Miracle-dz/UniFAR-FTUs huggingface dataset:
Improve Visual Question Answering Based On Text Feature Extraction

VQA requires the machine to be able to answer questions related to the image based on the image. VQA is mainly divided into four modules: text extraction module, image extraction module, text-image feature fusion module and answer prediction module. In this paper, the most mainstream VQA2.0[1] data set is selected for experiment, and the text extraction module is improved on the basis of a baseline model. The Glove vector is used to preprocess the problem text data, and the GRU network replaces the traditional LSTM network encoding and processing text vector. Experimental results show that the improvement of the text extraction module improves by 2.42% on the basis of the original model, and the GRU network can also accelerate the training speed.
Analytical Model to Study Turn-OFF Soft Switching Dynamics of SiC MOSFET in a Half-Bridge Configuration

Switching transients of SiC MOSFET are fast compared to its Si counterpart. Fast switching transient reduces switching loss but may induce prolonged oscillations, spurious turn-<sc>on</sc>, high device stress, and electromagnetic interference (EMI)-related issues, etc. In soft switched converters, use of external drain source capacitance can reduce these adverse effects along with the reduction in switching loss. However, the selection of external drain source capacitance is not straightforward. A large external capacitance reduces switching loss, <inline-formula><tex-math notation="LaTeX">$(dv/dt)$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$(di/dt),$</tex-math></inline-formula> and transient over-voltage but may also result in higher dead-time loss and reduced switching frequency. This article presents an analytical model that captures the soft turn-<sc>off</sc> switching dynamics of SiC MOSFET using parameters obtained from device datasheet along with the values of external circuit parasitics. Unlike linear approximation, a nonlinear channel current model is used along with a comprehensive model of transfer capacitance. The effect of external gate drain capacitance is also considered. This leads to a better estimation of switching transition time, actual loss, <inline-formula><tex-math notation="LaTeX">$(dv/dt)$</tex-math></inline-formula>, <inline-formula><tex-math notation="LaTeX">$(di/dt)$</tex-math></inline-formula>, and transient over voltage. Also, a method to select an optimal value of external drain source capacitance is presented. The proposed model is validated through behavioral simulation and experiment for two 1.2-kV discrete SiC MOSFETs.
Overheating assessment in flats with glazed balconies in warm-summer humid continental climate

Greenhouse systems in the form of glazed balconies may be accomplished both in the newly designed buildings and in the existing ones, raising their energy standard in a quick and inexpensive way. However, basic parameters influencing the efficiency of the systems are often chosen intuitively, not allowing to fully benefit from the sunspaces or causing overheating of the rooms and discomfort for the users. These issues are common drawbacks of passive systems and may become especially important in the aspect of anthropogenic climate changes, including temperature rise and summer heatwaves. The paper presents results of a long-term summer temperature monitoring in flats with glazed balconies of different construction. They were located in prefabricated multi-family buildings, in residential districts of Lublin and Zamość. The cities are situated in the south-eastern part of Poland, belonging to the warm-summer humid continental climate area. The monitoring enabled overheating assessment according to the concept of adaptive comfort and connecting it with the sunspace construction and the inhabitants’ behaviour. These issues are new aspects in the research area, and the work is a part of extensive studies including monitoring and dynamic simulations of dwellings with passive greenhouse systems in Poland. Practical application: Glazed balconies raise the energy standard of buildings in a quick and inexpensive way, but it is important to consider their function not only during the heating season but also in the summer. Recording of temperatures enabled the monitoring of thermal conditions in the flats and the overheating assessment. It is possible to keep the internal temperature in the rooms within the desired range thanks to the sunspace ventilation and occupants’ behaviour.
Volumetric Response of Brain Oligometastatic Disease to Focal Hypofractionated Radiation Therapy

Background: This study aimed to assess the volumetric response, morbidity and failure rates of hypofractionated radiation therapy (HFRT) for definitive focal management of brain oligometastatic disease. Methods: Patients managed with HFRT for unresected oligometastatic brain disease were entered into an ethics-approved database. HFRT was delivered using IMRT or VMAT with 30Gy or 25Gy in 5 fractions. Individual lesions had volumetric assessment performed at three timepoints. Primary endpoint was change of volume from baseline (GTV0) to one-month post-HFRT (GTV1); and to seven-months post-HFRT (GTV7). Secondary endpoints were local failure, survival, and rate of radiation necrosis. Results:One hundred and twenty-four patients with 233 lesions were managed with HFRT. Median follow-up was 23.5 months with thirty-two (25.8%) patients alive at censure. Median overall survival was 7.3 months, with 36.3% survival at 12 months, with superior survival predicted by GTV0 (p=0.003) and percentage volumetric response (p<0.001). Systemic therapy was delivered in 81.5% of cases. At one-month post-HFRT 206 metastases (88.4%) were available for assessment; at seven-months post-HFRT this had reduced to 118 metastases (50.6%). Median metastasis volume at GTV0 was 1.6cm3 (range: 0.1-19.1). At GTV1 and GTV7 this reduced to 0.7cm3 (p<0.001) and 0.3cm3 (p<0.001) respectively, correlating to percentage reductions of 54.9% and 83.3%. No significant predictors of volumetric response following HFRT were identified. Local failure was confirmed in 4.3% of lesions and radiation necrosis in 3.9%. Conclusion: HFRT is an effective method for oligometastatic disease in the brain to maximise initial volumetric response whilst minimising pseudoprogression and radiation necrosis.
A Review of Conflict and Cohesion in Social Relationships in Family Firms

The literature on conflict and cohesion in social relationships in family firms has developed rapidly in recent decades. To take stock and provide directions to move this flourishing research area forward, we conduct a systematic review of the literature. We examine the prevailing conceptualizations of conflict and cohesion in social relationships in family firms, depict their drivers and outcomes, highlight their theoretical and empirical underpinnings, and propose an ontological framework to synthesize this large body of research. Drawing on our review, we identify some important research gaps and suggest fruitful directions for future research.
Gamification and cultural institutions in cultural heritage promotion: a successful example from Italy

ABSTRACT This article deals with the application of serious games to cultural heritage. Through an overview of the growing importance of gamification in promoting cultural sites and museum collections, the research highlights the changing practices of cultural institutions, which are increasingly involved in producing serious games, considering them as strategic digital marketing tools to promote cultural heritage and tourism in addition to their traditional “learning by playing” function. The article explores a case study, namely Mi Rasna, a game on the Etruscan society realized in partnership with several Italian cultural institutions.
Effect of Social Media Adoption and Media Needs on Online Purchase Behavior: The Moderator Roles of Media Type, Gender, Age

Penetration of smartphones and increasing use of social media on always-on devices has attracted the attention of enterprises and organizations to benefit from such platforms for better understanding of customers’ needs and more effectively communicate with potential consumers. For this purpose, the present study investigates the impact of social media adoption and media needs on online purchasing behavior. This study has also examined the moderating role of media type, gender and age in the relationship between variables of social media adoption and online purchasing behavior. A total of 410 questionnaires were collected on social media such as Instagram, Telegram and WhatsApp. SEM technique using PLS used as the analytical method to empirically test the proposed hypotheses. Results show that the effects of tension dimension on online purchasing behavior is mediated by social media adoption. Moreover, the effects of social media adoption on online purchasing behavior are moderated by age and medium type based on Multi-Group Analysis. The IPMA matrix shows that social media adoption had the highest importance, but the lowest performance. This study is significant in terms of innovation due to the use of new indicators effective in the area of statistical analyses. The use of FIMIX, CTA, permutation test, MGA, and IPMA matrix analyses is part of it.
Task assignment in mobile edge computing networks: a deep reinforcement learning approach

Mobile Edge Computing (MEC) is a key technology to support the emerging low-latency Internet of Things (IoT) applications. With computing servers deployed at the network edge, the computational tasks generated by mobile users can be offloaded to these MEC servers and executed there with low latency. Meanwhile, with the ever-increasing number of mobile users, the communication resource for offloading and the computational resource allocated to each user would become quite limited. As a result, it would be difficult for the MEC servers alone to process all the tasks in a timely manner. An effective approach to deal with this challenge is offloading a proportion of the tasks at MEC servers to the cloud servers, such that both types of servers are efficiently utilized to reduce latency. Given multiple MEC and cloud servers and the dynamics of communication latency, intelligent task assignment between different servers is required. In this paper, we propose a deep reinforcement learning (DRL) based task assignment scheme for MEC networks, aiming to minimize the average task processing latency. Two design parameters of task assignment are optimized, including cloud server selection and task partitioning. Such a problem is formulated as a Markov Decision Process (MDP) and solved with a DRL-based approach, which enables the edge servers to capture the system dynamics and make optimized task assignment strategies accordingly. Simulation results show that the proposed scheme can significantly lower the average task completion latency.
Molecular insights into the pathogenesis of follicular lymphoma

: Follicular lymphoma (FL) represents a group of B-cell neoplasms derived from germinal center (GC) B cells. A better understanding of the pathogenic mechanisms of FL is important for developing innovative therapies. The most common form of FL is primarily nodal and associated with the t(14;18). Recent advances obtained via genomic profiling have provided unprecedented insights into the pathogenesis of FL. Conventional FL evolves through multiple independent or convergent genetic pathways. The classical pathogenesis of t(14;18)-positive FL is a multi-stage and multi-hit process escalating along accumulation of genetic and epigenetic alterations, which starts from FL-like B cells, through premalignant lesions, into full-blown malignancy. Early precursor lesions have been recognized in the form of FL-like B cells in normal peripheral blood, and both in-situ follicular neoplasia (ISFN) and duodenal-type FL. In comparison, t(14;18)-negative FL is much more heterogeneous at the molecular level, and the underlying mechanisms are less well understood. Some variants of FL, while lacking upregulation of BCL2, share a common mutational profile with conventional FL, including mutations in epigenetic modifiers. These cases also show some clinical overlap with BCL2-positive FL, including mainly nodal involvement. Other forms of FL show more profound differences, both clinically and biologically. These emerge more clearly as separate entities and include FL, grade 3B, testicular FL (TFL), pediatric-type FL (PTFL), and primary cutaneous follicle center lymphoma (PCFCL). Mutations in epigenetic regulators and 1p36/ TNFRSF14 abnormalities are highly recurrent and are seen across different subtypes of FL. Genetic profiling has offered important new insights, and will continue to impact the diagnostic approach, with changes in future classification schemes.
Application of IOT for Human Safety on Road

Two wheelers (motor bikes) are most used easy and economic means of transportation and it also has become unsafe because of the tremendous increase of road accidents. When two-wheeler met with an accident, it is difficult to spot the neighborhood of the accident and mammoth loss occurs due to time factor. This paper presents Internet of Things based accident detection and prevention system. This is a novel system divided into four parts: first to identify the accident to send signal to emergency center along with location using Arduino based Global Positioning System and Global System for Mobile Communication and remaining are to warn to prevent the accidents like an accelerometer to determine the velocity and tilt of the vehicle, Infrared sensor to detect any obstacles and an alcohol sensor.
Rapid production of AZ91 Mg alloy by extrusion based additive manufacturing process

AZ91 magnesium alloy parts were successfully produced using a novel extrusion-based additive manufacturing process. The green parts were built in two different orientations and subjected to debinding and sintering treatments. The as-built alloy showed a heterogeneous microstructure made up of α – Mg matrix, Al8Mn5 second phase particles, MgO layers and Mg17Al12continuous precipitates. After solution treatment followed by aging, Mg17Al12 discontinuous precipitates were observed. The sintered samples showed a yield strength and ultimate tensile strength of 79 and 207 MPa, respectively, in the horizontal configuration. Analysis of the fracture surfaces revealed that a continuous network of magnesium oxide layer around the powder particles act as preferential path for crack propagation.
The Effectiveness of Wells’ Meta-cognitive Therapy on Improvement of the Emotional Intelligence Levels of Male Students at High Schools in Khoramabad

The aim of the current study was to investigate the effectiveness of Wells’ metacognitive therapy on improvement of the level of emotional intelligence on male students of high schools in Khoramabad, during the academic year 2018-2019. The statistical Population of the study consisted of all the male students of high schools in Khoramabad. The study sample consisted of 30 students who were selected by multiphasic cluster sampling. The 30 selected subjects were randomly assigned into two groups of control and experimental; the experimental group received 7 sessions of Wells’ meta cognitive therapy. Both groups were evaluated in three stages of pretestposttest and follow-up. The study instruments consisted of Bar-On Emotional Intelligence-Inventory. The collected data were analyzed using descriptive statistical and inferential statistical (analysis of variance on repeated measures) and using the SPSS software. The Statistical results indicated that Wells’ meta-cognitive therapy was effective on improvement of the emotional intelligence level. Findings obtained from analysis of variance with repeated measures indicated that Wells’ metacognitive therapy is effective on improving the level of emotional intelligence of male students at high schools in Khorramabad (P≥0.001). According to the results of this study, the Wells’ metacognitive therapy can be used to improve the level of emotional intelligence of students.
Analysis of TCM Diagnosis and Treatment of Thyroid Diseases Based on Data Mining

Thyroid disease is the most common and frequently occurring endocrine disease. In recent years, the incidence rate of thyroid diseases has increased in recent years due to the gradual change of the ecological environment and the gradual popularization of iodine supplements for iodine deficiency disorders. At present, the prevalence of hyperthyroidism is 1.3%, hypothyroidism is 6.5%, thyroid nodules are 18.6%, and 2%-5% of thyroid nodules are thyroid cancer. Thyroid disease has become a high incidence and common disease in modern society. The number of thyroid disease patients in China has become the second largest disease after hypertension. Thyroid disease belongs to gall disease of traditional Chinese medicine. Compared with western medicine, traditional Chinese medicine has certain advantages in the treatment of gall disease. Compared with western medicine, traditional Chinese medicine can better improve clinical symptoms and reduce toxicity. In this paper, Stata and python were used to analyze and clean the data of TCM diagnosis thyroid medical records. Principal component analysis and factor analysis were used to analyze and clean the symptoms and pathogenesis of the medical record data. The data in the prescription were quantified. The feature selection of the data set was performed by principal component analysis to reduce the data dimension. In the process of treatment, different kinds of prescriptions will be used. Association analysis algorithm in data mining is used to mine the association rules of prescription medication and complications of thyroid disease, which provides a reference for clinical treatment drug selection and disease prevention of traditional Chinese medicine. Logistic regression was used to establish the prediction model of traditional Chinese medicine diagnosis prescription of thyroid by using the clinical data of traditional Chinese medicine.
Analysis of Coordinated Operation of the Clean Energy System Based on the Multiobjective Optimization Model

With the increase in the proportion of clean energy connected to the grid, the effective coordination of the operation of various energy power has become a new challenge facing the current power system scheduling. The coordinated operation of the clean energy power generation system can alleviate the contradiction between power generation and output power fluctuations and overcome the bottleneck of new energy development. Considering the natural characteristics of clean energy, this paper aims to make full use of clean energy, reduce system operating costs, increase system power generation, and reduce output fluctuations; we establish a multiobjective optimization model for coordinated scheduling of clean energy power systems. The model seeks to maximize power generation and minimize output fluctuations, power purchase costs, and maintenance costs under the constraints of the grid structure. In this paper, the joint algorithm has an accelerated effect on the target optimization calculation, and then the superiority of the algorithm is verified by the standard system. The standard IEEE39 node test system is used to verify the rationality and feasibility of the model built and provides a reference strategy for the coordinated operation mechanism of the clean energy system. According to the model, in the example in this paper, the maximum value of photovoltaic power prediction is 1290 MW, and the minimum value is 210 MW; the maximum value of wind power prediction is 780 MW, and the minimum value is 28 MW; the minimum cost of power purchase and maintenance is 56,950.395; the maximum generating capacity is 5.045 GW; the minimum output fluctuation is 0.120 GW.
Decision-Level and Feature-Level Integration of Remote Sensing and Geospatial Big Data for Urban Land Use Mapping

Information about urban land use is important for urban planning and sustainable development. The emergence of geospatial big data (GBD), increased the availability of remotely sensed (RS) data and the development of new methods for data integration to provide new opportunities for mapping types of urban land use. However, the modes of RS and GBD integration are diverse due to the differences in data, study areas, classifiers, etc. In this context, this study aims to summarize the main methods of data integration and evaluate them via a case study of urban land use mapping in Hangzhou, China. We first categorized the RS and GBD integration methods into decision-level integration (DI) and feature-level integration (FI) and analyzed their main differences by reviewing the existing literature. The two methods were then applied for mapping urban land use types in Hangzhou city, based on urban parcels derived from the OpenStreetMap (OSM) road network, 10 m Sentinel-2A images, and points of interest (POI). The corresponding classification results were validated quantitatively and qualitatively using the same testing dataset. Finally, we illustrated the advantages and disadvantages of both approaches via bibliographic evidence and quantitative analysis. The results showed that: (1) The visual comparison indicates a generally better performance of DI-based classification than FI-based classification; (2) DI-based urban land use mapping is easy to implement, while FI-based land use mapping enables the mixture of features; (3) DI-based and FI-based methods can be used together to improve urban land use mapping, as they have different performances when classifying different types of land use. This study provides an improved understanding of urban land use mapping in terms of the RS and GBD integration strategy.
Tracking and Monitoring Fitness of Athletes Using IoT Enabled Wearables for Activity Recognition and Random Forest Algorithm for Performance Prediction

Purpose: The progression in technology is made the best use of in every field. Sports analytics is an essential sector that has gained importance in this technology-driven era. It is used to determine the hidden relationships among different quantitative parameters that affect the performance of athletes. This type of analysis requires a large amount of data to be stored periodically. Cloud acts as a scalable centralized repository that can store the massive data essential for analysis purpose. From the technological perspective there are numerous wearable activity tracking devices, which will be able to provide feedback of physical activities. With the help of random forest (RF) algorithm it is possible to classify huge datasets to perform predictions. In this paper, different smart devices that can be used to measure physical activity, use of RF algorithm for converting data obtained from smart devices into knowledge are explored. A conceptual model that uses wearable devices for tracking and monitoring and RF algorithm to predict the performance is suggested. Methodology: The study was conducted by referring to scholarly documents available online and by referring to websites of companies offering healthcare and sports related services. A conceptual model is developed based on the theoretical perception that incorporates the components needed for measuring the physical activities to predict the performance of athletes. Findings/Result: In this paper the proposed system contains four major activities as Capture, Store, Analyze, and Predict. The model considers use of IoT-enabled wearable devices to measure the physical activities of athletes and the information collected will in turn be used to analyze predict their performance and suggest them how to increase the chances of winning. However, the outcome of a game does not only depend upon the PA of athletes. It depends also upon the physical, mental, emotional health, nutrition and many other factors. Originality: In this paper, a theoretical model is deduced to integrate IoT and RF Algorithm to track and monitor fitness of athletes using wearables for activity recognition and performance prediction. Paper Type: Conceptual Paper