IMPACT OF THE COVID-19 PANDEMIC ON SEPTIC ARTHRITIS MANAGEMENT

BackgroundSince 2020, the SARS-Cov-2 pandemic has disrupted the organization of healthcare systems worldwide.ObjectivesThis study aimed to assess the impact of this pandemic on septic arthritis management in a tertiary rheumatology department.MethodsIt was a single-center descriptive case-control study, which included patients hospitalized for septic arthritis between January 2018 and December 2021, whose diagnosis was retained after positive bacterial growthor on culture on according to presumptive criteria. Our patients were divided into two groups: G1: patients hospitalized during the COVID-19 pandemic (2020-2021), and G2: patients hospitalized during a similar period before the COVID-19 pandemic (2018-2019). In both groups, septic arthritis prevalence was calculated, socio-demographic characteristics, risk factors, clinical, paraclinical, and therapeutic data were collected. COVID-19 status was reported in the G1.ResultsTwenty-two patients were enrolled: G1 (n = 15), G2 (n = 7). The prevalence of septic arthritis was 0.77% and 0.36% respectively. The median age was 54.6±12.25 and 54.29±21.81 years old respectively. Diabetes was found in 26, 7% in G1 and 28.6% in G2. During the pandemic, arthropathy and oral corticosteroids use were noted in 53.3% and 28.6% of patients versus 26.7% and 14.3% in G2. The diagnosis delay and the prior use of antibiotic therapy were more significant in G1: 14.08[7-30] d versus 6.5[3.25-19.25] d, and 46.7% versus 14.3%. The knee was the most common localization in both groups. Other joints were affected in G1: shoulder (n = 2), hip (n = 1), and sacroiliac (n = 1). The most common germ was staphylococcus aureus. The duration of hospitalization and duration of antibiotic therapy in G1 and G2 were 26.07±9.12d versus 27.43±10.87d and 50±10d versus 48±25.79d, respectively. Concerning COVID-19 status, 33.3% of patients in G1 have received their vaccination and no recent SARS-Cov2 infection was noted before hospitalization. During the pandemic, synovectomy was required in three patients, one of whom was also transferred to intensive care for septic shock (two of these three patients are being followed for rheumatoid arthritis, and only one has never been vaccinated against COVID-19).ConclusionDuring the COVID-19 pandemic, the prevalence of septic arthritis in our department was higher and the diagnosis was delayed. Duration of hospitalization was not impacted, however, atypical localisations, prior use of antibiotics, recourse to synovectomy, and transfer to intensive care were reported. These results suggest an inadequate and difficult access to healthcare services during the lockdown, as well as an impact of social distancing on the immune system [1, 2]. More studies are needed to confirm these findings.References[1]Robinson E. Pires et al, What Do We Need to Know about Musculoskeletal Manifestations of COVID-19? A Systematic Review, JBJS Rev. 2022 Jun 3;10(6)[2]Pantea Kiani et al, Immune Fitness and the Psychosocial and Health Consequences of the COVID-19 Pandemic Lockdown in The Netherlands: Methodology and Design of the CLOFIT Study, Eur J Investig Health Psychol Educ. 2021 Feb 20;11(1):199-218Acknowledgements:NIL.Disclosure of InterestsNone Declared.

Control System for Indoor Safety Measures Using a Faster R-CNN Architecture

This paper presents a control system for indoor safety measures using a Faster R-CNN (Region-based Convolutional Neural Network) architecture. The proposed system aims to ensure the safety of occupants in indoor environments by detecting and recognizing potential safety hazards in real time, such as capacity control, social distancing, or mask use. Using deep learning techniques, the system detects these situations to be controlled, notifying the person in charge of the company if any of these are violated. The proposed system was tested in a real teaching environment at Rey Juan Carlos University, using Raspberry Pi 4 as a hardware platform together with an Intel Neural Stick board and a pair of PiCamera RGB (Red Green Blue) cameras to capture images of the environment and a Faster R-CNN architecture to detect and classify objects within the images. To evaluate the performance of the system, a dataset of indoor images was collected and annotated for object detection and classification. The system was trained using this dataset, and its performance was evaluated based on precision, recall, and F1 score. The results show that the proposed system achieved a high level of accuracy in detecting and classifying potential safety hazards in indoor environments. The proposed system includes an efficiently implemented software infrastructure to be launched on a low-cost hardware platform, which is affordable for any company, regardless of size or revenue, and it has the potential to be integrated into existing safety systems in indoor environments such as hospitals, warehouses, and factories, to provide real-time monitoring and alerts for safety hazards. Future work will focus on enhancing the system's robustness and scalability to larger indoor environments with more complex safety hazards.

Online Educational Ecosystem, Sustainability and Resiliency in Bangladesh: Scaffolding Pathways?

How is online tertiary education opening the student/teacher mind-set, particularly during the transitional learning/teaching process? The question is tested through a country-wide survey of students/teachers conducted against Independent University, Bangladesh's (IUB's) own transition. Students reported two broad changes: (a) micro-level infrastructural and resource issues resonating with macro-level interventions;and (b) the quality of learning vis-à-vis teaching amid-online platform transition merely exacerbating both teacher-level and student-level pressures. Accordingly, the chapter illustrates a handful of micro-level leverage points, based upon learner characteristics, needs, and university online ecosystem (including infrastructures, teacher's competency challenges, and Covid-19 impacts on the learning-teaching community), for future relevance. © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

Uptake of vaccination in older Indian patients with cancer: A cross-sectional observational study

Background: Older patients with cancer are at a higher risk of invasive infections. Vaccination is an effective approach to decrease the mortality and morbidity associated with infections. Objective(s): Our primary objective was to evaluate the proportion of older patients with cancer who had received routine vaccinations against pneumococcal, influenza, and coronavirus disease 2019 (COVID-19). Our secondary objective was to identify the factors associated with vaccine uptake such as age, sex, education, marital status, comorbidities, and place of residence. Material(s) and Method(s): This cross-sectional observational study was conducted in the geriatric oncology outpatient clinic of the Department of Medical Oncology at the Tata Memorial Hospital, a tertiary care cancer hospital in Mumbai, India, from February 2020 to January 2023. We included all patients aged >=60 years who were evaluated in the geriatric oncology clinic during the study period and for whom the immunization details were available. The uptake of COVID-19 vaccine was calculated from March 2021 onwards, which was when the COVID-19 vaccine became available to patients aged >=60 years in India. Result(s): We enrolled 1762 patients;1342 (76.2%) were male. The mean age was 68.4 (SD, 5.8) years;795 (45%) patients were from the west zone of India. Only 12 (0.68%) patients had received the pneumococcal vaccine, and 13 (0.7%) had received the influenza vaccine. At least one dose of the COVID-19 vaccine had been taken by 1302 of 1562 patients (83.3%). On univariate logistic regression, education, marital status, geographic zone of residence, and primary tumor site were correlated with the uptake of COVID-19 vaccine. Factors associated with a greater COVID-19 vaccine uptake included education (up to Std 10 and higher vs. less than Std 10: Odds Ratio [OR], 1.46;95% confidence interval [CI], 1.07-1.99;P = 0.018, and illiterate vs. less than Std 10: OR, 0.70;95% CI, 0.50-0.99;P = 0.041), marital status (unmarried vs. married: OR, 0.27;95% CI, 0.08-1.08;P = 0.046, and widow/widower vs. married: OR, 0.67;95% CI, 0.48-0.94;P = 0.017), lung and gastrointestinal vs. head-and-neck primary tumors (lung cancer vs. head-and-neck cancer: OR, 1.60;95% CI, 1.02-2.47;P = 0.038, and gastrointestinal vs.head-and-neck cancer: OR, 2.18;95% CI, 1.37-3.42;P < 0.001), and place of residence (west zone vs. central India: OR, 0.34;95% CI, 0.13-0.75;P = 0.015). Conclusion(s): Fewer than 1 in 100 older Indian patients with cancer receive routine immunization with influenza and pneumococcal vaccines. Hearteningly, the uptake of COVID-19 vaccination in older Indian patients with cancer is over 80%, possibly due to the global recognition of its importance during the pandemic. Similar measures as those used to increase the uptake of COVID-19 vaccines during the pandemic may be beneficial to increase the uptake of routine vaccinations.Copyright © 2023 Cancer Research, Statistics, and Treatment.

Case Report of Succesful Multidisiplinary Treatment for Patient with Metastatic Lung Cancer and Relapsed/Refractory Chronic Lymphatic Leukemia/Small Lymphocytic Lymphoma

Introduction: Croatian National Cancer Registry of Croatian Institute for Public Health reported that in year 2020 lung cancer was the second most common cancer site diagnosed in men with 16% and the third most common in women with 10% incidence among all cancer sites. Unfortunatelly lung cancer has the highest mortality in both men and women. Haematological malignancies had 7% share in all malignancies in both male and female cances cases. In 2020 190 newly diagnosed cases of lymphatic leukemia in men and 128 cases in women were reporeted, meaning 1.5 and 1.2% of all malignancies, respectively. Chronic lymphatic leukemia (CLL) is an advanced age disease and incidence increases with age. Impaired immunity, T and B cell dysfunction in CLL, chromosomal aberations, long-term immunosuppressive therapy and genetic factors can all cause secondary malignancies. Co- occurence of solid tumors and CLL is very rare. Although patiens with CLL have an increased risk of developing second primary malignancies including lung carcinoma, the data about their clinical outcomes are lacking. Parekh et al. retrospectively analyzed patients with simultaneous CLL and lung carcinoma over a 20-year period, and they found that ~2% of patients with CLL actually developed lung carcinoma. The authors claimed that up to 38% of patients will also develop a third neoplasm more likely of the skin (melanoma and basal cell carcinoma), larynx (laryngeal carcinoma) or colon. Currently there are no specific guidelines for concurrent CLL and non-small cell lung carcinoma (NSCLC) treatment. Usually, when the tumors are diagnosed simultaneously, treatment is based to target the most aggressive malignancy, as the clinical outcomes depend on the response of the tumor with the poorest prognosis. For this reason, a multidisciplinary approach is mandatory. Case report: A patient with history of coronary heart disease, myocardial infarction and paroxysmal atrial fibrillation was diagnosed in 2019 (at the age of 71) with B chronic lymphocytic leukemia with bulky tumor (inguinal lymph nodes 8x5 cm), stage B according to Binet, intermediate risk. He was treated with 6 cycles of chemoimmunotherapy (rituximab/cyclofosfamid/fludarabine). In 10/2019 remission was confirmed, but MSCT described tumor in the posterior segment of upper right lung lobe measuring 20x17 mm and bilateral metastases up to 11 mm. Bronchoscopy and biopsy were performed, and EGFR neg, ALK neg, ROS 1 neg, PD-L1>50% adenocarcinoma was confirmed. He was referred to Clinical Hospital Center Osijek where monotherapy with pembrolizumab in a standard dose of 200 mg intravenously was started in 01/2020. Partial remission was confirmed in October 2020. Immunotherapy was discontinued due to development of pneumonitis, dysphagia and severe weight loss (20kg), but without radiologically confirmed disease progression. At that time he was referred to our hospital for further treatment. Gastroscopy has shown erosive gastritis with active duodenal ulcus, Forrest III. Supportive therapy and proton pump inhibitor were introduced. After complete regression of pneumonitis, improvement of general condition and resolution of dysphagia, no signs of lung cancer progression were found and pembrolizumab was reintroduced in 12/2021. Hypothyroidism was diagnosed in 01/2021 and levothyroxine replacement ther apy was started. In 03/2021 he underwent surgical removal of basal cell carcinoma of skin on the right temporal region with lobe reconstruction. From 02/2021, when pembrolizumab was reintroduced, regression in tumor size was continously confirmed with complete recovery of general condition. He was hospitalized for COVID 19 infection in 09/2021, and due to complications pembrolizumab was discontinued till 11/2021. Lung cancer immunotherapy proceeded till 11/2022, when Multidisciplinary team decided to finish pembrolizumab because of CLL relapse. CLL was in remission till August 2022 when due to B symptoms, lymphcytosis, anemia and generalized lymphadenopathy, hematological workup including biopsy of cervical lymph node was performed and CLL/SLL relapse was confirmed. Initially chlorambucil was introduced, but disease was refractory. Based on cytogenetic test results (IGHV unmutated, negative TP53) and due to cardiovascular comorbidity (contraindication for BTK inhibitors) venetoclax and rituximab were started in 01/2023. After just 1 cycle of treatment normal blood count as well as regression of B symptoms and peripheral lymphadenopathy occured, indicating the probability of complete disease remission. In our patient with metastatic lung adenocarcinoma excellent disease control is achieved during 41 month of treatment in first line setting. Furthermore, relapsed/refractory CLL/SLL is currently in confirmed remission. Conclusion(s): Successful treatment of patients with multiple primary malignancies is based on multidisciplinarity, early recognition and management of side effects, treatment of comorbidities with the aim of prolonging life, controlling symptoms of disease and preserving quality of life.

COVID-19 risk reduce based YOLOv4-P6-FaceMask detector and DeepSORT tracker.

Wearing masks in public areas is one of the effective protection methods for people. Although it is essential to wear the facemask correctly, there are few research studies about facemask detection and tracking based on image processing. In this work, we propose a new high performance two stage facemask detector and tracker with a monocular camera and a deep learning based framework for automating the task of facemask detection and tracking using video sequences. Furthermore, we propose a novel facemask detection dataset consisting of 18,000 images with more than 30,000 tight bounding boxes and annotations for three different class labels namely respectively: face masked/incorrectly masked/no masked. We based on Scaled-You Only Look Once (Scaled-YOLOv4) object detection model to train the YOLOv4-P6-FaceMask detector and Simple Online and Real-time Tracking with a deep association metric (DeepSORT) approach to tracking faces. We suggest using DeepSORT to track faces by ID assignment to save faces only once and create a database of no masked faces. YOLOv4-P6-FaceMask is a model with high accuracy that achieves 93% mean average precision, 92% mean average recall and the real-time speed of 35 fps on single GPU Tesla-T4 graphic card on our proposed dataset. To demonstrate the performance of the proposed model, we compare the detection and tracking results with other popular state-of-the-art models of facemask detection and tracking.

The effect of research on COVID-19 and PM2.5 on the localization of humanitarian aid.

The spatiotemporal variation of the death and tested positive cases is poorly understood during the respiratory coronavirus disease 2019 (COVID-19) pandemic. On the other hand, COVID-19's spread was not significantly slowed by pandemic maps. The aim of this study is to investigate the connection between COVID-19 distribution and airborne PM2.5 (particulate matter with an aerodynamic diameter less than 2.5 µm). Long-term exposure to high levels of PM2.5 is significantly connected to respiratory diseases in addition to being a potential carrier of viruses. Between April 2020 and March 2021, data on COVID-19-related cases were gathered for all prefectures in Japan. There were 9159, 109,078, and 451,913 cases of COVID-19 that resulted in death, severe illness, and positive tests, respectively. Additionally, we gathered information on PM2.5 from 1119 air quality monitoring stations that were deployed across the 47 prefectures. By using the statistical analysis tools in the Geographical Information System (GIS) software, it was found that the residents of prefectures with high PM2.5 concentrations were the most susceptible to COVID-19. Additionally, the World Health Organization-Air Quality Guidelines (WHO-AQG) relative risk (RR) of 1.04 (95% CI: 1.01-1.08), which was used to compute the PM2.5-caused deaths, was employed as well. Approximately 1716 (95% CI: 429-3,432) cases of PM2.5-related deaths were thought to have occurred throughout the study period. Despite the possibility that the actual numbers of both COVID19 and PM2.5-caused deaths are higher, humanitarian actors could use PM2.5 data to localize the efforts to minimize the spread of COVID-19.

Identification and Localization of COVID-19Abnormalities on Chest Radiographs using Computer Vision

The Corona Virus (COVID 19) pandemic is quickly becoming the world's most deadly disease. The spreading rate is higher and the early detection helps in faster recovery. The existence of COVID 19 in individuals shall be detected using molecular analysis or through radiographs of lungs. As time and test kit are limited RT- PCR is not suitable to test all. The RT- PCR being a time-consuming process, diagnosis using chest radiographs needs no transportation as the modern X-ray systems are digitized. Deep learning takes an edge over other techniques as it deduces the features automatically and performs massively parallel computations. Multiple feature maps will help in accurate prediction. The objective of the proposed work is to develop a Computer Aided Deep Learning System identify and localize COVID-19 virus from other viruses and pneumonia. It helps to detect COVID-19 within a short period of time thereby improving the lifetime of the individuals. SIIM-FISABIO-RSNA benchmark datasets are used to examine the proposed system. Recall, Precision, Accuracy-rate, and F-Measure are the metrics used to prove the integrity of the system. © 2023 IEEE.

Gastrointestinal stromal tumors in elderly patients

Background: Gastrointestinal stromal tumors (GISTs) account for 1 to 3% of all primary malignant tumors of the gastrointestinal tract. The global incidence of GISTs varies in the range of 7-15 cases per 1 million people per year. In about 95% of cases, the incidence is sporadic. Despite the fact that some success has been achieved in the treatment of this pathology, the problem of GISTs treatment is urgent, especially in elderly and senile patients in particular. The aim of the study: To study the age-related characteristics of GISTs development in patients of older age groups. Material(s) and Method(s): A retrospective analysis of 56 clinical cases of GISTs in patients of different age groups according to the WHO classification was carried out in the study. Result(s): The most common variant of the immunohistochemical structure was the spindle cell one 62.5%. In most cases, tumors were localized in the stomach 82.2%. Elderly patients had larger tumor sizes compared with young and middle-Aged patients. In patients of older age groups, the disease was most often detected at stage II. In most cases, a comorbid pathology was detected, most often a combination of several diseases of the cardiovascular system. Conclusion(s): In patients of older age groups, the spindle cell structure of the GISTs is most common, the tumor was most often localized in the stomach (77.4%), most often the tumor was localized along the lesser curvature. In most cases, the tumor was up to 10.0 cm in diameter. On average, the disease was detected at stage II. Comorbid pathology occurred in 87.3% of cases. In 2020-2021, the disease was detected more often, the of tumors sizes were smaller, due to an increase in the number of CT scans of the chest for the diagnosis of the new coronavirus infection.Copyright © 2022 AME Publishing Company. All rights reserved.

Business, Government and the SDGs: The Role of Public-Private Engagement in Building a Sustainable Future

This book seeks to revise and challenge the roles and traditional realms of influence that national and local governments, and businesses at a critical juncture in terms of achieving sustainable development, faces when tackling the dual challenges of climate change and post-COVID recovery. Using the broader lens of the 2030 UN Sustainable Development Goals (SDGs) to assess the roles and responsibilities of each of these stakeholders and their relationships, the book offers policy, economic arguments, case studies and examples to argue that neither national or local governments, nor companies, could afford to deviate from the SDGs in the recovery from the current crisis, nor that the imperative of bold climate action should detract from the broader focus on sustainability. The analysis frames the debate of how a balance between people, planet, and profits can be achieved and how nations, regions and cities, and businesses, with their representative organizations, can achieve a sustainable recovery from the current global crisis, and contribute to climate smart, resilient and inclusive development. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023. All rights reserved.

Smartwatch-based ecological momentary assessments for occupant wellness and privacy in buildings

This paper describes the adaptation of an open-source ecological momentary assessment smartwatch platform with three sets of micro-survey wellness-related questions focused on i) infectious disease (COVID-19) risk perception, ii) privacy and distraction in an office context, and iii) triggers of various movement-related behaviors in buildings. This platform was previously used to collect data for thermal comfort, and this work extends its use to other domains. Several research participants took part in a proof-of-concept experiment by wearing a smartwatch to collect their micro-survey question preferences and perception responses for two of the question sets. Participants were also asked to install an indoor localization app on their phone to detect where precisely in the building they completed the survey. The experiment identified occupant information such as the tendencies for the research participants to prefer privacy in certain spaces and the difference between infectious disease risk perception in naturally versus mechanically ventilated spaces. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

Atlas of interactions between SARS-CoV-2 macromolecules and host proteins.

The proteins and RNAs of viruses extensively interact with host proteins after infection. We collected and reanalyzed all available datasets of protein-protein and RNA-protein interactions related to SARS-CoV-2. We investigated the reproducibility of those interactions and made strict filters to identify highly confident interactions. We systematically analyzed the interaction network and identified preferred subcellular localizations of viral proteins, some of which such as ORF8 in ER and ORF7A/B in ER membrane were validated using dual fluorescence imaging. Moreover, we showed that viral proteins frequently interact with host machinery related to protein processing in ER and vesicle-associated processes. Integrating the protein- and RNA-interactomes, we found that SARS-CoV-2 RNA and its N protein closely interacted with stress granules including 40 core factors, of which we specifically validated G3BP1, IGF2BP1, and MOV10 using RIP and Co-IP assays. Combining CRISPR screening results, we further identified 86 antiviral and 62 proviral factors and associated drugs. Using network diffusion, we found additional 44 interacting proteins including two proviral factors previously validated. Furthermore, we showed that this atlas could be applied to identify the complications associated with COVID-19. All data are available in the AIMaP database (https://mvip.whu.edu.cn/aimap/) for users to easily explore the interaction map.

Changes in the proteomics of exhaled breath condensate under the influence of inhaled hydrogen in patients with post-COVID syndrome.

Aim. To study the effect of inhalation therapy with an active hydrogen (AH) on the protein composition of exhaled breath condensate (EBC) in patients with post-COVID syndrome (PCS). Material and methods. This randomized controlled parallel prospective study included 60 patients after coronavirus disease 2019 (COVID-19) with PCS during the recovery period and clinical manifestations of chronic fatigue syndrome who received standard therapy according to the protocol for managing patients with chronic fatigue syndrome (CFS). The patients were divided into 2 groups: group 1 (main) - 30 people who received standard therapy and AH inhalations (SUISONIA, Japan) for 10 days, and group 2 (control) - 30 medical workers who received only standard therapy. Patients in both groups were comparable in sex and mean age. All participants in the study were sampled with EBC on days 1 and 10. Samples were subjected to tryptic digestion and high-performance liquid chromatography combined with tandem mass spectrometry analysis using a nanoflow chromatograph (Dionex 3000) in tandem with a high-resolution time-of-flight mass spectrometer (timsTOF Pro). Results. A total of 478 proteins and 1350 peptides were identified using high resolution mass spectrometry. The number of proteins in samples after AH therapy, on average, is 12% more than before treatment. An analysis of the distribution of proteins in different groups of patients showed that only half of these proteins (112) are common for all groups of samples and are detected in EBC before, after, and regardless of hydrogen therapy. In addition to the qualitative difference in the EBC protein compositions in different groups, quantitative changes in the concentration of 36 proteins (mainly structural and protective) were also revealed, which together made it possible to reliably distinguish between subgroups before and after treatment. It is worth noting that among these proteins there are participants of blood coagulation (alpha-1-antitrypsin), chemokine- and cytokine-mediated inflammation, and a number of signaling pathways (cytoplasmic actin 2), response to oxidative stress (thioredoxin), glycolysis (glyceraldehyde-3- phosphate dehydrogenase), etc. Conclusion. The use of hydrogen therapy can contribute to the switching of a number of physiological processes, which may affect the success of recovery in PCS patients. In particular, the obtained results indicate the activation of aerobic synthesis of adenosine triphosphate in mitochondria by hydrogen therapy, which correlates well with the decrease in the blood lactate level detected by laboratory studies. At the same time, this therapy can inhibit pro-inflammatory activity, negatively affecting the coagulation and signaling pathways of integrins and apoptosis, and, in addition, activate protective pathways, tricarboxylic acid cycle, FAS signaling, and purine metabolism, which may be essential for effective recovery after COVID-19.Copyright © 2023 Vserossiiskoe Obshchestvo Kardiologov. All rights reserved.

Contested geographies of localization(s): towards open-locales

In this paper, we identify localization(s) as an expanding set of spatial processes by which key economic and social mobilities are shifting towards regional, municipal and neighbourhood scales in response to interconnected crises of globalization, ecology, economy, politics, and public health. Localizations are already underway, and are likely to proliferate as these various crises intensify. They are diverse in their politics and have implications for all scales of social organization. Localizations raise important questions around inequalities and injustices, new topologies of (dis)connected communities, ethical dilemmas of obdurate globalizations, contesting turns to nativism, and ensuring the just and democratic construction of open-locales. Observing this trend of localizations from within the UK lockdowns of the global coronavirus pandemic, we argue that more geographically embedded but socially and politically interconnected futures are both an implication of this paper and should constitute the format of an important emerging research agenda around localizations. [ FROM AUTHOR] Copyright of Globalizations is the property of Routledge and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full . (Copyright applies to all s.)

Vision Transformers in medical computer vision-A contemplative retrospection

Vision Transformers (ViTs), with the magnificent potential to unravel the information contained within images, have evolved as one of the most contemporary and dominant architectures that are being used in the field of computer vision. These are immensely utilized by plenty of researchers to perform new as well as former experiments. Here, in this article, we investigate the intersection of vision transformers and medical images. We proffered an overview of various ViT based frameworks that are being used by different researchers to decipher the obstacles in medical computer vision. We surveyed the applications of Vision Transformers in different areas of medical computer vision such as image-based disease classification, anatomical structure segmentation, registration, region-based lesion detection, captioning, report generation, and reconstruction using multiple medical imaging modalities that greatly assist in medical diagnosis and hence treatment process. Along with this, we also demystify several imaging modalities used in medical computer vision. Moreover, to get more insight and deeper understanding, the self-attention mechanism of transformers is also explained briefly. Conclusively, the ViT based solutions for each image analytics task are critically analyzed, open challenges are discussed and the pointers to possible solutions for future direction are deliberated. We hope this review article will open future research directions for medical computer vision researchers.

Research on Intelligent Disinfection-Vehicle System Design and Its Global Path Planning

We aimed to research the design and path-planning methods of an intelligent disinfection-vehicle system. A ROS (robot operating system) system was utilized as the control platform, and SLAM (simultaneous localization and mapping) technology was used to establish an indoor scene map. On this basis, a new path-planning method combining the A* algorithm and the Floyd algorithm is proposed to ensure the safety, efficiency, and stability of the path. Simulation results show that with the average shortest distance between obstacles and paths of 0.463, this algorithm reduces the average numbers of redundant nodes and turns in the path by 70.43% and 31.1%, respectively, compared to the traditional A* algorithm. The algorithm has superior performance in terms of safety distance, path length, and redundant nodes and turns. Additionally, a mask recognition and pedestrian detection algorithm is utilized to ensure public safety. The results of the study indicate that the method has satisfactory performance. The intelligent disinfection-vehicle system operates stably, meets the indoor mapping requirements, and can recognize pedestrians and masks.

Outdoor Localization for a Mobile Robot under Different Weather Conditions Using a Deep Learning Algorithm

A fundamental issue in robotics is the precise localization of mobile robots in uncertain environments. Due to changing environmental patterns and lighting, localization under difficult perceptual conditions remains problematic. This paper presents an approach for locating an outdoor mobile robot using deep learning algorithms merge with 3D Light Detection and Ranging LiDAR data and RGB-D image. This approach is divided into three levels. The first is the training level, which involves scanning the localization area with a 3D LiDAR sensor and then converting the data into a 2.5D image based on the Principal Component Analysis. The testing is the second level in the Intensity Hue Saturation process. Then, the RGB and Depth images are combined to create a 2.5D fusion image. These datasets are trained and tested using Convolution Neural Networks. The K-Nearest Neighbor algorithm is used in the third level is the classification. The results show that the proposed approach is better in terms of accuracy of 97.46% and the Mean error distance is 0.6 meters.

Advanced, Innovative AIoT and Edge Computing for Unmanned Vehicle Systems in Factories

Post-COVID-19, there are frequent manpower shortages across industries. Many factories pursuing future technologies are actively developing smart factories and introducing automation equipment to improve factory manufacturing efficiency. However, the delay and unreliability of existing wireless communication make it difficult to meet the needs of AGV navigation. Selecting the right sensor, reliable communication, and navigation control technology remains a challenging issue for system integrators. Most of today's unmanned vehicles use expensive sensors or require new infrastructure to be deployed, impeding their widespread adoption. In this paper, we have developed a self-learning and efficient image recognition algorithm. We developed an unmanned vehicle system that can navigate without adding any specialized infrastructure, and tested it in the factory to verify its usability. The novelties of this system are that we have developed an unmanned vehicle system without any additional infrastructure, and we developed a rapid image recognition algorithm for unmanned vehicle systems to improve navigation safety. The core contribution of this system is that the system can navigate smoothly without expensive sensors and without any additional infrastructure. It can simultaneously support a large number of unmanned vehicle systems in a factory.