Miracle-dz/UniFAR-FTUs huggingface dataset:
A Temperature Sensor With an Inaccuracy of 1=+0.8 C Using 90-nm 1-V CMOS for Online Thermal Monitoring of VLSI Circuits

This paper proposes an accurate four-transistor temperature sensor designed, and developed, for thermal testing and monitoring circuits in deep submicron technologies. A previous three-transistor temperature sensor, which utilizes the temperature characteristic of the threshold voltage, shows highly linear characteristics at a power supply voltage of 1.8 V or more; however, the supply voltage is reduced to 1 V in a 90-nm CMOS process. Since the temperature coefficient of the operating point’s current at a 1-V supply voltage is steeper than the coefficient at a 1.8-V supply voltage, the operating point’s current at high temperature becomes quite small and the output voltage goes into the subthreshold region or the cutoff region. Therefore, the operating condition of the conventional temperature sensor cannot be satisfied at 1-V supply and this causes degradation of linearity. To improve linearity at a 1-V supply voltage, one transistor is added to the conventional sensor. This additional transistor, which works in the saturation region, changes the temperature coefficient gradient of the operating point’s current and moves the operating points at each temperature to appropriate positions within the targeted temperature range. The sensor features an extremely small area of 11.6 4.1 m and low power consumption of about 25 W. The performance of the sensor is highly linear and the predicted temperature error is merely 1.0 to +0.8 C using a two-point calibration within the range of 50 C to 125 C. The sensor has been implemented in the ASPLA CMOS 90-nm 1P7M process and has been tested successfully with a supply voltage of 1 V.
Mitigating the Knock-on-Effect of DDoS Attacks on Application Layer using Deep Learning Multi-Layer Perceptron

Distributed Denial of Service (DDoS) attacks are a significant threat to the Internet today, and application-layer DDoS attacks that use legitimate HTTP requests to overwhelm victim resources are imperceptible. As a result, neither the intrusion detection system (IDS) nor the victim's server can detect malicious packets. This research is intended to take a new approach, deep learning, to detect novel application layer DDoS attack strategies, known as increasing DDoS and proxy DDoS attack. The proposed attack detection model has been validated by performing simulation experiments with MATLAB and Python. Finally, the preliminary result compared with the various machine learning techniques for classification: Naïve Bayes, SVM, Decision trees, and Genetic Algorithm (GA) in terms of Accuracy Rate (AR), Detection Rate (DR), Sensitivity, specificity, (ROC) curve. The results show that our detection model effectively detects DDoS attacks at the application layer. The proposed deep learning multi-layer perceptron architecture can identify and use the most relevant high-layer features of packet flows with an accuracy of 98% on the generated dataset containing a novel DDoS attack.
Molecular Dynamics Simulations of Asphaltene Aggregation in Supercritical Carbon Dioxide with and without Limonene

We study the aggregation of asphaltene in supercritical carbon dioxide (sc-CO2) using molecular dynamics (MD) computer simulations. We use a well-defined asphaltene molecular structure (asphaltene “C” from Headen, T. F.; Boek, E. S.; Skipper, N. T. Energy Fuels 2009, 23, 1220−1229) obtained using a preliminary version of the quantitative molecular representation algorithm (Boek, E. S.; Yakovlev, D. S.; Headen, T. F. Energy Fuels 2009, 23, 1209−1219). We compare the aggregation of asphaltene in sc-CO2 with our previous MD aggregation studies in toluene and heptane. Also, we study asphaltene aggregation in the presence of limonene, a well-known aggregation inhibitor. First, we present simulations of sc-CO2 at a range of temperatures and pressures to test the ability of the force field used to correctly predict the system density. Simulations of asphaltenes were conducted in a similar fashion to those presented by Headen, T. F. ; Boek, E. S. ; Skipper, N. T. Energy Fuels 2009, 23, 1220−1229 for simulations in .
Detection of brain abnormalities using Internet of Things

—Today with the advancement of medical science, major part of the research is being focused on the diagnosis of the different abnormalities of the brain. Using brain waves one can detect the abnormal behavior of brain and these waves are recorded using EEG electrodes and the obtained artifacts can be analyzed to predict the mental wellness of the patient. These artifacts can be uploaded to cloud hub for easy access from all over the world. With the popularity of IoT(Internet of Things) reaching new heights and with each passing day, the devices getting smarter, it is very easy to access any information from any part of the world at a single touch of human hand. The IoT revolution has redesigned health care by narrowing the gap between medical condition and the response from the specialist. This work presents an idea to reform the existing access control system of detecting medical conditions related to brain and getting timely response from doctors in case of a medical emergency and hence this paper ensures that people can get good quality of healthcare facilities from doctors worldwide.
On the generality of the affective Simon effect.

In affective Simon studies, participants are to select between a positive and negative response on the basis of a nonaffective stimulus feature (i.e., relevant stimulus feature) while ignoring the valence of the presented stimuli (i.e., irrelevant stimulus feature). De Houwer and Eelen (1998) showed that the time to select the correct response is influenced by the match between the valence of the response and the (irrelevant) valence of the stimulus. In the affective Simon studies that have been reported until now, only words were used as stimuli and the relevant stimulus feature was always the grammatical category of the words. We report four experiments in which we examined the generality of the affective Simon effect. Significant affective Simon effects were found when the semantic category, grammatical category, and letter-case of words was relevant, when the semantic category of photographed objects was relevant, and when participants were asked to give nonverbal approach or avoidance responses on th.
Dissonance Reduction Strategies in Ride-Sharing Apps-A Case of Uber

Ride-sharing apps have both positive and negative impact in our daily lives. To study the negative impacts especially personal safety issues, this study explores the reaction of ride-sharing app users to such safety issues. We contextualize our study using a scenario-based survey. We found empirical support to all the hypotheses by surveying 421 individuals. This study contributes to the information systems literature in providing ways to apply dissonance reduction techniques that are a strong predictor of satisfaction. In addition, this study also addresses the safety issues faced by riders using ride-sharing apps and how co-creation would help address these issues.
Automatic Attendence Monitoring System Using Face Recognition Techniques

-Attendance recording of a student in an academic organization plays a vital role in judging students’ performance. As manual labor involved in this process is time consuming, an automated Attendance Management System (AMS) based on face detection and face recognition techniques is proposed in this paper. The system employs modified Viola-Jones algorithm for face detection, and PCA (Principal Component Analysis) face recognition algorithm for face recognition. After successful recognition of a student, the system automatically updates the attendance in the database. The proposed system makes the current attendance management systems more efficient by removing calling, marking and entry of attendance in institutional websites.
Transactions of the Korean Nuclear Society Virtual Autumn Meeting December 17-18 An Improved Deterministic Truncation of Monte Carlo Solutions for Nuclear Reactor Analysis

An improved deterministic truncation of Monte Carlo (iDTMC) solutions method has been preliminarily established by introducing a new calculational strategy applied with a coupled coarse-mesh finite difference (CMFD) and a decoupled fine-mesh finite difference (FMFD) methods in a Monte Carlo (MC) simulation [1]. The iDTMC method has been newly devised to overcome the numerical instability and eliminate potential bias of the solutions in big size nuclear reactor problems. The preliminary study showed that the iDTMC method can eliminate the numerical stability and enable early accurate truncation of the MC solution within only a few active cycles [1]. In the meantime, due to the strong correlation between the cycle-wise FMFD parameters, the variance of the deterministic solutions could be highly underestimated and slowly decrease along the simulation even though the variance is already much lower than that of conventional methods from the beginning of cycle. In this work, two different numerical schemes for the iDTMC method are considered and tested to lessen the correlation of the parameters and take further reduction of the stochastic errors. The concept of the iDTMC method will be presented, and numerical performance in terms of the solution estimation and variance reduction will be evaluated in a big size PWR problem.
Functional Delineation of Prefrontal Networks Underlying Working Memory in Schizophrenia: A Cross-data-set Examination

Abstract Background: Working memory (WM) impairment in schizophrenia substantially impacts functional outcome. Although the dorsolateral pFC has been implicated in such impairment, a more comprehensive examination of brain networks comprising pFC is warranted. The present research used a whole-brain, multi-experiment analysis to delineate task-related networks comprising pFC. Activity was examined in schizophrenia patients across a variety of cognitive demands. Methods: One hundred schizophrenia patients and 102 healthy controls completed one of four fMRI tasks: a Sternberg verbal WM task, a visuospatial WM task, a Stroop set-switching task, and a thought generation task (TGT). Task-related networks were identified using multi-experiment constrained PCA for fMRI. Effects of task conditions and group differences were examined using mixed-model ANOVA on the task-related time series. Correlations between task performance and network engagement were also performed. Results: Four spatially and temporally distinct networks with pFC activation emerged and were postulated to subserve (1) internal attention, (2) auditory–motor attention, (3) motor responses, and (4) task energizing. The “energizing” network—engaged during WM encoding and diminished in patients—exhibited consistent trend relationships with WM capacity across different data sets. The dorsolateral-prefrontal-cortex-dominated “internal attention” network exhibited some evidence of hypoactivity in patients, but was not correlated with WM performance. Conclusions: Multi-experiment analysis allowed delineation of task-related, pFC-anchored networks across different cognitive constructs. Given the results with respect to the early-responding “energizing” network, WM deficits in schizophrenia may arise from disruption in the “energization” process described by Donald Stuss' model of pFC functions.
Toward Automated Scenario Generation with Deep Reinforcement Learning in GIFT

Simulation-based training is an important tool for preparing learners to perform a broad range of complex tasks and skills. A key functionality of simulation-based training environments is delivering scenarios that drive learning interactions that approximate real-world situations. However, simulation-based training scenarios are typically resource-intensive to create. Some simulation environments provide authoring tools that enable new training scenarios to be manually created by subject matter experts, but these tools often require a high degree of specialized knowledge to be utilized effectively. Authored scenarios often cannot be reused in other training environments, and the knowledge associated with particular authoring tools has limited transferability. Further, learners are usually limited to training with a finite set of training scenarios provided by the system's designers. If learners have mastered the learning objectives associated with the available set of training scenarios, there is marginal benefit provided by further training with the simulation. Finally, training simulation scenarios are often delivered following a one-size-fits-all approach: they have limited capacity to dynamically respond to the broad range of individual differences in knowledge or behavior that are typical among learners.
Observing Early Colonizing Species On the Central Coast of British Columbia ES 470: Conservation and Biodiversity of Coastal British Columbia

Identifying early colonizing marine species is important for the recovery of an ecosystem after a disturbance, to determining the health of a system, and to understanding the community composition and species richness of an area. This study, conducted near Calvert Island in the Great Bear Rainforest, provides baseline information about healthy community systems for future reference. Ceramic mugs were used as settling plates; they were placed in the ocean for 12-days. The mugs were placed at sheltered and exposed locations with three depths at each location (2m, 10m, and 20m below surface). The organisms found on the mugs were divided into 11 groups based on similar characteristics; their presence or absence on each mug was recorded and multivariate statistics were used to analyze community structure. There was a significant difference between community structures at the exposed and sheltered sites across all depth groups. There was a significant difference in the species present only between depths of 2m and 20m across both exposure treatments. The percent contributions of each species to community structure were determined. From this analysis, inferences were made about the life history of species. The presence of algae near the surface was likely due to optimal photosynthetic conditions; the Amphipod and Copepod presence and percent contributions at almost all sites likely reflect r-selected characteristics; the Foam Algae's ability to handle lower photosynthetic levels may be suited to a niche at lower depths and shows that light reaches to at least 20m. The Algae seem to establish in sheltered areas and the Foam Algae may have been especially suited to low fetch areas.
Air-water flow and gas transfer at aeration cascades: A comparative study of smooth and stepped chutes

While stepped spillways are common design for energy dissipation purposes, aeration cascades are used for in-stream re-aeration, in water treatment plants to enhance the air-water transfer of atmospheric gases (e.g. oxygen, nitrogen) and for the removal of volatile organic components (VOC) such as methane and chlorine. The present study present new detailed characteristics of the air-water flows and interfacial properties. New experiments were performed in a large stepped cascade (35-m long, 0.5-m wide) to minimise scale effects. A comparative analysis of air-water gas transfer rate is developed between smooth-invert and stepped chute. The results demonstrates that the aeration cascade is at least 10 times more efficient than the corresponding smooth chute.
Microring resonator optical waveguide gyroscope based on coupling coefficient and coupling area modulating

How to improve the sensitivity of new pattern of gyroscope which is based on micro ring resonator is widely researched. And periodically modulating coupling coefficient and area are the two most common schemes. Moreover, when the area of the square difference is large enough, the precision of gyroscope of the latter is higher than that of the former by 1 to 2 orders of magnitude. But increasing modulation area will narrow the transmission band, it will impact on our measurement of the inertial rotation. What’s more, this modulation method is very sensitive to the coupling coefficient between the micro rings. Simultaneously, the slope of the center resonance peak will be quickly decayed with the increase of the coupling coefficient. Generally, there's no sense to our modulation when the coupled coefficient is over 0.1. In this paper, we use the way of combining the two schemes to study the performance of the gyroscope which includes it's precision and how to reduce interference when it applied to devices. We found that the accuracy of rotation produced by our scheme is higher than that of simply modulating coupling coefficient or coupling area. And the sensitivity of gyroscope achieved by the new method is times higher than that of the general of coupled resonator optical waveguide, and there is almost no limit to the coupled coefficient based on our coupling scheme. What’s more, the steepest transmission resonance of the transmission band appears at Ω = 0 and other resonance peaks around the center resonance are completely suppressed because of the effective superlattice structure, which will effectively reduce the interference during our measurement.
Machine Learning Prediction of Food Processing

Despite the accumulating evidence that increased consumption of ultra-processed food has adverse health implications, it remains difficult to decide what constitutes processed food. Indeed, the current processing-based classification of food has limited coverage and does not differentiate between degrees of processing, hindering consumer choices and slowing research on the health implications of processed food. Here we introduce a machine learning algorithm that accurately predicts the degree of processing for any food, indicating that over 73% of the U.S. food supply is ultra-processed. We show that the increased reliance of an individual's diet on ultra-processed food correlates with higher risk of metabolic syndrome, diabetes, angina, elevated blood pressure and biological age, and reduces the bio-availability of vitamins. Finally, we find that replacing foods with less processed alternatives can significantly reduce the health implications of ultra-processed food, suggesting that access to information on the degree of processing, currently unavailable to consumers, could improve population health.
Biologic Therapies for the Treatment of Post-ileal Pouch Anal Anastomosis Surgery Chronic Inflammatory Disorders: Systematic Review and Meta-analysis

Background and objective Chronic inflammatory disorders after ileal pouch-anal anastomosis (IPAA) are common. These include chronic pouchitis, crohns disease (CD) of the pouch, prepouch ileitis, and rectal cuff inflammation (cuffitis). The aim of this study was to evaluate the effectiveness of biologic therapy in achieving remission in these disorders. Method Systematic review of all published studies from inception to April 1st, 2021 was performed to investigate the effectiveness of biologic therapies for chronic inflammatory disorders of pouch. Outcome was the efficacy of biologic therapies in achieving remission as defined by each study. A subgroup analysis was performed for each disorder separately. Results 24 studies were identified by our search. A total of 682 patients were included in our analysis. Using a random effect model, the overall pooled efficacy of biologic therapies in achieving remission in patients with chronic inflammatory disorders of pouch was 60% (95% Confidence Interval (CI), 65 - 64). Specifically, the pooled efficacy of ustekinumab in achieving remission was 75% (95% CI, 64 - 85, p = 0.007), whereas the efficacy of vedolizumab was 60% (95% CI, 52 - 68, p = 0.172). In addition, the efficacy infliximab and adalimumab were 59% (95% CI, 53 - 64, p <0.01) and 51% (95% CI, 42 - 60, p = 0.452) respectively. Conclusion Ustekinumab , infliximab, vedolizumab and adalimumab are effective in achieving remission in chronic inflammatory disorders of the pouch. There is also a clear trend toward higher efficacy in patients with CD of the pouch compared to refractory pouchitis. More studies are needed to determine the efficacy of biologics in cuffitis.
Study on elavour components of teas:comparison of flavour components by Wulong tea and Raw tea

Simultaneous distillation extractor was used for extracting the flavour components in Wulong tea and Raw tea.The yield obtained in Wulong tea was 3.65% and in raw tea was 2.40%. 42 chemical components and 19 chemical components were identified by gas chromatography/massspectrometry (GC / MS ) method respectively.A comparison of flavour components and contents of Wulong tea and raw tea was made.
Synthesis of ZIF-68 Membrane on a ZnO Modified α-Alumina Support by a Modified Reactive Seeding Method

Large-pore ZIF-68 membranes offer adsorption-based selectivity for separation of gas mixtures or molecular sieving characteristics for the separation of large liquid molecules. ZIF-68 membranes can be grown on ZnO modified α-alumina supports by a modified reactive seeding method. The resultant membranes were around 40 μm in thickness and were determined to have limited nonselective defects given their adherence to Knudsen diffusion during single gas permeation measurements. Further pervaporation experiments showed that the ZIF-68 membranes synthesized via the modified reactive seeding method had a p-xylene pervaporation flux approximately 5.4 times as large as that reported for similar pore-sized MOF-5 membranes; however, pervaporation flux of larger molecule, ditert-butylbenzene, through the same two MOF membranes showed the flux of the ZIF-68 membrane was 3.4 times smaller than that reported for MOF-5. This reversal in pervaporation flux indicates the ZIF-68 structure is more readily accessible to molec.
Miniature Mass Spectrometer System with a Sampling Probe

Chen, Chien-Hsun. Ph.D., Purdue University, August 2015. Study of Miniature Mass Spectrometer System with a Sampling Probe. Major Professor: Zheng Ouyang. This thesis focuses on the development of miniature mass spectrometry systems and the sampling probes. Mass spectrometers are usually installed and used in the analytical laboratory. It requires complex sample preparation before the analysis. Use of miniature mass spectrometer enables the chemical analysis outside the lab and a sampling probe provides convenience for real-time analysis. Two miniature mass spectrometer systems have been developed in this work. A backpack mass spectrometer with a handheld probe was designed for on-site forensic analysis. The handheld probe consists of a mass analyzer, high vacuum pump, and in-line low temperature plasma (LTP) source. The wearable backpack unit contained other components can be easily carried by the user. A miniature pumping system with a 130g drag pump and a 350g scroll pump was used in this system. Direct surface analysis of illicit drug and explosives in tens of nanogram was demonstrated. Another portable mass spectrometer with a remote sampling probe was designed for in vivo tissue analysis in surgical and endoscopic procedures. The remote sampling probe has a dual-channel configuration, with one channel (0.5mmID) for providing the