Miracle-dz/UniFAR-FTUs huggingface dataset:
Significantly Improved Stability of PDMS- Packaged Silver Nanowire Film under Multiple Environments

Silver Nanowire (AgNW) transparent conductive film has been widely studied in recent years as alternative to ITO. In spite of the excellent light transmittance, conductivity and flexibility, the weak stability of AgNW film is a severe barrier to its application in electronic devices. However, transparent electrode is packaged when applied in electronic devices. In this work, the effects of package on the stability of AgNW film under multiple environments are tested. We demonstrate that the stability of AgNW film is significantly improved after packaged by PDMS in ambient atmosphere. After annealing at 180 °C for 50 h, the sheet resistance of PDMS-packaged AgNW film increased by 47%, compared to 1100% of pure AgNW film. In harsh environment of high humidity and high temperature, PDMS-packaged AgNW film also performs outstanding stability. When exposed to ultraviolet (UV) irradiation, the pure AgNW film lost conductivity in 40 min, accompanied with the sharp decrease of light transmittance. By contrast, the sheet resistance of PDMS-packaged AgNW film only changed about 20% with nearly unchanged light transmittance. Even at high current density of 250 mA cm-2, the PDMS-packaged AgNW film exhibited a lifetime 5.8 times that of pure AgNW film. The protective effect of package will promote the application of AgNW film in optoelectronic devices.
The Concerns of Innovating New Product Development Process in High Tech Companies: Evidence from Taiwan

The high-tech companies in Taiwan began to improve and innovate their new product development (NPD) process since late 1980s. However, it is found that even if companies desire to strategically innovate their NPD process, for some companies, it is difficult to effectively implement it. This paper aims to explore why it is difficult in Taiwan for companies to innovate and implement their NPD processes. We use interview and questionnaire survey method to conduct our research. According to the result from our research, there is no significant difference in company size and R&D team member's educational background regarding to how much the R&D team member supports the innovation and improvement of NPD process. Aside from that, the result implies that the cost of NPD and concerns of multi-tasking in cross-functional team are the major concerns for rejecting the innovation of NPD process. The study provides implications for R&D team managers to deal with the implementation of innovating the NPD process in terms of the cost in the process and the pressure within team members.
Integrating the Local Property and Topological Structure in the Minimum Spanning Tree Brain Functional Network for Classification of Early Mild Cognitive Impairment

Abnormalities in the brain connectivity in patients with neurodegenerative diseases, such as early mild cognitive impairment (EMCI), have been widely reported. Current research shows that the combination of multiple features of the threshold connectivity network can improve the classification accuracy of diseases. However, in the construction of the threshold connectivity network, the selection of the threshold is very important, and an unreasonable setting can seriously affect the final classification results. Recent neuroscience research suggests that the minimum spanning tree (MST) brain functional network is helpful, as it avoids the methodological biases while comparing networks. In this paper, by employing the multikernel method, we propose a framework to integrate the multiple properties of the MST brain functional network for improving the classification performance. Initially, the Kruskal algorithm was used to construct an unbiased MST brain functional network. Subsequently, the vector kernel and graph kernel were used to quantify the two different complementary properties of the network, such as the local connectivity property and the topological property. Finally, the multikernel support vector machine (SVM) was adopted to combine the two different kernels for EMCI classification. We tested the performance of our proposed method for Alzheimer's Disease Neuroimaging Initiative (ANDI) datasets. The results showed that our method achieved a significant performance improvement, with the classification accuracy of 85%. The abnormal brain regions included the right hippocampus, left parahippocampal gyrus, left posterior cingulate gyrus, middle temporal gyrus, and other regions that are known to be important in the EMCI. Our results suggested that, combining the multiple features of the MST brain functional connectivity offered a better classification performance in the EMCI.
Dynamic Models of Partially Connected Topologies for Population-Based Metaheuristics

This paper investigates the emergent properties of a self-organized dynamic network for structured population-based metaheuristics. The system displays complex and emergent behavior whose most visible trait is the self-organization of the population into dynamic clusters. Furthermore, relevant variables that describe the system display $1/f$ noise, which is a characteristic of many complex systems. These properties were previously detected with a time-invariant population (i.e., individuals with fixed fitness vales). In this work, the investigation is extended to dynamic populations (time-varying fitness values), a scenario that models more accurately the behavior of population-based metaheuristics. Several types of fitness variation rules were tested. The experiments show that dynamic populations also display the self-organizing properties and the behavioral patterns of the stationary fitness version, as long as the intensity of the changes is kept below a certain level.
A Many-Objective Configuration Optimization for Building Energy Management

For a commercial building or campus, the management of local energy production, storage, and consumption, promises substantial gains in efficiency and reduced costs and emissions. When facility managers are planning updates to an existing building complex, they face a variety of options for investment. This work targets to provide support for this investment decision by performing a many-objective optimization (MAO) of the system configuration considering initial investment cost, running costs, CO2 emissions, and system resilience. In our specific example the potential investment covers a photo voltaic (PV) system, a stationary battery, and a heat storage. We also consider potential changes to the operation of an existing co-generator for heat and power (CHP), by optimizing controller parameters. The proposed system is simulated using a Modelica-based software environment. In this work we show the results of our configuration optimization using the well-known NSGA-III algorithm and also consider the problem of variable run-times of the simulator on the optimization process especially for a parallel execution of fitness evaluations on a computing cluster.
Evaluation of Capacitance and Dielectric Dissipation Factor of Distribution Transformers - Experimental Results

This paper aims to evaluate the capacitance and the dielectric dissipation factor (DF) of brand new distribution transformers tested in the past two years at LAT-EFEI High Voltage Laboratory. Evaluation proposed consists to classify the capacitance between windings and between the windings and the ground, as well as the DF, according to the rated power, voltage class and number of phases. The DF analysis is based on IEEE Std. C57.152 limits and it aims to infer the insulation quality prior its operation on distribution networks. The parameters measurements were carried out on 170 distribution transformers made in Brazil, using a Schering bridge. The measurements showed a considerable number of samples with DF greater than the limit for a good insulation's condition.
Optimal Ensemble Classifiers Based Classification for Automatic Vehicle Type Recognition

In this work, a challenging vehicle type classification problem for automatic toll collection task is considered, which is currently accomplished with an Optical Sensors (OS) and corrected manually. Indeed, the human operators are engaged to manually correct the OS misclassified vehicles by observing the images obtained from the camera. In this paper, we propose a novel vehicle classification algorithm, which first uses the camera images to obtain the vehicle class probabilities using several Convolutional Neural Networks (CNNs) models and then uses the Gradient Boosting based classifier to fuse the continuous class probabilities with the discrete class labels obtained from two optical sensors. We train and evaluate our method using a challenging dataset collected from the cameras of the toll collection points. Results show that our method performs significantly (98.22% compared to 75.11%) better than the existing automatic toll collection system and, hence will vastly reduce the workload of the human operators. Moreover, we provide an in-depth analysis w.r. t. the learning strategies:e.g., choice of the optimization algorithm of the CNN model. Our results and analysis highlights interesting perspectives and challenges for the future work.
The Time Course of Symmetry Effect on Shape Perception: an Event-related Potential Study

Although symmetry is known to be an important determinant of visual processing and an appreciation of visual symmetry has been studied in several electroencephalography (EEG) experiments, it remains not well clear the mechanism of visual symmetry processing. To investigate this, we measured the processing of symmetrical stimulus (diagonal) and asymmetrical stimulus (control) with two levels of intensity by measured visual evoked potential (VEP). Our results showed significant difference in P8, P4 and O2, the right hemisphere for symmetry processing. In addition, the activity of visual related cortex for diagonal was weaker than that for control during the period of (160–190 ms), whereas the relationship was inverse during the period of (220–280 ms) in all intensity. This work provides a new account of neural mechanisms involved in visual symmetry perception.
A Process Model Integrated to Innovation Management Tools to Support Technology Entrepreneurship

Technology entrepreneurship (TE) has gaining increasing importance over the last decades. Therefore, a multidisciplinary research field was formed around research on technology-based innovation and entrepreneurship. However, despite the managerial and technological skills required in the challenge of new venture creation and development, little attention has been paid to the product/service and business levels of analysis of this phenomenon. An in-depth longitudinal study was conducted with four startups along 27 months. It was based on technology entrepreneurship, innovation management and new product development literatures. This research proposes a seven-step process model integrated to innovation management tools to support entrepreneurs in the context of startup creation and development. And then some theoretical and practical aspects of the proposed model are discussed. In particular the paper brings concerns on how the process and integrated tools helped TE management, how they were created and adapted to the startup context throughout the research, and which were the real contributions and limitations of well-known approaches, such as Lean Startup and other tools and processes.
Preview-Based Formation Control

In this paper, the discrete-time dynamic formation control (DT-DFC) problem is addressed for cooperative agents over directed graphs, in which the desired formation shape is time-varying and only the geometric structure is given. The continuous-time dynamic formation control (CT-DFC) problem has been analyzed through a matrix-valued Laplacian approach. However, the constraint in the information exchange renders the DT-DFC problem difficult to be handled. Moreover, notice that in the preview control philosophy, the future information can be utilized to improve the system performance. A preview-based controller is proposed and proven to be effective to address the DT-DFC problem with proper parameter setting. An illustrative example is given to demonstrate the efficacy of our theoretic results.
A Secure and High-Capacity Data-Hiding Method Using Compression, Encryption and Optimized Pixel Value Differencing

A high capacity data hiding method using lossless compression, advanced encryption standard (AES), modified pixel value differencing (MPVD), and least significant bit (LSB) substitution is presented. Arithmetic coding was applied on a secret message for the lossless compression, which provided ~22% higher embedding capacity. The compressed secret message is subjected to AES encryption; this provides higher security in the cases of steganalysis attacks. After compression and encryption, the LSB substitution and MPVD are applied. In MPVD, the adaptive non-overlapping <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> pixel blocks or a combination of <inline-formula> <tex-math notation="LaTeX">$3\times 3$ </tex-math></inline-formula> and <inline-formula> <tex-math notation="LaTeX">$2\times 2$ </tex-math></inline-formula> blocks are used in raster fashion. It is experimentally established that with the proposed method, significant enhancement in embedding capacity was achieved and 214 132 extra bits than existing methods could be embedded due to the use of arithmetic compression and MPVD. The MPVD and arithmetic coding together resulted into 25% enhanced embedding capacity than the earlier methods. The proposed method also provides high levels of visual quality with an average of 36.38 dB at 4.00 bpp. The proposed method is also proved to be secure against regular/singular (RS) steganalysis.
Region-based Object Detection and Classification using Faster R-CNN

With the advent of Deep Learning,the machine learning systems are able to recognize and classify objects of interest in an image.Various advancement has been done in the field of object recognition and classification.Our research work focusses on improving the R-CNN, Fast R-CNN,YOLO architecture.The work focussed on using Region Proposals Network(RPN) to extract region of interest in an image.RPN outputs an image based on the objectness score.The output objects are subjected to Roll Polling for classification.Our research work focusses on training Faster R-CNN using custom based data set of images. Our trained network efficiently detects objects from an image consisting of multiple objects.Our network requires minimum GPU capability of 3.0 or higher.
Educating Rural Women about Gender Specific Heart Attack and Prodromal Symptoms

Problem: Heart Disease (HD) remains the leading cause of mortality among women. Improvement of outcomes for morbidity and mortality in females with HD has not occurred at the same rate as in males. Rural populations often have more barriers to seeking timely intervention than their urban counterparts. Purpose: To test the efficacy of using acronyms to educate rural women on female MI and prodromal symptoms as well as the appropriate response to these symptoms and to assess if knowledge gained was sustained for a 2-month period of time. Method: A quasi-experimental design with two groups with site randomization of educational intervention with N = 137 rural women (RUCC codes of 5 or higher). Factor analysis, validity and reliability testing for the 23 item Matters of Your Heart Scale (v. 2) are discussed.
Dopamine neuron glutamate cotransmission evokes a delayed excitation in lateral dorsal striatal cholinergic interneurons

Dopamine neurons have different synaptic actions in the ventral and dorsal striatum (dStr), but whether this heterogeneity extends to dStr subregions has not been addressed. We have found that optogenetic activation of dStr dopamine neuron terminals in mouse brain slices pauses the firing of cholinergic interneurons in both the medial and lateral subregions, while in the lateral subregion the pause is shorter due to a subsequent excitation. This excitation is mediated mainly by metabotropic glutamate receptor 1 (mGluR1) and partially by dopamine D1-like receptors coupled to transient receptor potential channel 3 and 7. DA neurons do not signal to spiny projection neurons in the medial dStr, while they elicit ionotropic glutamate responses in the lateral dStr. The DA neurons mediating these excitatory signals are in the substantia nigra (SN). Thus, SN dopamine neurons engage different receptors in different postsynaptic neurons in different dStr subregions to convey strikingly different signals. Editorial note: This article has been through an editorial process in which the authors decide how to respond to the issues raised during peer review. The Reviewing Editor's assessment is that all the issues have been addressed (see decision letter).
LSHADE Algorithm with Rank-Based Selective Pressure Strategy for Solving CEC 2017 Benchmark Problems

Solving single-objective real-parameter optimization problems can still cause difficulties, for example if the optimized function is multimodal or has rotated trap problems. Such optimization problems can be found in various areas in real-world applications. Usually, these problems are very complex and computationally expensive. A new algorithm, which is a modification of the LSHADE algorithm with a rank-based selective pressure strategy, called LSHADE-RSP, is presented in this paper. The proposed algorithm is a new variant of the LSHADE algorithm, the basic idea of which consists in the adaptation of its mutation strategy using selective pressure. The experiments were performed on CEC 2018 benchmark functions. A comparison of the proposed LSHADE-RSP algorithm and the algorithm-participants of the CEC 2017 competition is presented. From the obtained results it can be concluded that LSHADE-RSP performs better in comparison with most alternative algorithms: using the CEC 2018 evaluation method, LSHADE-RSP obtained one of the best final scores among the algorithms that were winners of the previous competition.
GC-EIMS analysis, antifungal and anti-aflatoxigenic activity of Capsicum chinense and Piper nigrum fruits and their bioactive compounds capsaicin and piperine upon Aspergillus parasiticus

Abstract GC-EIMS analysis, antifungal- and anti-aflatoxigenic activities of the ethanolic extract of Capsicum chinense and Piper nigrum fruits and their main bioactive compounds were evaluated upon Aspergillus parasiticus. The GC-EIMS analysis showed capsaicin (50.49%) and piperine (95.94%) as the major constituents in C. chinense and P. nigrum, respectively. MIC50 values revealed that capsaicin (39 μg/mL) and piperine (67 μg/mL) were lower than those from fruit extracts of C. chinense (381 μg/mL) and P. nigrum (68 μg/mL). Extracts and bioactive compounds showed anti-aflatoxigenic activity. Maximum aflatoxin inhibition occurred at 150 µg/mL of extracts and compounds. The present study showed satisfactory results concerning the effects of ethanolic extract of C. chinense and P. nigrum fruits upon A. parasiticus, showing the capabilities of inhibiting fungal growth development and altering aflatoxins production. Graphical abstract
Rituximab Exhibits Altered Pharmacokinetics in Patients With Membranous Nephropathy

Background: Rituximab (RTX) is a chimeric monoclonal anti-CD20 antibody used off-label in the treatment of membranous nephropathy (MN). Unfortunately, limited information is available on the pharmacokinetics of therapeutic proteins such as RTX in patients with glomerular kidney diseases. Objective: The current study evaluated RTX pharmacokinetics in patients with MN (n = 20) who received 4 RTX weekly intravenous infusions (375 mg/m2) over a month, with a repeat of the identical treatment at 6 months. Baseline patient characteristics were gender (17 male/3 female), age (49 ± 13 years), and body surface area (2.2 ± 0.24 m2). Methods: Compartmental pharmacokinetic analyses were conducted using Phoenix, and comparisons of these parameters were made between the MN patients and published data from 2 reference populations without kidney diseases (follicular lymphoma and autoimmune disorders). Results: Patients with MN exhibited a shorter half-life, reduced volume of central compartment, decreased area under the serum concentration-time curve (exposure), and increased RTX clearance from the central compartment versus previous reports in the reference patient populations. Conclusions and Relevance: These results suggest that shorter half-life and lower exposures to RTX in patients with MN may necessitate higher doses and/or changes to dosing frequency to optimize the relationships between serum concentrations and therapeutic effects.
PCAOB Inspections: An Analysis of Entity-Level and Application-Level Control Audit Deficiencies

The Public Company Accounting Oversight Board (PCAOB) inspection process identifies deficiencies related to how firms conduct audits. Our work extends prior research by examining the type of internal control audit deficiencies (entity-level or application-level). Internal control audit deficiencies of both types may increase the risk of material misstatement, so the PCAOB is concerned with whether audit firms are performing appropriate procedures to identify entity-level and application-level internal control audit deficiencies, including those involving information technology general controls (ITGCs). Using text analysis to examine audit deficiencies by internal control type and firm size, we find that PCAOB inspection reports identify significantly more application-level than entity-level control audit deficiencies. Application-level control deficiencies generally involve revenue, inventory, and accounts receivable accounts whereas entity-level deficiencies often involve lack of centralized controls or controls over period end financial reporting. The number of application-level deficiencies identified for both Big 4 and second-tier firms varied between inspection years 2010 and 2015. However, the number of entity-level deficiencies, including ITGCs for both Big 4 and second-tier firms, held nearly steady during the period. Our findings should be of interest to practicing accountants, regulators, and other users of PCAOB inspection reports.
Travel mode identification using bluetooth technology

ABSTRACT Bluetooth technology has been widely used in transportation studies to collect traffic data. Bluetooth media access control (MAC) readers can be installed along roadways to collect Bluetooth-based data. This data is commonly used to measure traffic performance. One of the advantages of using Bluetooth technology to measure traffic performance is that travel time can be measured directly with a certain level of error instead of by estimation. However, travel time outliers can commonly be observed due to different travel mode on arterials. Since travel mode information cannot be directly obtained from the raw Bluetooth-based data, a mathematical methodology is in need to identify travel mode. In this study, a genetic algorithm and neural network (GANN)-based model was developed to identify travel mode. GPS-enabled devices were used to collect ground truth travel time. In order to additionally compare the model performance, K nearest neighbor (KNN) and support vector machine (SVM) were also implemented. N-fold cross validation was applied to statistically assess the models’ results. Since the model performances depend on the model inputs, seven collections of model inputs were tested in order to achieve the best travel mode identification performance. An arterial segment with four consecutive links and three intersections was selected to be the study segment. The results suggested that correctly identifying the three travel modes successfully every time was not possible, although the GANN based model had low misidentification rates. In our study, 6.12% of autos were misidentified as bikes and 10.53% of bikes were misidentified as autos using three links.