Reviewerly/exHarmony huggingface dataset:
Data from Relationship of Mammographic Density and Gene Expression: Analysis of Normal Breast Tissue Surrounding Breast Cancer

&lt;div&gt;Abstract&lt;p&gt;&lt;b&gt;Purpose:&lt;/b&gt; Previous studies of breast tissue gene expression have shown that the extratumoral microenvironment has substantial variability across individuals, some of which can be attributed to epidemiologic factors. To evaluate how mammographic density and breast tissue composition relate to extratumoral microenvironment gene expression, we used data on 121 patients with breast cancer from the population-based Polish Women's Breast Cancer Study.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Experimental Design:&lt;/b&gt; Breast cancer cases were classified on the basis of a previously reported, biologically defined extratumoral gene expression signature with two subtypes: an Active subtype, which is associated with high expression of genes related to fibrosis and wound response, and an Inactive subtype, which has high expression of cellular adhesion genes. Mammographic density of the contralateral breast was assessed using pretreatment mammograms and a quantitative, reliable computer-assisted thresholding method. Breast tissue composition was evaluated on the basis of digital image analysis of tissue sections.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; The Inactive extratumoral subtype was associated with significantly higher percentage mammographic density (PD) and dense area (DA) in univariate analysis (PD: &lt;i&gt;P&lt;/i&gt; = 0.001; DA: &lt;i&gt;P&lt;/i&gt; = 0.049) and in multivariable analyses adjusted for age and body mass index (PD: &lt;i&gt;P&lt;/i&gt; = 0.004; DA: &lt;i&gt;P&lt;/i&gt; = 0.049). Inactive/higher mammographic density tissue was characterized by a significantly higher percentage of stroma and a significantly lower percentage of adipose tissue, with no significant change in epithelial content. Analysis of published gene expression signatures suggested that Inactive/higher mammographic density tissue expressed increased estrogen response and decreased TGF-β signaling.&lt;/p&gt;&lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; By linking novel molecular phenotypes with mammographic density, our results indicate that mammographic density reflects broad transcriptional changes, including changes in both epithelia- and stroma-derived signaling. &lt;i&gt;Clin Cancer Res; 19(18); 4972–82. ©2013 AACR&lt;/i&gt;.&lt;/p&gt;&lt;/div&gt;
Data from Trial Designs for Personalizing Cancer Care: A Systematic Review and Classification

&lt;div&gt;Abstract&lt;p&gt;There is an increasing interest in the evaluation of prognostic and predictive biomarkers for personalizing cancer care. The literature on the trial designs for evaluation of these markers is diverse and there is no consensus in the classification or nomenclature. We set this study to review the literature systematically, to identify the proposed trial designs, and to develop a classification scheme. We searched MEDLINE, EMBASE, Cochrane Methodology Register, and MathSciNet up to January 2013 for articles describing these trial designs. In each eligible article, we identified the trial designs presented and extracted the term used for labeling the design, components of patient flow (marker status of eligible participants, intervention, and comparator), study questions, and analysis plan. Our search strategy resulted in 88 eligible articles, wherein 315 labels had been used by authors in presenting trial designs; 134 of these were unique. By analyzing patient flow components, we could classify the 134 unique design labels into four basic patient flow categories, which we labeled with the most frequently used term: single-arm, enrichment, randomize-all, and biomarker-strategy designs. A fifth category consists of combinations of the other four patient flow categories. Our review showed that a considerable number of labels has been proposed for trial designs evaluating prognostic and predictive biomarkers which, based on patient flow elements, can be classified into five basic categories. The classification system proposed here could help clinicians and researchers in designing and interpreting trials evaluating predictive biomarkers, and could reduce confusion in labeling and reporting. &lt;i&gt;Clin Cancer Res; 19(17); 4578–88. ©2013 AACR&lt;/i&gt;.&lt;/p&gt;&lt;/div&gt;
E-DIAGNOSTIC LAB ONLINE REOPRTING APP

E-Diagnostic Centres play a major role in the Healthcare Industry.Many hospitals run their diagnostic centre and many run individually.When people use to come up with the list of investigations that equipped with the latest technologies and machines.Diagnostic centres have made innovation in the health industry.It is an online diagnostic lab reporting mobile app that brings up various diagnoses working online.Here patients get an option to register on the site and log in using registered details.The patients may now see a variety of tests conducted by the lab along with their costs.The application allows the user to book any test needed and after successful booking of an appointment, the blood sample is collected from the user address and test the blood sample.After successful testing, the patient or the user can download their uploaded reports by using this app.It is a timesaving process.
Pseudo number generation based on the knight tour in chess board

The process of generating keys is an important topic in the field of information security and networks and there is a need to generate good keys that are not easily breakable. In this paper, a method is suggested for generating numbers based mainly on the movement of the knight tour in the chessboard and digital images. The generated keys used an image that could be found anywhere possible in mobile images or social media or websites. The image is taken and divided into blocks and then determine the primary path as well as Identifying possible knight tour movements with a condition not returning to the place in the track, the unused place in chessboard is merged at the end of the series for increasing randomness, as well as the exclusive or operation applied between the block and it's transposed before starting a movement. The results obtained show that the proposed approach can effectively produce pseudo-random numbers, high linear complexity, security properties, and good statistical. The quality measurement of the generated key is assigned using NIST tests, some of these tests are applied and passed the tests. It is possible to combine the method with other key generation methods such as future work.
TECNOLOGIAS EM SAÚDE UTILIZADAS COMO FERRAMENTAS PARA MITIGAÇÃO DA VIOLÊNCIA CONTRA O IDOSO: UMA REVISÃO INTEGRATIVA

Introduction: Incarcerated victims and traumatology require qualified care in Pre-Hospital Care, so it is necessary to guarantee a multidisciplinary team specialized in rescuing victims, using up-to-date techniques.The proposal of the Military Rescue Service of Mato Grosso do Sul was to develop an event that provided the strengthening of technical skills in the face of vehicular accidents and trauma.Objective: To describe the experiences of undergraduate Nursing students as spectators in a vehicle rescue and trauma challenge with realistic simulation, as well as to reflect on the care provided in a simulated way.Methodology: This is a descriptive study of an experience report nature, based on the experience of nursing students from Centro Universitário Unigran Capital, who participated as spectators in the 1st Challenge Sul-Mato-Grossense de Salvamento Veicular e Trauma, in the municipality of Campo Grande-MS.Results: Through the experience of the academics at the event, the relevance of the experience experienced for academic improvement was evident, since doubts were resolved about the necessary procedures for the care of victims, consequently increasing autonomy and reaffirming the extreme importance of the academic relationship with the simulations.Conclusions: Training based closer to reality is indispensable for stimulating holistic visualization, directly influencing the training of more qualified professionals, as they provide better patient care.
Machine Translation Quality Evaluation Model Based on Data Mining Algorithm

With the development of today's society, the contact between countries is getting closer and closer. People in different countries use different languages. It is very important to enable people in different countries to solve the first communication barrier and solve the language problem. In this environment, the technology of machine translation is becoming more and more mature. Relying on machine translation, people can not only understand the literature of different countries and regions, but also help complete the basic daily work. Moreover, the efficiency of machine translation is also higher than that of manual translation, which can not only save human resources, but also speed up the work efficiency and greatly improve the speed of social development. Similarly, there is the amount of data with the improvement of technology, so it is particularly important to apply data mining algorithms to design a model that can meet the needs of machine translation and its quality evaluation in the era of big data. This paper makes some research on the machine translation quality evaluation model based on the data mining algorithm. Based on the basic attributes of the algorithm and the background significance of machine translation, the quality evaluation model is obtained through relevant experimental simulation. By analyzing the experimental data, it can be seen that the machine translation quality evaluation model can basically meet the needs.
Personalized Teaching Knowledge Recommendation System Based on Artificial Intelligence Algorithm

With the in-depth development of the current artificial intelligence industry, more and more excellent artificial intelligence technologies have been successfully applied in various fields related to the society. A large number of commercial applications of artificial intelligence technologies make the education industry and its relevant practitioners have diversified means of analyzing and judging the trend of current industrial data changes. This paper analyzes several basic theories of artificial intelligence algorithm analysis, studies scholars’ improvement schemes of artificial intelligence algorithm, and selects hybrid artificial intelligence algorithm by comparing the recommendation effects of four classical artificial intelligence recommendation algorithms in different scenarios. In order to make the online teaching knowledge recommendation system better recommend course knowledge to users, the improved hybrid recommendation algorithm is combined to solve the teaching system cold start and knowledge recommendation long tail and other problems.
A Novel Hierarchical Storage System for Different Types of Data

As society grows and more data is generated, how to store this data becomes a key issue. In this paper, we present a novel and useful system types of data to solve this problem. When different types of data are input, the system will perform feature clustering on these data. After separating these different clustering results, classification will be performed on the basis of the clustering results. When the classification is completed, hierarchical storage is performed to ensure secure access to the data. It is certain that the system will provide a new idea for hierarchical storage of data.
Discovering Novelty via Transfer Learning

Modern-day intelligent systems, i.e., autonomous vehicles, smart manufacturing, etc., rely on distinct data-driven machine learning (ML) models for performing different safety-critical operations such as pedestrian detection, chemical plant control, among others. Since the performance of ML models depends on their generalizability capability, handling out-of-distribution data during the operational phase is of paramount importance for enhancing the adaptability of artificial intelligence (AI) systems. Hence, finding the root causes of novelty is critical for minimizing its impact on the performance of AI systems. However, detection of novelties is not trivial since each AI system operates in a specific environment or agent settings, whereas small changes cause the detection mechanism to adapt to different environmental constraints. For example, detecting novelties in identical intelligent navigation systems differs if the system is deployed in two different countries, though the operation of a navigation system is indistinguishable. In this paper, to reduce the dependability of novelty detection mechanisms on environment’s or agent’s attributes, we propose transfer learning-based novelty detection mechanisms for inter-domain applications. In this regard, we analyze the importance of feature transformation to enhance novelty detection systems’ transferability. The proposed detection mechanisms aim at augmenting AI systems with rapid responsiveness to novel surroundings, thus, making AI systems responsible and trustworthy. We conduct multiple experiments on state-of-the-art neural networks, i.e., ResNet50, Mobile-Net, and benchmark datasets, i.e., MNIST.
The Development of Android-Based Learning Mobile App to Practice Critical Thinking Skills for Elementary School Students

Today's increasingly rapid technological developments do not only appear in the field of communication but are also needed in education.In the education field, it is expected to instill students with adequate knowledge and critical thinking skills, which are needed for their future.Practicing critical thinking skills can be done with mobile applications because today's students are highly familiar with gadgets.In terms of education, students need a learning platform that is equipped with audio, images, and easy-to-understand text.To accommodate this, an Android-based application was designed to provide learning steps, such as analyzing, obtaining information, asking questions, and making conclusions.This study is a qualitative descriptive study using the ADDIE (analyzing, designing, developing, implementing, and evaluating) method.It includes material analysis, application design with smart apps creator (SAC), testing by media and materials experts, and overall evaluation.The trial of this application was carried out on 3 rd -grade elementary school students with questionnaires and interviews.Based on the validation process by IT experts and elementary school teachers, the development of the situation-based learning (SBL) App was considered valid.In other words, this application was properly designed according to the development of lower-grade students.Furthermore, the students were interested in SBL App and found it easier to obtain information, come up with good questions, and draw conclusions from a material using the app.Therefore, it can be concluded that the SBL App developed in this study is feasible and effective to be used as an Android-based learning application to train elementary school students' critical thinking skills.
The Impact of Digital Transformation on Customer Satisfaction to Digital Banking Service of Commercial Banks in Vietnam

The present study focuses on examining the interplay and correlation between the digital transformation process as assessed by the digital banking service quality components (Ease of use, Effectiveness, Interoperability, Privacy/ Security, Empathy, Responsiveness, Reliability, Service portfolios, Service charge) and customer satisfaction for digital banking services at commercial banks in Vietnam.The predictors (independent variables) for this study are the aforementioned service quality aspects and moderator is Service charge.The outcome variable (dependent variable) is customer satisfaction.The authors combined qualitative and quantitative research techniques to develop observed variables and assess the model's fit.This study can help banking leaders evaluate and improve the quality of digital banking services in the context of financial liberalization and globalization.
The Relationship of Learning Engagement and Cognitive Load of Online Learners: The Moderator Effect of Computer Self-efficacy

The study examined the effects of learning engagement and computer self-efficacy on cognitive load of online learners.The participants who recruited on the WeChat public platform completed the Utrecht Work Engagement Scale (UWES), the General Self-Efficacy Scale (GSES), and the Workload Profile Index Ratings (WP) on computers.The results of correlation analyses showed (The results of multi-level analyses showed) that both learning engagement and computer self-efficacy were significantly related to cognitive load; learning engagement was positively correlated with computer self-efficacy; Moreover, the hierarchical regression analyses showed that computer self-efficacy has a moderator role between learning engagement and cognitive load; and low computer self-efficacy could strengthen the positive effect of learning engagement on cognitive load.
Thinking on the path of mixed teaching reform under the concept of innovative development

Currently, the wave of informatization characterized by digitization, networking, and intelligence is in the ascendant, profoundly changing people's production, life, and learning methods.As an important position for cultivating talents, colleges and universities should comply with the current trend of information technology and take the opportunity of hybrid teaching reform to mix teaching concepts, teaching resources, teaching methods, and other aspects.They can innovate educational and teaching achievements by focusing on cultivating the ability of teachers and students to accept new technologies, improving the digital construction of education management, creating a good institutional innovation environment, and strengthening the construction of training bases, etc, We should actively serve the needs of teachers and students, thereby building a complete digital higher education system, and creating a more efficient and intelligent university education management model.
WASTEWATER TREATMENT USING ELECTROCOAGULATION: REVIEW

Due to its versatility, ease of setup, low footprint, and eco-friendliness, electrocoagulation (EC) is a prominent wastewater treatment alternative that has been extensively researched for a wide range of wastewater types.The following hybrid EC processes are currently being researched: EC-adsorption, EC-peroxidation, ECchemical coagulation (CC), photovoltaic EC, and EC-membrane.The hybrid EC-membrane process undisputedly stands out as the future of wastewater treatment due to its overall low carbon footprint requirement, environmental sustainability, and strong potential for continuous operation without the necessity of extensive control.The efficiency of electrocoagulation is affected by current density, anode-cathode gap, electrode arrangement, composition, and shape; initial pollutant concentration; solution composition and pH; and electrolysis time.The dramatic rise in urbanization and industrialization has significantly increased the amount and variety of waste, particularly wastewater, generated.Wastewater contains a wide range of organic and inorganic pollutants.Various wastewater treatment technologies have been developed.It has taken several decades to tackle the increasing concern regarding effective pollutant removal from wastewater.Electrocoagulation (EC) is one such broad-based, highly reliable, and cost-effective technology.Additionally, it boasts a high pollutant extraction efficiency and produces less sludge than other techniques.Despite being used effectively to treat a wide range of wastewater, a thorough examination of its efficiency under various process variables has not been critically analyzed.
Factors that Impact Blockchain Adoption in Vietnamese Logistics Enterprises

Blockchain has become one of the crucial technologies that are applied in various fields. However, the application of Blockchain technology in logistics businesses in Vietnam is uncommon and challenging. In this context, our study provides theoretical evaluations and practical analyses for the application of Blockchain technology in the logistics industry. Based on the development of a unified theoretical model of technology acceptance and use (UTAUT) combined with the TOE model, the study identifies the factors that influence the application of Blockchain by individuals in logistics enterprises in Vietnam. Using SPSS and AMOS tools to analyze 508 survey data collected from logistics businesses, the research team makes conclusions and proposes recommendations for the future.
An Assessment on the Impact of Motivation on Employee Performance at the Workplace

The primary goal of the study was to identify the driving forces behind employee performance at work.A total of 100 respondents were given semi-structured questionnaires to complete, and 84 of them responded, representing an 84% response rate.The research plan for the study used both qualitative and quantitative techniques.A descriptive statistical method was used to assess the data.The study findings were presented using tables and figures.According to the survey, the reasons for underperformance include low salary, nepotism, tribalism, and corruption in the promotion process.Training and growth, however, are the current motivating factors at work, it was noted.The study came to the conclusion that nepotism, corruption, tribalism, low remuneration, and a lack of empowerment are to blame for the lack of motivation and subpar performance of public sector workers.The strategies relating to training and development are, nevertheless, very well carried out.The researcher suggests that management reexamine its motivating tactics and put them into practice to inspire all personnel to improve their performance and, as a result, accomplish the organization's goals. 1.Introduction.The main resource of an organization is its workforce, who must be happy and motivated in order to achieve the objectives.Throughout the era of human resource management, motivation on workers' work performance was established globally as a strategy that was offered to increase the organization's efficiency and performance.In any firm, employee job performance is greatly influenced by motivation, 2006's Calder.The motivational aspects that significantly improve an employee's performance are intrinsic and extrinsic.