle class; 2) the lower working class is not so successful as the middle class in activating the resources at their disposal, turning them into their capital. These statistically significant two-level differences have to be considered when pursuing social policies on reducing differences between social classes.

This paper contributes to the current debate about Benefit Corporations, presenting the development of this organisational model in Italy, the first country to introduce this hybrid form after the US. Grounded on an institutional logic perspective, it provides a picture of the institutional dynamics that have characterised the rise of this new entrepreneurial form outside the US. The analysis provides an in-depth foundational study of the Italian case and highlights the relevant influence of different institutional pressures in explaining the rise of and constraints in the diffusion of Benefit Corporations. Empirically, it combines secondary data, available from different public sources, and primary data collected through interviews with a series of knowledgeable informants. Based upon the analysis, two aspects appear peculiar for the development of Benefit Corporations in Italy: the interplay between the Benefit Corporation legal form and the certified B Corp model, and the rise of tensions between social entrepreneurship and the third sector ecosystem. This paper concludes that the peculiar institutional pressures leading to the birth of Benefit Corporations in Italy may be source of permanent tensions and of concern for the diffusion of the model.

Nowadays, many political campaigns are using social influence (SI) in order to convince voters to support/oppose a specific candidate/party. In election control via SI problem, an attacker tries to find a set of limited influencers to start disseminating a political message in a social network of voters. A voter will change his opinion when he receives and accepts the message. In constructive case, the goal is to maximize the number of votes/winners of a target candidate/party, while in destructive case, the attacker tries to minimize them. Recent works considered the problem in different models and presented some hardness and approximation results. In this work, we consider multi-winner election control through SI on different graph structures and diffusion models, and our goal is to maximize/minimize the number of winners in our target party. We show that the problem is hard to approximate when voters' connections form a graph, and the diffusion model is the linear threshold model. We also prove the same result considering an arborescence under independent cascade model. Moreover, we present a dynamic programming algorithm for the cases that the voting system is a variation of straight-party voting, and voters form a tree.

Social media gives researchers an invaluable opportunity to gain insight into different facets of human life.Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and so on are common tools of conversations grouping. On the other hand, crowdsourcing is a concept that involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We build upon earlier work where we analyzed online forum posts from various geographical regions in the USA and Canada and characterized the readability scores of such users. Specifically, we collected 1000 tweets from the members of the US Senate and computed the Flesch-Kincaid readability score for the Senators. Comparing the Senators’ tweets to the ones from average citizens, we note the following. 1) US Senators’ readability based on their tweets rate is much higher affirming the gap between the academic performance of US Senators and their average citizen, and 2) the immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.

The number of people owning vehicles has been steadily growing, resulting in increased numbers of vehicles on the roads, making roads more congested, and increasing the risk of accidents. In addition, heavy rain, snow, and fog have increased due to abnormal weather caused by global warming. These bad weather conditions can also affect the safety of vehicles and drivers. The need to disseminate safety messages on the social internet of vehicles due to these problems has been steadily increasing. In this paper, we propose an efficient safety message dissemination scheme that focuses on urban environments with high vehicle density and mobility to address these problems. The proposed scheme reduces packet loss by considering frequent cluster departures and subscriptions through an efficient cluster management technique. In a vehicle-to-vehicle environment, the dissemination of safety messages is divided into an intra-cluster and an inter-cluster emergency as well as general safety message dissemination technique. In a vehicle to infrastructure environment, the proposed scheme reduces the number of processing requests and duplicate messages made to roadside units (RSUs) through a request operation process for each vehicle and an RSU scheduling technique. We conducted several performance evaluations of message packet loss and the number of RSU processing requests to demonstrate the superiority of the proposed scheme.

Fragile X syndrome (FXS), the most common monogenic cause of inherited intellectual disability and autism spectrum disorder, is caused by a full mutation (>200 CGG repeats) in the Fragile X Messenger Ribonucleoprotein 1 (FMR1) gene. Individuals with FXS experience a wide range of challenges related to social interaction (SI). Animal models, such as Drosophila melanogaster model for FXS where the only ortholog of human FMR1 (dFMR1) is mutated, have played a crucial role in understanding of FXS. The aim of this study was to investigate SI in the dFMR1B55 mutants using the novel Drosophila Shallow Chamber and a Python data processing pipeline based on social network analysis (SNA). In comparison with wild-type flies (w1118), SNA analysis in dFMR1B55 mutants revealed hypoactivity, fewer connections in their networks, a lower ability to efficiently transmit information, fewer alternative pathways for information transmission, a higher variability in the number of interactions they achieved, and tended to stay near the boundaries of the testing chamber. These observed alterations indicate the presence of characteristic strain-dependent social networks in dFMR1B55 flies, commonly referred to as the group phenotype. Finally, a combination of novel research tools is a valuable method for SI research in fruit flies.

During the last years our society was permanently disturbed by the coherent information waves of high amplitudes. These are waves of huge social energy. Often they are of the destructive character, a kind of information tsunami. But, they can carry as well positive improvements in the human society, as waves of decision making matching rational recommendations of societal institutes. The main distinguishing features of these waves are their high amplitude, coherence (homogeneous character of social actions generated by them), and short time needed for their generation and relaxation. We show that such social phenomenon can be modeled on the basis of the recently developed social laser theory. This theory can be used to model stimulated amplification of coherent social actions. ``Actions'' are treated very generally, from mass protests to votes and other collective decisions, as, e.g., acceptance (often unconscious) of some societal recommendations. We point to the main distinguishing features of the modern society simplifying social lasing: a) transformation of humans into social atoms - lost of individuality; b) generation by mass-media of powerful information fields leading to information overload of social atoms; c) creation of powerful social resonators based on internet Echo Chambers. In this paper, we analyze in very detail their functioning leading to increasing of the power fo the quantum information field as well as its coherence.

The meaning of a good social outcome in land use transport policy/planning has been relatively underdeveloped, compared to economic and environmental sustainability goals, but this is now changing. Cities are increasingly prioritizing reducing social exclusion, with the allied intent of providing all residents with equitable access to the benefits of their city. However, in jurisdictions that use cost-benefit analysis to guide government decision-making, this poses a challenge: monetization of the benefits of reducing social exclusion is poorly developed. An evaluation gap thus confronts benefit-cost based assessment of initiatives intended to reduce social exclusion, compared to initiatives directed towards societal economic and environmental goals. This is a particular problem for public transport services that primarily enable people at risk of mobility-related social exclusion to access more opportunities in their society (social transit). The benefits of reducing social exclusion associated with these services have not been monetized, so social transit initiatives are poorly placed to argue their merits against competing initiatives where benefit monetization is more advanced (e.g., mass public transport and roads). The paper summarizes Australian research that has developed monetized measures for the value of increased trip making, bridging/bonding social capital, sense of community and several conceptions of wellbeing, and of reducing neighbourhood disadvantage, as these contribute to reducing social exclusion. New Singaporean research on the value of trip making to support reduced exclusion is also presented, affirming the Australian trip values. Trip values are then used in case study examples to show they can make a material difference to the societal worth of initiatives intended to reduce exclusion. Using these trip values, and other pathway values linked to reducing social exclusion, closes or at least substantially reduces the evaluation gap confronting social goal achievement in land use transport policy and planning, strengthening the case for reducing exclusion. These values will assist jurisdictions that are seeking to provide their residents with more equitable access to the benefits of their city.

The evolution of digital media in adolescents has changed the patterns and motives of use and the impact on their communication choices in their social and family networks. The objectives of this study are to understand how peers communicate adopting a social network (SN) or by voice and their social desirability. After the informant consent signature, the adolescents completed a series of self-report questionnaires on the use of SN, on communication preferences, and on social desirability through online. Most of the adolescents belonged to the 17-19 age group (83.6%) and were female (68.9%). Adolescents spent more than 3 hours/day on Whatsapp and more than 2 hours/day on Instagram, while the use of Facebook was on average only 35 minutes/day. Females used digital media for longer than males. Adolescents aged 17-19 years choose more Facebook and voice modes compared to adolescents aged 14 and 16 years. The alternative modes of Whatsapp and voice were chosen more than the social networks in their communication strategies, especially for negative topics. Motives for use were, in addition to boredom, related to maintaining one's social sphere with peers. Some educative considerations were made based on these results.

Motivated by the #Metoo movement, we explore in this paper people’s perception of female bullying at workplace. We looked at #workplacebullying and found that 1) people were split between identifying the prevalence of workplace bullying against female and the view that such bullying simply does not exist and is a nuisance, 2) The tweets also showed the existence of psychological effects of cyberbullying, and 3) the tweets showed many intervention techniques that can minimize the effects of such bullying. We further explored the top three recurring hashtags mentioned under the #workplacebullying and found that the three top hashtags were #sexism, #feminism and #equality. Our results showed that the above hashtags represent the positive and negative approach to workplace bullying i.e. #feminism hashtag was mostly used by people who denied that workplace bullying against females exist while # sexism was mentioned as the prime cause by people who agree that such bullying exist. #equality overwhelmingly comprises of techniques to minimize workplace bullying against females.

The COVID-19 outbreak has focused attention on the use of social distancing as the primary defence against community infection, yet forcing social animals to maintain that physical distance has presented significant challenges for health authorities and law enforcement. Anecdotal media reports suggest widespread dissatisfaction with social distancing as a policy, yet there is little prior work aimed at measuring community acceptance of social distancing. In this paper, we propose a new approach to measuring attitudes towards social distancing by using social media and sentiment analysis. Over a 4 month period, we found 82.5% of tweets were in favour of social distancing. The results indicate a widespread acceptance of social distancing in the community. We conclude by examining options for estimating the optimal (minimal) social distance required at scale, and the implications for securing widespread community support and acceptance of public health messaging.

In recent years, online social media information has been subject of study in several data science fields due to its impact on users as a communication and expression channel. Data~gathered from online platforms such as Twitter has the potential to facilitate research over social phenomena based on sentiment analysis, which usually employs Natural Language Processing and Machine Learning techniques to interpret sentimental tendencies related to users opinions and make predictions about real events. Cyber attacks are not isolated from opinion subjectivity on online social networks. Various security attacks are performed by hacker activists motivated by reactions from polemic social events. In this paper, a methodology for tracking social data that can trigger cyber attacks is developed. Our main contribution lies in the monthly prediction of tweets with content related to security attacks and the incidents detected based on ℓ1 regularization.

Over the past decades, overweight and obesity has become a global epidemic and the leading threat for death. To prevent the serious risk, an overweight or obese individual must apply a long-term weight-management strategy to control food intake and physical activities, which is however, not easy. Recently, with the advances of information technology, more and more people can use wearable devices and smartphones to obtain physical activity information, while they can also access various health-related information from Internet online social networks (OSNs). Nevertheless, there is a lack of an integrated approach that can combine these two methods in an efficient way. In this paper, we address this issue and propose a novel mobile-social framework for health recognition and recommendation, namely, H-Rec². The main ideas of H-Rec² include (1) to recognize the individual's health status using smartphone as a general platform, and (2) to recommend physical activity and food intake based on personal health information, life science principles, and health-related information obtained from OSNs. To demonstrate the potentials of the H-Rec² framework, we develop a prototype that consists of four important components: (1) an activity recognition module that senses physical activity using accelerometer, (2) a health status modeling module that applies a novel algorithm to generate personalized health status index, (3) a restaurant information collection module that collects relevant information from OSN, and (4) a restaurant recommendation module that provides personalized and context-aware recommendation. To evaluate the prototype, we conduct both objective and subjective experiments, which confirm the performance and effectiveness of the proposed system.

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.

Improper design of friction pendulum bearings can lead to poor seismic reduction performance and may result in the failure of local vulnerable components. And the parameter design of friction pendulum bearings mainly relies on experience and verification calculations at present. This paper proposes an adaptive genetic algorithm considering the overall evolution state of the population, adjusting crossover and mutation probabilities adaptively based on individual fitness and population diversity. Compared to traditional algorithms, it exhibits better global search capabilities and convergence efficiency. Combining the improved genetic algorithm with finite element models, a parameter optimization method is proposed. The parameters of friction pendulum bearings are optimized, providing reference for the design of seismic isolation devices.

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.

Recently, the medical image processing is extensively used in several areas. In earlier detection and treatment of these diseases is very helpful to find out the abnormality issues in that image. Here there are number of methods available for segmentation to detect the lung nodule of computer tomography (CT) image. The main result of this paper, the earlier detection of lung nodules using Pre-processing techniques of top-hat transform, median and adaptive bilateral filter was compared both filtering methods and proved the adaptive bilateral filter is suitable method for CT images. The proposed segmentation technique uses novel strip method and the image is split into number of strips 3, 4, 5 and 6. A marker- watershed method based on PSO and Fuzzy C-mean Clustering method was proposed method. Firstly, the input image was reduced noise reduction and smoothing and the filter image is using strips method and then the image is segmented by marker watershed method. Secondly, the enhanced PSO technique was used to locate the better accurate value of the clustering centers of Fuzzy C-mean Clustering. Final stage, with the accurate value of centers and the enhanced target function and the small region of the segmented object was clustered by Fuzzy C-mean. In segmentation algorithm presented in this paper gives 95% of accuracy rate to detect lung nodules when strip count is 5.

This paper proposes a crack recognition method based on high-resolution line array cameras and adaptive lifting algorithm. By defining the crack rate, this algorithm calculates the ratio of the crack area to the area of the entire collected image to characterize the damage extent of the current section. The algorithm first uses image preprocessing to reduce the image noise, then uses histogram equalization to enhance the feature of the crack region, divides the whole image into multiple sub-blocks, and extracts region features in the sub-block. At the same time, this algorithm defines related feature descriptors, and constructs weak classifiers according to each feature descriptor, and converts the weak classifiers into strong classifiers by using an adaptive lifting algorithm. Finally, this algorithm realizes the division of the crack regions. Experimental results show that the proposed algorithm can meet the actual needs and is better than other classical algorithms.

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.

This systematic literature review explores the impact of social media use (SMU) and practices (SMPs) on team feedback (TF) and performance (TP) in construction project management. The study delves into the intricate dynamics between SMU and SMPs and their influence on communication, collaboration, and stakeholder engagement within construction projects. It investigates how these digital practices reshape the traditional methods of team interaction, feedback mechanisms, and overall team performance in the complex and multifaceted environment of construction sites. The review aims to provide a comprehensive understanding of the role of social media in modern construction project management, highlighting its significance in enhancing team communication, coordination, and performance. This research is crucial for understanding the evolving landscape of construction management, where digital tools and platforms are increasingly integral to successful project outcomes. The findings are expected to offer valuable insights for professionals in the construction industry, helping them leverage social media effectively for improved project management and team collaboration.

The endorsement from Christian circles in facilitating the rise of right-wing populism in Brazil, the United States, as well as the support and alliance of the Polish Catholic Church with the Law and Justice (PiS) government, is widely acknowledged. Embedded within the intricate fabric of multifaceted factors contributing to the surge of right-wing populism, the Culture War assumes a pivotal yet often underestimated role. Manifesting as a profound confrontation, the Culture War signifies an existential clash between contrasting perspectives concerning socio-cultural values grounded in religious scripture. Progressive endeavors strive to deconstruct longstanding norms, advocating for the ascendancy of post-materialistic ideals, while conservative domains portray an unwavering commitment to safeguarding national and religious socio-cultural values as sacrosanct. Both factions employ moral-ethical justifications in their respective pursuits, rendering the landscape of cultural discourse a nuanced tapestry of ideological contention. The influence of Christian doctrine on the conflict surrounding social justice, and the potential convergence between Christian doctrine and the positions of Populist right-wing movements on social justice issues, is underscored. By employing a singular case study focused on Portugal, owing to the nation's enduring relationship with Catholicism spanning from Monarchical times to the Republic and the Fascist Catholic regime, Portugal emerges as indisputably one of the most religious countries in the Western world, akin to Poland. Consequently, in theory, the support of the Portuguese Catholic Church for the nascent Populist right-wing and Christian-oriented party should be evident amidst the Culture War conflict, wherein conservative religious Christian values face existential challenge. However, the backing extended to CHEGA by both the Catholic Church and the Portuguese Episcopal Conference assumes an ambivalent and markedly diminished stance, diverging from the previously robust support observed in established center-right mainstream parties such as the PSD and CDS. Despite this reality and the absence of official support for CHEGA, numerous Christian politicians or activists have expressed endorsement or alignment with the right-wing party, departing from their former affiliations, primarily due to social justice concerns.

Loneliness is becoming prevalent across Japan in spite of its a dense population and communal culture. Surveys show 40% of adults report isolation across demographics. The contradiction emerges due to deteriorating social structure with modernization, economic parity, urbanization, and fading conventional setup. The continually social withdrawals, called “hikikomori." Relief seekers have embraced AI based emotive robots, digital avatars, and immersive VR as low-stakes social surrogates despite limited capacity to cultivate human intimacy. To stabilise government attempts awareness through reforms, scheme and support services. But a concrete transformation can be brough in with work-life balance, gender equality and rewire the metrics for self-esteem. Even a perfectly personalized AI cannot replace the human affection lacking in the society. Lasting solutions demand technological aids be balanced by confronting and reinventing community purpose and bring in structural change. Overall Japan’s endemic isolation reveals the societal fraying when modernization privileges independence over interdependence. The path forward rests on restoring cultural foundations to reverse loneliness and social isolation.

In today's digital landscape, the use of smartphones is almost inevitable, and so is the pervasive use of social media (SM), a constantly evolving system that has become an integral part of the daily lives of adolescents. Over the past seven years, the usage of social media platforms such as Instagram, Twitter, and TikTok has seen exponential growth among younger audiences. Excess SM use has raised concerns. This paper examines the possible negative consequences of the escalating trend in social media usage, and the implications of social media addiction (SMA), especially platforms featuring short-form video content, among teenagers. The paper also addresses the debate surrounding the validity of the term "addiction" concerning social media overuse and highlights the neurobiological processes associated with addiction, including the activation of dopamine-related brain structures. Furthermore, it discusses the researched structural and functional changes to the adolescent brain, including drawing parallels with substance addictions based on neuroimaging studies. Finally, this review advocates for exploring avenues of regulation to ensure a safer digital future, emphasizing the importance of policy to mitigate unforeseen long-term consequences of SMA and provides recommendations for reasonable and efficient future policy directions, guided by scientific and health concerns rather than political motives.

Purpose: Social responsibility has been accepted as a core value of nursing profession, particularly in the area of health disparity, but it seems to be difficult to understand for what is meant by social responsibility. The purpose of this study was to explore ways to define the concept of social responsibility of nursing in order to understand the meaning of social responsibility in the field of nursing profession. Methods: The concept analysis process by Walker and Avant was used to clarify the meaning of social responsibility of nursing. Results: Defining attributes of social responsibility of nursing included praxis, as approach to strategizing for social and structural change of inequity, care for an oppressed group which is under social suffering caused by the privilege and power, and action for health policy change in social and political unequal contexts. The antecedents of social responsibility of nursing included reflectivities in identifying personal characteristics, taking seriously the perspective of other vulnerable population, recognizing social environmental status quo (‘fair or unfair’), developing competences in ethical and moral reasoning with social issues. Consequences of social responsibility of nursing were achieving social justice, as equal access to basic human health need in societal level, equal access to effective nursing practice delivery system and development of health promotion policy for world health administrative practices (“health for all”) in nursing. Conclusion: This study provides a guidance to direct future studies through the procedure of identifying conceptual attributes in the context of social responsibility of nursing. Future studies are suggested to develop instruments for social responsibility as well as development of educational programs to promote social responsibility competences in nursing students and nurses.

At a basic level, entrepreneurship refers to an individual or a small group of partners that intend to create a new business. Entrepreneurship is at forefront of any competitive economy due to the creation of new products and services that comes from new ideas. In other words, entrepreneurship is associated with economic wealth, growth and innovation. The usage of resources in the creation of new businesses needs to be rational because investors normally are risk averse, and nowadays, the purpose of a new business needs to be for a long-term period, and the usage of resources has to take into mind the possible damage to the planet. In other words, entrepreneurs and the sustainability of the planet are interconnected. Beyond this, entrepreneurs need to take care of the corporate social responsibility of each new business, considering the interests of itself, its stakeholders, and the public. Nevertheless, existing research has not shown in full all the research streams, how they interact with each other and their potential knowledge development. Thus, a literature review on social entrepreneurship and corporate social responsibility in the last decade are opportune. This paper aims to identify research trends in the field through a Systematic Bibliometric Literature Review of social entrepreneurship and corporate social responsibility research. The review includes 69 articles published in the Scopus® database, presenting up-to-date knowledge on the topic.

In the age of social media your personal branding is critical to find a job. Information is highly available and handy for everyone looking for a job as well as for people trying to recruit employees at all levels. Currently, there is a first e-impression that encompasses employee education, qualifications, and relationships. This e-impression is given by your social media, specifically those websites that were built to facilitate interactions between employee and employer during the job-hunting process. There are different websites that enable you to establish a social network. For example, meet, Xing, Bark, Opportunity, Lunchmeet, and LinkedIn among others. This article is written by a group of young undergraduate and graduate researchers, who have noticed the importance of social networks to apply for graduate school and industry jobs. This manuscript offers ten simple rules that include resources to facilitate the journey on LinkedIn-like social networks, hoping that you have a successful career description in the most popular professional social networking site.

Social networks have become the scenario with the greatest potential for the circulation of disinformation, hence there is a growing interest in understanding how this type of information is spread, especially in relation to the mechanisms used by disinformation agents such as bots, trolls, among others. In this scenario, the potential of bots to facilitate the spread of disinformation is recognised, however, the analysis of how they do this is still in its initial stages. Taking into consideration what was previously stated, this paper aimed to model and simulate scenarios of disinformation propagation in social networks caused by bots based on the dynamics of this mechanism documented in the literature. For achieving the purpose, System dynamics was used as the main modelling technique. The results present a mathematical model, as far as disinformation by this mechanism is concerned, and the simulations carried out against the increase in the rate of activation and deactivation of bots. Thus, the preponderant role of social networks in controlling disinformation through this mechanism, and the potential of bots to affect citizens, is recognised.

As brachycephalic dog breed ownership increases, there is a growing concern for the welfare of these breeds due to extreme brachycephalism. Understanding the motivations and behaviours of those choosing to own these breeds is important if we wish to address these concerns. The aim of this study was to investigate how owners of brachycephalic and non-brachycephalic dog breeds use social media dog breed groups to discuss common health issues. The purpose of Facebook posts in relation to common health issues, owner awareness of health issues, and the role of Facebook facilitated social support were explored between brachycephalic and non-brachycephalic dog owners. Twelve Facebook breed groups were selected (brachycephalic breed groups, n=6, non-brachycephalic breed groups, n=6). Using key word searches we extracted the first twenty posts from each group. Thematic analysis revealed three overarching themes: advice seeking, advice giving and community bonding mechanisms. Whilst the purpose of posting did not differ between groups, non-brachycephalic owners appeared to display greater awareness of breed-specific health issues, whilst social support played a more prominent role in brachycephalic groups. This research highlights that social media groups can act as platforms for knowledge exchange and emotional support. These could be utilised by owners, veterinarians and welfare organisations to more effectively enhance dog health and wellbeing.

Social media platforms are not serving only as tools of communication rather they are acting like intricate systems managing our interactions. A major setback to our interactions occur when the flow of information in these networks gets distorted through polarization. It resembles a society where every debate turns into divide. Our research focuses exclusively on quantifying polarization as well as solving this issue head-on. In order to accurately gauge polarization, we’ve developed a new method that takes into consideration majority of its influencing factors. This helps us in correctly quantifying the polarization value of a given network and then by proposing it as an optimization problem we are attempting to maximally decrease polarization while staying within the set budget. We test our method on synthetic and real-world data sets, and find out that polarization is declining, and diversity is increasing. This proves that our recommendation engine has a penchant for finding the ideal connections between the nodes to start a dialogue. Our research presents a successful tactic to quantifying and reducing polarization in social media networks. The results indicate that our novel metric and intervention technique are effective in dismantling echo chambers, thereby promoting diversity in these networks.

Purpose – The paper aims to view how researchers have operationalized social impact assessment for construction projects over the last ten years. Design/methodology/approach – A systematic review was used to investigate case studies in the Social Life-Cycle Assessment (S-LCA) to analyze the application of the methodology. A qualitative study of 19 articles published between 2012 and 2022 was used to collect content on multiple categories impeding S-LCA through case studies in the construction industry. Findings – Our results showed the existence of limitations on the qualitative and quantitative aspects in measuring the social indicators. They were associated with the scoring method and the lack of data in some articles. Social implications – From this review, we understood that S-LCA has flaws in terms of the quality of the measurement, scoring method, and the lack of social data. Lack of social data means social impacts are being neglected and not assessed properly since there are several challenges pointed out throughout literature Originality/value – The originality of this research is that it focuses on case studies in the construction industry. It studies the operationalization of the S-LCA in this specific industry showing the different characteristics and challenges in the last 10 years.

This study investigated the effects of flamenco dance on mood and subjective well-being in older women. In a broader context, the question was raised about how participation in dance activities could influence the emotional well-being of this demographic group. Thirty-four older women, aged between 62 and 79 years, with a mean age of 70.11 years (SD = 5.13), voluntarily participated in the study after providing informed consent. All of them were enrolled in a 12-week flamenco dance workshop conducted by the Autoestima Flamenca Association, with one two-hour session per week. Mood measurements (sadness, anxiety, anger, and joy) were taken biweekly before and after each session, and subjective well-being assessments were conducted before and after the intervention. The results revealed significant improvements in the overall mood of the participants, as well as an increase in their subjective well-being. These improvements were observed at both the individual and group levels. In conclusion, participation in flamenco dance demonstrated a positive impact on the mood and subjective well-being of older women. These findings support the idea that physical and artistic activities, such as flamenco dance, can play an important role in promoting emotional well-being in this age group.

Social and academic integration variables have shown to be relevant for the understanding of university dropout. However, there is less evidence regarding the influence of these variables on dropout intention, as well as predictive models that explain their relationships. Improvements in this topic become relevant considering that dropout intention stands as a useful measure to anticipate and intervene on this phenomenon. The objective of the present study was to evaluate a predictive model for the university dropout intention that considers the relationships between social and academic variables, during the first university semester of 2020. The research was carried out using a cross-sectional associative-predictive design, with a convenience sampling (n=711) due the restrictions of pandemic period. The results showed a good fit of the proposed hypothetical model that explains 38.7% of dropout intention. Both social support and perceived social isolation predicted the sense of belonging, and through it, engagement. Previous academic performance predicted early academic performance, and through it, engagement. The set of variables predicted the intention to quit, through engagement. These results are a contribution both to the understanding of the phenomenon and to guide potential interventions in the early stages of the university experience.

We advance binational link-tracing sampling design, an innovative data collection methodology for sampling from transnational social fields, i.e., transnational networks embedding migrants and non-migrants. This paper shows the practical challenges of such a design, the representativeness of the samples and the qualities of the resulted networks. We performed 303 face-to-face structured interviews on sociodemographic variables, migration trajectories and personal networks of people living in a Romanian migration sending community (Dâmbovița) and in a migration receiving Spanish town (Castellón), simultaneously in both sites. Inter-connecting the personal networks, we built a multi-layered complex network structure embedding 4,855 nominated people, 5,477 directed ties (nominations) and 2,540 edges. Results indicate that the participants’ unique identification is a particularly difficult challenge, the representativeness of the data is not optimal (homophily on observed attributes was detected in the nomination patterns), and the relational and attribute data allow to explore the social organization of the Romanian migrant enclave in Castellón, as well as its connectivity to other places. Furthermore, we provide methodological suggestions for improving link-tracing sampling from transnational networks of migration. Our research contributes to the emerging efforts of applying social network analysis to the study of international migration.

Introduction and Background: Bangladesh as a country could prove its development potential over the past several years with its thriving economic growth and also with a significant level of positive changes made possible in its significantly important health and social indicators including MMR, IMR, Child nutrition, fertility regulation, child survival and Infectious disease prevalence. ^1,2,3 The country could make a commendable contribution in achieving Global development goal (MDG) at a significant level and also aiming to continue its effort to sustain that status quo and also making progressive changes consistently to be contributory to SDG goals and indicators towards positive development.^2,3 Purpose: This lyrical critic is an attempt to uphold the facts and evidences embedded in social development reality where the implementations are in constant challenge with urgency, need and continuity. Methodology: A quick and intensive desk review and web search made to capture the insights from secondary data facts, stories, evidences, news features and the findings blended with personal insights and experiences. Finally, compilation of insights and views through a laid over narrative analytics and described in a descriptive lyrical format. Purposefully ignored the figure and quantity data reflection in the write up as this write up is considered more as a social development lyric rather than a scientific write up. Conclusion: Our diversified marginalized community people are of vital importance from a social inclusion and exclusion point of view, to look into this more deeply whether they are socially, epidemiologically, statistically, economic indicator wise fall into the embracing practice of our democracy and inclusion culture of addressing the marginalized. This posed our country in a very challenging situation, a dilemma in between morality vs reality, emotion vs equity, social response vs political standpoint and so on. With a long end history of community responsive and socially sensitive works within /among our generalized poor, poverty stricken and marginalized people group, where the sustainable and ethically driven, gender sensitive social empowerment is still a far cry! The diversity in nature always claims to add on beauty, tranquility and completeness towards the sense of Equity management, but it’s very true that this diversity word has a very opposite and different connotation while it is relevant to diversity in marginalization and appears in a more critical and complex dynamics to seek solution. Therefore, the ultimate empowerment of community specially the marginalized people remain entrapped into the social development process of enduring response in embracing urgency in community care where the right response may not get right weightage into the community development priority response and also the development actors priority agenda.

Exposure to adverse childhood experiences (ACE) has been found to have a profound negative impact on multiple child outcomes, including academic achievement, social cognition patterns, and behavioral adjustment. However, these links have yet to be examined in preschool children that are already experiencing behavior or social-emotional problems. Thus, the present study examined the links between the caregiver's and the child's exposure to ACE and multiple child and caregiver's outcomes in a sample of 30 preschool children enrolled in a Therapeutic Nursery Program (TNP). Children are typically referred to this TNP due to significant delays in their social emotional development that often result in difficulty functioning in typical childcare, home, and community settings. Analyses revealed some contradictory patterns that may be specific to this clinical sample. Children with higher exposure to ACE showed more biased social information processing patterns and their caregivers reported lower child social skills than caregivers of children with less exposure, however their inhibitory control levels were higher (better control) and staff reported that these children exhibited better social skills as well as better approaches to learning than children with less exposure. No such contradictions were found in relation to the caregiver's exposure to ACE, as it was positively associated with a number of negative child and caregiver outcomes.

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.

This study presents an innovative approach named Optimal Adaptive Continuous Barrier Function Terminal Sliding Mode Control (OACBF-TSMC) approach with a novel switching manifold, to control mathematical model of nonlinear systems under model uncertainties and external disturbances. The proposed OACBF-TSMC approach combines the advantages of adaptive control, continuous barrier functions, and terminal sliding mode control to achieve robust stability of the mathematical model of nonlinear systems, and by using a genetic algorithm that tries to optimize the controller parameters as much as possible so that the proposed controller is the most to have efficiency and performance. The proposed method is designed to address the challenges of systems and provide superior performance in terms of stabilization accuracy, disturbance rejection, and robustness against uncertainties. However, the critical challenge facing the proposed controller is the complete elimination of the chattering phenomenon, which is the most important problem of ordinary sliding mode controllers. The simulation results show that the system trajectories in the proposed approach have converged to the origin with minimal overshoot and under-shoot in a finite time; In addition, the chattering problem in the control inputs has been well eliminated, which makes the proposed method suitable for various systems, including chaotic systems, energy sources, and distributed production systems. Furthermore, the OACBF-TSMC approach has the potential to enhance the stability and reliability of control systems, contributing to the integration of the system states.

This study presents finite-time synchronization of chaotic fractional order systems with disturbance uncertainty and unknown time delay. First, a PID sliding surface is presented. Then, for the finite-time synchronization of the master and slave systems, a robust sliding-adaptive control method is provided. Update rules have been retrieved to estimate the system parameters by using the Lyapunov function while establishing the stability of the suggested mechanism and assuring the convergence of errors to zero. The proposed approach was used to solve the fractional order system of Gensou-Tsei with time-varying parameters, and the simulation results show that the presented approach performs well.

In the development of a digital twin (DT) for hydropower turbines, dynamic modeling of the system (e.g., penstock, turbine, speed control) is crucial, along with all the necessary data interface, virtualization, and dashboard designs. Since the DT must mimic the actual dynamics of the hydropower turbine accurately, adaptive learning is required to train these dynamic models online so that the models in the DT can effectively follow the representation of the actual hydropower turbine dynamics accurately and reliably. This study presents an adaptive learning method for obtaining the hydropower turbine models for DT development of hydropower systems using recursive least squares algorithm. To simplify the formulation, the hydropower turbine under consideration was assumed to operate near a fixed operating point, where the system dynamics can be well represented by a set of linear differential equations with constant parameters. In this context, the well-known six-coefficient model for the Francis turbine was formulated as the starting point to obtain input and output models for the turbine. Then, an adaptive learning mechanism was developed to learn model parameters using the real-time data from a hydropower turbine testing system. This led to a semi-physical modeling in which the first principles and data-driven modeling are integrated to produce dynamic models for the DT development. Applications to a pilot system at the Norwegian University of Science and Technology (NTNU) were made, and the models learned adaptively using the data collected from the university’s pilot system. Desired modeling and validation results were obtained.

Self-adaptive systems are capable of reconfiguring themselves while in use to reduce the risks forced by environments for which they may not have been specifically designed. Run-time validation techniques are required because complex Self-adaptive systems must consistently offer acceptable behavior for important services. The run-time testing can offer further confidence that a Self-adaptive system will continue to act as intended even when operating in unknowable circumstances. This article introduces an evolutionary framework that supports adaptive testing for Self-adaptive systems. To ensure that the adaptive systems continue to operate following its requirements and that both test plans and test cases continuously stay relevant to shifting operational conditions. The proposed approach using the SPEA2 algorithm facilitates both the execution and adaptation of run-time testing operations.

A strong correlation between karyotype diversity and species richness in mammals was first reported over forty years ago: in mammalian phylogenetic clades, standard deviation of karyotype diversity (KD) closely corresponds to the standard deviation of species richness (SR). These initial studies, however, did not control for phylogenetic signal, raising the possibility that the correlation was due to phylogenetic relatedness among species in a clade. Accordingly, karyotype diversity trivially reflects species diversity simply as a passive consequence of adaptive radiations. A more recent study in mammals has controlled for phylogenetic signal and established the correlation as phylogenetically independent, suggesting that species diversity cannot, in itself, explain the observed corresponding karyotype diversity. The correlation is therefore remarkable because the mechanisms contributing to karyotype diversity are evolutionarily independent of the mechanisms contributing to species diversity. Recently, it was shown in salamanders that the two processes generating genetic diversity and species diversity are indeed independent and operate in parallel, suggesting a potential non-adaptive and non-causal relationship between the two fundamental variables involved in adaptive radiations. The following will examine the relationship between KD and SR in the context of Motoo Kimura’s theory of non-adaptive radiation.

The goal of this paper is the performance evaluation of a deep learning approach when deployed in fifth-generation (5G) millimeter wave (mmWave) multicellular networks. To this end, the optimum beamforming configuration is defined by two neural networks (NNs) that are properly trained, according to mean square error (MSE) minimization. The first network has as input the requested spectral efficiency (SE) per active sector, while the second network the corresponding energy efficiency (EE). Hence, channel and power variations can now be taken into consideration during adaptive beamforming. The performance of the proposed approach is evaluated with the help of a developed system level simulator via extensive Monte Carlo simulations. According to the presented results, machine learning (ML)-adaptive beamforming can significantly improve EE compared to the standard non-ML framework. Although this improvement comes at the cost of increased blocking probability (BP) and radiating elements (REs) for high data rate services, the corresponding increase ratios are significantly reduced compared to the EE improvement ratio. In particular, considering 21.6 Mbps per active user and ML adaptive beamforming, then EE can reach up to 5.3 Mbps/W, which is significantly improved compared to the non-ML case (0.9 Mbps/W). In this context, BP does not exceed 2.6%, which is slightly worse compared to 1.7% in the standard non-ML case. Moreover, approximately 20% additional REs are required, with respect to the non-ML framework.

Shape Memory Polymer (SMP) actuators are smart materials that adapt and switch shape in response to environmental conditions, allowing controlled and dynamic building systems. The integrated SMP strip enabled the adaptive facade shading system to operate independently and with flexibility under certain thermoresponsive conditions to regulate building performance. This study aims to improve adaptive façade performance in a hot-humid climate by determining optimal thermal activation factors for the shape memory polymer. This was achieved by comprehensively examining and analysing the impact of various SMP activation parameters on energy efficiency, thermal comfort, and visual comfort. In addition, dynamic IES-VE building energy simulation was used to evaluate the thermal performance of the adaptable facade. The results were validated using a multicriteria approach to compare with the existing glass façade of a high-rise apartment. The results showed that the coactivation technique, which combines solar radiation and air temperature triggers, proved to be the most efficient strategy to improve the performance of the adaptive façade. This strategy minimised heat gain, lowered indoor temperatures, reduced glare, and enhanced daylight comfort, resulting in notable energy savings through reduced cooling consumption. The study concluded that the utilisation of SMP actuators with fine-tuning activation parameters significantly optimised adaptive façade performance, promoting functionality, efficiency, and sustainability in buildings.

Background: With the increasing global incidence of diabetes mellitus (DM), diabetic retinopathy (DR) has become one of the leading causes of blindness in developed countries. DR leads to changes in retinal neurons and microcirculation. Rtx1TM (Imagine Eyes, France) is a microscope that allows histological visualisations of cones and retinal microcirculation throughout the DM duration. Objective: This study aimed to analyse the cones and retinal microvascular changes in 50 diabetic individuals and 18 healthy volunteers. The patients participated in the initial visit and two fol-low-up appointments: one and two years after the study, beginning with rtx1 image acquisition, visual acuity assessment, macular OCT scans and blood measurements. Results: The study revealed significant differences in the cone density, mosaic arrangement and vascular morphology between healthy and diabetic patients. The final measurements have shown decreased photoreceptor and microvascular parameters in the DR group compared to the control group. Furthermore, in the 2-year follow-up, both groups' rtx1-acquired morphological changes were statistically significant. Conclusions: Rtx-1 technology was successfully used as a non-invasive method of photoreceptors and retinal vasculature assessment over time in patients with diabetic retinopathy. The study has revealed a trend toward more vascular morphological changes occurring over time in diabetic patients.

The development of hybrid unmanned aerial underwater vehicles (HAUVs) compatible with the advantages of the aerial vehicles and the underwater vehicles is of great significance. This paper presents the first study on a new HAUV layout using four rotors to realize the medium crossing motion of a transverse slender body similar to the fuselage of a missile or a submarine, that is the hybrid aerial underwater quadrotor (HAUQ). Then a robust control strategy is proposed for the take-off HAUQ on the water in the presence of unknown disturbances and complex model dynamic uncertainties. As a semi-submersible HAUQ rises straightly from the water, the inside of the slender fuselage placed horizontally is filled with water. The center of the mass, the moment of inertia, and the arm of force of the HAUQ will change rapidly in the takeoff phase from the water since the rapid non-uniform change of mass caused by the passive fast drainage. It is difficult to establish a accurate mathematical model of the complex dynamic changes caused by the multi-media dynamics, the fast changing buoyancy, and the added mass crossing air–water surface. Therefore, an uncertain kinematic and dynamic model is established through the passive fast nonuniform change and the complex dynamics are considered as the unknown terms, and the external disturbances of gust and other factors are assumed as the bounded disturbance input. A robust design approach is introduced to deal with the fast time-varying mass disturbance based on the input-to-state stability (ISS) theorem. The complex dynamics are estimated using the basis function and the unknown weight parameters, and the adaptive laws are adopted for the on-line estimation of the unknown weight parameters. Consider the residual disturbance of the uncertain nonlinear system as a total disturbance term, a disturbance observer is introduced for disturbance observation. The numerical simulation shows the feasibility and robustness of the proposed algorithm.

Infrared detection plays an important role in the military, aerospace, and other fields, which has the advantages of all-weather, high stealth, and strong anti-interference. However, infrared dim small target detection suffers from complex backgrounds, low signal-to-noise ratio, blurred targets with small area percentages, and other challenges. In this paper, we proposed a multiscale YOLOv5-AFAM algorithm to realize high-accuracy and real-time detection. Aiming at the problem of target intra-class feature difference and inter-class feature similarity, the Adaptive Fusion Attention Module - AFAM was proposed to generate feature maps that are calculated to weigh the features in the network and make the network focus on small targets. This paper proposed a multiscale fusion structure to solve the problem of small and variable detection scales in infrared vehicle targets. In addition, the downsampling layer is improved by combining Maxpool and convolutional downsampling to reduce the number of model parameters and retain the texture information. For multiple scenarios, we constructed an infrared dim and small vehicle target detection dataset, ISVD. The multiscale YOLOv5-AFAM was conducted on the ISVD dataset, compared to YOLOv7, mAP@0.5 achieves a small improvement while the parameters are only 17.98% of it. By contrast with the YOLOv5s model, mAP@0.5 was improved by 4.3% with a 6.6% reduction in the parameters. Experiments results demonstrate that the multiscale YOLOv5-AFAM has a higher detection accuracy and detection speed on infrared dim and small vehicles.

The risks for complications of severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection are higher in obese individuals. Obesity is a state of chronic low-grade inflammation, with high leptin levels due to leptin resistance, high basal levels of other pro-inflammatory cytokines such as TNF-alpha, MCP-I and IL-6, and low adiponectin levels, thus contributing to a state of defective innate immunity as well as impaired B and T cell responses. Obesity is a risk factor for metabolic syndrome, diabetes, cardiovascular disease and hypertension. It has been observed that pre-existence of these diseases confers a higher risk of severe SARS CoV2 infection as well as the need for intensive care; even below the age of 60 years if their body mass index (BMI) is greater than 30 kg/m2, and even more so if it is > 35 kg/m2. The metabolic factors contributing to the changes in altering the immune mechanisms in obese individuals and how this enhances the susceptibility to infection and development of serious SARS-CoV2 infection have been the subject of many debates. Future development of targeted therapy and guidelines will be benefited by greater understanding into these metabolic pathways.

The main problem in creating anti-coronavirus vaccines that target mainly proteins of the outer membrane of the virus remains the rapid variability of the RNA genome of the pathogen that encodes these proteins. In addition, the introduction of technologies that can provide affordable and fast production of flexible vaccine formulas that easily adapt to the emergence of new subtypes of SARS-CoV-2 is required. Universal oligonucleotide vaccine can take into account the dynamics of rapid changes in the virus genome, as well as be synthesized on automatic DNA synthesizers in large quantities in a short time. In this brief report, the effectiveness of four phosphorothioate constructs of the La-S-so type oligonucleotide vaccine will be evaluated for the first time on transgenic mice [B6.Cg-Tg (K18-ACE2)2]. In our primary trials, the oligonucleotide vaccine increased the survival rate of animals infected with SARS-CoV-2 and also reduced the destructive effects of the virus on the lung tissue of mice. The obtained results show the perspective of the development of vaccine constructs of the La-S-so type for the prevention of coronavirus infections, including those caused by SARS-СoV-2.

Due to nonlinear components in the charging piles of electric vehicles, harmonics and non-stationary signals in the electric vehicle charging load brings voltage and current distortion, seriously affecting the accuracy of the power-related calculation in non-sinusoidal environments. This paper proposed a new approach to calculate the active power and root mean square values from decomposed components using the adaptive chirp mode decomposition (ACMD) method on voltage and current. The advantage of the ACMD-based method is that it correctly provides the power-related quantities of harmonics or non-stationary components for the electric vehicle charging load. The performance of the proposed method was verified using synthetic signals and simulation tests. The experimental results presented better estimations for each quantity defined in IEEE Standard 1459-2010, compared with the discrete wavelet transform approach.

Forest vegetation dynamics (succession and dominance) is an ecological phenomenon that is still difficult to characterize and integrate into management practices. In this study, the understanding of forest dynamics is explored based on Grime’s theoretical triangular model of plant adaptive strategies using the example of Lithuania’s forest ecosystems. The idea behind this is the hypothesis that forest dynamics is linked to natural selection as an evolutionary process that exhibits differential species responses to competition, stress, and disturbance. The aim of this study is to explore the adaptive relationships in hemi-boreal forests. Grime’s and Pierce’s secondary CSR strategies, which describe various equilibria between competitiveness (C), stress tolerance (S), and ruderalism (R), were considered to reflect four establishment and development adaptive specialization characteristics of forest tree species. As a result of the study, four types of tree functional groups were identified: stress-resistant ruderals, competitive stress-sensitive ruderals, ruderal stress-sensitive competitors, and stress-resistant competitors. Based on this, we propose that reforestation move away from single species regeneration by implementing the maintenance of these four types of functional groups. In conclusion, forest management must consider the existence of the established equilibria between plant competitiveness, stress tolerance, and ruderalism. The formal concepts presented in this article can serve as a guide for future relevant research and development of appropriate methods for studying real forests. This study is unique in that no previous work has linked forest dynamics and natural selection in the context of Lithuania’s forest ecosystems.

Abstract: Obstructive sleep apnea (OSA) is well known to often improve with non-supine positioning as opposed to su¬pine positioning. Emerging research supports a role for sleep position management in patients with central sleep apnea (CSA) as well. We report a case of de novo Complex Sleep apnea Syndrome (CompSAS) in a 78-year-old female, who presented after a car accident due to unclear syncope. Diagnostic polysomnography (PSG) showed a moderate OSA. A CompSAS developed under Automatic positive airway pressure (APAP), while download data of 4 years showed a good adherence. No significant benefit was reported under Adaptive Servo Ventilation (ASV) and BiPAP-ST, while a reduction of CSA in non-supine position was noticed. Oxygen and sleep positional therapy (SPT) were considered resulting in a significant improvement of CSA and sleep quality. Further research on the prevalence of positional CSA is needed.

This article investigates the dynamics of socio-ecological systems (SESs) unsustainability. By adopting a theoretical standpoint grounded in systems’ theory, the analysis shows how SESs’ teleology (or final cause) is of the utmost relevance for understanding unsustainability and how it is pivotal for envisioning possible evolutionary trajectories towards sustainability. Building on the contributions of both system and social scientists, the study argues that SES’s teleology is determined by dominant social ontologies that require a dialectical lens to be properly dealt with. The article therefore proposes the adoption of the adaptive cycle heuristic complemented by an historical-geographical approach based on world-ecology theory as a means to dynamically model of SESs’ behaviour. Such a perspective allows for the direct comparison between the four stages of the panarchy cycle (reorganization, exploitation, conservation, and release) and the four stages theorized by the world-ecology dialectics (expansion, appropriation, capitalization, crisis). In conclusion, the article claims that both system and social scientists would benefit from including in their analysis concepts and definitions from the other field, since both provide valuable insights about SESs' processes of change and both are necessary to envision transition pathways towards sustainability.

Introduction: The aim of our study was to evaluate the efficacy and toxicity of a daily-adaptive MR-guided SBRT on 1.5 T MR-linac in patients affected by lymphnode oligometastases from PCa. Materials and Methods: The present study is a prospective observational study conducted in a single institution (protocol n°: MRI / LINAC n. 23748). Patients with oligometastatic lymphnodes from PCa treated with daily-adaptive MR-guided SBRT on 1.5T MR-linac were included in the study. Minimum required follow-up of 3 months after SBRT. Primary end-point was local progression-free survival (LPFS). Secondary end-points were: nodal progression-free survival (NPFS), and progression-free survival (PFS), and toxicity. Results: 118 lymphnode oligometastases from PCa were treated with daily-adaptive 1.5T MR-guided SBRT in 63 oligometastatic patients. 63.5% patients were oligoprogressive and 36.5% oligoprogressive. Two-year LPFS was 94.5%. Median NPFS was 22.3 months, and the 2-year NPFS was 46.5%. Having received hormone therapy before SBRT was correlated with lower NPFS at the multivariate analysis (1-y NPFS 87.1% versus 42.8%; p= 0.002 - HR 0.199, 95% CI 0.073-0.549). Furthermore, the oligorecurrent state during ADT was correlated with a lower NPFS than the oligoprogressive state. Median PFS was 10.3 months, the 2-year PFS was 32.4%. Patients treated with hormone therapy before SBRT had a significantly lower 1-year PFS the others (28% versus 70.4%; p= 0.01 - HR 0.259, 95% CI 0.117-0.574). No acute and late toxicities occurred during treatment. Conclusion: the present is the largest prospective study of 1.5T lymphnode SBRT on MR-linac in patients with PCa. Lymphnode SBRT by 1.5T MR-linac provides high local control rates with an excellent toxicity profile.

It is a truism that whole energy system models underpin the development of policies for energy system decarbonisation. But recent reviews have thrown doubt on the appropriateness of such models for addressing the multiple goals for future energy systems, in the face of emergent real-world complexity and the evolution of stakeholder’s priorities. Without an understanding of the changing priorities of policy makers and expectations of stakeholders for future systems, system objectives and constraints are likely to be ill-defined, and there is a risk that models may be inadvertently instrumentalised. Adopting a system architecture perspective, the authors have undertaken a three-year programme of research to explore strategies for decarbonising heat in the UK, with interaction with and elicitation of needs from stakeholders at its heart. This paper presents the procedure, methods, and results of an exercise in which experts from stakeholder organisations across the energy system were interviewed. Analysis of interview data reveals two broad approaches to heat decarbonisation which can be broadly defined as either adaptive or transformative. Specific insights gained from these interviews enabled our modelling teams to refocus their work for exploration with a wider circle of stakeholders. Results suggests that this iterative approach to formalising model-policy interaction could improve the transparency and legitimacy of modelling and enhance its impact on policy making.

Breaking up prolonged sitting by short bouts of light physical activities including standing and walking has been shown to be beneficial for people with type 2 diabetes (T2D). This paper presents the development of an android mobile app to deliver a just-in-time adaptive intervention (JITAI) to reduce sedentary time in people with T2D. A total of six design workshops were conducted with seven experts to identify design requirements, a behavioural framework, and required contextual adaptations for the development of a bespoke mobile app (iMOVE). Moreover, a focus group was conducted among people with T2D as potential end-users (N=10) to ascertain their perceptions of the app. Feedback from the focus group was used in subsequent iterations of the iMOVE app. Data were analysed using an inductive qualitative thematic analysis. Based on workshops, key features of iMOVE were developed, including simplicity (e.g., navigation, login), colours and font sizes, push notifications, messaging algorithms and a triggering system for breaking up sitting time and moving more. Based on the user testing results, a goal setting tab was added, font sizes were made larger, the brightness of colours was reduced, and a colour indicator was used to indicate device connectivity with an activity tracker. A user-centric app was developed to support people with T2D to transition from sedentary to active lifestyles.

The ACC feature when activated augments the engine performance in real-time. This article presents a novel methodology to predict the optimal adaptive cruise control set speed profile (ACCSSP) by considering all the effecting parameters. This paper investigates engine operating conditions (EOC) criteria to develop a predictive model of ACCSSP in real-time. We developed a deep learning (DL) model using the NARX method to predict engine operating point (EOP) mapping the vehicle-level vectors (VLV). We used real-world field data obtained from Cadillac test vehicles driven by activating the ACC feature for developing the DL model. We used a realistic set of assumptions to estimate the VLV for the future time steps for the range of allowable speed values and applied them at the input of the developed DL model to generate multiple sets of EOP’s. We imposed the defined EOC criteria on these EOPs, and the top three modes of speeds satisfying all the requirements are derived for each second. Thus three eligible speed values are estimated for each second, and an additional criterion is defined to generate a unique ACCSSP for future time steps. Performance comparison between predicted and constant ACCSSPs indicates that the predictive model outperforms constant ACCSSP.

Computer systems are involved in many critical human applications today, so that a small error can lead to serious and dangerous problems. These errors can be from an error in the incorrect design of the user interface to an error in the program code. The success of a software product depends on several factors. Given that different organizations and institutions use software products, the need to have a quality and desirable Software according to the goals and needs of the organization makes measuring the quality of software products. an important issue for most organizations and institutions, To be sure of having the right software. It is necessary to use a standard quality model to examine the features and sub-features for a detailed and principled study in the quality discussion. In this study, the quality of Word software was measured by Adaptive Neural Fuzzy Inference System. In recent years, powerful systems called fuzzy inference systems on The basis of adaptive neural network (ANFIS) has been used in various sciences. Using the power of neural network training and the linguistic advantage of fuzzy systems, these types of systems have been able to realize the advantages of the two in terms of analyzing very powerful complex processes. Considering the importance of software quality and to have a good and usable software in terms of quality and measuring the quality of software during the study. It was applied at different levels to make the result of measuring the quality of Word software more accurate and closer to reality. In this research, the quality of the software product is measured based on the adaptive neural-fuzzy inference system in ISO standard. According to the results obtained in this study, it is understood that quality is a continuous and hierarchical concept and the quality of each part of the software at any stage of production can lead to high quality products.

The power system vulnerability leads to faults and the severity of the fault may lead to prolonged load-shedding. The power system needs to be configured in extreme failure scenarios for protecting the network from further contingencies and prolonged load-shedding. Distributed generation resources (DGs) can be useful to form intentional islands after faults to maintain the continuity of power supply to loads based on their weightage during faulty periods and to reduce overall load shedding duration. Power system is bound to collapses and secondary collapse in the formed island is possible. This research represents a novel method of impedance based path finding for intentional islanding, which adapts itself with the changes in the demands, DGs outputs or further severities during restoration period. In this adaptive islanding approach, network adjusts itself with the changes in either the load demands or renewable DGs outputs and rearranges the restoration plan by curtailing or adding some of the loads through controllable switches. Further a secondary collapse in the existing island is studied by injecting multiple faults at various positions of the network to validate the system resilience to cope with severities. A short-term load forecasting approach is used to predict changes in load demands and variations in DG outputs during the islanding scheme. During the restoration period, these variations are tracked and the islands are modified accordingly. In order to minimize the overall generation cost by using less fuel, an economical approach is used in the selection of controllable DGs. The proposed approach is formulated as a multi-objective, that incorporates several operational constraints and simulation is carried out using the modified IEEE 69-bus distribution system to assess the efficacy of the proposed model.

Novel coronavirus disease (COVID-19) has affected nearly 7 million individuals and claimed more than 0.4 million lives to date. There are several reports of gender differences related to infection and death due to COVID-19. This raises important questions such as “Whether there are differences based on gender in risk and severity of infection or mortality rate?” and “What are the biological explanation and mechanisms underlying these differences?” Emerging evidence has proposed sex-based immunological, genetic, and hormonal differences to explain this ambiguity. Besides biological differences, women have also faced social inequities and economic hardships due to this pandemic. Several recent studies have shown that independent of age males are at higher risk for severity and mortality in COVID-19 patients. Although susceptibility to SARS-CoV-2 was found to be similar across both genders in several disease cohorts, a disproportionate death ratio in men can be partly explained by the higher burden of pre-existing diseases and occupational exposures among men. From an immunological point of view, females can engage a more active immune response, which may protect them and counter infectious diseases as compared to men. This attribute of better immune responses towards pathogens is thought to be due to high estrogen levels in females. Here we review the current knowledge about sex differences in susceptibility, the severity of infection and mortality, host immune responses, and the role of sex hormones in COVID-19 disease.