Research Challenges Research Articles

An efficient test suit reduction methodology for regression testing

<span lang="EN-US">This paper's goal is to provide a more effective algorithm for reducing the amount of test cases in a test suit during the regression testing phase. This algorithm divide the entire test suit into equivalence classes in first step and then apply boundary value coverage to select test case out of repeated test cases which has same importance in test suit. This algorithm is based on the concept that before selecting best test cases out of repeated test case in test suit to prepare reduced test suit we can divide all test cases in number of equivalence classes so number of test case under consideration reduced by great extend. This paper proposed a method of experimentation involving test cases from different software application areas; minimization algorithms and the maths and algorithms of minimization algorithms in details. Test case techniques are equivalence portioning and boundary value analysis. Along with this concept, I also discuss a case study to verify and check new algorithm for its efficiency, for that I apply my algorithm on one of the program or group of program. This complete proposed methodology shall be applied to different software applications belonging to soft computing, Engineering software, Financial Software, Cloud Applications, Business Applications, AI and Machine Learning, Data Analytics are being identified. This selection has been made keeping in mind the trends, industrial importance, economic values and research challenges. Minimization in test cases would lead to lesser testing effort and desirable test completion.</span>

Gender Gap and Gender Data Gap in Futures Research: Focusing on the Studies Employing the Delphi Technique

The gender gap in futures research, rooted in male dominance, results in limited visibility of female futurists and neglect of women's issues. This study focuses on the gender data gap as an indicator of the existing gender gap in futures research and analyze the literature employing the Delphi technique—a widely employed research method in this field. When societal gender gaps influence data collection and utilization, a gender data gap arises, leading to underrepresentation of women's perspectives and experiences. The Delphi method, operates on the principle of expertise, may inadvertently exclude women experts whose expertise tends to be underrecognized. Our analysis, based on Temin and Roca (2016)’s definition of gender data, shows the gender data gap in the futures research literature. The gender gap in futures research, demonstrated by gender data gap, not only colonizes women's futures, but also impedes the potential for planning, social decision-making, and policies aimed at fostering a gender-equitable future. Futures research holds the potential to serve as a tool for decolonization. To achieve a future free of gender disparities, it’s imperative to address issues directly relevant to women but also crucial for humanity as the main challenges of futures research and ensure women’s participation.

Nanomaterials for advanced energy applications: Recent advancements and future trends

Nowadays, one of the most promising and significant challenges for our society is achieving highly efficient energy utilization. To address the upcoming demands in energy applications, which demonstrate considerable potential for future trends, continuous efforts are necessary to develop improved and higher-performing inorganic multifunctional nanomaterials. Multifunctional inorganic nanomaterials have been extensively researched to meet the requirements of various energy applications or to enhance them further. Specific attention is given to inorganic nanomaterials for advanced energy storage, conservation, transmission, and conversion applications, which strongly rely on the optical, mechanical, thermal, catalytic, and electrical properties of energy materials. At the nanometer-scale range, triboelectric, piezoelectric, thermoelectric, electrochromic, and photovoltaic materials have made significant contributions to numerous energy sector applications. Functional inorganic materials possess unique properties, including excellent thermal and electrical conductivity, chemical stability, and a large surface area, which enhance their competitiveness in energy-related applications. Herein, recent research, development, and advances in inorganic multifunctional nanomaterials to enhance their performance are discussed, highlighting how devices combine the functionalities of nanomaterials. In this manner, we aim to provide insights into future applications and elucidate the ongoing research challenges currently facing this field.

ShopAssistAI

Abstract: E-commerce sites are constantly searching for fresh and inventive approaches to boost customer satisfaction and engagement. Including chatbots with the ability to haggle with clients at no cost is a viable strategy. A detailed review of earlier research on chatbots used in e-commerce pricing negotiations is given in this study. We look at a variety of aspects, including the look, feel, effectiveness, and user experience of these chatbots. We also discuss the underlying technologies machine learning and natural language processing (NLP) that were utilized in the creation of these systems. We also highlight areas of possible interest and challenge for future research and development in this developing field. Our synthesis aims to provide practical advice for scholars, business experts, and organizations wishing to enhance the e-commerce experience through the usage of price negotiation chatbots.

Fake News Detection using Machine Learning

Abstract: In recent years, the proliferation of fake news has precipitated numerous social and political quandaries. As the predominant source of information shifts towards digital platforms, discerning accountability for disseminated opinions becomes increasingly challenging, hindering authentication of received information. Given the pervasive nature of ecological and societal dilemmas, the role of machine learning in combating fake messages on social media is paramount. The virality of messages, whether genuine or fabricated, underscores the necessity for an automated, resilient, dependable, and efficient detection mechanism amidst multifarious challenges. This review delves into the contemporary landscape of fake news detection mechanisms within social media. By examining the contextual backdrop of fake news and its ramifications on users, we explore various methodological approaches categorized as content-based, social context-based, and hybrid-based methods. Culminating with an elucidation of four pivotal research challenges, this paper aims to steer future endeavors towards advancing the field.

Novel cutting edge nano-strategies to address old long-standing complications in cardiovascular diseases. A comprehensive review.

Cardiovascular diseases (CVD) impact a substantial portion of the global population and represent a significant threat to experiencing life-threatening outcomes, such as atherosclerosis, myocardial infarction, stroke and heart failure. Despite remarkable progress in pharmacology and medical interventions, CVD persists as a major public health concern, and now ranks as the primary global cause of death and the highest consumer of global budgets. Ongoing research endeavours persist in seeking novel therapeutic avenues and interventions to deepen our understanding of CVD, enhance prevention measures, and refine treatment strategies. Nanotechnology applied to the development of new molecular probes with diagnostic and theranostic properties represents one of the greatest technological challenges in preclinical and clinical research. The application of nanotechnology in cardiovascular medicine holds great promise for advancing our understanding of CVDs and revolutionizing their diagnosis and treatment strategies, ultimately improving patient care and outcomes. In addition, the capacity of drug encapsulation in nanoparticles has significantly bolstered their biological safety, bioavailability and solubility. In combination with imaging technologies, molecular imaging has emerged as a pivotal therapeutic tool, offering insight into the molecular events underlying disease and facilitating targeted treatment approaches. Here, we present a comprehensive overview of the recent advancements in targeted nanoparticle approaches for diagnosing CVDs, encompassing molecular imaging techniques, underscoring the significant progress in theranostic, as a novel and promising therapeutic strategy.

Crosstalk between ROP GTPase signaling and plant hormones.

Rho of Plants (ROPs) constitute a plant-specific subset of small guanine nucleotide-binding proteins within the Cdc42/Rho/Rac family. These versatile proteins regulate diverse cellular processes, including cell growth, cell division, cell morphogenesis, organ development, and stress responses. In recent years, the dynamic cellular and subcellular behaviors orchestrated by ROPs have unveiled a notable connection to hormone-mediated organ development and physiological responses, thereby expanding our knowledge of the functions and regulatory mechanisms of this signaling pathway. This article delineates advancements in understanding the interplay between plant hormones and the ROP signaling cascade, centering primarily on the connections with auxin and abscisic acid pathways, alongside preliminary discoveries in cytokinin, brassinosteroid, and salicylic acid responses. It endeavors to shed light on the intricate, coordinated mechanisms bridging cell-level and tissue-level signals that underlie plant cell behavior, organ development, and physiological processes, and highlight future research prospects and challenges in this rapidly developing field.

Chinese Constitutonal Performance Unveiled: Text Mining Insights in Civil Litigations

Abstract This article presents a comprehensive examination of the role of constitutions in private disputes, with a specific focus on the Chinese Constitution. Utilizing an extensive dataset of digitized court adjudications, this study delves into a relatively unexplored area, yielding crucial factual and legal insights. The research reveals that the Chinese Constitution significantly influences civil adjudications, both implicitly and explicitly. Furthermore, court judgments emerge as pivotal forces shaping public awareness of constitutional principles. These findings hold profound implications for comprehending the role of constitutions in contexts beyond democratic frameworks, particularly within the private sphere. The study underscores how the capitalist market in China empowers individuals to assert and safeguard private rights, consequently affording the Constitution a protective role in this realm. Concurrently, the existing regime has systematically eroded the Constitution’s influence in public affairs, maintaining its symbolic and rhetorical role. Notably, the concept of judicial independence remains a sensitive subject in China, leading many civil courts to eschew constitutional provisions in favor of political expediency. This paradox underscores the necessity of establishing a reasonably independent judiciary as an integral facet of constitutionalism in such settings. In sum, this research challenges prevailing assumptions about constitutionalism’s contours. It transcends the conventional focus on political authority, illustrating how empirical investigations into constitutional performance within civil litigation can illuminate the emergence of constitutionalism in the shadows of non-democratic systems. These findings offer valuable insights into the role of constitutions in private disputes and pave the way for further exploration within this evolving scholarly domain. Ultimately, this study advances our comprehension of constitutional realities and their far-reaching impacts on the legal landscape and society within contemporary China.

A regulatory focus theory perspective on the dynamics between action and power.

Building on the strong consensus that the experience of power motivates individuals to take action, prior research postulates a positively reinforcing cycle wherein taking action leads to power, which in turn leads to subsequent actions. Applying regulatory focus theory, we differentiate between promotion-oriented and prevention-oriented actions to develop a within-person theory of when and why promotion-oriented and prevention-oriented actions should relate to power, and vice-versa. Across two studies, we find that when employees engaged in promotive voice behaviors, they were more likely to experience a sense of power; this effect was amplified for employees with trait promotion focus. When employees engaged in prohibitive voice behaviors, employees with trait prevention focus were less likely to experience a sense of power. The experience of power subsequently motivated employees to engage in promotive voice behaviors, but not prohibitive voice behaviors. By elucidating the differences between promotion and prevention pathways of action and power, our research challenges the notion that action and power positively reinforce each other. We discuss the theoretical and practical implications of our findings. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Maintenance Operations on Cloud, Edge, and IoT Environments: Taxonomy, Survey, and Research Challenges

The emergence of the Internet of Things (IoT) introduced new classes of applications whose latency and bandwidth requirements could not be satisfied by the traditional Cloud Computing model. Consequently, the IT community promoted the cooperation of two paradigms, Cloud Computing and Edge Computing, combining large-scale computing power and real-time processing capabilities. A significant management challenge in such complex infrastructure concerns the development of efficient maintenance strategies to preserve the environment’s performance and security. While the abundant resources from the academic literature could support the design of novel maintenance solutions, extracting actionable insights from the existing approaches is challenging, given the massive number of published papers. Furthermore, existing review papers, which could help summarize the state-of-the-art, scope their investigations to the maintenance of certain components in particular scenarios. This work fills this gap with a broader literature analysis that covers maintenance strategies targeting physical and logical components in cloud, edge, and IoT environments. First, we introduce a taxonomy that organizes existing solutions according to several characteristics. Then, we review the literature following the taxonomy structure to facilitate the understanding of the research landscape and the comparison between existing works. Finally, we shed light on open challenges that represent promising research directions.

A Survey on AI-based Parkinson Disease Detection: Taxonomy, Case Study, and Research Challenges

Parkinson Disease (PD) is most common diseases from majority of disease encountered all over the world, with more than 7 million individuals being affected. PD is a type of progressive nervous system disease, causing deterioration in health or function. The timely identification of PD is a significant challenge because it rarely shows symptoms in the early stages. Moreover, it is typically encountered in older people where the symptoms sometimes coincide with age-related issues. Deep Learning (DL) can be integrated into many methodologies in diagnosing PD, such as Magnetic Resonance Imaging (MRI) and Single-Photon Emission Computed Tomography (SPECT). DL algorithms can detect PD based on observing some common symptoms. Moreover, it can also be detected using brain MRI images. So, in this study, we reviewed existing DL algorithms for timely identification of PD. We also have developed a CNN model for the timely identification of PD. We used 3D brain MRI images of PPMI datasets and achieved the 88% accuracy.  

Carrying out research in Nepal: perceptions of scholars about research environment and challenges

Carrying out research in Nepal: perceptions of scholars about research environment and challenges

Ecological city concept: Challenge and future research agenda in urban ecology perspective

Cities are no longer viewed as creatures with a linear-climax-established cycle but as ecosystems with dynamic and complicated processes, with people as the primary component. Thus, we must understand urban ecology’s structure and function to create urban planning and appreciate the mechanisms, dynamics, and evolution that connect human and ecological processes. The ecological city (ecocity) is one of the city conceptions that has evolved with the perspective of urban ecology history. The concept of ecocity development within urban ecology systems pertains to recognizing cities as complex ecosystems primarily influenced by human activities. In this context, individuals actively engage in dynamic problem-solving approaches to address environmental challenges to ensure a sustainable and satisfactory quality of life for future generations. Therefore, it is necessary to study how ecocity has developed since it was initiated today and how it relates to the urban ecology perspective. This study aims to investigate the progression of scholarly publications on ecocity research from 1980 to 2023. Additionally, it intends to ascertain the trajectory of ecological city research trends, establish connections between scientific concepts, and construct an ecological city science network using keyword co-occurrence analysis from the urban ecology perspective. The present study used a descriptive bibliometric analysis and literature review methodology. The data was obtained by utilizing the Lens.org database, was conducted using the VOS (Visualization of Similarities) viewer software for data analysis. The urban ecology research area ecology of cities can be studied further from density visualization of ecosystem services and life cycle assessment. Finally, the challenges and future agenda of ecocity research include addressing humans by modeling functions or processes that connect humans with ecosystems (ecology of cities), urban design, ecological imperatives, integration research, and improving the contribution to environmental goals, spatial distribution, agriculture, natural resources, policy, economic development, and public health.

From Blur to Brilliance: The Ascendance of Advanced Microscopy in Neuronal Cell Biology.

The intricate network of the brain's neurons and synapses poses unparalleled challenges for research, distinct from other biological studies. This is particularly true when dissecting how neurons and their functional units work at a cell biological level. While traditional microscopy has been foundational, it was unable to reveal the deeper complexities of neural interactions. However, an imaging renaissance has transformed our capabilities. Advancements in light and electron microscopy, combined with correlative imaging, now achieve unprecedented resolutions, uncovering the most nuanced neural structures. Maximizing these tools requires more than just technical proficiency. It is crucial to align research aims, allocate resources wisely, and analyze data effectively. At the heart of this evolution is interdisciplinary collaboration, where various experts come together to translate detailed imagery into significant biological insights. This review navigates the latest developments in microscopy, underscoring both the promise of and prerequisites for bending this powerful tool set to understanding neuronal cell biology.

Bone morphogenetic protein-2 loaded triple helix recombinant collagen-based hydrogels for enhancing bone defect healing

The development of efficacious bone substitute biomaterials remains a major challenge for research and clinical surgical. Herein, we constructed triple helix recombinant collagen (THRC) -based hydrogels loading bone morphogenetic protein-2 (BMP-2) to stimulate bone regeneration in cranial defects. A series of in situ forming hydrogels, denoted as THRC-oxidized carboxymethylcellulose (OCMC)-N-succinyl-chitosan (NSC) hydrogels, was synthesized via a Schiff base reaction involving OCMC, THRC and NSC. The hydrogels underwent rapid formation under physiological pH and temperature conditions. The composite hydrogel exhibits a network structure characterized by uniform pores, the dimensions of which can be tuned by varying THRC concentrations. The THRC-OCMC-NSC and THRC-OCMC-NSC-BMP2 hydrogels display heightened mechanical strength, substantial biodegradability, and lower swelling properties. The THRC-OCMC-NSC hydrogels show exceptional biocompatibility and bioactivity, accelerating cell proliferation, adhesion, and differentiation. Magnetic resonance imaging, computed tomography and histological analysis of rat cranial defects models revealed that the THRC-OCMC-NSC-BMP2 hydrogels substantially promote new bone formation and expedite bone regeneration. The novel THRC-OCMC-NSC-BMP2 hydrogels emerge as promising candidates for bone substitutes, demonstrating substantial potential in bone repair and regeneration applications.

Sliding Mode Control Based on Jaya Algorithm of Phase Shift for Two-Rotor Vibration Unit

   In this paper, a sliding mode control based on the Jaya algorithm o f phase shift for a two-rotor vibration unit is presented. A two-rotor vibration unit is a highly nonlinear system that has many applications in practical systems. The control problem for this model is to design controllers to ensure phase shift according to the desired value and synchronize the speed of the two rotors. To solve this problem, the authors use the PI controllers for synchronizing the speed of the two rotors and the sliding mode controller for the phase shift. PI control is an effective method to design controllers for practical systems. Besides, Sliding Mode Control (SMC) is one of the most effective methods for designing robust controllers for nonlinear systems affected by external disturbances. However, defining the coefficients of PI controllers and sliding mode controllers is the challenge for researchers. In this study, the Jaya algorithm is used to find the coefficients of these controllers that guarantee the desired phase shift of two rotors, the synchronizing speed of two rotors, and the limitation of control laws. The simulation results are illustrated to show the effectiveness of the proposed method.

The Promise and Challenges of Practice-Oriented Research: A Commentary on the Special Issue.

At the centre of POR is the concept of collaboration between patients, therapists, agencies, and third-party payers. For this commentary, I review the articles of the special issue with attention to both the opportunities and challenges offered by practice-oriented research (POR).I also reviewed some previous research on practice-research networks and how that research might inform POR. The use of routine outcome monitoring (ROM), artificial intelligence (AI), and program evaluation (PE) models show promise for advancing POR. However, each comes with its challenges. The use of ROM to improve patient outcomes has research support. However, researchers have identified problems with implementing ROM because of low uptake by clinicians and because clinicians may see ROM as a potential intrusion. AI shows promise to improve patient outcomes by potentially providing therapists with immediate and nuanced data to inform interventions and interpersonal stances specific to each patient. However, the scaling up of AI runs the risk of dehumanizing psychological interventions. PE may provide a context for allowing therapists to engage in POR to address real-world processes and outcomes of mental health interventions. However, like ROM PE faces the challenge of trust among clinicians and patients who may be reluctant to participate. Despite these challenges, and because of efforts to nurture and maintain respectful collaborations, articles in this special issue highlight how POR can play a pivotal role in bridging the gap between theory and practice.

Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders.

To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research. An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided. In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets. There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice. Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.

A Survey on Graph Counterfactual Explanations: Definitions, Methods, Evaluation, and Research Challenges

Graph Neural Networks (GNNs) perform well in community detection and molecule classification. Counterfactual Explanations (CE) provide counter-examples to overcome the transparency limitations of black-box models. Due to the growing attention in graph learning, we focus on the concepts of CE for GNNs. We analysed the SoA to provide a taxonomy, a uniform notation, and the benchmarking datasets and evaluation metrics. We discuss fourteen methods, their evaluation protocols, twenty-two datasets, and nineteen metrics. We integrated the majority of methods into the GRETEL library to conduct an empirical evaluation to understand their strengths and pitfalls. We highlight open challenges and future work.

Opportunities and Challenges of Qualitative Research in Academic Health Sciences in Ethiopia

Background. The goal of qualitative research is to learn more about the opinions and experiences of the subjects being studied in relation to a particular question. There is a paucity of information on opportunities and challenges encountered to conduct qualitative research among the academic staffs in the health sciences. The purpose of this study was to examine the opportunities and challenges of conducting qualitative research among academic staff of health sciences in Ethiopia. Methods. A descriptive qualitative study design was conducted among health sciences academic staffs of selected public universities in Ethiopia. The study was conducted at three universities’ health sciences colleges, namely, Addis Ababa University, Dilla University, and Wolaita Sodo University. Data were collected using focus group discussions, in-depth interviews, and key informant interviews. MAXQDA 2020 software was used for coding and data management. A thematic analysis approach was followed to present the results. Both inductive and deductive coding approaches were used. Results. Three main themes have been identified as follows: general research practice, opportunities for qualitative research, and challenges to conduct qualitative research. Participants indicated that the availability of research opportunities will have a good impact on qualitative research engagement. Availability of grants, funding, and other incentives and demand for qualitative research in healthcare were described as opportunities for qualitative research practice. Despite the available opportunities, study participants uncovered different challenges that encountered by health academic staffs in conducting qualitative research. Resource limitations, lack of knowledge, negative attitudes or beliefs, publication, and language issues were raised as main challenges. Conclusion. Qualitative research practices were found to be low among health sciences academics. Lack of resources, training, and expertise, problems associated with publication, a lack of funding, and a shortage of experts were the main challenges in conducting qualitative research in health sciences academic settings in Ethiopia.