Multiple Search Research Articles

Historical Research on Aerosol Number Concentrations, Classifications of Air Pollution Severity and Particle Retention: Lessons for Present-Day Researchers

Research into the adverse health effects of air pollution exposure has repeatedly considered smaller particles, to the point where particle number concentration might be a more relevant metric than mass concentration. Here, we highlight some historical research which developed metrics for air pollution severity based on particle number concentration. Because this work was published in a national journal and prior to the internet and open access, this historical research is not easy to find, and it was more through the history of the aerosol research community in Ireland that this work is now being presented. Multiple online searches for published research papers on “particle number concentrations” and “air pollution severity” were undertaken. Even when specific searches were undertaken using the author names and publication year, these featured papers were not found on any internet search. O’Dea and O’Connor proposed that air pollution severity could be classified based on particle number concentration of condensation nuclei, with ‘little’ air pollution <50 × 103 particles per cm3, ‘mean’ 50–70 × 103 particles per cm3, ‘strong’ 70–100 × 103 particles per cm3, and ‘very strong’ >100 × 103 particles per cm3. Applying their assumptions on density and mean particle size, equated to mass concentrations for a mean of 6 µgm−3, strong at 8.5 µgm−3, and very strong >10 µgm−3. These are consistent with the current WHO guideline values for PM2.5. Additionally, we highlight the 1955 work by Burke and Nolan on the retention of inhaled particles, where ~40% of the inhaled number concentration is retained in the respiratory system. This is also consistent with the more recently published work on particle retention. In summary, the proposed categories of pollution severity, based on number concentrations, could form a basis for the development of future guidelines. This paper highlights that sometimes research has already been published, but it is difficult to find. We challenge researchers to find publications from their own countries which pre-date the WWW to inform current and future research. Additionally, there is scope for a repository for such information on historical publications. We have presented historical research on aerosol number concentrations, classifications of air pollution severity, and particle retention, which present lessons for current researchers.

The evaluation of transcription factor binding site prediction tools in human and Arabidopsis genomes

BackgroundThe precise prediction of transcription factor binding sites (TFBSs) is pivotal for unraveling the gene regulatory networks underlying biological processes. While numerous tools have emerged for in silico TFBS prediction in recent years, the evolving landscape of computational biology necessitates thorough assessments of tool performance to ensure accuracy and reliability. Only a limited number of studies have been conducted to evaluate the performance of TFBS prediction tools comprehensively. Thus, the present study focused on assessing twelve widely used TFBS prediction tools and four de novo motif discovery tools using a benchmark dataset comprising real, generic, Markov, and negative sequences. TFBSs of Arabidopsis thaliana and Homo sapiens genomes downloaded from the JASPAR database were implanted in these sequences and the performance of tools was evaluated using several statistical parameters at different overlap percentages between the lengths of known and predicted binding sites.ResultsOverall, the Multiple Cluster Alignment and Search Tool (MCAST) emerged as the best TFBS prediction tool, followed by Find Individual Motif Occurrences (FIMO) and MOtif Occurrence Detection Suite (MOODS). In addition, MotEvo and Dinucleotide Weight Tensor Toolbox (DWT-toolbox) demonstrated the highest sensitivity in identifying TFBSs at 90% and 80% overlap. Further, MCAST and DWT-toolbox managed to demonstrate the highest sensitivity across all three data types real, generic, and Markov. Among the de novo motif discovery tools, the Multiple Em for Motif Elicitation (MEME) emerged as the best performer. An analysis of the promoter regions of genes involved in the anthocyanin biosynthesis pathway in plants and the pentose phosphate pathway in humans, using the three best-performing tools, revealed considerable variation among the top 20 motifs identified by these tools.ConclusionThe findings of this study lay a robust groundwork for selecting optimal TFBS prediction tools for future research. Given the variability observed in tool performance, employing multiple tools for identifying TFBSs in a set of sequences is highly recommended. In addition, further studies are recommended to develop an integrated toolbox that incorporates TFBS prediction or motif discovery tools, aiming to streamline result precision and accuracy.

Multiple Symmetric Multi-link Search and Rescue Robot Based on a New Movable Structure

In the event of disasters such as fires and earthquakes, the efficiency of rescue operations is often limited due to the difficulty for firefighters to enter narrow spaces. To address this issue, our team has innovated on traditional search and rescue robots by incorporating advanced sensing systems, obstacle-crossing capabilities, and autonomous return functions. This enables the robots to quickly reach disaster sites for personnel search and significantly improves rescue efficiency.

Use model averaging instead of model selection in pulsar timing

ABSTRACT Over the past decade and a half, adoption of Bayesian inference in pulsar timing analysis has led to increasingly sophisticated models. The recent announcement of evidence for a stochastic background of gravitational waves by various pulsar timing array (PTA) projects highlighted Bayesian inference as a central tool for parameter estimation and model selection. Despite its success, Bayesian inference is occasionally misused in the pulsar timing community. A common workflow is that the data is analysed in multiple steps: a first analysis of single pulsars individually, and a subsequent analysis of the whole array of pulsars. A mistake that is then sometimes introduced stems from using the posterior distribution to craft the prior for the analysis of the same data in a second step, a practice referred to in the statistics literature as ‘circular analysis’. This is done to prune the model for computational efficiency. Multiple recent high-profile searches for gravitational waves by PTA projects have this workflow. This letter highlights this error and suggests that Spike and Slab priors can be used to carry out model averaging instead of model selection in a single pass. Spike and Slab priors are proved to be equal to log-uniform priors.

Artificial meerkat algorithm: a new metaheuristic algorithm for solving optimization problems

Abstract In this study, a novel artificial meerkat optimization algorithm (AMA) is proposed to simulate the cooperative behaviors of meerkat populations. The AMA algorithm is designed with two sub-populations, multiple search strategies, a multi-stage elimination mechanism, and a combination of information sharing and greedy selection strategies. Drawing inspiration from the intra-population learning behavior, the algorithm introduces two search mechanisms: single-source learning and multi-source learning. Additionally, inspired by the sentinel behavior of meerkat populations, a search strategy is proposed that combines Gaussian and Lévy variations. Furthermore, inspired by the inter-population aggression behavior of meerkat populations, the AMA algorithm iteratively applies these four search strategies, retaining the most suitable strategy while eliminating others to enhance its applicability across complex optimization problems. Experimental results comparing the AMA algorithm with seven state-of-the-art algorithms on 53 test functions demonstrate that the AMA algorithm outperforms others on 71.7% of the test functions. Moreover, experiments on challenging engineering optimization problems confirm the superior performance of the AMA algorithm over alternative algorithms.

DIGITAL TOOL FOR CALCULATING PROJECTED INITIAL RECOVERABLE OIL RESERVES AND OIL RECOVERY FACTOR

In today's realities, digitalization of production activities of companies and enterprises, including those involved in hydrocarbon production, is becoming increasingly important, allowing to create a single digital space for all employees, ensure transparency of the results obtained and provide prompt access to relevant information stored in a single database. The formation of a unified digital space implies the creation of an environment for employees, within which it is possible to use unified tools in solving production tasks. Data transparency eliminates the variability of results, demonstrating each step performed to achieve the result recorded in the database system. A unified database allows you to quickly familiarize yourself with all available relevant information, saving your employees time and effort. All these points are ultimately aimed at improving the efficiency of production processes, as well as optimizing the activities of employees working in different geographical locations, excluding from the work activities such routine processes as: manual recalculation of multiple indicators, search and review of information stored in different sources. Digitalization of production processes implies the use of digital technologies and tools formed on a certain technological basis. Based on the above, the authors have identified the criteria for the effectiveness of digital tools to perform production tasks: reducing the share of routine processes, providing digital transparency and storing results in a single database. The authors of the paper consider an example of digitalization of the oil recovery factor calculation process thanks to the implemented «Target Oil Recovery Factor» tool as part of the implementation of the RN-KIN Express corporate software.The purpose of the work under consideration is to confirm the performance of the implemented digital tool «Targeted EOR Express» (correct calculation of oil recovery factor and initial recoverable oil reserves) by means of retrospective analysis at fields in Western Siberia and Bashkortostan, and compliance with the stated criteria of an effective digital tool.Thanks to the retrospective analysis, the results confirming the accuracy of calculations of forecast values of residual recoverable oil reserves and oil recovery factor were obtained.Based on the results of the work, the implementation of «Targeted EORExpress» tool complies with the general trend of digitalization of production processes and includes all the criteria of an effective digital tool for production tasks.

Self-learning salp swarm algorithm for global optimization and its application in multi-layer perceptron model training

Optimization problems are common across various fields, and one effective solution is the swarm intelligence algorithm.It is essential for the algorithm to deliver high-quality solutions for problems with varying characteristics. However, most existing swarm intelligence rely on fixed and monotonic search strategies, which limits their ability to handle the diverse and complex situations encountered when solving real-world optimization problems with unknown fitness landscapes. To extend the applicability of swarm intelligence and thus offer users an efficient black-box optimizer for various applications, a novel self-learning mechanism is proposed and applied to the Salp Swarm Algorithm (SSA) to develop the self-learning salp swarm algorithm (SLSSA) in this paper. In SLSSA, four distinct search strategies, including a novel multiple food sources search strategy, are adopted to strengthen the search agents’ abilities to conquer various difficulties in the search space. To improve the efficiency of the search process, the self-learning strategy dynamically determines the execution probability of each search strategy according to the quality of solutions it produced previously. Moreover, a parameter setting method is proposed in this paper, which eliminates the need for a trial-and-error approach and allows for straightforward configuration of the parameters that optimize the performance of SLSSA. In comparison with several highly regarded state-of-the-art peer algorithms, the performance of SLSSA in solving the CEC2014 benchmark functions was thoroughly examined. Subsequently, SLSSA was applied to train multi-layer perceptron classifiers and test on the UCI machine-learning datasets. The experimental results and analysis on benchmark functions and multi-layer perceptron classifier training problems demonstrate that SLSSA outperforms the competing algorithms in terms of solution accuracy, stability, and overall convergence speed. Moreover, computational time comparisons reveal that SLSSA achieves significant performance improvement with only a marginal increase in time cost compared to the original SSA.

Optimization of protein identifications through the use of different chromatographic approaches and bioinformatic pipelines.

Selection of proteomic workflows for a given project can be a daunting task. This research provides a guide outlining the impact on protein identification of different steps such as chromatographic separation, data acquisition strategies, and bioinformatic pipelines. The data presented here will help experts and nonexpert proteomic users to increase proteome coverage and peptide identification. HeLa protein digests were analyzed through different C18 chromatographic columns (15 and 50 cm in length), using top 12 data-dependent acquisition (DDA), top 20 DDA, and data-independent acquisition (DIA) with a nanospray source in positive mode in a Thermo Q Exactive instrument. The raw data were analyzed using different search engines, rescoring approaches, and multi-engine searches. The results were analyzed in the context of peptide and protein identifications, precursor properties, and computation requirements to understand the differences between methods. Our results showed that higher column lengths and top N DDA approaches were able to significantly increase protein identifications. The use of multiple search engines yielded limited gains, whereas the use of rescoring methods clearly outperformed other strategies. Finally, DIA approaches, although successful at generating new identifications, had a limited performance influenced by the previous collection of DDA data, which could prohibitively increase instrument time. Nonetheless, the use of library-free methods showed promising results. Our results highlight the impact of different experimental approaches on proteome coverage. Changes in chromatographic columns, data acquisition, or bioinformatic analysis can significantly increase the number of protein identifications (>400%). Thus, this research provides a reference upon which to build a successful proteomic workflow with different considerations at every step.

Aerodynamic Parameter Identification Based on Multi Group Adaptive Search Optimization Algorithm

Abstract Due to the strong coupling and nonlinearity of spinning-stabilized correction projectile’s motion and dynamic model, its aerodynamic parameter identification can be transformed into a high-dimensional multi extremum optimization problem. To avoid the excessive computational complexity and susceptibility to local optima, this paper proposes a multiple swarm adaptive search optimization method. This method automatically divides the samples into several subpopulations through an adaptive density peak clustering method, and then searches and updates in each subpopulation. To balance convergence speed and population diversity, the update operator for each generation combines the random search and optimal convergence step size, whose weights can be adaptively adjusted based on the update rate and dispersion characteristics of the subpopulations. Applying this method to the aerodynamic parameter identification, and comparing with improved particle swarm optimization and genetic algorithm, simulation results show that a better identification result can be obtained at the same iterations.

Redox Imbalance and Cardiovascular Pathogenesis: Exploring the Therapeutic Potential of Phytochemicals

Abstract: The intriguing role of the oxidation system in cardiovascular disease lies in its contribution to chronic and acute increases in intracellular reactive oxygen species (ROS), driving the progression of cardiovascular diseases (CVDs). ROS, produced as by-products of oxidative physiological and metabolic events, act as mediators in various signaling pathways contributing to cardiovascular pathology. The delicate equilibrium between the production of free radicals and antioxidant defense shifts in favor of the former, resulting in redox imbalance and extensive cellular damage. Among CVDs, coronary artery disease (CAD) remains as the leading cause of death globally. Understanding the significance of oxidative damage in the dysfunction of endothelial cells, atherosclerosis, and other pathogenic events and pathways is crucial for preventing and managing CVD. Consequently, it is imperative to comprehend the mechanism/s underlying the pathogenic alterations of CVD due to oxidative damage to develop effective prevention strategies. Many studies have reported bioactive phytochemical/s as potential therapies against CVDs, modulating ROS generation, controlling the CVD-related inflammatory mediators and protecting the vascular system. Therefore, this review provides an update for understanding how the phytoconstituents exhibit preventive roles in oxidative stress-related CVD, thus improving the quality of life of people. This study conducted a thorough literature search on CVD, oxidative imbalance, and phytoconstituents. The search was performed using multiple search engines and the main keywords, and only English publications until June 2023 were included. However, there is a need for more research and clinical trials to fully elucidate the efficacy and safety of these phytochemicals for managing the disease.

THANGA URAM: A DUAL PERSPECTIVE REVIEW OF ITS PHARMACOLOGICAL BENEFITS IN MODERN AND SIDDHA MEDICINE

The Siddha system of medicine uses mineral and metallic compounds for their high bioavailability and efficacy at low doses. Thanga uram (TU) is one such Siddha Metallo-mineral formulation. A review of the scientific literature on the ingredients of Thanga uram (TU) was undertaken. This wide-ranging assessment involved searching through Siddha literature as well as relevant publications in multiple databases and electronic search engines, including Google scholar, Embase, Mendeley, Pub Med Central, Science Direct, and the National Library of Medicine (NLM). The aim was to gather all pertinent information available on the subject. Thanga uram, with its validated antimicrobial, spermatogenic, cognitive enhancement, and styptic properties, and favourable safety profile, stands as a promising Siddha formulation. Its blend of traditional knowledge and scientific validation underscores its potential in contemporary medicine. The pharmacological actions and organoleptic characteristics of the substance align closely with the indications cited in the literature. This congruence suggests that the substance can be effectively utilized in the treatment of various diseases. Its efficacy is supported by both traditional use and modern scientific validation, making it a promising option for therapeutic applications

Higher Precision Is Not Always Better: Search Algorithm and Consumer Engagement

On decentralized e-commerce platforms, search algorithms play a critical role in matching buyers and sellers. A typical search algorithm routinely refines and improves its catalog of data to increase search precision, but the effects of a more precise search are little known. We evaluate such effects via a 2019 quasiexperiment on a world-leading e-commerce platform in which the search algorithm refined some product categories into finer subgroups to allocate consumer queries to more relevant product listings. Our data cover millions of consumers’ search and purchase behaviors over six months across multiple search sessions and product categories, enabling us to investigate trade-offs over time and across categories. We find that a more precise search algorithm improves consumers’ click-through and purchase rates drastically and instantaneously, but it comes at the cost of a significant decrease in consumer engagement and unplanned purchases over a longer time horizon. On average, consumers who used to spend more time searching now conduct 5.5% fewer searches, spend 4.1% less time on the platform, and decrease their spending on related categories by 2.2% in the week after the search precision increases. Our examination of the mechanisms behind these consequences calls for more careful search algorithm designs that account for not only instant conversion based on search precision but also consumer engagement and sellers’ strategic responses in the longer horizon. This paper was accepted by Anindya Ghose, information systems. Funding: This work was supported by the Key Program of the National Natural Science Foundation of China [Grants 72141305 and 72192803] and the Ministry of Education, People’s Republic of China (Fundamental Research Funds for the Central Universities), both to L. Fang. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.00478 .

Pedagogical content knowledge in physical education preservice teachers in view of researching on lesson studies

PurposeResearching on lesson studies in initial training of physical education (PE) teachers in Portugal, our group is especially interested in the potential of this methodology for developing the pedagogical content knowledge (PCK) of future PE teachers. The purpose of the present text is to synthesize the knowledge relating to this subject.Design/methodology/approachExploratory searches in four multiple search databases were carried out, resulting in 51 records. Information about authors, year of publication, purposes, methodology and results was collected and analyzed.FindingsResults highlight the diversity of purposes. The interview is recurrent in the qualitative studies, while the questionnaire is used in all the large quantitative studies. Regarding the findings, it appears the centrality of PCK, its development potential and its measurability, the consistency of the concept and its evolutionary dynamics, as well as the constructive criticism of initial teacher training models and practices.Originality/valueTo emphasize the relationship between lesson studies and the development of PCK in initial teacher training of PE teachers.

SMCVdb: a database of experimental cellular toxicity information for drug candidate molecules.

Many drug discovery exercises fail because small molecules that are effective inhibitors of target proteins exhibit high cellular toxicity. Early and effective assessment of toxicity and pharmacokinetics is essential to accelerate the drug discovery process. Conventional methods for toxicity profiling, including in vitro and in vivo assays, are laborious and resource-intensive. In response, we introduce the Small Molecule Cell Viability Database (SMCVdb), a comprehensive resource containing toxicity data for over 24 000 compounds obtained through high-content imaging (HCI). SMCVdb seamlessly integrates chemical descriptions and molecular weight data, offering researchers a holistic platform for toxicity data aiding compound prioritization and selection based on biological and economic considerations. Data collection for SMCVdb involved a systematic approach combining HCI toxicity profiling with chemical information and quality control measures ensured data accuracy and consistency. The user-friendly web interface of SMCVdb provides multiple search and filter options, allowing users to query the database based on compound name, molecular weight range, or viability percentage. SMCVdb empowers users to access toxicity profiles, molecular weights, compound names, and chemical descriptions, facilitating the exploration of relationships between compound properties and their effects on cell viability. In summary, the database provides experimentally derived cellular toxicity information for over 24 000 drug candidate molecules to academic researchers, and pharmaceutical companies. The SMCVdb will keep growing and will prove to be a pivotal resource to expedite research in drug discovery and compound evaluation. Database URL: http://smcvdb.rcb.ac.in:4321/.

Olax dissitiflora Oliv. (small-fruited olax): Current status and future prospects as medicinal plant

Olax dissitiflora Oliv. (O. dissitiflora) has several medicinal uses, some of them known since prehistoric times. The present review compiles existing knowledge of the chemical and pharmacological properties, current medicinal uses, and further use potential and applications of O. dissitiflora. Multiple searches on existing literature on the medicinal, phytochemistry and pharmacological properties of O. dissitiflora were conducted in online databases such as Scopus, JSTOR, PubMed, Google Scholar and Science Direct as well as using pre-electronic literature sources obtained from the university library. This study showed that the bark, leaves, roots and twigs of O. dissitiflora are used as aphrodisiac, emetic, mosquito repellent and purgative, and as traditional medicines against dental problems, gonorrhoea, hernia, venereal diseases and wounds. The phytochemical evaluation of the plant revealed that it contains reducing sugars, alkaloids, flavonoids, phenolic compounds, quinones, saponins, steroids, tannins, terpenoids, 21-epimachaerinic acid, oleanolic acid, hederagenin, oleanolic acid 3-O-glucuronide, amphotericin, santalbic acid, exocarpic acid and octadic-9,11-diynoic acid. The pharmacological assessments showed that the crude extracts and phytochemical compounds isolated from the species have antibacterial, antifungal, antiplasmodial, larvicidal and adulticidal activities. The collected ethnomedicinal and ethnopharmacological properties of O. dissitiflora documented in this review will assist to understand the therapeutic relevance and potential of the species as well as providing baseline data required for ethnopharmacological assessments required in the discovery of novel plant-based health products. There is also need for more studies focusing on phytochemistry, pharmacological properties and toxicological evaluations, in vivo and clinical research on O. dissitiflora to compliment and validate the ethnomedicinal properties of the species.

In silico PCR analysis: a comprehensive bioinformatics tool for enhancing nucleic acid amplification assays.

Nucleic acid amplification assays represent a pivotal category of methodologies for targeted sequence detection within contemporary biological research, boasting diverse utility in diagnostics, identification, and DNA sequencing. The foundational principles of these assays have been extrapolated to various simple and intricate nucleic acid amplification technologies. Concurrently, a burgeoning trend toward computational or virtual methodologies is exemplified by in silico PCR analysis. In silico PCR analysis is a valuable and productive adjunctive approach for ensuring primer or probe specificity across a broad spectrum of PCR applications encompassing gene discovery through homology analysis, molecular diagnostics, DNA profiling, and repeat sequence identification. The prediction of primer and probe sensitivity and specificity necessitates thorough database searches, accounting for an optimal balance of mismatch tolerance, sequence similarity, and thermal stability. This software facilitates in silico PCR analyses of both linear and circular DNA templates, including bisulfited treatment DNA, enabling multiple primer or probe searches within databases of varying scales alongside advanced search functionalities. This tool is suitable for processing batch files and is essential for automation when working with large amounts of data.

IntAct Database for Accessing IMEx's Contextual Metadata of Molecular Interactions.

The International Molecular Exchange Consortium (IMEx) has evolved into a vital partnership of open resources dedicated to curating molecular interaction data from the scientific literature. This consortium, which includes IntAct, MINT, MatrixDB, and DIP, is a collaborative effort with a central mission of aggregating detailed molecular interaction experimental evidence in a machine-readable format, supported by controlled vocabularies and standard ontologies. The IntAct molecular interaction database (www.ebi.ac.uk/intact), as an IMEx partner, serves as a valuable portal for accessing IMEx data through user-friendly search options and an array of interactive filters. The resource currently hosts an extensive repository of 1,293,508 binary interactions meticulously captured from 75,098 experiments documented in 23,366 publications (as of the February 2024 release), with this corpora being added to by regular data releases. IMEx curation policy has consistently prioritized a fine-grained data and curation model, with a focus on capturing the relevant experimental details essential for interpreting molecular interaction data effectively. Our curation process is designed to support the generation of interactomes tailored to contexts such as disease-specific or tissue-/cell-type-specific interactomes. These interactions are ranked according to a scoring system based on the Proteomics Standard Initiative Molecular Interaction (PSI MI) standards. This scoring system allows users to assess the degree of confidence in binary interactions, enhancing the value of the data. The resource provides insights into the nature of relationships among interacting partners as defined by the experimental setup and the associated biological context. Interactive filters enable users to navigate these rich, multilayered data, promoting a deeper understanding of biological complexity. Additionally, the IntAct website fosters the creation of networks for collaborative analyses by the scientific community. The recent transformation of the IntAct website, supported by a graph-type database, empowers users to execute custom queries tailored to their specific research interests. This article illustrates the diverse levels of annotations available for interactions and the multiple search options at users' disposal to access data of interest. © 2024 European Molecular Biology Laboratory, European Bioinformatics Institute. Current Protocols published by Wiley Periodicals LLC. Basic Protocol 1: Using Quick Search, network visualization, and filters Support Protocol: Accessing fine annotations from intact: Unlocking the molecular details Alternate Protocol: Using batch search: Querying multiple interactors Basic Protocol 2: Using advanced search: Precision and customization.

Traffic Road Congestion System Using by the Internet of Vehicles (IoV): A Systematic Literature Review

Traffic problems have increased in modern life due to the significant number of vehicles, urbanization, and non-compliance with traffic regulations. Vehicular ad hoc networks (VANETs) have made improvements to the traffic system in the past, playing a crucial role in establishing efficient traffic control systems in large cities. However, VANETs alone cannot effectively address certain problems under specific conditions. Presently, the development of new Internet of Things (IoT) technologies has enabled collaborative and efficient task execution. This technology has been implemented in the transportation system, transforming it into an intelligent transportation system (ITS), known as the Internet of Vehicles (IoV). This study investigates traffic issues within the traditional system and explores the benefits, enhancements, and reasons for improving IoV through a comprehensive Systematic Literature Review (SLR). The SLR approach involved targeted searches using multiple search phrases, including 25 articles published between 2016 and 2023. Furthermore, discussion on the necessary IoV technologies and tools required to establish IoV and address specific traffic challenges. Simulation of Urban Mobility (SUMO) is employed for the design and simulation of road traffic and we aim to contribute to the development of an optimal traffic control system. This paper analyzes two vehicular congestion control models, selects the most optimized and efficient model, and provides evidence for its effectiveness through a Systematic Literature Review (SLR)-based investigation based on its efficient features, in the end, we propose IoV based on vehicular clouds as a superior model, surpassing the capabilities of the traditional model and enhancing the network system.

Robotic navigation with deep reinforcement learning in transthoracic echocardiography

Abstract Purpose The search for heart components in robotic transthoracic echocardiography is a time-consuming process. This paper proposes an optimized robotic navigation system for heart components using deep reinforcement learning to achieve an efficient and effective search technique for heart components. Method The proposed method introduces (i) an optimized search behavior generation algorithm that avoids multiple local solutions and searches for the optimal solution and (ii) an optimized path generation algorithm that minimizes the search path, thereby realizing short search times. Results The mitral valve search with the proposed method reaches the optimal solution with a probability of 74.4%, the mitral valve confidence loss rate when the local solution stops is 16.3% on average, and the inspection time with the generated path is 48.6 s on average, which is 56.6% of the time cost of the conventional method. Conclusion The results indicate that the proposed method improves the search efficiency, and the optimal location can be searched in many cases with the proposed method, and the loss rate of the confidence in the mitral valve was low even when a local solution rather than the optimal solution was reached. It is suggested that the proposed method enables accurate and quick robotic navigation to find heart components.

Expanding the Gluino Exclusion Limits Using Events with One Highly Energetic Final State Jet and Missing Transverse Momentum

Multiple searches of theoretically hypothesized particles in Supersym-metry scenarios have been conducted through the years, many of which take place at the Large Hadron Collider (LHC) facility. A recent search of the ATLAS Collaboration [ 7] focuses on √s = 13 TeV pp collisions characterized by highly energetic jets and missing transverse momentum, resulting in model-independent limits for selected Signal Regions and providing exclusion limits for squarks. In this paper, we reinterpret the observations in terms of exclusion limits on gluino pair production. Monte Carlo simulations of gluino pair production events are made, varying both the masses of gluino and neutralino. In the end, we present the results in the mass plane of m˜g vs. m˜g − m ˜χ0 1 , including the effect of various sources of uncertainty.