Miracle-dz/UniFAR-FTUs huggingface dataset:
Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs

Background Patients with breast cancer have a variety of worries and need multifaceted information support. Their accumulated posts on social media contain rich descriptions of their daily worries concerning issues such as treatment, family, and finances. It is important to identify these issues to help patients with breast cancer to resolve their worries and obtain reliable information. Objective This study aimed to extract and classify multiple worries from text generated by patients with breast cancer using Bidirectional Encoder Representations From Transformers (BERT), a context-aware natural language processing model. Methods A total of 2272 blog posts by patients with breast cancer in Japan were collected. Five worry labels, “treatment,” “physical,” “psychological,” “work/financial,” and “family/friends,” were defined and assigned to each post. Multiple labels were allowed. To assess the label criteria, 50 blog posts were randomly selected and annotated by two researchers with medical knowledge. After the interannotator agreement had been assessed by means of Cohen kappa, one researcher annotated all the blogs. A multilabel classifier that simultaneously predicts five worries in a text was developed using BERT. This classifier was fine-tuned by using the posts as input and adding a classification layer to the pretrained BERT. The performance was evaluated for precision using the average of 5-fold cross-validation results. Results Among the blog posts, 477 included “treatment,” 1138 included “physical,” 673 included “psychological,” 312 included “work/financial,” and 283 included “family/friends. ” The interannotator agreement values were 0.67 for “treatment,” 0.76 for “physical,” 0.56 for “psychological,” 0.73 for “work/financial,” and 0.73 for “family/friends,” indicating a high degree of agreement. Among all blog posts, 544 contained no label, 892 contained one label, and 836 contained multiple labels. It was found that the worries varied from user to user, and the worries posted by the same user changed over time. The model performed well, though prediction performance differed for each label. The values of precision were 0.59 for “treatment,” 0.82 for “physical,” 0.64 for “psychological,” 0.67 for “work/financial,” and 0.58 for “family/friends. ” The higher the interannotator agreement and the greater the number of posts, the higher the precision tended to be. Conclusions This study showed that the BERT model can extract multiple worries from text generated from patients with breast cancer. This is the first application of a multilabel classifier using the BERT model to extract multiple worries from patient-generated text. The results will be helpful to identify breast cancer patients’ worries and give them timely social support.
Research Progress on the Application of Machine Learning in Power System Security

Machine learning is considered to be an emerging technology that can be widely used. The construction of power system has always been an important field of social livelihood security and development in China. In recent years, the application of machine learning in power system security has also made great progress in the environment of increasing attention to the research on the intersection and integration of machine learning and various disciplines. According to the latest progress, the application of machine learning in this field is mainly divided into hardware layer and software layer. The research on security guarantee of hardware layer mainly focuses on the prediction and evaluation of transient stability and load forecasting, while the research on software layer mainly focuses on network attack detection, and most of the research focuses on model and method innovation. Using machine learning to quickly evaluate and predict the operation state of power system, so as to realize in-depth analysis and facilitate the operation and maintenance personnel to make appropriate human intervention, which can represent the current research trend of machine learning in the field of power system security.
Multistep-Ahead Chaotic Time Series Prediction Based on Hierarchical Echo State Network With Augmented Random Features

Multistep-ahead chaotic time series prediction is a kind of highly nonlinear problem, which puts forward higher requirements both for the dynamical memory and nonlinearity of the model. Echo state network (ESN) is frequently employed in the realm of chaotic time series modeling and prediction, but the basic ESN has been proved to have an antagonistic tradeoff between nonlinear transformation and memory capacity. To overcome this tradeoff, a new architecture named hierarchical ESN with augmented random features (HESN-ARF) is proposed. On the basis of the traditional linear random projection, the proposed HESN-ARF further leverages nonlinear kernel transformation to construct augmented random features, which can enable the linear and nonlinear properties to be fully represented. Moreover, the HESN-ARF utilizes low-rank kernel approximation to further reduce the computational cost, preserving the advantage of efficient modeling as much as possible while ensuring the capacities of nonlinear transformation and dynamical memory simultaneously. The proposed HESN-ARF can mine and learn the latent evolution patterns hidden in the dynamic system layer by layer through the hierarchical strategy, and achieves excellent performance in multistep-ahead chaotic time series prediction, as demonstrated by experimental findings on two synthetic chaotic systems and a real-world meteorological data set.
Research on web CAPP process resources database access technique

It the requirement for process resources management in networked manufacturing environment is analyzed,Web database access technique is researched.Through analyzing the work principle of ASP and Web database access technique by using ADO,the concrete method and step of accessing CAPP Web process database by using ASP is put forward.It affords the technology support for sharing process information and resources inside or between enterprises.
Dampak Penggunaan Gadget Terhadap Karakter Peduli Sosial Anak

Penggunaan gadget dapat memengaruhi karakter peduli sosial anak. Ditemukan kasus di Desa Kedungwaru Kidul RT 01 RW 04 anak-anak yang menggunakan gadget kerap kali acuh tak acuh, bertindak tidak sopan santun, dan enggan membantu orang lain. Penelitian ini bertujuan untuk menganalisis karakter peduli sosial anak yang menggunakan gadget. Penelitian ini menggunakan pendekatan kualitatif studi kasus. Teknik pengumpulan data dalam penelitian menggunakan observasi, wawancara, dokumentasi dan pencatatan lapangan. Keabsahan data diperoleh dengan triangulasi sumber dan triangulasi teknik. Analisa data yang digunakan yaitu analisis data kualitatif dengan menggunakan model Miles dan Huberman. Hasil penelitian yaitu karakter peduli sosial anak belum terlihat. Hal ini ditunjukkan dengan perilaku anak yang enggan dalam memberikan bantuan/pertolongan pada orang lain, suka marah-marah, berkata kasar, dan tidak menghormati orang lain dikarenakan anak lebih asik bermain gadget dibanding peduli dengan orang disekitarnya. Kesimpulan penelitian ini yaitu penggunaan gadget memengaruhi belum terlihatnya karakter peduli sosial anak-anak di Desa Kedungwaru Kidul RT 01 RW 04 Demak.
Comparing visual representations of collaborative map interfaces for immersive virtual environments

Virtual reality offers unique benefits to support remote collaboration. However, the way of representing the scenario and interacting within the team can influence the effectiveness of a collaborative task. In this context, this research explores the benefits and limitations of two different visual representations of the collaboration space, shared experience and shared workspace, in the specific case of map-based collaboration. Shared experience aims at reproducing face-to-face collaboration in a realistic way whilst shared workspace translates to the virtual world the functionalities of 2D collaborative spaces. The goal is to understand whether sophisticated interfaces with realistic avatars are necessary, or if simpler solutions might be enough to support efficient collaboration.We performed a user study (n = 24, 12 pairs) through a collaborative task with two roles in a emergency crisis intervention scenario that typically uses map-based interfaces. Despite that a shared experience scenario might provide a better personal experience to the user in terms of realism, our study provides insights that suggest that a shared workspace could be a more effective way to represent the scenario and improve the collaboration.
Investigation on comparison between the SMITH predictive compensation control and the PID control

Two kinds of controller used in industrial process control were compared.Four methods for tuning the parameters of the PID controller were presented.The Smith predictive compensation controller was mainly used in the long-time delay industrial process to improve the step response performance of the system.In theory,the Smith predictive compensation controller could provide an effective way to improve the control effect.The Matlab/Simulink was employed to compare the two fore-mentioned controllers.The outcome indicates that under the condition of a long-time delay,the Smith predictive compensation controller can achieve better results than those achieved by the PID controller.
The Diffusion of Lean Operations Practices in MNCs: A Knowledge-Based, Plant Level, Cross-Firm Study

Abstract Rolling out lean operations practices in MNCs’ plants is a complex knowledge transfer process whose design and implementation, though critical to operations performance, to date has not been investigated by operations management, international business, strategy, and organizational design research. Applying conceptual tools drawn from various theoretical approaches to knowledge management, transfer and diffusion, this exploratory study: (a) classifies and interprets lean roll-out processes in MNCs, framing them in terms of (i) knowledge replication strategies (template vs. principles-based), (ii) decentralization of decision making (degree of plant autonomy), and (iii) type of organizational ambidexterity (structural vs. contextual) underlying the process; (b) develops, through seven case studies of lean roll-outs in MNCs’ plants, three testable propositions about what might enhance the lean roll-out process performance, arguing about the individual and combined effect of the three above mentioned dimensions on lean roll-out effectiveness and efficiency. We posit that an approach characterized by principles-based knowledge replication, larger decentralization, and prevalence of contextual ambidexterity positively impacts on roll-out process performance.
THE ANALYSIS OF THE EFFECT OF ENHANCEMENT VILLAGE STATUS (THE VILLAGE BUILDING INDEX) ON REDUCTION THE POVERTY RATE IN THE PROVINCE OF WEST KALIMANTAN

Village Building Index (IDM) is a measuring instrument issued by the Ministry of Villages & PDTT to measure the success of development and use of Village Funds in a village. This measuring instrument includes 50 indicators divided into three large groups, namely The Social Resilience Index (IKS), The Economic Resilience Index (IKE), and The Environmental Resilience Index (IKL). The three groups have the same score in measurement and will determine the status of a village. The village's class divide into five categories that are very lagging, lagging, developing, advanced, and independent. Based on the measurement, the village level with a maximum score will have the independent level and get autonomous status while still low in scoring will achieve a very lagging status. This study aims to determine the dorp level score to reduce the poverty rate in the village. Because the rise should follow the increasing IDM status of a town in a village's social and economic classes, this village's position makes the villagers' rising economy. The growing economy of the villagers should help reduce the poverty rate in the town.
A deep field weakening control for the PMSM applying a modified overmodulation strategy

A field weakening control applying a modified overmodulation strategy is proposed to improve the stability of the PMSM drive system. The modified overmodulation is combined with the voltage feedback control to achieve a robust deep field weakening range for the machine. With the proposed control strategy, no switching is required between the MTPA, constant power and MTPV operating regions so that a seamless operation of the PMSM can be achieved. The proposed overmodulation guarantees that the current of the PMSM stays around its limitation during the transient state in the field weakening region. With an additional compensation term, a simple and stable control of MTPV operation for the PMSM can be achieved. In the end, the proposed control scheme is validated by simulation and experimental results.
Waiting/cruising location recommendation based on mining on occupied taxi data

Recently, lots of researchers are attracted to constructing information system for efficient taxi business. In general, there are three types of methods in order to catch a passenger for a taxi driver: 'waiting', 'cruising', and 'wireless/order'. Conventional systems decline to support 'wireless/order' method, and it is not sufficient to support 'waiting' and/or 'cruising' one. Therefore, in the present paper, we try to support 'waiting' and 'cruising' based on mining of occupied taxi data and try to catch a passenger more efficiently. According to the result of evaluation experiment, our proposition is effective.
Research Paper: Effects of Methanol Extract on Bioac tive Property and in Vitro Antioxidant Activity of Palma Christi ( Anthocleista nobilis G. Don.) Root

Background: Plants have diverse phytochemicals with different solubility levels and medicinal efficacy. This study aimed to determine the presence of phytochemical constituents and in vitro antioxidant activity of methanol root extract of Anthocleista nobilis . Objectives: Determining the bioactive principles and free radical scavenging properties of A. nobilis root. Methods: The preliminary phytochemical investigations were performed using standard analytical procedures. The in vitro antioxidant properties were assessed by 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging assay, nitric oxide (NO) scavenging activity, ferric reducing antioxidant power (FRAP) assay, total antioxidant capacity (TAC), and hydrogen peroxide (H 2 O 2 ) scaven ging activity. Results: The phytochemical analyses revealed the presence of several compounds: saponins, reducing sugar, alkaloids, tannins, flavonoids, cardiac glycosides, and mucilages. The concentrations of these compounds were different. A considerable quantity of phytochemicals wa s found in the methanol extract. The impact of the extract on DPPH, NO, FRAP, TAC, H 2 O 2 radicals were dependently and ascendingly concentrated. Solvent extract demonstrated better antioxidant activity, with DPPH and NO showing maximum antioxidant capability in conjunction with IC50 values of 5.45 µg/mL. Conclusion: The study results prove that A. nobilis is a possible source of natural antioxidants, justifying its use in indigenous medicine.
Title: A Precision Medicine Approach to Metabolic Therapy for Breast Cancer in Mice

One Sentence Summary: We identify a driver mutation signature by which glucose-wasting metabolic therapy (dapagliflozin) enhances the efficacy of chemotherapy in mice with breast cancer. Abstract: Increasing evidence highlights the possibility for approaches targeting metabolism as potential adjuvants to cancer therapy. Sodium-glucose transport protein 2 (SGLT2) inhibitors are the newest class of antihyperglycemic therapies, and have recently been highlighted as a novel therapeutic approach to breast cancer. To our knowledge, however, SGLT2 inhibitors have not been applied in the neoadjuvant setting as a precision medicine approach to combining metabolic therapy with standard of care therapy for this devastating disease. In this study we combine the SGLT2 inhibitor dapagliflozin with paclitaxel chemotherapy in both lean and obese mice. We show that dapagliflozin enhances the efficacy of paclitaxel, reducing tumor glucose uptake and prolonging survival in an insulin-dependent manner in some but not all breast tumors. Our data find a genetic signature for breast tumors most likely to respond to dapagliflozin in combination with paclitaxel. Tumors driven by mutations upstream of canonical insulin signaling pathways are likely to respond to such treatment, whereas tumors driven by mutations downstream of canonical insulin signaling are not. These data demonstrate that dapagliflozin enhances the response to chemotherapy in mice with breast cancer and suggest that breast cancer patients with driver mutations upstream of canonical insulin signaling may be most likely to benefit from this neoadjuvant approach. A clinical trial is currently in preparation, with an application recently submitted for Yale Human Investigations Committee approval, to test this hypothesis in breast cancer patients.
Energy Requirements of a Multi-Sensor Based Demand Control Ventilation System In Residential Buildings

In Korea, in 2006, the building regulation was revised to apply 0.7 ACH (Air Change Rate) ventilation systems to improve indoor air quality in residential apartment housing. The purpose of this study is to evaluate energy requirement and indoor contaminant level characteristics for residential building applying with sensor-based DCV (Demand Control Ventilation) system. It has been simulated both in a setting of the constant volume of 0.7 ACH intakes as recommended by the Korean Indoor Air Act and in a sensor-based DCV system controlled with CO2 and chemical material such as TVOC (Total Volatile Organic Compound). Our study demonstrates that the DCV system is energy efficient and maintains better indoor air quality because the indoor contamination level is reduced by controlling the outdoor air intakes. DCV system consists of a hardware module of sensor units and a control algorithm that implements several integrated ventilation strategies to meet the ventilation requirements. In this study, the energy requirements have been evaluated with two ventilation control strategies; one for the conventional ventilation type and the other for a sensor-based DCV system. DCV is a real time, occupancy and contamination level based ventilation approach that can lead to significant energy saving over the traditional fixed OA (Outdoor Air) intake ventilation system. Our approach enables the incorporated system to maintain proper IAQ (Indoor Air Quality) at all times in a space where adequately ventilated to reduce the indoor pollutant level and thus improves the air quality in an indoor environment.
Testing homogeneity, stationarity and trend in climatic series at Udaipur – a case study

Three seasonal climatological time series, i.e., rainfall, minimum temperature, and maximum temperature of Udaipur in three seasons, i.e. summer, rainy and winter were tested for the presence of homogeneity, stationarity, and trend components. Box plot indicated normality in the rainy season rainfall. Minimum temperature series of rainy season was more uniform and normal than summer and winter season time series. Maximum temperatures during rainy season follow normal distribution. Homogeneity of seasonal time series was tested by using von-Neumann, Cumulative Deviations and Bayesian tests, which indicated that all seasonal rainfall and maximum temperature time series were homogenous. Homogeneity was present only in summer and rainy season minimum temperature time series. Stationarity and trends in the time series were detected by using Mann-Whitney test and Mann-Kendall test, respectively, which suggested that stationarity was present in all seasonal maximum and minimum temperature, and winter season rainfall time series and absent in summer and rainy season rainfall time series. Based on the results of Mann-Kendall tests, all the time series under study were free from any kind of trend.
Global alignment of HD maps and satellite imagery for Autonomous Navigation

Autonomous driving is one of the most important thrust areas for application of AI and computer vision. Datasets in this space are created by leveraging sets of highly detailed maps that are developed partly with manual annotation. These are meticulously annotated and provide information about various lanes, signs and traffic rules, speed limits, etc. Such datasets exist for a few cities and provide accurate annotations. However, this method consumes a lot of resources to create, update while also not being tenable to develop at scale. While these datasets are useful, they do not provide a Birds Eye View of the road which has been shown to be highly valuable for navigation. Aerial images spatially cover the entire world and consume far less resources for acquisition. We could use these aerial images along with the HD maps provided by autonomous driving companies [3] to train the deep learning models for automating the tasks such as semantic segmentation. However, aerial images are acquired from a different source and thus, have to be accurately aligned with the existing HD maps before they can be used for autonomous navigation. Alignment issues between these two sources are mainly due to HD maps being created by assuming a flat-view of the world while satellite imagery is taken from a spherical Earth. In this work, we seek to minimize the alignment error between satellite imagery and HD maps by formulating it as an energy minimization problem [18].
Handling Distribution Shift in Tire Design

The recent success of machine learning methods in the industrial sector open new perspectives for the design of innovative products. However, these promising results are often challenged when it comes to industrial model deployment. Indeed, it frequently appears that the performance of the model is degraded when used on application data due to the distribution shift between the training and the targeted data. This issue is even more critical for model dedicated to the research of innovative designs as the model is mainly used on unseen regions of the design space. In this work, we present, on a real application of tire design, how distribution shifts impact the model performance and what can be expected from several domain adaptation methods. In an objective of industrial model deployment, we conduct this benchmark with the use of unsupervised evaluation metrics that considerably help the model selection.