Research Challenges Research Articles

Knowledge management and knowledge sharing in maintenance department of high-tech industries

PurposeThe competition and competitive advantage among firms in various industries have shifted toward a knowledge-based economy. This is particularly evident in knowledge-intensive industries where a company’s competitive edge relies heavily on its ability to generate and utilize new knowledge solutions. Knowledge management (KM) is essential for all organizations, but it holds greater significance in specific functional units, such as maintenance. Industrial maintenance is a knowledge-intensive field and knowledge sharing (KS) motivation is low due to the tacit and intricate nature of knowledge. Despite these challenges, sharing knowledge between experienced and novice employees is fundamental to industrial maintenance, and much remains to be examined in this area. This study explores how novices' knowledge and KS with other workers can be improved via “Training by Novices” program in industrial maintenance.Design/methodology/approachThis study adopted an action-research approach within a high-tech company’s maintenance department, involving 103 maintenance employees. The research unfolded in three pivotal phases: first, an initial evaluation of KM and KS was carried out; second, training programs by novices aimed at strengthening KS between novices and department members were implemented and third, data were collected to assess changes and facilitate improvement in knowledge exchange and collaboration within the department.FindingsThe study demonstrates that involving novices in training programs as trainers within high-tech industries' maintenance departments enhances KM and KS among all employees. The training programs led to substantial increases in knowledge for novices and experienced workers and fostered a culture of continuous learning. Despite the short training duration, participants' overall knowledge increased, and they found the program effective and expressed willingness to continue, indicating potential for long-term improvements in KS practices.Research limitations/implicationsThis research challenges conventional wisdom regarding KS among novices in industrial maintenance. Showcasing the effectiveness of novices as trainers offers a fresh perspective on enhancing KS practices within knowledge-intensive industries.Practical implicationsThis study provides actionable insights for maintenance managers grappling with KS challenges. Organizations can boost employees' knowledge and KS practices by advocating for structured training programs led by novices, fostering a more collaborative workplace culture.Originality/valueThis study introduces a novel approach by empowering novices to engage actively in KS within industrial maintenance settings. By utilizing novices as trainers, the research demonstrates a practical means of enhancing both their own knowledge and that of their peers. This innovative strategy not only addresses common challenges in KM within maintenance units but also promotes a culture of continuous learning and development. This research offers valuable insights into improving KS practices and fostering a more collaborative organizational culture.

Biofunctional applications of Chitosan in Dentistry – An Overview

Chitosan due to its unique properties has attracted materials scientists globally to explore it for bio-dental applications. In dentistry, chitosan is also shown wide applications. The creation of effective biomaterials for the prevention and treatment of dental disorders is a challenge for researchers in the present scenario. The natural semi-synthetic polysaccharide based biomaterial Chitosan is obtained from the marine source chitin. Due to its peculiar characteristics, chitosan offers benefits in biomedicine and can be utilized in the development of capsules (micro and nanoparticles), powders, scaffolds, films, beads, hydrogels, and bandages. Chitosan offers its benefits as an anti-microbial agent, mucoadhesive, biomimetic mineralization Teeth hardening action due to its remineralizing property which leads to the search and focus of chitosan towards dental applications. Translation of research to clinical applications is better suited for chitosan in terms of its multifactorial activities. Therefore, this article provides an overview of chitosan, which mainly covers the basic information of chitosan, with a focus on different techniques of preparation of chitosan based formulations. It also explores the usage of chitosan in the treatment of various dental disorders.

Guiding treatment decisions in renal cell carcinoma: the role of biomarkers and clinical factors.

Immune checkpoint inhibitors (ICIs) have transformed the treatment landscape for metastatic renal cell carcinoma (mRCC), significantly improving overall survival and achieving durable responses. This review is timely due to the increasing number of ICI-based regimens now considered standard care for RCC. There is an urgent need to identify reliable biomarkers that can predict therapeutic responses and resistance, a key challenge in current research. While tumor-specific factors such as pathological characteristics, genomic mutations, and transcriptional profiles have been extensively studied, no definitive predictive biomarker has yet emerged. Additionally, advanced technologies are being explored to address tumor heterogeneity. Recent research has focused on novel areas such as the microbiome, radiomics, and spatial transcriptomics, which show promise as potential biomarkers. The translation of these emerging biomarker findings into clinical practice is essential to improving personalized treatment strategies for RCC. Until reliable biomarkers are clinically available, clinical factors may play a pivotal role in guiding individualized treatment decisions to optimize patient outcomes.

Multivariate stochastic generation of meteorological data for building simulation through interdependent meteorological processes

In recent years, the uncertainty of weather conditions and the impact of future climate change on building energy assessment has received increasing attention. As an important part of these studies, several types of methods for generating stochastic meteorological data have also been developed. Since solar radiation drops to zero at night, unlike the continuous 24-hour data for elements such as temperature and humidity, this has posed challenges for previous research to fully account for the simultaneity among multiple elements. Therefore, this study proposes a framework for meteorological data generation: First, perform multivariate time series modeling of meteorological data of air temperature, solar radiation and absolute humidity at 12:00 of each day of a typical year based on the S-vine copula method and simulating daily series data at 12:00 for 365 days. Then, based on the probability of change of each element evaluated from the historical meteorological observation data, the daily series data at 12:00 were expanded to 24 h, after which the yearly stochastic weather data were obtained. The analysis of 30 years of stochastic data generated by this method, compared with the original data, reveals that air temperature and solar radiation closely match the original distribution characteristics, except for a minor deviation in the absolute humidity’s kurtosis. Furthermore, the comparison of thermal load distributions for office buildings shows that the original data curve falls within the range of the generated data. This suggests that the generated data includes more information about uncertainty but still keeps the original data’s characteristics.

Volatile Organic Compounds in Exhaled Breath, Blood, and Urine Detected in Patients with Thyroid Carcinoma Using Gas Chromatography-Ion Mobility Spectrometry- A Pilot Study.

Background
Distinguishing between malignant and benign thyroid nodules remains a significant challenge for clinicians and researchers globally. The use of volatile organic compounds (VOCs) has emerged as a novel approach in cancer diagnosis. This prospective pilot study aims to identify VOCs in exhaled breath, blood, and urine that can differentiate benign from malignant thyroid nodules using gas chromatography-ion mobility spectrometry (GC-IMS).
Methods
Patients with thyroid nodules scheduled for surgery were enrolled at the Maastricht University Medical Center (MUMC+). Breath samples were analyzed using a BreathSpec GC-IMS machine (G.A.S. Dortmund, Germany), specifically designed for breath analysis. All blood and urine samples were analyzed with a separate GC-IMS device, the FlavourSpec® (G.A.S., Dortmund, Germany).
Results 
In this proof-of-concept study, 70 patients undergoing thyroid surgery at MUMC+ were consecutively included. Of these patients, 29 were confirmed to have thyroid cancer after surgical resection. The overall analysis did not reveal statistically significant differences in VOCs in breath, urine and blood, between patients with benign and malignant thyroid cancer. 
Conclusion
This proof-of-concept study demonstrated that GC-IMS was unable to adequately distinguish between the VOC profiles of malignant and benign thyroid nodules. However, this study had a small sample size and future larger studies are needed to investigate the potential of using VOCs to distinguish between benign and malignant thyroid nodules. Furthermore, future research should focus on investigating potential confounders that affect patient VOC profiles.
(NCT04883294)&#xD.

ReMiND: Recovery of missing neuroimaging using diffusion models with application to Alzheimer’s disease

Abstract Missing data is a significant challenge in medical research. In longitudinal studies of Alzheimer’s disease (AD) where structural magnetic resonance imaging (MRI) is collected from individuals at multiple time points, participants may miss a study visit or drop out. Additionally, technical issues such as participant motion in the scanner may result in unusable imaging data at designated visits. Such missing data may hinder the development of high-quality imaging-based biomarkers. To address the problem of missing MRI data in studies of AD, we introduced a novel 3D diffusion model specifically designed for imputing missing structural MRI (Recovery of Missing Neuroimaging using Diffusion models (ReMiND)). The model generates a whole-brain image conditional on a single structural MRI observed at a past visit or conditional on one past and one future observed structural MRI relative to the missing observation. The performance of models was compared with two alternative imputation approaches: forward filling and image generation using variational autoencoders. Experimental results show that our method can generate 3D structural MRI with high similarity to ground-truth images at designated visits. Furthermore, images generated using ReMiND show relatively lower differences in volume estimation between the imputed and observed images compared to images generated by forward filling or autoencoders. Additionally, ReMiND provides more accurate estimated rates of atrophy over time in important anatomical brain regions than the two comparator methods. Our 3D diffusion model can impute missing structural MRI data at a single designated visit and outperforms alternative methods for imputing whole-brain images that are missing from longitudinal trajectories.

Surviving academia: Narratives on identity work and intersectionality

AbstractThis paper examines the lived experiences of four early career researchers (ECRs), from various intersecting backgrounds, who pursued their degrees at a prestigious UK university. Bonded through solidarity and care, the authors came together in an online writing group (OWG), which serves as a means of liberation from structural, colonial, and patriarchal oppression. Built upon autoethnographic vignettes, this paper reveals fragile narratives to capture subjective experiences of neglect through the lens of intersectionality and Goffmanian “passing” and “revealing”. Motivated to explore the coping mechanisms of marginalized ECRs, this paper's co‐constructed data collection and co‐generative analysis unpack how a meso‐organization (here, the OWG) allows foreign identities to transcend the constraints of the expected academic professional identity; thus freeing authors from stigma. This paper calls for help and advocates for the urgency of support groups for ECRs as they face issues around finances, political conflicts, and family from pre‐ to post‐COVID‐19 pandemic. Additionally, the research challenges internalized norms of academic productivity and responsibilization and calls for a re‐examination of systemic inequalities. By advocating for collaborative autoethnography and embodied personal storytelling, this study contributes to a richer, more inclusive exploration of intersectionality in academia and emphasizes the need for structural reforms to create more equitable academic environments.

Navigating ethical and legal challenges in the HEALthy Brain and Child Development Study: Lessons learned from the ethics, law, policy working group

The HEALthy Brain and Child Development (HBCD) Study, a multi-site prospective longitudinal cohort study, will examine human brain, cognitive, behavioral, social, and emotional development beginning prenatally and planned through early childhood. The HBCD study has faced several ethical and legal challenges due to its goal of enrolling pregnant people (including those with substance use disorder) and their newborns. Challenges not fully anticipated at the outset emerged from the rapidly changing legal landscape around reproductive rights in the United States. By embedding scholars in bioethics and law within research teams and engaging them in conversation with each other and other study personnel, we were able to address many challenges proactively and respond promptly to unanticipated challenges. In this paper, we highlight several important ethical and legal challenges that arose from the first phase of funding through the beginning of participant enrollment. We explain the methods used to address these challenges, the ethical and legal tradeoffs that arose, and the resolution of challenges through the design of the study. Based on this experience, we provide recommendations for research teams, sponsors, and reviewers to address legal risks and promote the ethical conduct of studies with pregnant people and caregivers. We highlight the importance of collaboration with bioethics and legal scholars in studies involving complex and evolving legal risks, as well as the necessity of designing robust approaches to informed consent and maintaining participant trust while navigating ethical challenges in research.

Unlocking the Impact of Phyto-bioactive Compounds against Neurodegenerative Disorders

Neurodegenerative disorders (NDs) are one of the prominent worldwide issues recently. Neurodegenerative disorders, like amyotrophic lateral sclerosis, Parkinson's disease, Alzheimer's disease, and Huntington's disease, are characterized by ongoing neuronal failure and loss of structure and function, which ultimately results in neuronal death. There are many established synthetic approaches to target pathogenesis of NDs and to mitigate it but having several challenges researchers are now focusing the significant use of plant derived bio-active constituents against several NDs which ultimately leads safer and potent results. Among these phyto-compounds such as carotenoids, essential oils, essential fatty acids, polyphenols, have attracted great animus due to their strong antioxidant and anti-incendiary properties effective against NDs. Considering these green compounds having significant role to manage various NDs as potent therapeutic approach, we reviewed the sources, application, safety and clinical aspects of phyto-constituents against NDs.

LÍNGUA BRASILEIRA DE SINAIS: BREVE HISTÓRICO DOS ASPECTOS EDUCACIONAIS DOS SURDOS

This article aims to present a compendium of the evolutionary history of Brazilian Sign Language (Libras) and its reflections on the deaf community, particularly concerning education for all. Schooling must be ensured for everyone, and therefore, addressing any disparities caused by specific needs is crucial. This dynamism reflects the shared responsibility of public and private agencies within legislative and educational contexts to provide specific training related to the language and prepare the environment for promoting teaching and learning. The method used involved exploratory bibliographic research in scientific articles and books, with filters for the central theme of this work. The results reveal a concerning state of involvement and interaction of public policies and effective actions for the integration and inclusion of deaf individuals in social and educational contexts, highlighting the challenge for more in-depth research on the topic to promote solutions and address the social disparities faced by these individuals.

Bottom-Up Formation of III-Nitride Nanowires: Past, Present, and Future for Photonic Devices.

The realization of semiconductor heterostructures marks a significant advancement beyond silicon technology, driving progress in high-performance optoelectronics and photonics, including high-brightness light emitters, optical communication,and quantum technologies. In less than a decade since 1997, nanowires research has expanded into new application-driven areas,highlighting a significant shift toward more challenging and exploratory research avenues. It is therefore essential to reflecton the past motivations for nanowires development, and explorethe new opportunities it can enable. The advancement of heterogeneous integration using dissimilar substrates, materials, and nanowires-semiconductor/electrolyte operating platforms is ushering in new research frontiers, including the development of perovskite-embedded solar cells, photoelectrochemical (PEC) analog and digital photonic systems, such as PEC-basedphotodetectors and logic circuits, as well as quantum elements, such assingle-photon emitters and detectors. This review offersrejuvenating perspectives on the progress of these group-III nitride nanowires,aiming to highlight the continuity of research toward high impact, use-inspired researchdirections in photonics and optoelectronics.

Design of ultra-broadband long-wave to ultra-long-wave infrared absorber based on machine learning

Abstract long-wave infrared (LWIR) and ultra-long-wave infrared (ULWIR) radiation find extensive utility in atmospheric monitoring, night reconnaissance, deep space exploration, and various other fields. However, achieving ultra-wideband absorption within these spectral ranges has persistently posed a significant challenge for researchers. Metasurfaces have attracted great interest due to their ability to manipulate electromagnetic (EM) waves with unprecedented precision and efficiency. In this paper, a metasurface absorber with a simple structure is proposed. Combined with machine learning (ML) of Random Forest algorithm for design optimization, the absorber achieves ultra-wideband perfect absorption of 42.54 μm within the LWIR-ULWIR band. Furthermore, the stability and accuracy of the Random Forest algorithm in absorber design are also evaluated and compared with other classical ML algorithms. The successful realization of this work can offer the advancement of thermal imaging, sensing, and communication systems operating within the LWIR and ULWIR spectra.

Anti-Rain Clutter Interference Method for Millimeter-Wave Radar Based on Convolutional Neural Network

Millimeter-wave radars are widely used in various environments due to their excellent detection capabilities. However, the detection performance in severe weather environments is still an important research challenge. In this paper, the propagation characteristics of millimeter-wave radar in a rainfall environment are thoroughly investigated, and the modeling of the millimeter-wave radar echo signal in a rainfall environment is completed. The effect of rainfall on radar detection performance is verified through experiments, and an anti-rain clutter interference method based on a convolutional neural network is proposed. The method combines image recognition and classification techniques to effectively distinguish target signals from rain clutter in radar echo signals based on feature differences. In addition, this paper compares the recognition results of the proposed method with VGGnet and Resnet. The experimental results show that the proposed convolutional neural network method significantly improves the target detection capability of the radar system in a rainfall environment, verifying the method’s effectiveness and accuracy. This study provides a new solution for the application of millimeter-wave radar in severe weather conditions.

Reflecting on a Decade of Evolution: MapReduce‐Based Advances in Partitioning‐Based, Hierarchical‐Based, and Density‐Based Clustering (2013–2023)

ABSTRACTThe traditional clustering algorithms are not appropriate for large real‐world datasets or big data, which is attributable to computational expensiveness and scalability issues. As a solution, the last decade's research headed towards distributed clustering using the MapReduce framework. This study conducts a bibliometric review to assess, establish, and measure the patterns and trends of the MapReduce‐based partitioning, hierarchical, and density clustering algorithms over the past decade (2013–2023). A digital text‐mining‐based comprehensive search technique with multiple field‐specific keywords, inclusion measures, and exclusion criteria is employed to obtain the research landscape from the Scopus database. The Scopus‐obtained data is analyzed using the VOSViewer software tool and coded using the R statistical analysis tool. The analysis identifies the numbers of scholarly articles, diversities of article sources, their impact and growth patterns, details of most influential authors and co‐authors, most cited articles, most contributing affiliations and countries, and their collaborations, use of different keywords and their impact, and so forth. The study further explores the articles and reports the methodologies employed for designing MapReduce‐based counterparts of traditional partitioning, hierarchical, and density clustering algorithms and their optimizations and hybridizations. Finally, the study lists the main research challenges encountered in the past decade for MapReduce‐based partitioning, hierarchical, and density clustering. It suggests possible areas for future research to contribute further in this field.

Botnets Unveiled: A Comprehensive Survey on Evolving Threats and Defense Strategies

ABSTRACTBotnets have emerged as a significant internet security threat, comprising networks of compromised computers under the control of command and control (C&C) servers. These malevolent entities enable a range of malicious activities, from denial of service (DoS) attacks to spam distribution and phishing. Each bot operates as a malicious binary code on vulnerable hosts, granting remote control to attackers who can harness the combined processing power of these compromised hosts for synchronized, highly destructive attacks while maintaining anonymity. This survey explores botnets and their evolution, covering aspects such as their life cycles, C&C models, botnet communication protocols, detection methods, the unique environments botnets operate in, and strategies to evade detection tools. It analyzes research challenges and future directions related to botnets, with a particular focus on evasion and detection techniques, including methods like encryption and the use of covert channels for detection and the reinforcement of botnets. By reviewing existing research, the survey provides a comprehensive overview of botnets, from their origins to their evolving tactics, and evaluates how botnets evade detection and how to counteract their activities. Its primary goal is to inform the research community about the changing landscape of botnets and the challenges in combating these threats, offering guidance on addressing security concerns effectively through the highlighting of evasion and detection methods. The survey concludes by presenting future research directions, including using encryption and covert channels for detection and strategies to strengthen botnets. This aims to guide researchers in developing more robust security measures to combat botnets effectively.

Fog Computing: A Systematic Review of Architecture, IoT Integration, Algorithms and Research Challenges with Insights into Cloud Computing Integration

With the continuous evolution of technology, the prevalence of the Internet of Things (IoT) has grown significantly. However, with this rise comesa number of challenges for integrated Cloud Computing (CC), including security, performance, latency, and network issues. Fortunately, Fog Computing is a solution that addresses these concerns by bringing CC closer to IoT devices. Essentially, fog serves as a data hub that processes and stores information locally on the fog node, rather than sending it to a cloud server. This results in faster response times and higher- quality services compared to those offered by traditional cloud servers. Fog Computing can optimize service delivery for multiple IoT clients by allowing the administrationof services and resource provisioning outside of CC, nearer to devices, or at specific locations for Service Level Agreements (SLAs). It's important to note that Fog Computing is not intended to replace CC, but rather to complement itas a critical component. In this paper, we explore various computing paradigms, examine the features and architecture of Fog Computing, analyze its relationship with IoT, and assess differentalgorithms used in Fog Computing systems. Furthermore, we tackle the distinctive challenges that emerge with Fog Computing as an intermediate layer between IoT sensors/devices and data centers. Keywords: Fog Computing, Internet of Things(IOT), Cloud Computing (CC).

Quantifying mobile robot localization safety for an EKF-based SLAM estimator: An integrity monitoring approach

The safety of SLAM-based estimation for mobile robots is a challenging research problem, particularly for life- or mission-critical exploratory applications. To address this problem, this work proposes a method to utilize integrity risk, a widely used performance metric in aviation, to quantify SLAM-based mobile robot’s localization safety. More importantly, the approach accounts for sensor measurement faults, unknown deterministic errors that cannot be modeled via Gaussian white noise. The method is tailored for an EKF-based SLAM estimator, a chi-squared failure detector, and a local nearest neighbors data association criterion. The results show that data association errors can cause significant positioning performance degradation that can only be predicted using the proposed integrity risk metric. Furthermore, the study demonstrates that as the map’s landmark density increases, mobile robot localization safety improves, except when the landmark map’s density is too high to make the features indistinguishable, which leads to positioning safety degradation.

Multimodal Seed Data Augmentation for Low-Resource Audio Latin Cuengh Language

Latin Cuengh is a low-resource dialect that is prevalent in select ethnic minority regions in China. This language presents unique challenges for intelligent research and preservation efforts, primarily due to its oral tradition and the limited availability of textual resources. Prior research has sought to bolster intelligent processing capabilities with regard to Latin Cuengh through data augmentation techniques leveraging scarce textual data, with modest success. In this study, we introduce an innovative multimodal seed data augmentation model designed to significantly enhance the intelligent recognition and comprehension of this dialect. After supplementing the pre-trained model with extensive speech data, we fine-tune its performance with a modest corpus of multilingual textual seed data, employing both Latin Cuengh and Chinese texts as bilingual seed data to enrich its multilingual properties. We then refine its parameters through a variety of downstream tasks. The proposed model achieves a commendable performance across both multi-classification and binary classification tasks, with its average accuracy and F1 measure increasing by more than 3%. Moreover, the model’s training efficiency is substantially ameliorated through strategic seed data augmentation. Our research provides insights into the informatization of low-resource languages and contributes to their dissemination and preservation.

Older people in the world of esport: a qualitative study

IntroductionThe inclusion of older players in the gaming world is growing rapidly, leading to the emergence of specific categories for them at major esport events. The aim of this study is to analyze the psychological issues faced by older people when engaging with the competitive context of esport.Materials and methodsThe study was conducted in 2021 within the French association Silver Geek, which offers digital workshops using Nintendo Wii consoles to older people living in retirement homes and organizes annual amateur competitions between teams of older adults. This qualitative study involved 16 participants (eight women, eight men) aged 63 to 97 years. Semi-structured interviews were used to explore the motivations, emotional well-being, and social impact of esport on older people.ResultsThe results show that older people who participate in esport are motivated by the discovery and mastery of new game skills, as well as the pleasure of performance through social comparison. All experience a psychological well-being that translates into intense positive emotions. Moreover, the esport context, whether during the pre-competition workshops or the competitions themselves, proved to be a strong vector for positive social relationships, especially with the young e-sports coaches. However, this activity had little impact on existing family relationships.ConclusionThis study highlights the potential of esport as a recreational activity to promote mental health and social integration in older adults. The challenge for future research is to identify the levers that will enable this activity to become a true medium for intergenerational communication.

Cannabis and Cannabinoid Signaling: Research Gaps and Opportunities.

Cannabis and its products have been used for centuries for both medicinal and recreational purposes. The recent widespread legalization of cannabis has vastly expanded its use in the United States across all demographics except for adolescents. Meanwhile, decades of research have advanced our knowledge of cannabis pharmacology and particularly of the endocannabinoid system with which the components of cannabis interact. This research has revealed multiple targets and approaches for manipulating the system for therapeutic use and to ameliorate cannabis toxicity or cannabis use disorder. Research has also led to new questions that underscore the potential risks of its widespread use, particularly the enduring consequences of exposure during critical windows of brain development or for consumption of large daily doses of cannabis with high content Δ 9-tetrahydrocannabinol. This article highlights current neuroscience research on cannabis that has shed light on therapeutic opportunities and potential adverse consequences of misuse and points to gaps in knowledge that can guide future research. SIGNIFICANCE STATEMENT: Cannabis use has escalated with its increased availability. Here, the authors highlight the challenges of cannabis research and the gaps in our knowledge of cannabis pharmacology and of the endocannabinoid system that it targets. Future research that addresses these gaps is needed so that the endocannabinoid system can be leveraged for safe and effective use.