Assessment of challenges and strategies for driving energy transitions in emerging markets:A socio-technological systems perspective

The pursuit of improved quality of life standards has significantly influenced the contemporary mining model in the 21st century.This era is witnessing an unprecedented transformation driven by pressing concerns related to sustainability,climate change,the just energy transition,dynamic operating environments,and complex social challenges.Such transitions present both opportunities and obstacles.The aim of this study is to provide an extensive literature review on energy transition to identify the challenges and strategies associated with navigating transformations in energy systems.Understanding these transformations is particularly critical in the face of the severe consequences of global warming,where an accelerated energy transition is viewed as a universal remedy.Adopting a socio-technological systems perspective,specifically through the application of Actor Network Theory(ANT),this research provides a theoretical foundation while categorising challenges into five distinct domains and outlining strategies across these different dimensions.These insights are specifically tailored for emerging market countries to effectively navigate energy transition while fostering the development of resilient societies.Furthermore,our findings highlight that energy transition encompasses more than a mere technological shift;it entails fundamental changes in various systemic socio-economic imperatives.Through focusing on the role of social structures in transitions,this study makes a significant and innovative contribution to ANT,which has historically been criticised for its limited acknowledgement of social structures.Consequently,we propose an emerging market energy transition framework,which not only addresses technological aspects,but also integrates social considerations.This framework paves the way for future research and exploration of energy transition dynamics.The outcomes of this study offer valuable insights to policymakers,researchers,and practitioners engaged in the mining industry,enabling them to comprehend the multifaceted challenges involved and providing practical strategies for effective resolution.Through incorporating the social dimension into the analysis,we enhance the understanding of the complex nature of energy system transformations,facilitating a more holistic approach towards achieving sustainable and resilient energy transitions in emerging markets and beyond.

Social Media-Based Surveillance Systems for Health Informatics Using Machine and Deep Learning Techniques:A Comprehensive Review and Open Challenges

Social media(SM)based surveillance systems,combined with machine learning(ML)and deep learning(DL)techniques,have shown potential for early detection of epidemic outbreaks.This review discusses the current state of SM-based surveillance methods for early epidemic outbreaks and the role of ML and DL in enhancing their performance.Since,every year,a large amount of data related to epidemic outbreaks,particularly Twitter data is generated by SM.This paper outlines the theme of SM analysis for tracking health-related issues and detecting epidemic outbreaks in SM,along with the ML and DL techniques that have been configured for the detection of epidemic outbreaks.DL has emerged as a promising ML technique that adaptsmultiple layers of representations or features of the data and yields state-of-the-art extrapolation results.In recent years,along with the success of ML and DL in many other application domains,both ML and DL are also popularly used in SM analysis.This paper aims to provide an overview of epidemic outbreaks in SM and then outlines a comprehensive analysis of ML and DL approaches and their existing applications in SM analysis.Finally,this review serves the purpose of offering suggestions,ideas,and proposals,along with highlighting the ongoing challenges in the field of early outbreak detection that still need to be addressed.

Analysis and Modeling of Mobile Phone Activity Data Using Interactive Cyber-Physical Social System

Mobile networks possess significant information and thus are considered a gold mine for the researcher’s community.The call detail records(CDR)of a mobile network are used to identify the network’s efficacy and the mobile user’s behavior.It is evident from the recent literature that cyber-physical systems(CPS)were used in the analytics and modeling of telecom data.In addition,CPS is used to provide valuable services in smart cities.In general,a typical telecom company hasmillions of subscribers and thus generatesmassive amounts of data.From this aspect,data storage,analysis,and processing are the key concerns.To solve these issues,herein we propose a multilevel cyber-physical social system(CPSS)for the analysis and modeling of large internet data.Our proposed multilevel system has three levels and each level has a specific functionality.Initially,raw Call Detail Data(CDR)was collected at the first level.Herein,the data preprocessing,cleaning,and error removal operations were performed.In the second level,data processing,cleaning,reduction,integration,processing,and storage were performed.Herein,suggested internet activity record measures were applied.Our proposed system initially constructs a graph and then performs network analysis.Thus proposed CPSS system accurately identifies different areas of internet peak usage in a city(Milan city).Our research is helpful for the network operators to plan effective network configuration,management,and optimization of resources.

Effectiveness of sensory integration therapy in children,focusing on Korean children:A systematic review and meta-analysis

BACKGROUND Sensory integration intervention is highly related to the child's effective interaction with the environment and the child's development.Currently,various sensory integration interventions are being applied,but research methodological problems are arising due to unsystematic protocols.This study aims to present the optimal intervention protocol by presenting scientific standards for sensory integration intervention through meta-analysis.AIM To prove the effectiveness of sensory integration therapy,examine the latest trend of sensory integration studies in Korea,and provide clinical evidence for sensory integration therapies.METHODS The database of Korean search engines,including RISS,KISS,and DBpia,was used to search for related literature published from 2001 to October 2020.The keywords,“Children”,“Sensory integration”,“Integrated sensory”,“Sensorymotor”,and“Sensory stimulation”were used in this search.Then,a meta-analysis was conducted on 24 selected studiesRISS,KISS,and DBpia,was used to search for related literature published from 2001 to October 2020.The keywords,“Children”,“Sensory integration”,“Integrated sensory”,“Sensorymotor”,and“Sensory stimulation”were used in this search.Then,a meta-analysis was conducted on 24 selected studies.RESULTS Sensory integration intervention has been proven effective in children with cerebral palsy,autism spectrum disorder,attention deficit/hyperactivity disorder,developmental disorder,and intellectual disability in relation to the diagnosis of children.Regarding sensory integration therapies,1:1 individual treatment with a therapist or a therapy session lasting for 40 min was most effective.In terms of dependent variables,sensory integration therapy effectively promoted social skills,adaptive behavior,sensory processing,and gross motor and fine motor skills.CONCLUSION The results of this study may be used as therapeutic evidence for sensory integration intervention in the clinical field of occupational therapy for children,and can help to present standards for sensory integration intervention protocols.

Does Parental Migration Affect Left-Behind Children’s Social Anxiety?A Systematic Review and Meta-Analysis

Social anxiety is a common psychological problem among left-behind children(LBC)and has been a popular issue in recent years.Children with higher levels of social anxiety have more emotional and behavioral problems and are prone to negative life events.Although several studies have explored the differences in social anxiety between LBC and non-left-behind children(N-LBC),the findings have not been consistent.In this study,a systematic review and meta-analysis method was used,with 411 papers retrieved on October 01,2023,from Pubmed,Embase,Web of Science,and Chinese databases(CNKI,VIP,and Wanfang)(PROSPERO registry number:CRD42023472463).Twenty-one studies met the research criteria and included 11,254 LBC and 13,096 N-LBC.LBC scored significantly higher for social anxiety([WMD(95%CI):0.35[0.23,0.48],p<0.001])and social avoidance and distress([WMD(95%CI):0.35[0.23,0.48],p<0.001]).Subgroup analyses showed significant differences in effect sizes for the overall proportion of children left behind(p=0.02).In addition,different types of parental migration may influence the social anxiety of LBC,double-parent migration was associated higher social anxiety than father migration(p<0.001).Future research should focus on treatments to decrease social anxiety of left-behind children.These findings suggest that due to the long-term absence of parental migration,LBC are more vulnerable to negative emotional experiences and behaviours such as anxiety,distress,and avoidance during social interaction,especially for those with both parents absent from the home.Future research should focus on treatments to reduce social anxiety in LBC.

An Expert System to Detect Political Arabic Articles Orientation Using CatBoost Classifier Boosted by Multi-Level Features

The number of blogs and other forms of opinionated online content has increased dramatically in recent years.Many fields,including academia and national security,place an emphasis on automated political article orientation detection.Political articles(especially in the Arab world)are different from other articles due to their subjectivity,in which the author’s beliefs and political affiliation might have a significant influence on a political article.With categories representing the main political ideologies,this problem may be thought of as a subset of the text categorization(classification).In general,the performance of machine learning models for text classification is sensitive to hyperparameter settings.Furthermore,the feature vector used to represent a document must capture,to some extent,the complex semantics of natural language.To this end,this paper presents an intelligent system to detect political Arabic article orientation that adapts the categorical boosting(CatBoost)method combined with a multi-level feature concept.Extracting features at multiple levels can enhance the model’s ability to discriminate between different classes or patterns.Each level may capture different aspects of the input data,contributing to a more comprehensive representation.CatBoost,a robust and efficient gradient-boosting algorithm,is utilized to effectively learn and predict the complex relationships between these features and the political orientation labels associated with the articles.A dataset of political Arabic texts collected from diverse sources,including postings and articles,is used to assess the suggested technique.Conservative,reform,and revolutionary are the three subcategories of these opinions.The results of this study demonstrate that compared to other frequently used machine learning models for text classification,the CatBoost method using multi-level features performs better with an accuracy of 98.14%.

Enhancing User Security on Instagram: A Multifaceted AI System for Filtering Abusive Comments

Social media platforms like Instagram have increasingly become venues for online abuse and offensive comments. This study aimed to enhance user security to create a safe online environment by eliminating hate speech and abusive language. The proposed system employed a multifaceted approach to comment filtering, incorporating the multi-level filter theory. This involved developing a comprehensive list of words representing various types of offensive language, from slang to explicit abuse. Machine learning models were trained to identify abusive messages through sentiment analysis and contextual understanding. The system categorized comments as positive, negative, or abusive using sentiment analysis algorithms. Employing AI technology, it created a dynamic filtering mechanism that adapted to evolving online language and abusive behavior. Integrated with Instagram while adhering to ethical data collection principles, the platform sought to promote a clean and positive user experience, encouraging users to focus on non-abusive communication. Our machine-learned models, trained on a cleaned Arabic language dataset, demonstrated promising accuracy (75.8%) in classifying Arabic comments, potentially reducing abusive content significantly. This advancement aimed to provide users with a clean and positive online experience.

Digital Human Rights from the Perspective of System Theory——Concept Definition, Social Function, and Constitutional Basis

The three core issues in the“digital human rights”debate are whether“digital human rights”are possible,necessary,and feasible.Both sides of the debate focus on discovering the value of“digital human rights”to individuals from a semantic level,but ignore the significance of“digital human rights”to the whole society and its subsystems at the level of social structure.By introducing Niklas Luhmann's System Theory,this observation blind spot can be eliminated.Fundamental rights are devoted to directly shaping not a physiological-psychological“individual”as a social environment but a social“person”that can be included by social systems.It is clear that digital human rights are the right to participate in digital communication of a“human”as a“person”,so they are possible in terms of conceptual definition.Digital human rights can help“people”lower the threshold for participation in digital communication,limit the excessive expansion of social systems,and promote the free and complete expression of body and mind,so they are necessary for social functions.There are limitations in the existing two ideas of“incorporating digital human rights into the constitution”.Based on the new construction idea of System Theory of Law,digital human rights as the right to participate in digital communication can be typified into digital communication in social sub-fields such as politics,economy,science,and art.The right to participate constructs a complete digital human rights system,making it feasible on the basis of the constitution.

Construction of an Evaluation Indicator System for Civic and Political Applications in Professional Practice Courses Based on CIPP:Taking the VBSE Practical Training Course as an Example

This study takes the virtual business society environment(VBSE)practical training course as a case study and applies the theoretical framework of the context,input,process,product(CIPP)model to construct an evaluation indicator system for the application of civic and politics in professional practice courses.The context evaluation is measured from the support of the VBSE practical training course into course civic and politics,teachers’cognition,and the integration of course objectives;the input evaluation is measured from the matching degree of teachers’civic and political competence,and the matching degree of teaching resources;the process evaluation is measured from the degree of implementation of civic and politics teaching and the degree of students’acceptance;and the product evaluation is measured from the degree of impact of civic and politics teaching.

Emerging Trends in Social Networking Systems and Generation Gap with Neutrosophic Crisp Soft Mapping

This paper aims to introduce the novel concept of neutrosophic crisp soft set(NCSS),including various types of neutrosophic crisp soft sets(NCSSs)and their fundamental operations.We define NCS-mapping and its inverse NCS-mapping between two NCS-classes.We develop a robust mathematical modeling with the help of NCS-mapping to analyze the emerging trends in social networking systems(SNSs)for our various generations.We investigate the advantages,disadvantages,and natural aspects of SNSs for five generations.With the changing of the generations,it is analyzed that emerging trends and the benefits of SNSs are increasing day by day.The suggested modeling with NCS-mapping is applicable in solving various decision-making problems.

An Extended Fuzzy-DEMATEL System for Factor Analyses on Social Capital Selection in the Renovation of Old Residential Communities

China has been promoting the renovation of old residential communities vigorously.Due to the financial pressure of the government and the sustainability of the renovation of old residential communities,public-private partnerships(PPP)have already gained attention.The selection of social capital is key to improving the efficiency of the PPP model in renovating old residential communities.In order to determine the influencing factors of social capital selection in the renovation of old residential communities,this paper aims to find an effective approach and analyze these factors.In this paper,a fuzzy decision-making and trial evaluation laboratory(fuzzy-DEMATEL)technique is extended and amore suitable systemis developed for the selection of social capital using the existing group decisionmaking theory.In the first stage,grounded theory is used to extract the unabridged key influencing factors for social capital selection in the renovation of old residential communities.Secondly,by considering the impact of expert weights,the key influencing factors are identified.The interactions within these influencing factors are discussed and the credibility of the results is verified by sensitivity analysis.Finally,these key influencing factors are sorted by importance.Based on the results,the government should focus on a technical level,organizationalmanagement abilities,corporate reputation,credit status,etc.This study provides the government with a theoretical basis and a methodology for evaluating social capital selection.

A Real-Time Pedestrian Social Distancing Risk Alert System for COVID-19

The COVID-19 virus is usually spread by small droplets when talking,coughing and sneezing,so maintaining physical distance between people is necessary to slow the spread of the virus.The World Health Organization(WHO)recommends maintaining a social distance of at least six feet.In this paper,we developed a real-time pedestrian social distance risk alert system for COVID-19,whichmonitors the distance between people in real-time via video streaming and provides risk alerts to the person in charge,thus avoiding the problem of too close social distance between pedestrians in public places.We design a lightweight convolutional neural network architecture to detect the distance between people more accurately.In addition,due to the limitation of camera placement,the previous algorithm based on flat view is not applicable to the social distance calculation for cameras,so we designed and developed a perspective conversion module to reduce the image in the video to a bird’s eye view,which can avoid the error caused by the elevation view and thus provide accurate risk indication to the user.We selected images containing only person labels in theCOCO2017 dataset to train our networkmodel.The experimental results show that our network model achieves 82.3%detection accuracy and performs significantly better than other mainstream network architectures in the three metrics of Recall,Precision and mAP,proving the effectiveness of our system and the efficiency of our technology.

Face Mask and Social Distance Monitoring via Computer Vision and Deployable System Architecture

The coronavirus(COVID-19)is a lethal virus causing a rapidly infec-tious disease throughout the globe.Spreading awareness,taking preventive mea-sures,imposing strict restrictions on public gatherings,wearing facial masks,and maintaining safe social distancing have become crucial factors in keeping the virus at bay.Even though the world has spent a whole year preventing and curing the disease caused by the COVID-19 virus,the statistics show that the virus can cause an outbreak at any time on a large scale if thorough preventive measures are not maintained accordingly.Tofight the spread of this virus,technologically developed systems have become very useful.However,the implementation of an automatic,robust,continuous,and lightweight monitoring system that can be efficiently deployed on an embedded device still has not become prevalent in the mass community.This paper aims to develop an automatic system to simul-taneously detect social distance and face mask violation in real-time that has been deployed in an embedded system.A modified version of a convolutional neural network,the ResNet50 model,has been utilized to identify masked faces in peo-ple.You Only Look Once(YOLOv3)approach is applied for object detection and the DeepSORT technique is used to measure the social distance.The efficiency of the proposed model is tested on real-time video sequences taken from a video streaming source from an embedded system,Jetson Nano edge computing device,and smartphones,Android and iOS applications.Empirical results show that the implemented model can efficiently detect facial masks and social distance viola-tions with acceptable accuracy and precision scores.

Psychosocial Group Intervention at a Low-Resource Setting Environment for Women Who Are Diagnosed and Treated for Breast Cancer: A Systematic Review

Africa faces significant challenges in terms of material and personnel resources for oncology interventions. This is particularly evident in South Africa, where resources are divided into high- and low-resource settings. High-resource settings cater to those with financial means to access private oncology facilities. However, many breast cancer patients receive care in South Africa’s low-resource settings, such as public hospital oncology clinics. Unfortunately, these settings have limited service providers and fail to offer comprehensive interventions, resulting in poor outcomes. However, recent research has highlighted the significance of socially supportive relationships in promoting healing and overall individual well-being, and spirituality has been identified as a source of positive outcomes in cancer patients. This systematic review paper explores the feasibility of implementing support group cancer care and interventions that incorporate social support networks available in community settings, and spiritual practices facilitated by traditional healers, and religious/spiritual leaders. These interventions can be provided within low-resource settings to women diagnosed with breast cancer. Inclusive participation of spouses, children, and extended family in the support group cancer care can facilitate healing for the entire system. Focusing on the strengths and resources within communities and incorporating these complementary services, can enhance the well-being and quality of life for Black African women diagnosed with breast cancer, despite low-resource settings. This approach acknowledges the potential of community-based support networks and encourages collaboration between various stakeholders, including community health educators, nurses, lay counselors, and community volunteers, to address the complex needs of these patients.

Marx’s Ontology of Social Power System for Ecological Justice

Historical materialism provides the ontology basis to understand the contemporary ecological justice problem,which is the perspective for analyzing ecological interests from the nature,structure,and transition of the social power system.The transcendence of Marx’s thoughts on western mainstream environmental justice theory lies that it does not based on the“speculative ontology”of metaphysics,but on the basis of“realistic ontology”of social power system.

Factors influencing quality of life in Chinese patients with dementia: a systematic review

Background:Dementia is a group of nervous system diseases characterized by progressive cognitive decline,leading to a loss of self-care ability and a decline in well-being.This places a significant burden on the global healthcare system,with Chinese patients accounting for approximately one-quarter of the world’s dementia cases.Therefore,it is crucial to identify factors that impact the quality of life(QOL)among elderly Chinese individuals with dementia.Method:To achieve this,we conducted a comprehensive search of several databases,including PubMed,Embase,Web of Science,the Cochrane Library,China National Knowledge Infrastructure,Wanfang Data,China VIP Database and China Biomedical Literature Database.We reviewed cross-sectional studies from the inception of these databases until March 27,2022.QOL outcomes were assessed using standardized scales in the studies included in this review.Results:The search yielded a total of 1,235 relevant articles,from which we finally included 21 cross-sectional studies and one longitudinal study after rigorous quality assessment.Among these,10 studies were classified as high quality,while 12 were classified as fair quality.Through our analysis,we identified 28 patient-rated QOL factors and 14 caregiver-rated QOL factors.These factors were categorized into three groups:patient,disease-related and caregiver.Factors commonly found to influence patient-rated QOL included age,education,marital status,depression,self-care ability,dementia severity,cognitive function,behavioral and psychological symptoms of dementia and caregiver burden.Similarly,factors commonly influencing caregiver-rated QOL included economic status,depression,self-care ability,dementia severity,cognitive function,behavioral and psychological symptoms of dementia and caregiving time.Conclusion:This review clarifies the factors that influence the QOL of Chinese individuals with dementia.When implementing interventions,it is crucial to consider the differences between patient-rated QOL and caregiver-proxy-rated QOL,as well as their respective influencing factors.

Formulating Innovative Societal Educational Learning Systems Using Gamification Techniques in 3D Environments

Gamified education has set the ground for the delineation of state-of-the-art literacy skills,enabling learners to develop their digital,cognitive,emotional and social competencies,through active experimentation,motivation and engagement,all while ensuring that pedagogical objectives are being effectuated,therefore capacitating the optimization of the learning process,as a whole.In this paper,we commence by assessing some of the most fundamental frameworks,models and theories evolved around the concept of gamification.We,additionally,showcase schemes through which it stimulates the actualization of active,multidimensional learning,by promoting the application of technological advancements,for the enhancement of learners’hard and soft skills,within time and cost effective frameworks.Ultimately,we,thoroughly,present a newly introduced,cross-platform,innovative educational learning system product,funded by the Hellenic Republic Ministry of Development and Investments,howlearn.Using gamification techniques,in 3D virtual environments,for the realization of STEAM related experiments which cover the vast majority of learners’subject material,while,simultaneously functioning as an authoring tool,whilst essentially accounting for accessibility,geographical and other socio-economic considerations,howlearn advocates youth-centered learning,providing the foundations towards the establishment of gamified,socially sustainable,multifaceted,inclusive educational learning systems.

Polygynous Mating System and Behavioural Reason of Black Crested Gibbon (Nomascus concolor jingdongensis) at Dazhaizi,Mt.Wuliang, Yunnan,China

The field research on five black crested gibbon groups, recently performed at Dazhaizi, Mr. Wuliang, Central Yunnan, China, showed that all groups in the local population consisted of one adult male, two adult females and 2 - 5 sub-adults, juveniles and itfants. The mean group size was 6.2 in August 2003 and 6.4 in August 2005. Two subadult males disappeared from their natal home range and three newborns were given birth in Group 3 （G3） and G4 during this study. The two adult females in G1, G2 and G3 gave births and/or carried babies but at different times. There was no aggressive or dominating behaviour observed between the two adult females. One floating female was first seen in G3＇s territory on April 15, 2005. The two resident females interrupted her duet with adult male and chased her. We did not observe adult male chased this floating female and she left G3＇s territory 10 days later. Sub-adult males often kept distance with the family, and they often sang solo bouts in their natal territory before they dispersed. The sub-adult males and females dispersed from natal territory and two adult resident females rejected the third one, which might were the reasons why the black gibbon groups were polygyny in Dazhaizi.

Melatonin improves synapse development by PI3K/Akt signaling in a mouse model of autism spectrum disorder

Autism spectrum disorders are a group of neurodevelopmental disorders involving more than 1100 genes,including Ctnnd2 as a candidate gene.Ctnnd2knockout mice,serving as an animal model of autis m,have been demonstrated to exhibit decreased density of dendritic spines.The role of melatonin,as a neuro hormone capable of effectively alleviating social interaction deficits and regulating the development of dendritic spines,in Ctnnd2 deletion-induced nerve injury remains unclea r.In the present study,we discove red that the deletion of exon 2 of the Ctnnd2 gene was linked to social interaction deficits,spine loss,impaired inhibitory neurons,and suppressed phosphatidylinositol-3-kinase(PI3K)/protein kinase B(Akt) signal pathway in the prefrontal cortex.Our findings demonstrated that the long-term oral administration of melatonin for 28 days effectively alleviated the aforementioned abnormalities in Ctnnd2 gene-knockout mice.Furthermore,the administration of melatonin in the prefro ntal cortex was found to improve synaptic function and activate the PI3K/Akt signal pathway in this region.The pharmacological blockade of the PI3K/Akt signal pathway with a PI3K/Akt inhibitor,wo rtmannin,and melatonin receptor antagonists,luzindole and 4-phenyl-2-propionamidotetralin,prevented the melatonin-induced enhancement of GABAergic synaptic function.These findings suggest that melatonin treatment can ameliorate GABAe rgic synaptic function by activating the PI3K/Akt signal pathway,which may contribute to the improvement of dendritic spine abnormalities in autism spectrum disorders.

Brain region-specific roles of brain-derived neurotrophic factor in social stress-induced depressive-like behavior

Brain-derived neurotrophic factor is a key factor in stress adaptation and avoidance of a social stress behavioral response.Recent studies have shown that brain-derived neurotrophic factor expression in stressed mice is brain region–specific,particularly involving the corticolimbic system,including the ventral tegmental area,nucleus accumbens,prefrontal cortex,amygdala,and hippocampus.Determining how brain-derived neurotrophic factor participates in stress processing in different brain regions will deepen our understanding of social stress psychopathology.In this review,we discuss the expression and regulation of brain-derived neurotrophic factor in stress-sensitive brain regions closely related to the pathophysiology of depression.We focused on associated molecular pathways and neural circuits,with special attention to the brain-derived neurotrophic factor–tropomyosin receptor kinase B signaling pathway and the ventral tegmental area–nucleus accumbens dopamine circuit.We determined that stress-induced alterations in brain-derived neurotrophic factor levels are likely related to the nature,severity,and duration of stress,especially in the above-mentioned brain regions of the corticolimbic system.Therefore,BDNF might be a biological indicator regulating stress-related processes in various brain regions.