Advantage Of Diversity Research Articles

Conductive hydrogels: Intelligent dressings for monitoring and healing chronic wounds

Abstract Conductive hydrogels (CHs) represent a burgeoning class of intelligent wound dressings, providing innovative strategies for chronic wound repair and monitoring. Notably, CHs excel in promoting cell migration and proliferation, exhibit powerful antibacterial and anti-inflammatory properties, and enhance collagen deposition and angiogenesis. These capabilities, combined with real-time monitoring functions, play a pivotal role in accelerating collagen synthesis, angiogenesis, and continuous wound surveillance. This review delves into the preparation, mechanisms, and applications of CHs in wound management, highlighting their diverse and significant advantages. It emphasizes the effectiveness of CHs in treating various chronic wounds, such as diabetic ulcers, infected wounds, temperature-related injuries, and athletic joint wounds. Additionally, it explores the diverse applications of multifunctional intelligent CHs in advanced wound care technologies, encompassing self-powered dressings, electrically-triggered drug delivery, comprehensive diagnostics and therapeutics, and scar-free healing. Furthermore, the review highlights the challenges to their broader implementation, explores the future of intelligent wound dressings, and discusses the transformative role of CHs in chronic wound management, particularly in the context of the anticipated integration of artificial intelligence (AI). Additionally, this review underscores the challenges hindering the widespread adoption of CHs, delves into the prospects of intelligent wound dressings, and elucidates the transformative impact of CHs in managing chronic wounds, especially with the forthcoming integration of artificial intelligence (AI). This integration promises to facilitate predictive analytics and tailor personalized treatment plans, thereby further refining the healing process and elevating patient satisfaction. Addressing these challenges and harnessing emerging technologies, we postulate, will establish CHs as a cornerstone in revolutionizing chronic wound care, significantly improving patient outcomes.

Diverse roles, advantages and importance of deep eutectic solvents application in solid and liquid-phase microextraction techniques – A review

Diverse roles, advantages and importance of deep eutectic solvents application in solid and liquid-phase microextraction techniques – A review

Unveiling the determinants of online shopping: Insights from a developing nation

This study examined the factors influencing online purchases among consumers in Bangladesh, employing a modified version of the Technology Acceptance Model (TAM). Data from 353 individuals in Bangladesh revealed that perceived ease of use, social influence, security, convenience, trust, emotional experience, and functional experience significantly positively affect the intention to purchase online. Additionally, results show that the intention to purchase online significantly positively affects actual online purchases. Findings further highlighted that intention to make online purchases mediated the influence of perceived ease of use, social influence, security, convenience, trust, emotional experience, and functional experience over online purchases. The study provides significant practical recommendations to help businesses and consumers support online purchasing with diverse advantages.

Improved Migration Algorithms for Uplink Transmission Secrecy Sum Rate Maximization in MIMO-NOMA

The most significant technology that could assist future high-speed wireless systems in meeting their growing capacity demands is Non-Orthogonal Multiple Access (NOMA). Nevertheless, NOMA should strive to achieve a harmonious equilibrium between equitable distribution among users and optimal network performance. A wireless communication network is a system that enables connections with limited power resources, such as Internet of Things (IoT) devices. Horizontal multiplexing is a feature of MIMO networks that allows for the concurrent transmission of multiple data streams. Nevertheless, there is a correlation between space multiplexing and the advantages of diversity. The issue of Secrecy Sum Rate (SSR) is complex and challenging. To address this, a first-order Taylor estimate is employed to transform the initial problem into a more manageable convex problem. Striking the right balance to optimize network performance and maximize overall rate can be quite challenging, particularly in time-sensitive scenarios. The total rate that can be achieved is reduced due to interference from different users and the use of multiple transmitting antennas. To optimize performance and achieve the highest possible data transmission rate, it is crucial to employ efficient techniques for managing interference. These may include coding methods, adjustments to positioning, or coordinating multiple users. Thus, this work proposes the utilization of optimization to enhance SSR in the MIMO-NOMA uplink transmission framework. The Singular Value Decomposition (SVD) method is commonly used for the decomposition of channels in the MIMO-NOMA system. The primary goal of the developed MIMO-NOMA uplink transmission framework is to enhance the capacity of the Base Station (BS) by optimizing the power allocation measures of the near user and far user using an Improved Migration Algorithm (IMA). In addition, the SSR is optimized and multiple experimental validations are generated to appraise the suggested model. Ultimately, the suggested outcomes are contracted with conventional Orthogonal Multiple Access (OMA) and other traditional approaches to assess the efficacy of the designed model, and developed model given the SSR to be 30bit/s/Hz.

Wild goose chase or not? Equilibrium in a hybrid ride-hailing market

We consider a hybrid ride-hailing platform, such as Uber, Lyft, and Didi, offering passengers the choice between a ride-sourcing service and a ride-pooling service. A passenger who chooses the ride-sourcing service can enjoy the trip alone, while passengers who pool can benefit from a lower trip fare; however, the latter may take more travel time (i.e., waiting time and in-vehicle time) and may require passengers to share their ride with strangers. When a passenger is matched to a distant driver, the ride-sourcing or ride-pooling service may get stuck in an inefficient wild-goose-chase (WGC) regime, wherein the driver spends a lot of time picking up passengers. To explore the non-WGC regime of ride-sourcing and ride-pooling services in the hybrid ride-hailing market, we build an equilibrium model that maximizes the profit of the platform, obtain the optimal trip fares of the two services, derive the non-WGC conditions for both services, and then compare them to those in the single market. We find that the interaction between the two services not only contributes to demand shifting effect, which adjusts fares and promotes differentiated pricing strategies, but also alters the non-WGC conditions of each service in the hybrid market, deepening their interdependence so that the waiting time of one service has a non-monotonic effect on the non-WGC regime of the alternative service. Moreover, benefiting from the coordinated complementarity of the two services with each other, passenger demand is more evenly distributed, which enhances real capacity and turnover, reduces the waiting time threshold (WTT) for each service to fall into the WGC regime under the mild condition, and contributes to travel efficiency overall. Such advantages of market diversity lay a solid foundation for ride-hailing market platforms to be able to offer more diverse service options.

ANALYSIS OF SOCIAL CAPITAL AND ENTREPRENEURIAL INTENTION OF FARMERS IN NIGERIA

This study aimed at analysing social capital and the entrepreneurial intention of farmers in Nigeria. The study specifically investigated the effect of social capital on Attitude towards Behaviour (ATB) of farmers; and Perceived Social Norms (PSN) of farmers. This study employed survey. 120 questionnaires were mailed to respondents, and 158 were physically administered. The study analysed data using both descriptive and inferential analytical technique (regression analysis). Social trust, network density, network size and demographic diversity were used as proxies of social capital, and ATB of farmers and PSN of farmers as entrepreneurial intention. Finding showed that network size, network and social trust have significant positive effects on ATB of farmers in Nigeria, and that demographic diversity has significant negative effects on ATB of farmers in Nigeria. Furthermore, finding showed that network density and social trust have significant negative effects on PSN of farmers in Nigeria. The study concluded that entrepreneurial intention of farmers depends on social capital. The study recommended that network size, network density and social trust within the social capital should be improved and sustained, and that farmers should take advantage of network size and demographic diversity as these contribute to PSN.

How to combine different types of prefabricated components in a building to reduce construction costs and carbon emissions?

In the context of industrialized construction and carbon emission reduction globally, prefabrication has gained considerable attention. However, most studies cared the carbon emissions and construction costs from the perspective of a whole prefabricated building rather than the prefabricated components. It makes it difficult to understand the role of prefabricated components play and make improvements from the prefabricated components level. To fill this gap, this study proposes a model to optimize the selection and combination of prefabricated components in a building project to simultaneously reduce the construction costs and carbon emissions. Six common component types were considered: shear wall, beam, floor slab, stair, balcony, and air-conditioning slab. Their costs and carbon emissions were quantified when adopting prefabrication and cast-in-situ technologies. To obtain the optimal solution, we developed an improved multi-objective mayfly algorithm (IMOMA), which incorporates enhanced circle chaos mapping and a global best guiding strategy to generate Pareto solution sets. The performance of IMOMA was evaluated using the Zitzler-Deb-Thiele series test functions, demonstrating its advantages in convergence, diversity, and comprehensive performance through metrics such as generation distance, diversity metric Δ, and inverse generational distance. A case study validated the effectiveness of this proposed method, demonstrating an average reduction of 9.05% in total construction costs and 8.04% in carbon emissions when compared to the initial scheme. Further study revealed that different prefabrication rates correspond to different optimal components combination schemes, with different trends in the costs and carbon emission curves as the prefabrication rate increases. Additionally, carbon trading policies influence the components combination, with construction costs reduced by 10.88% at a carbon trading price of 42.80 CNY/t CO2eq. This study provides valuable guidance for general contractors to design optimal component combinations and for governments to promote the sustainable development of prefabricated construction.

Explainable Machine Learning Model to Accurately Predict Protein-Binding Peptides

Enzymes play key roles in the biological functions of living organisms, which serve as catalysts to and regulate biochemical reaction pathways. Recent studies suggest that peptides are promising molecules for modulating enzyme function due to their advantages in large chemical diversity and well-established methods for library synthesis. Experimental approaches to identify protein-binding peptides are time-consuming and costly. Hence, there is a demand to develop a fast and accurate computational approach to tackle this problem. Another challenge in developing a computational approach is the lack of a large and reliable dataset. In this study, we develop a new machine learning approach called PepBind-SVM to predict protein-binding peptides. To build this model, we extract different sequential and physicochemical features from peptides and use a Support Vector Machine (SVM) as the classification technique. We train this model on the dataset that we also introduce in this study. PepBind-SVM achieves 92.1% prediction accuracy, outperforming other classifiers at predicting protein-binding peptides.

Nonconjugated Polyurethane Derivatives with Aggregation-Induced Luminochromism for Multicolor and White Photoluminescent Films.

A simple and effective strategy to obtain solid-state multicolor emitting materials is a particularly attractive topic. Nonconventional/nonconjugated polymers are receiving widespread attention because of their advantages of rich structural diversity, low cost, and good processability. However, it is difficult to control the molecular conformation or to obtain the crystal structure of amorphous molecules, which means it is a challenge to obtain nontraditional polymeric materials with multicolor emission. In this work, a polyurethane derivative (PUH) with red-shifted emission was synthesized by a simple one-pot polymerization reaction. By exploiting the aggregation-induced luminochromism of PUH, a series of plastic films with tunable emission from blue to orange, and white-light emission, was obtained by doping different amounts of PUH into poly(methyl methacrylate) (PMMA), thereby changing the aggregation degree of PUH. This work demonstrates the excellent promise of polyurethane derivatives for the simple fabrication of large-scale flexible luminescent films.

From Nanozymes to Multi-Purpose Nanomaterials: The Potential of Metal-Organic Frameworks for Proteomics Applications.

Metal-organic frameworks (MOFs) have the potential to revolutionize the biotechnological and medical landscapes due to their easily tunable crystalline porous structure. Herein, the study presents MOFs' potential impact on proteomics, unveiling the diverse roles MOFs can play to boost it. Although MOFs are excellent catalysts in other scientific disciplines, their role as catalysts in proteomics applications remains largely underexplored, despite protein cleavage being of crucial importance in proteomics protocols. Additionally, the study discusses evolving MOF materials that are tailored for proteomics, showcasing their structural diversity and functional advantages compared to other types of materials used for similar applications. MOFs can be developed to seamlessly integrate into proteomics workflows due to their tunable features, contributing to protein separation, peptide enrichment, and ionization for mass spectrometry. This review is meant as a guide to help bridge the gap between material scientists, engineers, and MOF chemists and on the other side researchers in biology or bioinformatics working in proteomics.

Thermal Metamaterials with Configurable Mechanical Properties.

Thermal metamaterials are typically achieved by mixing different natural materials to realize effective thermal conductivities (ETCs) that conventional materials do not possess. However, the necessity for multifunctional design of metamaterials, encompassing both thermal and mechanical functionalities, is somewhat overlooked, resulting in the fixation of mechanical properties in thermal metamaterials designed within current research endeavors. Thus far, conventional methods have faced challenges in designing thermal metamaterials with configurable mechanical properties because of intricate inherent relationships among the structural configuration, thermal and mechanical properties in metamaterials. Here, a data-driven approach is proposed to design a thermal metamaterial capable of seamlessly achieving thermal functionalities and harnessing the advantages of microstructural diversity to configure its mechanical properties. The designed metamaterial possesses thermal cloaking functionality while exhibiting exceptional mechanical properties, such as load-bearing capacity, shearing strength, and tensile resistance, thereby affording mechanical protection for the thermal metadevice. The proposed approach can generate numerous distinct inverse design candidate topological functional cells (TFCs), designing thermal metamaterials with dramatic improvements in mechanical properties compared to traditional ones, which sets up a novel paradigm for discovering thermal metamaterials with extraordinary mechanical structures. Furthermore, this approach also paves the way for investigating thermal metamaterials with additional physical properties.

Kinetic insights into heavy crude oil upgrading in supercritical water

Heavy crude oil upgrading in a supercritical water environment is a promising technology owing to diverse advantages such as heteroatom removal, high yields of low molecular weight fractions, and inhibition of coke production. In order to increase the performance of this process, some challenges need to be overcome by researching the reaction mechanisms and developing proper numerical models. Therefore, the kinetic studies reported in the literature for hydrothermal upgrading of heavy crude oil under supercritical water conditions and their results were systematically analyzed and discussed to achieve a better understanding of the diverse approaches considered to determine the distribution of reaction products and limitations. Additionally, fundamental aspects of the kinetic models, including experimental considerations and numerical methodologies applied for kinetic parameter estimation, are reviewed to obtain representative information about the reactant system that can be subsequently integrated into reactor design models or reservoir simulations.

A Comparative Analysis of Business Models and their Impacts on Selected Indigenous Communities in Aklan, Philippines: Towards Sustainable Practices in Ancestral Domains

This study investigates the impacts of various economic models on indigenous communities in Aklan, Philippines, with a special focus on sustainable practices within their ancestral lands. It examines the socio-economic, cultural, and environmental consequences of different business models. Additionally, it delves into the community members’ perspectives and aspirations regarding sustainable development, highlighting the primary challenges and opportunities they face. The study utilized a quantitative research approach, employing structured surveys to collect data from participants. The poll encompassed inquiries regarding demographic profiles, assessments of commercial effects, as well as the difficulties and possibilities encountered by the communities. A Pearson correlation analysis was performed to ascertain the association between demographic characteristics and perceived impacts. The study found that business models had a favorable effect on the socio-economic status, cultural preservation, and environmental conservation of indigenous groups. Diverse advantages were noted in the benefits offered by various business structures. Perceptions of these impacts were significantly related to age, educational background, career, and length of involvement in community activities. The main obstacles encompassed issues related to financial accessibility, infrastructure development, regulatory barriers, and educational deficiencies. Potential areas for development were recognized in eco-tourism, sustainable agriculture, partnerships with non-governmental organizations, market demand for indigenous products, and technology. The findings highlight the capacity of business models to improve the socio-economic, cultural, and environmental welfare of indigenous communities. It is essential to have customized company strategies that take into account the demographic variety. To achieve the successful adoption of sustainable business models, it is crucial to tackle financial, infrastructural, and regulatory obstacles. It is advised to improve financial assistance by providing grants and microfinance, investing in infrastructure, streamlining regulatory procedures, and establishing education and training initiatives. It is crucial to promote eco-tourism, enhance partnerships with non-governmental organizations (NGOs) and stakeholders, and utilize technology to implement sustainable practices. It is crucial to have policies that effectively combine economic growth with the preservation of culture and the sustainability of the environment. Keywords: Indigenous communities, sustainable development, business models, Aklan, socio-economic impacts

The Value of Digital Narrative in Oral Second Language Teaching

Digital Narrative has long been formed, developed and mature in foreign countries, but the domestic attention to it is still less, the lack of a complete theoretical system and practical model, so it is rarely used in classroom practice. As the product of the combination of digital information technology and traditional narrative, Digital Narrative is derived from video teaching. The initial form of Digital Narrative is relatively simple, based on the cognitive theory of multimedia learning and the theory of multiple literacy. In practice, Digital Narrative can be mediated by extra-curricular activities, network platforms, and theme competitions, which is of great value in oral second language teaching due to its unique advantages of novelty, diversity and situationality.

Top Management Team Diversity and Competitive Advantage: An Emirical Investigation of Insurance Companies in Kenya

Top Management Team Diversity and Competitive Advantage: An Emirical Investigation of Insurance Companies in Kenya

Study of heavy crude oil upgrading in supercritical water using diverse kinetic approaches

Heavy crude oil upgrading under a supercritical water environment has demonstrated to have diverse advantages. For commercial applications, it is necessary to develop mathematical models that can predict the reactive behavior of the different components of crude oil under these operating conditions. However, the consideration of different experimental and modeling approaches can significantly influence the determination and evaluation of reaction rates, which complicates providing essential insights into the reaction system and thus generates numerical simulations and strategies that allow for increasing the efficiency of these processes. Therefore, this study analyzes the effect of the supercritical water atmosphere on the diverse reactions among oil components as well as different kinetic modeling approaches reported in the literature to predict the yields of reactive compounds. Complex kinetic models were developed using proposed reaction schemes and experimental data obtained during the upgrading of heavy crude oil under supercritical water conditions. The predictions with the models show good agreement concerning the experimental data. In addition, it was shown that the conversion of asphaltenes to resins and gas compounds under supercritical conditions presents a significant increase in reaction rates compared with experiments under non-catalytic and catalytic thermal cracking conditions.

Diversity our strength? Exploring the influence of power dynamics on shaping suburban ethnic strips and place-making

ABSTRACT In Canada’s multicultural context, diversity is often celebrated in city branding and promotion, yet, it is not clear what diversity means, to whom, and whether the advantages of urban diversity are well acknowledged to support inclusive place-making. Situated in two of Toronto’s most diverse inner suburbs, this paper explores how diversity and place are perceived, produced, and experienced by local immigrant and racialized communities in obsolete and stigmatized strip mall environments, and how power dynamics are presented and influenced through the place-making process. The findings reveal that, although the population is culturally diverse and immigrants actively contribute to suburban place-making, municipal plans and policies do not prioritize immigrant communities and businesses. This results in ineffective public engagement and a disconnect with the diverse communities, thereby impacting the outcomes of ethnic places. The current city-wide Business Improvement Area program also requires a renewed focus on diversity and inclusion. Without equity and inclusion as fundamental considerations in city planning and engagement processes, there is no guarantee that diversity can be effectively leveraged as an urban asset or strength. This oversight can adversely affect the outcomes of ethnic place-making, a process that aims to empower the relationships between people and place.

The Role of Building-Integrated Greenery Systems in Building Sustainability Rating Systems

Building rating systems allow for the evaluation of environmental buildings’ impact throughout their lifecycle, thereby enabling improved design. The integration of vegetation into building envelopes, through green roofs and facades, provides multiple benefits that enhance the sustainability of a built environment. In arid climates, Building-Integrated Greenery Systems (BIGSs) contribute to energy savings and the improvement of the urban environment through evaporative cooling. However, the maintenance of these green systems requires efficient water use. This study thoroughly reviews six selected building sustainability certifications to determine the extent to which BIGSs are considered in the certification process. The findings indicate that BIGSs are not yet well integrated directly into these certifications. While the certifications recognize the biophilic effects on users and contributions to sustainable construction, they often overlook scientifically proven benefits such as acoustic insulation and urban noise reduction. This study highlights the importance of updating certification frameworks to fully incorporate the diverse advantages of BIGSs, especially in enhancing indoor environments and achieving energy savings.

A Comprehensive Review on Brain–Computer Interface (BCI)-Based Machine and Deep Learning Algorithms for Stroke Rehabilitation

This literature review explores the pivotal role of brain–computer interface (BCI) technology, coupled with electroencephalogram (EEG) technology, in advancing rehabilitation for individuals with damaged muscles and motor systems. This study provides a comprehensive overview of recent developments in BCI and motor control for rehabilitation, emphasizing the integration of user-friendly technological support and robotic prosthetics powered by brain activity. This review critically examines the latest strides in BCI technology and its application in motor skill recovery. Special attention is given to prevalent EEG devices adaptable for BCI-driven rehabilitation. The study surveys significant contributions in the realm of machine learning-based and deep learning-based rehabilitation evaluation. The integration of BCI with EEG technology demonstrates promising outcomes for enhancing motor skills in rehabilitation. The study identifies key EEG devices suitable for BCI applications, discusses advancements in machine learning approaches for rehabilitation assessment, and highlights the emergence of novel robotic prosthetics powered by brain activity. Furthermore, it showcases successful case studies illustrating the practical implementation of BCI-driven rehabilitation techniques and their positive impact on diverse patient populations. This review serves as a cornerstone for informed decision-making in the field of BCI technology for rehabilitation. The results highlight BCI’s diverse advantages, enhancing motor control and robotic integration. The findings highlight the potential of BCI in reshaping rehabilitation practices and offer insights and recommendations for future research directions. This study contributes significantly to the ongoing transformation of BCI technology, particularly through the utilization of EEG equipment, providing a roadmap for researchers in this dynamic domain.

A Semi-amorphous Ti-BDC-derived Ti/C nanocomposite for efficient electromagnetic wave absorption

Electromagnetic (EM) interference presents a crippling risk to both human health and the dependability of technological devices. Due to their diverse advantages, metal-organic framework (MOF) derivatives have received much attention in developing novel EM wave-absorbing materials. Nevertheless, achieving desirable EM wave absorption capabilities in magnetic carbon-based absorbers through deliberate adjustment of MOFs remains a difficult task. In this study, a novel nanoporous carbon material (TiO2/C) has been successfully synthesized by a semi-amorphous material (Ti-BDC), which has excellent EM wave absorption properties. In detail, the minimum reflection loss (RL) of −49.7 dB and broad effective absorbing bandwidth (EAB) of 4.16 GHz can be reached with an absorbent thickness of 2.2 mm. The improved performance is mostly due to the dipole polarization of the functional groups and the even distribution of the nanoparticles, which form a seamless network. This promotes the dissipation of polarization and dielectric energy at the interface. In addition, the TiO2 nanoparticles on the carbon material improve the impedance matching, further enhancing the material's absorption performance.