Miracle-dz/UniFAR-FTUs huggingface dataset:
DMSO_A_363692 1831..1843

Purpose: The main purpose of this study was to examine the effect of the cumulative exposure of blood lipid parameters on type 2 diabetes mellitus (T2DM). Another purpose was to explore whether the cumulative burden of blood lipid parameters plays a certain role in the pathogenesis of diet affecting T2DM. Patients and Methods: A total of 63 cases of diabetes occurred from 2017 to 2020, with an incidence density of 3.71 person-years. The dietary intake of the residents was obtained by using a dietary frequency questionnaire (FFQ). Cumulative lipid parameter burden was calculated according to the number of years (2016–2020) multiplied by total cholesterol (TC), high density lipoprotein (HDL), low density lipoprotein (LDL) and triglyceride (TG). A Cox proportional hazard model was used to estimate the effect of cumulative lipid burden on T2DM. A mediating analysis of accelerated failure time (AFT) was used to investigate the mediating effects of certain foods, the cumulative lipid parameter burden and T2DM. Results: A higher cumulative TG load corresponded to a higher risk of T2DM onset (Ptrend=0.021). After adjusting for covariates, the highest quartile cumulative TG burden had a 3.462 times higher risk of T2DM than that in the lowest quartile (HR=3.462, 95% CI: 1.297–9.243). Moreover, a higher cumulative HDL load corresponded to a lower risk of T2DM onset (Ptrend =0.006). After adjusting for covariates, the risk of T2DM was 0.314-fold lower in the highest quartile of cumulative HDL burden than that in the lowest quartile (HR=0.314, 95% CI: 0.131–0.753). Cumulative TG burden partially mediated the association between red meat and T2DM. Conclusion: The increase in cumulative HDL burden and the decrease in cumulative HDL burden are related to the incidence of T2DM. Cumulative TG burden was shown to play a partial mediating role in the pathogenesis of red meat and diabetes.
Six Decades of Research on 2D Materials: Progress, Dead Ends, and New Horizons

The present paper guides the reader through six decades of research on 2D materials, thereby putting special focus on the use of these materials for electronic devices. It is shown that after a slow start and only little activity over many years, since 2004 the exploration of 2D materials advanced at an enormous pace. While some of the high expectations raised in the so-called golden era of graphene did not fulfil, other electronic applications for 2D materials that originally were not on the agenda gain increasing attention now. One of the main research topics in the field of 2D materials during the early 2000s was high-performance graphene transistors. This effort, however, led to a dead end due the consequences of the missing bandgap in graphene. On the other hand, the semiconducting 2D materials show potential for different device concepts including stacked-channel 2D nanosheet MOSFETs and 2D memristors. The former may become the transistor architecture of choice at the end of the CMOS roadmap and 2D memristors represent a promising device concept for future neuromorphic computing, a type of information processing that shows great potential for artificial intelligence applications where energy efficiency is a key requirement.
Improving Generalizability of ML-enabled Software through Domain Specification

While the conventional software components implement pre-defined specifications, Machine Learning (ML)-enabled Software Components (MLSC) learn the domain specifications from the training samples. Thus, the MLSC’s data-driven and inductive reasoning becomes highly reliant on the quality of the training dataset, which are often arbitrarily collected in ad hoc manners. The random collection of samples leads to a significant gap between the actual specifications of a real-world concept, and the picture that a dataset represents of the concept, reducing MLSC generalizability, particularly in perceptual tasks where understanding the environment is an important factor of accurate prediction.To fill the gap between the conceptualization of a targeted domain’s concept and its visualization in the MLSC dataset, we propose exploiting semantic specification of the concept to identify the concepts’ missing variants in the data. We first, semantically specify hard-to-specify targeted domain’s concepts and second, refer to the derived specifications to evaluate the diversity and relative completeness of MLSC collected datasets. The systematic augmentation of training datasets, with respect to the semantics of the domain, improves the quality of an arbitrarily collected dataset and potentially yields more reliable models. As a proof of concept, we automatically acquired the existing semantic knowledge for specifying the automotive domain concept “pedestrian. ” Augmenting the state-of-the-art pedestrian datasets accordingly, the evaluations showed that semantic augmentation outperforms brute-force machine learning in satisfying the MLSC accuracy requirements.CCS CONCEPTS• Software and its engineering → Requirements analysis; Software reliability; • Computing methodologies → Object detection; Semantic networks.
Analysis and Control of Electrolytic Capacitor-Less LED Driver Based on Harmonic Injection Technique

AC-DC LED drivers may have a lifespan shorter than the lifespan of LED chips if electrolytic capacitors are used in their construction. Using film capacitors solves this problem but, as their capacitance is considerably lower, the low-frequency ripple will increase. Solving this problem by limiting the output ripple to safe values is possible by distorting the input current using harmonic injection technique, as long as these harmonics still complies with Power Factor Regulations (Energy Star). This harmonic injection alleviates the requirements imposed to the output capacitor in order to limit the low-frequency ripple in the output. This idea is based on the fact that LEDs can be driven by pulsating current with a limited Peak-To-Average Ratio (PTAR) without affecting their performance. By considering the accurate model of LEDs, instead of the typical equivalent resistance, this paper presents an improved and more reliable calculation of the intended harmonic injection. Wherein, its orders and values can be determined for each input/output voltage to obtain the specified PTAR and Power Factor (PF). Also, this harmonic injection can be simply implemented using a single feedback loop, its control circuit has features of wide bandwidth, simple, single-loop and lower cost. A 21W AC-DC buck converter is built to validate the proposed circuit and the derived mathematical model and it complies with IEC61000 3-2 class D standard.
NEMIANA: Cross-Platform Execution Migration for Debugging

Many IoT devices are compromised by exploiting their software vulnerabilities in these devices. A technique during a development phase to fix such vulnerabilities efficiently is leveraging advantages of various platforms (e.g., single-board computers, emulators, and FPGA (Field Programmable Gate Array) boards). As a debugging scenario, developers discover some vulnerability-candidates of software under test on an emulator like QEMU with rich debugging plugins. They then check those candidates in detail on a single-board computer (i.e., a real device) with GDB. To efficiently conduct this kind of debugging, we propose NEMIANA (Noncomplex Execution Migration Integrated Architecture for Nonstop Analyses), an architecture for automatically conducting cross-platform execution migration. It can migrate execution-state of software at any execution points (e.g., 500 steps after the beginning) from a platform (e.g., a single-board computer) to another platform (e.g., a FPGA board). Developers then seamlessly continue debugging at that execution point on the destination platform. A key idea for cross-platform migration is an abstraction model of execution-state (e.g., CPU register/memory values), which can be applied to various platforms (if they have CPUs of the same ISA (Instruction Set of Architecture)). Another key idea is to trace all instructions executed by software. This incurs a system performance overhead; however, it enables developers to choose any execution points, which we hope is very helpful for debugging. In the evaluation, a prototype system of NEMIANA demonstrates three case studies: a typical debugging with GDB, benchmarking, and vulnerability discovery. It shows developers can use NEMIANA for their debugging purposes. CCS CONCEPTS • Software and its engineering → Software testing and debugging; • Security and privacy → Embedded systems security.
Automatic Anti-Pattern Detection in Microservice Architectures Based on Distributed Tracing

The successful use of microservice-based applications by large companies has popularized this architectural style. One problem with the microservice architecture is that current techniques for visualising- and detecting anti-pattern are inadequate. This study contributes a method and tool for detecting anti-patterns in microservice architecture based on distributed execution traces. We demonstrate this on an industrial case study.
Comparing and Combining File-based Selection and Similarity-based Prioritization towards Regression Test Orchestration

Test case selection (TCS) and test case prioritization (TCP) techniques can reduce time to detect the first test failure. Although these techniques have been extensively studied in combination and isolation, they have not been compared one against the other. In this paper, we perform an empirical study directly comparing TCS and TCP approaches, represented by the tools Ekstazi and FAST, respectively. Furthermore, we develop the first combination, named Fastazi, of file-based TCS and similarity-based TCP and evaluate its benefit and cost against each individual technique. We performed our experiments using 12 Java-based open-source projects. Our results show that, in the median case, the combined approach detects the first failure nearly two times faster than either Ekstazi alone (with random test ordering) or FAST alone (without TCS). Statistical analysis shows that the effectiveness of Fastazi is higher than that of Ekstazi, which in turn is higher than that of FAST. On the other hand, FAST adds the least overhead to testing time, while the difference between the additional time needed by Ekstazi and Fastazi is negligible. Fastazi can also improve failure detection in scenarios where the time available for testing is restricted. CCS CONCEPTS • Software and its engineering →Software testing and debugging.
Retrieval of Similar Evolution Patterns from Satellite Image Time Series

Technological evolution in the remote sensing domain has allowed the acquisition of large archives of satellite image time series (SITS) for Earth Observation. In this context, the need to interpret Earth Observation image time series is continuously increasing and the extraction of information from these archives has become difficult without adequate tools. In this paper, we propose a fast and effective two-step technique for the retrieval of spatio-temporal patterns that are similar to a given query. The method is based on a query-by-example procedure whose inputs are evolution patterns provided by the end-user and outputs are other similar spatio-temporal patterns. The comparison between the temporal sequences and the queries is performed using the Dynamic Time Warping alignment method, whereas the separation between similar and non-similar patterns is determined via Expectation-Maximization. The experiments, which are assessed on both short and long SITS, prove the effectiveness of the proposed SITS retrieval method for different application scenarios. For the short SITS, we considered two application scenarios, namely the construction of two accumulation lakes and flooding caused by heavy rain. For the long SITS, we used a database formed of 88 Landsat images, and we showed that the proposed method is able to retrieve similar patterns of land cover and land use.
photooxidation of eugenol

Aqueous reactions may turn precursors into light-absorbing and toxic products, leading to air quality deterioration and adverse health effects. In this study, we comprehensively investigated eugenol photooxidation (a representative biomass-burning-emitted, highly substituted phenolic compound) in the bulk aqueous phase with direct photolysis, a hydroxyl radical (OH), and an organic triplet excited state (3C∗). Results show that the degradation rates of eugenol followed the order of 3C∗ >OH> direct photolysis. During the 3C∗-initiated oxidation, different reactive oxygen species (ROS), including 3C∗, OH, O2, and O q− 2 , can participate in the oxidation of eugenol, quenching experiments verified 3C∗ was the most important one, while, during OH-initiated oxidation, O q− 2 was a more important ROS than OH for degrading eugenol. The rate constants under saturated O2, air, and N2 followed the order of kO2 > kAir > kN2 for both direct photolysis and OH-initiated oxidation but changed to kAir > kN2 > kO2 for 3C∗-mediated oxidation. pH and dissolved oxygen (DO) levels both decreased during oxidation, indicating the formation of acids and the participation of DO in oxidation. Ultraviolet and visible (UV-vis) light absorption spectra of the reaction products showed a clear absorbance enhancement in the 300–400 nm range for all three sets of experiments, and new fluorescence at excitation/emission= 250/ (400– 500) nm appeared, suggesting the formation of new chromophores and fluorophores (brown carbon species). These species were likely attributed to humic-like substances (HULIS), as shown by the increases in HULIS concentrations during oxidation. Large mass yields of products (140 %–197 %) after 23 h of illumination were obtained, and high oxidation degrees of these products were also observed. Correspondingly, a series of oxygenated compounds were identified, and a detailed reaction mechanism with functionalization as a dominant pathway was proposed. At last, the dithiothreitol (DTT) assay was applied to assess the oxidation potential of the reaction products, and the end products of all three sets of experiments showed higher DTT consumption rates than those of eugenol, indicating that more toxic species were produced upon aqueous oxidation. Overall, our results from using eugenol as a model compound, underscore the potential importance of the aqueous processing of biomass burning emissions in secondary organic aerosol (SOA) formation. Published by Copernicus Publications on behalf of the European Geosciences Union. 7794 X. Li et al.: Aqueous photooxidation of eugenol
HVAC duct system modeling through the component mode synthesis for structural vibration analysis

HVAC systems (heating, ventilating and air conditioning) are responsible for making an environment remain within a desirable range of temperature, humidity and cleanliness. To get the desire environment, the HVAC systems use a set of rigid ducts with different cross section. In addition to increasing the comfort in industrial spaces, is emphasized the importance of the structural analysis, referring to the vibratory behavior, in this way avoiding the occurrence of phenomena such as resonance. In the field of structural dynamics, the finite element method (FEM) is becoming increasingly important to the industry. Attempting to reduce the size and time response of different complex models on numerical software, component mode synthesis (CMS) is one of the main techniques for modelling complex or large sized systems. The present work uses an application of the CMS in modal analysis of an HVAC duct system. In order to analyze the structural vibrational behavior the CMS uses a modal superset that consists in to modelling individual components of a structure separately and then to couple them to form an assembled system. The principal objective of this article is to use the CMS technique for numerical estimation of natural frequencies and vibration modes of a duct system.
Finite element modelling of a cold curved steel plate girder

Cold bending is a cost-effective solution that is sometimes used for curving structural steel girders. Current usage for bridge structures is limited to projects that fall outside the jurisdiction of American Association of State Highway and Transportation Officials (AASHTO) because of the lack of technical knowledge surrounding this technique. This paper presents results from a three-dimensional finite element model to assess the structural behaviour exhibited by steel girders during bending for a proprietary cold curving system. A non-linear FE model is validated against measured data obtained from a previously tested girder. The FE model is extended to explore the performance of all structural components of the girder during bending such as deformations in flanges and web, residual stresses and plastic strains. Findings from this paper provide a framework for accurately predicting the cold bent geometry and how to incorporate residual stresses and plastic strains in the design of curved girders.
Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction

To enhance the prediction performance for building energy consumption, this paper presents a modified deep belief network (DBN) based hybrid model. The proposed hybrid model combines the outputs from the DBN model with the energy-consuming pattern to yield the final prediction results. The energy-consuming pattern in this study represents the periodicity property of building energy consumption and can be extracted from the observed historical energy consumption data. The residual data generated by removing the energy-consuming pattern from the original data are utilized to train the modified DBN model. The training of the modified DBN includes two steps, the first one of which adopts the contrastive divergence (CD) algorithm to optimize the hidden parameters in a pre-train way, while the second one determines the output weighting vector by the least squares method. The proposed hybrid model is applied to two kinds of building energy consumption data sets that have different energy-consuming patterns (daily-periodicity and weekly-periodicity). In order to examine the advantages of the proposed model, four popular artificial intelligence methods—the backward propagation neural network (BPNN), the generalized radial basis function neural network (GRBFNN), the extreme learning machine (ELM), and the support vector regressor (SVR) are chosen as the comparative approaches. Experimental results demonstrate that the proposed DBN based hybrid model has the best performance compared with the comparative techniques. Another thing to be mentioned is that all the predictors constructed by utilizing the energy-consuming patterns perform better than those designed only by the original data. This verifies the usefulness of the incorporation of the energy-consuming patterns. The proposed approach can also be extended and applied to some other similar prediction problems that have periodicity patterns, e.g., the traffic flow forecasting and the electricity consumption prediction.
Floating top gate-induced output enhancement of a-InGaZnO thin film transistors under single gate operations

This work compares dual gate and single gate a-InGaZnO thin film transistor devices under single gate operations. In both devices, an abnormal drain current increase in the dual gate structures was observed. The results of structural geometry experiments, Technology Computer-Aided Design, and theoretical calculations matching the experimental results provide evidence for a larger voltage potential distribution located near the top gate even when the top gate is floating. Since an additional voltage is formed near the top gate, a better gate control capability will lead to more inverted carriers. Therefore, these dual gate structures have a larger drain current than does the single gate. Finally, both positive bias stress and negative bias illumination stress in both structures are discussed. The results of positive bias stress have shown good quality of the gate insulator layer and negative bias illumination stress was discussed to confirm the coupled voltage.This work compares dual gate and single gate a-InGaZnO thin film transistor devices under single gate operations. In both devices, an abnormal drain current increase in the dual gate structures was observed. The results of structural geometry experiments, Technology Computer-Aided Design, and theoretical calculations matching the experimental results provide evidence for a larger voltage potential distribution located near the top gate even when the top gate is floating. Since an additional voltage is formed near the top gate, a better gate control capability will lead to more inverted carriers. Therefore, these dual gate structures have a larger drain current than does the single gate. Finally, both positive bias stress and negative bias illumination stress in both structures are discussed. The results of positive bias stress have shown good quality of the gate insulator layer and negative bias illumination stress was discussed to confirm the coupled voltage.
A Compact WiMAX Band-Notched UWB MIMO Antenna with High Isolation

A multiple-input-multiple-output (MIMO) antenna is proposed for ultra-wideband (UWB) applications with high isolation capability. The proposed MIMO structure consists of two simple square monopole antennas with slotted ground plane structure with S11 bandwidth of 2.2 to 10.8 GHz and isolation level better than –30 dB. U-shaped and L-shaped slots are adopted to realize a notched band within 3.3–3.8 GHz relating to WiMAX frequency band. With the aim of enhancing the isolation between the monopole antennas, two parasitic structures are wisely embedded between the monopole antennas on backside of the substrate. The MIMO antenna prototype with a compact size of 25 × 38 mm is fabricated and measured. Based on the simulation and experimental results, the proposed MIMO antenna well-performs in ultra-wideband (UWB) band-notched MIMO application.
Spatial Information on Voice Generation from a Multi-Channel Electroglottograph

In the acoustics of human voice, an important role is reserved for the study of larynx movements. One of the most important aspects of the physical behavior of the larynx is the proper description and simulation of swallowing and singing register changes, which require complex laryngeal manoeuvres. In order to describe (and solve, in some cases) these actions, it is fundamental to analyze the accurate synchronization of vocal fold adduction/abduction and the change of the larynx position. In the case of dysfunction, which often occurs for professional singers, this synchronization can be disturbed. The simultaneous assessment of glottal dynamics (typically electroglottograph, EGG signal) and larynx position might be useful for the diagnosis of disordered voice and swallowing. Currently, it is very difficult to instantaneously gather this information because of technology problems. In this work, we implemented a time-multiplex measurement approach of space-resolved transfer impedances through the larynx (Multi-Channel electroglottograph MC-EGG). For this purpose, we developed specific software (Labview code) for the visualization of the main waveforms in the study of the EGG signals. Moreover, the data acquired by the Labview code have been used to create a theoretical algorithm for deriving the position of the larynx inside the neck. Finally, we verified the results of the algorithm for the 3D larynx movement by comparing the data acquired with the values described in the literature. The paths of the larynx and the displacement on the sagittal and transverse plans matched the ones known for the emission of low/high notes and for swallowing. Besides, we have introduced the possibility to study the movement on the coronal (x) plan (so far, unexplored), which might be a starting point for further analysis.