Miracle-dz/UniFAR-FTUs huggingface dataset:
Monitoring And Managing Depression In Adolescents With Epilepsy: Current Perspectives

Abstract Epilepsy is associated with a significantly increased risk of developing depressive disorder during adolescence. On the other hand, depression is highly detected in adolescents with epilepsy. These findings highlight the importance of early identification and proper management of comorbid depression in adolescent age. The prevalence of depressive disorders in adolescents with epilepsy ranges between 8 and 35% and is higher than the general population of the same age. The relationship between epilepsy and depression is complex and potentially bidirectional, thereby suggesting a common underlying pathophysiology. Furthermore, failure to detect and treat depressive disorder mostly in adolescence could lead to several negative implications such as an increased risk of suicidal ideation or behavior and poor quality of life. A number of methods are available to detect depressive disorder, such as psychiatric or psychological assessments, structured or semi-structured interviews, and self-report screening tools. Thus, physicians should be able to regularly screen depressive symptoms in youths with epilepsy. Recently, the NDDI-E-. Y inventory has been developed from the adult NDDI-E, and has been validated in many countries. NDDI-E-Y has showed reliable validity, being a brief screening tool (12 items) that can be easily included in routine epilepsy care. The first step to be considered for the management of depressive disorder in adolescents with epilepsy is to consider potential reversible causes of anxiety and depression (i.e., a new AEDs; seizure control). Secondly, great attention has to be given to the education of the child/adolescent and his/her family, trying to improve knowledge about epilepsy as well as to decrease parental stress and improving the child’s sense of competence. Pharmacological treatment should also be considered in adolescents diagnosed with depression.
Breakthroughs in medicine and bioimaging with up-conversion nanoparticles

Abstract Nanomedicine is a medical application of biochemistry incorporated with materials chemistry at the scale of nanometer for the purpose of diagnosis, prevention, and treatment. New models and approaches are typically associated with nanomedicine for precise multifunctional diagnostic systems at molecular level. Hence, employing nanoparticles (NPs) has unveiled new opportunities for efficient therapies and remedy of difficult-to-cure diseases. Among all types of inorganic NPs, lanthanide-doped up-conversion nanoparticles (UCNPs) have shown excellent potential for biomedical applications, especially for multimodal bioimaging including fluorescence and electron microscopy. Association of these visualization techniques plus the capability for transporting biomaterials and drugs make them superior agents in the field of nanomedicine. Accordingly, in this review, we firstly presented a fundamental understanding of physical and optical properties of UCNPs and secondly, we illustrated some of the prominent associations with bioimaging, theranostics, cancer therapy, and optogenetics.
DMSO_A_222066 1923..1930

1Department of Endocrinology, Metabolism and Genetic Disorders, Children’s Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 2Department of Emergency Intensive Care Unit, The First Affiliated Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 3Department of Children Health Care, Children’s Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China; 4Department of Cardiology, Children’s Hospital of Soochow University, Suzhou, Jiangsu, People’s Republic of China
School motivation, goal orientation and academic performance in secondary education students

Background In the students' school stage can appear many psychological variables that can positively or negatively affect the academic life of the students as well as their learning process. Therefore, the present study aims to explore the relation between school motivation, goal orientation and academic performance in adolescent students. Methods This cross-sectional study was performed on adolescent students (N=3512) from 18 public Secondary Education (ESO) centers. The Scale of School Motivation (EME-S), the Perception of Success Questionnaire (POSQ) and academic performance, quantified by each student’s mean mark, were instruments used. Results The results revealed significant relations among, on the one hand, intrinsic school motivations, task-oriented goal orientations and academic performance in a more adaptive pattern behavior and, on the other hand, relations between extrinsic motivations and ego-oriented goal orientations. The influence of intrinsic motivations, task-oriented goal orientations and low amotivation indices on predicting academic performance was deduced. Conclusion The importance of promoting adaptive behaviors that improve adolescent students’ academic lives focus on adequate levels of school motivation and task-oriented goal orientations at their education centers was revealed.
Humeral shaft fracture: a randomized controlled trial of nonoperative versus operative management (plate fixation)

Purpose This randomized controlled trial was conducted to investigate the outcomes of humeral shaft–fracture management with the functional Sarmiento brace (nonoperative) versus open reduction internal fixation (ORIF). Methods Sixty humeral shaft–fracture patients with a minimum age of 18 years were randomly assigned into two groups: operative treatment with open reduction–internal fixation (ORIF) or functional brace (Sarmiento). A similar postoperative rehabilitation program was applied for all subjects for the next 12 months. The outcomes of each method were measured in terms of nonunion rate, union time, “quick” Disabilities of Arm, Shoulder, and Hand (DASH) questionnaire scores, and rate of complications, such as malunion, infection, and radial nerve injury. Results The two groups had similar baseline characteristics, including age, sex, smoking status, and type and mechanism of fracture. The mean union time was about 4.8 weeks shorter in the ORIF group (13.9 weeks in operative group versus 18.7 weeks in nonoperative group), indicating a definite significant superiority (p=0.001) of ORIF management to functional Sarmiento bracing. However, a comparison of quick DASH scores revealed a borderline-significant difference between the groups (p=0.065). Additionally, we found that treatment of humeral shaft fractures using functional bracing was associated with slightly higher risk of nonunion; however this was not significant (p=0.492). Conclusion According to the present findings, there is remarkable superiority of ORIF over functional Sarmiento bracing in the management of patients with humeral shaft fracture.
Tuning the surface coating of IONs toward efficient sonochemical tethering and sustained liberation of topoisomerase II poisons

Background Iron oxide nanoparticles (IONs) have been increasingly utilized in a wide spectrum of biomedical applications. Surface coatings of IONs can bestow a number of exceptional properties, including enhanced stability of IONs, increased loading of drugs or their controlled release. Methods Using two-step sonochemical protocol, IONs were surface-coated with polyoxyethylene stearate, polyvinylpyrrolidone or chitosan for a loading of two distinct topo II poisons (doxorubicin and ellipticine). The cytotoxic behavior was tested in vitro against breast cancer (MDA-MB-231) and healthy epithelial cells (HEK-293 and HBL-100). In addition, biocompatibility studies (hemotoxicity, protein corona formation, binding of third complement component) were performed. Results Notably, despite surface-coated IONs exhibited only negligible cytotoxicity, upon tethering with topo II poisons, synergistic or additional enhancement of cytotoxicity was found in MDA-MB-231 cells. Pronounced anti-migratory activity, DNA fragmentation, decrease in expression of procaspase-3 and enhancement of p53 expression were further identified upon exposure to surface-coated IONs with tethered doxorubicin and ellipticine. Moreover, surface-coated IONs nanoformulations of topo II poisons exhibited exceptional stability in human plasma with no protein corona and complement 3 binding, and only a mild induction of hemolysis in human red blood cells. Conclusion The results imply a high potential of an efficient ultrasound-mediated surface functionalization of IONs as delivery vehicles to improve therapeutic efficiency of topo II poisons.
Cubic-Spline Interpolation for Sparse-View CT Image Reconstruction With Filtered Backprojection in Dynamic Myocardial Perfusion Imaging

We investigated a projection interpolation method for reconstructing dynamic contrast-enhanced (DCE) heart images from undersampled x-ray projections with filtered backprojecton (FBP). This method may facilitate the application of sparse-view dynamic acquisition for ultralow-dose quantitative computed tomography (CT) myocardial perfusion (MP) imaging. We conducted CT perfusion studies on 5 pigs with a standard full-view acquisition protocol (984 projections). We reconstructed DCE heart images with FBP from all and a quarter of the measured projections evenly distributed over 360°. We interpolated the sparse-view (quarter) projections to a full-view setting using a cubic-spline interpolation method before applying FBP to reconstruct the DCE heart images (synthesized full-view). To generate MP maps, we used 3 sets of DCE heart images, and compared mean MP values and biases among the 3 protocols. Compared with synthesized full-view DCE images, sparse-view DCE images were more affected by streak artifacts arising from projection undersampling. Relative to the full-view protocol, mean bias in MP measurement associated with the sparse-view protocol was 10.0 mL/min/100 g (95%CI: −8.9 to 28.9), which was >3 times higher than that associated with the synthesized full-view protocol (3.3 mL/min/100 g, 95% CI: −6.7 to 13.2). The cubic-spline-view interpolation method improved MP measurement from DCE heart images reconstructed from only a quarter of the full projection set. This method can be used with the industry-standard FBP algorithm to reconstruct DCE images of the heart, and it can reduce the radiation dose of a whole-heart quantitative CT MP study to <2 mSv (at 8-cm coverage).
Watch Spaces: A Spatial User Interface for Smart Watches

We present a platform to prototype spatial user interfaces for smart watches relative to the user's body. We show the general feasibility and present 2 applications. The first application scenario enable users "pin" applications in the air around them and get back to them by moving the smart watch display at the same position again. The second application shows a zoom use case for maps or other larger displays/visualizations. The smart watch acts like a digital "magnifying glass" enabling the user to see details he cares about.
Analytical Models for the Scalability of Dynamic Group-key Agreement Protocols and Secure File Sharing Systems

Dynamic group key agreement protocols are cryptographic primitives to provide secure group communications in decentralized and dynamic networks. Such protocols provide additional operations to update the group key while adding new participants into the group and removing existing participants from the group without re-executing the protocol from the beginning. However, the lack of scalability emerges as one of the most significant issues of dynamic group key agreement protocols when the number of participants in the group increases. For instance, frequent participant join requests for large groups may cause an effect similar to a Distributed Denial of Service (DDoS) attack and violate the system availability due to the increase in group key update time. Therefore, analyzing the scalability of dynamic group key agreement protocols is crucial to detect conditions where the system becomes unavailable. In this article, we propose an analytical performance model to evaluate the scalability of dynamic group key agreement protocols by using queueing models. We also extend our performance model for evaluating the scalability of secure file sharing systems that utilize group key agreement protocols. Moreover, we present a demonstrative use case to show the applicability of our performance model on an example group key agreement protocol and a secure file sharing system.
A FPGA Parameterizable Multi-Layer Architecture for CNNs

Advances in hardware platforms boosted the use of Convolutional Neural Networks (CNNs) to solve problems in several fields such as Computer Vision and Natural Language Processing. With the improvements of algorithms involved in learning and inferencing for CNNs, dedicated hardware architectures have been proposed with the goal to speed up the CNNs' performance. However, the CNNs' requirements in bandwidth and processing power challenge designers to create architectures fitted for ASICs and FPGAs. Embedded applications targeting IoT (including sensors and actuators), health devices, smartphones, and any other battery-powered device may benefit from CNNs. For that, the CNN design must follow a different path, where the cost function is a small area footprint and reduced power consumption. This paper is a step towards this goal, by proposing an architecture for the main modules of modern CNNs. The proposal uses as case-study the Alexnet CNN, targeting Xilinx FPGA devices. Compared to the literature, results show a reduction up to 9 times in the amount of required DSP modules.
WatchPen: Using Cross-Device Interaction Concepts to Augment Pen-Based Interaction

Pen-based input is often treated as auxiliary to mobile devices. We posit that cross-device interactions can inspire and extend the design space of pen-based interactions into new, expressive directions. We realize this through WatchPen, a smartwatch mounted on a passive, capacitive stylus that: (1) senses the usage context and leverages it for expression (e.g., changing colour), (2) contains tools and parameters within the display, and (3) acts as an on-demand output. As a result, it provides users with a dynamic relationship between inputs and outputs, awareness of current tool selection and parameters, and increased expressive match (e.g., added ability to mimic physical tools, showing clipboard contents). We discuss and reflect upon a series of interaction techniques that demonstrate WatchPen within a drawing application. We highlight the expressive power of leveraging multiple sensing and output capabilities across both the watch-augmented stylus and the tablet surface.
A Mixture Approach with Some Bayesian Models: Application to Determine Effective Variables of Fiscal and Monetary Policy on Economic Growth

In this paper, we estimated affecting and important variables of fiscal and monetary policy on economic growth with comparing 3 models (BMA), (DMA) and (DMS) base on accuracy review of estimate model (MSFA), (MAFA). We have understood (BMA) model is an optimal model due to lower prediction error indicators that have shown in table [1]. So we identified the most important and affecting variables on economic growth with considering 62 indicators base on empirical data for determining affecting variables on economic growth, that with using BMA model. By doing the calculations and investigating the effect of 62 factors that have been effective in empirical studies on economic growth, it has been determined that the effect of 11 variables will be meaningful and these variables have always retained their effect, and in the presence of other variables, they have retained their effect. These variables are determined according to the posterior probability (i.e., the highest importance in explaining the economic growth and in other words the most likely to be present in the model). Capital Investment(K), Employment(L), Liquidity(M), Interest Rate(R), Inflation Rate(P), Government Expenditure(G), Balance of Payments (BP), Taxes(T), Oil Revenues (Troil), Gini Coefficient (Gni) and Saving Rate (S) are the affecting and important variables of fiscal and monetary policy on economic growth.
The Multi-case Analysis on the Factors of Source Innovation: Based on the ISM

Source innovation is a new innovation activity that build new concept, combine existing resources and realize new value. Taking 5 famous innovation cases such as Wanda, Alibaba, Meituan, DiDi and Haier as samples, this paper analyzes 25 factors of firms' source innovation, and constructs interpretative structural model of factors. The results show that, social pain point, policy orientation, environmental change and customer needs are the external objective factors of source innovation. Open sharing, proactive collaboration, innovation atmosphere, faith and confidence are the internal cultural factors that influence the source innovation. Business opportunity discovery, organizational forgetfulness, absorptive capacity, technical capacity, performance basis, human capital, and social network constitute the internal capacity factors of source innovation. Stratagem pioneer, integration ability and reform courage directly affect source innovation, which is an important guarantee for the success of source innovation.
Switch off a Robot, Switch off a Mind?

A future world populated by robots is a projection that has long inspired and still inspires science-fiction stories. How to consider these artificial agents is a tremendous question for human society as it highlights an eternal issue "What defines human? " More precisely, "What defines the human mind? " In these two studies, we investigate how individuals' representation of mind may define attitudes towards robots and pro/antisocial behaviours towards them. Also, we evaluate the role of human-robot interaction as a moderator of the above-mentioned process. Our study's results demonstrate that the conceptualization of mind as a function results in more positive attitudes towards robots, and a lesser likelihood of acting negatively toward them. The opposite is true for participants who consider the mind as a human essence. Finally, we show that interaction with a robot seems to level out these differences. These results are discussed in terms of attitudes towards robots measured and implications for future human-robot interaction (HRI).
Comparison between Direct and Indirect Learnings for the Digital Pre-distortion of Concurrent Dual-band Power Amplifiers

Current radio-communication systems demand high linearity and high efficiency. The digital baseband pre-distorter (DPD) is a cost-effective solution to guarantee the required linearity without compromising the efficiency. In the design of a DPD for a single band power amplifier (PA), the position of the inverse system is exchanged during the identification procedure to avoid the necessity of a PA model within a cumbersome closed-loop process. However, in a practical environment where only an approximation to the inverse is achieved, the linearization capability is affected by shifting the post-inverse placed after the PA to a pre-inverse located before the PA. In DPD intended for concurrent dual-band PAs, an additional advantage of such approach is that the post-inverse identifications for each band are completely independent of each other. This work performs a comparative analysis between two learning architectures applied to the linearization of two concurrent dual-band PAs stimulated by 2.4 GHz Wi-Fi and 3.5 GHz LTE signals. For the first PA, an exact PA model is known and the replacement of a post-inverse to a pre-inverse produces only negligible degradation in linearity. For the second PA, only an approximate PA model is available and the accuracy of such PA model produces a major impact on the linearization capability than the shifting of the inverse.
Fraud Payment Research: Payment through Credit Car

Credit card payment is called CNP which stands for "card not present". Security issues about CNP are critical. We propose an empirical study research for analyzing payment transactions using K-means clustering and decision table method of data mining techniques. The data sets we used are credit card payment from government open data website and credit card fraud data set shared from Weka. In this paper, we found that EC (Electronic Commerce) payment was the majority credit card payment fraud from the first data set and got a close look about similar attributes of fraud customer payments.
Towards Graphical User Interface Redefinition without Source Code Access: System Design and Evaluation

Nowadays several interactive computing systems (ICSs) still have Graphical User Interfaces (GUIs) that are inadequate in terms of usability and user experience. Numerous improvements were made in the development of better GUIs however, little has been done to improve existing ones. This might be explained by the fact that most ICSs do not provide source code access. In most cases, this means that only persons with source code access can (easily) enhance the respective GUI. This paper presents a tool using computer vision (CV) algorithms to semi-automatically redefine existing GUIs without accessing their source code. The evaluation of a new GUI obtained from the redefinition of an existing GUI using the tool is described. Results show statistically significant improvements in usability (reduction of interaction mistakes), improved task completion success rate and improved user satisfaction.
Predictive Modeling of Stock Indexes Using Machine Learning and Information Theory

Stock market indexes play an important role in summarizing the overall performance of stocks that belong to a certain group, thus provide indication into the general market performance and could guide the trading strategy of relevant stocks. In this study, we combine the information theory and recurrent neural network modeling to first reveal important non-linear lead-lag relationships among different stock indexes, and then predictively model the stock index dynamics. We show that multiple strong directional information flows exist among different stock indexes, further some of them exhibit large time lags, thus provide useful prior knowledge for predictive modeling. Our recurrent neural network model successfully captures the temporal dynamics of the S&P 500 index. Sensitivity analysis further reveals that internal memory length in the neural network is one of the most important controlling factors contributing the model performance.
Communicating Multimodal Wayfinding Messages for Visually Impaired People via Wearables

People with a visual impairment (PVI) often experience difficulties with wayfinding. Current navigation applications have limited communication channels and do not provide detailed enough information to support PVI. By transmitting wayfinding information via multimodal channels and combining these with wearables, we can provide tailored information for wayfinding and reduce the cognitive load. This study presents a framework for multimodal wayfinding communication via smartwatch. The framework consists of four modalities: audio, voice, tactile and visual. Audio and voice messages are transmitted using a bone conduction headphone, keeping the ears free to focus on the environment. With a smartwatch vibrations are directed to a sensitive part of the body (i.e., the wrist), making it easier to sense the vibrations. Icons and short textual feedback are viewed on the display of the watch, allowing for hands-free navigation.