Current Research Research Articles

Narrative Testimony and Characteristics of Rottlerin for the Treatment of Various Kinds of Cancer

Abstract: From ancient times until now, scientists have focused on herbal medicaments for treating various diseases. Rottlerin, a potent PKCδ inhibitor, is one of the greatest herbal medications. Over the years, it was identified that rottlerin has several cellular and molecular targets that could be involved in the fight against cancer. The past and present research has clearly shown that rottlerin inhibits the transcription factors, enzymes, and signaling molecules that regulate the death and life of cancer cells. Although the anticancer property of rottlerin has primarily been attributed to the induction of autophagy and apoptosis, current research has revealed the existence of different damage pathways. The major part described in this mini-review is the antitumor/anticancer effects of rottlerin in various organs of the human body affected by breast cancer, pancreatic cell cancer, bladder cancer, NSCLC, prostate cancer, ovarian cancer, nasopharyngeal cancer, etc.

Toward a Framework for Trust Building between Humans and Robots in the Construction Industry: A Systematic Review of Current Research and Future Directions

Toward a Framework for Trust Building between Humans and Robots in the Construction Industry: A Systematic Review of Current Research and Future Directions

Art Teaching: Inclusive Factor and Attention to Diversity

<p style="text-align: justify;">The current research aims to analyze art teaching as an inclusive factor and pays attention to diversity in excluded populations to meet the needs of students in the pandemic context. In regard to methodology, we used the naturalistic paradigm, a qualitative approach, and an inductive method with a phenomenological and hermeneutical design. The data collection techniques we used were semi-structured interviews with teachers of the Regular Basic Education in Peru. The findings show that through the mediation of diverse artistic languages and the implementation of different didactic strategies, it is possible to achieve an interaction free from exclusion barriers, driven by good teaching practices that enable the inclusion and attention to the diversity of students, providing them with opportunities to develop capacities and skills to establish better coexistence. We concluded that art education comprises a lasting learning process that guarantees a healthy environment for peaceful coexistence, which today, inclusive teachers, parents, and students should keep in mind.</p>

A Survey of Algorithmic Methods for Competency Self-Assessments in Human-Autonomy Teaming

Humans working with autonomous artificially intelligent systems may not be experts in the inner workings of their machine teammates, but need to understand when to employ, trust, and rely on the system. A critical challenge is to develop machine agents with the capacity to understand their own capabilities and limitations, and the ability to communicate this information to human partners. Self-assessment is an emerging field that tackles this challenge through the development of algorithms that enable autonomous agents to understand and communicate their competency. These methods can engender appropriate trust and align human expectations with autonomous assistant abilities. However, current research in self-assessment is dispersed across many fields, including artificial intelligence, robotics, and human factors. This survey connects work from these disparate areas and reviews state-of-the-art methods for algorithmic self-assessments that enable autonomous agents to estimate, understand, and communicate valuable information pertaining to their competency, with focus on methods that can improve interactions within human-machine teams. To better understand the landscape of self-assessment approaches, we present a framework for categorizing work in self-assessment based on underlying algorithm type: test-based , learning-based , or knowledge-based . We synthesize common features across these approaches and discuss relevant future directions for research in this emerging space.

A comprehensive review on different types of fuel cell and its applications

This review article provides an overview of various types of fuel cells that are currently being researched and developed. Fuel cells are electrochemical devices that convert chemical energy directly into electrical energy, making them a promising technology for clean and efficient energy production. The review covers the principles of operation and key characteristics of proton exchange membrane fuel cells (PEMFCs), solid oxide fuel cells (SOFCs), alkaline fuel cells (AFCs), direct methanol fuel cells (DMFCs), and microbial fuel cells (MFCs). The article also discusses the advantages and limitations of each type of fuel cell, as well as the current research and development efforts aimed at improving their performance and reducing their costs. Overall, this review provides a comprehensive understanding of the various types of fuel cells and their potential applications in the field of energy production.

Numerical simulation and experimental investigation on Phase Change Materials based energy storage system for cooling the water in process industries towards water conservation and environmental sustainability

Water conservation is one of the significant concerns in the industrial sector in cooling processes. Water is lost predominantly in the cooling towers by evaporation, drift, and blowdown. A new Phase Change Material (PCM) based cooling system is proposed as a replacement for cooling towers to eliminate water losses completely. In the current research, the temperature drop of water known as range in the cooling tower is investigated in the proposed system by varying the process parameters such as water velocity, tube materials and tube lenght. In the present study, OM50, an organic PCM with a melting temperature of 500 C is chosen for experimental and numerical analysis as the normal operating temperature of water in the cooling towers is in this range. Based on the experiment, it is observed that the temperature range of water almost remains constant for the melt fraction values of PCM from about 0.1 to 0.8 and taking about 35 % of the total time required for completely melting the PCM. However the cooling range steeply decreases for the melt fraction variation from 0.8 to 1. Hence the designing of PCM based cooling system will be effective for the melting of the PCM from 0.1 to 0.8 leading to constant cooling range of temperature. The experimental results obtained are also validated by CFD simulation using ANSYS 18.1 and the results obtained are in close agreement with each other. The range obtained in the present research is almost in the same range of cooling tower used in the chiller plant presented in the case study. The proposed system may be able to replace cooling towers towards conservation of water and environmental sustainability.

The perspectives of visual arts teachers on postmodernism

<p>Postmodernism has evolved in the field of art, and its presence and definition have been the topic of several studies. The overall perspectives of the visual arts teachers on postmodernism are important for incorporating postmodernism issues into the high school curriculum in Turkey, as they are teaching art history and conveying the practice of visual arts. A review of the literature reveals that there are no comparable studies that take visual arts teachers’ perspectives on postmodernism into account. Therefore, this study aims to discover the perspectives of visual arts teachers on postmodernism. This article presents the findings and conclusions of the first research question of the Master’s thesis, titled “examination of the reflections of postmodernism on art education”. The study used a case study design, one of the qualitative research approaches. The sample of the study consisted of four visual arts teachers working in one of the central and three district schools in Yalova, Turkey. In the current research, two semi-structured interview forms and document analysis were utilized as data collection tools. The qualitative data were analyzed by the thematic analysis method. The themes of “teachers’ general perceptions”, “teaching process” and “reflection on students” were identified through the thematic analysis. This article includes a detailed analysis of the theme of “teachers’ general perceptions”. The results of the research identified the artistic and intellectual perspectives of the participating teachers toward postmodernism as outstanding features.</p>

Performance analysis of photovoltaic panel using machine learning method

Demand for energy is increasing as the world’s population grows, fossil fuels deplete on a daily basis, and climate conditions change. Renewable energy is more important than ever. Solar energy is the most accessible and cost-effective renewable energy source available today. Photovoltaic (PV) cells are the most promising way to convert solar energy into electricity. Wind speed, ambient temperature, incident radiation rate, and dust deposition are some of the internal and external variables that affect photovoltaic panel performance. Unwanted heat from the sun’s rays raises panel temperatures, reduces the amount of energy that solar cells can produce, and lowers conversion efficiency. Solar panels must be adequately cooled. The current research is focused on improving photovoltaic panel performance. The experimental system includes a fully automated photovoltaic panel, a microcontroller (NodeMCU8266), a DC pump, voltage and temperature sensors. The experiment was carried out with and without cooling of the PV panel. The findings suggest that keeping PV panel temperatures close to ambient temperatures improves performance. The Wi-Fi module collects real-time data on PV panel temperature, irradiation, ambient temperature, water temperature, and PV panel power output. The collected data was analyzed using machine learning. The PV panel’s performance was analyzed using the linear regression method.

Preparation of high-performance materials based on carbon nanofibers and analysis of mechanical properties at room and high temperatures

The shortcomings of conventional building materials, such as inadequate tensile strength and fire resistance, have been significantly exacerbated with the emergence of complex and diverse engineering practices. However, the current research on high-performance building materials is still in its nascent stage. Therefore, our study aims to investigate the potential improvements in the mechanical properties of building materials through the incorporation of carbon fiber and nano silica. Specifically, we focus on assessing the mechanical properties of these modified materials at both room and elevated temperatures. Our experimental results demonstrate a notable enhancement in compressive strength. At room temperature, the H2 group exhibited a 15.1% increase in compressive strength, while the H5 group experienced a 7.3% increase. Notably, the compressive strength of the H7 group reached its highest value at 47.13 MPa. Additionally, the H2 group displayed the highest bending strength at 5.18 MPa. Our electron scanning microscopy analysis revealed the uniform dispersion of carbon fibers within the building materials, without any clumping. This suggests that when the dosage of Carbon Fiber reaches 0.6% of the mass of building materials, it can be uniformly dispersed within the mixture. We have also evaluated the performance of the modified materials under high temperatures. It was observed that the prefabricated building materials group experienced a 65% reduction in compressive strength when subjected to 800 °C. Conversely, the carbon fiber building materials exhibited a compressive strength loss of over 62%. Overall, the carbon fiber building materials outperformed the standard building materials under high temperature conditions, with significantly higher compressive strength. Moreover, the modified materials demonstrated an improvement in the residual splitting strength, ranging from 26% to 37% when exposed to temperatures over 800 °C. These findings indicate a substantial enhancement in the mechanical properties of the proposed modified materials. In conclusion, the incorporation of carbon fiber and nano silica into the building materials resulted in substantial improvements in their mechanical properties. These modified materials hold great potential for practical construction projects, offering higher performance and enhanced durability.

Scientific Mapping of Chatbot Literature: A Bibliometric Analysis

The use of chatbots for customer service has gained momentum in recent years. Increasing evidence has shown that chatbots can transform the customer service landscape. Nevertheless, this topic currently lacks adequate bibliometric and visualization research. In order to review and summarise the research on chatbots, the study employs a bibliometric analysis approach to gain a comprehensive understanding of chatbots. The study uses bibliometric analysis of 798 documents sourced from the Scopus database from 2001 to 2022. The combination of biblioshiny (web interface application of Bibliometrix) and VOS viewer software was used to visualize the analysis. The study's findings reveal three prominent areas in the current research: antecedents of the adoption of chatbots, application of chatbots and behavioural & relational outcomes of the application of chatbots. The future directions and implications have been discussed in the study's conclusion.

Global Publication Trends and Research Hotspots of the Immune System and Osteoporosis: A Bibliometric and Visualization Analysis from 2012 to 2022.

Osteoporosis (OP) is a systemic bone metabolism disorder in which the immune system and bone metabolism interact. The purpose of this study was to explore the research status, hot spots and trends regarding the influence of the immune system on OP and to provide a basis for research directions and applications in this field. We searched and collected literature about the immune system and OP published from 2012 to 2022 in the Web of Science Core Collection database. All the included studies were subjected to bibliometrics analysis using Hiplot Pro, VOSviewer and CiteSpace software to produce statistics and visual analyses of the literature output, countries, institutions, authors, keywords and journals. A total of 1201 papers were included, and the number of citations of these articles reached 31,776. The number of publications and citations on the immune system and OP has increased year by year. The top three countries with the greatest number of papers published were China, the United States of America (USA) and Italy. The two institutions with the largest number of papers published were Sichuan University and Soochow University, both located in China. De Martinis Massimo (Italy) and Ginaldi Lia (Italy) are prolific authors in this field. The representative academic journals are Osteoporosis International, Frontiers in Immunology, Journal of Bone and Mineral Research, PloS One and Bone. The results of the keyword cooccurrence analysis showed that the research topics in this field mainly focused on T cells, cytokines, signaling pathways, vitamin D, postmenopausal OP and immune diseases. The keyword burst results showed that zoledronic acid, chain fatty acids and gut microbiota are the frontiers and trends of future research on this topic. The influence of the immune system on OP has been widely studied, and the current research in this field focuses on the effect or mechanism of immune-related cytokines, signaling pathways and vitamin D on OP. Future research trends in this field should focus on the immune regulation mechanism and clinical transformation of zoledronic acid, chain fatty acids and the gut microbiota in OP.

Wikstroemia: A Review on its Phytochemistry and Pharmacology.

Wikstroemia (the family Thymelaeaceae) consists of medicinal plants which established great value in traditional medicines for many years. For instance, W. indica is always recommended for treatments of syphilis, arthritis, whooping cough, and cancer. No systematic review of bioactive compounds from this genus has been recorded to date. The objective of the current study is to review phytochemical investigations and pharmacological effects of Wikstroemia plant extracts and isolates. By searching on the internet, the relevant data about Wikstroemia medicinal plants were retrieved from internationally renowned scientific databases, such as Web of Science, Google Scholar, Sci-Finder, Pubmed, and so on. More than 290 structurally diverse metabolites were separated and identified from this genus. They include terpenoids, lignans, flavonoids, coumarins, mono-phenols, diarylpentanoids, fatty acids, phytosterols, anthraquinones, and others. Pharmacological records indicated that Wikstroemia plant crude extracts and their isolated compounds bring out various beneficial effects, such as anticancer, antiinflammatory, anti-aging, anti-viral, antimicrobacterial, antimalarial, neuroprotective, and hepatoprotective activities Conclusion: Wikstroemia has been regarded as a worthy genus with numerous phytochemicals and various pharmacological potentials. Modern pharmacological studies have successfully provided evidence for traditional uses. Nonetheless, their action mechanisms need to be further investigated. Although various secondary metabolites were identified from Wikstroemia plants, the current pharmacological research mainly concentrated on terpenoids, lignans, flavonoids, and coumarins.

Image enhancement in palmprint recognition: a novel approach for improved biometric authentication

Several researchers have used image enhancement methods to reduce detection errors and increase verification accuracy in palmprint identification. Divergent opinions exist among experts regarding the best method of image filtering to improve image palmprint recognition. Because of the unique characteristics of palmprints and the difficulties in preventing counterfeiting, image-filtering techniques are the subject of this current research. Researchers hope to create the best biometric system possible by utilizing various techniques. These techniques include image enhancement, Gabor orientation scales, dimension reduction techniques, and appropriate matching strategies. This study investigates how different filtering approaches might be combined to improve images. The palmprint identification system uses a 3W filter, which combines wavelet, Wiener, and weighted filters. Optimizing results entails coordinating the 3W filter with Gabor orientation scales, matching processes, and dimension reduction methods. The research shows that accuracy may be considerably increased using a 3W filter with a Gabor orientation scale of [8×7], the kernel principal component analysis (KPCA) dimension reduction methodology, and a cosine matching method. Specifically, a value of 99.722% can be achieved. These results highlight the importance of selecting appropriate settings and techniques for palmprint recognition systems.

Alzheimer's Disease and Cancer: Common Targets.

There is growing epidemiologic evidence of an inverse association between cancer and AD. In addition, both cell survival and death are regulated by the same signaling pathways, and their abnormal regulation may be implicated in the occurrence and development of cancer and AD. Research shows that there may be a common molecular mechanism between cancer and AD. This review will discuss the role of GSK3, DAPK1, PP2A, P53 and CB2R in the pathogenesis of cancer and AD and describe the current research status of drug development based on these targets.

Climate Change and Stroke: A Topical Narrative Review.

The impacts of accumulating atmospheric greenhouse gases on the earth's climate are now well established. As a result, there have been increases in ambient temperatures and resultant higher frequency and duration of temperature extremes and other extreme weather events, which have been linked to a wide range of adverse health outcomes. This topical narrative review provides a summary of published evidence on the links between climate change and stroke. There is consistent evidence of associations between stroke incidence and mortality and increasing ambient temperature and air pollution. Associations have also been shown for changes in barometric pressure, wildfires, and desert dust and sandstorms, but current evidence is limited. Flooding and other extreme weather events appear to primarily cause service disruption, but more direct links to stroke may emerge. Synergies between dietary changes that reduce stroke risk and may also reduce carbon footprint are being explored. We also discuss the impact on vulnerable populations, proposed pathophysiologic mechanisms, mitigation strategies, and current research priorities. In conclusion, climate change increasingly impacts the stroke community, warranting elevated attention.

UniRecSys: A unified framework for personalized, group, package, and package-to-group recommendations

Recommender systems aim to enhance the overall user experience by providing tailored recommendations for a variety of products and services. These systems help users make more informed decisions, leading to greater user engagement with the platform. However, the implementation of these systems largely depends on the context, which can vary from recommending an item or package to a user or a group. This requires careful exploration of several models during the deployment, as there is no comprehensive and unified approach that deals with recommendations at different levels. Furthermore, these individual models must be closely attuned to their generated recommendations depending on the context to prevent significant variation in their generated recommendations. In this paper, we propose a novel unified recommendation framework that addresses all four recommendation tasks, namely, personalized, group, package, and package-to-group recommendation, filling the gap in the current research landscape. The proposed framework can be integrated with most of the traditional matrix factorization-based collaborative filtering (CF) models. This research underscores the significance of including group and package information while learning latent representations of users and items for personalized recommendations. These components help in exploiting a rich latent representation of the user/item by enforcing them to align closely with their corresponding group/package representation. We consider two prominent CF techniques, namely Regularized Matrix Factorization and Maximum Margin Matrix factorization, as the baseline models and demonstrate their customization to various recommendation tasks. Experimental results on two publicly available datasets are reported, comparing them to other baseline approaches for various recommendation tasks.

Bibliometric insights into data mining in education research: A decade in review

This bibliometric study on data mining in education synonymous with big educational data utilizes VOSviewer and Harzing’s Publish and Perish to analyze the metadata of 1,439 journal articles found in Scopus from 2010 to 2022. As bibliometric analyses in this field are lacking, this study aims to provide a comprehensive outlook on the current developments and impact of research in this field. This study employs descriptive and trends analysis, co-authorship analysis, co-citation analysis, co-occurrences of keywords, terms map analysis, and analysis of the impact and performance of publications. It also partially replicates a similar study conducted by Wang et al. (2022), who used the Web of Science (WoS) database. The study is reported in an article entitled ‘Big data and data mining in education: A bibliometrics study from 2010 to 2022’. Results show that data mining in education is a growing research field. There is also a significant difference between the publications in Scopus and WoS. The study found several research areas and topics, such as student academic performance prediction, e-learning, machine learning, and innovative data mining techniques, to be the core basis for collaborating and continuing current research in this field. These results highlight the importance of continuing research on data mining in education, guiding future research in tackling educational challenges.

Research on mathematical beliefs: systematic literature review

This study uses the systematic literature review (SLR) method to identify knowledge trends in beliefs about the nature of mathematics, learning mathematics and identify new development opportunities for further research. The Scopus database was used, and 41 documents were found as the primary source for further analysis. The results show that mathematical beliefs have become a current research trend. The number of publications increased significantly starting in 1989, accompanied by many citations. Based on these primary documents, it is known that the development of mathematical beliefs research topics. The foundation of this topic begins with studying the topic of knowledge, activities, and learning mathematics. Then it is developed into other, more varied studies, including teacher’s beliefs transition; mathematical beliefs comparison; mathematics teacher’s belief, teaching, and learning; mathematical beliefs and problem-solving; inquiry-based learning (IBL); affective factors; technology utilization; common misconceptions; and development of mathematics teacher’s beliefs instrument. This research investigates the core of scientific work to provide information to researchers and institutions as material for consideration of research to be carried out.

A Data-driven Approach for Planning Stock Keeping Unit (SKU) in a Steel Supply Chain

In response to the growing complexities in supply chain management, there is an imperative need for a data-driven methodology aimed at optimizing inventory allocation strategies. The purpose of this research is to enhance the efficiency of allocation and operational scheduling, particularly concerning the stock keeping units (SKUs). To achieve this, one year's operational data from a specific organization's SKUs is taken and machine learning tools are employed on the data collected. These tools are instrumental in identifying clusters of SKUs that exhibit similar behaviour. Consequently, this research offers recommendations for rational inventory allocation strategies that are finely attuned to the unique characteristics of each SKU cluster. Results obtained reveals substantial disparities between the recommended strategies for the organization's SKUs and those typically found in the literature such as same strategy cannot be used for all different types for products. This underscores the critical importance of adopting a tailored approach to supply chain management. Furthermore, the research demonstrates the remarkable efficiency of unsupervised machine learning algorithms in determining the optimal number of segments within the SKUs. The current research differentiates from others in a way that in most of the research, the holistic data-driven approach is underutilized, right from the selection of the clustering algorithm to the validation of segments.

Role of TBK1 Inhibition in Targeted Therapy of Cancer.

TANK-binding kinase 1 (TBK1) is a serine/threonine protein that plays a crucial role in various biological processes like immunity, autophagy, cell survival, and proliferation. The level and kinase activity of the TBK1 protein is regulated through post-translational modifications (PTMs). TBK1 mainly mediates the activation of IRF3/7 and NF-κB signaling pathways while also participating in the regulation of cellular activities such as autophagy, mitochondrial metabolism, and cell proliferation. TBK1 regulates immune, metabolic, inflammatory, and tumor occurrence and development within the body through these cellular activities. TBK1 kinase has emerged as a promising therapeutic target for tumor immunity. However, its molecular mechanism of action remains largely unknown. The identification of selective TBK1 small molecule inhibitors can serve as valuable tools for investigating the biological function of TBK1 protein and also as potential drug candidates for tumor immunotherapy. The current research progress indicates that some TBK1 inhibitors (compounds 15,16 and 21) exhibit certain antitumor effects in vitro culture systems. Here, we summarize the mechanism of action of TBK1 in tumors in recent years and the progress of small molecule inhibitors of TBK1.