Emerging Research Domain Research Articles

Cyphos Nitrate Selectively Separates Y(III) from Sr(II) Present in Acidic Feed Solution: An Efficient Ionic Liquid based Extraction Process

AbstractMutual separation of Y(III) and Sr(II) from their waste solution has been an emerging research domain due to their various industrial applications. In this investigation, we emphasize the feasibility of selective separation of Y(III) from Sr(II) present in nitric acid medium using undiluted cyphos nitrate ionic liquid (IL): trihexyl(tetradecyl)phosphonium nitrate ([P66614][NO3]) as the ligating phase. The set experimental parameters were the feed phase acidity, initial nitrate ion concentration, initial feed metal ion concentration, equilibration time and temperature to unveil the efficacy of the proposed system. Efficient extraction of Y(III) selectively in presence of Sr(II) using only a salting agent in aqueous phase without additional extractant in IL phase was highlighted. A remarkable separation factor (SF) obtained for Y(III) over Sr(II) explored the efficacy of the proposed IL system. Y(III) loading in IL phase both in unirradiated and irradiated conditions was compared to evaluate the coordination behavior of irradiated moieties. In addition, a most important advantage of the proposed system is that it employs Milli‐Q water for the back extraction (stripping) of the loaded Y(III) from IL phase and does not demand any complexing agent (water soluble). This undoubtedly corroborates the economic and environmental benefits of the proposed IL system.

GBERT: A hybrid deep learning model based on GPT-BERT for fake news detection

The digital era has expanded social exposure with easy internet access for mobile users, allowing for global communication. Now, people can get to know what is going on around the globe with just a click; however, this has also resulted in the issue of fake news. Fake news is content that pretends to be true but is actually false and is disseminated to defraud. Fake news poses a threat to harmony, politics, the economy, and public opinion. As a result, bogus news detection has become an emerging research domain to identify a given piece of text as genuine or fraudulent. In this paper, a new framework called Generative Bidirectional Encoder Representations from Transformers (GBERT) is proposed that leverages a combination of Generative pre-trained transformer (GPT) and Bidirectional Encoder Representations from Transformers (BERT) and addresses the fake news classification problem. This framework combines the best features of both cutting-edge techniques—BERT's deep contextual understanding and the generative capabilities of GPT—to create a comprehensive representation of a given text. Both GPT and BERT are fine-tuned on two real-world benchmark corpora and have attained 95.30 % accuracy, 95.13 % precision, 97.35 % sensitivity, and a 96.23 % F1 score. The statistical test results indicate the effectiveness of the fine-tuned framework for fake news detection and suggest that it can be a promising approach for eradicating this global issue of fake news in the digital landscape.

Toward next generation mixed reality games: a research through design approach

Mixed reality (MR) games refer to games that integrate physical entities with digitally mediated contents. Currently, it entails game creators to integrate heterogeneous virtual and physical components, which is often time-consuming and labor-intensive, without the support of a coherent technology stack. The underlying technodiversity manifested by the research corpus suggests a complicated, multi-dimensional design space that goes beyond merely technical concerns. In this research, we adopted a research-through-design approach and proposed an MR game technology stack that facilitates flexible, low-code game development. As design grounding, we first surveyed 34 state-of-the-art studies, and results were synergized into three different spectra of technological affordances, respectively activity range, user interface and feedback control, to inform our next design process. We then went through an iterative prototyping phase and implemented an MR game development toolset. A co-design workshop was conducted, where we invited 15 participants to try the prototype tools and co-ideate the potential use scenarios for the proposed technology stack. First-hand user feedback was collected via questionnaires and semi-structured interviews. As a result, four conceptual game designs with three major design implications were generated, which conjointly reflect a broader understanding on MR gameful experience and contribute fresh insights to this emerging research domain.

Digital product passport for sustainable and circular supply chain management: a structured review of use cases

ABSTRACT Digital product passport (DPP) is a key regulatory element of the latest European Union’s Circular Economy Action Plan. By enhancing product traceability and supply chain transparency, its upcoming mandatory implementation aspires to improve resource circularity, cut emissions, and strengthen supply chain governance. However, the extant literature lacks a comprehensive understanding of DPP use cases in the supply chain and how they enable sustainable and circular supply chain management (SCM). To address this knowledge gap, this study first provides an overview of the current state of DPP in policy and practice. Based on a structured review of 82 DPP supply chain use cases in academic literature, we synthesise the functional areas that DPP can add value to improving supply chain sustainability and circularity. Informed by the review findings, we first discuss theoretical and practical implications of DPP-enabled sustainable and circular SCM, and then prioritise research needs in the emerging research domain.

Decoding the nuances of scholarly output and publication metrics in orthodontics

This comprehensive manuscript endeavors to furnish orthodontic researchers with the necessary tools and knowledge to adeptly navigate the multifaceted landscape of academic publishing, thereby enhancing the efficacy and reach of their scholarly endeavors. It meticulously imparts critical insights and methodologies for comprehending and leveraging publication metrics, such as citation counts, the h-index, and Journal Impact Factors, to strategically plan research trajectories. Furthermore, it offers guidance on adeptly engaging with evaluation agencies such as the American Dental Association (ADA) and the National Institute of Dental and Craniofacial Research (NIDCR), thereby optimizing alignment with grant opportunities. Through the adept utilization of orthodontic bibliometrics, researchers can gain invaluable insights into prevailing collaboration trends and emerging research domains, thus facilitating informed decision-making and prioritization of scholarly pursuits. Additionally, the manuscript delves into the nuanced optimization of publication guidelines to maximize research impact. Spanning both established domains such as biomechanics, anchorage control, and aligner therapy, as well as burgeoning frontiers including 3D printing and artificial intelligence applications in aligner treatment, this manuscript equips orthodontic researchers with the requisite acumen to embark upon a journey of impactful scholarly contributions, thereby catalyzing advancements in patient care within the discipline.

Literature Review of Mixed Reality Research

The global evolution of mixed reality, driven by recent technological advancements, promises to transform industries and daily life by enhancing functionalities and services. This short review assesses highly cited papers over the last two decades from an over a thousand of research papers on mixed reality. Emphasizing papers published in 2022 and 2023, it delves into the contemporary applications of mixed reality, highlighting its potential in digitally enhanced simulations and utilities amidst the expansive language model era. Additionally, the review examines both the possibilities and constraints of these innovative solutions, shedding light on emerging research domains like education, telemedicine, remote control, and graphics optimization in real-time rendering.

Multimodal Dual-Embedding Networks for Malware Open-Set Recognition.

Malware open-set recognition (MOSR) is an emerging research domain that aims at jointly classifying malware samples from known families and detecting the ones from novel unknown families, respectively. Existing works mostly rely on a well-trained classifier considering the predicted probabilities of each known family with a threshold-based detection to achieve the MOSR. However, our observation reveals that the feature distributions of malware samples are extremely similar to each other even between known and unknown families. Thus, the obtained classifier may produce overly high probabilities of testing unknown samples toward known families and degrade the model performance. In this article, we propose the multi \ modal dual-embedding networks, dubbed MDENet, to take advantage of comprehensive malware features from different modalities to enhance the diversity of malware feature space, which is more representative and discriminative for down-stream recognition. Concretely, we first generate a malware image for each observed sample based on their numeric features using our proposed numeric encoder with a re-designed multiscale CNN structure, which can better explore their statistical and spatial correlations. Besides, we propose to organize tokenized malware features into a sentence for each sample considering its behaviors and dynamics, and utilize language models as the textual encoder to transform it into a representable and computable textual vector. Such parallel multimodal encoders can fuse the above two components to enhance the feature diversity. Last, to further guarantee the open-set recognition (OSR), we dually embed the fused multimodal representation into one primary space and an associated sub-space, i.e., discriminative and exclusive spaces, with contrastive sampling and ρ -bounded enclosing sphere regularizations, which resort to classification and detection, respectively. Moreover, we also enrich our previously proposed large-scaled malware dataset MAL-100 with multimodal characteristics and contribute an improved version dubbed MAL-100 + . Experimental results on the widely used malware dataset Mailing and the proposed MAL-100 + demonstrate the effectiveness of our method.

ΜοS2 nanoensembles prepared by a simple solvothermal route for hydrogen evolution reaction

Electrochemical hydrogen evolution reaction (HER) is an emerging research domain aiming to supply a means of renewable energy. Transition metal dichalcogenides (TMDs) have a good potential as promising low-cost alternatives to platinum-based catalysts. Molybdenum disulfide (MoS2) nanostructures with different shapes are increasingly becoming attractive materials for HER electrocatalysis, thanks to their peculiar physical properties which depend on their composition and morphology. It is still challenging to produce MoS2 nanomaterials simply and straightforwardly. In this work, MoS2 nanoensembles and small nanoparticles were fabricated via facile solvothermal protocols. The produced structures display a competitive activity in HER, with the nanoensembles performing better than the isotropic particles. This was attributed to the abundance of their electrochemically active sites and their robust structural stability, which endowed them with remarkable endurability in electroactivity. The nanoensemble morphology ensured the creation of a well-connected array of channels for charge transport, thus favouring an ameliorated electrochemical activity.

Untargeted white-box adversarial attack with heuristic defence methods in real-time deep learning based network intrusion detection system

Network Intrusion Detection System (NIDS) is a key component in securing the computer network from various cyber security threats and network attacks. However, consider an unfortunate situation where the NIDS is itself attacked and vulnerable; more specifically, we can ask, “How to defend the defender?“. In Adversarial Machine Learning (AML), the malicious actors aim to fool the Machine Learning (ML) and Deep Learning (DL) models to produce incorrect predictions with intentionally crafted adversarial examples. These adversarial perturbed examples have become the biggest vulnerability of ML and DL based systems and are major obstacles to their adoption in real-time and mission-critical applications such as NIDS. AML is an emerging research domain, and it has become a necessity for the in-depth study of adversarial attacks and their defence strategies to safeguard the computer network from various cyber security threads. In this research work, we aim to cover important aspects related to NIDS, adversarial attacks and its defence mechanism to increase the robustness of the ML and DL based NIDS. We implemented four powerful adversarial attack techniques, namely, Fast Gradient Sign Method (FGSM), Jacobian Saliency Map Attack (JSMA), Projected Gradient Descent (PGD) and Carlini & Wagner (C&W) in NIDS. We analyzed its performance in terms of various performance metrics in detail. Furthermore, the three heuristics defence strategies, i.e., Adversarial Training (AT), Gaussian Data Augmentation (GDA) and High Confidence (HC), are implemented to improve the NIDS robustness under adversarial attack situations. The complete workflow is demonstrated in real-time network with data packet flow. This research work provides the overall background for the researchers interested in AML and its implementation from a computer network security point of view.

Analysing the Impact of Omni-channel Customer Delight on Repurchase Intention

Merely satisfying the customers is not enough; delighting them by offering unexpected, innovative and surprising services has become crucial. For this, firms invest in several channels and attract delighted customers. The current research examines the impact of Omni-channel customer delight on customers’ repurchase intention. The proposed conceptual model develops several hypotheses relating to customer delight in the Omni-channel context by reviewing rich literature on customer delight. Data were collected from 496 respondents using convenience sampling, representing 24 states of India. Using Smart PLS software, collected data were examined through structural equation modelling. Surprise and joy are crucial to offline customer delight, while satisfaction, usefulness and entertainment play significant roles in online customer delight. Also, Omni-channel customer delight significantly influences repurchase intentions; therefore, paying attention to all antecedents of customer delight is essential. The research provides a basic understanding of customer delight in Omni-channel contexts and offers directions to formulate various strategies to delight customers beyond satisfaction. The article adds value to existing knowledge relating to Omni-channel retailing. Omni-channel customer delight is undoubtedly an emerging research domain in the current Omni-channel retailing.

Interactive Neural Painting

In the last few years, Neural Painting (NP) techniques became capable of producing extremely realistic artworks. This paper advances the state of the art in this emerging research domain by proposing the first approach for Interactive NP. Considering a setting where a user looks at a scene and tries to reproduce it on a painting, our objective is to develop a computational framework to assist the user’s creativity by suggesting the next strokes to paint, that can be possibly used to complete the artwork. To accomplish such a task, we propose I-Paint, a novel method based on a conditional transformer Variational AutoEncoder (VAE) architecture with a two-stage decoder. To evaluate the proposed approach and stimulate research in this area, we also introduce two novel datasets. Our experiments show that our approach provides good stroke suggestions and compares favorably to the state of the art.

Bibliometric analysis of social enterprise literature: Revisit to regroup

Social enterprise (SE) studies are gaining ground as an emerging research domain owing to the duality characterizing their business models for tapping the triple bottom line (TBL) principle, which is a framework measuring the three pillars of sustainability: people, planet, and profit. This rising attention to SE has led to scattering in the research structure and knowledge spillover. Although publications have attempted to regroup this domain, extant analyses lack scientific coverage. By reforming previously adopted research approaches and redesigning metadata retrieval constraints, this study revisits, dissects, and synthesizes relevant SE-related studies to identify the current dominance, recent developments, and future directions of SE studies. Through a bibliometric review that combines descriptive, network, and content analyses, our findings reveal ten avenues worth pursuing, categorized under the research scope, research trajectory, and analytical dimensions. Finally, this study presents practical implications that support the institutionalization of SEs that holistically meet the TBL principle.

Domain knowledge decomposition of building energy consumption and a hybrid data-driven model for 24-h ahead predictions

The task of building energy prediction (BEP) is essential to several emerging research domains, including energy management, control optimization and fault detection. An accuracy prediction model contributes significantly to the improvement of building energy efficiency and flexibility. However, most existing prediction models are either based on single time series model or static model, making them perform poorly for long-term predictions. To solve this problem, this paper proposes a hybrid prediction model, which combines the deep ensemble (DE) model and autoregressive (AR) model together. Its motivation comes from the domain knowledge analysis of building energy consumption, where the energy consumption is decomposed into a global part and a local part. The deep ensemble model is adopted to predict its global part, and the AR model is employed to predict its local part. Comprehensive data experiments are conducted based on 50 real buildings across five building types to validate the model performance. The prediction horizon is the 24-h ahead sliding window prediction for one-year energy consumption. The results indicate that the hybrid prediction model outperforms the LSTM, DE-only, and ARIMA-only model, where its relative improvements of CV-RMSE are 28.7%, 35.98% and 18.47%, respectively. The experimental results also reveal that the types of buildings, i.e., office, lodging, will affect the model performance, which is attributed to their different user-behaviors. Based on the experimental results, it is demonstrated that the integration of static model and time series model is robust to the varying of building types and prediction steps, which should be the preferred choice of BEP task.

RAIF: A deep learning‐based architecture for multi‐modal aesthetic biometric system

AbstractHuman aesthetics play a significant role in video game development, emotional‐aware robot design, online recommender systems, digital human, and other domains of research focusing on human‐computer interactions. Social network user recognition based on aesthetic preferences is an emerging research domain. In this paper, a novel deep learning architecture is proposed for multi‐modal audio‐visual person identification that combines audio and visual aesthetic features. A pre‐trained ResNet architecture is utilized to extract high‐level features from a set of user‐preferred audio and image samples. A novel deep learning‐based fusion technique called residual‐aided intermediate fusion (RAIF) is introduced in order to effectively merge the audio and visual features. The proposed RAIF method achieved an accuracy of 98% and a loss of 0.01 on a proprietary multi‐modal dataset, indicating its effectiveness in fusing audio and visual information.

Blockchain technology for sustainable supply chains: a network cluster analysis and future research propositions.

Achieving the sustainable goals of the United Nations requires improving supply chain sustainability (SSC). Blockchain technology (BCT) has attracted attention on a global level with the ability to transform supply chain management and sustainability efforts. Recognizing this, this study investigates how BCT plays a role in SSC. The current study looks into the importance of BCT in order to move supply networks towards sustainability by performing bibliometric analysis, and network cluster analysis. Through the literature review, the current literature was analyzed and future research directions were concluded. We begin our study by selecting 297 papers on the relevant subjects by applying various filters to the Web of Science (WoS) database. Influential individuals, journals, and organizations in this field were identified using bibliometric analysis. A network analysis was performed to identify influential co-author, and keywords, and for page rank, and cluster analysis. The network analysis was revealed ten distinct study clusters, and ten propositions were suggested from the analysis of these clusters. Additionally, a conceptual framework for the research was proposed, which can advise managers, practitioners, and researcher communities on the key trends and topics in this emerging research domain. Furthermore, to guide research scholars in this field, 33 future research directions were suggested.

Lignin-Based Admixtures: A Scientometric Analysis and Qualitative Discussion Applied to Cement-Based Composites.

The development of lignin-based admixtures (LBAs) for cement-based composites is an alternative to valorizing residual lignins generated in biorefineries and pulp and paper mills. Consequently, LBAs have become an emerging research domain in the past decade. This study examined the bibliographic data on LBAs through a scientometric analysis and in-depth qualitative discussion. For this purpose, 161 articles were selected for the scientometric approach. After analyzing the articles' abstracts, 37 papers on developing new LBAs were selected and critically reviewed. Significant publication sources, frequent keywords, influential scholars, and contributing countries in LBAs research were identified during the science mapping. The LBAs developed so far were classified as plasticizers, superplasticizers, set retarders, grinding aids, and air-entraining admixtures. The qualitative discussion revealed that most studies have focused on developing LBAs using Kraft lignins from pulp and paper mills. Thus, residual lignins from biorefineries need more attention since their valorization is a relevant strategy for emerging economies with high biomass availability. Most studies focused on production processes, chemical characterizations, and primary fresh-state analyses of LBA-containing cement-based composites. However, to better assess the feasibility of using different LBAs and encompass the multidisciplinarity of this subject, it is mandatory that future studies also evaluate hardened-sate properties. This holistic review offers a helpful reference point to early-stage researchers, industry professionals, and funding authorities on the research progress in LBAs. It also contributes to understanding the role of lignin in sustainable construction.

Less is more! A pathway to consumer's transcendence

In the 1987 film ‘Wall Street’, apart from telling the world that “Greed, for lack of a better word, is good.“, Gordon Gekko, portrayed by Michael Douglas, notably remarked, “I create nothing. I own.” As a result, the last part of the millennium was given to excess and the never-ending pursuit of wealth and material possessions. However, recent years have seen an alternate mindset take shape. From the ‘KonMari’ method to Dorothy Breininger's ‘five-point scale’, we are now being urged to discard, declutter, and refrain from purchasing. But there are questions about why such a ‘minimalist’ lifestyle resonates with so many consumers worldwide. Building upon self-determination theory, the objective of the current research is to reveal the motivational goal (what), intrinsic (why) and extrinsic (when) motives that underlie minimalistic consumption. Our study examines the relationship between minimalism and perceived transcendence, along with the mediating and moderating roles of moral identity and descriptive norms, respectively. Findings from a cross-sectional sample of 529 shoppers show that minimalistic value enables consumers to fulfill their aspiration for transcendence, and that moral identity and descriptive norms explain why and when, respectively, consumers are motivated to reach this aspiration. Our study signifies the importance of cultivating the value of minimalism that helps navigate human well-being since its development provides us with a better understanding of our self-awareness as a membership in a universal unity of being, thereby expanding moral identity from self to all. We also provide theoretical and practical implications for consumers, marketers, and policymakers and shed light on further research in this emerging research domain.

A Survey of Manifold Learning and Its Applications for Multimedia

Manifold learning is an emerging research domain of machine learning and helps to overcome the limits of standard neural networks which are restricted to Euclidean spaces. In this work, we give an introduction into manifold learning and how it is employed for important application fields in multimedia like similarity search, image classification, synthesis & enhancement, video analysis, 3D data processing and nonlinear dimension reduction. Furthermore, we present available open source software frameworks for manifold learning. One can see from this survey that manifold learning has a lot of potential for computer vision and multimedia.

Job scheduling problem in fog-cloud-based environment using reinforced social spider optimization

Fog computing is an emerging research domain to provide computational services such as data transmission, application processing and storage mechanism. Fog computing consists of a set of fog server machines used to communicate with the mobile user in the edge network. Fog is introduced in cloud computing to meet data and communication needs for Internet of Things (IoT) devices. However, the vital challenges in this system are job scheduling, which is solved by examining the makespan, minimizing energy depletion and proper resource allocation. In this paper, we introduced a reinforced strategy Dynamic Opposition Learning based Social Spider Optimization (DOLSSO) Algorithm to enhance individual superiority and schedule workflow in Fog computing. The extensive experiments were conducted using the FogSim simulator to generate the dataset and an energy-efficient open-source tool utilized to model and simulate resource management in fog computing. The performance of the formulated model is ratified using two test cases. The proposed algorithm attained the optimized schedule with minimized cost function concerning the CPU processing period and assigned memory. Our simulation outcomes show the efficacy of the introduced technique in handling job scheduling issues, and the results are contrasted with five existing metaheuristic techniques. The results show that the proposed method achieves 10% - 15% better CPU utilization and 5%-10% less energy consumption than the other techniques.

Integrated assessment across building and urban scales: A review and proposal for a more holistic, multi-scale, system-of-systems approach

Humanity is facing major societal challenges that are complex and systemic in the nature of their drivers, interactions, and impacts. Because buildings and cities play a substantial role in these societal challenges, we need reliable approaches that can be used to assess their resilience and sustainability. Given that building and urban systems are usually tightly coupled, we critically review nine building-scale assessment frameworks and seven urban-scale assessment frameworks, ranking them from high to low in terms of the causality among component systems. We identify four major knowledge gaps that, to varying degrees, span the entire range of assessment frameworks: (1) causality among component systems and their subsystems is limited; (2) sustainability and resilience are too narrowly defined; (3) social systems are inadequately addressed; and (4) building- and urban-scale assessments are poorly connected. To address these limitations, we briefly introduce several closely-related fields of research including integrated assessment and modeling, social-ecological systems research, land systems science, socio-environmental systems modeling, modeling of human behavior, multi-scale modeling, and multi-fidelity modeling. Building on these rapidly emerging research domains, we conclude by proposing a more holistic, multi-scale, system-of-systems approach that connects across building and urban scales using several common systems.