Miracle-dz/UniFAR-FTUs huggingface dataset:
Adolescent Substance Use Following a Deadly U.S. Tornado Outbreak: A Population-Based Study of 2,000 Families

Despite conceptual links between disaster exposure and substance use, few studies have examined prevalence and risk factors for adolescent substance use and abuse in large, population-based samples affected by a recent natural disaster. We addressed this gap using a novel address-based sampling methodology to interview adolescents and parents who were affected by the 4th deadliest tornado outbreak in U.S. history. Postdisaster interviews were conducted with 2,000 adolescent–parent dyads living within a 5-mile radius of the spring 2011 U.S. tornadoes. In addition to descriptive analyses to estimate prevalence, hierarchical linear and logistic regression analyses were used to examine a range of protective and risk factors for substance use and abuse. Approximately 3% reported substance abuse since the tornado. Greater number of prior traumatic events and older age emerged as consistent risk factors across tobacco and alcohol use and substance abuse since the tornado. Tornado incident characteristics, namely, greater loss of services and resources after the tornado and posttraumatic stress disorder since the tornado, were associated with greater alcohol consumption. Service loss increased risk for binge drinking, whereas, for substance abuse, posttraumatic stress disorder increased risk and parent presence during the tornado decreased risk. Greater family tornado exposure was associated with a greater number of cigarettes smoked in female but not male teen participants. Both trauma and non-trauma-related factors are relevant to postdisaster substance abuse among adolescents. Future research should examine the role of broader ecological systems in heightening or curtailing substance use risk for adolescents following disaster exposure.
On the Modeling of the Avalanche Multiplication Coefficient in SiGe HBTs

This paper focuses on the modeling of the avalanche multiplication coefficient in state-of-the-art SiGe:C HBTs suffering from radical self-heating and impact-ionization effects. Experimental data are measured in a wide current range on devices fabricated during the DOTFIVE project and used as a reference. A comparative analysis of the available empirical descriptions for the avalanche coefficient is made at low current density; the best model is then extended to account for medium/high-current effects by resorting to a suitable parameter optimization methodology. The same procedure is adopted to examine the accuracy of physics-based formulations implemented in advanced transistor models for circuit design.
Topic Propagation Prediction Based on Dynamic Probability Model

As social networks play an increasingly important role in people’s lives, people are more likely to discuss hot topics on social networks. Predicting the spread of hot topics, known as topic propagation prediction is an important task. Due to the unpredictability of the users and topics in social networks, predicting the topic propagation trend is still a major challenge. Different users play different roles in topic propagation. However, existing studies have not utilized user role analysis. In this paper, we propose a topic propagation prediction method (TPP) based on user role analysis and dynamic probability model. First, we describe our user role analysis, which incorporates four user-factors to characterize user attributes along two dimensions. Second, we combine dynamic probability model with user role analysis to accurately predict the topic propagation trend. Finally, we prove the efficiency of TPP by experiments.
Ultra Low Power Digital Front-End for Single Lead ECG Acquisition

A low power single lead electrocardiogram front end acquisition system in 0.18um CMOS process with supply voltage 0.5V is presented in this paper. The analog blocks in Low Noise Amplifier (LNA), filters and passive elements that performs amplification, DC offset cancellation is replaced by a Moving Average Voltage to Time Converter (MA-VTC) to get amplification and alias removal in the time domain. A digital feedback algorithm is used to cancel out the DC offset. The front-end structure is designed in the subthreshold region of MOS to reduce the power consumption in the circuit. The proposed architecture consumes 50nW of power with a gain of 670us/V.
Semi-Supervised Classification With Graph Structure Similarity and Extended Label Propagation

Graph-based semi-supervised learning (GSSL) has attracted great attention over the past decade. However, there are still several open problems: 1) how to construct a graph that effectively represents the underlying structure of data and 2) how to incorporate label information of the labeled samples into a procedure of label propagation. Our solution mainly focuses on two aspects: 1) we propose a new graph construction technique by fusing local and global structural similarity (FLGSS). Based on an initial graph structure such as K-nearest neighbors (KNN), we utilize different types of link prediction algorithms to extract local and global graph structure information. These two types of structure information are fused into a graph structure that enhances the ability to represent the data correlation. 2) By incorporating the label correlation with feature similarity of samples, we propose an extended label propagation algorithm (ELP). Through experiments on three different types of datasets, it is shown that our method outperforms other widely used graph construction methods. The extended label inference algorithm achieves better classification results than some state-of-the-art methods. The proposed FLGSS method starts from KNN graph and two link prediction algorithms are performed to construct the graph. With the time complexity analysis, we theoretically deduce that the time complexity of FLGSS is not beyond that of KNN. Meanwhile, the time complexity of ELP remains the same as that of the traditional LP algorithm.
Propagation Graphs for Indoor Sensor Network Planning

Sensor network planning poses several challenges including the selection of appropriate locations for the sensors and actuators, for the gateway and possibly for the required repeaters. We use propagation graphs to model indoor wave propagation on the basis of coarse floor plans, and show how the graphs can be used in the scope of network planning. Efficient methods from graph theory are presented for path loss and coverage prediction. These efficient prediction methods facilitate optimal repeater placement.
Latent fingerprint development with biosynthesized Nano rust

Biosynthesis of nanoparticles are gaining importance and has been suggested as possible alternative for several chemical and physical methods due to its cost effectives and green approach. The present work reports a facile and cost-effective green synthesis of nano rust (iron oxide) using Camellia sinensis leaf extract. In the present study iron oxide nanoparticles were successfully employed as a promising developer for the visualization of latent fingerprints. Distinctive friction ridge patterns were developed on both porous and nonporous surfaces developed with iron oxide nanoparticles. Moreover biosynthesized nanomaterial provides a wide variety of advantages including facile green fabrication methodology, easy application, and high stability and shows enhanced contrast
Using DMAIC Methodology to Reduce Defects in Sport Bar Products

The DMAIC process in Six Sigma technique was utilized for studying and analyzing to reduce defects in sport bar products. From gathering primary data, the major problem was on the products' surfaces accounted for 19.40%, included scratches, dent, pinhole and tool mark. The way to improve consisted of five phases, which were 1) Define: collect historical data to specify the scope of problem and use the Pareto chart to highlight important defects 2) Measure: identify and prioritize causes using failure mode and effects analysis (FMEA) 3) Analyze: detect root causes and develop possible actions from Why-why analysis 4) Improve: follow the guidelines, including material replacement for welding fixtures, new wrapping material during transport and work instruction for polishing machine, and 5) Control: summarize via standardizing the operating manual. After doing all five phases, the result found that the proportion of the defects decreased to be 2.5%.
Preparation of Graphene Colloids in Different Dispersing Devices by Electric Spark Discharge Method and Analysis of Their Suspension Stability

In this study, electrical discharge machine (EDM) was used to prepare graphene colloids by the electric spark discharge method (ESDM). Machine-lathed 99% pure graphite rods were used as the electrodes, deionized water (DI water) was used as the dielectric liquor, and a magnetic stirrer and ultrasonic cleaner were used as the dispersing devices. According to the analysis results from ultraviolet-visible spectroscopy (UV-Vis), Zetasizer, and transmission electron microscope (TEM), the characteristic wavelength of graphene was 260 nm with an absorbance of 0.219 and a zeta potential of -36.6 mV, and many graphene laminates could be seen based on the microscale. This study discussed the two different dispersing devices used to make graphene colloids and analyzed their impact on suspension stability. The results showed that a magnetic stirrer was suitable for making graphene colloids with high suspension stability and low concentration, while an ultrasonic cleaner was suitable for making graphene colloids with low suspension stability and high concentration. The graphemes made by the chemical method are difficult to suspend in water and chemical or dispersant additives are required to maintain their properties; furthermore, which increases the production time. However, with no need for chemical additions and a short preparation time, ESDM is a safe, environmentally-friendly, and fast green manufacturing method.
Novel Method for Recording High Frequency Human Skin Temperature Oscillations

Circuitry solution has been developed to create a prototype of the system for the registration of the high frequency human skin temperature oscillations. As they connected with blood circulation, such method allowing in the future to provide the registration of the pulse wave signal, for measuring heart rate in portable devices like smart watches and fitness bracelets. The miniature semiconductor chip diodes sensor has been used. The electronic system for the calibration of the device has been designed. For providing operation of the system in a full voltage range using the automatic set has been used for the maintenance of the operating point and of the necessary slope of the conversion factor (Volts / °C). The structural and circuit diagram has been developed. They consisted of two parts: the remoted preamplifier with the temperature sensor and the main block including all the other digital and analog parts. The developed system has following technical characteristics: the temperature resolution to be 0.01 °C and the thermal time constant of the sensor 0.05 s. The registration system testing has been performed in two ways: in a thermostat and on the human skin surface. Device contains plethysmo- and ECG-channels for obtaining data on the phases of cardiac cycles and synchronous analysis of physiological processes. The cross correlation coefficient between the temperature oscillations and the pulse wave signals laid in the diapason from 0.53 to 0.65.
Leaf Instance Segmentation and Counting Based on Deep Object Detection and Segmentation Networks

Accurately extracting the shape and number of leaves is a hot topic in plant phenotype studies recently. Traditional statistical methods could not handle the problems when very small-shaped leaves or large number of leaves with heavily overlapping appear in the images. This study proposes to adopt the excellent object detection and segmentation algorithm, named as Mask R-CNN, in the exploration of leaf instance segmentation and counting, and then evaluates the effectiveness of the algorithm on the Leaf Segmentation Challenge (LSC) datasets from CVPPP 2017 organizers using the metrics of Average Precision (AP), Symmetric Best Dice (SBD), Difference in Count (DiC) and the absolute value of DiC (|DiC|). The outstanding results for leaf instance segmentation and counting show that the object detection and segmentation algorithms with deep convolutional networks may be promising tools for the plant phenotype studies.
MOS Varactor RO Architectures in Near Threshold Regime Using Forward Body Biasing Techniques

Due to small output swings and series stack transistors, Differential Ring Oscillators (DRO) and current starved ROs are not well suited for Near Threshold Voltage (NTV) regime. MOS varactor based Single Ended Ring Oscillators (SERO) is well suited in NTV regime as it gives full swing characteristics, wide tuning range and has very low power consumption. This paper proposes different architectures using MOS Varactor SERO (VBRO) that gives high oscillation frequency, wide tuning range, low area and power consumption without degrading the phase noise as compared to existing VCO topologies in NTV regime. The improvement in the tuning range of the VCO is because of the change in body capacitance of the DTMOS configuration used in the VBRO. The change in the center frequency with PVT variations is compared with that of an NTV DRO. Post layout simulations have been performed on parasitic extracted netlist using HSPICE in industrial 65nm CMOS Process Design Kit (PDK). Our VBRO architecture has tuning range of 0.425 - 2.13 GHz with phase noise of -95dBc/Hz at 0.6V supply. The power consumption is only 127µW and the Figure Of Merit (FOM) is 164.96dBc/Hz.
Identifying Malicious Dealers in Goods and Services Tax

Tax evasion is pervasive in most of the nations. It impedes the economic progress of the nation. In this paper, we devise a technique that detects groups of people who commit evasion in the goods and services tax by manipulating their sales and (or) purchases with the motivation to reduce their tax liability. In particular, we perform clustering analysis on certain sensitive parameters pertaining to each of the dealers. Then by analyzing the number of dealers across the different clusters, we identified the suspicious set of dealers who are further investigated by looking at other sensitive parameters. This work is designed and implemented for the Government of Telangana, India.
Write Variation Aware Non-volatile Buffers for On-Chip Interconnects

With the advancement in CMOS technology and multiple processors on the chip, communication across these cores is managed by a Network on Chip (NoC). Power and performance of these NoC interconnect have become a significant factor. In particular, the buffers used at every port consume considerable dynamic as well as leakage power. This paper attempts to reduce leakage power consumption of NoC buffers by use of non-volatile memory (NVM) technology based STT-RAM buffers. STT-RAM technology has the advantage of high density and low leakage but suffers from high write energy and low endurance. In particular, if some buffers get written more number of times compared to some other buffers, the heavily written buffers may wear out faster compared to others. This has an impact on the lifetime of the router as a whole. Here we propose a virtual channel (VC) allocation policy that helps to uniformly distribute the writes across the buffers to improve their effective lifetime. Pure STT-RAM buffers, however, impact the network latency. In order to mitigate this, we propose a hybrid policy that uses an alternative VC made of SRAM technology in case of heavy network traffic. Experimental evaluation on full system simulation shows that the proposed policy reduces the write variation by 99% and improves lifetime by 3.4 times and 24 times, respectively for both the proposals. We also get respectively 93% and 90% gains in the EDP.
Relationship Between Autonomous and Evoked Activities in a Living Neuronal Network of a Neurorobot, "Vitaenoid".

Toward neuroprostheses, establishing a model system for bi-directional communication between a living neuronal circuit and electrical devices is critical. We developed a neu-rorobot with a dissociated hippocampal culture system. Small neuronal circuits on a culture dish with planar microelectrode arrays were used to detect the electrical activity patterns of a neuronal circuit. Responses of neurons against an input fluctuate by internal autonomous activity. If this fluctuation includes certain information, the interface should detect such embedded information. Thus, handling such fluctuation of autonomous activity is critical, which is the focus of this research. In this study, we proposed neurorobot system as a model system for bidirectional communication between a living neuronal circuit and a robot body. We call this system " Vitaenoid, " which means the robot like (-oid) livs (Vitae) with Neuron (N). To illustrate Vitaenoid, we attempted a self-organizing map (SOM) based method for generating robot behavior. SOM was designed so that the distribution of winner nodes reflected the trend of spatiotemporal electrical patterns. We confirmed that the SOM-based " Vitaenoid " performed a collision avoidance behavior, and the distribution of winner node revealed that some parts of spontaneous activity shared the patterns with evoked responses and suggested that the spontaneous activity and evoked responses were not strictly discriminated. In addition, the ratio of winners shared with spontaneous activity tended to decrease during a 60-min-robot-run. Inputs from outer world to a living neuronal circuit are considered not to be tightly coupled to their response patterns, instead of that, they loosely coupled with the specific activity pattern, which is also expressed as spontaneous activity. We propose this hypothesis as "the loose-coupling hypothesis in input-and-output relationship of the neuronal circuit. "
An Approach to Improve the Service Quality of Interior Design Company

This study develops a service quality assessment system, based on the SERVQUAL, to assess the existing service performance of interior design companies. A real case study from interior and architecture experts is applied to conduct the service quality improvement processes. The result reveals that the proposed approach can be provided a guideline for companies seeking to improve sustainable business advantages and would be an effective decision tool for interior design company strategy optimization.
Stability of a Complex Traffic Flow Problem: A Delay-Dependent Perturbation Filter Design

Complex traffic flow is a huge concern towards achieving an efficient transportation system. It involves handling all the operational issues and effects while regulating the traffic system. These operational issues also have situation of uncertainties and vagueness. In this paper, the uncertainties in the form of perturbation have been the focus of study in the theory of traffic. To achieve that, the moving-block train model has been utilized for the perturbation analysis. Firstly, a delay-dependent moving-block model is represented. This model considered the dynamics of position and velocity in the perturbation modeling of system. These dynamics have been extracted from a measurement sensor. Secondly, an augmented Lyapunov- Krasoviskii function (ALKF) is built on this model for the realization of feasible region of stability. Numerical evaluations were made on a moving-block model. Results showed an effective implementation of the proposed scheme.
A Randomized Trial of a Community-Based Cognitive Intervention for Obese Senior Adults

Objectives: To determine whether a cognitive intervention delivered by lay health educators (LHEs) in senior centers was effective in improving cognition in obese older adults. Methods: This cluster randomized trial was conducted in 16 senior centers from which 228 senior adults were recruited. The centers were randomized to either the cognitive intervention or a control, weight-loss intervention. The primary outcome variable, cognitive function, was measured using the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS). Results: Analyses of RBANS indices as continuous variables did not indicate significant differences between arms. However, after adjusting for baseline delayed memory, gender, and baseline body mass index, seniors in the cognitive intervention arm had a 2.7 times higher odds of a reliable improvement (clinically significant) in delayed memory from baseline as compared to those in the control intervention (95% CI, 1.3-5.6, p = .011). The intervention effect was not significant for the proportion showing reliable improvement in immediate memory or in attention. Attendance at the 12-session program was high with an average of 83% (67%-92%) sessions attended and 87% of participants in the cognitive arm indicating they would recommend the program. Discussion: Cognitive interventions can be effectively delivered in the community by LHEs.
Validity of DSM-IV subtypes of attention-deficit/hyperactivity disorder: a family study perspective.

OBJECTIVE To test the hypothesis that the clinical severity of subtypes paralleled a gradient of familial severity. METHOD One hundred forty children with attention-deficit/hyperactivity disorder (ADHD) and 120 normal control children and their biological relatives were studied: Because these data had been collected prior to the publication of DSM-IV, DSM-III-R symptoms were used to approximate DSM-IV subtypes using a method the authors had validated in prior work. RESULTS The first prediction from the hypothesis was true: rates of ADHD among relatives of each subtype group were greater than rates among relatives of controls. But the second prediction did not hold: rates of ADHD were not significantly higher among relatives of combined-typed probands compared with relatives of other probands. The "gradient model" also predicted that subtypes would not "breed true" (i.e., the subtype of the relative would not be the same as that of the proband). The prediction of nonspecificity was refuted for the inattentive and combined subtypes, but hyperactive-impulsive ADHD was found almost exclusively among relatives of hyperactive-impulsive probands. CONCLUSIONS Although the results are limited by some small subsamples along with the use of a DSM-III-R-ascertained sample, they provide little evidence for the idea that DSM-IV subtypes of ADHD correspond to familially distinct conditions. They also do not confirm the idea that the subtypes fall along a gradient of familial severity. Instead, they suggest that symptom differences among subtypes are due to nonfamilial, environmental causes.