Substantial Help Research Articles

A Guideline for Guidelines: A Novel Method to Assess the Helpfulness of Medical Guidelines.

Background/Objectives: The recommendations included in medical guidelines (GLs) provide important help to medical professionals for making clinical decisions regarding the diagnosis and treatment of various diseases. However, there are no systematic methods to measure the helpfulness of GLs. Thus, we developed an objective assessment of GLs which indicates their helpfulness and quality. We hypothesized that a simple mathematical analysis of 'Recommendations' and 'Evidence' would suffice. Methods: As a proof of concept, a mathematical analysis was conducted on the '2020 European Society of Cardiology Guidelines on Sports Cardiology and Exercise in Patients with Cardiovascular Disease Guideline' (SCE-guideline). First, the frequencies of Classes of Recommendations (CLASS) and the Levels of Evidence (LEVEL) (n = 159) were analysed. Then, LEVEL areas under CLASS were calculated to form a certainty index (CI: -1 to +1). Results: The frequency of CLASS I ('to do') and CLASS III ('not to do') was relatively high in the SCE-guideline (52.2%). Yet, the most frequent LEVEL was C (41.2-83.8%), indicating only a relatively low quality of scientific evidence in the SCE-guideline. The SCE-guideline showed a relatively high CI (+0.57): 78.4% certainty and 21.6% uncertainty. Conclusions: The SCE-guideline provides substantial help in decision making through the recommendations (CLASS), while the supporting evidence (LEVEL) in most cases is of lower quality. This is what the newly introduced certainty index showed: a tool for 'quality control' which can identify specific areas within GLs, and can promote the future improvement of GLs. The newly developed mathematical analysis can be used as a Guideline for the Guidelines, facilitating the assessment and comparison of the helpfulness and quality of GLs.

Optimization Design of Automotive Rear Tails for Enhancing the Downforce and Grip Effect during High Speed

Vehicle motion control and safety have always been key research directions in the field of automotive engineering. However, the traditional rear tail design has limitations in providing downforce, which cannot adequately meet the needs of high-speed driving. In this study, a computational fluid dynamics (CFD) simulation technique combined with an optimization algorithm was used to systematically investigate the influence of rear tail shape and layout on vehicle downforce. By introducing a parametric rear fin design into the CFD model and applying a multi-objective optimization algorithm, various design options can be systematically explored to improve the downforce performance. It was found that with the optimized tail fin design, the car was able to achieve higher downforce and provide better grip and stability during high speed. Specifically, downforce gains of up to 20% were observed, leading to significant improvements in vehicle handling and stability compared to conventional designs. The results of this research are not only of great significance to the field of automotive engineering but also of substantial help to improve the motion performance and safety of vehicles. optimizing the rear tail design can effectively improve the grip of the car in the process of high speed, provide a safer and more stable driving experience for the driver, and is also expected to play an important role in motorsport and other fields.

Accuracy of the Information on Sudden Sensorineural Hearing Loss From Chat Generated Pre-Trained Transformer

Background and Objectives Chat generated pre-trained transformer (ChatGPT) is a conversational artificial intelligence model, which has recently attracted worldwide attention by enabling natural conversations based on huge information from deep learing in various fields. Several studies have reported usefullness and reliability of medical information obtained by ChatGPT, but there are no studies explaining reliability and accuracy in the field of otorhinolarynoglogy. On this regard, we investigated the accuracy of information on sudden sensorineural hearing loss obtained by ChatGPT.Materials and Method Twenty-five questions and answeres related to sudden sensorineural hearing loss were recorded from ChatGPT based on textbook from Korean Society of Otorhinolaryngology-Head and Neck Surgery and Clinical Guidelines of American Academy of Otolaryngology-Head and Neck Surgery. Answers were shown to one specialist in otorhinolaryngoly in a blind test and asked to assess their accuracy. Each question was rated as ‘accurate’ or ‘inaccurate.’ If the contents were not found in the textbook or the guidelines, it was rated as ‘unreliable.’Results Of the 25 questions, 19 (76%) were identified as ‘accurate,’ 6 (24%) were ‘inaccurate,’ and 0 (0%) were ‘unreliable.’ Questions about definition, prevalence, hearing rehabilitation, diagnosis and treatment were found to be more accurate than the average, while causes and prognosis were less accurate than the average.Conclusion The information on sudden sensorineurla hearing loss obatined from ChatGPT was quite accurate. It is expected to provide substantial help to patients and doctors. As medicine and medical artificial intelligence develop together, further research is needed on reliability and accuracy in vaious diseases and fields of otorhinolaryngology-head and neck surgery.

School Counseling Before and During the COVID-19 Pandemic: Comparing the Perceptions of School Counselors at Two Points in Time

The restrictions implemented in response to the COVID-19 pandemic created a unique opportunity to examine various aspects of the field of school counseling. The study focused on school counselors’ perception of their work during the COVID-19 pandemic, comparing it to the time period that immediately preceded the pandemic. The eleven school counselors who participated in the study were interviewed about various aspects of their work during COVID-19: the enforced routine of life during the pandemic, their feelings about several characteristics of their profession, and the extent of change from before the start of the pandemic. The study’s findings indicated that the counselors were under considerable pressure due to the consequences of the quarantines and online learning. On the other hand, their work became more systematic in nature and was limited to training and support for teachers, which they perceived as an improvement in comparison to the period before the pandemic. They were also provided with extensive support from psychological counseling services and professional peer groups, which they described as a positive change that provided substantial help in dealing with the situation. The study concludes that COVID-19 led to a reformulation of the profession of school counseling. This conclusion further accentuates the image of school counseling as a flexible profession that moves across a systemic/ individual scale and adapts according to the circumstances and needs of the educational setting.

The Natural History of Disability and Caregiving Before and After Long-Term Care Entry

ImportanceMany older persons move into long-term care facilities (LTCFs) due to disability and insufficient home caregiving options. However, the extent of disability and caregiving provided around the time of entry is unknown.ObjectiveTo quantitatively describe disability and caregiving before and after LTCF entry, comparing nursing home (NH), assisted living (AL), and independent living (IL) entrants.Design, Setting, and ParticipantsA longitudinal cohort study using prospectively collected annual data from the National Health and Aging Trends Study from 2011 to 2020 including participants in the continental US. Overall, 932 community-dwelling Medicare beneficiaries entering LTCF from 2011 to 2019 were included. Entry into LTCF was set as t = 0, and participant interviews from 4 years before and 2 years after were used.Main Outcomes and MeasuresPrevalence of severe disability (severe difficulty or dependence in ≥3 activities of daily living), prevalence of caregivers, and median weekly caregiving hours per entrant, using weighted mixed-effects regression against time as linear spline.ResultsAt entry, mean (SD) age was 84 (8.4) years, 609 (64%, all percentages survey weighted) were women, 143 (6%) were Black, 29 (3%) were Hispanic, 30 (4%) were other (other race and ethnicity included American Indian, Asian, Native Hawaiian, and other), and 497 (49%) had dementia. 349 (34%) entered NH, 426 (45%) entered AL, and 157 (21%) entered IL. Overall, NH and AL entry were preceded by months of severe disability and escalating caregiving. Before entry, 49% (95% CI, 29%-68%) of NH entrants and 10% (95% CI, 3%-24%) of AL entrants had severe disability. Most (>97%) had at least a caregiver, but only one-third (NH, 33%; 95% CI, 20%-50%; AL, 33%; 95% CI, 24%-44%) had a paid caregiver. Median care was 27 hours weekly (95% CI, 18-40) in NH entrants and 18 (95% CI, 14-24) in AL entrants. On NH and AL entry, severe disability rose to 89% (95% CI, 82%-94%) and 28% (95% CI, 16%-44%) on NH and AL entry and was 66% (95% CI, 55%-75%) 2 years after entry in AL residents. Few IL entrants (<2%) had severe disability and their median care remained less than 7 hours weekly before and after entry.ConclusionsThis study found that persons often enter NHs and ALs after months of severe disability and substantial help at home, usually from unpaid caregivers. Assisted living residents move when less disabled, but approach levels of disability similar to NH entrants within 2 years. Data may help clinicians understand when home supports approach a breaking point.

Drought risk assessment on the eastern part of Indian peninsula-a study on Purulia district, West Bengal.

This study focuses on measuring the spatial nature of drought risk which is conceived as the product of drought severity, drought vulnerability, and drought exposure in the Purulia district, located in the eastern part of the Indian peninsula. Drought severity is measured using the Standard Precipitation Index and drought vulnerability is calculated as the average condition of meteorological, hydrological, agricultural, and socio-economic drought. The drought types and drought exposure conditions are the outcome of multi-criteria analysis where the Fuzzy Analytical Hierarchy Process is used for assigning weights to the respective parameters and the Analytical Hierarchy Process is used for determining the class ranks. 31.46% of the total district area has registered moderate to high and high vulnerability to drought situations, while 16.57% of the entire district area has been found moderate to high and highly exposed to drought situations. Similarly, 39.39% of the district's total area is under a significant drought risk. Blocks like Barabazar (75.49%), Jhalda-I (71.85%), and Purulia-II (52.66%) have the majority of their area under extreme drought risk conditions. The modeled outcome of drought vulnerability was found significant while being tested with phenomena highly correlated to drought events, land surface temperature, and aridity index. The computed spatial profile of the districts' drought risk condition is of substantial help for the policymakers in preparing effective drought mitigation measures to restrict drought impacts reasonably.

CASE STUDY OF INNOVATION BUSINESS MODEL TO PROMOTE SUSTAINABLE AGRICULTURE OF PRIVATE FARM IN TAIWAN

This study investigation modern sustainable agricultural business model of private farm in Taiwan. Enter the real field, understand the rural environment on the spot, and hope to bring substantial help to the current agricultural environment in Taiwan. Conduct Zhang-Jia farm as the case, based on local ecology, production and life to analyse the management characteristics of the farm. Combine with the theory of ideas explored in the literature to verify the relevance of its actual mode of operation and goals. Observe the local tribal ecology and industrial context to find problems, and compile various conceptual factors that affect the sustainable development of Taiwan's agriculture based on the literature. Through developing organic agriculture, promoting leisure agriculture, developing diversified agricultural products, promoting food agricultural education, and organized production and marketing groups as strategies. Through diversification activities, construct a field environmental relationship brimming with "agricultural production", "peasant life" and "rural ecology", and practice the concept of respect for nature. To maintain the survival of the farm and leave a beautiful homeland, further to increase agricultural production, increase income, maintain natural ecology, create the best areas for farming and living, attract more young people to return to the countryside, and to promote sustainable agriculture in Taiwan.

MA-QC: Free online software for prostate MR quality control and PI-QUAL assessment

PurposeTo validate MRI Analyzer Quality Control (MA-QC), a free and open-source online software designed to facilitate MR data acquisition quality control and PI-QUAL score calculation. Material and methodsMA-QC is a web-based software, designed for analysing DICOM data related to MR acquisition parameters. The software allows automatic extraction of 18 technical criteria, and manual input of 12 visual criteria, to calculate the PI-QUAL score. We collected 100 prostate MRI datasets from four MR device manufacturers to test data compatibility, automatic sequence recognition, and robustness of technical criteria extraction from DICOM data. The main issue was to determine the spatial resolution in the phase and frequency directions, due to variable encoding of the DICOM datasets. ResultsAcquisition data could be extracted from all sample examinations (100%), with a median analysis speed of 15.2 ± 4.4 images per second and mean processing time of 96 [11–326] seconds per examination. MA-CQ automatically detected the optimal T2-w, DWI and DCE sequences in 71 out of 100 (71%) cases, and required manual selection of at least one sequence in 29 out of 100 (29%) cases to get the best parameters. Display of technical criteria for the 3 sequences was instantaneous. PI-QUAL score could be calculated in all cases. ConclusionThis software brings substantial help in the quality assessment of prostate MRI examinations, by providing fast extraction of series data and the 18 technical parameters of PI-QUAL. PI-QUAL scoring can be performed in less than two minutes, helping to focus on the visual criteria, allowing use of this software in the clinical workflow in the aim of improving overall image quality in prostate MR imaging.

Chlamydia psittaci pneumonia in Wuxi, China: retrospective analysis of 55 cases and predictors of severe disease.

More and more patients with community-acquired pneumonia have been detected with Chlamydia psittaci (C. psittaci) infected using metagenomic next-generation sequencing (mNGS). Previously, this was unheard of, and several patients presented with severe pneumonia and even required ECMO. We aimed to clarify the clinical characteristics of C. psittaci pneumonia and find out if there are any possible predictors of severe C. psittaci pneumonia. In this retrospective study, we included all confirmed cases of C. psittaci pneumonia in Wuxi. Epidemiological, clinical, and radiological features, as well as laboratory data, were collected and analyzed. We enrolled 55 patients with C. psittaci pneumonia, with 30 (54.5%) having a history of exposure to birds or their internal organs. 50 (90.9%) patients were diagnosed by mNGS. Patients with C. psittaci pneumonia had many complications, among which, that deserve sufficient attention from clinicians were vascular embolic events (3, 5.5%). High fever was the most common clinical manifestation (41, 74.5%). The majority of patients had a significant increase in neutrophils ratio, neutrophils to lymphocytes ratio (NLR), rapid c-reactive protein, creatine kinase (CK), and lactate dehydrogenase (LDH), as well as a decrease in lymphocytes ratio, albumin, serum sodium, serum potassium, and serum phosphorus. Chest computed tomography scans revealed unilateral pneumonia (70.9%), consolidation (87.3%), air bronchogram (76.4%), and ground-glass opacity (69.1%). The neutrophil ratio, NLR, LDH, and CK were all factors that could identify severe pneumonia. Both AUCs exceeded 0.8; the respective 95% CIs were 0.715-0.944, 0.710-0.963, 0.677-0.937, and 0.718-0.950; all p < 0.05 (0.01, 0.001, 0.007, 0.007 respectively). The ORs were 10.057, 9.750, 10.057, and 9.667, respectively; the 95% CIs were 2.643-38.276, 2.339-40.649, and 2.643-38.276, respectively; all p-values were less than 0.05 (0.001, 0.002, 0.001, 0.001 respectively). C. psittaci pneumonia is a very complex disease that changes all the time. Some patients showed severe pneumonia. Patients will have a poor prognosis if they are not treated promptly and effectively. We discovered that many clinical indicators were typical. Meanwhile, significant increases in neutrophil ratio, NLR, LDH, and CK predicted severe pneumonia. Timely detection of mNGS provided substantial help for clinical diagnosis and early treatment.

Palliative care needs and specialist services post stroke: national population-based study

Objectives(1) To compare palliative care needs of patients admitted primarily with stroke and (2) to determine how the care needs of these patients affect their use of different types of...

중국 반독점법 2022년 개정법의 실체법적 개정사항

On June 24, 2022, at the 35th meeting of the Standing Committee of the 13th National People's Congress, the amendment to the Anti-monopoly Law of the People's Republic of China was passed. It was decided to go into effect on August 1st. This is the first amendment to China's Anti-monopoly Law since its implementation in 2008. A new purpose was proposed in maintaining an orderly market system. This amendment adds innovation encouragement to the legislative purpose of the Anti-monopoly Law, in line with Xi Jinping's leadership's keynote on state affairs, which has emphasized technological innovation. It is specified in this law that it is responsible. In the 2018 central organizational reorganization, the newly established State Administration for Market Regulation, the Anti-monopoly Bureau, which was in charge of anti-monopoly work that had been distributed among the Ministry of Commerce, Development and Development, and the General Administration of Industry and Trade, was separated into a vice-ministerial level organization and upgraded to the State Anti-monopoly Bureau. The amendment bans monopoly, exclusion, and competition restrictions that abuse administrative power, and strengthens the level of punishment, such as significantly increasing the level of fines and linking antitrust penalties with credit records. Clarify the criteria for screening and classifying managers to strengthen screening of managers related to the national economy and livelihood, while introducing a safe harbor rule to exclude managers from sanctions if they prove that their market share is lower than the anti-monopoly standards made with In addition, managers who provide “substantial help” in concluding and implementing monopoly agreements are also subject to sanctions. The amendment is a review of China's anti-monopoly law enforcement over the past 10 years during the deliberation of the revision of the Chinese anti-monopoly law, and can be seen as a summary of legal practice experience. This is a demand inherent in the development of China's socialist market economy, and can help meet the objective demand of the new development pattern. It is conducive to introducing Chinese anti-monopoly law supervision and strengthening anti-monopoly law enforcement, which will be scientifically and objectively conducive.

QUANTITATIVE ANALYSES OF THE ESC GUIDELINES ON SPORTS CARDIOLOGY AND EXERCISE

Objective: Exercise and sports are important components of preventing/delaying the development of cardiovascular disease (CVD) as evidenced by reduced all-cause morbidity and mortality. Because of that, the European Society of Cardiology (ESC) published “2020 ESC Guidelines on sports cardiology and exercise in patients with cardiovascular disease” (GL-SCE) summarizing recommendations and supporting evidence to ‘prescribe’ various types of exercise and sports regimens that can be used to prevent, delay, or alleviate CVD. Thus we aimed to assess quantitatively the efficacy of the GL-SCE providing help in the sport- /exercise-related treatment decision-making in specific cardiovascular condition Design and method: An analytic study was conducted on the GL-SCE regarding the Classes of Recommendations (CLASS) and the Levels of Evidence (LEVEL) (n = 159). Then LEVEL areas under CLASS were calculated to form a Certainty Index (CI: -1 to +1). Results: The frequency of CLASS I and CLASS III was relatively high in GL-SCE (52.2%) and GL-CS) (in Clinical Settings: 50.7%). Yet, the most frequent LEVEL was C (41.2%–83.8%), indicating only a relatively low quality of scientific evidence in GL-SCE. The GL-SCE showed a relatively high CI (+0.57), 78.4% Certainty, and 21.6% Uncertainty. Conclusions: The GL-SCE provides substantial help in decision-making through the recommendations (CLASS), but the supporting evidence (LEVEL) in most cases is only lower quality, which is well reflected in the newly developed Certainty Index identifying issues that should be clarified and investigated in future studies.

Classification of precancerous lesions based on fusion of multiple hierarchical features

PurposeTo investigate an identification method for precancerous gastric cancer based on the fusion of superficial features and deep features of gastroscopic images. The purpose of this study is to make most use of superficial features and deep features to provide clinicians with clinical decision support to assist the diagnosis of precancerous gastric diseases and reduce the workload of doctors. MethodsAccording to the nature of gastroscopic images, 75-dimensional shallow features were manually designed, including histogram features, texture features and high-order features of the image; then, based on the constructed convolutional neural networks such as ResNet and GoogLeNet, before the output layer. A fully connected layer is added as the deep feature of the image. In order to ensure consistent feature weights, the number of neurons in the fully connected layer is designed to be 75 dimensions. Therefore, the superficial and deep features of the image are concatenated, and a machine learning classifier is used to identify gastric polyps, there are three types of gastric precancerous diseases such as gastric polyps, gastric ulcers and gastric erosions. ResultsA dataset with 420 images was collected for each disease, and divided into a training set and a test set with a ratio of 5:1, and then based on the dataset, three methods, such as traditional machine learning, deep learning, and feature fusion, were used respectively. For model training and testing of traditional machine learning and feature fusion, SVM, RF and BP neural network are used as the classification results of the classifier. For deep learning, the GoogLeNet, ResNet, and ResNeXt were implemented. The test results of the model on the test set show that the recognition accuracy of the proposed feature fusion method reaches (SVM: 85.18%; RF: 83.42%; BPNN: 85.18%), which is better than the traditional machine learning method (SVM: 80.17%; RF: 82.37%; BPNN: 84.12%) and the deep learning method (GoogLeNet: 82.54%; ResNet-18: 81.67%; ResNet-50: 81.67%; ResNeXt-50: 82.11%), which proves that this method has obvious advantages. ConclusionThis study provides a new strategy for the identification of precancerous gastric cancer, improving the efficiency and accuracy of precancerous gastric cancer identification, and hopes to provide substantial practical help for the identification of gastric precancerous diseases.

Evaluation of medical decision support systems (DDX generators) using real medical cases of varying complexity and origin

BackgroundMedical decision support systems (CDSSs) are increasingly used in medicine, but their utility in daily medical practice is difficult to evaluate. One variant of CDSS is a generator of differential diagnoses (DDx generator). We performed a feasibility study on three different, publicly available data sets of medical cases in order to identify the frequency in which two different DDx generators provide helpful information (either by providing a list of differential diagnosis or recognizing the expert diagnosis if available) for a given case report.MethodsUsed data sets were n = 105 cases from a web-based forum of telemedicine with real life cases from Afghanistan (Afghan data set; AD), n = 124 cases discussed in a web-based medical forum (Coliquio data set; CD). Both websites are restricted for medical professionals only. The third data set consisted 50 special case reports published in the New England Journal of Medicine (NEJM). After keyword extraction, data were entered into two different DDx generators (IsabelHealth (IH), Memem7 (M7)) to examine differences in target diagnosis recognition and physician-rated usefulness between DDx generators.ResultsBoth DDx generators detected the target diagnosis equally successfully (all cases: M7, 83/170 (49%); IH 90/170 (53%), NEJM: M7, 28/50 (56%); IH, 34/50 (68%); differences n.s.). Differences occurred in AD, where detection of an expert diagnosis was less successful with IH than with M7 (29.7% vs. 54.1%, p = 0.003). In contrast, in CD IH performed significantly better than M7 (73.9% vs. 32.6%, p = 0.021). Congruent identification of target diagnosis occurred in only 46/170 (27.1%) of cases. However, a qualitative analysis of the DDx results revealed useful complements from using the two systems in parallel.ConclusionBoth DDx systems IsabelHealth and Memem7 provided substantial help in finding a helpful list of differential diagnoses or identifying the target diagnosis either in standard cases or complicated and rare cases. Our pilot study highlights the need for different levels of complexity and types of real-world medical test cases, as there are significant differences between DDx generators away from traditional case reports. Combining different results from DDx generators seems to be a possible approach for future review and use of the systems.

Application of cascaded GAN based on CT scan in the diagnosis of aortic dissection

PurposeCurrently, Computed Tomography Angiography (CTA) is the most commonly used clinical method for the diagnosis of aortic dissection, which is much better than plain CT. However, CTA examination has some disadvantages such as time-consuming image processing, complicated procedure and injection of developer. CT plain scanning is widely used in the early diagnosis of arterial dissection because of its convenience, speed and popularity. In order not to delay the optimal diagnosis and treatment time of patients, we use deep learning technology and network model to synthesize plain CT images into CTA images. Patients can be timely professional related departments of clinical diagnosis and treatment, and reduce the rate of missed diagnosis. In this paper, we propose a CTA image synthesis technique for cardiac aortic dissection based on the cascaded generative adjunctive network model. MethodFirstly, we registered CT images, and then used nnU-Net segmentation network model to obtain CT and CTA paired images containing only the aorta. Then we proposed a CTA image synthesis method for aortic dissection based on cascaded generative adversarial. The core idea is to build a cascade generator and double discriminator network based on DCT channel attention mechanism to further enhance the synthesis effect of CTA. ResultsThe model is trained and tested on CT plain scan and CTA image data set of aortic dissection. The results show that the proposed model achieves good results in CTA image synthesis. In the CT data set, the nnU-Net model improves 8.63% and reduces 10.87mm errors in the key index DSC and HD, respectively, compared with the benchmark model U-Net. In CTA data set, nnU-Net model improves 10.27% and reduces 6.56mm error in key index DSC and HD, respectively, compared with benchmark model U-Net. In the synthesis task, the cascaded generative adm network is superior to Pix2pix and Pix2pixHD network models in both PSNR and SSIM, which proves that our proposed model has significant advantages. ConclusionThis study provides new possibilities for CTA image synthesis of aortic dissection, and improves the accuracy and efficiency of diagnosis, and hopes to provide substantial help for the diagnosis of aortic dissection.

Evaluation of Internet Addiction Exercise Intervention Effect for Teenagers Based on Intelligent Psychological Strategy Technology

Modern information has led to Internet addiction among some young people. Some studies have shown that Internet addiction has serious harm to young people, such as affecting young people’s learning and endangering young people’s physical health. Even more serious, it may lead to depression, suicide, and other problem appearance. At present, most of the research on Internet addiction focuses on the development of questionnaires, symptoms, and causes of Internet addiction, and there is still a lack of treatment and intervention. Therefore, in the era of rapid development of artificial intelligence, let artificial intelligence use psychological strategies. Technology to intervene adolescents’ Internet addiction is a new technology required by current intelligent psychological terminal equipment, and this technology is still in the field of data preprocessing and classification. For the intelligent psychological strategy technology that is applied and located in the terminal device, the ability to intelligently identify whether the Internet addiction is an inevitable trend of the development of modern intelligent devices. Looking back at this technology, we can often see two prominent problems with this technology during the development process, namely, accuracy and efficiency. Due to the development of 5G technology and cloud computing, the intelligent identification process can be transferred to the cloud for rapid completion, and the terminal device only needs to receive feedback to solve the efficiency problem as much as possible. The problem of relative accuracy is the difficulty that the current intelligent psychological strategy technology needs to overcome, and it is also the focus of this article. This paper introduces the related technologies in the process of intelligent psychological technology intervention, the extraction of Internet addiction problems, and the use of related technologies to intervene and explore the improvement under the current mainstream algorithm conditions. Intelligent technology improves the accuracy of psychological intervention. Through the discussion of the actual comparison experiment of the algorithm, we can understand that the influencing factors of the accuracy rate are mainly concentrated in the influence of the Internet, such as unreasonable game play and video browsing. We are committed to ensuring that the intelligent psychological strategy technology of terminal equipment can accurately intervene the Internet addiction problem of young people without changing the equipment conditions. First of all, the optimized algorithm is easier to understand and less difficult to use than ordinary algorithms, which greatly reduces the difficulty for psychoanalysts or intervention evaluators to deal with problems. Secondly, the intelligent psychological strategy technology is the product of many experiments and is gradually widely recognized and accepted by everyone. It can carry out statistics, calculation, and visual display through various data of intelligent programs and realizes automatic intervention for multiple problems, which can significantly reduce the user’s time and cost investment, reduce the burden of staff, and provide analysts and evaluators with substantial information help.

Between Hospitality and Hostility: The Experience of Migration Through Things

ABSTRACT In this article, I propose the category of ‘hospitable things’, and argue that it can improve our understanding of the relationships between subjects and objects in the situation of migration. The analysis uses a case study of Polish migration to territories of pre-war Germany and the Free City of Danzig – the so-called Recovered Territories [Ziemie Odzyskane] – in the twilight of World War II. Based on primary sources from the time, I scrutinise how the new Polish settlers transformed German property into ‘hospitable things’ with substantial help from the post-war propaganda. I reconstruct the affective atmosphere of the period and confront these expectations with the actual experience of hostility materialised by everyday things. The image of ‘hospitable things’ – waiting and ready to be taken – has been formed through the experience of migration in radically inhospitable circumstances. ‘Hospitable things’ enable people to cope with the emotions accompanying the fragile experience of displacement; they help to form and transform fears and insecurities, but also desires and wants.

Lung image segmentation based on DRD U-Net and combined WGAN with Deep Neural Network

PurposeCOVID-19 is a hot issue right now, and it's causing a huge number of infections in people, posing a grave threat to human life. Deep learning-based image diagnostic technology can effectively enhance the deficiencies of the current main detection method. This paper proposes a multi-classification model diagnosis based on segmentation and classification multi-task. MethodIn the segmentation task, the end-to-end DRD U-Net model is used to segment the lung lesions to improve the ability of feature reuse and target segmentation. In the classification task, the model combined with WGAN and Deep Neural Network classifier is used to effectively solve the problem of multi-classification of COVID-19 images with small samples, to achieve the goal of effectively distinguishing COVID-19 patients, other pneumonia patients, and normal subjects. ResultsExperiments are carried out on common X-ray image and CT image data sets. The results display that in the segmentation task, the model is optimal in the key indicators of DSC and HD, and the error is increased by 0.33% and reduced by 3.57 mm compared with the original network U-Net. In the classification task, compared with SMOTE oversampling method, accuracy increased from 65.32% to 73.84%, F-measure increased from 67.65% to 74.65%, G-mean increased from 66.52% to 74.37%. At the same time, compared with other classical multi-task models, the results also have some advantages. ConclusionThis study provides new possibilities for COVID-19 image diagnosis methods, improves the accuracy of diagnosis, and hopes to provide substantial help for COVID-19 diagnosis.

Deep learning-based medical image segmentation of the aorta using XR-MSF-U-Net

PurposeThis paper proposes a CT images and MRI segmentation technology of cardiac aorta based on XR-MSF-U-Net model. The purpose of this method is to better analyze the patient's condition, reduce the misdiagnosis and mortality rate of cardiovascular disease in inhabitants, and effectively avoid the subjectivity and unrepeatability of manual segmentation of heart aorta, and reduce the workload of doctors. MethodWe implement the X ResNet (XR) convolution module to replace the different convolution kernels of each branch of two-layer convolution XR of common model U-Net, which can make the model extract more useful features more efficiently. Meanwhile, a plug and play attention module integrating multi-scale features Multi-scale features fusion module (MSF) is proposed, which integrates global local and spatial features of different receptive fields to enhance network details to achieve the goal of efficient segmentation of cardiac aorta through CT images and MRI. ResultsThe model is trained on common cardiac CT images and MRI data sets and tested on our collected data sets to verify the generalization ability of the model. The results show that the proposed XR-MSF-U-Net model achieves a good segmentation effect on CT images and MRI. In the CT data set, the XR-MSF-U-Net model improves 7.99% in key index DSC and reduces 11.01 mm in HD compared with the benchmark model U-Net, respectively. In the MRI data set, XR-MSF-U-Net model improves 10.19% and reduces 6.86 mm error in key index DSC and HD compared with benchmark model U-Net, respectively. And it is superior to similar models in segmentation effect, proving that this model has significant advantages. ConclusionThis study provides new possibilities for the segmentation of aortic CT images and MRI, improves the accuracy and efficiency of diagnosis, and hopes to provide substantial help for the segmentation of aortic CT images and MRI.

Forecasting Staphylococcus aureus Infections Using Genome-Wide Association Studies, Machine Learning, and Transcriptomic Approaches.

ABSTRACTStaphylococcus aureus is a major human and animal pathogen, colonizing diverse ecological niches within its hosts. Predicting whether an isolate will infect a specific host and its subsequent clinical fate remains unknown. In this study, we investigated the S. aureus pangenome using a curated set of 356 strains, spanning a wide range of hosts, origins, and clinical display and antibiotic resistance profiles. We used genome-wide association study (GWAS) and random forest (RF) algorithms to discriminate strains based on their origins and clinical sources. Here, we show that the presence of sak and scn can discriminate strains based on their host specificity, while other genes such as mecA are often associated with virulent outcomes. Both GWAS and RF indicated the importance of intergenic regions (IGRs) and coding DNA sequence (CDS) but not sRNAs in forecasting an outcome. Additional transcriptomic analyses performed on the most prevalent clonal complex 8 (CC8) clonal types, in media mimicking nasal colonization or bacteremia, indicated three RNAs as potential RNA markers to forecast infection, followed by 30 others that could serve as infection severity predictors. Our report shows that genetic association and transcriptomics are complementary approaches that will be combined in a single analytical framework to improve our understanding of bacterial pathogenesis and ultimately identify potential predictive molecular markers.IMPORTANCE Predicting the outcome of bacterial colonization and infections, based on extensive genomic and transcriptomic data from a given pathogen, would be of substantial help for clinicians in treating and curing patients. In this report, genome-wide association studies and random forest algorithms have defined gene combinations that differentiate human from animal strains, colonization from diseases, and nonsevere from severe diseases, while it revealed the importance of IGRs and CDS, but not small RNAs (sRNAs), in anticipating an outcome. In addition, transcriptomic analyses performed on the most prevalent clonal types, in media mimicking either nasal colonization or bacteremia, revealed significant differences and therefore potent RNA markers. Overall, the use of both genomic and transcriptomic data in a single analytical framework can enhance our understanding of bacterial pathogenesis.