Assistant Tool Research Articles

The co-morbidity of DSM-V Gambling with DSM-V mental disorders and substance abuse in a Kenyan context of high risk schizophrenia

IntroductionThere is evidence that gambling disorder shares similarities with other types of addictive behavior, such as occurs in substance abuse. In addition, co-morbidity of gambling with mental disorders has been established in school-going students.AimThis study aimed at determining the comorbidity of DSM-V gambling disorder with DSM-V mental disorders and substance abuse in high school, college and university students in Kenya.MethodsThis was a cross-sectional study among 536 high school, college and university students. We collected data on socio-demographic characteristics, economic indicators, DSM-V diagnosis including DSM-V gambling disorder and substance use disorders using the WHO ASSIST tool. Descriptive and inferential analyses were done.ResultsA total of 536 students participated in the study, of which 11.4% (61 out of 536) had DSM-V gambling disorder. Male gender (AOR = 12.0, 95% CI: 4.99–34.3), antisocial personality disorder (AOR = 3.42, 95% CI: 1.34–8.54), tobacco use (AOR = 4.42, 95% CI: 1.15–18.3) and conduct disorder (AOR = 7.56, 95% CI: 2.34–25.1) were predictors of gambling disorder.ConclusionGambling is highly prevalent in Kenya learning institutions at 11.4% and is associated with mental disorders and substance use. There is a need for public awareness of gambling among Kenyan youths.

Impact of VR Application in an Academic Context

Traditional learning has faced major changes due to the COVID-19 pandemic, highlighting the necessity for innovative education methods. Virtual reality (VR) technology has the potential to change teaching and learning paradigms by providing a gamified, immersive, and engaging education. The purpose of this study is to evaluate the impact of virtual reality in academic context by using a VR software instrument (called EduAssistant). The system’s features such as virtual amphitheater, search by voice recognition, whiteboard, and a video conference system have fostered a sense of connection and community interaction. The study involved 117 students for VR experience, out of which 97 watched a pre-recorded video and 20 students used the VR headset, and an additional 20 students for traditional learning. The students who used the VR headset achieved a significantly higher mean quiz score of 8.31 compared to 7.55 for the traditional learning group with a two-tailed p-value of 0.0468. Over 80% of the total number of participants were satisfied (4 or 5 out of 5) with the experience and the confidence level when searching through voice recognition was over 90%. The study demonstrates that virtual reality is an excellent approach for changing conventional education. The research results, based on samples, simulations, and surveys, revealed a positive impact of VR and its gamification methods on the students’ cognitive performance, engagement, and learning experience. Immersion provided by a virtual assistant tool helped to promote active and deep learning. Experiments based on EduAssistant features suggest that virtual reality is also an effective strategy for future research related to students with disabilities.

Developing an integrated Local Culture Padlet Based Writing Course Using Systemic Functional Linguistics Genre Based Pedagogy

Abstract. Due to the implementation of distance learning policy in Indonesia, the usual class is suspended as a response to the COVID-19 pandemic outbreak. Since then, the demand of innovative teaching involving the use of computer assisted learning tools increases progressively. Undeniable, a distance learning scheme has not been able to outperform a conventional face-to-face classroom learning. One of the problems was encountered in Production Écrite Elimentaire (hereafter PEE) class, where lecturers could not monitor students’ writing progress directly. This study aims at developing a Padlet based writing course by integrating local culture using Systemic Functional Linguistic Genre Based pedagogy (SFL-GBP) adopting ADDIE approach. In order to achieve this objective, three lecturers and 32 students were involved to validate the result of Padlet product. This research used two kinds of instruments i.e., an open-ended questionnaire and an experts’ rubric. The open-ended questionnaire was used to identify students’ needs in PEE course, and the experts’ rubric was used to examine Padlet by regarding the appropriateness of material and language, the procedure of SFL-GPB, and the media. The overall mean scores of the experts' judgement towards the Padlet feasibility shows significant figures, i.e., the material aspects gained 3.7, the appropriateness was 3.8, and the media reached 3.7. On the other hand, the results of the students' opinion analysis also show a positive figure where the mean score for material as well as language aspects were 3.9, and media aspect had 3.75 mean score. The findings. The findings of this study were expected to have a further contribution to the development of comprehensive teaching syllabuses, materials, as well as lesson plans for French-language classrooms in the university, especially integrating local culture and the SFL Pedagogy approach. Keywords: Padlet, Writing Course, Local Culture, SFL-GBP

MP60-18 EFFICIENT AUGMENTED INTELLIGENCE STRATEGY WITH POTENTIAL USE FOR REAL-TIME BLADDER TUMOR DETECTION

You have accessJournal of UrologyCME1 Apr 2023MP60-18 EFFICIENT AUGMENTED INTELLIGENCE STRATEGY WITH POTENTIAL USE FOR REAL-TIME BLADDER TUMOR DETECTION Okyaz Eminaga, Timothy Lee, Mark Laurie, Jessie Ge, Vinh LA, Jin Long, Eugene Shkolyar, Xiao Jia, Axel Semjonow, Martin Bogemann, Hubert Lau, Lei Xing, and Joseph Liao Okyaz EminagaOkyaz Eminaga More articles by this author , Timothy LeeTimothy Lee More articles by this author , Mark LaurieMark Laurie More articles by this author , Jessie GeJessie Ge More articles by this author , Vinh LAVinh LA More articles by this author , Jin LongJin Long More articles by this author , Eugene ShkolyarEugene Shkolyar More articles by this author , Xiao JiaXiao Jia More articles by this author , Axel SemjonowAxel Semjonow More articles by this author , Martin BogemannMartin Bogemann More articles by this author , Hubert LauHubert Lau More articles by this author , Lei XingLei Xing More articles by this author , and Joseph LiaoJoseph Liao More articles by this author View All Author Informationhttps://doi.org/10.1097/JU.0000000000003318.18AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookLinked InTwitterEmail Abstract INTRODUCTION AND OBJECTIVE: Development of intelligence systems for bladder tumor detection is cost- and labor-intensive. Specifically, image annotation is one of the most expensive tasks in the development of intelligence systems. Moreover, previous studies utilized selective screenshots or frame sequences for model development and validation, despite that cystoscopy is a dynamic visual inspection impacted by random noises. The current work proposes an efficient strategy to develop augmented intelligence strategy ready for real-time bladder tumor detection as computer-aided assistance tool for clinicians. METHODS: We used a previously published educational cystoscopy atlas (n=312 images) and our deep learning models (ConvNeXt, PlexusNet, MobileNet, SwinTransformer) covering a variety of model complexity and computation efficacy to estimate the ratio between cancer and normal confidence scores; We applied an image augmentation strategy called RandAugment to populate the training set for model training and externally validated on video records for the initial diagnostic cystoscopy prior to TURBT from 68 cases with benign and bladder cancers tumors (i.e., region of interest, ROI) at a single center. Each frame of the video was labeled by ROI status. ROI was confirmed by pathology examination and the Delphi method. The ROI status was predicted based on the ratio (if the ratio>1, then the frame is positive for ROI, otherwise negative). On external validation, areas of adequate illumination in video frames were considered as input for models. The performance measurement included specificity and sensitivity at frame level, frame sequence (block) level, and ROI level for each case. The block level split each full-length video into small segments according to the ROI status. RESULTS: Specificity was statistically comparable between four models at frame (range: 30.0–44.8%) and block levels (56–67%) by p>0.05. While sensitivity at frame level (range: 81.4 – 88.1) statistically differed between the models, sensitivity at block level (100%) and ROI level (100%) were comparable between these models, indicating that all ROI are detectable by these models and that the frame-level model performance is impacted by the random noises. MobileNet and PlexusNet were computationally more efficient (22 and 19 frames per second, retrospectively) and suitable for real-time detection task than ConvNeXt and SwinTransformer (13 and 17 frames per second, retrospectively). CONCLUSIONS: Educational cystoscopy atlas and cost-effective model development strategy facilitates the development of accurate and efficient intelligence systems with potential use for real-time bladder tumor detection. Source of Funding: The work was supported by National Institutes of Health R01 CA260426 to JCL. © 2023 by American Urological Association Education and Research, Inc.FiguresReferencesRelatedDetails Volume 209Issue Supplement 4April 2023Page: e850 Advertisement Copyright & Permissions© 2023 by American Urological Association Education and Research, Inc.MetricsAuthor Information Okyaz Eminaga More articles by this author Timothy Lee More articles by this author Mark Laurie More articles by this author Jessie Ge More articles by this author Vinh LA More articles by this author Jin Long More articles by this author Eugene Shkolyar More articles by this author Xiao Jia More articles by this author Axel Semjonow More articles by this author Martin Bogemann More articles by this author Hubert Lau More articles by this author Lei Xing More articles by this author Joseph Liao More articles by this author Expand All Advertisement PDF downloadLoading ...

Attention is not all you need: the complicated case of ethically using large language models in healthcare and medicine.

Large Language Models (LLMs) are a key component of generative artificial intelligence (AI) applications for creating new content including text, imagery, audio, code, and videos in response to textual instructions. Without human oversight, guidance and responsible design and operation, such generative AI applications will remain a party trick with substantial potential for creating and spreading misinformation or harmful and inaccurate content at unprecedented scale. However, if positioned and developed responsibly as companions to humans augmenting but not replacing their role in decision making, knowledge retrieval and other cognitive processes, they could evolve into highly efficient, trustworthy, assistive tools for information management. This perspective describes how such tools could transform data management workflows in healthcare and medicine, explains how the underlying technology works, provides an assessment of risks and limitations, and proposes an ethical, technical, and cultural framework for responsible design, development, and deployment. It seeks to incentivise users, developers, providers, and regulators of generative AI that utilises LLMs to collectively prepare for the transformational role this technology could play in evidence-based sectors.

Legal regulation of electronic evidence and the practice of their use in the judiciary of Ukraine

The article, based on the analysis of judicial practice (criminal, civil, administrative and economic proceedings), analyzes the assessment and use of evidence received in electronic form by courts, and draws a conclusion regarding the lack of unanimity in these issues; in particular, regarding the very concept of "electronic evidence" (in the Criminal Procedural Code of Ukraine, unlike other procedural codes, this term is not defined) and its duplicate or copy, the method of recording and certification (by whom: by the provider or another entity), the use screenshot seizure, etc. Ukraine's signing on November 30, 2022 of the Second Additional Protocol to the Convention on Cybercrime, which is aimed at improving the process of electronic data collection, marks the beginning of a new law-making process to improve Ukraine's national legislation in accordance with international standards. The basis for this is the created legal basis for international direct cooperation with service providers, accelerated forms of cooperation for disclosure of information about the subscriber and traffic data, accelerated cooperation and disclosure of information in emergency situations, additional tools for mutual assistance, data protection and other guarantees of the rule of law are also provided , the definition of "emergency situation" is given for the first time, which should be understood as a situation that creates a significant and unavoidable risk to the life or safety of any natural person, and "personal data" is considered to be information related to the identification of a person. On the basis of updated international standards, current legislation and practice, it is proposed to start an in-depth study of the "Theory of electronic evidence", which will contribute to the development of verified bills and the adoption by the parliament of a law that would specify the unambiguous terms "electronic evidence", a copy/duplicate of electronic evidence, the order of withdrawal, records or certificates, the provider's authority to assist in legal proceedings.

Positive climate effects when AR customer support simultaneous trains AI experts for the smart industries of the future

Initially, Artificial Intelligence (AI) focused on diagnostics during the 70s and 80s. Unfortunately, it did not gain trust and few industries embraced it, mostly due to the extensive manual programming effort that AI required for interpreting data and act. In addition, the computer capacity, for handling the amounts of data necessary to train AI, was lacking the disc dimensions we are used to today, which made it go slowly. Not until the 2000 s confidence in AI was established in parallel with the introduction of new tools that was paving the way for PLS, PCA, ANN and soft sensors. Year 2011, IBM Watson (an AI application) was developed and won over the jeopardy champion. Today's machine learning (ML) such as “deep learning” and artificial neural networks (ANN) have created interesting use cases. AI has therefore regained confidence and industries are beginning to embrace where they see appropriate uses. Simultaneously, Internet of Things (IoT) tools have been introduced and made it possible to develop new capabilities such as virtual reality (VR), augmented reality (AR), mixed reality (MR) and extended reality (XR). These technologies are maturing and could be used in several application areas for the industries and form part of their digitalization journey. Furthermore, it is not only the industries that could benefit from introducing these technologies. Studies also show several areas and use cases where augmented reality has a positive impact, such as on students' learning ability. Yet few teachers know or use this technology. This paper evaluates and analyze AR, remote assistance tool for industrial purposes. The potential of the tool is discussed for frequent maintenance cases in the mining industry. Further on, if we look into the future, it is not surprising if we will be able to see that today's concepts of reality tools have evolved to become smarter by being trained by multimedia recognition and from people who have thus created an AI expert. Where the AI expert will support customers and be able to solve simple errors but also those that occur rarely and thus be a natural part of the solution for future completely autonomous processes for the industry. The article demonstrates a framework for creating smarter tools by combining AR, ML and AI and forms part of the basis creating the smarter industry of the future. Natural Language Processing (NLP) toolbox has been utilized to train and test an AI expert to give suitable resolutions to a specific maintenance request. The motivation for AR is the possible energy savings and reduction of CO₂ emissions in the maintenance field for all business trips that can be avoided. At the same time saving money for the industries and expert manhours that are spent on traveling and finally enhancing the productivity for the industries. Tests cases have verified that with AR, the resolution time could be significantly reduced, minimizing production stoppages by more than 50% of the time, which ultimately has a positive effect on a country's GDP. How much energy can be saved is predicted by the fact that 50% of all the world's business flights are replaced by one of the reality concepts and are estimated to amount to at least 50 Mton CO₂ per year. This figure is probably slightly higher as business trips also take place by other means of transport such as trains, buses, and cars. With today's volatile employees changing jobs more frequently, industry experts are becoming fewer and fewer. Since new employee stays for a maximum of 3–5 years per workplace, they will not stay long enough to become experts. Introducing an AI expert trained by today's experts, there is a chance that this knowledge can be maintained.

Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence

The advent of generative artificial intelligence (AI) offers transformative potential in the field of education. The study explores three main areas: (1) How did ChatGPT answer questions related to science education? (2) What are some ways educators could utilise ChatGPT in their science pedagogy? and (3) How has ChatGPT been utilised in this study, and what are my reflections about its use as a research tool? This exploratory research applies a self-study methodology to investigate the technology. Impressively, ChatGPT’s output often aligned with key themes in the research. However, as it currently stands, ChatGPT runs the risk of positioning itself as the ultimate epistemic authority, where a single truth is assumed without a proper grounding in evidence or presented with sufficient qualifications. Key ethical concerns associated with AI include its potential environmental impact, issues related to content moderation, and the risk of copyright infringement. It is important for educators to model responsible use of ChatGPT, prioritise critical thinking, and be clear about expectations. ChatGPT is likely to be a useful tool for educators designing science units, rubrics, and quizzes. Educators should critically evaluate any AI-generated resource and adapt it to their specific teaching contexts. ChatGPT was used as a research tool for assistance with editing and to experiment with making the research narrative clearer. The intention of the paper is to act as a catalyst for a broader conversation about the use of generative AI in science education.

An improved text mining-based space mission risk classification approach

In this paper, an improved approach for space mission risk classification based on text mining is proposed. The method consists of the text mining of extracted bibliographic data with an improved accuracy compared to previous works. Based on these data, a weight is attributed to each of the identified risk parameters and a total mission class score is calculated. A quantification of the interrelation of these parameters, with the use of the t-score metric, together with the appropriate visualization is also provided for further analysis. The proposed method serves as an assistant tool to the appointed panel of experts that is traditionally tasked with the assignment of weights to the parameters. It allows for the consideration of parameters of widely different importance, even by almost an order of magnitude. Eight classification parameters are assessed including: (a) Criticality to the Agency, (b) Objective importance, (c) Cost, (d) Lifetime, (e) Complexity, (f) Manned mission, (g) Destination, and (h) Omega Factor. The most important parameter is shown to be the Complexity of the mission contributing around one fourth of the total weight. Finally, the NASA Mars Perseverance mission is used to demonstrate the application of the classification algorithm.

The Perceptual Science of Augmented Reality.

Augmented reality (AR) systems aim to alter our view of the world and enable us to see things that are not actually there. The resulting discrepancy between perception and reality can create compelling entertainment and can support innovative approaches to education, guidance, and assistive tools. However, building an AR system that effectively integrates with our natural visual experience is hard. AR systems often suffer from visual limitations and artifacts, and addressing these flaws requires basic knowledge of perception. At the same time, AR system development can serve as a catalyst that drives innovative new research in perceptual science. This review describes recent perceptual research pertinent to and driven by modern AR systems, with the goal of highlighting thought-provoking areas of inquiry and open questions.

Atrial fibrillation detection using Poincare geometry and heart beat intervals

AbstractDetection of atrial fibrillation (AF) remains one of the major concerns in the field of medical research. AF is one of the main cause for stroke. AF is characterized by irregular heartbeats and absence of P waves in electrocardiogram (ECG) signal. In this article, we propose a method, combining Poincare plot derived and RR interval‐based features to classify given ECG signal into normal, AF and other rhythms. Classification and regression tree, K‐nearest neighbor, support vector machine, artificial neural network, ResNet18, convolutional neural network (CNN)‐long short term memory (LSTM) are implemented for classification of ECG signal. The class specific accuracies for the three rhythms are computed. Physionet challenge 2017 database is used for evaluation and testing of the developed algorithm. The database has 5154 normal, 771 AF, 2557 other rhythm and 46 noisy signals. Three Poincare derived features viz: SD1, SD2, and ratio of SD1 to SD2, three RR interval features viz: Mean stepping increment of RR interval, approximate entropy and sample entropy are computed and are given to classifiers. During fivefold cross‐validation, CNN‐LSTM classifier showed best result with class specific accuracy for normal of 96.65%, AF of 97.55%, other rhythms of 94.87%, overall accuracy of 96.17% and F1 score of 0.9589. The developed technique can bring change in conventional practice in AF diagnosis and can aid the physician as an assisted tool.

External Validation of an Extreme Gradient Boosting Model for Prediction of Delayed Cerebral Ischemia After Aneurysmal Subarachnoid Hemorrhage

Delayed cerebral ischemia (DCI) may significantly worsen the functional status of patients with aneurysmal subarachnoid hemorrhage (aSAH). Several authors have designed predictive models for early identification of patients at risk of post-aSAH DCI. In this study, we externally validate an extreme gradient boosting (EGB) forecasting model for post-aSAH DCI prediction. A 9-year institutional retrospective review of patients with aSAH was performed. Patients were included if they underwent surgical or endovascular treatment and had available follow-up data. DCI was diagnosed as new-onset neurologic deficits at 4-12 days after aneurysm rupture, defined as worsening Glasgow Coma Scale score for ≥2 points, and new ischemic infarcts at imaging. We collected 267 patients with aSAH. At admission, median Hunt-Hess score was 2 (range, 1-5), median Fisher score 3 (range, 1-4), and median modified Fisher score 3 (range, 1-4). One-hundred and forty-five patients underwent external ventricular drainage placement for hydrocephalus (54.3%). The ruptured aneurysms were treated with clipping (64%), coiling (34.8%), and stent-assisted coiling (1.1%). Fifty-eight patients (21.7%) were diagnosed with clinical DCI and 82 (30.7%) with asymptomatic imaging vasospasm. The EGB classifier correctly predicted 19 cases of DCI (7.1%) and 154 cases of no-DCI (57.7%), achieving sensitivity of 32.76% and specificity of 73.68%. The calculated F1 score and accuracy were 0.288% and 64.8%, respectively. We validated that the EGB model is a potential assistant tool to predict post-aSAH DCI in clinical practice, finding moderate-high specificity but low sensitivity. Future research should investigate the underlying pathophysiology of DCI to allow the development of high-performing forecasting models.

Deep Hierarchical Attention Active Learning for Mental Disorder Unlabeled Data in AIoMT

In the Artificial Intelligence of Medical Things (AIoMT), Internet-Delivered Psychological Treatment (IDPT) effectively improves the quality of mental health treatments. With the advent of COVID-19, psychological tasks have become overloaded and complicated for medical professionals due to the overlap of sentimental values. The development of an AIoMT tool requires labeling of data to achieve clinical-level performance. Text data requires an appropriate set of linguistic features for vector latent representation and segmentation. Emotional biases could lead to incorrect segmentation of patient-authorized texts, and labeling emotional data is time-consuming. In this article, we propose an assistant tool for psychologists to assist them in mental health treatment and note-taking. We first extend the word and emotion lexicon and then apply a hierarchical attention method to support data labeling. The learned latent representation uses word position prediction and sentence-level attention to create a semantic framework. The augmented vector representation helps in highlighting words and classifying nine different symptoms from the text written by the patient. Our experimental results show that the emotion lexicon helps to increase the accuracy by 5% without affecting the overall results, and that the hierarchical attention method achieves an F1 score of 0.89.

Smart Low Level Laser Therapy System for Automatic Facial Dermatological Disorder Diagnosis.

Computer-aided diagnosis using dermoscopy images is a promising technique for improving the efficiency of facial skin disorder diagnosis and treatment. Hence, in this study, we propose a low-level laser therapy (LLLT) system with a deep neural network and medical internet of things (MIoT) assistance. The main contributions of this study are to (1) provide a comprehensive hardware and software design for an automatic phototherapy system, (2) propose a modified-U2Net deep learning model for facial dermatological disorder segmentation, and (3) develop a synthetic data generation process for the proposed models to address the issue of the limited and imbalanced dataset. Finally, a MIoT-assisted LLLT platform for remote healthcare monitoring and management is proposed. The trained U2-Net model achieved a better performance on untrained dataset than other recent models, with an average Accuracy of 97.5%, Jaccard index of 74.7%, and Dice coefficient of 80.6%. The experimental results demonstrated that our proposed LLLT system can accurately segment facial skin diseases and automatically apply for phototherapy. The integration of artificial intelligence and MIoT-based healthcare platforms is a significant step toward the development of medical assistant tools in the near future.

Noninferiority of Artificial Intelligence–Assisted Analysis of Ki-67 and Estrogen/Progesterone Receptor in Breast Cancer Routine Diagnostics

Image analysis assistance with artificial intelligence (AI) has become one of the great promises over recent years in pathology, with many scientific studies being published each year. Nonetheless, and perhaps surprisingly, only few image AI systems are already in routine clinical use. A major reason for this is the missing validation of the robustness of many AI systems: beyond a narrow context, the large variability in digital images due to differences in preanalytical laboratory procedures, staining procedures, and scanners can be challenging for the subsequent image analysis. Resulting faulty AI analysis may bias the pathologist and contribute to incorrect diagnoses and, therefore, may lead to inappropriate therapy or prognosis. In this study, a pretrained AI assistance tool for the quantification of Ki-67, estrogen receptor (ER), and progesterone receptor (PR) in breast cancer was evaluated within a realistic study set representative of clinical routine on a total of 204 slides (72 Ki-67, 66 ER, and 66 PR slides). This represents the cohort with the largest image variance for AI tool evaluation to date, including 3 staining systems, 5 whole-slide scanners, and 1 microscope camera. These routine cases were collected without manual preselection and analyzed by 10 participant pathologists from 8 sites. Agreement rates for individual pathologists were found to be 87.6% for Ki-67 and 89.4% for ER/PR, respectively, between scoring with and without the assistance of the AI tool regarding clinical categories. Individual AI analysis results were confirmed by the majority of pathologists in 95.8% of Ki-67 cases and 93.2% of ER/PR cases. The statistical analysis provides evidence for high interobserver variance between pathologists (Krippendorff's α, 0.69) in conventional immunohistochemical quantification. Pathologist agreement increased slightly when using AI support (Krippendorff α, 0.72). Agreement rates of pathologist scores with and without AI assistance provide evidence for the reliability of immunohistochemical scoring with the support of the investigated AI tool under a large number of environmental variables that influence the quality of the diagnosed tissue images.

Automatic product copywriting for e‐commerce

AbstractProduct copywriting is a critical component of e‐commerce recommendation platforms. It aims to attract users' interest and improve user experience by highlighting product characteristics with textual descriptions. In this paper, we report our experience deploying the proposed Automatic Product Copywriting Generation (APCG) system into the JD.com e‐commerce product recommendation platform. It consists of two main components: (1) natural language generation, which is built from a transformer–pointer network and a pretrained sequence‐to‐sequence model based on millions of training data from our in‐house platform; and (2) copywriting quality control, which is based on both automatic evaluation and human screening. For selected domains, the models are trained and updated daily with the updated training data. In addition, the model is also used as a real‐time writing assistant tool on our live broadcast platform. The APCG system has been deployed in JD.com since February 2021. By September 2021, it has generated 2.53 million product descriptions, and improved the overall averaged click‐through rate (CTR) and the conversion rate (CVR) by 4.22 and 3.61%, compared to baselines, respectively, on a year‐on‐year basis. The accumulated gross merchandise volume (GMV) made by our system is improved by 213.42%, compared to the number in February 2021.

Multi-category Words of Verb-Noun in Chinese Learners Mobile App Dictionary and Their Applications for Dictionary Compilation

One of the most important and heated topics in modern Chinese studies is the classification of word types. Among all word types, the word of multi-categories is a special and difficult case, especially multi-category words of verb-noun (VNs) for students learning Chinese as the second language. To help them learn this kind of word, several teaching methods and textbook compilation methods have been proposed, with only a few studies focusing on dictionary compilation. However, as a significant tool for second language learning, the dictionary plays an important role in helping learners improve their productive ability in using multi-category words. This paper examined the definitions of the multi-category word of verb-noun by different scholars and chose Yus criteria from the need of learning Chinese as the second language. Common colligations and collocations of five VNs in native Chinese were extracted from two corpora and Online Chinese Collocation Assistant tool. Then, takingas an example, this paper analyzed the differences in its use by native Chinese and Chinese learners with a particular focus on its colligations and collocations. The results reveal that it was overused by Chinese learners as a verb, while underused as a noun. There was an imbalance between the use of the part of speech as verb and noun. Finally, this study proposed a better way to represent in a learning dictionary to help Chinese learners use this word.

Automatic quality assurance of radiotherapy treatment plans using Bayesian networks: A multi-institutional study.

Artificial intelligence applications in radiation oncology have been the focus of study in the last decade. The introduction of automated and intelligent solutions for routine clinical tasks, such as treatment planning and quality assurance, has the potential to increase safety and efficiency of radiotherapy. In this work, we present a multi-institutional study across three different institutions internationally on a Bayesian network (BN)-based initial plan review assistive tool that alerts radiotherapy professionals for potential erroneous or suboptimal treatment plans. Clinical data were collected from the oncology information systems in three institutes in Europe (Maastro clinic - 8753 patients treated between 2012 and 2020) and the United States of America (University of Vermont Medical Center [UVMMC] - 2733 patients, University of Washington [UW] - 6180 patients, treated between 2018 and 2021). We trained the BN model to detect potential errors in radiotherapy treatment plans using different combinations of institutional data and performed single-site and cross-site validation with simulated plans with embedded errors. The simulated errors consisted of three different categories: i) patient setup, ii) treatment planning and iii) prescription. We also compared the strategy of using only diagnostic parameters or all variables as evidence for the BN. We evaluated the model performance utilizing the area under the receiver-operating characteristic curve (AUC). The best network performance was observed when the BN model is trained and validated using the dataset in the same center. In particular, the testing and validation using UVMMC data has achieved an AUC of 0.92 with all parameters used as evidence. In cross-validation studies, we observed that the BN model performed better when it was trained and validated in institutes with similar technology and treatment protocols (for instance, when testing on UVMMC data, the model trained on UW data achieved an AUC of 0.84, compared with an AUC of 0.64 for the model trained on Maastro data). Also, combining training data from larger clinics (UW and Maastro clinic) and using it on smaller clinics (UVMMC) leads to satisfactory performance with an AUC of 0.85. Lastly, we found that in general the BN model performed better when all variables are considered as evidence. We have developed and validated a Bayesian network model to assist initial treatment plan review using multi-institutional data with different technology and clinical practices. The model has shown good performance even when trained on data from clinics with divergent profiles, suggesting that the model is able to adapt to different data distributions.

Design of an Energy Policy for the Decarbonisation of Residential and Service Buildings in Northern Portugal

The decarbonisation of the building sector is crucial for Portugal’s goal of achieving economy-wide carbon neutrality by 2050. To mobilize communities towards energy efficiency measures, it is important to understand the primary drivers and barriers that must be overcome through policymaking. This paper aims to review existing Energy Policies and Actions (EPA) in Portugal and assess their effectiveness in improving Energy Efficiency (EE) and reducing CO2 emissions in the building sector. The Local Energy Planning Assistant (LEPA) tool was used to model, test, validate and compare the implementation of current and alternative EPAs in the North of Portugal, including the national EE plan. The results indicate that electrification of heating and cooling, EE measures, and the proliferation of Renewable Energy Sources (RES) are crucial for achieving climate neutrality. The study found that the modelling of alternative EPAs can be improved to reduce investment costs and increase Greenhouse Gas (GHG) emissions reduction. Among the alternatives assessed, the proposed one (Alternative 4) presents the best returns on investment in terms of cost savings and emissions reduction. It allows for 52% investment cost savings in the residential sector and 13% in the service sector when compared to the current national roadmap to carbon neutrality (Alternative 2). The estimated emission reduction in 2050 for Alternative 4 is 0.64% for the residential sector and 3.2% for the service sector when compared to Alternative 2.

Disaster Rescue via Multi-Robot Collaboration: Development, Control, and Deployment

Aging social infrastructure needs maintenance and inspection for which robot technology is highly effective. It is also effective for disaster rescue and recovery operations. Tunnel disaster rescue is risky for human workers. Robot technology can perform this work easily and accelerate rescue and recovery operations. This paper introduces a framework for the rescue robot development, control, and deployment steps. This framework also proposes automated and hybrid robot control systems in addition to conventional manual control. A multi-robot collaboration system causes the proposed control system to be convenient for deployment, reliable, and accelerate rescue work. In the development phase, controllers and different additional assistance tools for the robot have been developed in addition to the basic robot. As the proposed framework has been cultivated through our development and team collaboration training for the World Robot Summit (WRS) and the actual competition of the WRS, we will also present what we have achieved at the WRS.