Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved and accurate fault diagnostic model for gas turbine based on 2D-wavelet transform and generative adversarial network

Severe working environments cause gas turbines to break down, which can directly affect their performance. Research on the diagnostic methods for gas turbine faults, such as, gas path faults and sensor failures, has always raised concerns. However, traditional fault diagnosis algorithms mostly use instantaneous data rather than time-series data, because they cannot efficiently use time-series analysis to extract fault features and improve algorithm accuracy. Problems with sparse fault samples and categories are also encountered with these algorithms. In this study, a gas turbine fault diagnostic method based on a 2D-wavelet transform and generative adversarial network (GAN) was proposed. The data preprocessing method, 2D-wavelet transform, of multiple time series images was used to obtain fault features. Based on the Fréchet inception distance, a performance evaluation index, an optimal generator built from a deep convolutional GAN model was selected to solve sparse or imbalanced datasets. The classification accuracy of the four algorithms, namely, random forest, support vector machine, convolutional neural network, and deep neural network, verified the performance of the data preprocessing and dataset building methods mentioned earlier. Compared with the original data, the 2D wavelet transform effectively improved the model accuracy. The generated samples also improved the misclassification issue caused by the imbalanced dataset; however, the ratio of real and generated samples in datasets still requires more attention.
Numerical evaluation of air gap effect on double-glazed solar air heater performance

Abstract Double glazing is one of the effective ways to improve the thermal performance of flat-plate solar air heaters. In this regard, by considering this type of heat exchanger, the free convection flow between two glass covers and the forced convection flow inside the main air channel were analyzed in the present study. The exact implementation of this idea was numerically investigated using COMSOL Multiphysics commercial software. Due to the occurrence of Bénard convective flows in the space between two glasses, the air gap thickness must be optimized so that the positive effect of the presence of the air layer as a suitable thermal insulation is not affected by this phenomenon and does not lead to thermal dissipation. By choosing air gaps of 1 cm to 5 cm and sun radiative heat fluxes of 600, 800, and 1000 W/m2, it was found that the higher rate of heat transfer from the absorber plate into laminar forced convection airflow took place in the air heater with 3 cm air gap. Under the solar irradiation of 1000 W/m2, computations indicated the values of 11.2 K, 9.7 K, and 8.8 K for air temperature increase along the air heaters with the air gap thicknesses of 3 cm, 5 cm, and 1 cm, respectively. Comparisons between the present numerical findings and experimental data reported in the literature revealed an excellent consistency.
Combination of ozone-based advanced oxidation process and nanobubbles generation toward textile wastewater recovery

The intricate nature of various textile manufacturing processes introduces colored dyes, surfactants, and toxic chemicals that have been harmful to ecosystems in recent years. Here, a combination ozone-based advanced oxidation process (AOP) is coupled with a nanobubbles generator for the generation of ozone nanobubbles (NB) utilized the same to treat the primary effluent acquired from textile wastewaters. Here we find several key parameters such as chemical oxygen demand ammonia content (NH3), and total suspended solids indicating a substantial recovery in which the respective percentages of 81.1%, 30.81%, and 41.98%, upon 300 min residence time are achieved. On the other hand, the pH is shifted from 7.93 to 7.46, indicating the generation of hydrogen peroxide (H2O2) due to the termination reaction and the self-reaction of reactive oxygen species (ROS). We propose that the reactive oxygen species can be identified from the negative zeta potential measurement (−22.43 ± 0.34 mV) collected in the final state of treatment. The combined method has successfully generated ozone nanobubbles with 99.94% of size distributed in 216.9 nm. This highlights that enhancement of ozone’s reactivity plays a crucial role in improving the water quality of textile wastewater towards being technologically efficient to date.
Investigating the Interaction of an Anticancer Nucleolipidic Ru(III) Complex with Human Serum Proteins: A Spectroscopic Study

Ruthenium(III) complexes are very promising candidates as metal-based anticancer drugs, and several studies have supported the likely role of human serum proteins in the transport and selective delivery of Ru(III)-based compounds to tumor cells. Herein, the anticancer nanosystem composed of an amphiphilic nucleolipid incorporating a Ru(III) complex, which we named DoHuRu, embedded into the biocompatible cationic lipid DOTAP, was investigated as to its interaction with two human serum proteins thought to be involved in the mechanism of action of Ru(III)-based anticancer drugs, i.e., human serum albumin (HSA) and human transferrin (hTf). This nanosystem was studied in comparison with the simple Ru(III) complex named AziRu, a low molecular weight metal complex previously designed as an analogue of NAMI-A, decorated with the same ruthenium ligands as DoHuRu but devoid of the nucleolipid scaffold and not inserted in liposomal formulations. For this study, different spectroscopic techniques, i.e., Fluorescence Spectroscopy and Circular Dichroism (CD), were exploited, showing that DoHuRu/DOTAP liposomes can interact with both serum proteins without affecting their secondary structures.
Characterization of the Bacterial Microbiome in Natural Populations of Barley Stem Gall Midge, Mayetiola hordei, in Morocco

Simple Summary Barley stem gall midge, Mayetiola hordei (Kieffer), is one of the most devastating barley pests in many regions of the world, causing significant losses to agricultural output. In this work, we explore the presence of reproductive symbionts, including Wolbachia, Spiroplasma, Cardinium, and Arsenophonus, which can be employed as a substitute for chemical pesticides in pest management, in addition to the impact of their geographic origin on bacterial composition, as well as the diversity of the natural populations of barley stem gall midges detected in four Moroccan barley-growing regions. The screening revealed the presence of Wolbachia; in contrast, none of the samples analyzed had Spiroplasma, Cardinium, or Arsenophonus infections. Overall, 5 phyla, 7 classes, and 42 genera were detected throughout all samples, with a significant environmental influence on the taxonomic assortment of the microbiota. Abstract Mayetiola hordei (Kieffer), known as barley stem gall midge, is one of the most destructive barley pests in many areas around the world, inflicting significant qualitative and quantitative damage to crop production. In this study, we investigate the presence of reproductive symbionts, the effect of geographical origin on the bacterial microbiome’s structure, and the diversity associated with natural populations of M. hordei located in four barley-producing areas in Morocco. Wolbachia infection was discovered in 9% of the natural populations using a precise 16S rDNA PCR assay. High-throughput sequencing of the V3-V4 region of the bacterial 16S rRNA gene indicated that the native environments of samples had a substantial environmental impact on the microbiota taxonomic assortment. Briefly, 5 phyla, 7 classes, and 42 genera were identified across all the samples. To our knowledge, this is the first report on the bacterial composition of M. hordei natural populations. The presence of Wolbachia infection may assist in the diagnosis of ideal natural populations, providing a new insight into the employment of Wolbachia in the control of barley midge populations, in the context of the sterile insect technique or other biological control methods.
Large-Eddy Simulation of the Convective Atmospheric Boundary Layer

Abstract Large-eddy simulations of a free convective atmospheric boundary layer with an overlying capping inversion are considered. Attention is given to the dependence of the results upon the various factors influencing the simulation: the subgrid model, the domain size, and the mesh resolution. By providing artificial constraints upon the convection the results also provide extra insight into the underlying dynamics. The gross features of the boundary layer, such as the overall energy budget, are not sensitive to the details of the simulations but a number of important factors are revealed. It has been found that near the surface the subgrid diffusivity must be larger than is usually supposed, in order for the vertical velocity skewness to have the correct sign. This region of the flow has a significant subgrid-scale heat flux and it seems that the subgrid model requires improvement in such cases. A revised model which under statically unstable conditions allows the mixing-length of the subgrid-scale tu.
The characteristics of particle charging and deposition during powder coating processes with ultrafine powder

In a preceding work, the mechanisms of particle charging and deposition during powder coating processes were explored with coarse polyurethane powder. In this paper, the developed mechanisms were further examined with ultrafine polyurethane powder in order to meet the growing needs for ultrafine powder in finishing industries. This study first verified the previous findings in particle deposition, which account for a cone-shaped pattern formed by deposited particles on the substrate and a rise in particle accumulation in the fringe region. It was further demonstrated with ultrafine powder that, as disclosed by using coarse powder, the primary charging of in-flight particles competes with back corona in particle deposition processes, and the highest deposition efficiency is a compromise by balancing their effects. In comparison with coarse powder, ultrafine powder presents a faster reduction in the deposition rate with extended spraying duration, but shows some superiority in the uniformity of the deposited layer. In the case of charging characteristics of the deposited particles, it was further substantiated with ultrafine powder that the secondary charging mechanism takes predominance in determining the distribution of local charge-to-mass ratios. It was also disclosed that ultrafine powder shows a decreasing charge-to-mass ratio with increased charging voltage in the deposited layer, opposite to the increasing tendency of coarse powder. However, it was commonly demonstrated by both coarse and ultrafine powders that the charge-to-mass ratio of the deposited particles decreases with the extended spraying durations. In comparison, ultrafine powder is more likely to produce uniform charge-to-mass ratio distributions in the deposited layer, which contrast sharply with the ones associated with the coarse powder. In conclusion, it is believed that this study supplements the preceding study and is of great help in providing a comprehensive understanding of the mechanisms of corona charging processes of different powders.
White Cane-Type Holdable Device Using Illusory Pulling Cues for Orientation & Mobility Training

For visually impaired people, a white cane is an important aid for obtaining information about the surrounding environment. Although the visually impaired should attend this training by orientation & mobility (O&M) specialists to learn proper cane technique, the number of specialists is limited. In our previous study, we developed a training system that enabled the visually impaired to learn white cane techniques using illusory pulling cues. In the previous prototype system, a cane grip-type device was developed with a built-in vibrator to generate asymmetric vibration stimuli, and illusory pulling cues were fed according to the position of the cane tip. Although the prototype system showed positive results, issues related to the lack of user-friendliness of the device associated with the wired drive and the impractical gripping method were observed. This study developed a more practical white-cane-type device for O&M training, envisioning independent training for the visually impaired, which improved the above-mentioned issues. Specifically, the device was wireless and had a vibration mechanism for the gripping method of the general cane technique, and its validity was demonstrated by evaluation experiments. In addition, the paper explains the usefulness of the device as per discussion with an active O&M specialist. The results of the evaluation experiments indicated that the devices were implemented to satisfy the requirements for cane training.
A Systematic Review and Meta-Analysis of the Effectiveness of Adherence Therapy and Its Treatment Duration in Patients with Schizophrenia Spectrum Disorders

Background Adherence therapy (AT) is an intervention for improving medication adherence of people with schizophrenia spectrum disorders, but no systematic reviews have yet summarized the effectiveness and minimum treatment duration of AT. We here aimed to systematically examine the effectiveness of AT in improving outcomes versus treatment as usual (TAU) and its minimum effective duration for people diagnosed with schizophrenia spectrum disorders. Patients and Methods Eligible randomized controlled trials were identified from four databases (Web of Science, PubMed, CINAHL and Cochrane Library) for the period 1 January 2006 to 1 January 2023. Of 1087 retrieved candidate studies, five studies with a total of 726 participants met the inclusion criteria. The search terms consisted of adherence therapy, compliance therapy, schizophrenia, schizoaffective disorder, schizophrenic disorder, medication adherence, and medication compliance (combined with OR and AND). Two investigators independently selected studies, extracted data, and conducted bias risk assessments. Random-effects models were used to analyze the pooled data. Results Meta-analysis of the five selected studies showed that AT had a significantly greater positive effects than TAU on psychiatric symptoms, but no significant effects on adherence behaviors and attitudes. Conclusion The AT had limited effects in terms of improving the attitudes and behaviors of people with schizophrenia spectrum disorders with respect to medication adherence, but did improve patients’ psychiatric symptoms. The therapy should be performed by trained staff for maximum benefit, and the recommended duration of the intervention is 12 hours.
H2 and H∞ Filtering of Discrete-time Markov Jump Linear Systems through Linear Matrix Inequalities

Abstract This paper addresses the H 2 and H ∞ filtering design problems of discrete-time Markov jump linear systems. First, under the assumption that the Markov parameter is measured, the main contribution is on the LMI characterization of all filters such that the estimation error remains bounded by a given norm level, yielding the complete solution of the mode-dependent filtering design problem. Based on this result, a robust filter design to deal with convex bounded parameter uncertainty is considered. Second, from the same LMI characterization, a procedure for mode-independent filtering design is proposed. An example is solved for illustration and comparisons with the methods available in the literature.
Fusionless All-Pedicle Screws for Posterior Deformity Correction in AIS Immature Patients Permit the Restoration of Normal Vertebral Morphology and Removal of the Instrumentation Once Bone Maturity Is Reached

The aim of this study was to report the restoration of normal vertebral morphology and the absence of curve progression after the removal of instrumentation in AIS patients that underwent posterior correction of the deformity by a common all-screws construct without fusion. A series of 36 AIS immature patients (Risser 3 or less) were included in the study. Instrumentation was removed once the maturity stage was complete (Risser 5). The curve correction was assessed pre- and postoperatively, before instrumentation removal, directly post-removal, and more than two years after instrumentation was removed. Epiphyseal vertebral growth modulation was assessed by the coronal wedging ratio (WR) at the apical level of the main curve (MC). The mean preoperative coronal Cobb was corrected from 53.7° ± 7.5 to 5.5° ± 7.5° (89.7%) at the immediate postop. After implant removal (31.0 ± 5.8 months), the MC was 13.1°. T5–T12 kyphosis showed significant improvement from 19.0° before curve correction to 27.1° after implant removal (p < 0.05). Before surgery, the WR was 0.71 ± 0.06, and after removal, 0.98 ± 0.08 (p < 0.001). At the end of the follow-up, the mean sagittal range of motion (ROM) of the T12-S1 segment was 51.2 ± 21.0°. The SRS-22 scores improved from 3.31 ± 0.25 preoperatively to 3.68 ± 0.25 at the final assessment (p < 0.001). In conclusion, a fusionless posterior approach using common all-pedicle screws correctly constructed satisfactory scoliotic main curves and permitted the removal of instrumentation once bone maturity was reached. The final correction was highly satisfactory, and an acceptable ROM of the previously lower instrumented segments was observed.
Repeated Lung Ultrasound versus Chest X-ray—Which One Predicts Better Clinical Outcome in COVID-19?

The purpose of this study was to evaluate whether changes in repeated lung ultrasound (LUS) or chest X-ray (CXR) of coronavirus disease 2019 (COVID-19) patients can predict the development of severe disease and the need for treatment in the intensive care unit (ICU). In this prospective monocentric study, COVID-19 patients received standardized LUS and CXR at day 1, 3 and 5. Scores for changes in LUS (LUS score) and CXR (RALE and M-RALE) were calculated and compared. Intra-class correlation was calculated for two readers of CXR and ROC analysis to evaluate the best discriminator for the need for ICU treatment. A total of 30 patients were analyzed, 26 patients with follow-up LUS and CXR. Increase in M-RALE between baseline and follow-up 1 was significantly higher in patients with need for ICU treatment in the further hospital stay (p = 0.008). Both RALE and M-RALE significantly correlated with LUS score (r = 0.5, p < 0.0001). ROC curves with need for ICU treatment as separator were not significantly different for changes in M-RALE (AUC: 0.87) and LUS score (AUC: 0.79), both being good discriminators. ICC was moderate for RALE (0.56) and substantial for M-RALE (0.74). The present study demonstrates that both follow-up LUS and CXR are powerful tools to track the evolution of COVID-19, and can be used equally as predictors for the need for ICU treatment.
Reservoir Connectivity Identification and Robust Production Forecasting Using Physics Informed Machine Learning

Routine well-wise injection/production data contain significant information which can be used for closed-loop reservoir management and rapid field decisions. Traditional physics-based numerical reservoir simulation can be computationally prohibitive for short-term decision cycles, and also requires detailed geologic model. Reduced physics models provide an efficient simulator free workflow, but often have a limited range of applicability. Pure machine learning models lack physical interpretability and can have limited predictive power. We propose a hybrid machine learning and physics-based approach for rapid production forecasting and reservoir connectivity characterization using routine injection/production and pressure data. Our framework takes routine measurements such as injection rate and pressure data as input and multiphase production rates as output. We combine reduced physics models into a neural network architecture by utilizing two different approaches. In the first approach, the reduced physics model is used for pre-processing to obtain approximate solutions that feed it into a neural network as input. This physics-based input feature can reduce the model complexity and provide significant improvement in prediction performance. The second approach augments the residual terms in the neural network loss function with physics-based regularization that relies on the governing partial differential equations (PDE). Reduced physics models are used for the governing PDE to enable efficient neural network training. The regularization allows the model to avoid overfitting and provides better predictive performance. Our proposed hybrid models are first validated using a 2D benchmark reservoir simulation case and then applied to a field-scale reservoir case to show the robustness and efficiency of the method. The hybrid models are shown to provide superior prediction performance than pure machine learning models and reduced physics models in terms of multiphase production rates. Specifically, in the second method, the trained hybrid neural network model satisfies the reduced physics model, making it physically interpretable, and provides inter-well connectivity in terms of well flux allocation. The flux allocation estimated from the hybrid model was compared with streamline-based flux allocation, and excellent agreement was obtained. By combining the reduced physics model with the efficacy of deep learning, model calibration can be done very efficiently without constructing a geologic model. The proposed hybrid models with physics-based regularization and preprocessing provide novel approaches to augment data-driven models with underlying physics to build interpretable models for understanding reservoir connectivity between wells and robust future production forecasting.
Effects of forward mask duration variability on the temporal dynamics of brief facial expression categorization

The Japanese and Caucasian Brief Affect Recognition Task (JACBART) has been proposed as a standardized method for measuring people's ability to accurately categorize briefly presented images of facial expressions. However, the factors that impact performance in this task are not entirely understood. The current study sought to explore the role of the forward mask's duration (i.e., fixed vs. variable) in brief affect categorization across expressions of the six basic emotions (i.e., anger, disgust, fear, happiness, sadness, and surprise) and three presentation times (i.e., 17, 67, and 500 ms). Current findings do not demonstrate evidence that a variable duration forward mask negatively impacts brief affect categorization. However, efficiency and necessity thresholds were observed to vary across the expressions of emotion. Further exploration of the temporal dynamics of facial affect categorization will therefore require a consideration of these differences.
Emission and detection of terahertz radiation using two dimensional plasmons in semiconductor nano-heterostructures for nondestructive evaluations

This paper reviews recent advances in emission and detection of terahertz radiation using two dimensional (2D) plasmons in semiconductor nano-heterostructures for nondestructive evaluations. The 2D plasmon resonance is introduced as the operation principle for broadband emission and detection of terahertz radiation. The device structure is based on a high-electron mobility transistor and incorporates the authors’ original asymmetrically interdigitated dual grating gates. Excellent terahertz emission and detection performances are experimentally demonstrated by using InAlAs/InGaAs/InP and/or InGaP/InGaAs/GaAs heterostructure material systems. Their applications to nondestructive material evaluation based on terahertz imaging are also presented.