Research Opportunities Research Articles

Toward Safe and Reliable Aqueous Ammonium Ion Energy Storage Systems

AbstractAqueous batteries using non‐metallic charge carriers like proton (H+) and ammonium (NH4+) ions are becoming more popular compared to traditional metal‐ion batteries, owing to their enhanced safety, high performance, and sustainability (they are ecofriendly and derived from abundant resources). Ammonium ion energy storage systems (AIBs), which use NH4+ ions with tetrahedral geometry, a small hydrated ionic radius, and relatively low ionic weight, are emerging as strong candidates in non‐metal ion battery chemistry. Various electrode materials (both inorganic and organic) are investigated as hosts for NH4+ ions, however, a comprehensive understanding of these mechanisms is still lacking. This gap limits the ability to optimize performance and identify further research opportunities in AIBs. In this review, the charge storage mechanisms in AIBs are discussed, offering insights into the interactions between NH4+ ions and different electrode materials. Additionally, existing literature on electrode materials, highlighting key structural, and electrochemical achievements are summarized. The review also outlines various electrolytes and examines how their properties influence AIB performance. Finally, the shortcomings of current research are discussed and potential solutions are suggested to advance AIB technology, with the goal of inspiring researchers to explore real‐world applications for ammonium ion batteries.

Chicken intestinal organoids: a novel method to measure the mode of action of feed additives.

There is a rapidly growing interest in how the avian intestine is affected by dietary components and feed additives. The paucity of physiologically relevant models has limited research in this field of poultry gut health and led to an over-reliance on the use of live birds for experiments. The development of complex 3D intestinal organoids or "mini-guts" has created ample opportunities for poultry research in this field. A major advantage of the floating chicken intestinal organoids is the combination of a complex cell system with an easily accessible apical-out orientation grown in a simple culture medium without an extracellular matrix. The objective was to investigate the impact of a commercial proprietary blend of organic acids and essential oils (OA+EO) on the innate immune responses and kinome of chicken intestinal organoids in a Salmonella challenge model. To mimic the in vivo prolonged exposure of the intestine to the product, the intestinal organoids were treated for 2 days with 0.5 or 0.25 mg/mL OA+EO and either uninfected or infected with Salmonella and bacterial load in the organoids was quantified at 3 hours post infection. The bacteria were also treated with OA+EO for 1 day prior to challenge of the organoids to mimic intestinal exposure. The treatment of the organoids with OA+EO resulted in a significant decrease in the bacterial load compared to untreated infected organoids. The expression of 88 innate immune genes was investigated using a high throughput qPCR array, measuring the expression of 88 innate immune genes. Salmonella invasion of the untreated intestinal organoids resulted in a significant increase in the expression of inflammatory cytokine and chemokines as well as genes involved in intracellular signaling. In contrast, when the organoids were treated with OA+EO and challenged with Salmonella, the inflammatory responses were significantly downregulated. The kinome array data suggested decreased phosphorylation elicited by the OA+EO with Salmonella in agreement with the gene expression data sets. This study demonstrates that the in vitro chicken intestinal organoids are a new tool to measure the effect of the feed additives in a bacterial challenge model by measuring innate immune and protein kinases responses.

Prg4-Expressing Chondroprogenitor Cells in the Superficial Zone of Articular Cartilage.

Joint-resident chondrogenic precursor cells have become a significant therapeutic option due to the lack of regenerative capacity in articular cartilage. Progenitor cells are located in the superficial zone of the articular cartilage, producing lubricin/Prg4 to decrease friction of cartilage surfaces during joint movement. Prg4-positive progenitors are crucial in maintaining the joint's structure and functionality. The disappearance of progenitor cells leads to changes in articular hyaline cartilage over time, subchondral bone abnormalities, and the formation of ectopic ossification. Genetic labeling cell technology has been the main tool used to characterize Prg4-expressing progenitor cells of articular cartilage in vivo through drug injection at different time points. This technology allows for the determination of the origin of progenitor cells and the tracking of their progeny during joint development and cartilage damage. We endeavored to highlight the currently known information about the Prg4-producing cell population in the joint to underline the significance of the role of these cells in the development of articular cartilage and its homeostasis. This review focuses on superficial progenitors in the joint, how they contribute to postnatal articular cartilage formation, their capacity for regeneration, and the consequences of Prg4 deficiency in these cells. We have accumulated information about the Prg4+ cell population of articular cartilage obtained through various elegantly designed experiments using transgenic technologies to identify potential opportunities for further research.

Industrial revolution and smart farming: a critical analysis of research components in Industry 4.0

PurposeThe domains of Industry 4.0 and Smart Farming encompass the application of digitization, automation, and data-driven decision-making principles to revolutionize conventional sectors. The intersection of these two fields has numerous opportunities for industry, society, science, technology and research. Relatively, this intersection is new, and still, many grey areas need to be identified. This research is a step toward identifying research areas and current trends.Design/methodology/approachThe present study examines prevailing research patterns and prospective research prospects within Industry 4.0 and Smart Farming. This is accomplished by utilizing the Latent Dirichlet Allocation (LDA) methodology applied to the data procured from the Scopus database.FindingsBy examining the available literature extensively, the researchers have successfully discovered and developed three separate research questions. The questions mentioned above were afterward examined with great attention to detail after using LDA on the dataset. The paper highlights a notable finding on the lack of existing scholarly research in the examined combined field. The existing database consists of a restricted collection of 51 scholarly papers. Nevertheless, the forthcoming terrain harbors immense possibilities for exploration and offers a plethora of prospects for additional investigation and cerebral evaluation.Research limitations/implicationsThis study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.Practical implicationsThe paper proposes that the incorporation of Industry 4.0 technology into agricultural operations can enhance efficiency, production and sustainability. Furthermore, it highlights the significance of creating user-friendly solutions specifically tailored for farmers and companies. The study indicates that the implementation of supportive legislative frameworks, incentive programmes and resource conservation methods might encourage the adoption of smart agricultural technologies, resulting in the adoption of more sustainable practices.Social implicationsThis study examines the Industrial Revolution's and Smart Farming's practical effects, focusing on Industry 4.0 research. The proposed method could help agricultural practitioners implement Industry 4.0 technology. It could additionally counsel technology developers on innovation and ease technology transfer. Research on regulatory frameworks, incentive programs and resource conservation may help policymakers and government agencies.Originality/valueBased on a thorough examination of existing literature, it has been established that there is a lack of research specifically focusing on the convergence of Industry 4.0 and Smart Farming. However, notable progress has been achieved in the field of seclusion. To date, the provided dataset has not been subjected to analysis using the LDA technique by any researcher.

Transient and elusive intermediate states in self‐assembly processes: An overview

AbstractThe transient and elusive intermediate states are the keys in self‐assembly processes, which are common phenomena shaping the structure, properties, and functionalities of assembled materials across many scientific domains. However, the understanding about the intermediate states of self‐assembly process is always challenging and limited. In this review, we focus on these states by combining theoretical and experimental approaches. By examining a wide variety of self‐assembly systems that span from biological to metal–organic nanostructures, this review uncovers the wealth of intermediate states of self‐assembled materials. In addition to combining the current knowledge, it will identify challenges and provide a new insight into the opportunities for future research.

School libraries in Australia: A preliminary analysis of the Knowledge Bank of Australian and New Zealand School Libraries

In 2023, academics from the School of Information and Communication Studies at Charles Sturt University completed a two-stage project documenting the published output related to Australian and New Zealand school libraries and teacher librarianship from 1900 to the present. The resultant output of the project was the Knowledge Bank of Australian and New Zealand School Libraries. This article discusses a preliminary analysis of the Australian corpus recorded in the Knowledge Bank and what the evidence provided in the corpus can tell us about the history, development and intellectual output associated with teacher librarianship and school libraries in Australia over time. This insight provides opportunities for further research and a broader understanding of the contribution of this sector to both education and librarianship internationally.

Quantifying CO-release from a photo-CORM using 19F NMR: An investigation into light-induced CO delivery

Carbon monoxide (CO) is an innate signaling molecule that can regulate immune responses and interact with crucial elements of the circadian clock. Moreover, pharmacologically, CO has been substantiated for its therapeutic advantages in animal models of diverse pathological conditions. Given that an excessive level of CO can be toxic, it is imperative to quantify the necessary amount for therapeutic use accurately. However, estimating gaseous CO is notably challenging. Therefore, novel techniques are essential to quantify CO in therapeutic applications and overcome this obstacle precisely. The classical Myoglobin (Mb) assay technique has been extensively used to determine the amount of CO-release from CO-releasing molecules (CORMs) within therapeutic contexts. Nevertheless, specific challenges arise when applying the Mb assay to evaluate CORMs featuring innovative molecular architectures. Here, we report a fluorinated photo-CORM (CORM-FBS) for the photo-induced CO-release. We employed the 19F NMR spectroscopy approach to monitor the release of CO as well as quantitative evaluation of CO release. This new 19F NMR approach opens immense opportunities for researchers to develop reliable techniques for identifying molecular structures, quantitative studies of drug metabolism, and monitoring the reaction process.

Gender and Mentorship in Military Medicine: A Survey Study.

Mentorship is essential for professional development and advancement within the military. In civilian medicine, the intersection between gender and mentorship holds important implications for research opportunities, academic success, and career progression. However, the intersection of gender and mentorship has not yet been explored within the field of military medicine. The purpose of this study, therefore, was to investigate the role of gender in mentorship within the field of military medicine. We investigated gender and mentorship within military medicine by developing and distributing a 16-item Likert survey to active-duty military physicians. We used the Chi-squared test of independence and the independent samples t-test to examine the role of gender in mentorship among the 16 questions. The male respondents reported more same-gender mentors than females (male [M]: 61.9% vs. female [F]: 33.0%), whereas the female respondents had significantly more mentors of the opposite gender than males (M: 12.4% vs. F: 47.3%). A higher proportion of males indicated no preference compared to females. Conversely, a significant preference for a same-sex mentor was expressed by female respondents compared to male respondents (M: 5.2% vs. F: 30.1%). In contrast, female respondents considered having a mentor more important than their male counterparts (t(206) = -2.26, P = .012, F: 3.86 vs. M: 3.57). Both male and female respondents had significantly more female mentees ($\chi _{\left( 1 \right)}^2$ = 12.92, P < .001, Cramer's V = 0.254). While the female participants in our study preferred female mentors, the male participants had more same-gender mentors than the female participants. In addition, more females reported that they did not have the time to be mentored. Our results, therefore, suggest that training males to be better mentors to females and providing accessible mentorship training to females may promote equitable career development within military medicine.

IMPLEMENTATION OF A BASE OF RULES FOR DIFFERENTIAL DIAGNOSIS OF CLINICAL AND HEMATOLOGICAL SYNDROMES BASED ON MORPHOLOGICAL CLASSIFICATION ALGORITHM

The evolving landscape of modern medicine underscores the growing importance of automating diagnostic processes. This advancement is not merely a convenience but a necessity to harness the full potential of technological progress, aiming to elevate research and clinical outcomes to new heights. Among the innovative strides in this field, the development of diagnostic systems based on morphological classification algorithms stands out. Such systems, rooted in comprehensive rule bases for differential diagnosis, promise to revolutionize the way we approach complex medical conditions. This paper introduces a cutting-edge system that epitomizes this evolution. Designed to harness the power of data analysis, it paves the way for groundbreaking research opportunities. At the heart of this system is a sophisticated set of rules derived from a morphological classification algorithm. This foundation enables the system to perform automated diagnoses of a wide array of clinical and hematological syndromes with unprecedented accuracy. A notable application of this technology is its ability to diagnose anemia by analyzing six distinct blood parameters and further categorize the anemia type based on biochemical criteria. The implications of such diagnostic capabilities are profound. By enabling the systematic collection and analysis of statistical data, the system facilitates in-depth research into the prevalence of diseases across different demographic groups. It aids in identifying disease patterns and supports preventive medicine efforts, potentially shifting the paradigm from treatment to prevention. This study not only highlights the system's capacity for enhancing diagnostic precision but also emphasizes its role as a catalyst for medical research and the improvement of healthcare delivery. The integration of such technologies into the medical field promises to enhance the quality of care, streamline diagnostic processes, and open new avenues for medical research, ultimately contributing to the advancement of global health standards.

A Historical Survey of Advances in Transformer Architectures

In recent times, transformer-based deep learning models have risen in prominence in the field of machine learning for a variety of tasks such as computer vision and text generation. Given this increased interest, a historical outlook at the development and rapid progression of transformer-based models becomes imperative in order to gain an understanding of the rise of this key architecture. This paper presents a survey of key works related to the early development and implementation of transformer models in various domains such as generative deep learning and as backbones of large language models. Previous works are classified based on their historical approaches, followed by key works in the domain of text-based applications, image-based applications, and miscellaneous applications. A quantitative and qualitative analysis of the various approaches is presented. Additionally, recent directions of transformer-related research such as those in the biomedical and timeseries domains are discussed. Finally, future research opportunities, especially regarding the multi-modality and optimization of the transformer training process, are identified.

Qualitative research in the metaverse, a primer

New technology can be a source of great optimism and a force for positive change in the research industry. As such, it is important to discuss the merits of emerging technology. In this research note, the emergent opportunity for qualitative research in the metaverse is discussed. This paper looks at how rich sensory virtual environments with multiple sensory modalities might help address some of the shortcomings of traditional online qualitative research. The importance of establishing social presence in virtual qualitative research is addressed. Then, how the metaverse might help remove some of the constraints of ‘big qual’ is debated. The potential for the metaverse to improve participant inclusivity is considered, before finally discussing the issue of research ethics in a decentralised virtual world.

Studying complex causal processes in technological innovation and entrepreneurship with set-theoretic mediation models

Researchers often turn to linear mediation models to understand the complex causal processes inherent within innovation and entrepreneurship phenomena. However, these models are not always the most appropriate methods for increasing our understanding of these phenomena. This is because linear models depend on the principle of reductionism – which separates causal processes into their independent components – and overlooks systemwide attributes. To advance research findings that do not adequately address complex causal processes, we advocate using set-theoretic mediation models that offer analytical features better suited for holistically uncovering interdependent and intervening pathways. This method enables investigating complex causal processes associated with the conjunction, equifinality, and asymmetry that can occur with multiple interdependent variables. We provide researchers with practical guidance on constructing and testing set-theoretic mediation models using widely available software while demonstrating these procedures with an illustrative analysis. In doing so, we seek to guide researchers interested in integrating these models into their studies and recommend best practices for implementation. We argue that set-theoretic meditation models can be utilized in various contexts, as they offer new research opportunities for exploring unified necessity and sufficiency relational systems in ways existing methods have yet to address.

Mundane urban (im)mobilities: work, domestic, and family lives of migrant building workers in China

ABSTRACT Interdisciplinary research from China has generated a wealth of quantitative and qualitative insights into trans-, national, regional, and local migration patterns over long and short distances through attention to diverse topics, socio-economic groups, and case-study locations. Our contribution in this paper is to advance recent critical work that is directing focus from rural-to-urban “circular movements” toward more complex spatialities and temporalities. To add value to that project, we not only engage with studies of urban (im)mobilities per se but also elaborate productive new avenues from across interdisciplinary mobilities research. Specifically, we draw on feminist, materialist, and more-than-representational thinking to highlight the contingent relatedness of migrants’ daily lives across diverse work, domestic, and family times/spaces both within and beyond cities. In doing so, we show how exploration of mundane urban (im)mobilities offers new insights to challenge binary depictions of city/countryside, home/destination, movement/stasis, belonging/marginalization, and moored/unmoordness. The conclusion signposts new research opportunities.

Finding the needle in the haystack: archival research in European political science

AbstractThis short article offers a practical introduction to archival research for political scientists working on European politics. Archival documents are increasingly recognized as a relevant data source for process tracing analyses in small-N or mixed methods studies. Previously classified archival documents are exceptionally trustworthy due to their original confidentiality. Their rich and detailed content facilitates the understanding of causal mechanisms. Still, the hurdles for working with archival sources are high for political scientists. Lack of experience, no special training in handling historic documents, and a shortage of textbooks meeting their demands are a few of the problems political scientists planning archival research face. In the article, I highlight the opportunities of archival research and demonstrate how challenges can be overcome. I emphasize that the archival field trip should only be planned once researchers have gained substantive context knowledge. In their preparation, researchers should use all the resources archives offer and develop measurable expectations from theories.

The Best Protocol to Treat Equine Skin Wounds by Second Intention Healing: A Scoping Review of the Literature.

Equine skin wound treatment continues to be a challenge for veterinarians. Despite being a frequent practice, it remains difficult to choose an evidence-based treatment protocol. This study aimed to comprehensively explore the literature and provide a scoping review of therapeutic strategies for equine skin wounds and identify knowledge gaps and opportunities for future research. This review was conducted using specific criteria to select literature that described methods to manage second intention wound healing. After removing duplicates and screening papers for suitability, 81 manuscripts were included for data extraction. Of these, 59 articles were experimental studies, 10 were case reports, 9 were case series, and 3 were clinical studies. The most frequent wound location was the distal limbs. Macroscopic assessment was the main tool used to evaluate treatment effectiveness. All of the case reports, case series, and clinical studies reported positive outcomes with regard to the treatment used, while only 36% of the experimental studies found significant healing improvement in treated wounds compared to control groups. It was found that there are many treatments that have exhibited controversial results, and there exists a lack of evidence for the adoption of specific treatment protocols.

Understanding the emergence and significance of behavioral cybersecurity: A bibliometric analysis

This thorough bibliometric analysis, using R-Studio and Biblioshiny, presents a comprehensive view of the evolving field of behavioral cybersecurity. It explores how human behaviors in online cyber activities have become a crucial area of research. Despite being relatively new, behavioral cybersecurity has gained considerable attention in academia and practical applications. The analysis reveals essential details such as key sources, influential authors, participating countries, affiliated institutions, and prevalent research themes in this field. It's an essential guide for new researchers, helping them identify gaps in knowledge and understand the importance of this growing research area. Examining 468 literature sources from Scopus up to January 11, 2024, shows a gradual start followed by significant growth, especially after 2012. This dynamic field offers numerous research opportunities by incorporating human aspects into cybersecurity discussions. The focus is on understanding behavioral patterns to shape strategies, guidelines, and user-centered software solutions. Spotlighting behavior in cybersecurity highlights user cognitive biases and the impact of emotions and social factors on decision-making in the cybersecurity landscape. This holistic approach underscores the crucial role of human behavior in shaping cybersecurity practices and strengthening digital defenses.

Centralized exchanges vs. decentralized exchanges in cryptocurrency markets: A systematic literature review

Research on cryptocurrency exchanges, consisting of both centralized exchanges (CEXs) and decentralized exchanges (DEXs), has seen a significant increase in contributions in recent years, driven by growing interest in the conceptual design of cryptocurrency markets. Through a comprehensive review of literature published between January 2019 and September 2023, I identify and analyze different dimensions of the ongoing CEX vs. DEX debate. While DEXs emphasize decentralization, user control, and resistance to censorship, CEXs offer higher liquidity, advanced trading features, and a more established track record. Regulatory challenges, such as Know Your Customer (KYC) and Anti-Money Laundering (AML) compliance, also feature prominently in the literature and influence the choice of exchange for both traders and policymakers. In addition, I observe a growing interest in the design of pricing functions for CEXs and DEXs, particularly in the area of automated market makers (AMMs). Finally, based on my findings, I outline future research opportunities in this context and derive research gaps as well as recommended actions for practitioners.

Challenges and Opportunities of Education and Scientific Research in the United Arab Emirates

This study addressed the challenges and opportunities of education and scientific research in the United Arab Emirates. It discussed the relationship between educational outcomes, scientific research, and real-world requirements. It also highlighted the density of curricula and the accumulation of knowledge, noting that current educational curricula do not align with the demands of e-learning, which has emerged as a strong and new system in future education. The study emphasized the role of higher education and scientific research institutions in advancing and keeping pace with advanced countries. It stressed the importance of academic and research collaboration among higher education institutions, academic accreditation for programs, and the required skills for each specialization to serve both learners and the country. Regarding scientific research, the study observed a continuous decline due to reduced funding for research and decreased allocated funds by universities. It also discussed faculty members' challenges in their academic and administrative duties. The study concluded by proposing innovative solutions to address the challenges of education and scientific research in the United Arab Emirates.

Deep learning on spatiotemporal graphs: A systematic review, methodological landscape, and research opportunities

Deep learning approaches, given their low cost and high reliability, have gained much popularity in different subjects, such as computer vision and natural language processing, and more recently in graph data types. Spatiotemporal graph-based neural networks have been more and more developed to solve problems related to spatiotemporal data, mainly for analyzing changes over time and for provisioning purposes. In this systematic literature review, we have aimed to answer the most important questions regarding spatiotemporal graph deep learning architectures in different applications domains, such as traffic related topics, medical imaging, and geographical data analysis. We have selected more than 50 papers that cover a wide range of applications and very different architectures and innovations. We have also noticed that most of them consider the spatiotemporal graphs to be quite classic graphs without any further sophisticated modeling of temporal and spatial data evolution. Moreover, core problems (such as node classification, frequent pattern recognition, etc.) and application domains are not sufficiently addressed by the state-of-the-art. This study thus opens many perspectives to new developments in spatio-temporal graph deep learning with different strategies in order to solve various end-to-end tasks and other problems related to this special kind of graph.

Bridging Dense and Sparse Maximum Inner Product Search

Maximum inner product search (MIPS) over dense and sparse vectors have progressed independently in a bifurcated literature for decades; the latter is better known as top- \(k\) retrieval in Information Retrieval. This duality exists because sparse and dense vectors serve different end goals. That is despite the fact that they are manifestations of the same mathematical problem. In this work, we ask if algorithms for dense vectors could be applied effectively to sparse vectors, particularly those that violate the assumptions underlying top- \(k\) retrieval methods. We study clustering-based approximate MIPS where vectors are partitioned into clusters and only a fraction of clusters are searched during retrieval. We conduct a comprehensive analysis of dimensionality reduction for sparse vectors, and examine standard and spherical KMeans for partitioning. Our experiments demonstrate that clustering-based retrieval serves as an efficient solution for sparse MIPS. As byproducts, we identify two research opportunities and explore their potential. First, we cast the clustering-based paradigm as dynamic pruning and turn that insight into a novel organization of the inverted index for approximate MIPS over general sparse vectors. Second, we offer a unified regime for MIPS over vectors that have dense and sparse subspaces, that is robust to query distributions.