Miracle-dz/UniFAR-FTUs huggingface dataset:
Design principles and fabrication method for a miniaturized fuel gas combustion reactor

Wobbe Index meter is widely used to reflect the energy content and gas quality of a fuel gas mixture. It is highly in demand to downscale from the conventionally large Wobbe Index facility to a miniaturized Wobbe Index meter. Therefore spontaneously combustion of the fuel gas/air mixtures on a MEMS-based combustor can provide in-line temperature monitoring of the combustion produced heat. However, flame can be quenched by the channel walls when the walls are closely spaced. Therefore, it is crucial to design a micro-combustor with good thermal management and strong mechanical stability. MEMS-based Trench-Assisted Surface Channel Technology is designed and developed to realize these large-volume suspended combustor structures.
Adversarial Bi-Regressor Network for Domain Adaptive Regression

Domain adaptation (DA) aims to transfer the knowledge of a well-labeled source domain to facilitate unlabeled target learning. When turning to specific tasks such as indoor (Wi-Fi) localization, it is essential to learn a cross-domain regressor to mitigate the domain shift. This paper proposes a novel method Adversarial Bi-Regressor Network (ABRNet) to seek more effective cross- domain regression model. Specifically, a discrepant bi-regressor architecture is developed to maximize the difference of bi-regressor to discover uncertain target instances far from the source distribution, and then an adversarial training mechanism is adopted between feature extractor and dual regressors to produce domain-invariant representations. To further bridge the large domain gap, a domain- specific augmentation module is designed to synthesize two source-similar and target-similar inter- mediate domains to gradually eliminate the original domain mismatch. The empirical studies on two cross-domain regressive benchmarks illustrate the power of our method on solving the domain adaptive regression (DAR) problem.
International Journal of Renewable Energy Development

This paper presents the kinetic modeling of biomass gasification in bubbling fluidized bed (BFB) gasifiers and optimization methods to maximize gasification products. The kinetic model was developed based on two-phase fluidization theory. In this work, reaction kinetics, hydrodynamic conditions, convective and diffusion effect, and the thermal cracking of tar kinetics were considered in the model. The model was coded in MATLAB and simulated. The result depicted good agreement with experimental work in literature. The sensitivity analysis was carried out and the effect of temperature ranging from 650 °C to 850 °C and steam to biomass ratio (S/B) ranging from 0.1 to 2 was investigated. The result showed that an increase in temperature promoted H2 production from 18.73% to 36.87%, reduced that of CO from 39.97% to 34.2%, and CH4 from 18.01% to 11.65%. Furthermore, surface response was constructed from the regression model and the mutual effect of temperature and S/B on gasification products and heating value was investigated. In addition, the desirability function was employed to optimize gasification product and heating value. The maximum gasification product yield was obtained at 827.9 °C and 0.1 S/B. The response predicted by desirability function at these optimum operational conditions was 30.1 %, 44.1%, 13.2%, 12.9%, 14.035 MJ/Nm3, and 14.5 MJ/Nm3 for H2, CO, CO2, CH4, LHV, and HHV, respectively. Kinetic modeling of the biomass gasification in BFB process is still under development, which considers the diffusion effect, tar cracking, reaction kinetics, and hydrodynamic behavior. Moreover, the large number of previous studies gave priority to a single parameter investigation. However, this investigation can be extended to various parameters analysis simultaneously, which would give solid information on system performance analysis.
Ecological Adaptive Cruise Control and Energy Management Strategy for Hybrid Electric Vehicles Based on Heuristic Dynamic Programming

In this paper, an ecological adaptive cruise controller (ECO-ACC) for parallel hybrid electric vehicles (HEVs) in a car-following scenario is presented to improve the fuel economy and to maintain a desired inter-vehicle distance from the preceding vehicle. An ACC based on action dependent heuristic dynamic programming (ADHDP) is proposed to obtain an ecological velocity profile and realize an active distance control in normal driving situations. ADHDP is able to adapt internal parameters online and can thus deal with systems with disturbances. Furthermore, an adaptive energy management strategy for HEVs is introduced to control the gear shift and power split for fuel consumption optimization. The gear shift command is designed by enumeration, and the power distribution between the engine and the electric motor is performed by ADHDP. The developed ACC and energy management strategy are finally combined to an ECO-ACC to achieve a multi-objective optimization. Only the current velocity and acceleration of the preceding vehicle are used while knowledge about the future velocity is not needed. The simulations of different driving cycles indicate that the ECO-ACC can lead to near-optimal fuel economy and comfortable driving.
The Role of Trust and Automation in an Intelligence Analyst Decisional Guidance Paradigm

Trust in automation has been linked to a multitude of performance improvements and implicated in the reduction of human error, stress, and workload. In the present study, trust in automation was examined in an experiment measuring the efficacy of linguistic annotation schemes for decision support and human performance. An automated aid provided decisional guidance to assist in intelligence task performance. Four hundred and fifty-eight participants were randomly assigned to one of three annotation schemes and then subsequently performed three simulated intelligence analysis task. The results indicated that trust played a significant role in intelligence task performance, though a significant trust by annotation scheme interaction did not emerge. Specifically, an increase in trust accompanied an increase in performance across the task types. We conclude with a discussion of trust and automated annotation schemes, which has implications for the intelligence operations community.
Counting, Sampling, and Synthesis: The Quest for Scalability

The current generation of symbolic reasoning techniques excel at the qualitative tasks (i.e., when the answer is Yes or No); such techniques sufficed for traditional systems whose design sought to achieve deterministic behavior. In contrast, modern computing systems crucially rely on the statistical methods to account for the uncertainty in the environment, and to reason about behavior of these systems, there is need to look beyond qualitative symbolic reasoning techniques. We will discuss our work focused on the development of the next generation of automated reasoning techniques that can perform higher-order tasks such as quantitative measurement, sampling of representative behavior, and automated synthesis of systems. From a core technical perspective, our work builds on the SAT revolution, which refers to algorithmic advances in combinatorial solving techniques for the fundamental problem of satisfiability (SAT), i.e., whether it is possible to satisfy a given set of constraints. The SAT revolution offers the opportunity to develop scalable techniques for problems that lie beyond SAT from complexity perspective and, therefore, stand to benefit from the availability of powerful SAT engines. Our work seeks to enable a Beyond SAT revolution via design of scalable techniques for three fundamental problems that lie beyond SAT: constrained counting, constrained sampling, and automated synthesis.
Contribution to Robot System Identification: Noise Reduction using a State Observer

: Conventional identification approach based on the inverse dynamic identification model using least-squares and direct and inverse dynamic identification techniques has been effectively used to identify inertial and friction parameters of robots. However these methods require a well-tuned filtering of the observation matrix and the measured torque to avoid bias in identification results. Meanwhile, the cutoff frequency of the low-pass filter f c must be well chosen, which is not always easy to do. In this paper, we propose to use a Kalman filter to reduce the noise of the observation matrix and the output torque signal of the PID controller.
Tube‐type plasma‐enhanced atomic layer deposition of aluminum oxide: Enabling record lab performance for the industry with demonstrated cell efficiencies >24%

In this work, single‐side aluminum oxide (Al2O3) deposition enabled by a new tube‐type industrial plasma‐assisted atomic layer deposition (PEALD) technique is presented to meet the increasingly stringent requirements for high‐efficiency solar cell mass production. Extremely low emitter saturation current densities, J0e, down to 15 fA/cm2 are achieved on an industrial textured boron emitter with a sheet resistance of 104 Ω/sq, passivated by PEALD Al2O3/PECVD SiNx stack after firing. An implied open‐circuit voltage of up to 721 mV is obtained on symmetrical lifetime samples. The underlying passivation mechanisms of this new tube‐type PEALD Al2O3 are investigated by contactless corona‐voltage measurements. The results indicate that the superior passivation is mainly attributed to a low interface defect density down to 1.1 × 1011 cm−2 eV−1 and a high negative fixed charge density up to 4.5 × 1012 cm−2. Simulations show that the obtained J0e is close to its intrinsic limit. Lastly, the developed tube‐type PEALD Al2O3 is applied to industrial TOPCon solar cells achieving an average cell efficiency above 24% and a maximum Voc of 707 mV. This work shows that the record level of surface passivation available from lab‐scale PEALD reactors is now available in a flexible high‐throughput industrial PEALD platform, which opens a new route for mass production of high‐efficiency industrial TOPCon solar cells with a lean process at low costs.
Ladder-Like Low Iodine Delivery Rate Injection Protocols Based on BMI at 70 KV by Automated Tube Voltage Selection in Coronary CTA

Objective To evaluate the feasibility of reducing the injection velocity and volume of contrast agent according to BMI, and the effect of body weight (BW), body surface area（BSA）, body mass index（BMI），and blood volume (BV) on aortic contrast enhancement when the voltage of third-generation dual-source CT is selected at 70 KV. Methods A total of 280 patients selected at 70 KV were randomly divided into an experimental group and a control group. Each group was divided into 7 subgroups according to BMI ≤20, 20–21, 21-22, 22-23, 23-24, 24-25, and 25–26. The experimental group uses 2.3/2.4/2.5/2.6/2.7/2.8/2.9 ml/s injection speed with 350 mgI/ml contrast agents according to the subgroups; injection time was fixed at 10 s. In the control group, the fixed injection flow rate was 3.5 ml/s, time was 12 s with a total of 42 ml. Subjects in both groups were inspected to adaptive prospective ECG-gating sequence scanning, and subjective and objective image quality of the two groups were compared using Student's t-test. BMI, BSA, and BV were calculated from the patient's body weight and height. We assess the relationship between aortic attenuation and BW, BMI, BV, and BSA using regression analysis or correlation analysis. Results Significant differences exist in vascular enhancement between the two groups; SNR and CNR of objective image quality in the experimental group were lower than those in the control group (P < 0.05). Both groups had the same subjective image scores (P > 0.05). The number of vessels in the optimal enhancement range counts more in the experimental group than in the control group (χ2 value = 334.25, P ＜ 0.05). In the control group, a weak to medium correlation was seen between vascular enhancement and BMI (r = −0.20), BW (r = −0.42), BSA (r = −0.46), and BV (r = −0.48) (P ＜ 0.05 for all). Conclusions Compared to BW, BSA, and BV, a weaker negative correlation exists between vascular enhancement and BMI when ATVS selects 70 KV. However, as a much easier way to operate, the stepped low flow and low-contrast agent injection based on BMI was feasible, and the image quality was more homogenized than that of the control group.
Migrating to Long-Read Sequencing for Clinical Routine BCR-ABL1 TKI Resistance Mutation Screening

Objective: The aim of this project was to implement long-read sequencing for BCR-ABL1 TKI resistance mutation screening in a clinical setting for patients undergoing treatment for chronic myeloid leukemia. Materials and Methods: Processes were established for registering and transferring samples from the clinic to an academic sequencing facility for long-read sequencing. An automated analysis pipeline for detecting mutations was established, and an information system was implemented comprising features for data management, analysis and visualization. Clinical validation was performed by identifying BCR-ABL1 TKI resistance mutations by Sanger and long-read sequencing in parallel. The developed software is available as open source via GitHub at https://github.com/pharmbio/clamp Results: The information system enabled traceable transfer of samples from the clinic to the sequencing facility, robust and automated analysis of the long-read sequence data, and communication of results from sequence analysis in a reporting format that could be easily interpreted and acted upon by clinical experts. In a validation study, all 17 resistance mutations found by Sanger sequencing were also detected by long-read sequencing. An additional 16 mutations were found only by long-read sequencing, all of them with frequencies below the limit of detection for Sanger sequencing. The clonal distributions of co-existing mutations were automatically resolved through the long-read data analysis. After the implementation and validation, the clinical laboratory switched their routine protocol from using Sanger to long-read sequencing for this application. Conclusions: Long-read sequencing delivers results with higher sensitivity compared to Sanger sequencing and enables earlier detection of emerging TKI resistance mutations. The developed processes, analysis workflow, and software components lower barriers for adoption and could be extended to other applications.
A Big Data Analytics Approach for Construction Firms Failure Prediction Models

Using 693 000 datacells from 33 000 sample construction firms that operated or failed between 2008 and 2017, failure prediction models were developed using artificial neural network (ANN), support vector machine, multiple discriminant analysis (MDA), and logistic regression (LR). The accuracy of the models on test data surprisingly showed ANN to have only a slightly better accuracy than LR and MDA. The ANN's number of units in the hidden layer and weight decay hyperparameters were consequently tuned using the grid search. Tuning process led to tedious machine computation that was aborted after many hours without completion. The state of art big data analytics (BDA) technology was, for the first time in failure prediction, consequently employed and the tuning was completed in some seconds. Mean accuracy from cross validation was used for selection of the model with best parameter values, which were used to develop a new ANN model that outperformed all previously developed models on test data. Subsequent use of selected variables to develop new models led to reduced tuning computational cost, but not improved performance. Since the real-life effect of a misclassification cost is greater than the tedious computation cost, it was concluded that BDA is the best compromise.
Complex Disease Individual Molecular Characterization Using Infinite Sparse Graphical Independent Component Analysis

Identifying individual mechanisms involved in complex diseases, such as cancer, is essential for precision medicine. Their characterization is particularly challenging due to the unknown relationships of high-dimensional omics data and their inter-patient heterogeneity. We propose to model individual gene expression as a combination of unobserved molecular mechanisms (molecular components) that may differ between the individuals. Considering a baseline molecular profile common to all individuals, these molecular components may represent molecular pathways differing from the population background. We defined an infinite sparse graphical independent component analysis (isgICA) to identify these molecular components. This model relies on double sparseness: the source matrix sparseness defines the subset of genes involved in each molecular component, whereas the weight matrix sparseness identifies the subset of molecular components associated with each patient. As the number of molecular components is unknown but likely high, we simultaneously inferred it and the weight matrix sparseness using the beta-Bernoulli process (BBP). We simulated data from a double sparse ICA with 10/30 components with specific sparseness structures for 100/500 individuals and 500/1000/5000 genes with different noise variance levels to evaluate the reconstruction of the latent structures by our model. For all simulations, the isgICA was able to reconstruct with higher accuracy than 2 state-of-the-art methods (ica and fastICA) the number of components, the weight and source matrix sparsenesses (correlation simulated/estimated >.8). Applying our model to the expression of 1063 genes of 614 breast cancer patients, the isgICA identified 22 components. According to the source matrix, 7 of these 22 components seemed to be specifically related to 3 known molecular pathways with a prognostic effect in early breast cancer (immune system, proliferation, and stroma invasion). This proposed algorithm provides an insight into individual molecular heterogeneity to better understand complex disease mechanisms.
He–Laplace method for nonlinear vibration in shallow water waves

This study is based on an analytical approach for nonlinear vibration of nonlinear evaluation equations which arises in shallow water waves. This approach is, coupled with variational iteration method and Laplace transformation, known as He–Laplace method. In order to show the capability of the proposed method, He’s polynomials are employed to handle nonlinear terms in illustrated problems due to specific limit of Laplace transformation. Significant results and their graphical representations demonstrated that He–Laplace method is most suitable for nonlinear problems, and it is enormously active for nonlinear vibrations and nonlinear waves.
A Novel Approach to Functional Equivalence Testing

: In Model Based Software Development, sometimes it is required to transform the model and respective software code from one platform to another platform that is having different tool setup. For example, transforming a legacy model to the newly adapted architecture or transforming a model supplied from third party to production model and so on. Once the model is adapted to the new platform, there will be changes in the model, hence the code generated from the model can be different from that of legacy artifacts. After transformation activity, developers have to be sure that the new model and code is functionally equivalent to that of the old set. It is also important from quality standpoint that there are no deviations in the functionality after migration. With most of the compilation toolchains being closed source it is difficult to identify the issues during migration unlike in systems engineering. Achieving functional equivalence between the production artifacts and the reference/legacy artifacts provide conformity to the engineer of successful migration. In this paper, a methodology is proposed in which even with non-availability of few artifacts from legacy setup, functional equivalence is achieved using Model in loop and software in loop simulation results matchup. The method and the results are presented in the paper with two different use cases.
Corneal Cross-linking in Thin Corneas: From Origins to State of the Art

Corneal cross-linking (CXL) can halt ectasia progression and involves saturating the stroma with riboflavin, followed by ultraviolet-A (UV-A) light irradiation. This generates reactive oxygen species that covalently cross-link together stromal molecules, strengthening the cornea. The ‘Dresden protocol’ left a 70 µm uncross-linked region at the base of the stroma to protect the corneal endothelium from UV damage; however, this limited CXL to corneas ≥400 µm. Approaches made to overcome this limitation involved artificial corneal thickening to ≥400 μm through swelling the stroma with hypo-osmolaric riboflavin, applying riboflavin-soaked contact lenses during UV irradiation or leaving ‘epithelial islands’ over the thinnest corneal regions. The drawbacks to these three approaches are unpredictable swelling, suboptimal stiffening and unpredictable cross-linking effects, respectively. Newer approaches adapt the irradiation protocol to the cornea to deliver CXL that maintains the 70 μm uncross-linked stroma safety margin. The sub400 protocol employs an algorithm that models the interactions between UV-A energy, riboflavin, oxygen diffusion and stromal thickness. It requires only corneal pachymetry measurements at the thinnest point and the selection of the appropriate UV irradiation time from a look-up table to cross-link corneas as thin as 200 µm safely and effectively.
Classification of Users' Opinions and Posts on Facebook Using Machine Learning Approaches

— In this research work, four classifiers are adopted, analyzed, and discussed. The classifiers are Naïve Bayes (NB), Support Vector Machine (SVM), Stochastic Gradient Descent (SGD), and Logistic Regression (LR). The classifiers are operated on a dataset with more than eight-thousands of instances. The dataset contains the users' reviews and their opinions about the quality of service of restaurants. The reviews are collected from the Arabic Facebook posts. Several experiments are done to evaluate the performance of the adopted classifiers. Moreover, some features selection methods are also applied to improve the classification process. The feature selected methods are based on term-weights with N-grams, correlation, chi-square, and mutual information. Some criteria are considered to evaluate the performance of the classification process mainly: precision, recall, F-measure, and learning time. From the experimental results, the SVM classifier outperforms the other adopted ones. Also, the feature selection method based on the correlation between the individual features and the target class outperforms the other chosen methods. The same concluding remarks are expected to take place for other datasets containing comments or reviews from social media.