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Background: In the realm of breast cancer diagnosis and treatment, accurately discerning molecular subtypes is of paramount importance, especially when aiming to avoid invasive tests. The updated guidelines for diagnosing and treating HER2 positive advanced breast cancer, as presented at the 2021 National Breast Cancer Conference and the Annual Meeting of the Chinese Society of Clinical Oncology, highlight the significance of this approach. A new generation of drug-antibody combinations has emerged, expanding the array of treatment options for HER2 positive advanced breast cancer and significantly improving patient survival rates. Triple-negative breast cancer (TNBC), on the other hand, may indicate survival outcomes following multi-agent adjuvant chemotherapy. DISCO is a more recent DCE MRI technique that has achieved high spatial and temporal resolution and minimized image artifacts in cases like malignant focal liver lesions, enhanced focal breast lesions, and intracranial aneurysms. Objective: To employ the method mentioned above to differentiate between triple-negative and non-triple-negative as well as HER2 positive and HER2 negative cancer lesions, and to assess the value of quantitative and semi-quantitative parameters in molecularly typing breast cancer. Methods: All participants were scanned with a 3.0-T MR scanner (GE SIGNA™ Premier) using a 16-channel phased-array body coil. Each participant underwent a DISCO DCE-MRI with a scan time of approximately 1 minute and 40 seconds. The ROIs were outlined with the GenIQ software, avoiding regions with blood vessels, susceptibility artifacts, hemorrhage, and necrosis. We evaluated four quantitative parameters (Ktrans, kep, ve, vp) and four semi-quantitative parameters (TTP, MAX Conc, AUC, MAX Slope). The carcinomas were segregated into respective subgroups (HER2+ vs HER2-, TNBC vs non-TNBC, HER2+ vs TNBC) and we compared the eight parameters across these groups. The AUC of the models was compared using DeLong's test as per the ROC analysis. Results: We analyzed a total of 96 female patients, revealing significant differences in the semi-quantitative parameters such as TTP, MAX Conc, AUC, and MAX Slope among different groups. HER2-positive versus HER2-negative exhibited significant differences in quantitative parameters (Ktrans: 0.22 min-1 vs. 0.43 min-1, kep: 0.11 min-1 vs. 0.35 min-1, vp: 0.01 vs. 0.04, all P < 0.05). TNBC versus non-TNBC revealed statistical variations in quantitative parameters (Ktrans: 1.03 min-1 vs. 0.15 min-1, kep: 0.61 min-1 vs. 0.19 min-1, vp: 0.18 vs. 0.01, all P < 0.05). Additionally, HER2-positive compared to TNBC demonstrated significant differences in quantitative parameters (Ktrans: 0.22 min-1 vs. 1.03 min-1, kep: 0.11 min-1 vs. 0.61 min-1, vp: 0.01 vs. 0.18, all P < 0.05). As per ROC analysis, Ktrans, kep, vp, TTP, and MAX Conc effectively differentiated TNBC from non-TNBC, with TTP being the strongest determinant for TNBC. Furthermore, these parameters successfully distinguished between HER2 positive and HER2 negative, with kep being particularly effective in identifying HER2. Importantly, Ktrans, kep, vp, TTP, and MAX Conc were effective in discriminating HER2 positive from TNBC, with kep and TTP exhibiting notable efficacy in this context. Conclusion: Our study suggests that DISCO DCE-MRI derived parameters could serve as reliable quantitative biomarkers for differentiating between TNBC and HER2 positive breast cancer. [ABSTRACT FROM AUTHOR]

Uveal melanoma is the most common intraocular malignant tumor in adults. For patients presenting with cataracts and glaucoma, it is recommended to assess whether an intraocular lesion is present as the primary cause. The present study describes the case of a 52-year-old man with primary intraocular malignant melanoma. The patient experienced painless vision loss in the right eye for 1 year, with recent onset of eye swelling and pain in the week prior to seeking medical attention. A slit-lamp examination revealed a shallow anterior chamber in the right eye, a visibly opaque lens and a faint reflection of the tumor surface in the vitreous humor. In addition, the intraocular pressure of this eye was >60 mmHg. Magnetic resonance imaging revealed a large tumor behind the lens measuring 16x18x14 mm. Pathological examination confirmed the diagnosis of malignant melanoma. No BRCA-associated protein-1 somatic mutation was detected, whereas germline mutations of MutL protein homolog 1, RAD54 like, and SWI/SNF related, matrix associated, actin dependent regulator of chromatin, subfamily a, member 4 were identified. Extensive systemic examination excluded the possibility that the tumors originated from another part of the body. The present case report highlights the crucial role of slit-lamp examination in the detection of ocular tumors. It is advocated that for patients presenting with cataracts, attention should be paid to the possibility of intraocular tumors. Meticulous slit-lamp microscopy may reveal a reflection of the surface of a malignant melanoma, preventing misdiagnosis. [ABSTRACT FROM AUTHOR]

Small-scale systems based on periodic boundary conditions often cannot accurately describe real-world situations, especially when conducting molecular dynamics simulations to study phase transitions, where it is very necessary to use large-scale systems. However, studying phase transitions in large-scale systems is an important and difficult task. Though ab initio molecular dynamics (AIMD), based on density functional theory (DFT), provides advantages in terms of accuracy, it is very difficult to study phase transitions in large-scale systems due to the considerable computational time required. In addition, although traditional empirical potentials are faster, their lower calculation accuracy makes it difficult to use them for phase transition studies. It is crucial to devise a method that has enabled a promising fusion of computational efficiency and precision to effectively investigate phase transitions in large-scale systems. In this work, the obtained machine learning potential function of carbon through deep neural networks not only demonstrates strong scalability but also effectively enables the study of the formation mechanisms of amorphous diamond and polycrystalline diamond using C60 crystals and graphene as precursors under high-pressure high-temperature conditions (HPHT). Furthermore, the structure search software (AIRSS) was used to generate numerous initial structures which were optimized using the machine learning potential, a process which led to finding new structural clusters of carbon. Interestingly, the predictive capabilities of the machine learning potential for symmetric and asymmetric carbon clusters aligned well with the Gaussian approximation potential (GAP), yet the former demonstrated higher computational efficiency, making it more suitable for carbon material research. The results of this work signify significant progress in the field of carbon transition study, opening up new possibilities for exploring and understanding carbon materials with improved computational efficacy. [ABSTRACT FROM AUTHOR]

Over the past decade, excessive groundwater extraction has been the leading cause of land subsidence in Taiwan's Chuoshui River Alluvial Fan (CRAF) area. To effectively manage and monitor groundwater resources, assessing the effects of varying seasonal groundwater extraction on groundwater levels is necessary. This study focuses on the CRAF in Taiwan. We applied three artificial intelligence techniques for three predictive models: multiple linear regression (MLR), support vector regression (SVR), and Long Short-Term Memory Networks (LSTM). Each prediction model evaluated the extraction rate, considering temporal and spatial correlations. The study aimed to predict groundwater level variations by comparing the results of different models. This study used groundwater level and extraction data from the CRAF area in Taiwan. The dataset we constructed was the input variable for predicting groundwater level variations. The experimental results show that the LSTM method is the most suitable and stable deep learning model for predicting groundwater level variations in the CRAF, Taiwan, followed by the SVR method and finally the MLR method. Additionally, when considering different distances and depths of pumping data at groundwater level monitoring stations, it was found that the Guosheng and Hexing groundwater level monitoring stations are best predicted using pumping data within a distance of 20 kilometers and a depth of 20 meters. [ABSTRACT FROM AUTHOR]

Barium titanate (BaTiO3, BT)/poly (vinylidene fluoride, PVDF) composites containing 0%–50% of ceramic filler volume fraction were fabricated by spin‐coating method, and it was shown that the permittivity of BT/PVDF composites increased with the addition of ceramic filler. In order to enhance the interfacial bonding between the two phases, the surface modification treatment of BT ceramic particles was carried out using different mass fractions of γ‐Aminopropyl‐triethoxysilane (KH550). The results indicated that the dielectric properties of the modified composites were enhanced. The dielectric constant of the BT/PVDF composite with 10 vol.% ceramic filler treated with 1 wt.% KH550 is 20.3, which is twice as much as pure PVDF. A model of BT/PVDF dielectric composite was employed by using COMSOL Multiphysics to simulate and analyze the effects of filler particle size, volume fraction, and degree of dispersion on the properties of composite dielectric films from the perspective of microstructural. Calculated results show that the addition of ceramic particles triggers local electric field distortion, which affects the dielectric properties of the composites. Meanwhile, the degree of dispersion of the filler also affects the breakdown resistance of the composite material, when the filler is uniformly dispersed, the local electric field is reduced, which effectively enhances the breakdown resistance of the material. Finite element simulation analyzes the macroscopic dielectric properties from a microstructural point of view, which is beneficial for further understanding of ceramic‐polymer composites. Highlights: The dielectric properties of the polymer matrix were enhanced by doping BT.The KH550 can enhance the dispersion of BT particles in the PVDF.The surface‐modified BT improved the dielectric properties of the composite.COMSOL simulation confirmed the effectiveness of the interface modification. [ABSTRACT FROM AUTHOR]

Backgrounds: Hematocrit is found an independent risk factor for acute kidney injury (AKI) in certain patients, but this effect in patients with acute myocardial infarction (AMI) is unclear. We aim to identify the relationship between hematocrit and AKI in patients with AMI. Methods: The patient data for the discovery and validation cohorts were extracted from the electronic Intensive Care Unit database and the Medical Information Mart for Intensive Care III database, respectively, to identify the relationship between hematocrit and AKI. With normal hematocrit as the reference, patients were divided into five groups based on the initial hematocrit value. The primary outcome was AKI during hospitalization. A multivariable logistic regression and a marginal effect analysis were used to evaluate the relationship between hematocrit and AKI. Results: In this study, a total of 9692 patients diagnosed with AMI were included, with 7712 patients in the discovery cohort and 1980 patients in the validation cohort. In the discovery cohort, hematocrit in 30-33%, 27-30% or <27% were independent risk factors for AKI in the multivariate logistic analysis, with odds ratio (OR) of 1.774 (95% confidence interval [CI]: 1.203-2.617, p = 0.004), 1.834 (95% CI: 1.136-2.961, p = 0.013) and 2.577 (95% CI: 1.510-4.397, p < 0.001), respectively. Additionally, in the validation cohort, low hematocrit levels independently contributed to an increased risk of AKI among patients with AMI. During the analysis of marginal effects, a significant negative linear relationship between hematocrit levels and AKI was observed. Conclusions: Decreased hematocrit was an independent risk factor for AKI in patients with AMI. The relationship between hematocrit and AKI was negative linear. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs; online courses delivered over the Internet) enable distance learning without time and place constraints. MOOCs are popular; however, active participation level among students who take MOOCs is generally lower than that among students who take in-person courses. Students who take MOOCs often lack guidance, and the courses often fail to provide personalized learning materials. Artificial intelligence (AI) has been applied to manage increasing amounts of learning data in learners' online activity records. Driven by the trend in big data, AI technology has drawn increasing attention in various fields. AI-based recommendation systems (RSs) are powerful tools for improving resource acquisition through supply customization, and they can provide personalized learning materials as study guides. In this study, a personalized learning path for MOOCs based on long short-term memory (LSTM) was proposed to meet students' personal needs for learning. According to students' video-watching behaviors, we proposed an MOOC material RS that identifies students with similar learning behaviors through clustering and then uses the clustering results and the learning paths of each group of students to construct an LSTM model to recommend learning paths. The system's learning path recommendations can effectively improve the online participation of learners, and students who received recommendations progressed from the slow-progress group to the medium-progress or fast-progress group. In addition, the learning attitude questionnaire results indicated that the proposed system not only motivated learners to continue learning and achieve high learning capacity but also supported their study planning according to their individual learning needs. [ABSTRACT FROM AUTHOR]

The increasing use of artificial intelligence (AI) models in the classroom not only brings a large number of benefits, but also has a variety of ethical implications. To provide effective education, it is now necessary to understand the ethical implications of using AI models in the classroom, and the principles for avoiding and addressing these ethical implications. However, existing research on the ethical implications of using AI models in the classroom is rather sparse, and a holistic overview is lacking. Therefore, this study seeks to offer an overview of research on the ethical implications, ethical principles and the future research directions and practices of using AI models in the classroom through a systematic literature review. Out of 1,445 initially identified publications between 2013 and 2023, 32 articles were included for final coding analysis, identified using explicit inclusion and exclusion criteria. The findings revealed five main ethical implications, namely algorithmic bias and discrimination, data privacy leakage, lack of transparency, decreased autonomy, and academic misconduct, with algorithmic bias being the most prominent (i.e., the number of existing studies is the most), followed by privacy leakage, whereas decreased autonomy and academic misconduct were relatively understudied; and six main ethical principles, namely fairness, privacy, transparency, accountability, autonomy and beneficence, with fairness being the most prominent ethical principle (i.e., the number of existing studies is the most), followed by privacy, while autonomy and beneficence were relatively understudied. Future directions of research are given, and guidelines for future practice are provided: (1) further substantive discussion, understanding and solution of ethical implications are required; (2) the precise mechanism of ethical principles of using AI models in the classroom remains to be elucidated and extended to the implementation phase; and (3) the ethical implications of the use of AI models in the classroom require accurate assessment. [ABSTRACT FROM AUTHOR]

Land subsidence is a serious issue primarily caused by the human over-extraction of groundwater, excluding natural factors. To achieve a balance between supply and demand, the real-time monitoring and management of groundwater resources are currently implemented using sensors. However, many developed management platforms often overlook the actual usage and experience of the users, potentially resulting in a poor user experience. In this study, we applied the Technology Acceptance Model to explore users' acceptance of and satisfaction with a groundwater management platform. We found that the proposed management platform obtained higher levels of user acceptance and satisfaction, and can be practically applied to the monitoring and management of groundwater resources. [ABSTRACT FROM AUTHOR]

The digital skills divide has been raised as a serious issue during the COVID-19. However, few studies explored the predictive influence of personality traits on college students' digital skills in online learning. To address this gap, this study took the second-level digital divide as the focus to conduct a two-round survey of college students over nearly 2 years based on cross-sectional and longitudinal methods to explore whether there is a digital skill divide or personality trait differences between urban and rural college students while learning online, and whether college students' personality traits can predict their digital skills. The results confirmed the rural and urban college students' digital skills divide. There were significant differences in all dimensions of their digital skills except for mobile skills. In addition, the digital skills divide of these college students persisted for nearly 2 years. Specifically, this study further confirmed that there were significant differences in the urban and rural college students' extraversion, neuroticism, and agreeableness, but there was no significant difference in their openness and conscientiousness. Additionally, there was no change in the urban and rural college students' personality trait differences in nearly 2 years. Personality traits could positively predict college students' digital skills. This study provides evidence for bridging the second-level digital divide of the rural and urban college students from the perspective of personality traits. [ABSTRACT FROM AUTHOR]

The innovation of nanocellulose as reinforcement filler in composites has been a topic of interest in the development of new biomaterials. The objective of this study was to investigate the mechanical properties of a nanohybrid dental composite made of rice husk silica and loaded with different percentages of kenaf nanocellulose. Kenaf cellulose nanocrystals (CNC) were isolated and characterized using a transmission electron microscope (TEM) (Libra 120, Carl Zeiss, Germany). The experimental composite was fabricated with fiber loadings of 1 wt%, 2 wt%, 3 wt%, 4 wt%, and 6 wt% silane-treated kenaf CNC, and subjected to a flexural and compressive strength test (n = 7) using an Instron Universal Testing Machine (Shimadzu, Kyoto, Japan), followed by a scanning electron microscopic assessment of the flexural specimen's fracture surface using a scanning electron microscope (SEM) (FEI Quanta FEG 450, Hillsborough, OR, USA). Commercial composites Filtek Z350XT (3M ESPE, St. Paul, MN, USA), Neofil (Kerr Corporation, Orange, CA, USA) and Ever-X Posterior (GC Corporation, Tokyo, Japan) were used as a comparison. The average diameter of kenaf CNC under TEM was 6 nm. For flexural and compressive strength tests, one-way ANOVA showed a statistically significant difference (p < 0.05) between all groups. Compared to the control group (0 wt%), the incorporation of kenaf CNC (1 wt%) into rice husk silica nanohybrid dental composite showed a slight improvement in mechanical properties and modes of reinforcement, which was reflected in SEM images of the fracture surface. The optimum dental composite reinforcement made of rice husk was 1 wt% kenaf CNC. Excessive fiber loading results in a decline in mechanical properties. CNC derived from natural sources may be a viable alternative as a reinforcement co-filler at low concentrations. [ABSTRACT FROM AUTHOR]

Background. During counter movement jumps, adding weight in the eccentric phase and then suddenly releasing this weight during the concentric phase, known as accentuated eccentric loading (AEL), has been suggested to immediately improve jumping performance. The level of evidence for the positive effects of AEL remains weak, with conflicting evidence over the effectiveness in enhancing performance. Therefore, we proposed to theoretically explore the influence of implementing AEL during constrained vertical jumping using computer modelling and simulation and examined whether the proposed mechanism of enhanced power, increased elastic energy storage and return, could enhance work and power. Methods. We used a simplified model, consisting of a ball-shaped body (head, arm, and trunk), two lower limb segments (thigh and shank), and four muscles, to simulate the mechanisms of AEL. We adjusted the key activation parameters of the muscles to influence the performance outcome of the model. Numerical optimization was applied to search the optimal solution for the model. We implemented AEL and non-AEL conditions in the model to compare the simulated data between conditions. Results. Our model predicted that the optimal jumping performance was achieved when the model utilized the whole joint range. However, there was no difference in jumping performance in AEL and non-AEL conditions because the model began its push-off at the similar state (posture, fiber length, fiber velocity, fiber force, tendon length, and the same activation level). Therefore, the optimal solution predicted by the model was primarily driven by intrinsic muscle dynamics (force-length-velocity relationship), and this coupled with the similar model state at the start of the push-off, resulting in similar push-off performance across all conditions. There was also no evidence of additional tendon-loading effect in AEL conditions compared to non-AEL condition. Discussion. Our simplified simulations did not show improved jump performance with AEL, contrasting with experimental studies. The reduced model demonstrates that increased energy storage from the additional mass alone is not sufficient to induce increased performance and that other factors like differences in activation strategies or movement paths are more likely to contribute to enhanced performance. [ABSTRACT FROM AUTHOR]

To understand students' learning behaviors, this study uses machine learning technologies to analyze the data of interactive learning environments, and then predicts students' learning outcomes. This study adopted a variety of machine learning classification methods, quizzes, and programming system logs, found that students' learning characteristics were correlated with their learning performance when they encountered similar programming practice. In this study, we used random forest (RF), support vector machine (SVM), logistic regression (LR), and neural network (NN) algorithms to predict whether students would submit on time for the course. Among them, the NN algorithm showed the best prediction results. Education-related data can be predicted by machine learning techniques, and different machine learning models with different hyperparameters can be used to obtain better results. [ABSTRACT FROM AUTHOR]

With the inherently interdisciplinary and hands-on nature of the STEAM system, this study changes the way the English language is taught, no longer confining it to monotonous tests and dull textbooks. Teaching via Augmented Reality (AR) technology tends to pique students' interest, making them more willing to participate in learning. This study used AR technology in conjunction with gaming, where the participants were split into pairs, which promoted friendly competition among themselves, and, in turn, drove them to learn more. Moreover, the ability to track their own performance was added to the system, as well as an in-depth explanation for every question. The teacher used our teaching aids system in the lessons to make the teaching process more interesting and enjoyable. There were two English course experiments, each lasting for 4 weeks. The goal of the first experiment was to gain a general understanding of how students felt about the lessons and how well they could operate the system. After analyzing the results, some improvements for problems that arose were made in the second experiment. The goal of the second experiment was to find out how students felt about the lesson content and teaching aids and to compare the results of the two experiments. To help us find potential oversights and check how participants felt about this approach, surveys regarding STEAM learning and overall ease of use were administered. The results showed that AR English learning was widely considered helpful by the students, and also, our system was easy to operate, and competing with peers really motivated them to do better. [ABSTRACT FROM AUTHOR]

In recent years, STEM education has developed students' fundamental subject knowledge, and has allowed students to integrate STEM cross-domain knowledge. Universities emphasize innovative thinking, practice and application, problem-solving, and teamwork to nurture students while learning STEM cross-domain knowledge development in remote STEM program education. When students take online STEM programs, they may encounter unanswered questions and may give up on trying to solve them. Therefore, this study proposed a problem-based learning approach with an online programming system integrated into an online STEM programming course. To help students solve the past programming assignments, the assignments were simplified, decomposed, and reorganized. The teacher guided the students to understand the STEM programming problems and taught them to use appropriate problem-solving skills to motivate them to complete the STEM programming assignments. The experiment was conducted with students in an online STEM programming course at a university in northern Taiwan. In the experimental activities, we used a problem-based learning approach for the online STEM programming activity. The problem-based learning method can be divided into four steps, namely stating the problem, understanding the problem, developing a solution plan, and executing the plan, reflecting, and debugging. This study used a problem-based learning approach and an online programming system integrated into a STEM programming curriculum to explore the differences in students' perceptions of STEM learning, learning outcomes, and learning behaviors. The experimental results found a significant difference between students' prior knowledge and learning outcomes. Students showed significant gains in learning the STEM programming content using the problem-based learning approach and the online programming system. In the analysis of their STEM learning perceptions, we found that there were significant differences in students' responses for each dimension. This shows that using the problem-based learning approach with the online programming system helped students learn the course content. The analysis of students' behaviors in answering the STEM programming assignments indicated that some students had the habit of taking notes. This helped them to easily associate and integrate STEM cross-domain knowledge with what they had learned in the online course, and enhanced their ability to implement STEM programs. In addition, students could take the initiative and focus on repeatedly watching the teacher solve the material in the online course. Students could try different solving plans to pass the code validation of the STEM programming assignments. This revealed that students wanted to complete the STEM programming assignments to achieve good learning performance. [ABSTRACT FROM AUTHOR]

Digital reality technologies (such as AR, VR, and MR) have recently become a key component of promoting creative and cultural industries (CCIs) worldwide to transform static cultural heritage exhibits into more engaging, entertaining, and immersive experiences. These technologies present an exciting example of studying how consumers would respond to the potential invasion of privacy due to these technologies. This literature review study mainly focuses on one essential branch of CCIs: museums and their applications of digital reality technologies. Because many of these location-based AR applications by museums are inherently sensitive to users' locational information, there is also a rising concern of the potential infringement of personal privacy (RQ1). A thorough examination of existing literature on how consumers respond to privacy concerns related to the museum's AR applications will help uncover how scholars have approached and studied these crucial issues in the literature (RQ2). Unlike traditional literature review analyses, we employed a text mining of retrieved 715 studies articles from Business Source Complete and Engineering Village (E.I.) databases to answer our two research questions. Our study found that privacy and user(s)/visitor(s) has dramatically increased since 2017, echoing the rising concerns of other privacyinvasive technologies. Most notably, key phrases extracted from the literature corpus include "security and privacy," "privacy and security," "privacy risks," "privacy concerns," "privacy issues," "user privacy," "location privacy," "privacy protection," and "privacy preserving" that are most pertinent to the rapid implementation of AR technology in the museum sector. Discussions and implications are provided. [ABSTRACT FROM AUTHOR]

Objective: To analyze the clinical therapeutic effect of acupuncture for patient with depression after cerebral stroke and the effect on neural function of patient. Methods: A total of 60 cases of patients with depression after cerebral stroke were selected in the study. The patients were randomly divided into the control group and the observation group, with 30 cases in each group. Patients in the two groups were treated with conventional anti-depression drugs and drugs for cerebral stroke. Patients in the observation group were treated with acupuncture on the basis of conventional anti-depression drugs and drugs for cerebral stroke. Patients in the two groups were treated and observed for 3 months. The scores of Hamilton Depression Scale (HAMD) and National Institutes of Health Stroke Scale (NIHSS), clinical therapeutic effect and changes of score of quality of life of cerebral stroke patient in the two groups were observed and compared before and after treatment. Results: After 3 months of treatment, the scores of NIHSS and HAMD significantly decreased comparing with those before treatment. Moreover, the scores of NIHSS and HAMD of the observation group were significantly lower than those of the control group, and the differences were statistically significant (P < 0.05). After 3 months of treatment, the total effective rate of depression in the observation group was significantly higher than that in the control group, and the difference was statistically significant (P < 0.05). After 3 months of treatment, the total effective rate of cerebral stroke in the observation group was significantly higher than that in the control group, and the difference was statistically significant (P < 0.05). After 3 months of treatment, the quality of life of patients in the two groups increased comparing with that before treatment. Moreover, the quality of life of patients in the observation group was significantly higher than that in the control group, and the difference was statistically significant (P < 0.05). Conclusion: Acupuncture can effectively increase the neural function of patient with depression after cerebral stroke, decrease the depressive emotion of patients, and improve clinical therapeutic effect and quality of life of patients. The therapeutic method has relatively high clinical value. [ABSTRACT FROM AUTHOR]

The article proposes the application of cloud computing and Internet of Things (IoT) technologies to develop a hydrological and land subsidence monitoring system for the Choshui River Alluvial Fan in Taiwan. It outlines the steps of the workflow of data processing of the groundwater monitoring system using Phython programming and Structured and Unstructured Query Languages for the databases. The study observes real-time groundwater and rainfall levels and subsidence between alluvial fan areas.

A novel concentrating photothermal reactor with the ultrafast heating rate at around 1800 °C/min was developed to study the effects of CO 2 and H 2 O on coal pyrolysis for oxy-fuel combustion. The mass loss rate with the ultrafast heating rate increased by several decuples than that with the slow heating rate. Coal pyrolysis process with the ultrafast heating rate was promoted by CO 2 within 50% concentration but inhibited for a further higher concentration (70%). The addition of H 2 O into 30% CO 2 exhibited a continuous positive role in coal pyrolysis with the ultrafast heating rate. Different from the satisfied prediction of chemical reaction models with the slow heating rates, diffusion models were of higher correlation coefficients for the ultrafast-heating pyrolysis process. The correction factors for CO 2 and H 2 O were determined accordingly, and the modified three-dimensional diffusion mechanism model based on Jander equation (D3 model) well described the ultrafast-heating coal pyrolysis process. • Coal pyrolysis with the ultrafast heating rate of around 1800 °C/min in CPR was studied. • Coal pyrolysis was promoted within 50% CO 2 , but inhibited in 70% CO 2. • The addition of H 2 O into 30% CO 2 exhibited a positive role in coal pyrolysis process. • The correction factors for CO 2 and H 2 O were determined. • The modified D3 model well described the ultrafast-heating coal pyrolysis process. [ABSTRACT FROM AUTHOR]

Stepping into the 21st century, "graphene fever" swept the world due to the discovery of graphene, made of single-layer carbon atoms with a hexagonal lattice. This wonder material displays impressive material properties, such as its electrical conductivity, thermal conductivity, and mechanical strength, and it also possesses unique optical and magnetic properties. Many researchers see graphene as a game changer for boosting the performance of various applications. Emerging consumer electronics and electric vehicle technologies require advanced battery systems to enhance their portability and driving range, respectively. Therefore, graphene seems to be a great candidate material for application in high-energy-density/high-power-density batteries. The "graphene battery", combining two Nobel Prize-winning concepts, is also frequently mentioned in the news and articles all over the world. This review paper introduces how graphene can be adopted in Li-ion/Li metal battery components, the designs of graphene-enhanced battery materials, and the role of graphene in different battery applications. [ABSTRACT FROM AUTHOR]

Massive open online courses (MOOCs) provide numerous open-access learning resources and allow for self-directed learning. The application of big data and artificial intelligence (AI) in MOOCs help comprehend raw educational data and enrich the learning process for students and instructors. Thus, we created two deep neural network models. The first model predicts learning outcomes on the basis of learning behaviors observed when students watch videos. The second is a novel exercise-based model that predicts if a student will correctly answer examination questions on relevant concepts. The study data were collected from two courses conducted on the National Tsing Hua University's MOOCs platform. The first model accurately evaluated student performance on the basis of their learning behaviors, and the second model efficiently predicted student performance according to how they answered the exercise questions. In conclusion, our AI system remedies the present-day inability of MOOCs to evaluate student performance. Instructors can use the systems to identify poor-performing students and offer them more assistance on a timely basis. [ABSTRACT FROM AUTHOR]

Due to their applications on varied and complex issues, Artificial Intelligence (AI) and Internet of Things (IoT) (collectively, AIoT) have become popular new-generation courses, but the learning of such courses needs to consider actual situations and to analyze complicated problems, making it difficult for students to improve their academic performance. The concept of computational thinking education is to allow students to find solutions to problems in different fields by understanding the problems and analyzing solution steps. In this study, we integrate the concept of computational thinking education into a course on plot image-based virtual reality (VR). It combined shooting of the actual field, instructions on the movie required to be shot, and VR in order to enable students to get immersed in thinking about problems and understanding the actual field through plot image-based VR, further plan to learn the essence of problems, and develop possible solutions. Through experimental results of experimental and control groups, we found that plot image-based VR can effectively improve students' interests in learning and computational thinking. [ABSTRACT FROM AUTHOR]

Advanced metering infrastructure (AMI) plays a key role in power systems. Since smart meters and meter collectors are synchronised to the time synchronisation devices (TSDs) in the head end system (HES) of AMI, they are vulnerable to global positioning system (GPS) spoofing-based time synchronisation attack (TSA). Impacts of GPS spoofing-based TSA on AMI are investigated in this study. It is uncovered that, since AMI is a distributed networked system and metering data and control commands transmitted in AMI could be of large latency, data and commands with large latency over the specific threshold are considered to be invalid according to validity verification mechanism of average distributed system. Therefore, the disorder in time synchronisation induced by GPS spoofing-based TSA could disable functions of HES of AMI, such as meter reading and remote control. A time jitter detection-based approach is developed to identify and prevent from GPS spoofing-based TSA. A high-precision oven-controlled crystal oscillator with cumulative error compensation is utilised to identify time jitter of the satellite clock and help ride through sustained GPS spoofing-based TSA. Simulation on FPGA demonstrates the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

Aiming at the problems of excessive dependence on manual work, low detection accuracy and poor real-time performance of current probe flow anomaly detection in power system network security detection, a detection method for calculating information entropy of probe flow and random forest classification is proposed. Firstly, the network probe stream data are captured and aggregated in real-time to extract network stream metadata. Secondly, by calculating Pearson correlation coefficient and maximum mutual information coefficient, feature selection of network metadata is carried out. Finally, the information entropy and stochastic forest algorithm are combined to detect the anomaly of probe traffic based on the selected key feature groups, and the probe traffic is accurately classified by multiple incremental learning. The results show that the proposed method can quickly locate the abnormal position of probe traffic and analyze the abnormal points, which greatly reduces the workload of application platform for power system security monitoring, and has high detection accuracy. It effectively improves the reliability and early warning ability of power system network security. [ABSTRACT FROM AUTHOR]

This article presents a coupled microstrip line microwave phase shifter using ferroelectric thin film varactors with parallel plate electrodes integrated on high-resistivity silicon substrate. This design is proposed as a component in low-cost beam scanning phased array antennas. The phase shifter structure consists of coupled microstrip line periodically loaded with eight BaxSr1-xTiO3 (BST) varactors and two planar Marchand baluns. The planar Marchand baluns work as the transformers of odd mode excitation and a transmission stop circuit for even mode excitation as well as the impedance matching networks. A differential phase shift of 45° was obtained with a low dc bias of 20 V at frequency of 9 GHz, which corresponds to a figure of merit of ∼23°/dB. These results represent significant progress toward compact size, low loss, and ferroelectric thin film-based phase shifters at room temperature. [ABSTRACT FROM AUTHOR]

Columnar BaxSr1-xTiO3 (BST) thin films with x=0.25 and 0.5 were grown by pulsed laser deposition on single crystal LaAlO3 and Pt/Ti/SiO2/Si substrates, respectively. Ferroelectric varactors were then fabricated from the BST thin films by photolithographic techniques based on parallel-plate capacitors (PPCs) and coplanar interdigital capacitors (IDCs). The microstructure, dielectric constant [variant_greek_epsilon], loss tan δ, tunability τ, and temperature dependent characters of the BST film with IDC and PPC electrodes were investigated and compared in a wide frequency range from 1 kHz to 10 GHz. The Ba0.5Sr0.5TiO3 films exhibited larger permittivity, tunability, and loss tan δ at the level of IDC and PPC structures compared to Ba0.25Sr0.75TiO3 films. As measured by IDC and PPC electrodes, their in-plane dielectric properties also exhibited a larger permittivity, lower loss tan δ, and relative lower tunability than that of out-of-plane dielectric properties. Finally, the influence on the dielectric properties of the columnar BST grains due to the use of different types of electrodes was discussed. [ABSTRACT FROM AUTHOR]

We report an approach of controlling liquid crystals using a laser beam without external bias. In our design, in addition to liquid crystals, a patterned semiconductor was also included. We discovered that the average pretilt angle of liquid crystals can be controlled by adjusting excitation power, and the relationship was found to follow the possible predicted photovoltaic effect. Besides, the photogenerated field was found to have both perpendicular and horizontal components, which is very useful to control the orientation of liquid crystals to an arbitrary direction. Optical switch devices can be possible to design using our method. [ABSTRACT FROM AUTHOR]

Products are generally classified in a binary manner (conforming or non-conforming) in attributed control charts. This format may not be the most suitable if the product quality changes gradually rather than abruptly. Therefore, refining the partition into multiple categories will lead to a reduction of quality information loss. To enhance the detection ability of a control chart, linguistic variables are used here to express the quality characteristics and a sequential probability ratio test scheme is constructed under the estimated probability distribution function of the linguistic data. The simulation results show that the sequential probability ratio test charting procedure has a better performance than the Shewhart control chart for the small magnitude of process mean shifts in terms of average run lengths. [ABSTRACT FROM AUTHOR]

Objective. 1e aim of this study is to explore the potential pathogenesis of juvenile dermatomyositis by bioinformatics analysis of gene chips, which would screen the hub genes, identify potential biomarkers, and reveal the development mechanism of juvenile dermatomyositis. Material and Methods. We retrieved juvenile dermatomyositis's original expression microarray data of message RNAs (mRNAs) and microRNAs (miRNAs) from NCBI's Gene Expression Omnibus database (GEO, http://www.ncbi.nlm.nih. gov/geo/); through the R package of limma in Bioconductor, we can screen the differentially expressed miRNAs and mRNAs, and then we further analyzed the predicted target genes by the methods such as Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and miRNA-mRNA regulatory network construction and protein-protein interaction (PPI) network using Cytoscape 3.6.1. Results. Compared with normal juvenile skin tissues, 6 upregulated microRNAs and 5 downregulated microRNAs were identified from 166 downregulated microRNAs and 58 upregulated microRNAs in juvenile dermatomyositis tissues. 1e enrichment pathways of differentially expressed microRNAs include cell adhesion molecules (CAMs), autoimmune thyroid disease, Type I diabetes mellitus, antigen and presentation, viral myocardium, graft-versus-host disease, and Kaposi sarcoma-associated herpes virus infection. By screening of microRNA-messenger RNA regulatory network and construction of PPI network map, three target miRNAs were identified, namely, miR-193b, miR-199b-5p, and miR-665. Conclusion. We identified mir-193b, mir-199b-5p, and mir-6653 target miRNAs by exploring the miRNA-mRNA regulation network mechanism related to the pathogenesis of juvenile dermatomyositis, which will be of great significance for further study on the pathogenesis and targeted therapy of juvenile dermatomyositis. [ABSTRACT FROM AUTHOR]

The main purpose of this paper is to construct pliable fractional repetition (FR) codes that are locally recoverable for distributed storage systems (DSSs). FR codes are integral in constructing a class of distributed storage codes with exact repair by transfer. Pliable FR codes are a new type of FR codes in which both the per-node storage and repetition degree can easily be adjusted simultaneously; thus, pliable FR codes are vital for DSSs in which parameters can dynamically change over time. However, the constructions of pliable FR codes with repair locality remain unknown. In addition, the tradeoffs between the code minimum distance of an FR code and its repair locality are not fully understood. To address these problems, this paper first presents general results regarding FR codes. Subsequently, this paper presents an improved Singleton-like bound for locally recoverable FR codes under an additional requirement that each node must be part of a local structure that, upon failure, allows it to be exactly recovered by a simple download process. Moreover, this paper proposes a construction of locally recoverable FR codes that can achieve the proposed Singleton-like bound; this construction is based on bipartite graphs with a given girth. In particular, this paper also proposes a general bipartite-graph-based approach to constructing optimal pliable FR codes with and without repair localities; in this approach, a new family of bipartite graphs, called matching-feasible graphs, is introduced. Finally, this paper proposes the explicit constructions of optimal pliable FR codes by using a family of matching-feasible graphs with arbitrary large girth. Notably, in addition to attaining a Singleton-like bound for FR codes, the explicit pliable FR codes are optimal locally recoverable FR codes from two perspectives of repair locality. The explicit pliable FR codes can also be used as FR batch codes to provide load balancing in DSSs. [ABSTRACT FROM AUTHOR]

A distributed phase shifter of coplanar waveguide (CPW) using ferroelectric (Ba0.25Sr0.75TiO3) film varactors with parallel-plate electrodes is presented. In order to satisfy the requirement of small dimension of the parallel-plate varactor for the design of phase shifter and impedance match of the circuit, two parallel-plate varactors of 8×10 μm2 are connected in series. The fabricated CPW phase shifter exhibited low insertion loss of 2.3 dB and large differential phase shift of 170° at 7 GHz with a dc bias voltage of 25 V. [ABSTRACT FROM AUTHOR]

Platinum nanoparticles (Pt NPs) immobilized on a N-doped graphene@ Al2O3 hybrid support (Al2O3@CNx) were synthesized and employed for low temperature CO oxidation. The superior catalytic activity was attributed to a strong metal--support interaction between Pt NPs and the N-doped graphene surface which was also confirmed in the direct dehydrogenation reaction. [ABSTRACT FROM AUTHOR]

The mechanism of CO oxidation by O2 on a single Au atom supported on pristine, mono atom vacancy (m), di atom vacancy (di) and the Stone Wales defect (SW) on single walled carbon nanotube (SWCNT) surface is systematically investigated theoretically using density functional theory. We determine that single Au atoms can be trapped effectively by the defects on SWCNTs. The defects on SWCNTs can enhance both the binding strength and catalytic activity of the supported single Au atom. Fundamental aspects such as adsorption energy and charge transfer are elucidated to analyze the adsorption properties of CO and O2 and co-adsorption of CO and O2 molecules. It is found that CO binds stronger than O2 on Au supported SWCNT. We clearly demonstrate that the defected SWCNT surface promotes electron transfer from the supported single Au atom to O2 molecules. On the other hand, this effect is weaker for pristine SWCNTs. It is observed that the high density of spin-polarized states are localized in the region of the Fermi level due to the strong interactions between Au (5d orbital) and the adjacent carbon (2p orbital) atoms, which influence the catalytic performance. In addition, we elucidate both the Langmuir–Hinshelwood (LH) and Eley–Rideal (ER) mechanisms of CO oxidation by O2. For the LH pathway, the barriers of the rate-limiting step are calculated to be 0.02 eV and 0.05 eV for Au/m-SWCNT and Au/di-SWCNT, respectively. To regenerate the active sites, an ER-like reaction occurs to form a second CO2 molecule. The ER pathway is observed on Au/m-SWCNT, Au/SW-SWCNT and Au/SWCNT in which the Au/m-SWCNT has a smaller barrier. The comparison with a previous study (Lu et al., J. Phys. Chem. C, 2009, 113, 20156–20160.) indicates that the curvature effect of SWCNTs is important for the catalytic property of the supported single Au. Overall, Au/m-SWCNT is identified as the most active catalyst for CO oxidation compared to pristine SWCNT, SW-SWCNT and di-SWCNT. Our findings give a clear description on the relationship between the defects in the support and the catalytic properties of Au and open a new avenue to develop carbon nanomaterial-based single atom catalysts for application in environmental and energy related fields. [ABSTRACT FROM AUTHOR]

Long noncoding RNAs (lncRNAs) participate in multiple biological processes especially human diseases, of which, tumor seems to be one of the most significant. Angiogenesis has been deemed to have a pivotal role in a series of tumor biological behaviors in tumorigenesis, progression and prognosis. Emerging evidences suggested that lncRNAs are involved in tumor angiogenesis and lncRNAs have already been verified to be potential biomarkers and promising therapeutic targets. This review summarized emerging angiogenesis-related lncRNAs, discussed their mechanisms interacting with cytokines, cancer stem cells, miRNAs and tumor hypoxia microenvironment, and demonstrated if lncRNAs could be new candidate targets of antiangiogenesis therapy. [ABSTRACT FROM AUTHOR]

Hybrid ferroelectric thin-film varactor device architecture is presented that allows for integrating the features of both structures of coplanar and parallel-plate varactors. In this structure, a high-resistivity oxide thin film is used as dc bias bottom electrode, while the existence of the high-resistivity oxide thin-film electrode is considered as disturbance to the original electric field in the coplanar varactor. Using analytic solution of electromagnetic field, the disturbance caused by high-resistivity thin film in microwave TEM wave is derived. The result shows that this disturbance could be modeled as serial or shunt resistance in the transmission line equivalent circuit and the magnitude of it is proportional to the conductivity and thickness of the conductor thin film. With conductivity and thickness low enough, the conductor film will be “transparent” to microwave field. Using high-resistance dc bias electrode “transparent” to microwave is expected to be able to bring much more flexibility in the developing of microwave ferroelectric tunable devices. [ABSTRACT FROM AUTHOR]

Sarcopenia is defined as the progressive loss of muscle mass with age, and poses a serious threat to the physiological and psychological health of the elderly population with consequential economic and social burdens. Chronic low-grade inflammation plays a central role in the development of sarcopenia such that it alters cellular protein metabolism to favor proteolysis over synthesis, and thereby accelerates muscular atrophy. The purpose of this review is to highlight how exercise and nutrition intervention strategies can attenuate or treat sarcopenia. Resistance exercise increases not only muscle mass but also muscle strength, while aerobic exercise is able to ameliorate the age-related metabolic disorders. Concurrent exercise training integrates the advantages of both aerobic and resistance exercise, and may exert a significant synergistic effect in the aging organism. Higher protein intakes rich in the amino acid leucine appear to restore skeletal muscle protein metabolism balance by rescuing protein synthesis in older adults. There is good reason to believe that a multimodal treatment, a combination of exercise and increased leucine consumption in the diet, can combat some of the muscle loss associated with aging. Future research is needed to consolidate these findings to humans, and to further clarify to what extent and by which mechanisms protein metabolism might be directly involved in sarcopenia pathogenesis and the multimodal treatment responses. [ABSTRACT FROM AUTHOR]

For many cancer types, cancer cells invade into surrounding tissues by collective movement of cell groups that remain connected via cell-cell junctions. This migration is completely distinguished from single-cell migration, in which cancer cells disrupt the tight intercellular junctions and gain a mesenchymal phenotype. Recently, emerging evidence has revealed that collective cell invasion depends on not only cell-intrinsic mechanisms but also on extracellular mechanisms by bidirectional interplay between the tumor cell and the tumor environment. Herein, in this review we discuss the role and underline mechanisms of tumor microenvironment in collective tumor cell invasion, particularly focusing on extracellular matrix remodeling and cross-talk between tumor and stromal cells. [ABSTRACT FROM AUTHOR]

Background: Transforming growth factor-beta (TGF-β) is associated with a higher incidence of distant metastasis and decreased survival. Whether TGF-β can be used as a prognostic indicator of colorectal cancer (CRC) remains controversial.Methods: The Medline, EMBASE and Cochrane databases were searched from their inception to March 2016. The studies that focused on TGF-β as a prognostic factor in patients with CRC were included in this analysis. Overall survival (OS) and disease-free survival (DFS) were analysed separately. A meta-analysis was performed, and hazard ratios (HR) with 95% confidence intervals (CI) were calculated.Results: Twelve studies were included in the analysis, of which 8 were used for OS and 7 for DFS. In all, 1622 patients with CRC undergoing surgery were included. Combined HRs suggested that high expression of TGF-β had a favourable impact on OS (HR = 1.68, 95% CI: 1.10-2.59) and DFS (HR = 1.11, 95% CI: 1.03-1.19) in CRC patients. For OS, the combined HRs of Asian studies and Western studies were 1.50 (95% CI: 0.61-3.68) and 1.80 (95% CI: 1.33-2.45), respectively. For DFS, the combined HRs of Asian studies and Western studies were 1.42 (95% CI: 0.61-3.31) and 1.11 (95% CI: 1.03-1.20), respectively.Conclusions: This meta-analysis demonstrates that TGF-β can be used as a prognostic biomarker for CRC patients undergoing surgery, especially for CRC patients from Western countries. [ABSTRACT FROM AUTHOR]

Several studies have indicated a positive influence of leucine supplementation and aerobic training on the aging skeletal muscle signaling pathways that control muscle protein balance and muscle remodeling. However, the effect of a combined intervention requires further clarification. Thirteen month old CD-1® mice were subjected to moderate aerobic exercise (45 min swimming per day with 3% body weight workload) and fed a chow diet with 5% leucine or 3.4% alanine for 8 weeks. Serum and plasma were prepared for glucose, urea nitrogen, insulin and amino acid profile analysis. The white gastrocnemius muscles were used for determination of muscle size and signaling proteins involved in protein synthesis and degradation. The results show that both 8 weeks of leucine supplementation and aerobic training elevated the activity of mTOR (mammalian target of rapamycin) and its downstream target p70S6K and 4E-BP1, inhibited the ubiquitin-proteasome system, and increased fiber cross-sectional area (CSA) in white gastrocnemius muscle. Moreover, leucine supplementation in combination with exercise demonstrated more significant effects, such as greater CSA, protein content and altered phosphorylation (suggestive of increased activity) of protein synthesis signaling proteins, in addition to lower expression of proteins involved in protein degradation compared to leucine or exercise alone. The current study shows moderate aerobic training combined with 5% leucine supplementation has the potential to increase muscle size in fast-twitch skeletal muscle during aging, potentially through increased protein synthesis and decreased protein breakdown. [ABSTRACT FROM AUTHOR]