Current Development Research Articles

Recent Advances in Therapeutic Approaches Against Ebola Virus Infection.

Ebola virus (EBOV) is a genus of negative-strand RNA viruses belonging to the family Filoviradae that was first described in 1976 in the present-day Democratic Republic of the Congo. It has intermittently affected substantial human populations in West Africa and presents itself as a global health menace due to the high mortality rate of patients, high transmission rate, difficult patient management, and the emergence of complicated autoimmune disease-like conditions post-infection. EBOV or other EBOV-like species as a biochemical weapon pose a significant risk; hence, the need to develop both prophylactic and therapeutic medications to combat the virus is unquestionable. In this review work, we have compiled the literature pertaining to transmission, pathogenesis, immune response, and diagnosis of EBOV infection. We included detailed structural details of EBOV along with all the available therapeutics against EBOV disease. We have also highlighted current developments and recent advances in therapeutic approaches against Ebola virus disease (EVD). The development of preventive vaccines against the virus is proving to be a successful effort as of now; however, problems concerning logistics, product stability, multi- dosing, and patient tracking are prominent in West Africa. Monoclonal antibodies that target EBOV proteins have also been developed and approved in the clinic; however, no small drug molecules that target these viral proteins have cleared clinical trials. An understanding of clinically approved vaccines and their shortcomings also serves an important purpose for researchers in vaccine design in choosing the right vector, antigen, and particular physicochemical properties that are critical for the vaccine's success against the virus across the world. Our work brings together a comprehensive review of all available prophylactic and therapeutic medications developed and under development against the EBOV, which will serve as a guide for researchers in pursuing the most promising drug discovery strategies against the EBOV and also explore novel mechanisms of fighting against EBOV infection.

Medicinal (Radio)Chemistry: Building Radiopharmaceuticals for the Future.

Radiopharmaceuticals are increasingly playing a leading role in diagnosing, monitoring, and treating disease. In comparison with conventional pharmaceuticals, the development of radiopharmaceuticals does follow the principles of medicinal chemistry in the context of imaging-altered physiological processes. The design of a novel radiopharmaceutical has several steps similar to conventional drug discovery and some particularity. In the present work, we revisited the insights of medicinal chemistry in the current radiopharmaceutical development giving examples in oncology, neurology, and cardiology. In this regard, we overviewed the literature on radiopharmaceutical development to study overexpressed targets such as prostate-specific membrane antigen and fibroblast activation protein in cancer; β-amyloid plaques and tau protein in brain disorders; and angiotensin II type 1 receptor in cardiac disease. The work addresses concepts in the field of radiopharmacy with a special focus on the potential use of radiopharmaceuticals for nuclear imaging and theranostics.

Overview of Path Planning Algorithms

Background: Path-planning algorithms are widely used in robotics, vehicles, UAVs, carrier-based aircraft towing vehicles, etc. Using these algorithms, an optimal path with safe obstacle avoidance and high efficiency can be planned. In recent years, path-planning algorithms have received more and more attention from scholars at home and abroad. Objective: In order to promote the application and development of path-planning algorithms, an in-depth analysis of the current development status of path-planning algorithms was presented. The most widely used and representative algorithms in several representative results were extracted, and the characteristics and advantages and disadvantages of each algorithm were analyzed and elaborated in detail for the readers' reference. Methods: The existing path-planning algorithms were classified, and a brief overview of each traditional algorithm was given, followed by an in-depth study of the improved algorithms to summarize the advantages and disadvantages of each type of algorithm. Finally, based on these research results, the future development trend of path-planning algorithms was projected. Results: Through the research and analysis of the path-planning algorithm, it was found that the path- planning algorithm before the improvement had the problems of an unsmooth path, low computational efficiency, a slow response time, the inability to safely avoid obstacles, etc. After the improvement and optimization of the path-planning algorithm, the performance was greatly improved. Conclusion: After research and analysis, it was found that multi-algorithm fusion had great potential for development in path-planning compared to the application and optimization of a single algorithm. In addition, further research is needed in the areas of algorithm adaptability, fusion, intelligent algorithms, and extending the range of algorithmic applications.

DONGLE 2.0: Direct FPGA-Orchestrated NVMe Storage for HLS

Rapid growth in data size poses significant computational and memory challenges to data processing. FPGA accelerators and near-storage processing have emerged as compelling solutions for tackling the growing computational and memory requirements. Many FPGA-based accelerators have shown to be effective in processing large data sets by leveraging the storage capability of either host-attached or FPGA-attached storage devices. However, the current HLS development environment does not allow direct access to host-or FPGA-attached NVMe storage from the HLS code. As such, users must frequently hand off between HLS and host code to access data in storage, and such a process requires tedious programming to ensure functional correctness. Moreover, since the HLS code uses radically different methods to access storage compared to DRAM, the HLS codebase targeting DRAM-based platforms cannot be easily ported to NVMe-based platforms, resulting in limited code portability and reusability. Furthermore, frequent suspension of HLS kernel and synchronization between CPU and FPGA introduce significant latency overhead and require sophisticated scheduling mechanisms to hide latency. To address these challenges, we propose a new HLS storage interface named DONGLE 2.0 that enables direct FPGA-orchestrated NVMe storage access. By providing a unified interface for storage and memory access, DONGLE 2.0 allows a single-source HLS program to target multiple memory/storage devices, thus making the codebase cleaner, portable, and more efficient. DONGLE 2.0 is an extension to DONGLE 1.0 [ 1 ] but adds support for host-attached storage. While its primary focus is still on FPGA NVMe access in near-storage configurations, the added host storage support ensures its compatibility with platforms that lack native support for FPGA-attached NVMe storage. We implemented a prototype of DONGLE 2.0 using an AMD/Xilinx Alveo U200 FPGA and Solidigm DC-P4610 SSD. Our evaluation on various workloads showed a geometric mean speed-up of 2.3× and a reduction in lines of code (LoC) by 2.4× compared to the state-of-the-art commercial platform when using FPGA-attached NVMe storage. Moreover, DONGLE 2.0 demonstrated a geometric mean speed-up of 1.5× and a reduction in LoC by 2.4× compared to the state-of-the-art commercial platform when using host-attached NVMe storage.

Numerical investigation of rip currents near a barred beach induced by irregular waves

Rip currents pose a common natural hazard at coastal tourist beaches, presenting a significant threat to the safety of beachgoers and beach management. To better understand the characteristics of rip currents, particularly those induced by irregular waves on a barred beach, this study utilized a Boussinesq-type, phase-resolving hydrodynamic model for numerical simulations. We investigated various factors, including significant wave height, peak period, incident angle, and bottom friction, to assess their impact on rip currents formation. The results of our research reveal some key insights. Firstly, an increase in significant wave height and peak period fosters the development of rip currents. However, beyond a certain threshold, the intensity of the rip currents decreases. Conversely, a larger incident angle tends to reduce the intensity of rip currents. Additionally, higher bottom friction leads to increased energy dissipation during wave propagation, consequently resulting in decreased rip currents intensity. In an effort to create a more precise representation of real sea conditions, multi-directional waves were employed as incident waves in this study. An analysis of different standard deviations of directional distributions revealed that as the directional spread of incident waves widens, the waves become more susceptible to breaking, ultimately leading to a reduction in the intensity of rip currents.

How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools.

In recent years, narrative visualization has gained much attention. Researchers have proposed different design spaces for various narrative visualization genres and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, increasingly more tools have been designed and developed. In this study, we summarized six genres of narrative visualization (annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos) based on previous research and four types of tools (design spaces, authoring tools, ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and automation level of the tools. We surveyed 105 papers and tools to study how automation can progressively engage in visualization design and narrative processes to help users easily create narrative visualizations. This research aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

Insight into Quantum Computing and Deep Learning Approach for Drug Design

: In recent years, substantial modelling breakthroughs have been achieved in artificial intelligence due to new algorithms, improved computer power, and expanded storage capacity. These factors have made it possible to process large amounts of data in a short amount of time. By using quantum computing in conjunction with deep learning models, it has been possible to explain the characteristics of ligands and their interactions with biological targets. This contributes to the process of ligand identification and ultimately results in the optimization of drug design. This review explains the extensive use of quantum deep learning in the development of drug design from traditional to quantum-powered deep learning neural networks that cover some domains like variational quantum Eigen solver, variational quantum circuits, quantum convolutional deep neural networks, QC-based deep neural networks for QSAR, as well as quantized generative models for the discovery of small drug molecules. Quantum computing can execute incredible computational work tenfold faster than current technology, transforming drug design, development, and post-marketing surveillance. This will reduce the time and resources needed to develop a medicine. Scientific research is moving toward quantum computing since it is anticipated that QC-based deep learning technologies can predict and mimic the characteristics, structures, and activities of molecules more efficiently than different ML techniques or conventional computers.

Treatment of MRSA Infection: Where are We?

Staphylococcus aureus is a leading cause of septicemia, endocarditis, pneumonia, skin and soft tissue infections, bone and joint infections, and hospital-acquired infections. In particular, methicillin-resistant Staphylococcus aureus (MRSA) is associated with high morbidity and mortality, and continues to be a major public health problem. The emergence of multidrug-resistant MRSA strains along with the wide consumption of antibiotics has made anti-MRSA treatment a huge challenge. Novel treatment strategies (e.g., novel antimicrobials and new administrations) against MRSA are urgently needed. In the past decade, pharmaceutical companies have invested more in the research and development (R&D) of new antimicrobials and strategies, spurred by favorable policies. All research articles were collected from authentic online databases, including Google Scholar, PubMed, Scopus, and Web of Science, by using different combinations of keywords, including 'anti-MRSA', 'antibiotic', 'antimicrobial', 'clinical trial', 'clinical phase', clinical studies', and 'pipeline'. The information extracted from articles was compared to information provided on the drug manufacturer's website and Clinical Trials.gov (https://clinicaltrials.gov/) to confirm the latest development phase of anti-MRSA agents. The present review focuses on the current development status of new anti-MRSA strategies concerning chemistry, pharmacological target(s), indications, route of administration, efficacy and safety, pharmacokinetics, and pharmacodynamics, and aims to discuss the challenges and opportunities in developing drugs for anti-MRSA infections.

Current Development of Data Resources and Bioinformatics Tools for Anticoronavirus Peptide.

Since December 2019, the emergence of severe acute respiratory syndrome coronavirus 2, which gave rise to coronavirus disease 2019 (COVID-19), has considerably impacted global health. The identification of effective anticoronavirus peptides (ACVPs) and the establishment of robust data storage methods are critical in the fight against COVID-19. Traditional wet-lab peptide discovery approaches are timeconsuming and labor-intensive. With advancements in computer technology and bioinformatics, machine learning has gained prominence in the extraction of functional peptides from extensive datasets. In this study, we comprehensively review data resources and predictors related to ACVPs published over the past two decades. In addition, we analyze the influence of various factors on model performance. We have reviewed nine ACVP-containing databases, which integrate detailed information on protein fragments effective against coronaviruses, providing crucial references for the development of antiviral drugs and vaccines. Additionally, we have assessed 15 peptide predictors for antiviral or specifically anticoronavirus activity. These predictors employ computational models to swiftly screen potential antiviral candidates, offering an efficient pathway for drug development. Our study provides conclusive results and insights into the performance of different computational methods, and sheds light on the future trajectory of bioinformatics tools for ACVPs. This work offers a representative overview of contributions to the field, with an emphasis on the crucial role of ACVPs in combating COVID-19.

The Future of Process Industry: A Cyber-Physical-Social System Perspective.

The process industry is an industrial field of interdisciplinary nature involving electrical engineering, energy, petroleum, chemical, and metallurgy, which play a key role in the sustainable development. As a main source of CO2 emissions, the process industry will undertake a large part of the emission reduction task. In order to incorporate the impact of social factors, such as environment, society, and human to support the future process industry, the cyber-physical-social system (CPSS) framework should be considered as a promising way to enhance the transformation of the process industry. The development of CPSS technologies will fundamentally change the infrastructure of conventional industrial systems, offering a great opportunity for the greenization, high-value, and digitalization in the process industry. This article first presents the current status of the process industry. Through a CPSS framework, the current developments of the process industry as well as the main challenges and opportunities are discussed. A vision for the future process industry based on CPSS is described by focusing on three aspects, namely, the greenization and low carbon, high-value and high-end, digitalization, and intellectualization in process manufacturing. Finally, the advanced technologies and approaches in CPSS driven by artificial intelligence and industrial digitalization, which are important in achieving the sustainable development of the process industry, are outlined. The development of the comprehensive digital technologies, such as virtual reality, digital twin, blockchain, and big data, will stimulate the implementation of a ground-breaking concept formed in the CPSS framework called industrial metaverse.

Flavonoid-metal ion Complexes as Potent Anticancer metallodrugs: A comprehensive Review.

Flavonoids and their analogous are mainly found in pink lady apples, green and black tea (catechins), celery and red peppers, onions, broccoli and spinach, berries, cherries, soybean, citrus fruits, and fungi. The different derivatives of flavonoids belonging to polyphenolic compounds such as 3,4',5,7-Tetrahydroxyflavylium (pelargonidin), 2-(3,4-Dihydroxyphenyl)chromenylium-3,5,7-triol (cyanidin), 3,3',4',5,5',7-Hexahydroxyflavylium (delphinidin), 3,3',4',5,7-Pentahydroxy-5'-methoxyflavylium (petunidin), and 3,4',5,7-Tetrahydroxy-3',5'-dimethoxyflavylium (malvidin) can act as good chelating agents for metal-chelate complex formation. These flavonoid-metal complexes have been reported to have various biomedical and pharmacological activities. Flavonoid-metal ion complexes display a broad spectrum of biological properties such as antioxidant, anti-inflammatory, anti-allergic, antiviral, anticarcinogenic, and cytotoxic activity. The literature survey showed that flavonoid metal complexes have potential therapeutic properties against various cancerous cells. The objective is to gain insight into the current perspective and development of novel anticancer metallodrug drugs. The flavonoid-metal ion complexes can be prepared by reacting flavonoid ligand with appropriate metal salt in aqueous or alcoholic reaction medium under stirring or refluxing conditions. In this review article, the various reported methods for the synthesis of flavonoid-metal complexes have been included. The utility of synthetic methods for flavonoid-metal complexes will support the discovery of novel therapeutic drugs. In this review study, short libraries of flavonoid-metal ion complexes were studied as potential anticancer agents against various human cancer cell lines. The review report reveals that metal ions such as Fe, Co, Ni, Cu, Zn, Rh, Ru, Ga, Ba, Sn etc., when binding to flavonoid ligands, enhance the anticancer activity compared to free ligands. This review study covered some important literature surveys for the last two decades. It has been concluded that flavonoid metal complexes have been associated with a wide range of biological properties that could be noteworthy in the medicinal field. Therefore, to develop a new anticancer drug, it is essential to determine the primordial interaction drug with DNA under physiological or anatomical conditions. The study of numerous flavonoid metal complexes mentioned in this paper could be the future treatment against various cancerous diseases.

Review of Recent Patents on Smart Bearing

Background: Smart bearing is based on the traditional bearing, which is integrated by sensing devices and control devices, formating a unique bearing structure unit. It uses information processing, automatic control, and other technologies to achieve real-time online monitoring of bearing operating conditions, fault detection, and state control. Compared with ordinary bearings, smart bearings have the characteristics of self-awareness, self-decision, and self-regulation. By analyzing the composition of various types of smart bearings and their working principles, it is beneficial to prospect the future development direction of bearings. Objective: We analyze and organize the patents on smart bearings in recent years, summarize the problems encountered in the development of smart bearings, and propose the development direction of smart bearings in the future to provide a reference for future scientific research. Methods: This paper reviews the patents related to smart bearings and analyzes the composition and structure of smart bearings. The main smart bearings include rolling bearing, sliding bearing, magnetic bearing, and rubber bearing. The principle and structure of these bearings are analyzed. Their advantages and shortcomings are discussed. Results: Through the analysis of relevant patents and papers on smart bearings, the current development status and problems of smart bearings are summarized, the future design of smart bearings is proposed, and the research ideas and directions are proposed. Conclusion: The development of smart bearings is conducive to the timely detection of bearing conditions in the operation process. Except for detection, smart bearings have the functions of judgment, feedback, processing, etc. There are many factors that interfere with the accuracy of the smart bearing judgment and information transfer, so it is necessary to improve the structure and working principle of smart bearings through a variety of measures. In the future, more smart bearings will be invented.

A Survey of Cutting-edge Multimodal Sentiment Analysis

The rapid growth of the internet has reached the fourth generation, i.e., web 4.0, which supports Sentiment Analysis (SA) in many applications such as social media, marketing, risk management, healthcare, businesses, websites, data mining, e-learning, psychology, and many more. Sentiment analysis is a powerful tool for governments, businesses, and researchers to analyse users’ emotions and mental states in order to generate opinions and reviews about products, services, and daily activities. In the past years, several SA techniques based on Machine Learning (ML), Deep Learning (DL), and other soft computing approaches were proposed. However, growing data size, subjectivity, and diversity pose a significant challenge to enhancing the efficiency of existing techniques and incorporating current development trends, such as Multimodal Sentiment Analysis (MSA) and fusion techniques. With the aim of assisting the enthusiastic researcher to navigating the current trend, this article presents a comprehensive study of various literature to handle different aspects of SA, including current trends and techniques across multiple domains. In order to clarify the future prospects of MSA, this article also highlights open issues and research directions that lead to a number of unresolved challenges.

Generation and Application of Aryl Radicals Under Photoinduced Conditions.

Photoinduced aryl radical generation is a powerful strategy in organic synthesis that facilitates the formation of diverse carbon-carbon and carbon-heteroatom bonds. The synthetic applications of photoinduced aryl radical formation in the synthesis of complex organic compounds, including natural products, physiologically significant molecules, and functional materials, have received immense attention. An overview of current developments in photoinduced aryl radical production methods and their uses in organic synthesis is given in this article. A generalized idea of how to choose the reagents and approach for the generation of aryl radicals is described, along with photoinduced techniques and associated mechanistic insights. Overall, this article offers a critical assessment of the mechanistic results as well as the selection of reaction parameters for specific reagents in the context of radical cascades, cross-coupling reactions, aryl radical functionalization, and selective C-H functionalization of aryl substrates.

Mapping the Viewer Experience in Cinematic Virtual Reality: A Systematic Review

Abstract In recent years, the creative media landscape has witnessed growing interests surrounding the utilization of virtual reality (VR) as a novel visual narrative approach for both filmmakers and audiences. This trend is accompanied by an increase in studies aimed at scientifically examining the characteristics and principles of immersive visual storytelling. This paper intends to contribute to this growing field by offering a comprehensive review on the current research development in cinematic virtual reality (CVR), which employs VR technology to produce immersive, cinematic experiences for audiences. While extant research has focused on the content generation techniques and human performance implicated in virtual environments, such investigations may not fully explain the medium adaptation differences or emotional dimensions of narrated immersive experiences. These aspects are especially crucial in the context of visual storytelling through VR film, 360-degree video production, or other narrated experiences. The proposed study systematically categorizes CVR-related research, revealing the field's current state by narrowing the focus to specific topics and themes within CVR literature and highlighting key subdomains of interest centered on viewers’ experience measurement techniques. The findings of this review are expected to establish formal categories for implementing visual CVR to achieve immersive visual storytelling and provide a comprehensive analysis of current viewer experience measurements.

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.

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.

A Review of Recent Patents on LoRaWAN

Background: LoRaWAN technology enables IoT devices with ubiquitous connectivity, which further empowers a broad spectrum of applications, including civil engineering, agriculture, and smart home. Although LoRaWAN has low-power energy consumption and long-range communication capability, it has many features that should be carefully managed to improve its performance. Objective: This paper aimed to review recent patents on improving the performance of LoRaWAN. Methods: The most related patents on improving the performance of LoRaWAN were discussed and analyzed. Results: A total of 71 patents were reviewed. This paper proposed a 2D taxonomy to categorize the existing patents. These patents aimed at focusing on the main components of LoRaWAN, i.e., end node, gateway, and server, and improving the performance of LoRaWAN from six aspects, i.e., energy efficiency, communication range, transmission efficiency, collision management, network security, and interoperability. The methodologies used in the current patent development were also discussed. Conclusion: Tremendous efforts have been devoted to improving the performance of LoRaWAN in recent years. To continue the growth of LoRaWAN technology, future patent development may focus on scalability, cost-effectiveness, and ease of implementation.

Current Prospects in Rheumatoid Arthritis: Pathophysiology, Genetics, and Treatments.

An autoimmune inflammatory disease, rheumatoid arthritis (RA), predominantly affects the synovium joint lining, augmenting disability, early mortality, and socioeconomic difficulty. Therefore, current updates on pharmacological therapies are crucial for developing drugs to treat the disease at each stage. This review attempts to compile a thorough analysis of current developments in our knowledge of RA pathogenesis and disease-modifying drugs, with the aim of providing insights for next-generation RA therapeutics. According to the literature, the most successful drugs for treatment techniques described so far in this include (cs) DMARDs (sub-class of DMARDs), tsDMARDS (targeted synthetic DMARDS), and bDMARDs (biological DMARDs). However, current pharmacologic therapy (consisting of biological, conventional, and creative views of small molecule anti-rheumatic drugs that treat the disease or DMARD) remains the cornerstone of rheumatoid arthritis treatment with which significant progress toward disease remission has been accomplished. The pathobiology of RA involves cytokine messengers such as B and T-cells, and an intricate interplay of pro-inflammatory cytokines responsible for activating and developing effector cells, in turn, accountable for local disease and systemic symptoms. Despite the fact that the cause of rheumatoid arthritis is not known, new treatments have been created as a result of better approaches towards the biology of the disease. As they target molecules directly implicated in the genesis of rheumatoid arthritis, these drugs may be more effective, targeted, and less harmful in the short and long term than standard therapies.

Systematic Review on the Current Situation and Development of Local Antimicrobial Agents in Revision Arthroplasty for Periprosthetic Joint Infection (PJI)

Systematic Review on the Current Situation and Development of Local Antimicrobial Agents in Revision Arthroplasty for Periprosthetic Joint Infection (PJI)