Analysis of the first genetic engineering attribution challenge

In our current research, we are developing a practical mobile vehicle navigation system which is capable of controlling multiple vehicles on the general environment. In order to realize the practical use, navigation system should have high accuracy and be low cost. Therefore, in this paper, we propose the vehicle navigation system which realizes vehicles navigation using high accuracy localization scheme by ceiling cameras with infrared filters and LED markers. Our key ideas are two topics. One topic is system integration which consists of high accuracy recognition system for LED marker and AGV controller with low cost. The other topic is novel AGV navigation scheme with high accuracy under occlusion condition. For practical use, mobile vehicles are required to continue its tasks with safety and high accuracy on temporary occlusion condition. Our developed system can navigate target vehicle based on estimation odometry error with simple algorithm. The performance of our proposed control system is experimented with our prototype.

ABSTRACTWe aimed to review strategies for identifying SARS-CoV-2 infection before the availability of molecular test results, and to assess the reporting quality of the studies identified through the application of the STARD guideline. We screened 3,821 articles published until 30 April 2021, of which 23 met the inclusion criteria: including at least two diagnostic variables, being designed for use in clinical practice or in a public health context and providing diagnostic accuracy rates. Data extraction and application of STARD criteria were performed independently by two researchers and discrepancies were discussed with a third author. Most of the studies (16, 69.6%) included symptomatic patients with suspected infection, six studies (26.1%) included patients already diagnosed and one study (4.3%) included individuals with close contact to a COVID-positive patient. The main variables considered in the studies, which included symptomatic patients, were imaging and demographic characteristics, symptoms, and lymphocyte count. The values for area under the receiver operating characteristic curve (AUC)ranged from 53-97.4. Seven studies (30.4%) validated the diagnostic model in an independent sample. The average number of STARD criteria fulfilled was 17.6 (maximum, 27 and minimum, 5). High diagnostic accuracy values are shown when more than one diagnostic variable is considered, mainly imaging and demographic characteristics, symptoms, and lymphocyte count. This could offer the potential to identify individuals with SARS-CoV-2 infection with high accuracy when molecular testing is not available. However, external validation for developed models and evaluations in populations as similar as possible to those in which they will be applied is urgently needed.IMPORTANCE According to this review, the inclusion of more than one diagnostic test in the diagnostic process for COVID-19 infection shows high diagnostic accuracy values. Imaging characteristics, patients’ symptoms, demographic characteristics, and lymphocyte count were the variables most frequently included in the diagnostic models. However, developed models should be externally validated before reaching conclusions on their utility in practice. In addition, it is important to bear in mind that the test should be evaluated in populations as similar as possible to those in which it will be applied in practice.

PURPOSE. To consider the problems of modeling the processes of run-out, self-starting of a group of asynchronous electric motors (AM) in case of short-term power outages (NEC) and voltage drops in external short circuits (SC), convenient for programming and practical use. To establish the integral reaction of the AM group during self-start to the disturbing effect, taking into account their characteristics and duration to establish the permissible limit values of the NEC. To develop an algorithm for the transient process of self-starting of the AM group when using matrix and vector data representation when solving the basic equation of the rotor motion and its computer implementation. METHODS. When solving the problem, the following methods were used: successive approximations when solving the basic electromechanical equation, taking into account electromagnetic transient processes; Gauss-Seidel method with accelerating the convergence of the iterative process when solving the equations of the parameters of the regime; method of nodal stresses. The algorithm is implemented in VBA and tested in Matlab Simulink. RESULTS. The article describes the relevance of the topic, considers a model of AM according to catalog data, an algorithm for self-starting a group of an AM with NEC and external short circuits, taking into account electromagnetic transient processes, which has high accuracy and is convenient for practical use. CONCLUSION. The use of asynchronous motor catalogs makes it possible not to carry out laborious preliminary calculations of the parameters of asynchronous motors. The application of the Gauss-Seidel method with acceleration of convergence provides a decrease in the number of iterations. Taking into account electromagnetic transients and the effect of displacement of the rotor current allows you to evaluate the mutual influence of motors and increase the accuracy of calculations. The use of the method of nodal voltages makes it possible to determine the residual voltage on the busbar section with AM, if at the first moment the motors are switched on to short circuit. The implementation of the algorithm in the VBA environment is convenient for practical use.

Pan-mediastinal neoplasm diagnosis via nationwide federated learning: a multicentre cohort study

Background Early relapse in children with Crohn’s Disease (CD) after reaching remission is associated with a more severe disease course that significantly impairs quality of life. We have previously shown that a Bayesian approach predicted clinical course in children with CD following the first year after diagnosis with high accuracy when ensuring samples were truly treatment-naïve. Here, we aimed to assess the impact on the accuracy when taking a broader timeframe of stool collection to baseline, in order to facilitate use in clinical trials and eventually daily practice. Methods We selected de novo paediatric CD patients with PCDAI>10 from the RISK cohort with baseline stool samples within 14 days after start of induction treatment. We assessed if they sustained remission at 12 months (PCDAI≤10). Using QIIME2 sequences were demultiplexed, joined, and denoised (deblur) to obtain amplicon sequence variants (ASVs). The ASVs were classified (classify-sklearn) using a pre-trained SILVA database. We used hierarchical Bayesian model for microbial community structure (BioMiCo), previously trained on treatment-naïve stool samples to predict treatment outcomes at 6 months according to baseline gut microbiome differences. Results Patient metadata and 16S rRNA amplicon data were available from 197 stool samples of newly diagnosed paediatric CD patients as part of the RISK cohort. Previous analysis of 42 truly treatment-naïve samples lead to prediction of samples in patients maintaining remission without early treatment escalation and those that did not in 81% and 75% (AUC=0.79). In this analysis, we selected 13 samples of children with PCDAI that were taken within 14 days after start of induction therapy. PCDAI varied from 15–47.5 at baseline. Therapy regimens started within the first 14 days were EEN, 5ASA, corticosteroids, immunomodulators and antibiotics. The Bayesian model predicted 12-month outcome of patients that maintained remission with a positive predictive value of 75% and negative predictive value of 60% (AUC 0.76). Conclusion Using treatment-naïve faecal samples only, a Bayesian approach predicted clinical course in treatment-naïve children with CD over the first year after diagnosis with high accuracy. When taking a broader timeframe of stool collection after start of treatment, the accuracy of the model decreased only slightly. Further exploration of microbiome signatures and potential use in practice should therefore emphasize the importance of treatment naivety of samples, but not necessarily rule them out for prediction of treatment outcome.

To reduce the pressure of medical triage in the hospitals, this paper proposes a medical triage system that could classify patients' questions or texts about their symptoms into several given categories to give suggestions on which kind of consulting room patients could choose. First, we have done extensive research on the medical care situation and the hospitals' problems in China and conclude that reducing the triage pressure is of great importance for hospitals. We then collect the medical Question Answering datasets, including questions and answers with symptom tags. According to the form of our data, we use BERT, a mainstream model in Natural Language Processing, as the base of our system and modify it with additional components specified to our task. We developed two models based on different datasets. One is trained by data from the five most frequent symptom tags. And for the other one, we use the whole dataset by identifying the appearance of special words, measuring the overlap of all the tags, and merging them into 20 categories. Both of them utilize several training techniques and result in relatively high accuracy: top1 85% accuracy and top2 accuracy 96% on the smaller dataset, top1 accuracy 66.2%, and top2 accuracy 78.3% on the other one. Then we analyze the results and build up our web system for medical use. If given real-world data with similar data distribution, our system could help patients judge diseases and alleviate the triage problem in medical treatment to a certain extent. Moreover, a similar strategy of our model could also be adapted for use in different fields like book searching in libraries. Therefore, our system has a broad application prospect.

Collaborative development, as one of the most widely used development models, is very effective at increasing development efficiency and software quality. In collaborative development, developers deploy services on the Internet and provide stable business functionality to the public. However, developers with different levels of development experience and code levels may classify the services into the wrong categories. As a result, reusing these services is impossible due to the confusing service categories, which has a significant impact on developers’ development efficiency. Most existing service classification approaches rely on textual information such as Web service descriptions and names, which may not provide enough information to determine their categories when the descriptions and names are too brief or vague. This paper proposes a Service Classification Approach based on Feature Fusion (SCAFF), which combines semantic and structural features. SCAFF first extracts service semantic feature vectors and service structural feature vectors by SimCSE, MixGCF, and LightGCN. Then it combines these two features and finally obtains the service category using two fully connected layers. Compared with TextCNN, LSTM, and ServeNet, SCAFF has higher accuracy on Top-1 accuracy and Top-5 accuracy.

Dam safety assessment is typically made by comparison between the outcome of some predictive model and measured monitoring data. This is done separately for each response variable, and the results are later interpreted before decision making. In this work, three approaches based on machine learning classifiers are evaluated for the joint analysis of a set of monitoring variables: multi-class, two-class and one-class classification. Support vector machines are applied to all prediction tasks, and random forest is also used for multi-class and two-class. The results show high accuracy for multi-class classification, although the approach has limitations for practical use. The performance in two-class classification is strongly dependent on the features of the anomalies to detect and their similarity to those used for model fitting. The one-class classification model based on support vector machines showed high prediction accuracy, while avoiding the need for correctly selecting and modelling the potential anomalies. A criterion for anomaly detection based on model predictions is defined, which results in a decrease in the misclassification rate. The possibilities and limitations of all three approaches for practical use are discussed.

In order to assess the practical efficacy of the S-Detect Thyroid artificial intelligence-based program for automatic detection and analysis of thyroid lesions, the prospective assessment of 84 focal thyroid lesions was carried out. he risk of malignancy was stratified according to the EU-TIRADS at the same time. A fine-needle aspiration biopsy was performed for all detected nodules of EU-TIRADS 3–5 category and a diameter ≥10 mm. Cytological examination was performed using the Bethesda classification. According to the cytology data, all patients were divided into two groups: 73 patients with benign thyroid nodules (Bethesda II) and 11 patients with malignant nodules (Bethesda V). Patients with “uncertain” Bethesda categories “I”, “III”, and “IV” were excluded from the study. The results of the study showed that the use of the S-Detect program based on artificial intelligence allows for differential diagnostics of benign (Bethesda II) and malignant (Bethesda V) thyroid nodules with a sensitivity of 90.9%, specificity of 94.5%, positive and negative predictive value of 71.4% and 98.6%, accuracy of 94%, and AUC 0.941. In our opinion, the best results of all program settings show the S-Detect “High Accuracy” mode, which we recommend for practical use. In some cases, there was disagreement between the S-Detect and the doctor's opinion in characterizing the nodule structure and echogenicity, as well as in determining the presence of macro- and microcalcifications. In our opinion, the use of the S-Detect benign/malignant criteria as indications for needle aspiration biopsy would avoid obviously unnecessary diagnostic interventions in some patients with thyroid nodules classified as EU TIRADS 3–5. However, the S-Detect artificial intelligence program cannot currently fully replace the doctor's intellect, erudition, and experience.

Introduction: The successful “Divergent” sci-fi trilogy by writer Veronica Roth portrays a dystopian and post-apocalyptic society where the population is divided into five groups called “Factions,” each with a specific social role and associated to a specific set of psychological traits. Though fictional, such typology is compelling and may provide a significant contribution to personality studies.Objectives: To investigate the accuracy of the classification of psychological and sociocultural traits into five Factions as described in Divergent and their potential practical usefulness for understanding work life choices and experiences in organizations.Method: A total of 217 Brazilian adult men and women of various ages, socioeconomic status and ethnicities were submitted to measures of several psychological and sociocultural variables, as well as of how strongly they supposedly manifest each Faction. The resulting dataset was studied using Smallest Space Analysis (SSA) and Facet Theory.Results: The Factions were shown not only to be associated to psychological variables in ways consistent with the descriptions from Divergent, but also to be related to specific aspects of one’s work life in organizations.Conclusion: The five Factions conceived by Roth appear to constitute an original set of constructs that are psychologically valid and, at the same time, of practical use in predicting work life choices and experiences. This justifies engaging in future empirical and theoretical work toward a new scientific model of potential practical value.

The growing popularity of the concepts of circular economy and resource cascade has intensified the need for consistent handling of multifunctionality‐related challenges when modeling multiple cycles in life cycle assessment (LCA). In LCA, end‐of‐life upcycling and downcycling effects (also known as quality changes), triggered by the presence of multiple life cycles, have only recently begun to be studied from a consequential perspective, and no studies exist investigating attributional aspects. In this paper, a novel approach that considers quality in attributional LCA is proposed. The attributional, cut‐off, open loop, and proposed approaches are compared in the form of a cascading case study. The implications of integrating quality in both perspectives are contrasted by modeling the same case study under a consequential perspective. By performing sensitivity analysis on the quality parameters in attributional LCA, we found that the integration of quality influences the results of the proposed approach by up to 15%. In the case of consequential LCA, the implementation of quality yields an influence between 97% and 138% of the results for each unit variation of quality. Comparison between the two perspectives of quality shows the same trend of supporting high‐quality cascades. However, the attributional perspective of quality accomplishes this by redistributing impacts, while the consequential perspective affects the external benefits generated by the cascade. Considering the influence of quality on the results of both perspectives, future work should focus on establishing the technical or economic properties that would allow for practical use of quality in various circular economy and resource cascade applications.

ABSTRACTIn order to remove the residual nitrogen from the secondary-treated milking centre wastewater, the electrochemical reaction including NH4-N oxidation and NOx-N reduction has been known as a relatively simple technique. Through the present study, the electrochemical reactor using the Ti-coated IrO2 anode and stainless steel cathode was optimized for practical use on farm. The key operational parameters [electrode area (EA) (cm2/L), current density (CD) (A/cm2), electrolyte concentration (EC) (mg/L as NaCl), and reaction time (RT) (min)] were selected and their effects were evaluated using response surface methodology for the responses of nitrogen and colour removal efficiencies, and power consumption. The experimental design was followed for the central composite design as a fractional factorial design. As a result of the analysis of variance, the p-values of the second-order polynomial models for three responses were significantly fit to the empirical values. The nitrogen removal was significantly influenced by CD, EC, and RT (p < .05), whereas colour removal was significantly governed by EA, CD, RT, the interaction of EA and EC (p < .05). For higher efficiency of nitrogen removal over 90%, the combination of [EA, 20 cm2/L; CD, 0.044 A/cm2; EC, 3.87 g/L as NaCl; RT, 240 min] was revealed as an optimal operational condition. The investigation on cathodic reduction of NOx-N may be required with respect to nitrite and nitrate separately as a future work.

We investigate generalized stochastic resonance (GSR) in a fractional harmonic oscillator with time delay and fluctuation damping. Considering that nonlinear noise widely exists in actual systems compared with linear noise, the fluctuation of damping is modeled as polynomial trichotomous noise. The analytical expression of the output amplitude gain is derived by applying small delay approximation and stochastic averaging. Simulation results show that the output amplitude gain curves, as functions of time delay versus noise parameters, behave non-monotonically and exhibit typical GSR. Furthermore, nonlinear phenomena of stochastic multi-resonance with two, three, and four peaks are observed. Finally, we provide the phase diagrams for GSR versus time delay with different system parameters. Furthermore, the mechanism underlying the stochastic multi-resonance with two, three, and four peaks, as well as their connections, is elucidated. This study could serve as a theoretical basis for the practical use and control of GSR in future works.

This book proposed novel methods for building deformable machines with conceptually infinite degrees of freedom. The bodies of these machines consist of actively deformable materials, which are generally referred to as artificial muscles. Recent advances in electroactive polymers provided promising classes of materials for these artificial muscles. Despite their lack of durability, strength and safety, preventing them from being of complete practical use, we were still motivated for realizing deformable machines. Rather than solving the material problems, we tackled the following mechanical problems: organized the universal difficulty to embody deformable machines made of active materials into two parts. One originates in deformability of the machines, and the other derives from activeness of the materials applied. It was referred to as the deformability problem and the activeness problem. The major finding of this research is that these problems are complimentary, and one solves the other. Gel robots were prototyped utilizing electroactive polymers in order to solve these problems. Modelling, design and control methodologies were explored, which form the foundations in addressing the key issues.

Natural language interfaces are becoming more and more common, because they are powerful and easy to use. Examples of such interfaces are voice controlled navigation devices, Apple’s personal assistant Siri, Google Voice Search, and translation services. However, such interfaces are extremely difficult to build, to maintain, and to port to new domains. We present an approach for building and porting such interfaces quickly. NLCI is a natural language command interpreter that accepts action commands in English and translates them into executable code. The core component is an ontology that models an API. Once the API is “ontologized”, NLCI translates input sentences into sequences of API calls that implement the intended actions. Two radically different APIs were ontologized: openHAB for home automation and Alice for building 3D animations. Construction of the ontology can be automated if the API uses descriptive names for its components. In that case, the language interface can be generated completely automatically. Recall and precision of NLCI on a benchmark of 50 input scripts are 67 and 78 %, resp. Though not yet acceptable for practical use, the results indicate that the approach is feasible. NLCI accepts typed input only. Future work will use a speech front-end to test spoken input.