This article describes the transition of an undirected active network, used for numerical and logical analysis, and made up of communicating operators and nodes, and being self-extensible, into a network of semantic structures, and the extensions necessary to handle language. The network, called Active Structure, was infused with knowledge in the form of tens of thousands of definitions taken from a human-readable dictionary. Where the dictionary fell short, from circularity, lack of a hierarchical form for its senses and sub senses, or inadequate detail around verbs, the entries were extensively curated. The result is a machine which has access to every meaning of every word in its vocabulary, and needs to manage the right meaning for every use of a word, a task that our unconscious minds now handle. To demonstrate that such a network can aspire to human-level tasks, we have begun loading an Anti-Money Laundering Act so that the machine can devise and test strategies against money launderers. There is a higher goal being aimed at, the breaking of the Four Pieces Limit. That is, the network is not intended to save money on human cognitive labor, but to raise an absurdly low limit on it.

Recent years have shown that traditional regulatory techniques alone are not effective in achieving behavior change in important fields such as environmental sustainability. Governments all over the world have been progressively including behaviourally informed considerations in policy and law-making with the aim of improving the acceptance and impact of sustainability-oriented measures. This led to the arrival of alternative regulatory tools, such as nudges. The effectiveness of nudges for environmental sustainability (green nudges) has been largely reported but the practical and ethical implications are still largely neglected by academic research. In this contribution, “nudges” are conceptually distinguished from “boosts” and their ethical briefly explained. The analysis is made at the light of the current mostly European and US American academic literature.

Chan and colleagues in their paper titled “Human origins in a southern African palaeo-wetland and first migrations” (https://www.nature.com/articles/s41586-019-1714-1) report 198 novel whole mitochondrial DNA (mtDNA) sequences and infer that ‘anatomically modern humans’ originated in the Makgadikgadi–Okavango palaeo-wetland of southern Africa around 200 thousand years ago. This claim relies on weakly informative data. In addition to flawed logic and questionable assumptions, the authors surprisingly disregard recent evidence and debate on human origins in Africa. As a result, the emphatic and high profile conclusions of the paper are unjustified.

The human virome is an area of increasing interest with relation to human health and disease. It has been demonstrated to alter in concert with the bacterial microbiome in early life and was also found to be different in patients with certain diseases such as inflammatory bowel disease. However, all virome analyses are hampered by a lack of annotated representative database sequences, often referred to as the ‘viral dark matter’. Here we provide the first description of the gut DNA virome in elderly individuals (>65 years old) as well as the description of novel bacteriophages not present in current reference databases. Diversity analysis comparing elderly persons from different residence locations (community living vs long term care facilities) did not reveal any difference in their virome diversity profiles despite the reported differences at the bacteriome level. An abundance of Microviridae of the subfamily Gokushovirinae were present in the faeces of elderly individuals. Several novel members of the order Caudovirales were also characterized and annotated. Assignment of host bacteria to detected viral genomes was attempted using a combination of CRISPR spacers, tRNA genes and a probabilistic approach. Further characterization of the viral dark matter is necessary for developing tools and expanding databases to study the human virome. This study focused on the virome of an aging human cohort with the goal of illuminating part of the viral dark matter.

This qualitative study explores the dynamics of supplier relationships among Italian restaurant managers, aiming to uncover the strategic practices and challenges in supplier relationship management (SRM) within the culinary industry. Through semi-structured interviews with a diverse sample of restaurant managers across Italy, the study examines how trust, communication, adaptive sourcing strategies, and innovation influence supplier interactions and operational outcomes. Findings reveal that trust-building is foundational to establishing reliable supplier partnerships, crucial for ensuring consistent delivery of high-quality ingredients essential for culinary excellence. Effective communication channels and negotiation techniques are pivotal in clarifying expectations, resolving conflicts, and fostering collaborative relationships with suppliers. Adaptive sourcing strategies, including diversification of supplier bases and local sourcing initiatives, enable managers to navigate market dynamics and meet consumer demands effectively. Innovation emerges as a catalyst for differentiation and sustainability in supplier relationships, with managers engaging in collaborative product development and embracing sustainability initiatives to align with consumer preferences and corporate values. Despite facing challenges such as pricing pressures, supply chain logistics complexities, and regulatory compliance, proactive management strategies and operational agility enable managers to mitigate risks and capitalize on opportunities in supplier management. Theoretical implications enrich understanding of SRM dynamics in the culinary sector, while practical implications suggest strategies for optimizing supplier relationships, enhancing operational efficiency, and sustaining competitive advantage. Future research could explore technological advancements, consumer trends, and longitudinal impacts of SRM practices on business performance within the restaurant industry.

Supplier collaboration and partnership in procurement are essential elements for enhancing organizational efficiency, innovation, and resilience in today's globalized markets. This qualitative study investigates the dynamics of effective supplier relationships, focusing on trust, communication, innovation, quality, cost savings, and resilience as critical factors. Data were gathered through semi-structured interviews, case studies, and document analysis across diverse industries, highlighting insights from procurement professionals and supplier representatives. Key findings underscore the foundational role of trust and communication in fostering successful collaborations. Establishing transparent, ongoing communication channels facilitates mutual understanding, reduces conflicts, and enhances overall satisfaction. Collaborative innovation emerged as pivotal, enabling organizations to pool resources and expertise for product development and process improvement. Quality and reliability were identified as significant outcomes of close partnerships, particularly in industries with stringent standards such as aerospace and healthcare. Furthermore, the study reveals substantial cost savings and efficiency gains through collaborative efforts in process optimization and waste reduction. Effective risk management practices within these partnerships enhance supply chain resilience, enabling organizations to anticipate and mitigate disruptions proactively. Despite these benefits, challenges such as cultural differences, technological integration, and regulatory compliance require strategic mitigation strategies. In conclusion, organizations can optimize supplier collaborations by prioritizing trust-building, fostering a culture of innovation, and investing in robust relationship management practices. This approach not only enhances operational performance but also positions organizations to navigate market uncertainties and achieve sustainable growth in a competitive landscape.

Supply chain transparency plays a crucial role in shaping consumer perceptions and building brand trust in today's competitive marketplace. This qualitative study explores the impact of supply chain transparency on consumer behavior, focusing on its influence on brand trust and purchase decisions. Through semi-structured interviews with 30 participants, the research examines consumer attitudes towards transparency, highlighting key factors that influence trustworthiness and credibility perceptions of brands. The findings reveal that consumers prioritize brands that demonstrate openness about their sourcing, production practices, and ethical standards, viewing transparency as a critical indicator of corporate responsibility and integrity. Factors such as product safety, environmental sustainability, and labor practices within supply chains emerge as significant concerns driving consumer preference for transparent brands. Demographic insights indicate that younger consumers and those with higher education levels exhibit heightened sensitivity to transparency issues, underscoring a generational and educational divide in consumer expectations. Moreover, income levels influence the perceived importance of transparency, with higher-income participants showing greater preference for brands that prioritize ethical and sustainable practices. Challenges associated with supply chain transparency, including information overload and concerns about greenwashing, highlight the complexities brands face in effectively communicating their ethical commitments to consumers. The study concludes by advocating for strategic transparency initiatives that integrate sustainability, technology-enabled verification, and stakeholder engagement to build consumer trust and competitive advantage. By addressing these insights, brands can navigate the evolving landscape of consumer expectations and regulatory requirements, fostering long-term relationships based on trust and ethical business practices.

This qualitative study investigates how supply chain disruptions affect brand loyalty by analyzing consumer experiences during crises. Through in-depth interviews with diverse participants, the research explores the nuanced factors shaping consumer perceptions and behaviors when supply chains face challenges. Findings reveal that trust is pivotal, with transparent communication and consistent updates fostering consumer confidence and loyalty. Brand reputation emerges as crucial, influencing perceptions of reliability and ethical conduct during disruptions. Effective crisis management strategies, including agility and proactive communication, mitigate negative impacts on brand loyalty. Consumer decision-making is influenced by the availability of substitutes and perceptions of disruption severity, highlighting the importance of brand resilience and continuity. Emotional responses, such as frustration and anxiety, significantly influence consumer loyalty, underscoring the role of empathetic engagement and support from brands. Aligning with consumer expectations and delivering on promises are essential for sustaining loyalty and reducing the risk of consumer defection. The study concludes that navigating supply chain disruptions requires brands to prioritize trust, reputation, crisis management, consumer-centric strategies, emotional intelligence, and alignment with consumer expectations. These insights contribute to a deeper understanding of consumer behavior in crisis contexts and provide strategic implications for brands seeking to enhance resilience and maintain loyalty in dynamic market environments.

Consumer trust in supply chain certifications plays a pivotal role in shaping contemporary consumer behavior and marketing effectiveness. This qualitative research investigates consumer perceptions of supply chain certifications and their impact on purchasing decisions and brand loyalty. Through semi-structured interviews and focus group discussions with 30 participants, the study explores themes such as credibility, transparency, perceived value, and the influence of certifications on consumer trust. Findings reveal that consumers value certifications that signal ethical sourcing practices and sustainability commitments, viewing them as assurances of product quality and social responsibility. Credibility and transparency in certification processes emerged as critical factors influencing consumer trust, while perceived benefits such as environmental sustainability and fair labor practices positively influenced purchase intentions. Moreover, certifications were found to enhance brand loyalty by aligning with consumers' ethical values and beliefs. However, the study identifies challenges including high certification costs, complexity in compliance, and consumer skepticism, which hinder broader adoption and effectiveness of certifications. The implications of these findings suggest that businesses can enhance consumer trust and market competitiveness by investing in rigorous certification standards, clear communication strategies, and sustainable practices. Addressing barriers through improved transparency and consumer education is crucial for maximizing the impact of certifications on ethical consumption. Future research directions include longitudinal studies on certification impacts and comparative analyses across industries and global markets to further enrich understanding of consumer behavior and certification influences.

This qualitative research explores the impact of digital transformation on supply chain collaboration, delving into its implications for organizational dynamics, processes, and outcomes. Through in-depth interviews with key stakeholders in diverse industries, insights are gathered into how digital technologies reshape collaboration within supply chains. Findings reveal a shift towards greater connectivity, visibility, and agility enabled by digital platforms and ecosystems, fostering innovation and value creation. However, challenges such as data security, organizational silos, and trust issues hinder the realization of collaborative potential. The study underscores the importance of embracing digital technologies, fostering a collaborative culture, and prioritizing trust and transparency to unlock the full benefits of digital collaboration. Moving forward, future research should focus on exploring evolving dynamics, identifying best practices, and developing frameworks for effective implementation in diverse organizational contexts. By leveraging digital transformation, organizations can enhance their supply chain capabilities, strengthen competitiveness, and drive sustainable growth in an interconnected, digitalized world.

This study investigates the factors influencing consumers' intentions to purchase overseas travel packages via social media in Thailand, with a focus on consumer trust and perceived risks. A mixed-methods approach was employed, combining qualitative and quantitative research techniques. In the qualitative phase, the Fuzzy Set Delphi Method was applied to gather expert consensus from 19 participants. For the quantitative phase, a survey was conducted among 600 individuals who had experience purchasing overseas travel packages on social media in Thailand. First-order and second-order confirmatory factor analyses were used to examine the relationships between eight identified factors: social media influencer, e-WOM, trust, perceived risk, brand image, rating review, personal attitude, and destination image. The results indicate that trust and perceived risk are the most significant factors affecting purchase intention. Trust plays a crucial role in customers' decision-making processes, while perceived risk influences their tendency to avoid purchasing packages if the risk is perceived as high. The insights gained from this study contribute to the understanding of factors influencing purchase intentions for overseas travel packages through social media in Thailand, providing valuable information for travel agencies to develop effective marketing strategies.

Brand trust holds significant importance in the Mongolian cashmere industry. This study examines the primary predictors of customer brand trust among Mongolian cashmere users. Our research includes a survey with 800 participants and 20 individual interviews conducted among both Mongolian cashmere users and non-users. We utilized an online platform to reach participants, employing convenience sampling. The study investigates the relationship between various factors and brand trust within the context of Mongolian cashmere brands. Through a combination of in-dividual interviews and statistical analysis, we test six hypotheses. The findings indicate that higher brand quality, a strong reputation, greater transparency, effective issue resolution, commitment to environmental and social responsibility, and pre-purchase research on such commitments positively impact brand trust. Key insights gleaned from the interviews emphasize the importance of quality, reputation, transparency, customer service, and environmental responsibility in shaping consumer trust. These results offer valuable implications for cashmere brands aiming to bolster consumer trust and foster loyalty.

Renewable Energy Communities (RECs), strongly sustained by the European Community as a viable way to fasten low carbon transition, slowly develop in Italy for regulatory . This study delves into the framework of RECs and explores the perspective of the specifically small and medium industries in the Lombardy region of Italy through a survey. Our findings reveal that while SMEs exhibit interest in RECs, there is a notable gap in knowledge and engagement. We argue that the active involvement of Public Administration Bodies (PABs) is crucial in disseminating information about RECs and highlighting their benefits. The survey results, elaborated through a non-parametric test (Mann-Whitney U test) indicate that PABs play a pivotal role in influencing SMEs' perceptions and willingness to participate in RECs. The data suggest that more direct and effective communication strategies by PABs could significantly enhance SMEs' understanding and involvement in RECs. This research underscores the need for targeted and strategic efforts by PABs to foster a more informed and proactive approach among SMEs towards renewable energy initiatives. Such endeavors could be instrumental in accelerating the adoption of RECs, contributing to a more sustainable and resilient energy future.

Context and aim: Environmental awareness is the first step to climate change risk perceptions and environmental behaviour. However, this pathway is not always possible within different population segments and trust contexts. This study explores the conditional effect of trust on environmental behaviour in the Zed Generation in Albania. The consideration of the Zed generation due to two opposing traits, such as social courage and anti-social, make them an interesting topic to understand, especially for their importance in online communities. Method: The youth engagement environmental programme and the climate change risk perceptions framework are adopted in an input-output design. Environmental awareness (input) is the initiating factor, and climate change risk perception and community engagement as mediators of environmental behaviour(output). Individual and institutional trust is the moderating factor that inhibits/enhances engagement and environmental behaviour. Results: The moderation-mediation show that climate change risk perceptions did not mediate environmental behaviour. Environmental awareness directly affects it. However, low institutional trust inhibits the general trust that, in turn, lowers the impact of awareness in climate change perceptions and community engagement toward environmental protection. Practical recommendation: The application of the input-output model shows that the pathway that directly affects environmental behaviour among Zed Generation in Albania is not Climate change risk perception. In low-trust contexts, positive environmental behaviour can be generated through community engagement. Policymakers and educational programmes in low-middle-income countries might consider community engagement to mitigate trust issues and effectively deal with environmental problems with a global segment such as the Zed generation.

Aiming to investigate the impact of strategic knowledge disclosure on the continuous realization of digital innovation in enterprises, this study examines data from 168 listed companies across three high-tech industries in China from 2012 to 2021. By examining investor trust, consumer trust, worker trust, and rival trust as mediating variables within the market trust dimension, this research explores the impact mechanism of strategic knowledge disclosure on the sustainability of input and output in digital innovation. The findings reveal that strategic knowledge disclosure significantly promotes both input and output sustainability in digital innovation. Specifically, investor trust positively mediates the relationship between strategic knowledge disclosure and the sustainability of digital innovation input. Similarly, consumer trust and worker trust positively mediate the association between strategic knowledge disclosure and both input and output sustainability in digital innovation. However, it is important to note that rival trust negatively mediates the link between strategic knowledge disclosure and both input and output sustainability in digital innovation. The research findings possess significant theoretical value and provide valuable guidance for promoting the sustainable development of digital innovation in enterprises.

The current global financial sector is dedicated to providing customers with more advanced and strategic services. With channels like internet banking, mobile phone banking, and telephone banking, the industry delivers multi-channel banking services to satisfy its clients. The COVID-19 pandemic has directly impacted the global economy, as well as the financial sector, with the banking sector facing lockdown initiatives to curb the virus's spread. As a result, mobile payment methods have been favored by people over physical money for transactions. This study analyzes the factors that influenced mobile banking usage during the pandemic in Sri Lanka.. Convenience sampling was used, comprising of 200 online questionnaires conducted among customers in Sri Lanka. The collected data was analyzed using descriptive analysis, exploratory factor analysis, and multiple linear regression through IBM SPSS 25. Research findings revealed that trust and risk were significant factors affecting mobile banking usage in Sri Lanka. Trust had a positive effect while Risk negatively effects to the mobile banking usage. Ease of use and Usefulness were irrelevant factors during the COVID-19 pandemic in Sri Lanka.

With the rapid development of Internet of Vehicles (IoV), particularly the introduction of mobile edge computing (MEC), vehicles can efficiently share data with one another. However, edge computing nodes are vulnerable to various network attacks, posing security risks to data storage and sharing. Moreover, the presence of abnormal vehicles during the sharing process poses significant security threats to the entire network. To address these issues, this paper proposes a novel reputation management scheme, which proposes an improved multi-source multi-weight subjective logic algorithm. This algorithm fuses direct and indirect opinion feedback of nodes through the subjective logic trust model while considering factors such as event validity, familiarity, timeliness, and trajectory similarity. Vehicle reputation values are periodically updated, and abnormal vehicles are identified through reputation thresholds. Finally, blockchain technology is employed to ensure the security of data storage and sharing. By analyzing real vehicle trajectory datasets, the algorithm is proven to effectively improve the differentiation and detection rate of abnormal vehicles.

Indonesian crypto asset owners are expanding despite the government's ban. Crypto assets are too risky for Indonesian investors. This study uses the Theory of Planned Behavior and exogenous variables like financial literacy, trust, and government regulation to determine the causes of the intention to invest in crypto assets. Exogenous variables and behavioral intentions were moderated by gender. The study employs a quantitative approach, collecting data through a questionnaire survey of 149 adults over the age of 17 who have invested in non-crypto assets. SmartPLS was used to analyze research data. Financial literacy and trust, according to the study's findings, have an impact on the intention to invest in crypto assets. Gender can moderate the relationship between belief and behavioral intention. Women are more likely than men to invest in crypto assets due to the block chain system's security, transparency, and ease of use. This research is expected to help the government address the risks associated with investing in crypto assets by increasing transaction security with futures clearing and monitoring by using third parties as depositors. It also governs the laws governing crypto exchanges' liability to their investors.

Exoskeletons designed to assist patients with activities of daily living are becoming increasingly popular, but still are subject to research. In order to gather requirements for the design of such systems, long-term gait observation of the patients over the course of multiple days in an environment of daily living are required. In this paper a wearable all-in-one data acquisition system for collecting and storing biomechanical data in everyday life is proposed. The system is designed to be cost efficient and easy to use, using off-the-shelf components and a cloud server system for centralized data storage. The measurement accuracy of the system was verified, by measuring the angle of the human knee joint at walking speeds between 3 and 12 km/h in reference to an optical motion analysis system. The acquired data was uploaded to a cloud database via a smartphone application. Verification results showed that the proposed toolchain works as desired. The system reached an RMSE from 2.9° to 8°, which is below most of the comparable system. The system provides a powerful, scalable platform for collecting and processing biomechanical data, which can help to automize the generation of an extensive database for human kinematics.

Porous coatings on prosthetic implants encourage implant fixation. Enhanced fixation may be achieved using a magneto-active porous coating that can deform elastically in vivo on application of an external magnetic field, straining in-growing bone. Such coating, made of 444 ferritic stainless steel fibres, was previously characterised in terms of its mechanical and cellular responses. In this work, co-cultures of human osteoblasts and endothelial cells were seeded into a novel fibrin-based hydrogel embedded in a 444 ferritic stainless steel fibre network. Albumin was successfully incorporated into fibrin hydrogels improving the specific permeability and the diffusion of fluorescently-tagged dextrans without affecting their Young’s modulus. The beneficial effect of albumin was demonstrated by upregulation of osteogenic and angiogenic gene expression. Furthermore, mineralisation, extracellular matrix production and formation of vessel-like structures were enhanced in albumin-enriched fibrin hydrogels compared to fibrin hydrogels. Collectively, the results indicate that the albumin-enriched fibrin hydrogel is a promising bio-matrix for bone tissue engineering and orthopaedic applications.

Excessive vibration of temporary grandstand owing to crowd has lateral rhythmic motions can cause human in panic, which attracted increasing attention in recent years. This paper focuses on experiments that a temporary grandstand occupied 20 participants is oscillated by a shaking table with a series of random waves and induced by crowd with rhythmic swaying motions at lateral direction, respectively. And then a series of vibration perception questionnaires about crowd reaction are obtained. Evaluation of annoying level derives in concept of degree of membership and annoying rate method is proposed, then the human health risk of vibration serviceability of temporary grandstand is assessed with acceleration vibration dose value(VDV). From these results it is clear that standing crowd is more tolerant to vibration than seated crowd. The measured vibrations generated by crowd activities on temporary grandstand can cause panic in crowd. New relationship between the annoyance rate and structural acceleration VDV at logarithmic coordinate is proposed. The findings of this study can be utilized to manage the vibration of temporary grandstand and assess the human health risk.

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

Along with the economic growth, the companies must contribute to social progress and promote environmental sustainability in equal harmony. Sustainable human resource management (SHRM) strategies make it possible to attain the economic, social and environmental goals of a firm. In this regard, a survey method is discussed using the literature review and online questionnaire to identify the main factors/indicators during the SHRM evaluation of manufacturing firms in India. Uncertainty is commonly occurred in the assessment of SHRM factors. As a generalized version of fuzzy sets, single-valued neutrosophic set (SVNS) has been demonstrated as a valuable tool to illustrate the indeterminate, inconsistent and uncertain data of realistic decision-making problems. Considering the idea of SVNSs, this study develops a hybrid multi-criteria group decision-making (MCGDM) approach for assessing the SHRM of manufacturing firms under uncertainty settings. For this purpose, an SVN-alternative ranking order method accounting for two-step normalization (AROMAN) is proposed based on VIFI-score function-based decision experts’ (DEs’) weighting tool and integrated criteria weight-determining model to solve the MCGDM problems with fully unknown DEs and criteria weights. In this regard, we develop new SVN-distance measure to compute the degree of difference between SVNSs. Some examples are presented to demonstrate the efficacy of developed measure over the existing ones. In addition, new criteria weight-determination model is presented with the integration of objective weights through IVIF-distance measure-based model and subjective weights through ranking comparison (RANCOM) tool on SVNSs. The proposed ranking method is applied to an empirical study of SHRM assessment for manufacturing firms in India, which shows its applicability and feasibility. In this study, the evaluation criteria are characterized into social, environmental and economic aspects with DE’s opinions. Comparative and sensitivity analyses are made to show the strength and steadiness of presented approach. This study provides an innovative MCGDM analysis framework, which makes a significant contribution to the SHRM assessment problem under indeterminate, inconsistent and uncertain setting.

Wastewater-Based Epidemiology (WBE) is currently used to monitor not only the spread of the pandemic virus SARS-CoV-2 but also for other viruses in endemic conditions, particularly in the absence of syndromic surveillance. Continuous monitoring of sewage requires high costs and significant time investment, highlighting the need for standardized methods and structured monitoring strategies. In this context, we conducted weekly wastewater monitoring in northwestern Italy, targeting Human Adenovirus (HAdV), Norovirus Genogroup II (NoVggII), Enterovirus (EV), and SARS-CoV-2. Samples collected at the entrance of treatment plants were concentrated using PEG/NaCl precipitation and viral nucleic acids were extracted and detected by real-time (RT-)qPCR. NoVggII resulted the most identified target (84.4%), followed by HAdV, SARS-CoV-2, and EV. Only HAdV and EV exhibited seasonal peaks in spring and summer. SARS-CoV-2 results compared with data previously collected in the same study area (February 2021 to September 2021) revealed a shift from an epidemic to a pandemic status, likely due to the evolution of variants. In conclusion, WBE, using standardized methods and an efficient monitoring strategy, proves valuable for virus surveillance in pandemic and epidemic scenarios, enabling the identification of temporal-local distribution patterns that are useful for making informed public health decision.

As the global population rapidly ages with longer life expectancy and declining birth rates, the need for healthcare services and caregivers for older adults is increasing. Current research envisions addressing this shortage by introducing domestic service robots to assist with daily activities. The successful integration of robots as domestic service providers in our lives requires them to possess efficient manipulation capabilities, provide effective physical assistance, and have adaptive control frameworks that enable them to develop social understanding during human-robot interaction. In this context, human factors, especially quantifiable ones, represent a necessary component. The objective of this paper is to conduct an unbiased review encompassing studies on human factors studied in research involving physical interactions and strong manipulation capabilities. We identified the prevalent human factors in physical human-robot interaction (pHRI), noted the factors typically addressed together and determined the frequently utilized assessment approaches. Additionally, we gathered and categorized proposed quantification approaches based on measurable data for each human factor. We also formed a map of common contexts and applications addressed in pHRI for a comprehensive understanding and easier navigation of the field. We found out that most of the studies in direct pHRI (when there is direct physical contact) focus on social behaviors with belief being the most commonly addressed human factor type. Task collaboration is moderately investigated, while physical assistance is rarely studied. In contrast, indirect pHRI (when the physical contact is mediated via a third item) studies often involve industrial settings, with physical ergonomics being the most frequently investigated human factor. More research is needed on human factors in direct and indirect physical assistance applications, including studies that combine physical social behaviors with physical assistance tasks. We also found that while the predominant approach in most studies involves the use of questionnaires as the main method of quantification, there is a recent trend that seeks to address quantification approaches based on measurable data.

Of the roughly 20,000 canonical human protein sequences, as of September 15, 2020, 6,937 proteins have had their full or partial, medium- to high-resolution structures determined by x-ray crystallography or other methods. Which of these proteins dominate the Protein Data Bank (the PDB) and why? In this paper, we list the 273 top human protein structures based on the number of their PDB entries. This set of proteins accounts for more than 40% of all available human PDB entries and represent past trends as well as current status for protein structural biology. We briefly discuss the relationship which some of the prominent protein structures have with protein research as a whole and mention their relevance to human diseases. The top-10 soluble and membrane proteins are all well-known (most of their first structures being deposited more than 30 years ago). Overall, there is no dramatic change in recent trends in the PDB. Remarkably, the number of structure depositions has grown nearly exponentially over the last 10 or more years (with a doubling time of 7 yrs for proteins from all organisms). Growth in human protein structures is slightly faster (at 5.9 yrs, while E.Coli and Mouse+Rat protein structures accumulate more slowly, Zebrafish protein structures are growing most, at a doubling every 3.7 years, albeit starting from only approx. 100 structure entries in 2010). The information may be informative to senior scientists but also inspire researchers who are new to protein science, providing the year 2020 snap-shot for the state of protein structural biology.

It has been a challenge to visualize in nature some concepts from abstract subjects such as mathematics and philosophy. Irrationality is certainly among the least comprehensive. This essay aimed to propose that irrationality is simply an illusion resulting from the human inability to grasp the full nature of reality, rather than actual continuity flaws in the fabric of existence. There are three major arguments: (1) human senses have limitations and these are likely to be extended to the intellect, (2) there are no observable existential discontinuities in everyday experience, and (3) some rational phenomena have irrational components. These arguments point towards the existence of a physical existence of irrational quantities or distances, visible to the human eye but not fully comprehended through arithmetic abstraction.

The human virome is an area of increasing interest with relation to human health and disease. It has been demonstrated to alter in concert with the bacterial microbiome in early life and was also found to be different in patients with certain diseases such as inflammatory bowel disease. However, all virome analyses are hampered by a lack of annotated representative database sequences, often referred to as the ‘viral dark matter’. Here we provide the first description of the gut DNA virome in elderly individuals (>65 years old) as well as the description of novel bacteriophages not present in current reference databases. Diversity analysis comparing elderly persons from different residence locations (community living vs long term care facilities) did not reveal any difference in their virome diversity profiles despite the reported differences at the bacteriome level. An abundance of Microviridae of the subfamily Gokushovirinae were present in the faeces of elderly individuals. Several novel members of the order Caudovirales were also characterized and annotated. Assignment of host bacteria to detected viral genomes was attempted using a combination of CRISPR spacers, tRNA genes and a probabilistic approach. Further characterization of the viral dark matter is necessary for developing tools and expanding databases to study the human virome. This study focused on the virome of an aging human cohort with the goal of illuminating part of the viral dark matter.

Cloud robotics is becoming an alternative to support advanced services of robots with low computing power as network technology advances. Recently, fog robotics has gained attention since the approach has merit relieving latency and security issues over the conventional cloud robotics. In this paper, a Function-as-a-Service based Fog Robotic (FaaS-FR) for cognitive robots is proposed. The model distributes the cognitive functions according to the computational power, latency and security with a public robot cloud and fog robot server. During the experiment with a Raspberry Pi as an edge, the proposed FaaS-FR model shows efficient and practical performance in the proper distribution of the computational work of the cognitive system.

This paper attempts to compare two different approaches to solve the problem of accident rates prediction based on human factors for industrial workers. One of the methods has already been done using Fuzzy c-Means Clustering and proved to be working with decent results. The second method which will be covered in this paper is using Artificial Neural Networks. The primary goal of this work is to insure that ANN will work efficiently in such prediction problem. The second goal is to reveal the fact that which one of the two selected methodologies is better at defining the estimation of accident rates among people who work in different industrial fields. The purpose has been achieved when the outcome of the ANN was obtained and compared accordingly with the output of the research previously carried out with Fuzzy c-means clustering method. Comparing the outcomes of these two different methods gave an immense insight on which features are more important than others when it comes to laborers properties with completely different background such as varying levels of health, knowledge, experience, training and physical properties. At the end of the research, it becomes clear that accident rates estimation for laborers with properly trained Artificial Neural Network gives better results when it is compared with Fuzzy c-Means Clustering method. Standard deviation method was used to calculate the validity of ANN technique. The result was compared with Fuzzy c-mean clustering technique. Impressive improvement of 8.8% in the accident rate prediction was achieved using Tailored-Made-ANN.

This qualitative study explores the pivotal roles of trust and communication in supplier relationship management (SRM) within the e-commerce sector. Drawing on interviews and thematic analysis, the study examines how trust, comprising dimensions of competence, benevolence, integrity, and reliability, underpins collaborative partnerships and operational resilience between e-commerce platforms and suppliers. Effective communication strategies, including proactive engagement, transparent feedback mechanisms, and real-time updates via digital platforms, are identified as critical enablers for enhancing information exchange and fostering mutual understanding in SRM. Challenges such as cultural differences, technological complexities, and regulatory compliance requirements are also discussed, highlighting the need for strategic management and investment to mitigate risks and maintain trust in supplier engagements. Relational competencies such as empathy, active listening, conflict resolution, and trust-building skills are recognized as essential for navigating interpersonal dynamics and fostering collaborative relationships in e-commerce SRM. Strategically, integrating these findings into comprehensive SRM frameworks is emphasized to cultivate a positive organizational culture characterized by openness, ethical integrity, collaboration, and adaptability. This approach aims to enhance supplier relationships, drive innovation, and achieve sustainable growth in a competitive marketplace.

This qualitative study explores the challenges and successes in supplier development from the perspectives of procurement managers across diverse industries. Through in-depth interviews, key themes emerged, highlighting the critical role of communication, trust, performance measurement, capacity building, risk management, innovation, regulatory compliance, and technology integration in supplier relationship management. Procurement managers emphasized the importance of clear, continuous communication and trust-building activities to align objectives and foster collaborative partnerships. Performance measurement was crucial for evaluating and improving supplier performance, supported by capacity-building initiatives to enhance supplier capabilities. Risk management strategies focused on identifying and mitigating risks, while compliance efforts aimed to ensure adherence to industry standards. Innovation and technological integration were pivotal for driving efficiency and competitiveness, enabling faster time-to-market and improved product quality. Cultural sensitivity and relationship strengthening emerged as essential for navigating diverse cultural contexts and enhancing partnership effectiveness. Overall, the findings underscored positive outcomes including enhanced quality, cost efficiencies, and increased resilience within supply chains. This research contributes valuable insights into effective supplier management practices, emphasizing the strategic importance of supplier development initiatives for sustainable growth and competitive advantage in today's dynamic business environment.

This study explores the multifaceted role of culture in supply chain collaboration, focusing on how cultural differences shape communication, trust-building, negotiation, conflict resolution, risk management, and ethical standards. Utilizing a qualitative methodology, semi-structured interviews were conducted with 25 supply chain professionals from diverse cultural backgrounds and industries. The findings reveal that high-context cultures favor indirect communication and nuanced information exchange, which can lead to misunderstandings with partners from low-context cultures who prefer direct and explicit communication. Trust-building practices vary significantly, with high uncertainty avoidance cultures emphasizing formal agreements and procedural rigor, while low uncertainty avoidance cultures prioritize personal relationships and demonstrated reliability. Negotiation strategies also diverge, reflecting the underlying values of collectivism versus individualism, impacting the tactics and outcomes of supply chain negotiations. Conflict resolution approaches differ, with high power distance cultures favoring hierarchical and mediated solutions, and low power distance cultures preferring direct and egalitarian methods. The study highlights the need for adaptability to cultural changes and the development of cultural intelligence as essential competencies for managing cross-cultural supply chains. Additionally, cultural differences in risk management approaches and governance mechanisms affect the coordination and resilience of supply chains, while varying ethical standards influenced by cultural norms necessitate culturally sensitive strategies for promoting corporate social responsibility. The insights gained from this research underscore the importance of understanding and leveraging cultural differences to build stronger, more productive supply chain relationships, contributing to the broader literature on culture and supply chain management and offering practical implications for enhancing global supply chain collaboration.

This qualitative research explores the dynamics of collaborative marketing and supply chain strategies for enhanced product lifecycle management. Through in-depth interviews with industry stakeholders, key themes, challenges, and implications of collaboration emerge. Trust, communication, technology, strategic alignment, and sustainability are identified as critical factors driving successful collaboration. The study highlights the transformative potential of collaboration in driving innovation, efficiency, and customer satisfaction. Despite challenges such as aligning objectives and ensuring data security, collaborative strategies offer significant opportunities for value creation and differentiation. The findings underscore the importance of building trust, leveraging technology, and integrating sustainability considerations in collaborative efforts. Strategic alignment and cultural fit among partners are essential for overcoming differences and achieving common goals. The implications of the research extend to practitioners seeking to enhance collaboration in their organizations and researchers exploring collaborative practices in different industries and contexts. Future research could further explore the impact of collaboration on stakeholder perspectives and outcomes. By addressing challenges and leveraging opportunities, organizations can maximize the benefits of collaborative marketing and supply chain strategies to drive sustainable growth and competitive advantage in today's dynamic business landscape.

Human decision-making is increasingly supported by artificial intelligence (AI) systems. From medical imaging analysis to self-driving vehicles, AI systems are becoming organically embedded in a host of different technologies. However, incorporating such advice into decision-making entails a human rationalization of AI outputs for supporting beneficial outcomes. Recent research suggests intermediate judgments in the first stage of a decision process can interfere with decisions in subsequent stages. For this reason, we extend this research to AI-supported decision-making to investigate how intermediate judgments on AI-provided advice may influence subsequent decisions. In an online experiment (N=192), we found a consistent bolstering effect in trust for those who made intermediate judgments and over those who did not. Furthermore, violations of total probability were observed at all timing intervals throughout the study. We further analyzed the results by demonstrating how quantum probability theory can model these types of behaviors in human-AI decision-making and ameliorate the understanding the interaction dynamics at the confluence of human factors and information features.

This study investigates the influence of expectations and trust on farmers' decisions regarding contractual choices in land transfer. The dataset used for analysis consists of 1101 households in Guizhou Province. The research focuses on two aspects: the selection between written and oral contracts, and the decision to continue the contractual relationship. The findings reveal that farmers' choices are significantly affected by their expectations and levels of trust.Specifically, when farmers' expectations regarding the land transfer process are not met, they tend to prefer written contracts over oral ones. This preference can be attributed to the greater clarity and risk reduction offered by written contracts. On the other hand, higher levels of trust increase the likelihood of farmers continuing the land transfer contract, as trust fosters a sense of confidence and security in the ongoing relationship.Interestingly, trust also plays a moderating role. Farmers who have aligned expectations and high levels of trust are more inclined to opt for oral contracts. This preference for oral contracts can be attributed to their flexibility and emphasis on interpersonal relationships, allowing for better adaptation to the changing dynamics of land transfer.These findings contribute to a deeper understanding of the factors influencing farmers' decision-making processes regarding contractual choices in land transfer. They provide valuable insights for policymakers involved in land transfer, enabling them to optimize policies and facilitate farmers' decision-making processes.

Previous research has either neglected to consider misbehaviour or misconduct in the virtual workplace during the current COVID-19 pandemic or highlighted some challenges encountered with remote working or work from home but in isolation, e.g., misbehaviours or ethical concerns associated with improper use of technology when accessing clients’ confidential information, without understanding their aetiology and context in a holistic manner. This has impacted the ability of existing research in providing recommendations organisations can practically implement to improve the sustainability and productivity of the virtual workplace, especially as they seek to navigate the current pandemic and prepare for the global business environment in the next decade. Instead, this study, for the first time, proposes a novel holistic methodology to identify the aetiologies of ethical and performance challenges with focus to the Information Technology consulting sector, as well as developing a set of recommendations companies can adopt to manage such behavioural and productivity challenges. Thus, this study illustrates the key issues associated with remote working implemented to respond to the current COVID-19 pandemic, which mine not only the individual performance in an organisation but also its overall productivity, as well as suggests the main performance and reward management strategies that can be leveraged to mitigate them. Tailored strategies are described and discussed as referred to this industry to help in navigating a more unstable yet competitive market to manage the virtual employees’ misbehaviours in the workplace and drive growth for individuals and organisations alike.

As a potential motivation, psychological empowerment stimulates employees' work behaviors, and it determines the degree of effort and duration of employees' work. Only when employees are psychologically empowered, will they have an impact on their behavior when they believe that they are trusted. This paper chose to set the independent variable as the employee's perceived trust and the dependent variable as the company's work performance, and explored the mediating role of psychological empowerment in the two. The psychological empowerment of employees had a positive impact on work performance. Employees with high psychological empowerment tended to be proactive in their work, and had more input in the work, which in turn encouraged employees to have higher work performance. The four dimensions of psychological empowerment can positively affect employee task performance, and the ability and influence of psychological empowerment had a positive impact on relationship performance. Psychological empowerment as a whole perception played a part of the mediating role between the perception of superior dependency and task performance, and it played a part of the mediating role between perception of superior dependency and relationship performance. As a whole perception, psychological empowerment played a part of mediating role between perceived information disclosure and task performance, and part of mediating role between perceived information disclosure and relationship performance. In the study of perceived trust and work performance, this article focused on the mediating role of psychological empowerment, and further understood the internal mechanism of perceived trust.

A lack of trust in Energy Service Company (ESCo) is the most critical factor affecting the development of Energy Performance Contracting (EPC) in China compared with other constraints. One cannot easily estimate the energy-saving performance of an EPC project. Under that condition, lack of trust may cause the Energy-Consuming Unit (ECU) to suspect the energy-saving performance promised by the ESCo, thus leaving potentially profitable projects without necessary funding. Currently, specific studies taking an across-projects viewpoint on annual energy-saving performance of EPC projects in multiple subsectors objectively and quantitatively are lacking. This paper studies the regression relationships of annual energy-saving quantity in terms of revamping cost and the regression relationships of annual cost saving in terms of revamping cost. The regression results show that there are statistically significant correlations in the above relationships in the nine subsectors investigated. This is significant for ESCos and ECUs because knowledge on energy-saving performance could contribute to EPC investment decisions and trust relationships between ESCos and ECUs. Then a multiple linear regression model of revamping cost is set up to analyze its influencing factors. The model indicates that the subsector the sample belongs to, financing, registered capital of the ESCo, and contract period have significant effects on revamping cost. Thus, policy implications regarding innovation of EE promotion technology, clarifying ESCos’ exit mechanism, innovation of financing mechanism, and improving the market credit environment for promoting investment in EPC projects are provided.

The existence of low-cost and accurate tools that can equip the First Responders (FRs) to detect trapped victims and provide insights about their health condition in the aftermath of a disaster, is imperative. To address the problem of detecting trapped victims behind walls or debris, we have developed a tool that exploits an Ultra-Wide Band (UWB) radar sensor for data collection and Machine Learning (ML) algorithms for data analysis. To evaluate the efficacy of our approach, we collected data from nine humans, both in standing and lying down positions. Next, we applied various ML algorithms to the collected dataset for two discrete sub-tasks that are of interest from an FR’s perspective. The first task is the detection of the victim’s presence, where the algorithms attained more than 95% accuracy and F1-Score. The second task is the estimation of the distance between the radar sensor and the victim, where the tool showed an average error of less than 40 cm.

Propolis, or bee glue, is a complex mixture of resins from plant buds and flowers, wax, pollen and bee saliva. It also contains various vitamins, essential oils, mineral salts, trace elements, hormones and enzymes. It has been found that propolis possesses antimicrobial, antiviral and anti-inflammatory properties. We have studied the antiviral activity of six extracts of Bulgarian propolis collected from six districts of Bulgaria. The study was conducted against structurally different viruses: Human coronavirus strain OC-43 (HCoV OC-43) and Human respiratory syncytial virus type 5 (HRSV-5) (enveloped RNA viruses), Human herpes simplex virus type 1 (HSV-1) (enveloped DNA virus), Human rhinovirus type 14 (HRV-14) (non-enveloped RNA virus) and Human adenovirus type 2 (HadV-2) (non-enveloped DNA virus). The influence of the extracts on the internal replicative cycle of viruses was determined using the cytopathic effect (CPE) inhibition test. The virucidal capacity, their influence on the adsorption stage and their protective effect on uninfected cells were also determined by the end-point dilution method. Residual viral infectivity in propolis samples was compared to untreated controls and Δlgs were calculated. When administered during viral replication, most propolis extracts demonstrated the strongest activity against HCoV OC-43. The influence of propolis extracts on the viability of extracellular virions was expressed to a different degree in the various viruses studied, and the effect was significantly stronger in those with an envelope. Almost all extracts significantly inhibited the step of herpes virus adsorption to the host cell, and some of them showed a protective effect on healthy cells before they were subjected to herpes infection. Our results enlarge the knowledge about the action of propolis and could open new perspectives for its application in viral infections treatment.

Generative models for images, audio, text and other low-dimension data have achieved great success in recent years. Generating artificial human movements can also be useful for many applications, including improvement of data augmentation methods for human gesture recognition. The object of this research is to develop a generative model for skeletal human movement, allowing to control the action type of generated motion while keeping the authenticity of the result and the natural style variability of gesture execution. We propose to use a conditional Deep Convolutional Generative Adversarial Network (DC-GAN) applied to pseudo-images representing skeletal pose sequences using Tree Structure Skeleton Image format. We evaluate our approach on the 3D-skeleton data provided in the large NTU RGB+D public dataset. Our generative model can output qualitatively correct skeletal human movements for any of its 60 action classes. We also quantitatively evaluate the performance of our model by computing Frechet Inception Distances, which shows strong correlation to human judgement. Up to our knowledge, our work is the first successful class-conditioned generative model for human skeletal motions based on pseudo-image representation of skeletal pose sequences.

Human footprint is having a unique set of ridges unmatched by any other human being, and therefore it can be used in different identity documents for example birth certificate, Indian biometric identification system AADHAR card, driving license, PAN card, and passport. There are many instances of the crime scene where an accused must walk around and left the footwear impressions as well as barefoot prints and therefore it is very crucial to recovering the footprints to identify the criminals. Footprint-based biometric is a considerably newer technique for personal identification. Fingerprints, retina, iris and face recognition are the methods most useful for attendance record of the person. This time world is facing the problem of global terrorism. It is challenging to identify the terrorist because they are living as regular as the citizens do. Their soft target includes the industries of special interests such as defense, silicon and nanotechnology chip manufacturing units, pharmacy sectors. They pretend themselves as religious persons, so temples and other holy places, even in markets is in their targets. These are the places where one can obtain their footprints easily. The gait itself is sufficient to predict the behaviour of the suspects. The present research is driven to identify the usefulness of footprint and gait as an alternative to personal identification.

Living systems are intricate repositories of biomolecules, where the interactions between these molecules play an important role in maintaining homeostasis. Interruptions in these interactions can lead to diseases, underscoring the significance of biomolecular interactions and their molecular basis. HUGENICS (Human Genetic Database) is a comprehensive biological database that focuses on molecular interactions between human and microbial proteins, particularly emphasizing protein-protein interactions (PPIs). This database offers insights into disease pathways by elucidating the interplay between microbial surface/secretory proteins and human disease-related proteins. This white paper highlights the significance, scope, and potential applications of HUGENICS in advancing biomedical research and improving human health.

Given the broad and intense use of plastic, society is being increasingly affected by its degradation and by-products, particularly by microplastics (MPs) -fragments smaller than 5 mm in size-, and nanoplastics (NPs) -with sizes less than 1 µm. MPs and NPs may enter the body primarily through inhalation, consumption, and skin contact. Once ingested, MPs can penetrate tissues, deviating to other parts of the body, and hence potentially affecting important cellular pathways such as nonconforming chemokine receptors that control the communication between the fetus and the mother. Consequently, the potential health harm induced by MP internalization is a major issue, evidenced by multiple studies demonstrating harmful consequences in diverse animal models and human cells. Here, an overview of the various modes of exposure to MPs and NPs is presented, including inhalation, placental transfer, ingestion, breastmilk consumption, and skin absorption, as well as placental and fetal toxicity due to plastic particles based on animal and in vitro studies. Though MPs in our environment are becoming more recognized, their developmental toxicity is still scarcely known. Besides negatively affecting pregnancy, MPs and NPs have been shown to potentially harm the developing fetus, given their ability to cross the placental barrier. Still, considerable gaps remain in our understanding of the dispersion and toxicity of these particles in the environment, as well as the precise types of NPs and MPs bearing the greatest dangers. As a result, we advocate for larger-scale epidemiological investigations, the development of novel approaches for measuring NP and MP exposures, and the necessity of understanding the toxicity of various kinds of NPs to guide future research efforts.

Visual Patient Avatar ICU is an innovative approach to patient monitoring enhancing the user's situation awareness in intensive care settings. It dynamically displays the patient's current vital signs through changes in color, shape and animation. The technology can also indicate patient-inserted devices, such as arterial lines, central lines and urinary catheters, along with their insertion locations. We conducted an international, multi-center study using a sequential qualitative-quantitative design to evaluate users' perception of Visual Patient Avatar ICU among physicians and nurses. Twenty-five nurses and twenty-five physicians from the ICU participated in the structured interviews. Forty of them completed the online survey. Overall, ICU professionals expressed a positive outlook on Visual Patient Avatar ICU. They described Visual Patient Avatar ICU as a simple and intuitive tool that improved information retention and facilitated problem identification. However, a subset of participants expressed concerns about potential information overload and a sense of incompleteness due to missing exact numerical values. These findings provide valuable insights into user perceptions of Visual Patient Avatar ICU and encourage further technology development before clinical implementation.

This study attempts to investigate a method for creating an index from mobility data that not only correlates with the number of people who relocate to a place but also has causal influence on the number of such individuals. By creating an index based on human mobility data, it becomes possible to predict the influence of urban development on future residential movements. In this paper, we propose a method called the travel cost method for multiple places (TCM4MP) by extending the conventional travel cost method (TCM). We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of a neighborhood. However, conventional TCM does not assume that the opportunity cost of travel time varies according to the departure place. In this paper, TCM4MP is proposed to estimate the opportunity cost of travel time with respect to the departure place. We consider such estimation to be possible due to the use of massive mobility data. We assume that the opportunity cost of travel time on non-working days reflects the convenience and amenities of the neighborhood. Therefore, we consider that the opportunity cost of travel time has a causal influence on future residential mobility. In this paper, the validity of the proposed method is tested using the smart card data of public transportation in Western Japan. Our proposed method is beneficial for urban planners in estimating the effects of urban development and detecting the shrinkage and growth of a population.

The importance of predicting the exposure to environmental hazards is highlighted by issues like global climate change, public health problems caused by environment stresses, and property damages and depreciations. Several approaches have been used to assess potential exposure and achieve optimal results under various conditions, for example, for different scales, groups of people, or certain points in time. Micro-simulation tools are becoming increasingly important in human exposure assessment, where each person is simulated individually and continuously. This paper describes an agent-based model (ABM) framework that can dynamically simulate human exposure levels, along with their daily activities, in urban areas that are characterized by environmental stresses such as air pollution and heat stress. Within the framework, decision making processes can be included for each individual based on rule-based behavior to achieve goals under changing environmental conditions. The ideas described in this paper are implemented in a free and open source NetLogo platform. A simplified modeling scenario of the ABM framework in Hamburg, Germany, further demonstrates its utility in various urban environments and individual activity patterns, and portability to other models, programs and frameworks. The prototype model can potentially be extended to support environmental incidence management by exploring the daily routines of different groups of citizens and compare the effectiveness of different strategies. Further research is needed to fully develop an operational version of the model.

— In a smart healthcare system," Human Activity Recognition (HAR)" is considered as an efficient approach in pervasive computing from activity sensor readings. The "Ambient Assisted Living (AAL)" in the home or community helps the people to provide independent care and enhanced living quality. However, many AAL models are restricted to multiple factors that include both the computational cost and system complexity. Moreover, the HAR concept has more relevance because of its applications, such as content-based video search, sports play analysis, crowd behavior prediction systems, patient monitoring systems, and surveillance systems. This paper attempts to implement the HAR system using a popular deep learning algorithm, namely "Recurrent Neural Network (RNN)" with the activity data collected from smart activity sensors over time, and it is publicly available in the "UC Irvine Machine Learning Repository (UCI)". The proposed model involves three processes: (1) data collection, (b) optimal feature learning, and (c) activity recognition. The data gathered from the benchmark repository was initially subjected to optimal feature selection that helped to select the most significant features. The proposed optimal feature selection method is based on a new meta-heuristic algorithm called "Colliding Bodies Optimization (CBO)". An objective function derived from the recognition accuracy has been used for accomplishing the optimal feature selection. The proposed model on the concerned benchmark dataset outperformed the conventional models with enhanced performance.

The Zero Trust Architecture (ZTA) security system follows the "never trust, always verify" principle. The process constantly verifies users and devices trying to access resources. This paper describes how Microsoft Azure uses ZTA to enforce strict identity verification and access rules across the cloud environment to improve security. Implementation is time-consuming and difficult. Azure's extensive services and customizations require careful design and implementation. Azure administrators struggle to navigate and change configurations due to its complex user interface (UI). Each Azure ecosystem component must meet ZTA criteria. ZTA's comprehensive policy definitions, multi-factor and pass-wordless authentication, and other advanced features are tested in a mid-size business scenario. These configuration changes require a solid grasp of Azure's architecture and Zero Trust. Implementing an Azure ZTA reduces vulnerabilities and restricts authorized users' access to critical resources. Implementation is time-consuming and expensive, requires a significant commitment to changing IT procedures, and can be confusing because the same features are available in multiple places. Azure ZTA has shown great potential for both hybrid and cloud-native companies.

This qualitative study explores collaborative supplier relationships within the context of e-commerce, aiming to uncover the dynamics, challenges, strategies, impacts, and future trends shaping these partnerships. Through thematic analysis of interviews with e-commerce platform managers, supplier representatives, and industry experts, the study identifies three primary types of relationships: transactional, relational, and strategic. Critical factors influencing these relationships include trust, effective communication, technological integration, and regulatory compliance. Challenges such as information asymmetry, cultural differences, logistical complexities, and competitive pressures are examined, alongside strategies for enhancing collaboration, including clear communication protocols, performance evaluations, and technological advancements. The findings highlight the significant impact of collaborative supplier relationships on business performance, including cost efficiencies, supply chain resilience, innovation capacity, customer satisfaction, and market competitiveness. Looking forward, trends in digital transformation, sustainability initiatives, global supply chain networks, resilience-building strategies, and industry collaboration are discussed as shaping the future landscape of e-commerce partnerships. Embracing these trends presents opportunities for organizations to innovate, adapt, and sustain growth in the competitive digital marketplace.

This qualitative research investigates the impact of Supplier Relationship Management (SRM) on e-commerce innovation adoption. Through semi-structured interviews and document analysis, the study explores how SRM practices influence organizational capabilities to innovate in the context of digital commerce. Key themes emerged, emphasizing the critical role of trust, communication, collaboration, strategic alignment, technology integration, organizational culture, leadership, and external factors in shaping e-commerce innovation. Trust was found to be foundational, fostering transparent communication and collaborative relationships that facilitate innovation. Effective communication channels and collaborative initiatives enabled organizations and suppliers to leverage combined expertise, driving the development of innovative e-commerce solutions. Strategic alignment ensured that both parties worked towards shared goals, supported by technology integration that enhanced operational efficiency and decision-making. Organizational culture and leadership were identified as crucial in creating environments conducive to continuous innovation. External factors such as market dynamics, regulatory requirements, and technological advancements influenced innovation strategies, highlighting the need for adaptive and responsive SRM practices. The findings underscore the interconnectedness of these factors within the SRM framework, offering insights into how organizations can enhance their innovation capabilities and competitiveness in the digital economy. By strategically managing SRM practices and integrating sustainable initiatives, organizations can navigate challenges, seize opportunities, and achieve sustained success in e-commerce innovation.

In contemporary marketing and consumer behavior research, supply chain transparency has emerged as a critical determinant of brand image and consumer trust. This qualitative study explores how transparency in supply chain communication shapes brand perception and influences consumer behavior. Through in-depth interviews with marketing professionals, consumers, and supply chain managers, this research examines the impact of transparent practices on consumer trust, brand loyalty, and market differentiation. Findings reveal that transparent disclosure of sourcing, production methods, and sustainability initiatives enhances consumer perception of brand authenticity and ethical responsibility. Effective communication strategies such as detailed product labeling, interactive digital content, and third-party certifications play a pivotal role in educating consumers and reinforcing brand credibility. Despite the benefits, the study identifies challenges including global supply chain complexity, lack of standardized metrics, and economic considerations associated with transparency implementation. Technological advancements like blockchain and data analytics offer solutions to improve supply chain visibility and traceability, supporting brands in meeting ethical standards and consumer expectations. Moreover, transparency facilitates effective crisis management and reputation recovery by enabling brands to promptly address supply chain issues and maintain consumer trust during disruptions. The research highlights cultural and regional variations in consumer expectations regarding transparency, underscoring the need for brands to tailor communication strategies to local values and preferences. As brands navigate these complexities, prioritizing transparency not only strengthens consumer relationships but also positions them as leaders in ethical business practices. Ultimately, this study contributes to understanding the strategic importance of supply chain transparency in fostering sustainable brand growth and resilience in a competitive global market.

In the field of business environments, the integration of novel artificial intelligence (AI) techniques has been identified in the literature and theoretical discourse as an essential catalyst for enhancing the decision-making processes. In the contemporary landscape of digital transformation, the application of these techniques becomes essential, despite encountering various hurdles that impede their seamless integration into the rational decision-making process within e-government, which constitutes a critical component of digital government initiatives. This study explores whether similar advancements can be observed within the e-government sector and investigates the impact of using machine learning (ML) as a technique of artificial intelligence on rational decision-making (RDM). Employing an empirical research approach, the study utilized a quantitative methodology, relying on an electronically structured questionnaire survey administered to 163 employees in the e-government sector in Jordan through purposive random sampling. Data analysis was conducted using the SPSS v25 program, complemented by a media-tion analysis using AMOS v23 software. The research findings revealed a significant contribution of using machine learning to enhance rational decision-making and trust levels, with trust positively impacting RDM. Trust was identified as a beneficial mediator in the relationship between machine learning and rational decision-making. Despite certain limitations, this study's outcomes offer substantial insights for researchers and government policy-makers alike, contributing to the advancement of sustainable practices in the e-government domain.

This study examines the influences of live streaming servicescape on Chinese consumers' luxury purchase intention. Interestingly, for the sample without luxury purchase experience, the mediating effect of the perceived value of luxury goods is significant but effect of consumer trust is not. For the sample with luxury purchase experience, the result of mediating effect is just the opposite. The interaction between conspicuous consumption and live streaming servicescape plays a certain role in regulating the perceived value of luxury goods. These results show that luxury brands can adopt innovation in the live streaming servicescape for different types of consumers to promote live streaming marketing. This study enriches the servicescape theory of live streaming.

To achieve user retention through multifactor synergy, Internet enterprises must reduce costs and increase efficiency and sustainable development. In response to the dilemma that Internet compa-nies are experiencing increasingly high user acquisition costs and serious user churn, this paper uses a sample of 46,695 user reviews of nine product series from Xiaomi Ecological Chain. Case studies and qualitative comparative analysis are used to explore the influence mechanisms of quality of experience, brand trust, and brand attachment on users' retention intentions. Our find-ings are as follows. (1) Brand attachment alone is not necessary for high user retention intention, but user perception, cognition, and brand trust are necessary. (2) Quality of experience positively impacts brand trust, attachment, and user retention intention. Therefore, improving user perception and cognition is critical in generating high user retention intention. (3) Five configuration paths can achieve high user retention intention, three configuration paths lead to low user retention intention, and there is an asymmetric relationship between these paths. Among them, the role of quality of experience-driven configuration paths in generating user retention intention is the most prominent. (4) In the substitution relationship between configuration paths, user perception, and cognition can substitute with brand trust and attachment. Our findings have important theoretical and practical implications for revealing the realization paths of high user retention intention in Internet companies and provide a new perspective for future research.

Abstract In This research we will describe how to deal the system with users to make the work in the company or in any work space . in application forms interact with users inside the system . application form of any system must creates a many graphical interface for user ant we should take care about who is dealing with the system , Additionally, application forms used from different type of user so that we must take care about this points during designing the system for different users . Application Forms in the system consists of many programs, or components and function we are designing it. the instructions inside program can be execute by clicking on the command button and we can create specific process in the system.

Ancient DNA has given a new vision to the recent history of human evolution. However, by always relying on the information provided by whole genome sequencing, some relevant relationships between modern humans and its archaic relatives have been misinterpreted by hybridization and recombination causes. In contrast, the congruent phylogeny, obtained from non-recombinant uniparental markers, indicates that humans and Neanderthals are sister subspecies, and that the most recent common ancestor of modern humans was not of African origin but Eurasian.

Continual learning (CL), a.k.a lifelong learning, is an emerging research topic that has been attracting increasing interest in the field of machine learning. With human activity recognition (HAR) playing a key role in enabling numerous real-world applications, an essential step towards the long-term deployment of such systems is to extend the activity model to dynamically adapt to changes in people’s everyday behavior. Current research in CL applied to HAR domain is still under-explored with researchers exploring existing methods developed for computer vision in HAR. Moreover, analysis has so far focused on task-incremental or class-incremental learning paradigms where task boundaries are known. This impedes the applicability of such methods for real-world systems. To push this field forward, we build on recent advances in the area of continual learning and design a lifelong adaptive learning framework using Prototypical Networks, LAPNet-HAR, that processes sensor-based data streams in a task-free data-incremental fashion and mitigates catastrophic forgetting using experience replay and continual prototype adaptation. Online learning is further facilitated using contrastive loss to enforce inter-class separation. LAPNet-HAR is evaluated on 5 publicly available activity datasets in terms of its ability to acquire new information while preserving previous knowledge. Our extensive empirical results demonstrate the effectiveness of LAPNet-HAR in task-free CL and uncover useful insights for future challenges.

Since the 1970s, the application of microprocessor in industrial machinery and the development of computer systems have transformed the manufacturing landscape. The rapid integration and automation of production systems have outpaced the development of suitable human design criteria, creating a deepening gap where human factor was seen as an important source of errors and disruptions. Today the situation seems different: the scientific and public debate about the concept of Industry 4.0 has raised the awareness about the central role humans have to play in manufacturing systems, to the design of which they must be considered from the very beginning. The future of industrial systems, as represented by Industry 4.0, will rely on the convergence of several research fields such as Intelligent Manufacturing Systems (IMS), Cyber-Physical Systems (CPS), Internet of things (IoT), but also socio-technical fields such as social approaches within technical systems. This article deals with different Human dimensions associated with CPS and IoT and focuses on their conceptual evolution of automatization to improve the sociability of such automated production systems and consequently puts again the human in the loop. Hereby, our aim is to take stock of current research trends, and to show the importance of integrating human operators as a part of a socio-technical system based autonomous and intelligent products or resources. As results, different models of sociability as way to integrate human into the broad sense and/or the development of future automated production systems, were identified from the literature and analysed.

Multimodal user interfaces promise a natural and intuitive human machine interactions. But is the extra effort for the development of a complex multi-sensor system justified, or can users also be satisfied with one input modality already? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and use of multiple input modalities is valuable, and that it is difficult to predict the usability of individual input modalities for complex systems.

Proteins are the primary effectors of function in biology, and thus complete knowledge of their structure and properties is fundamental to deciphering function in basic and translational research. The chemical diversity of proteins is expressed in their many proteoforms, which result from combinations of genetic polymorphisms, RNA splice variants and post-translational modifications. This knowledge is foundational for the biological complexes and networks that control biology, yet remains largely unknown. We propose here an ambitious initiative to define the human proteome; that is to generate a definitive reference set of the proteoforms produced from the genome. Several examples of the power and importance of proteoform-level knowledge in disease-based research are presented, along with a call for improved technologies in a two-pronged strategy to accomplish the Human Proteoform Project.

Human-robot collaboration is a new trend in modern manufacture. The safety or human protection is of great significance due to the share of the same workshop between humans and robots. To achieve effective protection, in this paper, a contact force sensor based on the 8-shaped wound polymer optical fiber is proposed. The 8-shaped wound structure can convert the normal contact force to the shrinkage of the 8-shaped optical fiber ring. The macro-bending loss of the optical fiber is used to detect the contact force. Compared with conventional sensors, the proposed scheme has the advantage of high flexibility, low cost, fast response, high repeatability, which shows the great promise on actively alarming collision and passively reducing the hurt.

This qualitative research explores Supplier Relationship Management (SRM) within the context of subscription-based e-commerce models. Through in-depth interviews and thematic analysis, the study investigates key elements such as trust, collaboration, technology integration, sustainability, risk management, cultural alignment, innovation, cost management, globalization, supplier selection, and performance measurement. Findings reveal that trust is foundational in fostering reliable interactions and open communication between e-commerce businesses and suppliers. Collaboration emerges as crucial for innovation and operational efficiency, facilitating co-development and shared risk management. Technology integration, including AI and blockchain, enhances supply chain visibility and decision-making capabilities. Sustainability considerations drive businesses to engage with suppliers adhering to environmental standards, enhancing brand reputation and consumer loyalty. Effective risk management strategies mitigate disruptions and ensure supply chain resilience. Cultural alignment fosters harmonious partnerships and mutual understanding across diverse global markets. Innovation through supplier collaboration drives product development and market responsiveness. Cost management practices optimize operational efficiency and strategic supplier relationships. Globalization necessitates adaptive strategies to manage diverse cultural and regulatory landscapes. Strategic supplier selection and continuous performance measurement drive ongoing improvement and alignment with business objectives. This study contributes empirical insights and practical implications for enhancing SRM in subscription-based e-commerce, informing strategies to navigate challenges and leverage opportunities in a competitive marketplace.

Mobile phones have become an integral part of our lives in the last two decades, leaving a digital trace of our activities and communication. This study aims to develop a data processing framework to evaluate human mobility and socioeconomic status based on call detail records. The methodology proposed first calculates radius of gyration and entropy for each user, then estimates the socioeconomic status by the price and age of the subscribers' phones. Finally, an unsupervised machine learning algorithm was used to group the cells into clusters based on their mobility and socioeconomic metrics. The research showed differences between Buda and Pest during a large scale social event using mobile phone ages and prices. Additionally, the clustering results revealed homogenous groups of cells around Budapest, with similar mobility and socioeconomic metrics. The main conclusion is that mobile network data combined with mobile phone properties offer a useful tool for characterising urban mobility and socioeconomic status.

Depictions of robots as romantic partners for humans are frequent in popular culture. As robots are becoming part of human society, they gradually assume the role of partners for humans wherever necessary: as assistants, collaborators, or companions. Companion robots are sup-posed to provide social contact to those humans who could not have it otherwise. These com-panion robots are usually not designed to fulfill one of the most important human needs: the one for romantic and intimate contact. Human-robot intimacy remains a vastly unexplored ter-ritory. In this article, we review the state-of-the-art research in intimate robotics. We discuss major issues limiting acceptance of robots as intimate partners, public perception of robots in in-timate roles, and the possible influence of cross-cultural differences in these domains. We also discuss possible negative effects human-robot intimacy may have on human-human contact. Most importantly, we propose a new term “Intimate companion robots” to reduce the negative connotations of other terms used so far and improve the social perception of research in the domain. With this article, we provide an outlook on prospects for the development of intimate companion robots, considering the specific context of their use.

The use of new assistive technologies in general, and Socially Assistive Robots (SAR) in particular is becoming increasingly common for supporting people’s health and well-being. However, it still faces many issues regarding long-term adherence, acceptability and utility. Most of these issues are due to design processes that insufficiently take into account the needs, preferences and values of intended users. Other issues are related to the currently very limited amount of long-term evaluations, performed in real world settings, for SAR. This study presents the results of two regional projects that consider as a starting hypothesis that the assessment in controlled environments and/or with short exposures may not be enough in the design of a SAR deployed in a retirement home and the necessity of designing for and with users. Thus, the proposed methodology has focused in use cases definitions that follow a human-centred and participatory design approach. The main goals have been facilitating system acceptance and attachment by involving stakeholders in the robots design and evaluation, overcoming usage barriers and considering user’s needs integration. The implementation of the first use case deployed and the two-phase pilot test performed in the retirement home are presented. In particular, a detailed description of the interface re-design process based on improving a basic prototype with users’ feedback and recommendations is presented together with main results of a formal evaluation that has highlighted the impact of changes and improvements addressed in the first redesign loop of the system.

Collaborative robotics is a major topic in current robotics research posing new challenges, especially in Human-Robot Interaction. The crucial aspect in this area of research focuses on understanding the behavior of robots when engaging with humans, where reinforcement learning is a key discipline that allows us to explore sophisticated emerging reactions. This review aims to delve into the relevance of different sensors and techniques, with special attention to EEG (electroencephalography data on brain activity) and its influence on the behavior of robots interacting with humans. In addition, mechanisms available to mitigate potential risks during the experimentation process such as virtual reality will also be addressed. In the final part of the paper, future lines of research combining the areas of collaborative robotics, reinforcement learning, virtual reality and human factors will be explored, as this last aspect is vital to ensure safe and effective Human-Robot Interactions.

The onset of embryonic heart beating may be regarded as the defining feature for the beginning of personal human life. Clarifying the timing of the 1^st human heartbeat, therefore, has religious, philosophical, ethical, and medicolegal implications. This article reviews the historical and contemporary sources of knowledge on the beginning of human heart activity. Special attention is given to the problem of the determination of the true age of human embryos and to the problem of visualization of the human embryonic heart activity. It is shown that historical and current textbook statements about the onset of blood circulation in man do not derive from observations on living human embryos but derive from the extrapolation of observations on animal embryos to the human species. This fact does not preclude the existence of documented observations on human embryonic heart activity: Modern diagnostic (ultrasound) and therapeutic (IVF) procedures facilitate the visualization of early embryonic heart activity in precisely dated pregnancies. Such studies showed that the human heart started its pumping action during the 4^th post-fertilization week. A small number of direct observations on the heart activity of aborted human embryos were reported since the 19^th century but did not receive much recognition by embryologists.

The aim of the present study is to investigate themes related to visitors’ perceptions of captive wildlife in particular, attitudes towards non-human primates (henceforth, primates). This research took place in free-roaming, multi-species primate sanctuary, Monkeyland (South Africa), where 400 visitors were interviewed using an anonymous survey both before and after attending a guided tour. The answers were divided into different categories, in order to standardize the motivations behind tourists’ choices. The results of the survey demonstrated that most visitors agree that a primate would not be a good companion animal. Visitors’ desire to touch primates was found to be positively correlated with desire for companion primates and inversely associated with visitor age. In response to: “would you like to touch a monkey?”, the majority of tourists who expressed this desire seemed aware that such interactions are not appropriate, with concern for animal welfare and human health. Of the various primate species present in the sanctuary, visitors preferred the ring-tailed lemur (Lemur catta) and, generally speaking, expressed appreciation for primates’ “cuteness”. Our results indicate a general awareness by the visitors on the importance of animal welfare in the human interactions with captive wildlife, in agreement with the “hands-off” policy of Monkeyland primate sanctuary. We discuss the findings from a general to zooanthropological point of view, proposing some reflections on the attitudes of visitors toward non-human primates.

It is crucial to immediately curb the spread of a disease once an outbreak is identified in a pandemic. An agent based simulator will enable the policymakers to evaluate the effectiveness of different hypothetical strategies and policies with a higher level of granularity. This will allow them to identify the vulnerabilities and asses the threat level more effectively, which in turn can be used to build resilience within the community against a pandemic. This study proposes a PanDemic SIMulator (PDSIM ) which is capable of modeling complex environments while simulating realistic human motion patterns. The ability of PDSIM to track the infection propagation patterns, contact paths, places visited, characteristics of people, vaccination, and testing information of the population, allows the user to check the efficacy of different containment strategies and testing protocols. The results obtained based on the case studies of Covid-19 are used to validate the proposed model. However, it is highly extendable to all pandemics in general, enabling robust planning for more sustainable communities.

This qualitative research explores Supplier Relationship Management (SRM) practices within the e-commerce fashion industry, aiming to uncover key strategies and their impacts. Through semi-structured interviews with supply chain managers, procurement officers, and supplier representatives, critical themes emerged: communication, trust, risk management, sustainability, and digital technologies. Effective communication, characterized by transparency and regular updates, emerged as pivotal for aligning expectations and resolving issues promptly. Trust-building practices, including fair dealings and formal agreements, were essential in fostering long-term partnerships, reducing transaction costs, and enhancing collaboration. Robust risk management strategies, such as supplier diversification and contingency planning, were identified as crucial for mitigating disruptions and ensuring supply chain resilience. Sustainability was highlighted as a strategic imperative, driven by ethical sourcing and environmental standards, supported by technologies like blockchain for transparency. The transformative role of digital technologies—supply chain management software, blockchain, IoT, and data analytics—was evident in enhancing visibility and decision-making across global supply chains. Viewing suppliers as strategic partners, rather than mere vendors, promoted collaborative innovation and mutual growth. Despite challenges like cultural differences and regulatory complexities, opportunities exist to enhance SRM through integrated best practices. By prioritizing effective communication, trust-building, risk management, sustainability, and digital innovation, companies can strengthen supplier relationships, optimize supply chain performance, and achieve competitive advantage in the e-commerce fashion sector.

This qualitative study explores the intricacies of building sustainable supplier relationships in the e-commerce sector, focusing on best practices and strategic approaches. Through in-depth interviews with procurement managers, supply chain experts, and supplier representatives, the research identifies four critical themes: trust, technology integration, ethical practices, and performance measurement. Trust is found to be the foundation of effective supplier relationships, fostering open communication and collaboration, which are essential for addressing challenges and co-creating solutions. The integration of digital technologies such as supply chain management software, blockchain, and the Internet of Things significantly enhances transparency, decision-making, and operational efficiency, providing a competitive edge. Ethical practices, including adherence to fair labor standards and environmental sustainability, are increasingly crucial as consumer and regulatory expectations evolve, necessitating alignment between e-commerce companies and their suppliers. Performance measurement and continuous improvement are vital for maintaining quality and efficiency, with clear metrics and regular evaluations driving constructive feedback and optimization of supply chain processes. The study underscores the interdependence of these elements in fostering resilient and adaptable supplier relationships. The findings offer practical guidance for e-commerce businesses and suppliers, emphasizing the need for a holistic approach to supplier management that balances trust, technology, ethics, and performance. This approach not only enhances operational effectiveness but also ensures the long-term sustainability of supplier relationships in the dynamic e-commerce landscape. The research contributes to the theoretical understanding of supplier relationship management, providing a comprehensive framework for future studies and practical applications in the e-commerce industry.

This qualitative research explores Supplier Relationship Management (SRM) strategies within the context of e-commerce customization, aiming to understand how businesses effectively manage relationships with suppliers to meet personalized consumer demands. Through semi-structured interviews with industry professionals, key themes such as trust, communication, technological integration, strategic alignment, sustainability, and collaboration emerged as critical factors influencing SRM effectiveness. Trust was found to be foundational, fostering cooperation and commitment between e-commerce businesses and suppliers through transparent interactions. Effective communication and the integration of advanced technologies like AI and blockchain enhanced operational efficiency and innovation capabilities, enabling businesses to deliver tailored customer experiences. Strategic alignment between businesses and suppliers proved essential for shared success and mutual benefit, while sustainability practices increasingly shaped consumer preferences and brand reputation. Collaboration facilitated joint innovation and accelerated product customization, underscoring the importance of partnerships in achieving strategic goals. However, challenges including supply chain complexity, managing consumer expectations, and regulatory compliance highlighted the need for continuous improvement and adaptive strategies. Overall, this research contributes valuable insights for e-commerce businesses seeking to optimize SRM strategies, enhance supply chain efficiency, and navigate the complexities of customization in a competitive digital marketplace. By leveraging trust, communication, technology, strategic alignment, sustainability, and collaboration, businesses can effectively meet evolving consumer demands and achieve sustainable growth.

This qualitative study explores the dynamics of customer satisfaction and supplier collaboration within Italian restaurant operations. Through in-depth interviews, participant observations, and document analysis, the research investigates key factors influencing these relationships. Trust and communication emerged as foundational elements, crucial for ensuring reliable ingredient supply and maintaining culinary authenticity. Quality and authenticity of ingredients were identified as primary drivers of customer satisfaction, underscoring the strategic role of suppliers in delivering superior dining experiences. Customer feedback played a pivotal role in shaping operational practices, highlighting the iterative process of adapting to customer preferences to enhance service delivery and loyalty. Emotional intelligence among managers facilitated effective supplier relationships, fostering collaborative partnerships and navigating operational challenges with empathy and skill. Economic considerations were vital in balancing cost efficiencies with quality standards, guiding strategic decisions in supplier management. Technological integration enhanced supply chain management, optimizing logistics and responsiveness to market demands. The adoption of sustainable sourcing practices reinforced restaurants’ commitment to environmental stewardship and community engagement, aligning with consumer preferences for ethical dining options. The findings suggest actionable insights for restaurant managers to optimize supplier collaborations, enhance operational efficiency, and sustain competitive advantage in the hospitality industry.

This qualitative study explores consumer responses to sustainable supply chains and green marketing strategies, aiming to uncover insights into consumer behavior, perceptions, and attitudes towards sustainability in the marketplace. Through semi-structured interviews and focus group discussions with 30 participants, the study examines key factors influencing consumer trust in green marketing claims, barriers to adopting sustainable products, generational differences in attitudes towards sustainability, and the impact of social media on consumer perceptions. Findings reveal that consumers value transparency and credibility in green claims, preferring products certified by trusted third parties and companies that demonstrate transparent supply chain practices. Higher pricing, concerns about product effectiveness, and limited availability emerged as significant barriers to consumer adoption of sustainable products, highlighting the need for cost competitiveness and increased market accessibility. Generational cohorts, particularly Millennials and Generation Z, exhibit strong inclinations towards sustainability-driven behaviors, shaping market trends towards ethical consumption practices. Social media plays a pivotal role in influencing consumer perceptions, disseminating information about sustainability, and fostering engagement with environmental issues. The study underscores the importance of clear communication, consumer education, and supportive regulatory frameworks in promoting sustainable consumption behaviors. Practical implications include recommendations for businesses to enhance transparency, innovate sustainable products, align with consumer values, leverage social media platforms, and advocate for regulatory measures that uphold environmental standards. By addressing these insights, businesses and policymakers can effectively meet consumer expectations and contribute to sustainable development goals in the global marketplace.

This study explores the impact of supply chain visibility (SCV) on marketing strategies within the fast-moving consumer goods (FMCG) industry. In a rapidly evolving market characterized by complex supply chains and dynamic consumer demands, SCV offers a transformative capability by providing real-time data and insights into supply chain operations. Through semi-structured interviews with industry practitioners, the research identifies several key themes: enhanced demand forecasting, increased consumer trust through transparency, improved marketing agility, and the integration of advanced technologies. Findings suggest that SCV significantly improves the accuracy of demand forecasts by providing timely insights into inventory levels, production schedules, and market conditions. This enhanced forecasting aligns supply chain capabilities with marketing efforts, reducing stockouts and optimizing promotional activities. Moreover, SCV fosters consumer trust by enabling transparency regarding product sourcing and ethical practices, which can be effectively communicated in marketing campaigns to build brand loyalty. The agility provided by SCV allows companies to quickly adjust their marketing strategies in response to market disruptions and shifts in consumer preferences, making their campaigns more resilient and responsive. Advanced technologies such as IoT, blockchain, and analytics further enhance the benefits of SCV, providing deeper insights and more sophisticated capabilities for marketing. The study also highlights the importance of cross-functional collaboration between supply chain and marketing teams in leveraging SCV data effectively. Despite challenges such as the need for significant investment and organizational change, the advantages of SCV in enhancing marketing strategies and overall organizational performance are substantial. The research underscores SCV's critical role in shaping effective marketing strategies, offering FMCG companies a path to greater efficiency, agility, and consumer engagement in a competitive landscape.

Public trust in science was tested and relied on during the SARS-CoV-2 pandemic, which has shaped global events since the WHO declaration in March 11, 2020. Public trust has been impacted through the government recommendations and mandates informed by public health guidance, including non-pharmaceutical and pharmaceutical interventions. The free-flow of ideas and in-formation so essential to the functioning of science has faced unprecedented challenge from widespread censorship in both the media and in scientific journals. This has created a poisoned environment for the building of trust between science and society. Scientific norms and ac-countability must be restored in order to rebuild the vital relationship between scientists and the public they serve.

Gradient based methods are popularly used in training neural networks and can be broadly categorized into first and second order methods. Second order methods have shown to have better convergence compared to first order methods, especially in solving highly nonlinear problems. The BFGS quasi-Newton method is the most commonly studied second order method for neural network training. Recent methods have shown to speed up the convergence of the BFGS method using the Nesterov’s acclerated gradient and momentum terms. The SR1 quasi-Newton method though less commonly used in training neural networks, are known to have interesting properties and provide good Hessian approximations when used with a trust-region approach. Thus, this paper aims to investigate accelerating the Symmetric Rank-1 (SR1) quasi-Newton method with the Nesterov’s gradient for training neural networks and briefly discuss its convergence. The performance of the proposed method is evaluated on a function approximation and image classification problem.

In the absence of information on Borrelia species causing Lyme borreliosis (LB) in Serbia, we were interested in the pathogenic potential of local strains of Borrelia burgdorferi sensu lato isolated from ticks. The susceptibility of B. burgdorferi sensu lato isolates to human complement was tested in vitro. A total of 31 strains were analysed by serum susceptibility testing - 12 Borrelia afzelii, 12 Borrelia lusitaniae, three Borrelia bavariensis, two Borrelia garinii, and two Borrelia valaisiana. The serum-resistant strains (B. afzelii, B. bavariensis and B. valaisiana) expressing OspA and OspB or OspA and OspC proteins might be related to the different pathogenic potential of the specific Borrelia strains and their ability to cause different clinical manifestations of LB in humans. Among the B. lusitaniae strains from Serbia, two were significantly more motile than others (all p < 0.05) and equally motile as human isolate (both p > 0.05), and since they express OspC, their pathogenic potential remains unclear. For further studies, it is of particular interest to explore the mechanism of resistance of B. valaisiana and to explain the fair motility of B. lusitaniae, a predominant Borrelia species in Serbia, in the presence of human complement.

Integrated disaster risk reduction in schools represents a key component of safety strategies within the educational sector of every country. In this study, a series of 850 face-to-face interviews (650 with students and 200 with teachers) were conducted throughout 2023 in 10 out of the total 18 municipalities in the Western Morava Basin of the Republic of Serbia. The paper proposes two central hypotheses regarding school-based disaster risk reduction. Firstly, it suggests that gender, age, parent's employment, academic achievement, living situation, paternal and maternal education levels, and engagement with social media collectively influence students' perspectives on this matter. Secondly, it posits that gender, age, marital status, parenthood, and educational background significantly impact teachers' viewpoints on school-based disaster risk reduction. Multivariate linear regression was used to explore predictors of students' and teachers' insights on school-based disaster risk reduction. Various statistical tests including Chi-square, t-tests, one-way ANOVA, and Pearson's correlation were employed to investigate the influence of demographic and socioeconomic factors on these insights. The results of multivariate regression analyses indicate that age, gender and marital emerge as the primary predictors across various facets of students' and teachers' insights on school-based disaster risk reduction (awareness of disasters, disaster education activities, attitudes on disaster risk reduction education, enhancement of disaster information accessibility, etc). The results of this study provide insight into the key factors influencing students' and teachers' perceptions of disaster risk reduction in schools. As such, they can serve as a basis for improving educational programs, developing policies and strategies, normative educational frameworks, teacher training, and further research in disaster education.

Semi-autonomous vehicles (AVs) enable drivers to engage in non-driving tasks but require them to be ready to take control during critical situations. This "out-of-the-loop" problem demands a quick transition to active information processing, raising safety concerns and anxiety. Multimodal signals in AVs aim to deliver take-over requests and facilitate driver-vehicle cooperation. However, the effectiveness of auditory, visual, or combined signals in improving situational awareness and reaction time for safe maneuvering remains unclear. This study investigates how signal modalities affect driver’s behavior using virtual reality (VR). We measured drivers’ reaction times from signal onset to take-over response and gaze dwell time for situational awareness across twelve critical events. Furthermore, we assessed self-reported anxiety and trust levels using the Autonomous Vehicle Acceptance Model questionnaire. Results showed visual signals significantly reduced reaction times, whereas auditory signals did not. Additionally, any warning signal, together with seeing driving hazards, increased successful maneuvering. Analysis of gaze dwell time on driving hazards revealed that audio and visual signals improved situational awareness. Lastly, warning signals reduced anxiety and increased trust. These results highlight the distinct effectiveness of signal modalities in improving driver reaction times, situational awareness, and perceived safety, mitigating the “out-of-the-loop” problem and fostering human-vehicle cooperation.

This qualitative research investigates the influence of Corporate Social Responsibility (CSR) on Supplier Relationship Management (SRM) within the e-commerce sector. Through semi-structured interviews and secondary data analysis, the study explores motivations, challenges, impacts, and strategic outcomes of integrating CSR practices into supplier relationships. Motivations for adopting CSR include ethical considerations, regulatory compliance, and meeting stakeholder expectations, reflecting a strategic alignment with corporate values and societal demands. Challenges such as resource constraints, regulatory complexities, and cultural differences highlight operational hurdles and strategic considerations e-commerce firms face in implementing CSR initiatives effectively. The study reveals that CSR enhances trust, collaboration, and risk mitigation within e-commerce supply chains through transparent communication, shared values, and ethical practices. Strategically, CSR contributes to enhanced brand equity, competitive advantage, and long-term value creation by differentiating firms in competitive markets and attracting socially conscious consumers and investors. However, economic trade-offs, stakeholder divergence, and operational complexities require firms to adopt adaptive strategies that balance short-term financial goals with long-term sustainability objectives. The findings suggest avenues for future research exploring longitudinal impacts of CSR on supplier relationships, cross-sectoral comparisons, and technological advancements in CSR-driven SRM practices. Practically, e-commerce firms should prioritize strategic alignment of CSR initiatives with core business objectives, engage stakeholders in CSR strategy development, and embrace continuous improvement to foster ethical conduct, stakeholder trust, and sustainable business practices.

Supplier Relationship Management (SRM) plays a pivotal role in enhancing delivery performance within the e-commerce sector, yet understanding its nuanced impacts remains crucial for optimizing operational efficiencies and customer satisfaction. This qualitative research explores the intricate dynamics of SRM in e-commerce, focusing on strategic alignment, technological integration, trust, collaboration, and risk management as key determinants of delivery reliability. Through in-depth interviews and thematic analysis, the study reveals that strategic alignment of supplier capabilities with firm objectives is fundamental for maintaining a responsive and synchronized supply chain. Technological integration, including AI-driven analytics and blockchain applications, emerges as critical for real-time monitoring and adaptive decision-making, thereby improving delivery accuracy and efficiency. Trust and collaboration are identified as essential pillars for building resilient supplier relationships, fostering transparent communication and joint problem-solving. Effective risk management practices, such as supplier diversification and contingency planning, mitigate disruptions and ensure consistent delivery performance. Despite these benefits, challenges such as technological adoption barriers, cultural differences, communication complexities, resource constraints, and supplier reliability issues persist, necessitating strategic interventions. Recommendations include investing in advanced SRM technologies, promoting cultural understanding, enhancing communication protocols, allocating adequate resources, and fostering collaborative initiatives with suppliers. These strategies aim to overcome challenges and optimize SRM practices, ultimately enhancing delivery performance and competitiveness in the e-commerce landscape.

This ethnographic study investigates the cross-cultural challenges encountered in global supply chains, focusing on communication barriers, diverse business practices, trust-building, ethical and legal standards, and cultural competence. By embedding within the day-to-day operations of multinational corporations and conducting in-depth interviews with supply chain professionals, the research provides a nuanced understanding of the cultural dynamics that impact global supply chain management. The findings highlight that effective communication goes beyond language proficiency, requiring an understanding of varying communication styles and contextual nuances. Business practices shaped by cultural values such as power distance and uncertainty avoidance necessitate adaptive management strategies that balance local norms with global standards. Trust and relationship-building are critical, particularly in cultures where personal relationships underpin business transactions, demanding long-term investment in relationship-building activities. Ethical and legal standards vary significantly across regions, presenting challenges that require a nuanced approach to global governance. The study underscores the importance of cultural competence as a critical skill for supply chain professionals, enabling them to bridge cultural gaps and foster productive collaborations. Sustainability efforts, entrepreneurship, and innovation are also influenced by cultural factors, highlighting the need for culturally sensitive strategies. Emotional intelligence is identified as essential for managing cross-cultural interactions, with participants demonstrating high emotional intelligence more effectively navigating the emotional and social complexities of cross-cultural communication. The use of technology in managing cross-cultural challenges is acknowledged, but the findings suggest that it must be complemented by cultural competence. Overall, the study provides valuable insights for companies seeking to enhance their cross-cultural effectiveness, emphasizing the importance of cultural competence, communication, and relationship-building in achieving sustainable success in global supply chains.

The adoption of Industry 4.0 technology is essential for achieving energy efficiency, a critical aspect of Sustainable Development Goal (SDG) no. 12 aimed at addressing climate change. Understanding the impact of Sustainable Attitudes, Digital Trust, and Uncertainty Avoidance is crucial in the decision-making process regarding the implementation of Industry 4.0 technology. The slow adoption of Industry 4.0 technology is attributed to non-technological factors or people, while the need to achieve energy efficiency targets necessitates examining the influence of Sustainable Attitudes and Digital Trust as supporting variables, along with Uncertainty Avoidance, on technology adoption. The study employed a quantitative explanatory research approach to investigate the relationships and impacts of variables. It centered on employees of Schneider Electric Indonesia, a global Industry 4.0 technology provider, with a sample of 189 employees selected through random sampling, incorporating a 5% margin error from Slovin formula. The analysis involved the use of Structural Equation Modeling (SEM) with AMOS version 26. Industry 4.0 technology is essential for fostering innovation and business expansion, leading to a sustainable environment by offering principles, guidance, and energy-efficient technologies for companies. The involvement of Sustainable Attitude, moderated by Uncertainty Avoidance, is vital in reaching energy efficiency goals through Industry 4.0 implementation.

This paper estimates the effect of trust in government on rural residents’ contributions in China’s rural pension program using the Propensity Score Matching (PSM) method. We construct an analytical framework for rural residents' decision-making in pension program and provide analysis using data from China Family Panel Studies (CFPS) and 25 provincial Departments of the Human Resources and Social Security (DOHRSS) in China. Our analysis shows that rural residents’ trust in government will influence their contributions to the pension programs by affecting their expected return of the investments. Our results suggest that the government should improve rural residents’ trust in government in order to develop a successful and sustainable rural pension program.

Communication, compression of information, transmission of information through noisy channels, interconnecting different information systems, cryptography, gate construction –– these areas all depend on classical information theory. We show that, in classical terms, semantic aspects of communication are not at all irrelevant to the engineering problem, contrary to Shannon, and affect the message intended to be transmitted. This is revisited and captured by an analogy to trust, in that they are essential to the channel (for proper use), but cannot be transferred (under risk of flaws) through that same channel. Information is also described by, at least, a tri-state system — not by binary logic. The trust analogy semantics can be coded as the Curry-Howard relationship, connecting computer code with structural logic, by way of different categories. Two-state and Boolean logic (aka Shannon semantics) was used classically before, with Shannon theory, but without trust analogy semantics – found to be a sine qua non condition. This is now familiar in classical gate construction with physical systems with, e.g., Verilog and SystemVerilog. The applications to computation and quantum theory are further explored. The most fundamental entity in today`s theory of information is proposed to use at least three logical states, not bits, in all applications, including: cyber-physical systems, devices, in computation, and in quantum theory.

Access to medical care is a global issue. Technology-aided approaches have been applied in addressing this. Interventions have however not focused on medical diagnosis as a fully automated procedure and available applications employ mainly text-based inputs rather than conversation in natural language. We explored the utility of ontology-based chatbot technology for the design of intelligent agents for medical diagnosis through a systematic review of the most recent related literature. English articles published in 2011-2016 returned 233 hits which yielded 11 relevant articles after a 3-stage screening. Findings showed that the creation of expert systems had been the focus of many the studies which utilize the physician-system-patient framework with system training based mostly on expert knowledge for designing web- or mobile phone-based applications that serve assistive purposes. Findings further indicated gaps in the design and evaluation of more effective systems deployable as standalone applications, for example, on an embodied robotic system. The need for technology supporting the physical examination part of diagnosis, connection to data sources on patients’ vitals and medical history are also indicated in addition to the need for more qualitative work on natural language-based interaction. The system should be one that is continuously learning. Future works should also be directed towards the building of more robust knowledge base as well as evaluation of theory-based diagnostic methodological options

This paper aims to argue the fundamental significance of education in addressing notable gaps in the constitutive, performance, and evaluation criteria for corporate social responsibility (CSR) and endeavours to showcase the complementarity between education, human security, sustainable human development, and the pursuit of CSR as an ideal normative paradigm. An abstractive approach to studying CSR engages with corporate philanthropy, which has not been helpful outside traditional CSR paradigms without looking at key dynamics that can robustly underpin successful CSR. It relies on the systematic analysis of written data sources to theoretically explore the central significance, and complementarity between human security, education, and human dignity and how they can orchestrate a holistic CSR paradigm. In 1994, the UNDP in formulating the human security doctrine recognised the syncretic correlation between human security, education, and sustainable human development as fundamental elements that should underpin CSR. This paper contends that these are necessary intrinsic components of human dignity as the central factor that justifies a multi-dimensional and proactive approach to CSR. Despite the criticisms against human security, this paper identifies it as an important concept that could instrumentally orchestrate a model of CSR that uniformly mainstreams sustainable human development and human dignity that leverages education which has routinely and traditionally been seen as a constitutive component of CSR. It reveals how a holistic path to CSR can reinvigorate the central pillars of human security and human dignity using education as a springboard and makes a compelling case for supporting explicit references to education in company law with appropriate human security informed constitutive, performance, and evaluation criteria which are absent in existing legally orchestrated CSR.

Autonomous systems and intelligent machines are involved in almost all areas of human activity and they are now more and more present in our everyday life. The reason for this extensive use certainly resides in all the benefits these machines offer to the users. In experimental settings, numerous studies have demonstrated the positive effects that the introduction of autonomous systems have on human decision-making and performance. However, studies have shown in addition that the introduction of these systems can have important negative effects as well. Considering that autonomous systems are now introduced in sensitive domains like the military or medicine, we need more than ever a comprehensive understanding of the effects they cause on human performance and decision-making, and particularly in tasks and contexts with a social or moral dimension. The aim of this narrative review is threefold. First, we will provide an overview of the main effects on a human agent’s decision-making and performance produced by the introduction of autonomous systems. Second, we will review the conditions identified as underlying factors of these effects, and see how current models of human – autonomous systems interaction integrate those conditions. Third, we will conclude this review by highlighting new directions for future investigations.

This paper presents a multi-year research study conducted at the University of Toledo, aimed at improving human-machine teaming (HMT) methods and technologies. With the incorporation of artificial intelligence (AI) into 21st-century machines, collaboration between humans and machines has become highly complicated for real-time applications. The penetration of intelligent and synthetic assistants (IA/SA) in virtually every field has opened a path for the new area of HMT. When it comes to crucial tasks such as patient treatment and care, defense, and industrial production, the use of non-standardized HMT technologies may pose a risk to human lives as well as up to billions of taxpayer dollars. A thorough literature survey revealed that there are not many established standards or benchmarks for HMT. In this paper, we aim to address the lack of HMT design and development standards. We propose a method to design an HMT based on a generalized architecture. This design includes the development of an intelligent collaborative system and human team. Followed by processes and metrics to test and validate the proposed model, we present a novel human-in-the-loop (HIL) simulation method using two controlled HMT scenarios; first, emergency care provider (ECP) training, and second, patient treatment by an experienced medic. Both scenarios include humans processing visual data and performing actions that represent real-world applications while responding to a Voice-Based Synthetic Assistant (VBSA) as a collaborator that keeps track of actions. As part of these HMT simulation studies, the impact of each parameter related to machines, humans, and HMT (such as errors made by humans and by the VBSA) is presented from the perspective of performance, rules, roles, and operation limitations. In addition, based on the analysis of results from two case studies, we presented guidelines for designing future HMT. Our conclusions indicate that the proposed HIL can be used to perform standardization studies in the pursuit of developing techniques for benchmarking HMT that can be used in critical situations.

Carukia barnesi (Cb), Malo kingi (Mk) and Chironex fleckeri (Cf) are dangerous Australian box jellyfish species that provoke distinct and not well understood envenomation syndromes. Specifically, Cb and Mk are small, rare and able to induce a systemic syndrome of generalised muscle pain and catecholamine excess termed “Irukandji syndrome”; Cf has been widely regarded as one of the most venomous organisms in the animal kingdom causing severe sting site pain combined with potentially lethal cardiotoxicity. Building on past studies of major chirodropid and carybdeid species venoms, this study compared the utility of various cubozoan specific antibody reagents to better define the relationships between venom proteins from both exemplar Irukandji species (Cb and Mk) and the archetype C. fleckeri box jellyfish. With the aid of commercial ovine derived Cf-specific antivenom, mouse antibodies reactive to Cb and Mk and rabbit antibodies specific to two Cf toxins (CfTX-1 and 2), as well as human sera, the cross-reactivity of jellyfish species-specific polyclonal antibodies against these three cubozoan venoms was investigated. Immunoblot assays revealed distinc levels of immune recognition across the three species, indicating that Mk specific reagents may bind both Irukandji and Cf venoms. Irukandji venom appears to be antigenic with the exception of a few proteins in the range of 43/46 kDa maybe homologous to CfTX-1 and 2. The implications of such antibody binding for future antivenom development require further investigation.

Background: Artificial intelligence can help improve the quality of healthcare by analyzing vast amounts of data and providing more effective and personalized treatment plans. Researchers are working on developing AI-powered solutions that can help improve the outcomes of patients. Objective: To explore the potential of AI in improving healthcare outcomes and patient experience. Results: The study suggests that AI can improve healthcare efficiency and patient outcomes but cannot fully replace human healthcare professionals. AI can assist healthcare professionals in their work, leading to better resource utilization and improved patient care. However, there is still a need for human healthcare professionals to oversee AI systems and provide empathy and personalized care to patients. Conclusion: While there is immense potential for AI in healthcare, it is not yet feasible to replace human healthcare workers. Instead, it should be viewed as a tool that can help improve the efficiency and effectiveness of human healthcare.

This study explores the influence of cultural differences on Supplier Relationship Management (SRM) within global e-commerce, examining communication styles, trust-building, negotiation strategies, and strategic alignment across diverse cultural contexts. Through qualitative research including interviews with e-commerce professionals, the study identifies how cultural factors shape SRM practices and outcomes, highlighting the importance of cultural sensitivity and adaptive strategies in fostering effective supplier relationships. Findings underscore the significance of communication preferences, trust norms, and strategic orientations in navigating cultural complexities to enhance operational efficiency and mitigate risks in global markets. Practical implications suggest integrating cultural dimensions into SRM frameworks, prioritizing cultural competence development, and leveraging cultural diversity for innovation and collaboration. Future research directions include exploring digitalization's impact on cross-cultural SRM and longitudinal studies on evolving cultural dynamics in global business. By advancing understanding in this area, e-commerce firms can enhance resilience, maintain competitive advantage, and achieve sustainable growth in the global marketplace.

Background: Australia’s healthcare system is complex and fragmented which can create challenges in healthcare, particularly in rural and remote areas. Aboriginal people experience inequalities in healthcare treatment and outcomes. This study aimed to investigate barriers and enablers to accessing healthcare services for Aboriginal people living in rural and remote Australia. Methods: Semi-structured interviews were conducted with healthcare delivery staff and stakeholders recruited through snowball sampling. The communities were selected for their high proportion of Aboriginal people and geographical representation (coastal, rural, and border). Thematic analysis identified barriers and enablers. Results: Thirty-one interviews were conducted (n =5 coastal, n=13 remote, and n=13 border) and six themes identified: 1) Improved coordination of healthcare services; 2) Better communication between services and patients; 3) Trust in services and cultural safety; 4) Importance of prioritizing health services by Aboriginal people; 5) Importance of reliable, affordable and sustainable services; 6) Distance and transport availability. These themes were often present as both barriers and enablers to healthcare access for Aboriginal people. They were also present across the healthcare system and within all three communities. Conclusions: This study describes a pathway to better healthcare outcomes for Aboriginal Australians by providing insights into ways to improve access.

In financial time series modelling and forecasting, combining ARMA and GARCH models tend to produce superior and reliable models for volatility persistence, half-life volatility and backtesting (application of model in real life). In Nigeria, banking stocks are mostly traded because of its potential benefits to investors. This study modelled and forecasted the Guaranty Trust (GT) Bank daily stock returns from January 2^nd 2001 to May 8^th 2017 data set collected from a secondary source. The ARMA-GARCH models, persistence, half-life and backtesting were used to analysed the collected data using student t and skewed student t distributions, and the analyses are carried out R environment using rugard and performanceAnaytics Packages. The study revealed that using the lowest information criteria values only could be misleading rather we added the use of backtesing. The ARMA(1,1)-GARCH(1,1) models fitted exhibited high persistency in the daily stock returns while the days it takes for mean-reverting of the models ranges from 5 days to 100 days but unfortunately the models failed backtesting. The results further revealed ARMA(1,1)-eGARCH (2,2) model with student t distribution provides a suitable model for evaluating the GT bank stock returns among the competing models while it takes less than 30 days for the persistence volatility to return back to its average value of the stock returns. This study recommended that researchers should adopt backtesting approach while fitting GARCH models while GT bank stocks investor should be assured that no matter the fluctuations in the stock market, the GT bank stock returns has the ability to returns to its mean price return.

The genus Aeromonas is widely distributed in aquatic environments. It is frequent as a fish pathogen, but has also been described associated to human infections. Most human cases are caused by A. caviae, A. veronii biovar sobria, and A. hydrophila, though A. dhakensis is becoming increasingly important. Transmission happens primarily by ingestion or contact with aquatic collections, or by consumption of contaminated food, especially of aquatic origin. Increasing resistance has been described in Aeromonas to penicillins, including their combinations with classical b-lactamase inhibitors, cephalosporins and carbapenems. Among the b-lactam antibiotics, only the 4th generation cephalosporins remain almost uniformly active. In addition, the simultaneous presence of resistance genes to 3rd generation cephalosporins and carbapenems in the same isolates is increasing. Recently, the presence of mobile genes linked to colistin resistance has also been described, in some cases with resistance percentages higher than 30%. Most likely, this evolution of colistin resistance is linked to its use in aquaculture, and, together with the evolution of b-lactam resistance, may be turning this genus into a reservoir of resistance genes that may affect other species much more frequently linked to human infections, such as Enterobacterales.

The vibrant landscape of Malaysia serves as a dynamic backdrop for the convergence of artificial intelligence (AI) and social science, reshaping knowledge and inquiry amidst tradition and modernity. With Malaysia leading a technological renaissance driven by digital transformation and innovation, the ethical implications of AI-driven decision-making in public policy and governance emerge as pressing research problems. Addressing concerns such as algorithmic bias and discrimination requires interdisciplinary collaboration to ensure transparency, accountability, and fairness. Exploring questions regarding the distribution of welfare benefits and strategies for enhancing transparency and accountability in urban planning underscores the need for contextually relevant frameworks and stakeholder engagement. Through comprehensive research objectives, including investigating algorithmic biases and analyzing their social implications, scholars aim to contribute to a deeper understanding of the complex interplay between AI technology, social dynamics, and public policy. As Malaysia navigates the challenges and opportunities presented by AI deployment, prioritizing interdisciplinary collaborations, longitudinal studies, and capacity-building efforts will be essential for shaping a future that upholds principles of social justice and collective well-being.

Human papillomavirus (HPV) can potentially compromise both unassisted conception and success rates with assisted reproductive technology (ART). We aimed to evaluate the rates of HPV vaccination among women presenting for fertility care. This was a prospective study analyzing a self-reported intake form completed by all new patients presenting to a fertility clinic. The primary outcome was HPV vaccination status. Among the 523 new patients included in this study 172 (32.9%) were fully vaccinated, 237 (45.3%) were unvaccinated, and 95 (18.2%) patients didn’t respond. There was a significant age difference between fully vaccinated patients (33.1 years) compared to unvaccinated patients (37.4 years) p&lt;0.001 and a significant difference in gravidity between fully vaccinated patients (average 0.65) and unvaccinated patients (average 1.11) p=0.0005. No significant association was found between HPV vaccination status and history of abnormal Pap smears or relationship status (single vs. married). Less than half of patients presenting to our fertility center were vaccinated against HPV, despite being eligible to receive the vaccine. Younger patients with lower gravidity were more likely to be vaccinated against HPV than older patients with higher gravidity. Reproductive Endocrinologists have the opportunity to recommend HPV vaccination for eligible patients presenting for fertility care.