Shells, probably like no other product of nature, have played an important role in the history of mankind. The pre-Hispanic civilizations of Ibero-America also used certain type of shells profusely in their religious ceremonies, in particular, in Ecuador there were two species of main importance, the Spondylus princeps and the Spondylus calcifer broadly employed to manufacture ornaments that possess a strong symbolic, religious and social meaning and that were almost exclusively used by ruling classes. Among these ornaments, the faces carved on the Spondylus shells are little known. In the present study, we chose a total of fifteen (15) pieces from the Pastor Restrepo Lince´s archaeomalacology collection to understand the possible uses of these objects, through the interpretation of the gestures represented on the faces, their dimensions, and their geographical distribution in pre-Hispanic Ecuador. To achieve the proposed objective, we approach the present investigation from the perspective of the formal analysis of concepts, which is a mathematical theory of representation of knowledge, finding that these faces carved in Spondylus, were used daily or in special ceremonial occasions and that its use was common in all the regional cultures of ancient Ecuador, from the oldest such as Valdivia, and for more than 2000 years, indicating a long tradition of the use of Spondylus as an object of great symbolic and economic value until the arrival of the Spanish

Because of an often complicated and difficult-to-access care system, help-seeking for people with suspected dementia can be stressful. Difficulty in help-seeking may contribute to carer burden, in addition to other known stressors in dementia care. This study examined the relationship between perceived help-seeking difficulty and carer burden, and the barriers contributing to perceived difficulty. We interviewed 110 carers accessing a community-based dementia assessment service for suspected dementia of a family member for their perceived difficulty, delays, and barriers in help-seeking, and carers burden in terms of role strain, self-criticism, and negative emotions. Linear regression models showed that perceived help-seeking difficulty is associated with carer self-criticism, while carer role strain and negative emotions are associated with symptom severity of the person with dementia but not help-seeking difficulty. Inadequate knowledge about symptoms, service accessibility, and affordability together explained more than half of the variance in perceived help-seeking difficulty (Nagelkerke R2 = .56). Public awareness about symptoms, support in navigating service, and financial support may reduce perceived difficulty in help-seeking, which in turn may reduce carer self-criticism during the early course of illness.

Introduction: Oral health knowledge, beliefs and attitudes of parents have a direct influence on their child’s oral health maintenance, dietary habits and encourage healthy behaviors. The aim of this study was to assess the knowledge, and attitude of parents with regards to the oral health practice of their children and its associated factors. Methods: A self -administered structured questionnaire was administered to parents or guardians of learners aged 5 to 12 years at a low socio-economic rural primary school in the uGu district, KwaZulu Natal, South Africa to collect demographic variables, knowledge of dental health, use of toothbrush, use of toothpaste, dietary practices and dental visits; practice with respect to dental care and attitudes towards oral health. The questionnaire was translated from English to IsiZulu was used. Bivariate and multivariate logistic regressions were used to assess the association between parent’s sociodemographic factors with their oral health knowledge and practice scores. Results: One hundred and forty four completed survey instruments were received with an eighty percent (118) representation of mothers. Ninety five percent of the parents (136) had a positive attitude towards oral health with 86% (124) of the children brushed their tongue and 89% (128) of the children brushed their teeth happily. The mean knowledge score was 70% with a median of 72%. Children were significantly more likely to brush their tongues (AOR: 3.20 95% CI: 1.06-9.66) and were more likely to be happier when brushing their teeth (AOR: 4.65 95% CI: 1.41-15.38) when the caregivers were their mothers, and when parents had an above average knowledge score (AOR: 1.86 95% CI: 0.72-4.85) and had positive attitudes (AOR: 3.20 95%CI: 0.46-22.00). Conclusion: To reduce the gaps in knowledge, oral health promotion should be integrated into all point of care contact with parents at health facilities, increased community awareness and advertising campaigns as well as a more focused school health oral program that addresses primary prevention, screening and appropriate referrals to health facilities

Social media has become part and parcel of the world of today. These days, it’s still the most talked about thing. It cannot be overlooked because it plays a key role in our business functions such as marketing and advertising. Social Media is all about collaboration on files, ideas and projects that help users and stakeholders to successfully complete the project. It influences how people communicate, develop relationship, build trust, increase transparency and provide cultural context. The fundamental aim of this research is to investigate the capacity for project management in social media. This paper explains how social media is used for project management knowledge areas and process groups. Also this research aims to identify SM tools that can be suitable for project management processes. Two studies Delphi Study of three rounds and structured case study interview are used to investigate the impact on the performance of the project team and process robustness. These studies support social media use by accessing the contribution to relationship building, trusts, coordination and cohesion.

The growth of sustainable apparels provides grounding for the emergence of a new consumer market. Surprisingly, this movement has not only taken place in developed countries but also in emerging countries. To the best of the authors’ knowledge, no systematic reviews have been carried out on sustainable apparels, focused specifically on emerging countries. The goal of this systematic review is to analyse the existing literature on various factors that influence sustainable apparel consumption in emerging countries, as well as presenting future research directions. This detailed literature review is guided by the PRISMA Statement (Preferred Reporting Items for Systematic reviews and Meta-Analyses) review method, and its searches encompass sources such as Web of Science, Scopus and Google Scholar databases which, in result, identified 19 related studies. Furthermore, the reviewers segregated the factors into four major domains which include value, subjective norm, perceived behavioural control and knowledge. The results of this review demonstrate that most customers in the emerging countries have already experienced a growing interest in sustainable apparel with a strong development of knowledge, great concern, values and social circle. In sum, this systematic literature review provides an insightful information about sustainable apparel purchase behaviour and helps academicians, manufacturers and retailers to position and market their sustainable clothing brands.

Traditionally understood in reference to distance education, cognitive presence may be defined as "the extent to which the participants in any particular configuration of a community of inquiry can construct meaning through sustained communication. The purpose of this paper was to create a blueprint for the reflective ePortfolio as the capstone project for my graduate degree. The blueprint was accomplished by adapting for use cognitive presence as a tool for both analysis and framing, which has never been done to the best of my knowledge. I considered myself to be a participant in a “community of inquiry” model and substituted the result of my interaction with each of the required instructor-course content pairings I took to serve as fellow participants in this community model. The result of my participation was understood to be the knowledge and experience that I have gained, which was reflected in eight of my academic research papers. These selected papers were the artifacts around which my ePortfolio was ultimately developed and demonstrated my participation as an active member in this community of inquiry constructing meaning through sustained communication.

One of the most pivotal challenges that learners may face, during foreign language learning, is building a reliable lexicon. Insufficient vocabulary knowledge may put serious obstacles in the foreign language learning process. Thus, students need to equip themselves with different strategies to cope with these difficulties. Translation equivalence (TE) is one of these strategies. Since the success or failure of any vocabulary learning strategy depends on two main factors: developing the learners’ vocabulary knowledge and the extent of difficulty or ease with which the learner acquires new words, this research aims at investigating the impact of this strategy on EFL learners’ vocabulary knowledge and word learnability. 258 Tunisian 9^th graders participated in this project. Two vocabulary recognition tests, one using translation equivalence and the other using only-English strategies, were used to test the hypotheses. Different statistical tests and techniques were employed to analyze data. Findings showed that TE has a positive impact on learners’ vocabulary knowledge and revealed that students, at this level of proficiency, learn vocabulary through translation better than any other strategy using only English.

This study presents a comprehensive investigation on an innovative mechanical system inspired by recent advancements in metamaterials, more specifically the work is focused on Origami-type structures for their intriguing mechanical properties. Originating from the specific fields such as aerospace for their lightweight and foldable characteristics, Origami mechanical devices exhibit unique non-linear stiffness, in particular, when suitably designed, they show Quasi-Zero Stiffness (QZS) characteristics within a specific working range. The QZS property, aligned with the High Static Low Dynamic (HSLD) stiffness concept, suggests promising applications such as a low-frequency mechanical passive vibration isolator. The study explores the vibration isolation characteristics of Origami-type suspensions, with a particular emphasis on their potential application as low-frequency passive vibration isolators. The Kresling Origami Module (KOM) has been selected for its compactness and compatibility with 3D printers. A detailed analysis using 3D CAD, Finite Element Analysis, and experimental testing have been carried out. The investigation includes the analysis of the influence of geometric parameters on the non-linear Force-Displacement curve. Multibody simulations validate the low-frequency isolation properties within the QZS region, and disparities in dynamic properties beyond the QZS range. The study underscores the transformative potential of Origami-type metamaterials in enhancing low-frequency vibration isolation technology. It also highlights challenges related to material properties and loading mass variations, providing valuable insights for future developments in this promising field.

In this paper, we introduce a control method for the linear quadratic tracking (LQT) problem with zero steady-state error. It is achieved by augmenting the original system with an additional state representing the integrated error between the reference and actual outputs. In its essence, it is a linear quadratic integral (LQI) control embedded in a general LQT control framework, with the reference trajectory generated by a linear exogenous system. During the simulative implementation for the specific real-world system Car-in-the-Loop (CiL) test bench, we assume that the 'real' system is completely known. Therefore, for the model-based control, we can have a perfect model identical to the 'real' system. It becomes clear that stable solutions can scarcely be achieved with controller designed with the perfect model of the 'real' system. Contrary, we show that a model learnt via Bayesian Optimization (BO) can facilitate a much bigger set of stable controllers. It exhibits an improved control performance. To the best of the authors' knowledge, this discovery is the first in the LQT related literature.

Dealing with and maintaining high-quality standards in the design and construction phases is challenging, especially for on-site construction. Issues like improper implementation of building components and poor communication can widen the gap between design specifications and actual conditions. To prevent this, particularly for energy-efficient buildings, it's vital to develop resilient, sustainable strategies. These should optimize resource use, minimize environmental impact, and enhance livability, contributing to carbon neutrality by 2050 and climate change mitigation. Traditional post-occupancy evaluations, which identify defects after construction, are impractical for addressing energy performance gaps. A new, real-time inspection approach is necessary throughout the construction process. This paper suggests an innovative guideline for prefabricated buildings, emphasizing digital 'self-instruction' and 'self-inspection'. These procedures ensure activities impacting quality adhere to specific instructions, drawings, and 3D models, incorporating relevant acceptance criteria to verify completion. This methodology, promoting alignment with planned energy-efficient features, is supported by BIM-based software and Augmented Reality (AR) tools, embodying Industry 4.0 principles. BIM and AR bridge the gap between virtual design and actual construction, improving stakeholder communication and enabling real-time monitoring and adjustments. This integration fosters accuracy and efficiency, key for energy-efficient and Nearly Zero-Energy Buildings, marking a shift towards a more precise, collaborative, and environmentally sensible construction industry.

Over the years, the need for autonomous systems such as drones, also known as unmanned aerial vehicles (UAVs), has significantly increased in popularity across several industries, including the military. The Internet of Battlefield Things (IoBT) represents a modern technological advancement that significantly enhances the operational efficacy of defense systems. It establishes an integrated military force by connecting individuals to intelligent technology in drones, radios, equipment, antennas and ground stations. This is achieved through the utilization of cloud and edge computing, sensors, mobile devices, embedded systems, and IoT-based systems. However, IoBT security is significantly compromised by enemies that exploit the vulnerabilities and malicious intrusions that specifically target the external environment or to obtain sensitive data. In addition, a comprehensive understanding of cyberattacks and countermeasures is essential for assuring the security of IoBT. On the other hand, as cyber threats get more complicated, trust becomes increasingly important for the security of digital systems. Within this context, the zero trust architecture contends that the security of a complex network is inherently vulnerable to internal and external threats. In defense, UAV security system applications, for example, the primary purpose of the zero trust architecture is to limit the vulnerability of military UAV systems to cyberattacks, hence reducing the possibility of data breaches and illegal entry. This study conducts a systematic review aimed at identifying different aspects of cybersecurity strategies for protecting UAV systems within the IoBT domain. Furthermore, zero trust architecture is presented in the study as an effective prospective solution to the security issues that develop in defense UAV systems.

Education is one of the most important tools available to policymakers and non-governmental bodies to promote a change in the behavior of society and to address the climate crisis, in line with the 4^th and 13^th Sustainable Development Goals of the United Nations 2030 Agenda. Project-based learning (PBL) addresses real and global challenges and allows the academic and professional formation of students. As part of the reflection stage of the educational action research method using the Business Canvas Model (BMC), pedagogic deficiencies were identified in the PBL and a didactic proposal was elaborated to emulate the interaction of students with the ecosystems of society. A prototype for improving the management of organic manures as soil amendments was initially developed, to provide the students with a quick start on the first steps of the proposed project. In the 10 sessions designed for the PBL (Make it happen!), the students elaborate a more sophisticated artifact in response to the demands of potential clients, as per the outcomes of the primary market research (5^th session). Active teaching methods and tools, such as a modified template of the BMC with the Rumsfeld’s matrix, aid the metacognition of students and their competences development. In a double session of the PBL, the primary market research is organized with key stakeholders of the agroindustry to enquire about the feasibility of the implementation of the technological solution and the logistics at farm level. Finally, the evaluation relies on the suitability of the upgraded prototype to respond customers’ demands.

Graph unlearning has emerged as a crucial technique in privacy-preserving applications, particularly in scenarios where sensitive data must be erased from graph-based systems. This paper explores the fundamental concepts, challenges, and methodologies associated with graph unlearning, including its application to domains such as social networks, financial transaction systems, and healthcare networks. By delving into various algorithms, including local and global unlearning methods, we analyze the trade-offs between accuracy, privacy, and computational efficiency. The paper further investigates the security risks associated with graph unlearning, including adversarial and inference attacks, and proposes mitigation strategies to safeguard unlearning processes. We also examine the unique challenges posed by federated learning systems, where unlearning requires coordination across decentralized clients. Finally, the paper evaluates graph unlearning techniques using performance metrics such as time complexity, accuracy, and privacy guarantees, supported by real-world examples from diverse applications. The findings emphasize the importance of developing robust, scalable, and secure unlearning mechanisms to ensure data privacy and compliance with regulations. Evaluation and metrics for Graph Unlearning will also be discussed in this paper.

With the increasing use of automatic transmissions in passenger vehicles, there is a growing need for improved gear shifting control strategies that aim to enhance performance and fuel efficiency. This paper introduces a knowledge-based fuzzy control system designed specifically for automatic transmissions in light vehicles. The proposed system utilizes a fuzzy inference system as the controller, enabling the emulation of the driver’s perceptions during the gear shifting process. Consequently, the input signal consists of variables that are either sensitive to or observable by the driver, while the output represents the selected gear. The paper evaluates three distinct control strategies, each based on different inputs. These inputs include the longitudinal velocity of the vehicle, the angular velocity of the engine, or a combination of both, along with the time-derivative of the throttle valve opening. Through the evaluation, it is observed that the combination of inputs yields significantly improved performance. Therefore, the methodology presented in this paper demonstrates that the combined input approach is more effective for gear shifting control.

This comparative study aims to investigate environmental awareness, knowledge, and safety among students in Montenegro and North Macedonia, considering the unique socio-cultural and environmental contexts of both countries. A mixed-methods approach involving surveys and interviews was employed to gather data from students in educational institutions across Montenegro and North Macedonia. The study assessed various factors, including students' awareness of environmental issues, their perceived level of knowledge, and their attitudes towards safety measures. A comprehensive series of 400 face-to-face interviews was carried out and these interviews encompassed 200 students from Montenegro and an equal number from North Macedonia, taking place at two esteemed academic institutions: The University of Montenegro in Podgorica and The University of St. Clement of Ohrid, Bitola, Republic of North Macedonia. Our hypothetical conceptual framework proposes that a combination of variables, including gender (H1), age (H2), year of study (H3), and rate of study (H4), significantly influences the attitudes of students from Montenegro and North Macedonia towards environmental awareness, safety, and knowledge. Research findings indicate significant variations in environmental awareness and knowledge perception among students in the two countries. While both Montenegro and North Macedonia face environmental challenges, differences in educational systems and socio-economic factors contribute to distinct attitudes and behaviors towards environmental issues and safety practices. This study sheds light on the importance of understanding regional differences in environmental awareness and knowledge perception among students. By identifying areas of strength and areas needing improvement, policymakers and educators can develop targeted interventions to enhance environmental education and foster a culture of sustainability in both Montenegro and North Macedonia.

Scheduling and optimization have a central place in the research area of computing because it is increasingly important to achieve fully automated production processes to adjust manufacturing systems to the requirements of Industry 4.0. In this paper, we demonstrate how an automated wet-etch scheduling problem for a semiconductor industry can be solved by constraint answer set programming (CASP) and, its solver called clingcon. The considered scheduling problem includes a robot for lot transfers between the baths. CASP is a hybrid approach in automated reasoning that combines different research areas such as answer set programming, Constraint Processing and Satisfiability Modulo Theories. For a long time, exact methods such as constraint programming have displayed difficulties in solving real large-scale problem instances. Currently, the performance of state-of-the-art constrains solvers is comparatively better than a decade ago, and some complex combinatorial problems can be optimally solved. These theoretical and technical achievements open new horizons for declarative programming applications such as logistics. A successful solution to this problem is achieved and we found that in most cases tested, CASP is faster and obtains in most cases similar makespan values than solutions based on mixed integer linear programming and constraint paradigms.

We develop new theory from a broad case-study approach to explore a better understanding of what constitutes knowledge and its value as identifiable to human-machine teams. From past research, this trail of exploration will lead to these initial questions: What is the value of debate in the furtherance of knowledge? Will machines with AI be able to contribute to a debate if we humans cannot define it or determine its value sufficiently for a machine’s understanding, contribution, exploration and identification? Like humans, as teammates, machines must be able to determine with AI what constitutes the usable knowledge that contributes to a team’s success in the field (e.g., testing "knowledge" in the field, identifying new knowledge, using knowledge to develop innovation) or its failure (viz., trouble shooting; identifying weaknesses; discovering vulnerabilities, hiding by deception). It matters not whether a debate is public, private or unexpressed by an individual human or machine agent alone; we speculate in this exploration that the process advances the science of autonomous human-machine teams and assists in interpretable machine learning. We conclude with questions and a speculation: How does a human become aware or express its awareness of knowledge? Can a machine be as expressive as its human teammates? And how does a human-machine teammate become aware that its teammates possess sufficient knowledge to perform a task? We speculate that the structure of “knowledge," once found, is resistant to alternatives (i.e., it is ordered); that its functional utility is generalizable; and that its applications are multifaceted (akin to maximum entropy production). The complexity of the team is taken into consideration in our search for knowledge, which can also be used as an information metric.

The study explores the value of indigenous knowledge in mitigating and adapting to the effects of climate change in rural communities of Uganda. It provides an overview of how rural farmers use traditional and indigenous knowledge to predict weather changes to inform their adaptation and mitigation plans. The study intended to investigate how climate change effects were managed by the rural communities of Bakiga using indigenous knowledge and practices. Specifically, it sought to establish how local farmers’ innovations and practices were used to mitigate the effects of climate change and enhance food security. A case study research design with triangulation of data sources was employed. The study was conducted in Rukiga District, southwestern Uganda, where rural farmers were interviewed to explore their practices for reducing the effects of climate change to enhance food security. Thematic data analysis was used to get participants’ experiences and views regarding indigenous knowledge and effects of climate change mitigation. The findings indicate that rural farmers have indigenous practices of mitigating the effects climate change and contributing to the Sustainable Development Goals (SDGs).

The overexploitation of forest resources in the charcoal production basin of the city of Lubum-bashi (DR Congo) is reducing the resilience of miombo woodlands and threatening the survival of the riparian as well as urban human populations that depend on it. We assessed the socio-economic value and availability of plant-based non-timber forest products NTFPs in the rural area of Lubumbashi through ethnobotanical (100 respondents) and socio-economic (90 respondents) interviews, supplemented with floristic inventories, in two village areas selected on the basis of the level of forest degradation. The results show that 60 woody species, including 46 in the degraded forest (Maksem) and 53 in the intact forest (Mwawa), belonging to 22 families are used as sources of NTFPs in both villages. Among these species, 25 are considered priority species. NTFPs are collected for various purposes, including handcrafting, hut building, and traditional medicine. Moreover, the ethnobotanical lists reveal a similarity of almost 75%, indicating that both local communities surveyed use the same species for collecting plant-based NTFPs, despite differences in the level of degradation of the miombo woodlands in the two corresponding study areas. However, the plant-based NTFPs that are collected from miombo woodlands and traded in the urban markets have significant economic value, which ranges from 0.5 to 14.58 USD per kg depending on the species and uses. NTFPs used for handicraft purposes have a higher economic value than those used for other purposes. However, the sustainability of this activity is threatened due to unsustainable harvesting practices that include stem slashing, root digging, and bark peeling of woody species. Consequently, there is a low availability of plant-based NTFPs, particularly in the village area where forest degradation is more advanced. It is imperative that policies for monitoring and regulation of harvesting, and promoting sustainable management of communities’ plant-based NTFPs priority, be undertaken to maintain their resilience.

Antimicrobial resistance (AMR) remains a challenge in Kenya, while the extent remains unknown. To assess the knowledge, cultural beliefs, practices, and behavioral patterns among multisectoral stakeholders in Kenya. The cross-sectional survey was conducted in August 2021 among farmers, animal health service providers and AMR researchers. Regional digital data collection tool developed by FAO was shared and responses obtained through mail, phone calls and direct interviews. Descriptive and inferential analysis were conducted. Antimicrobials were mostly sourced from agro-veterinary shops and from veterinary professionals. Farmers, often implement self-treatment and reported overuse, unnecessary use, and sometimes fail to complete the dosage in livestock. More farmers reported to have heard about antibiotics as compared to antimicrobials, mostly from friends and radio program, however only 9.2% could correctly differentiate the two. Animal Health Service Providers (AHSP) were the source of information to farmers regarding AMR. AHSPs mainly relied on suppliers and distributors for information about antibiotics. Both farmers and AHSPs treated viral infections with antibiotics. One Health Partners (OHPs) had higher knowledge and largely favorable attitudes towards AMR. Up to 72.7% of OHPs from training institutions had AMR included in training curriculum however, they were optimistic livestock farmers and government were less concerned about AMR. Gaps in knowledge and practice on Antimicrobial Stewardship (AMS) were observed in all categories of stakeholders. Given the documented knowledge-practice gap, innovative solutions are needed for both AHSPs and farmers to promote good antimicrobial stewardship practices and to mitigate burdens of AMR. Outcomes of this research should deepen the understanding of critical information and trigger behavioral change in usage and stewardship of antimicrobials.

Using a variety of theoretical foundations, this paper examines the association between corporate social responsibility (CSR) and employees’ knowledge-sharing behavior, investigating the sequential mediation role of perceived organizational support and organizational commitment as well as the moderating role of ethical climate. Hypotheses were formulated to address both the sequential mediation effect of perceived organizational support and organizational commitment on the link between CSR and knowledge-sharing and the moderation effect of ethical climate on the relationship between CSR and perceived organizational support. In order to mitigate the problems of potential common method bias, data were gathered from a sample of 204 South Korean employees at three distinct time points. The results of the study offer compelling evidence that CSR activities exert a positive influence on employees’ knowledge-sharing behavior, an effect that is sequentially mediated by both perceived organizational support and organizational commitment. Furthermore, the research uncovers the positive moderating role played by ethical climate in the relationship between CSR and perceived organizational support. By integrating multiple theoretical frameworks, this study not only advances the extant literature but also offers invaluable insights for organizations striving to bolster knowledge-sharing through the strategic implementation of CSR initiatives.

Background: The outbreak of the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) resulted in the global COVID-19 pandemic. The urgency for an effective SARS-CoV-2 vaccine has led to the development of a first series of vaccines at unprecedented speed. The discovery of SARS-CoV-2 spike-glycoprotein mutants, however, and consequentially the potential to escape vaccine-induced protection and increased infectivity, demonstrates the persisting importance of monitoring SARS-CoV-2 mutations to enable early detection and tracking of genomic variants of concern. Results: We developed the CoVigator tool with three components: 1) a knowledge base that collects new SARS-CoV-2 genomic data, processes it and stores its results; 2) a comprehensive variant calling pipeline; 3) an interactive dashboard highlighting the most relevant findings. The knowledge base routinely downloads and processes virus genome assemblies or raw sequenc-ing data from the COVID-19 Data Portal (C19DP) and the European Nucleotide Archive (ENA), respectively. The results of the variant calling pipeline are visualized through the dashboard in the form of tables and customizable graphs, making it a versatile tool for tracking SARS-CoV-2 variants. We put a special emphasis on the identification of intrahost mutations and make available to the community what is, to the best of our knowledge, the largest dataset on SARS-CoV-2 intrahost mutations. In the spirit of open data, all CoVigator results are available for download. The CoVigator dashboard is accessible via covigator.tron-mainz.de. Conclusion: With increasing demand worldwide in genome surveillance for tracking the spread of SARS-CoV-2, CoVigator will be a valuable resource of up-to-date list of mutations, which can be incorporated into global efforts to sustainably prevent or treat infections.

Objective: Despite increasing interest in understanding factors influencing awareness, knowledge, and acceptability of the human papilloma virus (HPV) vaccine among Latino parents to date, limited information is available specific to Latino fathers living in the United States. Methods: This descriptive qualitative study explored Latino fathers’ awareness, knowledge, and acceptability of the HPV vaccine for their adolescent children. Data were collected through individual, semi-structured interviews and analyzed using a hybrid method of thematic analysis that incorporated deductive and inductive approaches. Results: Nineteen, majority foreign-born Latino fathers (63.2%; n = 12) fathers of male and female adolescents participated in the study. Four main themes and two subthemes emerged from the analyses. Results found fathers’ low awareness and knowledge of HPV and the HPV vaccine. Results also identified fathers’ positive attitude toward vaccines in general. Moreover, results revealed fathers’ trust in healthcare providers. This trust translated into an increased willingness to vaccinate their children against HPV if recommended by their child’s primary healthcare provider. Conclusion: Findings indicate the need for increased efforts to raise awareness and knowledge among Latino fathers of HPV and the HPV vaccine. In addition, findings underscore the critical role of healthcare providers’ recommendation of the HPV vaccine. Given the limited research focused on Latino fathers, this study’s findings are valuable in building a knowledge foundation needed for developing future studies and interventions to promote the HPV vaccine by targeting Latino fathers living in the United States. Future research should quantify Latino fathers' awareness, knowledge, and acceptability of the HPV vaccine for their children, and preferences for educational interventions to promote HPV vaccination.

Background and Objectives The socioeconomics working group of the European ME/CFS Research Network (EUROMENE) has conducted a review of the literature pertaining to GPs’ knowledge and understanding of ME/CFS. Materials and Methods A MEDLINE search was carried out. The papers identified were reviewed following the Synthesis Without Meta-analysis (SWiM) methodology, and were classified according to the focus of the enquiry (patients, GPs, database and medical record studies, evaluation of a training programme, and overview papers), and whether they were quantitative or qualitative in nature. Results 33 papers were identified in the MEDLINE search. The quantitative surveys of GPs demonstrated that a third to a half of all GPs did not accept ME/CFS as a genuine clinical entity and, even when they did, they lacked confidence in diagnosing or managing it. Patient surveys indicated that a similar proportion of patients was dissatisfied with the primary medical care they had received. These findings were consistent with the findings of the qualitative studies that were examined, and have changed little over several decades. Conclusions Lack of knowledge and understanding of ME/CFS among GPs is widespread, and the resultant diagnostic delays constitute a risk factor for severe and prolonged disease. Failure to diagnose ME/CFS renders problematic attempts to determine its prevalence, and hence its economic impact.

COVID-19 is an infectious disease spreading through human touch. This study explored the risk perception and knowledge towards COVID-19 infection among Bangladeshi adult participants. Two self-administered online surveys were administered at two different time points from 26-31 March 2020 (Early lockdown) and 11-16 May 2020 (Late lockdown) through social media on 1005 respondents (322 and 683 participants, respectively) during COVID-19 lockdown period in Bangladesh. Univariate and multiple linear regression models were used to examine factors associated with risk perception and knowledge towards COVID-19. The mean knowledge (8.4 vs. 8.1, P=0.022) and risk perception (11.2 vs. 10.6, P < 0.001) scores differ significantly between early and late lockdown. Compared to the early lockdown period, the scores for perceived risk of contracting COVID-19 decreased significantly while public knowledge about COVID-19 was lower but not statistically significant. Female participants who practiced high quarantine particularly those who did so at the public health order during the lockdown reported increased knowledge towards the spread of COVID-19 and perceived high risk of contracting COVID-19. Education intervention using awareness to increase public knowledge and perception towards COVID-19 in Bangladesh should target male participants who practiced low quarantine and are less worried about the spread of such novel coronavirus even as the physical distancing persists.

Background: Iranian traditional medicine is a holistic school of medicine with a long prolific history. It describes numerous concepts and the relationships between them. However, no unified terminology has been proposed for the concepts of this medicine up to the present time. Considering the extensive use of concepts in the numerous textbooks written by the scholars over centuries, comprehending the totality of the terminology is obviously a very challenging task. To resolve this issue and overcome the obstacles, and code the concepts in a reusable manner, constructing an ontology of the concepts of Iranian traditional medicine seems a necessity.Methods: Makhzan al-Advieh, an encyclopedia of materia medica compiled by Mohammad Hossein Aghili Khorasani, was selected as the resource to create an ontology of Mizaj. The steps followed to accomplish this task included (1) compiling the list of classes for Mizaj; (2) arranging the classes in taxonomy; (3) determining object properties and their cardinalities; (4) specifying annotation properties including codes, labels, synonyms, and definitions for each concept; (5) reviewing the fields pertaining to Mizaj of all monographs in Makhzan al-Advieh. The ontology was created using Protégé with adherence to the principles of ontology development provided by the Open Biological and Biomedical Ontology (OBO) foundry. Results: Mizaj ontology was constructed with a final inclusion of 105 classes, three object properties, and 1078 axioms in the Iranian Traditional Medicine General Ontology database, IrGO, freely available at http://jafarilab.com/irgo/. An indented tree view and an interactive graph view using WebVOWL were used to visualize the ontology. All classes were linked to their instances in the UNaProd database to create a knowledge-base of Mizaj. Conclusion: We constructed an ontology-based knowledge base of ITM concepts of Mizaj in the domain of materia medica to help offer a shared and common understanding of this concept, enable reuse of the knowledge, and make the assumptions explicit. Extending IrGO will bridge the gap between traditional and conventional schools of medicine and help guide future research on new treatment options.

Robotic decision-support systems must facilitate a robots interactions with their environment, this demands adaptability. Adaptability relates to awareness of the environment and `self-awareness', human behaviour exemplifies the concept of awareness to arrive at an optimal choice of action or decision based on reasoning and inference with learned preferences. A similar conceptual approach is required to implement awareness in autonomous robotic systems which must adapt to the current dynamic environment (the context of use). By incorporating `self-awareness' with knowledge of a Robot's preferences (in decision making) the decision maker interface should adapt to the current context of use. This paper proposes a novel approach to enable an autonomous robotics which implements path planning combining adaptation with knowledge reasoning techniques and hedge algebra to enable an autonomous robot to realise optimal coverage path planning under dynamic uncertainty. The results for a cleaning robot show that using our proposed approach demonstrated the capability to avoid both static and dynamic obstacles while achieving optimal path planning with increased efficiency. The proposed approach achieves the multiple decision-making objectives (path planning) with a high-coverage and low repetition rates. Compared to other current approaches, the proposed approach has demonstrated improved performance over the conventional robot control algorithms.

Pre-Western contact Hawaiʻi stands as a quintessential sustainability example of a large human population that practiced intensive agriculture, yet minimally displaced native habitats that comprised the foundation of its vitality. An explicit geospatial footprint of human-transformed areas across the pre-contact Hawaiian archipelago comprised less than 15% of total land area, yet provided 100% of human needs, supporting a thriving Polynesian society. A post-contact history of disruption of traditional Hawaiian land-use and its supplanting by Western land tenure and agriculture based on ranching, sugarcane, and pineapple, culminated in a landscape, in which over 50% of native habitats have been lost, while self-sufficiency has plummeted to 15% or less. Recapturing the ʻāina momona (productive lands) of ancient times can be accomplished through study of pre-contact agriculture, assessment of biological and ecological changes imposed on Hawaiian social-ecological systems, and conscious planned efforts to increase self-sufficiency and reduce importation. Impediments include the current tourism-based economy, competition from habitat-modifying introduced species, a suite of agricultural pests severely limiting traditional agriculture, and changes in climate rendering some pre-contact agricultural centers suboptimal. Modified agricultural methods will be required to counteract these limitations, and diversified agriculture to broaden the production base, without contributing to further degradation of native habitats.

Humanity, culture, societal development and language issues are mutually reinforcing much as they are intricately interwoven in a non-ceasing dynamic interrelationships within the matrix defined by language standardization or development, acquisition and use that mostly take up central place as unifying and propelling forces in language discourse. Within the confines of globalization which is neither homogenization nor convergence; but the beginning of drawing on the strength of indigenous capabilities to create knowledge ecology that would enliven socio-economic and scientific development of the world in far reaching interconnectivities and relationships across every clime, issues pertaining to language development, acquisition and use become imperative. It is against this background that this paper examines the development/standardization, acquisition and use of Esan language as one of several Nigerian indigenous languages to promote scientific and technological development through knowledge creation, preservation and dissemination. The obsession of using western model and epistemological outlook to achieve appropriate scientific and technological development to the utter neglect of home grown and culturally distilled efforts were also examined. In the light of the dare consequences and the harsh conditions globalization imposes on developing nations, this paper highlights standardization and use of indigenous languages in chatting appropriate trajectory for effectual developmental efforts in fast shrinking contemporary world.

The future of machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved in labs for the most common machining operations by using models based on equations describing the physical laws of the machining processes; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep-knowledge and models” that aid machine designers, production engineers or machinists to get the best of the machine-tools. This article proposes a methodology to reduce problems in machining by means of a simulation utility, which uses the main variables of the system&process as input data, and generates results that help in the proper decision-making and machining planification. Direct benefits can be found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter-free optimum cutting conditions and d) the right programming of cutting toolpaths at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies and are explained in the paper.

The next future using machine tools will be dominated by highly flexible and interconnected systems, in order to achieve the required productivity, accuracy and reliability. Nowadays, distortion and vibration problems are easily solved for the most common cases by sing models based on equations describing the physical laws dominating the machining process; however additional efforts are needed to overcome the gap between scientific research and the real manufacturing problems. In fact, there is an increasing interest in developing simulation packages based on “deep knowledge and models” that aid the machine designer, the production engineer, or machinists to get the best of their machines. This article proposes a systematic methodology to reduce problems in machining by means of a simulation utility, which recognizes, collects and uses the main variables of the system/process as input data, and generates objective results that help in the proper decision-making. Direct benefits by such an application are found in a) the fixture/clamping optimal design, b) the machine tool configuration, c) the definition of chatter free optimum cutting conditions and the right programming of cutting tool path at the Computer Aided Manufacturing (CAM) stage. The information and knowledge-based approach showed successful results in several local manufacturing companies.

The analysis of the key factors which prevent or contribute to the promotion and development of the Power Purchase Agreements (PPAs) in order to meet the sustainability goals represents an important issue which worth being investigated, especially in the situation in which entering in bilateral relations inside this contract has a powerful meaning in achieving new climate targets, developing and improving the use of green energy, and promoting a sustainable green economy. The main goal of this research is to identify and analyze the main factors which help or pre-vent the promotion and development of a PPA, in order to meet the sustainability goals by promoting a green economy. For this research survey based on comparative analysis, questionnaires and interviews with energy experts, Spearman’s correlation matrix and IBM SPSS Statistics for Window are also used. The results suggest that although there are positive interest and an open attitude towards the PPA from both sides (sellers and buyers), but there is still place for improvement, the lack of knowledge or the insufficient measures taken so far being two of the reasons way until now in Romania we do not have a developed PPA market and the level of knowledge regarding this type of mechanism is quite low.

Hydrogen plays an important role in the implementation of Australia’s Net-Zero Strategy. It competes with lithium-ion rechargeable batteries in the electrification of Australian land transportation sector, and can provide an alternative energy storage medium for the greening of the country’s electrical grid system. This paper reviews the role hydrogen plays in the current government’s Net-Zero strategy, and investigates the potential role of other bioenergy sources. It is found that hydrogen and batteries play essentially complementary roles in reducing transport emissions, and that the former energy source is more suited to the operation of long-distance commercial service vehicles than batteries. It is suggested that bioenergy fuels such as biomethane, second generation biofuels and HVO can provide suitable bridging alternatives in the event that Australia’s EV strategy is unable to meet the 2030 target.

Already more than 140 countries consider or have pledged to reach net-zero emission targets by 2050 or earlier and the share of global emissions falling into an emission pricing scheme has steeply increased over the past three years. Even where there are no direct implications for industry (yet), there is a series of subtle pressure points driving an increasing number of companies across the globe to work towards climate neutrality and pledging ambitious emission reduction goals. This article sheds light on the pressure points, the subtle triggers, the underlying considerations as well as the hoped-for benefits for industrial companies from achieving net-zero emissions. The observations and ideas presented in this paper are derived from quantitative data obtained via the Energy Efficiency Index of German Industry (EEI) and qualitative data. Not only societal, work force, supply chain and investor expectations play a large role, but also many strategic considerations which have the potential to make the company more resilient and profitable, particularly in time of crisis. Those companies that do not move towards decarbonisation, on the other hand, may face a costly late-mover disadvantage. This piece uncovers subtle interconnections, helping stakeholders from industry and beyond to grasp opportunities and challenges ahead.

Integrated photovoltaic-fuel cell (IPVFC) systems, amongst other integrated energy generation methodologies are renewable and clean energy technologies that have received diverse research and development attentions over the last few decades due to their potential applications in a hydrogen economy. This article systematically updates the state-of-the-art of IPVFC systems and provides critical insights into the research and development gaps needed to be filled/addressed to advance these systems towards full commercialisation. The design methodologies, renewable energy-based microgrid and off-grid applications, energy management strategies, optimisations and the prospects as self-sustaining power source were covered. IPVFC systems could play an important role in the upcoming hydrogen economy since they depend on solar hydrogen which has almost zero emissions during operation. Highlighted herein are the progresses as well as the technical challenges requiring research efforts to solve to realise numerous potential applications of IPVFC systems such as in unmanned aerial vehicles, hybrid electric vehicles, agricultural applications, telecommunications, desalination, synthesis of ammonia, boats, buildings, and distributed microgrid applications.

Recently, the relationship between emotional arousal and depression has been studied. Focusing on this relationship, we first developed an arousal level voice index (ALVI) to measure arousal levels using the Interactive Emotional Dyadic Motion Capture database. Then, we calculated ALVI from the voices of depressed patients from two hospitals (Ginza Taimei Clinic [GTC] and National Defense Medical College hospital [NDMC]) and compared them with the severity of depression as measured by the Hamilton Rating Scale for Depression (HAM-D). Depending on the HAM-D score, the datasets were classified into a no depression (HAM-D&lt;8) and a depression group (HAM-D≥8) for each hospital. A comparison of the mean ALVI between the groups was performed using the Wilcoxon rank-sum test and a significant difference at the level of 10% (p = 0.094) at GTC and 1% (p = 0.0038) at NDMC was determined. The area under the curve (AUC) of the receiver operating characteristic was 0.66 when categorizing between the two groups for GTC, and the AUC for NDMC was 0.70. The relationship between arousal level and depression severity was indirectly suggested via ALVI.

Acetylcholine (ACh) is the best one characterized neurotransmitter. Its central roles in the cholinergic areas and central nervous system (CNS) and peripheral (PNS) synapses are well known. It was the first molecule identified as a neurotransmitter and appears to be phylogenetically the oldest one signaling molecules. ACh was detected in bacteria, protozoa, fungi, algae and primitive plants, indicating that the cholinergic system was widely distributed in living organisms before its appearance in the nervous system. The autonomous nervous system (ANS) is an integral part animal kingdom history. It determines fight-escape reactions as well as outlining the vital and cognitive functions bio-organization rhythms. The virus (Covid-19) extraneous effects acting on it could help to better understand its functions. The Blast results show some factors like protein that can be implicated such as bungaro-toxins, phospholipase A2 and the similar prothrombin activator protein. A positive correlation with different toxins is obtained and the Covid-19 double pathogenic mechanism theory is proposed. The process allows us to outline the possibility about toxic-like factors presence.

An explanation of the origin of dark matter is suggested in this work. The argument is based on symmetry considerations about the concept of mass. In the Wigner's view, the rest mass and the spin of a free elementary particle in flat space-time are the two invariants that characterize the associated unitary irreducible representation of the Poincar\'e group. The Poincar\'e group has two and only two deformations with maximal symmetry. They describe respectively the de Sitter (dS) and Anti de Sitter (AdS) kinematic symmetries. Analogously to their shared flat space-time limit, two invariants, spin and energy scale for de Sitter and rest energy for Anti de Sitter, characterize the unitary irreducible representation associated with dS and AdS elementary systems. While the dS energy scale is a simple deformation of the Poincaré rest energy and so has a purely mass nature, AdS rest energy is the sum of a purely mass component and a kind of zero-point energy derived from the curvature. An analysis based on recent estimates on the chemical freeze-out temperature marking in Early Universe the phase transition quark-gluon plasma epoch to the hadron epoch supports the guess that dark matter energy might originate from an effective AdS curvature energy.

In this paper, we propose "Synergized Efficiency Optimization for Large Language Models" (SEO-LLM), a groundbreaking approach that integrates advanced data utilization and model compression techniques to significantly enhance the performance, efficiency, and scalability of large language models (LLMs). Our method synergistically combines Adaptive Data Augmentation (ADA), Transfer- Active Learning (TAL), Adaptive Iterative Pruning (AIP), and Synergistic Quantization and Distillation (SQD). These components work together to reduce the training data requirement by 30%, compress model size by 67.6%, and improve inference speed by up to 50%, while preserving or even enhancing model accuracy across various NLP tasks. ADA dynamically adjusts augmentation strategies to optimize model generalization, while TAL leverages pre-trained models to focus learning on the most informative data samples. AIP intelligently prunes less significant weights, and SQD harmonizes quantization with knowledge distillation to achieve high compression rates without significant performance loss. The synergy between these techniques makes SEO-LLM a robust solution for deploying LLMs in resource-constrained environments, maintaining state-of-the-art performance with a fraction of the computational and data resources.

In the context of crowds innovation and the generative design driven by big language models, the personalized requirements has become the best engine for innovative design. The exploration of personalized requirements has become a key in significantly improving product innovation, concepts feasibility, and design interaction efficiency. To mine a large number of vague and unexpressed implicit requirements of personalized products, a domain knowledge graph-based method is proposed in this research. First, based on the classical theory of design science, the characteristics and categories of personalized implicit requirements are analyzed. Next, in order to improve the practicability and construction efficiency of the domain knowledge graph, a more informative ontology is constructed, and better performing NLP models are proposed. Then, a series of mining methods based on knowledge graph are proposed for the personalized implicit requirements of different categories. Finally, A platform was developed based on the technical solution proposed in this study, and an example verification was conducted in the field of electromechanical engineering. The efficiency improvement of the training model proposed in the research was analyzed, and the practicality of implicit requirement mining methods were discussed.

Colorectal cancer (CRC) is a weighty cause of mortality worldwide, with growing incidence in the Middle East. Understanding the level of awareness about CRC screening among the Syrian pop-ulation is crucial public health interventions. A cross-sectional study was conducted from Sep-tember 22, 2023, to October 15, 2023, involving 928 participants from six major governorates in Syria. An online questionnaire containing socio-demographic, knowledge, and attitude sections was used to collect data. Descriptive statistics and chi-square tests were employed for data analysis using (SPSS) v28.0. The survey revealed a lack of awareness regarding CRC screening among the Syrian population, with only 499(53%) of the proportion demonstrating adequate knowledge about the disease and its screening methods. Significant associations were observed between the level of knowledge and socio-demographic factors including gender, work status, and economic status (p < 0.05). The findings emphasize the need for public health initiatives to enhance awareness and knowledge about CRC and its early screening methods in Syria. Education is essential for improving early detection and reducing the burden of colorectal cancer in the region. Efforts to increase public understanding of CRC screening are critical for reducing the incidence and mortality.

The menace of human papillomavirus (HPV) infections among low- and middle-income countries with no access to free HPV vaccine is a public health concern. HPV is one of the most common Sexually Transmitted Infections (STIs) in Nigeria with high-risk HPV-16 and 18 genotypes. In this study, we explored the predictors of self-reported HPV infections and HPV genital warts infection among a population of students, non-academic staff, and academic staff of Ibrahim Badamasi Babangida (IBB) university located in Lapai, Nigeria. We also assessed their knowledge about HPV infections and genotypes, and sexual behaviors. An online cross-sectional study was conducted by setting up a structured questionnaire on google form and it was distributed to the University community via Facebook and other social media platforms of the university. The form captured questions on HPV infection, knowledge about HPV infection and genotypes, as well as sexual health of the participants. All variables were described using frequencies and percentage distribution, chi-squared test statistics was used to explore the association between HPV infection (medical records of HPV infection) and the participants’ profile, and logistic regression analysis was performed to examine the factors associated with HPV genital warts infection among the population. This study reveals those participants between the ages of 26 – 40 years (81.3%) and those currently not in a union (26.4%) who have self-reported having HPV-16 and -18 genotypes. Also, participants between 26 – 40 years of age (OR: 0.45, 95%CI: 0.22 – 0.89) reported to be carriers of HPV genital warts. Therefore, this study reveals the factors associated with HPV infection and genital warts peculiar to IBB university students and staff. Hence, we suggest the need for HPV awareness programs and free HPV vaccine availability at IBB university.

Numerous studies have shown the multiple benefits of plant biodiversity and the crucial role of resident’s awareness in conservation and land management. Plant biodiversity is investigated in an analytical monitoring report conducted jointly with local population engaged in livestock activities and the young residents who are shifting toward hospitality and tourism. The study area, covering over 800 km2 in Sardinia, is half forested and has landscape interest. During the research, genotypes of Quercus ilex L. with sweet acorns and richer in polyphenols were discovered, the oldest ecosystem services in those rural communities. Collaborative monitoring focused attention on the benefits of plants in different habitats: 53% of these have been known to livestock farmers as food for local breeds of goats and pigs; 15% are officinal and 13% endemic. They have also been used in human nutrition within the Mediterranean diet, and they have landscape and garden interest. The study analyzes numerical data from a critical and educational perspective. These data serve as indicators of ecosystem health for the purpose of sustainable management policies, and attest to collaborative monitoring as a tool for analyzing human activities and the necessary balance between profit and biodiversity conservation, given the current challenging climate change conditions.

The study analyzed Ecuador's presence in the Web of Science (WoS) post-pandemic from a multivariate dynamic, highlighting a significant transformation in scientific communication towards open access (OA). The methodology integrated multivariate statistical techniques, using R and specialized tools, to process 9,085 relevant articles, 52% of the Ecuadorian publications in WoS between 2019 and 2021 were open access. This movement reflects a commitment to quality research and accessibility, showing an increase in publications and citations under OA. The research underscores the importance of establishing robust national OA policies in Ecuador to promote scientific collaboration and an equitable distribution of knowledge. Despite funding challenges and the lack of specific regulations, educational institutions in Ecuador have maintained a constant scientific output, adapting to the global environment and the demand for transparent information. The study concludes that there is an urgent need for a holistic approach to OA in the country, recognizing the progress made despite the difficulties.

Understanding the genetic architecture of a disease is crucial for development of valid diagnostic and therapeutic interventions. The analysis of genomic variations associated with pathological conditions is the starting point for uncovering disease-causing pathways (candidate processes). However, the complexity of intergenic and genetic-environmental interactions hinders the identification of pathogenic values of genomic changes. Furthermore, heredity, epigenetics and somatic mosaicism make the interpretation of genomic data even more sophisticated. To succeed, a variety of bioinformatic techniques are applied. Here, reviewing own and literature data, knowledge-based prioritization of genomic variations is described. Theoretical basis of the knowledge-based prioritization is given with a special regard to gene ontology, heuristics, hermeneutics (genomic hermeneutics) and analytics. Practical and methodological issues of prioritization using ontology- or pathway-based systems analysis are considered in the light of optimistic and realistic scenarios of cumulative phenotypic effects of the variome (the whole set of genomic variations in an individual or specific set of genomic variations for a phenotypic outcome). In the present communication, copy number variants (CNVs) in children with neurodevelopmental diseases are used as a practical foundation for the prioritization, inasmuch as these genome variations are systematically overlooked in the so-called NGS era. Nonetheless, it is highly likely that the prioritization is applicable to almost all types of genomic variations (e.g. chromosome abnormalities, gene mutations, functional synonymous variants etc.). The present methodology seems to be a valuable addition to current biomedical science widening the opportunities for medical genomics and genetics.

The unsupervised domain-adaptive vehicle reidentification approach aims to transfer knowledge from a labeled source domain to an unlabeled target domain, however there are knowledge differences between the target domain and the source domain. To reduce domain differences, existing unsupervised domain-adaptive rerecognition methods generally require access to the source domain data to assist in re-training the target domain model. But for security reasons, in many cases, data between different domains cannot communicate. To this end, this paper proposes an unsupervised domain-adaptive vehicle re-identification method based on source-free knowledge transfer. First, by constructing a passive domain knowledge migration module, the target domain is consistent with the source domain model output to train a generator to generate the "source-like samples". Then, it can effectively reduce the model knowledge difference and improve the model generalization performance. According to the experiment and testing of VeRi776 and VehicleID, two mainstream public data sets in this field, the rank-k and mAP indexes obtained are both improved, and are suitable for object re-recognition tasks without source data.

Abstract: Educating healthcare workers (HCWs) to take action against monkeypox (mpox) is an important part of public health prevention efforts. The study aimed to assess the knowledge, attitudes, and willingness to vaccinate against mpox among HCWs in the Kurdistan Region of Iraq. This study utilized an online cross-sectional survey that was disseminated via Google Forms between November, 1, 2022 and January, 15, 2023, employing a convenience sampling method. The researchers utilized logistic regression to ascertain the factors associated with knowledge, attitude and willingness to vaccinate. A total of 637 HCWs were included in the analysis (ages ranged between 21 and 51 years old). The mean overall score and standard deviation of the knowledge, attitude, and willingness assessment on mpox were 8.18±3.37 out of (0–16), 3.40±1.37 out of (0–5), 2.41±1.25 out of (0–5), respectively. The multivariate logistic regression analysis showed that HCWs who heard about mpox before 2022 had a higher level of knowledge (AOR: 4.85; 95% CI: 2.81-8.36; p < 0.001). In addition, those who had less than 1 year of practice had a positive attitude about mpox (AOR: 0.35; 95% CI: 0.20-0.59; p < 0.01). Finally, none of the variable groups had the capacity to predict willingness to be vaccinated against mpox. The research revealed that HCWs exhibit a relatively low level of knowledge and attitude towards mpox, as well as a low level of willingness to receive mpox vaccinations. Further, there is an urgent need to increase their knowledge and attitude, as the success of efforts to control the global epidemic depends on them.

Work in the copper mining industry is often associated with exposure to respirable dust and respirable crystalline silica. This exposure has the potential to cause silicosis, a non-curable occupational respiratory lung disease. This study aimed at establishing the relationship between knowledge, attitudes, work behaviour and practice, compliance with safety standards and exposure to respirable dust. A cross-sectional descriptive survey was conducted on 528 mine workers. Statistical package for social sciences (SPSS) was used for descriptives. Structural Equation Modelling (SEM) with latent variables and partial least-squares (LVPLS) analysis was employed to determine the relationship among these variables. The results indicated that of the four (4) hypotheses, two (2) were supported and two (2) were rejected showing that there is a significant relationship between exposure to respirable dust and work behaviour and practice, as well as compliance with safety standards. Knowledge and attitudes towards respirable dust exposure did not significantly influence exposure. According to the results from the survey, positive work behaviour and practice as well as compliance with safety standards was significantly associated with exposure to respirable dust. It is recommended that the mine should focus on the miners’ work behaviour and practice and compliance with safety standards.

This thesis presents the effects of work done on a software project for generative models and spreadsheets, allowing for a quick creation of the conceptual model of the aircraft. The subject of the work is a response to the current trends and needs prevailing in the field of computer design engineering CAD and aviation. In the initial chapters, theoretical issues related to the work being carried out were introduced and the methodology of creating software for construction and verification of the structure of aircraft along with the needs of interchange between databases of generative models was presented. In the next stages, the concepts and selected solutions for the user interface supporting the knowledge base were presented along with a set of procedures for its operation. Furthermore, the method of database integration with the methods of determining design features for the developed generative models and with the Siemens NX system. Furthermore, problems encountered in software development, as well as solution examples for model application are specified. The results obtained and the models generated on their basis were subjected to a strength analysis using Autodesk Inventor software and analysed in terms of meeting the initial assumptions. In the end, conclusions and observations resulting from the effects of the work presented in the project were formulated.

In considering knowledge graphs in a diverse range of domains of interest, graph neural networks have demonstrated significant improvements in node classification and prediction when applied to graph representation with learning node embedding to effectively represent hierarchical properties of graphs. DiffPool is a deep-learning approach using a differentiable graph pooling technique that generates hierarchical representations of graphs. In operation DiffPool is a differentiable graph pooling technique that generates hierarchical representations of graphs. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep graph neural network with nodes mapped sets of clusters. However, control of the learning process is difficult given the complexity and large number of parameters on an `end-to-end’ model. To address this difficulty we propose an novel approach termed FPool which is predicated on the basic approach adopted in DiffPool (where pooling is applied directly to node representations). Methods designed to enhance data classification have been developed and evaluated using a number popular and publicly available sensor data sets. Experimental results for FPool demonstrate improved classification and prediction performance when compared to alternative methods. Moreover, FPool shows an important reduction in the training time over the basic DiffPool framework.

A new research stream emerged in the 2000s dedicated to flexible work arrangements in public and private organizations, called “new ways of working” (NWW). This article aims to examine NWW from both a theoretical and empirical perspective, focusing on outcomes of this new concept and the debate between “mutual gains” vs. “conflicting outcomes.” Through a literature review, it examines this research field’s innovation and its rather vague theoretical foundations. Findings demonstrate that NWW definitions are diverse and somewhat imprecise, leading to fragmented research designs and findings; the research stream’s theoretical foundations should be better addressed. Findings also highlight the current lack of empirical data, which therefore does not allow any real conclusions on NWW’s effects on employees’ and organizations’ well-being and performance.

Urbanization has impacted biodiversity and ecosystems at a global scale. At the same time, it has been recognized as a driver of the gap between humans and nature. The lack of direct contact with nature can deteriorate several aspects of human wellbeing, and change knowledge and attitudes of people towards the environment. However, this phenomenon is still poorly understood in Megacities outside developed countries. Here, we explore the relationship between ecological knowledge and self-reported wellbeing in an important urban park in Santiago, Chile. We conducted semi-structured surveys to park users to explore their notions, preferences, ecological knowledge of plants and birds and self-reported wellbeing. Citizens associated urban parks mainly with “nature”, and particularly with the presence of trees and plants. Trees were recognized as the most relevant elements of urban parks, in turn, birds were ranked as the less relevant. Regarding ecological knowledge, respondents correctly identified an average of 2.01 plants and 2.44 birds out of a total of 10 for each taxon, and exotic species were more likely to be recognized. Park users also reported high scores for self-reported wellbeing. Interestingly, variance of self-reported wellbeing scores tended to increase at low levels of ecological knowledge of trees, but no significant relationship was detected with knowledge of birds, nor native species. These results suggest that parks can positively contribute to bring people closer to nature. Ecological knowledge was related to self-reported wellbeing. Improving ecological knowledge can be critical to restore the relationship between humans and nature in megacities.

The Himalayas are known for high floristic diversity and rich ethnobotanical practices. However, not all parts of the Himalayan regions are thoroughly studied. The present study aims to document the ethnoveterinary medicines used by migratory shepherds in Trans-Himalayan Rakchham-Chitkul Wildlife Sanctuary,Baspa (Sangla) valley of the Kinnaur district in Himachal Pradesh. The shepherds are very close to nature as they spend most of their time in forests with their livestock. Shepherding depends more on traditional healthcare practices based on local medicinal plants. In this study, we are reporting for the first time commonly used ethnoveterinary medicines in Rakchham and Chitkul Wildlife Sanctuary and their application, procedures of preparation, as well as listing 51 plant species. Such documentations are done first time in the Himachal Pradesh region of India as per our information. Our research emphasizes the urgent need to document traditional medicine preparation procedures from migratory shepherds. The required information on various ethnoveterinary medicines used by migratory shepherds was collected through personal field visits, participatory observations, interview and using a pretested questionnaire. It was observed that in all 51 species of ethnoveterinary were used by shepherds in Trans-Himalayan Rakchham-Chitkul Wildlife Sanctuary in Baspa (Sangla) valley of Kinnaur district. The results of this survey show that shepherds in tribal areas are highly dependent on ethnoveterinary remedies for their livestock which evolved over generations of practices for healthcare. There is an urgent need to document this vast knowledge of migratory shepherds concerning the use of ethnoveterinary remedies for animal health care in the regions of the Himalayas.

Background and objectives: The inadequate knowledge of complementary and alternative medicine (CAM) among health professionals may put their clients in risky situation because they then would find information about CAM from unreliable sources. Clinical psychologists (CPs), as health professionals, have also the opportunity to provide psychoeducation on the latest CAM scientific research to their clients. The current study aimed to explore knowledge and educational needs of CAM among CPs in Indonesia because previous studies on exploring CAM knowledge and educational needs of CAM were primarily conducted in Western countries. Materials and Methods: Data were collected through semi-structured face-to-face interviews with 43 CPs in public health centers (PHCs) in Indonesia. Most interviews were held at the PHCs where participants worked and interviews lasted for 55 minutes, on average. The interview recordings were transcribed and were analyzed using deductive thematic analysis. Results: Five main themes emerged within participants’ responses regarding CAM knowledge and educational needs. First (CAM understanding), participants’ responses ranged from those with little or no prior knowledge of CAM treatments and uses, to those with much greater familiarity. Second (source of knowledge), participants’ access ranged widely in terms of references, from popular to scientific literature. Third (why is it important?), participants identified CAM essentially as part of Indonesian culture and it was therefore crucial to have this cultural knowledge. Fourth (the challenges and what is needed?), the challenges for improving participants’ knowledge came from personal and institutional levels. Fifth (what and how to learn?), participants advised that only CAM treatments that fit in brief psychotherapy sessions should be introduced in professional training. Conclusions: This qualitative study discovered that CAM was neither well-known nor understood widely. Participants advised that professional associations and health institutions should work together in enhancing knowledge of CAM and incorporating CAM education into psychology education.

Food systems will need to undergo considerable transformation. To be better prepared for and resilient to uncertainty and disturbances in the future, resource users and managers need to further develop knowledge about the food and farming system, with its dominating feedback structures and complexities, and to test robust and integrated system-based solutions. This paper investigates how participatory system dynamics modeling can be adapted to groups at the community level with low or no formal educational background. The paper also analyzes the refinement of workshop participants’ mental models as a consequence of a participatory system dynamics intervention. For this purpose, we ran two workshops with small-scale farmers in Zambia. Analysis of workshop data and post-workshop interviews shows that participatory system dynamics is well adaptable to support an audience-specific learning-by-doing approach. The use of pictures, objects and water glasses in combination with the basic aspects of causal loop diagramming makes for a well-balanced toolbox. Participants acquire understanding that is also relevant beyond systems thinking in that is offers a range of practical insights such as a critical evaluation of common food security strategies.

The emergence of few-shot learning as a potential approach to address the problem of data scarcity by learning underlying pattern from a few training sample had so far given a mix-result especially on the suitability of model-agnostic meta learning, transfer learning, and optimization strategy to rapidly learn valid information from few sample. In this research, we did an in-depth evaluation of meta- learning to determine their plausibility and suitability for previously unknown cyberattack detection by first retrieving the original research artifacts of current state of the art meta learning to repeat the experiment with original dataset before replicating the experiment with two different malware dataset which had not been previously done with meta-transfer learning. On each of the experiments, meta-transfer learning gave good results on digital character recognition dataset but abysmal result on Malimg and Malevis malware images datasets thereby indicating its unreliability for detecting cyberattacks and the need for an improvement to the state-of-the-art meta transfer learning towards a better attack detection. Transfer learning performance is independent on imbalance and hence does not influence its performance since both malware dataset used for this experiment result in high validation loss and balancing the dataset doesn't result in reduced validation loss,the successful learning transfer seen on digital character recognition dataset is not unconnected to the fact that several languages have similar characters and digits thereby enhancing the successful learning transfer unlike malware datasets, and more importantly the finding that current meta-learning transfer approach doesn't generalize well on malware dataset and hence not suitable for detecting previously unseen out-of-distribution attack.

The main objective of the paper is to provide a thorough analysis of currently used modulation control schemes for single phase bidirectional dual active bridge DC/DC converter. In this article, it will be shown that single phase shift, extended phase shift and Dual phase shift modulation schemes are special cases of triple phase shift (TPS) modulation scheme. The article aims to highlight six TPS switching modes and their complements with the operational constraints. Unlike previous studies that regarded TPS as a complex scheme, this paper simplifies the analysis of each mode and aims to standardize the understanding of TPS modulation in DAB converters. Power equations, range of power transferred and zero voltage switching (ZVS) are derived for all the modes under TPS without assuming fundamental component analysis. Additionally, a generic optimization algorithm is developed to show the advantages of TPS modulation and thus, detailed analysis presented in this paper provides valuable insights for single phase DAB converter designers in identifying a wide range of optimization algorithms to achieve higher efficiency under TPS modulation. This analysis contributes to the advancement of bidirectional DAB converter technology and facilitates its application in various power electronic systems.

We present here a theory of Majorana excitons, photo-excited conduction electron-valence band hole pairs, interacting with Majorana Fermions in a Kitaev chain of semiconductor quantum dots embedded in a nanowire. Using analytical tools and exact diagonalisation methods we identify the presence of Majorana Zero Modes in the nanowire absorption spectra.

Background: Food waste has been a major social problem in recent years. Annually, 1.3 billion tons of food is wasted worldwide. It is recommended that food waste should be reduced at every stage of production. By-products from food processing have high nutritional value; therefore their use in the design of new products is advisable. Methods: By-products from tomato processing (tomato waste-TW) and pepper (defatted pepper seeds-DPS, pepper placenta-PP) were used at a level of 10-30% to produce extruded pasta. The farinographic characteristics, chemical composition, cooking quality, and color of the pasta were studied. Results: The results showed a significant up to 27% increase in the protein content in the TW30 samples, compared to the control. The TDF content increased over five times in DPS30 and TW30 (27.99% and 25.44%). The amino acid composition of the pasta improved with the fortification, but failed to achieve the complete protein by FAO. The DPS30, PP20, PP30, and all TW samples can be considered high-protein products according to the EU definition (min. 20% energy from protein). Conclusion: Vegetable waste can be a valuable additive for improvement of the nutritional value of food. Their addition is part of the zero-waste trend, which has a positive impact on the environment.

Considering increasingly ambitious pledges by countries, pressure from society, investors, and clients further up the supply chain, the question for companies is not so much whether to take decarbonisation action, but what action and by when. However, determining an ideal mix of measures to apply ‘decarbonisation efficiency’ requires more than knowledge of technically feasible measures and how to combine them to achieve the most economic outcome: In this paper, the author describes seven aspects which heavily influence the composition of an ‘ideal mix’ that executive leadership needs to take a (strategic) position on. These aspects consider underlying motivations and span across (socio-)economic, technical, regulatory, strategic, corporate culture and environmental factors and further underline the necessity of clarity of definitions. How these decisions influence the determination of the decarbonisation-efficient ideal mix of measures, both in principle but also in terms of specific impact is further explored by providing examples. What choices are taken by German manufacturers in several of the aspects is disclosed by insights from about 850 responses to the ‘Energy Efficiency Index of German Industry’. Knowledge of the status quo, and clarity in definitions, objectives, time frames and scope are key.

In 2010, Parties to the Convention on Biological Diversity (CBD) adopted the Strategic Plan for Biodiversity 2011–2020 to address the loss and degradation of nature. Subsequently, most biodiversity indicators continued to decline. Nevertheless, conservation actions can make a positive difference for biodiversity. The emerging Post-2020 Global Biodiversity Framework has potential to catalyze efforts to ‘bend the curve’ of biodiversity loss. Thus, the inclusion of a goal on species, articulated as Goal B in the Zero Draft of the Post-2020 Framework, is essential. However, as currently formulated, this goal is inadequate for preventing extinctions, and reversing population declines; both of which are required to achieve the CBD’s 2030 mission. We contend it is unacceptable that Goal B could be met while most threatened species deteriorated in status and many avoidable species extinctions occurred. We examine the limitations of the current wording and propose an articulation with robust scientific basis. A goal for species that strives to end extinctions and recover populations of all species that have experienced population declines, and especially those at risk of extinction, would help to align actors towards the transformative actions and interventions needed for humans to live in harmony with nature.

This study constructs a comprehensive line loss management evaluation knowledge graph, considering the impact of various factors such as power grid structure characteristics, equipment's physical parameters, power grid operation characteristics, electricity consumption features, and natural conditions. It establishes the association between line loss characteristics and line loss retrieval and configures the data source for line loss management evaluation indicators. Based on Principal Component Analysis (PCA), this study selects comprehensive evaluation indicators for line loss management, verifies the rationality of the indicators, and proposes an integrated evaluation method for municipal power grid line loss management. A full-process composite evaluation method based on DEMATEL-Entropy Weight-TOPSIS is constructed, and the accuracy of the evaluation method is verified through ablation experiments. The TOPSIS analysis method is applied to empirical research on cross-sectional data from a city in Guangxi from 2012 to 2022 to validate the evaluation system's effectiveness, scientific nature, and objectivity. The case analysis shows that the line loss evaluation index system established in this paper effectively reduces the redundancy of line loss data and can objectively and rationally reflect the actual status of line loss management work, serving as a basis for decision-making in future line loss management evaluations.

This study endeavors to elucidate the intricate interplay between gastronomic involvement, gastronomic knowledge, past gastronomic experiences, gastronomic attractiveness, destination attractiveness, tourist satisfaction, and the intention to revisit. In the context of this investigation, the research population comprises 300 international culinary tourists who have ventured to the Turkish Republic of Northern Cyprus (TRNC). Employing SPSS software, a confirmatory factor analysis (CFA) was conducted to rigorously assess construct validity, reliability, and the discriminant and convergent validity of the research model. The findings of this research study underscore the positive relationships that exist between various dimensions of gastronomy, namely involvement in gastronomy, knowledge of gastronomy, and previous gastronomic experiences, and the perceived attractiveness of gastronomy. These findings enhance our understanding of how culinary elements affect tourists' perceptions and experiences, highlighting the crucial role of gastronomy in destination appeal and visitor satisfaction. This research is a significant step towards enhancing our comprehension of the dynamic nexus between gastronomy and the tourism industry, ultimately benefiting destination management and strategy development.

The assessment of existing bridges constitutes a serious challenge for the researchers since their safety evaluation is a complex task due to several sources of uncertainty affecting the parameters identified in the knowledge process. In particular, a high level of uncertainties may arise in the case of prestressing systems requiring special investigation strategies and experimental technologies. The paper aims to investigate how the knowledge uncertainties influence the safety assessment of existing post-tensioned bridges. The semi-probabilistic approach is used, and uncertainties related to the knowledge process are described considering the number of tests, the confidence level assumed for the estimation of the problem parameters, and the measurement errors of in-situ tests. Results concerning a typical post-tensioned bridge are reported and the variability of the outcomes is discussed and compared with a reference case of “perfect knowledge”. The main results of the study confirm the general robustness of the semi-probabilistic Eurocodes safety format and provide an overview of dispersion due to different choices about the number of tests and confidence level. The measurement errors may lead to significant under-estimation of the computed structural capacity with respect to the reference one.

Stroke is a global leading cause of death and disability. Knowledge of related guidelines is crucial for Emergency Department (ED) staff, influencing early diagnosis and timely treatment. We investigated Greek-Cypriot ED healthcare professionals' (nurses and physicians) knowledge in recognizing and managing stroke. A descriptive cross-sectional study spanned November 2019 to April 2020, encompassing four private and seven public EDs in the Republic of Cyprus. The data were collected through a self-reported questionnaire developed by the research team, consisting of 37 questions. Eight questions focused on sociodemographic and employment characteristics, 28 assessed knowledge in stroke recognition and management (each item was equally weighted without deliberate prioritization), and one question addressed self-assessment of knowledge in stroke care. 255 nurses [Response Rate (RR): 74.1%] and 26 physicians (RR: 47.3%) completed the questionnaire. The average correct response rate was 12.9 out of 28 statements (SD: 4.2), with nurses and physicians scoring 12.6 (SD: 4.1) and 15.7 (SD: 4), respectively. Work experience significantly influenced stroke knowledge, with all groups demonstrating superiority over those with less than one year of experience. Participants with previous training scored an average of 1.45 additional correct answers while educational attainment did not significantly influence stroke knowledge. Investigating stroke knowledge among Emergency Department nurses and physicians in the Republic of Cyprus revealed significant deficits. The study stresses targeted interventions, including, education, yearly examinations, workshops with hands-on training, and repeated training, to address these gaps and enhance the overall stroke care capabilities of the healthcare professionals.

Drug-target affinity (DTA) prediction is crucial for understanding molecular interactions and aiding drug discovery and development. While various computational methods have been proposed for DTA prediction, their predictive accuracy remains limited, failing to delve into the structural nuances of interactions. With increasingly accurate and accessible structure prediction of targets, we developed a novel deep learning model, named S2DTA, to accurately predict DTA by fusing sequence and structural knowledge of drugs, targets, and pockets using heterogeneous models based on graph and semantic networks. Experimental findings underscored that complex feature representations imparted negligible enhancements to the model’s performance. However, the integration of heterogeneous models demonstrably bolstered predictive accuracy. In comparison to three state-of-the-art methodologies, the supremacy of S2DTA became strikingly apparent. It showcased a noteworthy 25.2% reduction in Mean Absolute Error (MAE) and an impressive 20.1% decrease in Root Mean Square Error (RMSE). Furthermore, S2DTA exhibited substantial advancements in other pivotal metrics, including Pearson Correlation Coefficient (PCC), Spearman, Concordance Index (CI), and R2. These metrics experienced remarkable increments of at least 19.6%, 17.5%, 8.1%, and a remarkable 49.4%, respectively. Finally, we conducted interpretability analysis on the effectiveness of S2DTA by bidirectional self-attention mechanism, fully proving that S2DTA is a valuable and accurate tool for predicting DTA. For further exploration, the source data and code repository can be accessed at https://github.com/dldxzx/S2DTA.

Measles remains a highly contagious and potentially severe infectious disease, necessitating high vaccine coverage. However, misinformation and measles vaccine hesitancy/resistance posed significant challenges to achieving this goal. The COVID-19 pandemic further exacerbated these challenges, leading to a measles outbreak in Jordan in 2023. This study aimed to investigate the acceptance of the Measles-Rubella (MR) vaccine among parents in Jordan and identify its associated determinants. This cross-sectional questionnaire-based study was conducted using a previously Arabic-validated version of the Parental Attitudes towards Childhood Vaccines (PACV) survey instrument. Data collection took place in October 2023, and the final study sample comprised a total of 391 parents, with mothers representing 69.8% of the participants (n = 273). The majority of participating parents expressed either resistance (n = 169, 43.2%) or hesitancy (n = 168, 43.0%) towards MR vaccination, while only 54 participants (13.8%) expressed MR vaccine acceptance. Multivariate analysis revealed that trust in vaccine safety/efficacy, behavior, and having fewer offspring were significantly associated with MR vaccine acceptance. The current study revealed a concerning level of MR vaccine hesitancy/resistance among parents in Jordan. Urgent and targeted interventions are strongly recommended to address this issue, including mass campaigns aimed at building trust in the MR vaccine safety/efficacy. Additionally, there is an urgent need for effective public health initiatives to ensure sufficient measles vaccine coverage to prevent future outbreaks of this serious disease.

Plant biotechnology is dependent today on accessing broader plant genetic resources for food and agriculture (PGRFA) diversity for ensuring food security for our future. Heterogenous agricultural lands are considered today as hotspot for biodiversity, and they also exist in the hilly mountain areas of Romanian’s Carpathians. The scope of this review is to describe traditional home-hardens related to the land use of households and cultivated species from Moșna commune, Sibiu County, Romania. The survey was realized with the support of authorities and local stakeholders from 2010 up to 2019. The results of this survey revealed that maintenance of crop species structure and land use of traditional households is longer than 100 years. Consequently at least 15 traditional Saxon home-gardens of 120 taken into the study may be defined. Today these home-garden covers a media of 26% of the total area of a household and should be part of a surface ranging between 5,000 and 1,500 m2. The first Local Council Decision in Romania in 2019 supporting the recognition of landraces and home-gardens as heritage values was officially adopted supporting the need to more access bottom-up approaches for supporting biodiversity conservation.

Despite air pollution being a leading cause of health issues in developing nations, public awareness and understanding of local air quality remains notably low. The present study assesses the perception, attitude, and environmental knowledge of local air pollution among adult urban residents (n=870) in a city with leading air pollution rates among cities of emerging economies: Astana, Kazakhstan. Structural equation modeling (SEM) was employed to investigate the causal relationship between perceived air quality, environmental literacy, and willingness to pay (WTP) for environmental protection. Findings indicate over 40% of residents neither consider the city being highly polluted nor recognize the association between air quality and adverse health outcomes, correlated with a generally low level of environmental literacy. The age, education, and health status of the participants significantly affected (p&lt;.001) their level of environmental knowledge and awareness. The SEM analysis indicates that knowledge is the major determinant in improving public awareness and perception of local air pollution (path value=0.626). Moreover, a close association between WTP and environmental attitude was also evident (path value=0.533). The findings of the present study may provide valuable insights for healthcare professionals, environmental researchers, and governmental institutions for implementing more effective public interventions to protect local air quality.

Environmental concern has attained a higher level than in the past, with numerous organisations, including the pioneering "Green Banking" institution, expressing their concern. This study examines the relationship between green brand innovativeness and perceived value, as well as its effect on green brand loyalty. It expands on previous research that investigated the differences between innovation and innovativeness and their effects on brand loyalty. In addition, the role of perceived green value as a mediator and consumer green knowledge as a moderator in the formation of green brand loyalty is investigated. To test the conceptual model, a convenience-sampling survey of 600 Ethiopian customers was administered to capture a sample of respondents for the conceptual model. For data analysis, structural equation modelling (SEM) was employed. The results indicate that consumers' perceptions of green value have indirect effects on brand loyalty and direct effects on the innovativeness of green brands. In addition, green knowledge mediates the relationship between green brand innovation and green value perception in a significant way. Therefore, organisations should concentrate on enhancing consumers' environmental literacy and nurturing positive attitudes towards green brands in order to increase green brand loyalty.

The development of knowledge graphs for water resources as a tool for studying sustainable regional development is a relevant task, as the increasing deterioration of water bodies affects the ecology, economy of the country, as well as the health of the population living near water bodies. Solving this problem requires the development of water resource monitoring methods based on semantic data analysis is required to detect and track sources of pollution and provide recommendations to support decision-making. This research proposes a method of semantic analysis for modeling knowledge graphs and constructing rules that enable the integration of data from heterogeneous sources, semantic compatibility, support for reasoning and inference, facilitate queries and search, and represent knowledge in the field of water resource monitoring. As a result, an ontology based on the SWRL language is created, which enables the expression of complex relationships and derivation of new knowledge, SSN for interaction between the ontology and sensor data, and Time Ontology, which models and analyzes information related to temporal aspects.After populating the ontology and establishing logical connections, SPARQL queries are implemented to retrieve new knowledge about water resources.

The General Theory of Information (GTI) tells us that information is represented, processed and communicated using physical structures. The physical universe is made up of structures combining matter and energy. According to GTI, “Information is related to knowledge as energy is related to matter.” GTI also provides tools to deal with transformation of information and knowledge. We present here, the application of these tools for the design of digital autopoietic machines with higher efficiency, resiliency and scalability than the information processing systems based on the Turing machines. We discuss the utilization of these machines for building autopoietic and cognitive applications in a multi-cloud infrastructure.

COVID-19 has emerged as deadly pandemic worldwide with no vaccine or suitable antiviral drugs to prevent or cure the disease. Because of the time-consuming process to develop new vaccines or antiviral agents, there has been a growing interest in repurposing some existing drugs to combat SARS-CoV-2. Vitamin D is known to be protective against acute respiratory distress syndrome (ARDS), pneumonia and cytokine storm. Recently it has been used as a repurposed drug for the treatment of H5N1 virus-induced lung injury. Circumstantial evidences indicate that people with low level of vitamin D are more susceptible to SARS-CoV-2. Although, vitamin D was suggested to interfere with viral replication, its interaction with any SARS-CoV-2 protein is unexplored yet. Beside this, ivermectin, a well-known anti-parasitic agent, exhibits potent anti-viral activities in vitro against viruses such as HIV-1 and dengue. Very recently, ivermectin has been found to reduce viral load of SARS-CoV-2 in vitro. We have analyzed available structures of SARS-CoV-2 proteins to identify probable binding partner(s) of vitamin D and ivermectin through knowledge-based docking studies and figured out possible implication of their binding in SARS-CoV-2 infection. Our observations suggest that the non-structural protein nsp7 possesses a potential site to house 25-hydroxyvitamin D3 (VDY) or the active form of Vitamin D, calcitrol. Binding of vitamin D with nsp7 likely to hamper the formation of nsp7-nsp8 complex which is required to bind with RNA dependent RNA polymerase (RdRP), nsp12 for optimal function. On the other hand, potential binding site of ivermectin has been identified in the S2 subunit of trimeric spike(S) glycoprotein of SARS-CoV-2. We propose that deeply inserted mode of ivermectin binding at three inter-subunit junctions may restrict large scale conformational changes of S2 helices which is necessary for efficient fusion of viral and host membrane. Our study, therefore, opens up avenues for further investigations to consider vitamin D and ivermectin as potential drugs against SARS-CoV-2.

Background: Ongoing molecular profiling studies enabled by advances in biomedical technologies are producing vast amounts of ‘omic’ data for early detection, monitoring, and prognosis of diverse diseases. A major common limitation is the scarcity of biological samples, necessitating integrative modeling frameworks that can make optimal use of available data for disease classification tasks. Related data sets are often available from different studies, but may have been generated using different technology platforms. Thus, there is a critical need for flexible modeling methods that can handle data from diverse sources to facilitate the discovery of robust biomarkers that underlie disease regulatory processes. Results: In this paper, we introduce a novel framework called Knowledge Augmented Rule Learning (KARL), which incorporates two sources of knowledge, domain, and data, for pattern discovery from small and high-dimensional datasets, such as transcriptomic data. We propose KARL as a transfer rule learning framework in which knowledge of the domain is transferred to the learning process on data in order to 1) improve the reliability of the discovered patterns, and 2) study the knowledge of the domain when used along with data for modeling. In this work, we generated KARL models on gene expression datasets for five types of cancer, including brain, breast, colon, lung, and prostate. As our knowledge of the domain, we used the Ingenuity Knowledge Base (IKB) to extract genes related to hallmarks of cancer and annotated these prior relationships before learning classifiers from these datasets. Conclusions: Our results show that KARL produces, on average, rule models that are more robust classifiers than the baseline without such background knowledge, for our tasks of cancer prediction using 25 publicly available gene expression datasets. Moreover, KARL helped us learn insights about previously known relationships in these gene expression datasets, along with new relationships not input as known, to enable informed biomarker discovery for cancer prediction tasks. KARL can be applied to modeling similar data from any other domain and classification task. Future work would involve extensions to KARL to handle hierarchical knowledge to derive more general hypotheses to drive biomedicine.

Two pilot scale desalination system employing carbon modified nano-sized, zero valent metals (n-ZVM) were maufactured and tested to determine (1) the degree to which high salt water (20 to 130 mS) could be desalinated and (2) if this degree of desalination could be maintained throughout an extended treatment period. Two pilot systems were tested (referred to as Generation 1 and Generation 2) consisting of parallel lines of 4 individual reactors in series, a settling tank and an activated carbon cell at the end of each reactor line. The system capacity was 300 gal in Generation 1 and 600 gal in Generation 2 total with a total hydraulic residence time of 6 hours per reactor line (one hour per cell/tank). A slurry of n-ZVM was introduced in the first reactor on each line to yield approximately 5 to 45 grams of nano-metal per 100 L of influent salt water. The n-ZVM slurry was made from mixing tea extract with metal salts (eg ferrous sulfate, ferric chloride) to produce the carbon modified metal slurry. Initial runs focused on dosing required to achieve maximum salt removal at each of three influent salt contents, 28 mS, 44 mS and 123 mS. Once dosing was set, continuous runs (14 days, 23 days and 9 days) were carried out to (1) examine the ability of the pilot to maintain the removal efficiency over an extended period, (2) to determine the weaknesses inherent in the pilot systems, and (3) to begin to develop CAPEX and OPEX estimates for the pilot and pre-design full scale mobile desalination unit (MDU) system. Results demonstrated that maximum removal occurred with 10 g/100L of salt for the 30 mS salt solution,16 g/100 L of salt for the 40 mS influent water and 40 g/100 L for the 130 mS influent. Salt removal (expressed as Na and Cl removed) approached 78% for the 30 mS influent and 41 mS influent respectively while removal for the highest salt influent (130 mS) approached 81 %. Addition of greater amounts of carbon modified slurry than those described resulted in a pH less than 6 which inhibits continued salt removal and would require addition of calcium or magnesium oxides/hydroxides or bicarbonate solutions at the influent side to achieve greater salt removal and of course greater cost per gallon. Continuous operation over the extended time-period showed no significant decrease in salt removal with typical day to day variation of no more than 10% suggesting that this approach to desalination could rapidly provide usable water from saline aquifers, seawater or even produced water. Treatment of saline waters at 30 mS or less yields effluent water in the range necessary for irrigation use of many crops while treatment of seawater or produced water levels of salt (40 mS or higher) yields effluent in the range of 8 to 10 mS a range ideal for RO influent that would prevent the production of large volumes of brine. Pilot testing continues with seawater extracted from Puget Sound, Seattle WA and higher levels of salt to better represent produced water.

This study develops into the application of a combined MFC unit with chemical coagulation for total treatment of inert contaminants in complex substrates. Microbial Fuel Cell (MFC) technology converts chemical energy in the form of organic matter, into bioelectricity in an environmentally friendly and effi-cient manner, reducing carbon emissions and increasing bioenergy production. An evaluation of a la-boratory scale chemical coagulation using an aluminum and poly-based coagulant on how effective it can remove bulk impurities such as particulate COD and turbidity to obtain the purest and most cost-effectively treated wastewater using a jar test is being conducted in this current study. This study aims to find the most effective treatment technologies for wastewater recovery in breweries in order to achieve zero liquid effluent discharge (ZLED). The preliminary results showed that adding a modest amount of poly and a 50 % alum alone treatment improved COD, color, and turbidity reduction. The turbidity removal efficiency achieved after chemical coagulation treatment was 90.50 % and 59.36 % COD removal, demonstrating the benefits of adopting an alum/poly based technique. To determine ZLED, this study clearly advised a combined treatment technique, specifically the MFC-flocculator unit for efficient organics and inorganics removal.

The textbook analysis of vacuum energy density (VED) in flat spacetime follows from Pauli’s lectures of 1951, in which quantum vacuum is modeled as a reservoir of free harmonic oscillators. In his lectures, Pauli shows that deriving a nearly vanishing VED is contingent upon fulfilling three corollary conditions called polynomial-in-mass-constraints. The goal of this work is to evaluate Pauli’s constraints against the Standard Model parameters and the Higgs mechanism of spontaneous symmetry breaking.

This paper presents an optimal design for the inner current control loop of the continuous current conduction mode (CCM) power factor correction (PFC) stage, which it can be used as the front stage of the two stages alternating current-direct current (AC-DC) telecom power supply. Conventional single-phase CCM-PFC boost converter usually implemented with using of the proportional-integral (PI) controllers in both of the voltage and current control loops, to regulate the output DC voltage to the specified value, moreover to maintains the input current follows the input voltage which offers converter with high power factor (P.F) and low current total harmonic distortion (THD). However, due to the slow dynamic response of the PI controller at the zero-crossing point of the input supply current, input current can’t fully follow the input voltage which leads to high THD. Digitally controlled PFC converter with an optimal design of the inner current control loop using doubly control loops IP controller to reduce the THD and to offer input current with unity P.F was performed in this paper. Furthermore, for the economic design of the digitally control PFC converter, two isolated AC and DC voltage sensors are proposed and designed for the interfacing with the microcontroller unit (MCU). PSIM software was used to test the converter performance with using the proposed designed current controllers and isolated voltage sensors. High power density digitally controlled telecom PFC stage with P.F of about 99.93%, full load efficiency of about 98.70% and THD less 5.50% is achieved in this work.

Molecule radiation can be used as a tool to study colder regions in switching arc plasmas like arc fringes in contact to walls and ranges around current zero (CZ). This is demonstrated in the present study for the first time for the case of ablation-dominated high–current arcs as key elements of self–blast circuit breakers. The arc in a model circuit breaker (MCB) in CO2 with and an arc in a long nozzle under ambient conditions with peak currents between 5 and 10 kA were studied by emission and absorption spectroscopy in the visible spectral range. The nozzle material was polytetrafluoroethylene (PTFE) in both cases. Imaging spectroscopy was carried out either with high-speed cameras or with intensified CCD cameras. A pulsed high-intensity Xe lamp was applied as background radiator for the broad-band absorption spectroscopy. Emission of Swan bands from carbon dimers was observed at the edge of nozzles only or across the whole nozzle radius with highest intensity in the arc center, depending on current and nozzle geometry. Furthermore, absorption of C2 Swan bands and CuF bands were found with the arc plasma serving as background radiator. After CZ, only CuF was detected in absorption experiments.

The move to a low carbon economy is very important for enhancing international competitiveness. The eco-innovation is the critical factor of the green paradigm. This study is designed to investigate deeply the determinants of eco-innovation of manufacturing firms in Korea by suggesting anticipated regulations, self-regulations, and industry-specific characteristics as external factors and open information sources as internal factors. The data used in the analysis is 1946 sample firms from Korean Innovation Survey 2010 based on the Oslo Manual. Using the multi-variate probit analysis and the zero-inflated negative binomial (ZINB) regression analysis, we have found out that the anticipated regulations and self-regulations have significant influences both on eco-process innovation and eco-product innovation, while industrial characteristics have no effects. The empirical results also show that the breadth of information sources has a positive effect on businesses in implementing eco-innovations. Our findings show that the Korean government should provide a good platform where firms can better understand the future trends of environmental policies, particularly policies on anticipated and self-regulations. At the same time, Korean firms should establish a voluntary system to control environmental activities so that they can improve eco-innovations through integrating external information.

The Energy Performance of Buildings Directive 2010 calls for the Nearly Zero Energy Standard for new buildings from 2021 onwards: Buildings using “almost no energy” are powered by renewable sources or energy produced by the building itself. For residential buildings, this ambitious new standard has already been reached. But for other building types this goal is still far away. The potential of these buildings to meet a Nearly Zero Energy Standard was investigated by analyzing ten case studies representing non-residential buildings with different uses. The analysis shows that the primary characteristics common to critical building types are a dense building context with a very high degree of technical installation (such as hospital, research and laboratory buildings). The large primary energy demand of these types of buildings cannot be compensated by building and property-related energy generation including off-site renewables. If the future Nearly Zero Energy Standard were to be defined with lower requirements because of this, the state related properties of Bavaria suggest that the real potential energy savings available in at least 85% of all new buildings would be insufficiently exploited. Therefore, it would be useful to instead individualize the legal energy verification process for new buildings to distinguish critical building types such as laboratories and hospitals.

Traffic Inspection (TI) work in this article is positioned as a specific module of road traffic with its primary function oriented towards monitoring and controlling safe traffic and the execution of significant events within a particular geographic area. Exploratory research on the significance of event execution in simple, complicated and complex traffic flow and process situations is related to the activities of monitoring and controlling functional states and performance of categorical variables, which include objects and locations of road infrastructure, communication infrastructure and networks of traffic inspection resources. It is emphasized that the words “work” and “traffic” have the semantic status as synonyms (in one world language), which is explained in the design of the Agent-based model of the complexity of content and contextual structure of TI work at the singular and plural level with 12 points of interest (POI) in the thematic research. An Event Execution Log (EEL) was created for on-site data collection with eight variables, seven of which are independent (event type, activities, objects, locations, host, duration-period and periodicity of the event) and one dependent (significance of the event) variable. The structured data set includes 10,994 input-output vectors in 970 categories collected in the EEL created by 32 human agents (traffic inspectors) over a 30-day period. An algorithmic presentation of the methodological research procedure for preprocessing and final data processing in the ensemble of machine learning models for classification and selection of TI tasks is given. Data cleaning was performed on the available dataset to increase data consistency for further processing. Vector elimination was carried out based on the Location variable, such that the total number of vectors equals the number of unique categories of this variable, which is 636. The main result of this research is the Classification modeling of the significance of events in TI work based on machine learning techniques and the Stacking ensemble. The created machine learning models for event significance classification modeling have high accuracy values. To evaluate the performance metrics of the Stacking ensemble of models, the confusion matrix, Precision, Recall, ND F1-score are used.

One of today’s most critical challenges is ensuring a sufficient food production and supply for the ever-expanding global population. Recently, many countries around the world, particularly those in Africa, grapple with severe hunger and food insecurity, frequently exacerbated by escalating global prices, persistent insecurity, and the repercussion of the COVID-19 pandemic. However, this study undertook an extensive review of the prevalence and severity of hunger and food insecurity across diverse countries and regions. The review utilised several metrics, including Global Hunger Index (GHI) scores, Global Food Security index (GFSI) scores, 2023 data (related to two Sustainable Development Goal 2 (SDG2) target) from the Food and Agriculture Organization (FAO), and other pertinent sources to assess the current status of the countries included in this study. The 20 countries with 2023 GHI scores below 5 were collectively ranked from 1 to 20, signifying low hunger levels. However, Central African Republic (CAR) received the lowest ranking at 125 out of 125, with a score of 42.3, indicating an alarming level of hunger. Finland achieved the highest GFSI score of 83.7, with Oman showing the most notable GFSI improvement at +13.8, while Syria experienced a significant decline in GFSI score by -10.5. Over time, many countries, particularly the developed ones, witnessed significant improvement in their GHI and GFSI scores. Meanwhile, most countries in sub-Saharan Africa experienced the most deterioration in their GFSI scores and notable increases in their GHI scores. The review documented the significant progress made by countries and regions in reducing levels of hunger and food insecurity, while highlighting the substantial burden borne by sub-Saharan Africa (SSA) in combating global hunger and food insecurity.

Indigenous traditional land uses, including hunting, fishing, sacred activies and land-based education at Red Sucker Lake First Nation (RSLFN) in Manitoba, Canada are impacted by mining. Traditional land use maps and interviews were undertaken with 21 Indigenous people from RSLFN, showing many traditional land uses are concentrated on greenstone belts. The interviews revealed that mining exploration has resulted in large petroleum spills, noise distress, personal property destruction, wildlife die-offs and animal population declines, which negatively impact RSLFN’s traditional land use practices, ecosystem integrity, and community health. Red Sucker Lake First Nation (RSLFN) people want their territories’ land and water protected for traditional uses, culture and ecological integrity. Towards this goal, their Island Lake Tribal Council sought support for an Indigenous-protected and conserved area (IPCA) in their territory outside of existing mining claims, but without success. Governments need to partner with Indigenous nations to reach their biodiversity targets, particularly considering northern Canada’s peatlands, including those in Island Lake, surpassing the Amazon forests for carbon storage. Critical minerals and gold’s role in renewable energy and geopolitics have colonial governments undermining Indigenous rights, climate stabilization and biodiversity. With extractivism prioritized, the environmental impacts of mining extend to not only the mines but also the extensive development required to facilitate extraction including roads, hydro and ports to ship the minerals with proposals for a national Northern Corridor to run nearby.

In Nigeria, Cervical cancer (CC) and lack of Human Papillomavirus (HPV) awareness poses significant HPV infection risk among women. This study was conducted at Ibrahim Badamasi Babangida (IBB) University in Nigeria; focused on assessing HPV awareness and knowledge, particularly concerning its association with CC and oral contraceptive (OC) pills among students and staff. We adapted and validated a 20-item HPV knowledge scale and explored factors influencing knowledge, vaccination rates, awareness, and the impact of ethnicity on HPV knowledge. Also, due to the non-normality of the knowledge score, non-parametric tests were done. Data were summarized using median and interquartile range (IQR), while categorical data were presented in frequency and percentage distribution. We established the reliability of the 20-item knowledge scale with a strong Cronbach's alpha coefficient of 0.913, indicating internal consistency. However, it revealed a concerning lack of awareness and knowledge about HPV, only 34.8% of participants were aware of HPV infection, and only 25.0% knew about HPV vaccine. Additionally, ethnicity was identified as a significant factor affecting HPV knowledge. Considering these findings, this study emphasizes the need for targeted interventions to enhance HPV awareness and knowledge, particularly among specific ethnic groups. Despite the development and validation of a reliable knowledge scale, the study underscores the importance of educational initiatives to address this knowledge gap, ultimately reducing the risk of HPV infection and CC in Nigeria.

This paper extends our seminal paper on KnoBAB for efficient Conformance Checking computations performed on top of a customised relational model. After defining our proposed temporal algebra for temporal queries (xtLTLf ), we show that this can express existing temporal languages over finite and non-empty traces such as LTLf . This paper also proposes a parallelisation strategy for such queries thus reducing conformance checking into an embarrassingly parallel problem leading to super-linear speed up. This paper also presents how a single xtLTLf operator (or even entire sub-expressions) might be efficiently implemented via different algorithms thus paving the way to future algorithmic improvements. Finally, our benchmarks remark that our proposed implementation of xtLTLf (KnoBAB) outperforms state-of-the-art conformance checking software running on LTLf logic, be it data or dateless.

Cervical cancer is mostly diagnosed cancer of women worldwide and the second most common cancer among middle-aged women in Bangladesh. Knowledge and perception of women are considered one of the major components of prevention strategy. We carried out this survey to assess the existing knowledge level of students and their perception to the role of vaccination in preventing cervical cancer. The participants were female undergraduate students studying Public Health in a university in Chattogram city of Bangladesh. A total of 275 female students were interviewed through prescribed questionnaire possessing questions on basic information of cervical cancer, its causal agent, risk factors, prevention methods, and vaccination. We marked the responses of participants with zero for wrong answer and one for every correct answer and ranked the knowledge level of students. The mean age of study participants was 20.68 years. In total, 69.1% students possessed poor knowledge whereas, 30.9% had good knowledge. The country of students has significant association with their level of knowledge. Most of the students got information on cervical cancer from traditional media (49.8%) and doctor (30.9%) and the effect of these two sources was statistically significant on the knowledge level of students. Students mostly identified vaccination (21.1%) and Gynecological examination (14.39%) as preventing methods. However, 65.5% of them acknowledge that vaccines cannot prevent this cancer and 33.4% believe in different risks of vaccine. Although students have heard of cervical cancer the overall knowledge was insufficient and perceived negative concepts about cancer vaccine. Therefore, we recommend the initiation of educational programs on basic reproductive health and health seeking behaviour targeting young women to successfully control and prevention of this life-threatening health problem.

Deep neural networks (DNN) have successfully delivered a cutting-edge performance in several fields. With the broader deployment of DNN models on critical applications, the security of DNNs becomes an active and yet nascent area. Attacks against DNNs can have catastrophic results, according to recent studies. Poisoning attacks, including backdoor and Trojan attacks, are one of the growing threats against DNNs. Having a wide-angle view of these evolving threats is essential to better understand the security issues. In this regard, creating a semantic model and a knowledge graph for poisoning attacks can reveal the relationships between attacks across intricate data to enhance the security knowledge landscape. In this paper, we propose a DNN Poisoning Attacks Ontology (DNNPAO) that would enhance knowledge sharing and enable further advancements in the field. To do so, we have performed a systematic review of the relevant literature to identify the current state. We collected 28,469 papers from IEEE, ScienceDirect, Web of Science, and Scopus databases, and from these papers, 712 research papers were screened in a rigorous process, and 55 poisoning attacks in DNNs were identified and classified. We extracted a taxonomy of the poisoning attacks as a scheme to develop DNNPAO. Subsequently, we used DNNPAO as a framework to create a knowledge base. Our findings open new lines of research within the field of AI security.

In ethnobotany, the availability hypothesis predicts that plants that are abundant and easily accessible to people are more likely to be medicinal than not. By protecting species diversity away from people, protected areas (PAs) may act as a limiting factor to a sustainable development of traditional knowledge concerning medicinal uses, and in so doing, PAs provide opportunity to prioritize ex-situ conservation for species that are PAs restricted. In this scenario, ex-situ conservation becomes the only chance for people to develop traditional knowledge on plants which otherwise wouldn’t be documented as traditionally useful to people. To test these expectations, we used data collected for almost 20 years of fieldworks on plant medicinal uses and their abundance inside and outside the Kruger National Park (KNP), South Africa. We fitted four different scenarios of structural equation models (SEMs) to the data collected. We found that total plant abundance (abundance outside + inside KNP) is a significant positive predictor of medicinal status, and so is abundance outside KNP, thus supporting the availability hypothesis. However, not only abundance inside KNP is not a direct significant correlate of medicinal status, but also the relationship between both is negative. The lack of predictive power of inside-abundance is most likely because some species are exclusively found inside KNP, and local communities do not have access to them. It also shows that the positive and direct correlation of total abundance with medicinal status is driven by outside-abundance. In addition, the negative relationships between inside abundance and medicinal status implies that abundant plants inside KNP tend to be not-medicinal, further providing evidence that PAs hinder the development of medicinal knowledge. Furthermore, when inside and outside abundance were included simultaneously in a model as two distinct variables, inside abundance was never a direct significant predictor of medicinal status, but it was so, via an indirect path mediated by outside abundance. This suggests that outside abundance is the key variable driving the development of medicinal plant knowledge. Cumulatively, our findings suggest that anything that promotes the growth of PA-restricted plants beyond the natural realized niches of these plants (ex-situ conservation) such as in botanical gardens, private gardens, in agroforestry systems, etc., is to be promoted so that people-plant interactions may continue for the benefits of ethnobotanical knowledge development.

The holy grail of Artificial Intelligence (AI) has been to mimic human intelligence using computing machines. Autopoiesis which refers to a system with well-defined identity and is capable of re-producing and maintaining itself and cognition which is the ability to process information, apply knowledge, and change the circumstance are associated with resilience and intelligence. While classical computer science (CCS) with symbolic and sub-symbolic computing has given us tools to decipher the mysteries of physical, chemical and biological systems in nature and allowed us to model, analyze various observations and use information to optimize our interactions with each other and with our environment, it falls short in reproducing even the basic behaviors of living organisms. We present the foundational shortcomings of CCS and discuss the science of infor-mation processing structures (SIPS) that allows us to fill the gaps. SIPS allows us to model su-per-symbolic computations and infuse autopoietic and cognitive behaviors into digital machines. They use common knowledge representation from the information gained using both symbolic and sub-symbolic computations in the form of system-wide knowledge networks consisting of knowledge nodes and information sharing channels with other knowledge nodes. The knowledge nodes wired together fire together to exhibit autopoietic and cognitive behaviors.

This research work describes an architecture for building a system that guide a user from a forecast generated by a machine learning model through a sequence of decision-making steps. The system is demonstrated in manufacturing demand forecasting use case and can be extended to other domains. In addition, the system provides means for knowledge acquisition by gathering data from users. Finally, it implements an active learning component and compares multiple strategies to recommend media news to the user. Such media news aims to provide additional context to demand forecasts and enhance judgment on decision-making.

This article argues that we need to look at living examples provided by non-state communities in various regions of the world that are, perhaps unwittingly, contributing to the maintenance of the Earth's optimal thermal balance. These fully sustainable communities have been living outside the mainstream for centuries, even millennia, providing examples in the global struggle against the degradation of social–ecological systems. They have all, to varying degrees, embraced simple forms of living that make them ‘exemplary ethical communities’ (EECs) – human communities with a track record of sustainability related to forms of traditional knowledge and the capacity to survive outside the capitalist market and nation-state system. The article proceeds in three steps: First, it condenses a large body of research on the limits of the existing nation-state system and its accompanying ideology, nationalism, identifying this institutional–ideological complex as the major obstacle to tackling climate change. Second, alternative social formations that could offer viable micro-level and micro-scale alternatives are suggested. These are unlikely to identify with existing nation-states as they often form distinct types of social communities. Taking examples from hunter-gatherer societies and simple-living religious groups, it is shown how the protection and maintenance of these EECs could become the keystone in the struggle for survival of humankind and other forms of life. Finally, further investigation is called for, into how researchers can come forward with more examples of actually existing communities that might provide pathways to sustainability and resistance to the looming global environmental catastrophe.

The copper mining industry is increasingly using artificial intelligence methods to improve cop-per production processes. Recent studies reveal the use of algorithms such as Artificial Neural Network, Support Vector Machine, and Random Forest, among others, to develop models for predicting product quality. Other studies compare the predictive models developed with these machine learning algorithms in the mining industry, as a whole. However, not many copper mining studies published compare the results of machine learning techniques for copper recovery prediction. This study makes a detailed comparison between three models for predicting copper recovery by leaching, using four datasets resulting from mining operations in northern Chile. The algorithms used for developing the models were Random Forest, Support Vector Machine, and Artificial Neural Network. To validate these models, four indicators or values of merit were used: accuracy (acc), precision (p), recall (r), and Matthew’s correlation coefficient (mcc). This paper describes dataset preparation and the refinement of the threshold values used for the predictive variable most influential on the class (the copper recovery). Results show both a precision over 98.50% and also the model with the best behavior between the predicted and the real. Finally, the models obtained show the following mean values: acc=94.32, p=88.47, r=99.59, and mcc=2.31. These values are highly competitive as compared with those obtained in similar studies using other approaches in the context.

Within the framework of theoretical developments in so-called participatory culture and the context of funds of identity, incorporated within what is known as the funds of knowledge approach, an innovative teaching methodology is implemented that allows students to actively participate in knowledge construction. The project translates into a proposal for educational contextualization and personalization based on the students’ funds of identity, that is, those resources (people, artifacts, places, activities, institutions) they consider to be most relevant and significant to define themselves. Once these have been identified through identity artifacts, such as collages or self-drawings, students link some of these funds of identity to curricular content of the subject and produce a video that shows the results of this academic work. The final product is shared on a YouTube channel containing the videos of all of the students in the class. The phases of the project are described and illustrated. We argue that the proposed teaching and learning project, which is cross-disciplinary in nature, allows for principles such as educational contextualization, funds of identity, and participatory culture to be incorporated into what we call here the funds of identity 2.0 approach, putting it into educational practice.

As digital communication becomes increasingly integral to personal and corporate activities, phishing attacks have emerged as a prevalent threat, ingeniously mimicking legitimate sources to illicitly acquire sensitive information. This research paper details the development of a sophisticated phishing detection application utilizing the DistilBERT-based model, finetuned on a diverse array of email datasets. The application significantly enhances the precision of phishing detection mechanisms, adeptly reducing the incidence of successful phishing attacks. Initial tests have demonstrated a precision rate of over 95% in detecting phishing emails, outperforming traditional rule-based filters substantially. The application exhibits robust defences against zero-day phishing attacks through its advanced machine learning framework, which dynamically adapts to emerging phishing strategies. This paper explores the methodology of developing the DistilBERT model, evaluates its efficacy against existing solutions, and discusses its implications for future cybersecurity practices. The study’s findings underscore the potential of AI-driven tools in transforming cybersecurity measures, offering a proactive approach to thwarting phishing attempts and safeguarding sensitive data.

This paper analyses the viability of different solutions to passively augment the axial stiffness of a horizontal axis radial levitation passive magnetic bearing (PMB) with topology previously studied. The zero-field cooling (ZFC) of high-temperature superconductor (HTS) bulks promotes higher magnetic impulsion and levitation forces and lower electromagnetic losses than with the field-cooling (FC), but on the other hand, the guiding stability is much lower than with FC. Because of stability reasons, FC was adopted in most superconducting maglev systems. The trend of this research group has been to develop an HTS ZFC horizontal axis radial levitation PMB presenting notable levitation forces with reduced electromagnetic losses, defined by a topology that creates guiding stability. Previous work has shown that even optimizing the bearing geometry to maximize magnetic guidance forces, could not be enough to guarantee the axial stiffness required for many applications. First, the extent to which guidance forces are augmented by increasing the number of HTS bulks in the stator is evaluated. Then, the axial stiffness augmentation by passively adding two limiting permanent magnet (PM) rings is evaluated. Results show that the axial stiffness is highly augmented by adding limiting PM rings with no significant additional investment. This change enables using the studied ZFC superconducting PMB in high-precision axial stability applications, such as precision gyroscopes, horizontal axis propellers and turbines.

There is an increased interest in the district-scale energy transition within interdisciplinary research community. Agent-based modelling presents a suitable approach to address variety of questions related to policies, technologies, processes, and the different stakeholder roles that can foster such transition. This state-of-the-art review focuses on the application of agent-based modelling for exploring policy interventions that facilitate the decarbonisation (i.e., energy transition) of districts and neighbourhoods while considering stakeholders’ social characteristics and interactions. We systematically select and analyse peer-reviewed literature and discuss the key modelling aspects, such as model purpose, agents and decision-making logic, spatial and temporal aspects, and empirical grounding. The analysis reveals that the most established agent-based models’ focus on innovation diffusion (e.g., adoption of solar panels) and dissemination of energy-saving behaviour among a group of buildings in urban areas. We see a considerable gap in exploring the decisions and interactions of agents other than residential households, such as commercial and even industrial energy consumers (and prosumers). Moreover, measures such as building retrofits and conversion to district energy systems involve many stakeholders and complex interactions between them that up to now have hardly been represented in the agent-based modelling environment.

A numerical study for the effect of crest width, breaking parameter and trunk permeability on hydrodynamics and flow behavior in the vicinity of rubble-mound, permeable, zero-freeboard breakwaters (ZFBs) is presented. The modified two dimensional Navier-Stokes equations for two-phase flows in porous media with a Smagorinsky model for the subgrid scale stresses were solved numerically. An immersed-boundary/level-set method was used. The numerical model was validated for the cases of wave propagation over a submerged impermeable trapezoidal bar and over a low-crested permeable breakwater. Five cases of breakwaters were examined, and the main results are: (a) The size of the crest width, B, does not notably affect the wave reflection, vorticity and currents in the seaward region of ZFBs, while wave transmission, currents in the leeward side, and mean overtopping discharge, all decrease with increasing B. A non-monotonic behavior of the wave setup is also observed. (b) As the breaking parameter decreases, wave reflection, transmission, currents, mean overtopping discharge, and wave setup decrease. This observation is also verified by relevant empirical formulas. (c) As the ZFB trunk permeability decreases, an increase of the wave reflection, currents, wave setup, and a decrease of wave transmission and mean overtopping discharge is observed.

Since engineering discipline is at the forefront of latest innovations and developments, there is an increasing emphasis on the critical responsibilities of engineers towards sustainability. There are several studies investigating the incorporation of sustainability principles into engineering curriculum but studies analyzing the knowledge, attitude, and behavior of engineering students are lacking. A deeper understanding of the current state of students’ knowledge, attitude, and behavior towards sustainability is crucial to determine the type of pro-sustainability trainings, strategies and pedagogical activities needed. Hence, the current study is aimed at assessing the level of sustainability cognizance of Mechanical Engineering students via an online Sustainability Consciousness Questionnaire. The final sample size was 134 students from a Higher Education Institute in India with 90% male responders and 52% from urban areas. Results indicate that 70% of the respondents have heard of the Sustainable Development Goals, only around 32% of the students demonstrate a positive behavior and attitude towards sustainability whereas 30% of the students do not hold any opinion towards sustainability principles. The current exploratory study contributes to the knowledge base about sustainability awareness and consciousness and advocates for measures to insert its principles in engineering courses.

Traditional Medicine (TM) is a culturally-rooted healthcare practice, and its use, including for treating illnesses among children, has been reported in many countries. Despite the cultural significance of TM in managing children’s illnesses, evidence suggests that child mortality remains high in many settings. This indicates that the sole use of TM within families and communities might act as a barrier to using biomedical services, contributing to avoidable deaths. Globally, Indonesia is in the top 10 countries with the highest child mortality rates. In addition, Belu is a district in East Nusa Tenggara province, Indonesia that exhibits a high number of child deaths within the last three years. However, the use of TM for the treatment of illnesses among children in Indonesia has received minimal academic attention. This study aimed to understand parental beliefs, knowledge, views, and perceptions of TM and the impact on the use of TM and biomedical services in the treatment of illnesses among their children in Belu district, Indonesia. The participants (n=25) were parents with the real-life experiences of using TM for treating their children’s illnesses. They were recruited using the snowballing sampling technique. Data were collected using in-depth qualitative interviews. A thematic data analysis was performed and guided by a qualitative data analysis framework. The findings were grouped into four main themes: (i) parental beliefs, knowledge, views, and perceptions of TM, (ii) the influence of these factors on parental use of TM and biomedical services for the treatment of children’s illnesses, (iii) social and cultural influences on the use of TM and biomedical services among their children, and (iv) the impact of the sole use of TM or late medical diagnosis on the health condition and the death of children. The findings show that the use of TM is a common practice within families and communities in Belu, however evidence suggests that child mortality remains high in the setting, indicating less effectiveness of the sole use of TM. Thus, there is a need for the development of collaborative models of healthcare programs and service delivery fostering mutual respect and understanding between traditional healers and healthcare professionals, which could significantly contribute to improving parental use of biomedical services for the treatment of their children’s illnesses.