Miracle-dz/UniFAR-FTUs huggingface dataset:
Reply to comment on “How Green is Blue Hydrogen?”

In their comment on our 2021 paper “How Green is Blue Hydrogen,” Romano et al. purport to provide “a more balanced perspective on blue hydrogen, which is in line with current best available practices. ” We strongly disagree. First, we categorically dismiss their presentation on methane emissions. Methane dominates the greenhouse gas footprint of blue hydrogen in our analysis, and our estimates were based on very recent, peer‐reviewed science. Romano et al., in sharp contrast, use only three sources: (1) a 2015 non‐peer‐reviewed report (which gave reasonable values, although at the low end, since based on older science, but nonetheless compatible with our paper); (2) a 2018 report from the International Energy Agency (which also gave values consistent with ours, but has been updated by the Agency in a 2022 report to give much higher values that are very consistent with ours); and (3) a value from a cartoon on a web site from an oil and gas industry trade group (i.e., not supported by any data or references and is simply wrong based on peer‐reviewed science). We cannot stress enough that the Romano et al. criticism of our methane emission rates is based totally on this industry web‐site cartoon. Beyond the methane issue, our analysis used actual data for capture of carbon dioxide from real‐world operations. Romano et al. dismiss the use of real‐world data, and instead rely on presentations from theoretical studies. We find this fanciful. But even if one accepts their theoretical values for carbon capture, the greenhouse gas footprint of blue hydrogen remains unacceptably high because of methane. We unequivocally stand by our analyses and conclusions: There is no room for blue hydrogen in a decarbonized energy future. The Romano et al. result derives from a cartoon and hypothetical guesses, not scientific data.
Cartdridged Food Materials for 3D Printing of Alternative Meat: Regarding Modified Texture of 3D Printed Food according to the Ionic State of Particle Surface exposed on Protein Powder

In the field of food processing, 3D food printing can be one of the foremost methods which allows to laminate various type of complex food structures. The technology makes it possible to manufacture food products with desired internal structures, tastes, flavors and nutrition and is attracting attention as an innovative way to customize food to meet each customer's needs. 3D printing can produce a wide range of foods with different textures and viscosities using the cartridge loaded with materials ranging from soft sauces to hard doughs. Additionally, for controlling the material while processing in the 3D printer, the food ink should be easily ejected through the nozzle, and pretreatment is required to homogenize and reduce the particle size. Since the ionic layer on the surface of the powder particles affects the bonding between the powders, zeta potential measured on the surface of these can be used as a standard for monitoring the organization of powdered materials in 3D printed substitute meat. In this study, various prototype of meat substitute was prepared using protein powder‐based materials with various processing parameters on the particle surface, and the texture was evaluated by comparing and analyzing the physical properties. Specifically, the particle surface state of the powder material was monitored by measuring various properties including the zeta potential, and the physico‐chemical and rheological properties of 3D printed meat substitute were analyzed to deduce the relationship for reaching the food processing aptitude.
Artificial Neural Network-Based Feed-Forward and Feedback Control Design and Convergence Analysis

A feed-forward and feedback control scheme based on artificial neural network (ANN) and iterative learning control is proposed. Iterative learning control and ANN are combined as a feed-forward controller, which makes the output track the desired trajectory. Feedback control is introduced to reduce the effect of disturbances. To combine the feed-forward controller and the feedback controller, the ANN is employed to simulate the plant. Since the ANN can update the weights online, it is always consistent with the plant. The convergence and robustness of the system are analyzed, and the simulation shows the feasibility of the proposed control scheme.
Controlling the nucleation of multiple precipitates in precipitation-strengthened steel through Mo partitioning

In this study, atom probe tomography and first-principles calculations were used to investigate the influence of Mo addition on multiple precipitates, including isolated NiAl-based precipitates as well as NiAl-based and Cu-rich co-precipitates, in high-strength ferritic steel. The co-NiAl-based precipitates were experimentally confirmed to be nucleated from the abundance of Cu atoms. The addition of Mo accelerated the nucleation process of Cu-rich precipitates and structural transformation of co-precipitates, led to a considerable increase in the nucleation rate of NiAl-based precipitates, and slightly promoted isolated NiAl-based precipitates. The results were discussed from the perspective of lattice misfits and mixing enthalpy.
SENTIMENT ANALYSIS FOR DEPRESSION DETECTION

The Covid-19 pandemic has dramatically changed the way we have used to live. The pandemic has been causing significant devastations in economy, and health, inter alia. Mental health, especially, has become a growing concern due to employment terminations, income loss, family stress and other uncertainties. The pandemic disproportionally affected mental health of younger population. Nowadays risk of early death is increasing due to mental illness which is mostly caused due to depression. Depression creates suicidal thoughts causing serious impairments in daily life. Sentiment analysis is a hot topic that’s been on research for decades, which intends to find the nature of text and classifies into positive, negative and neutral. In today’s digital world lot of data can be made available for sentiment analysis. Hence, our aim is to focus on creating a depression detection system from text, video & audio analysis. Sentiment Analysis and Natural Language Processing methods will be used to develop this system. The system will classify text, audio and video cues as positive or negative depending on the emotions inferred from user’s input.
Antibiotic Resistance in Non-Typhoidal Salmonella enterica Strains Isolated from Chicken Meat in Indonesia

The increase in antibiotic resistance in non-typhoidal Salmonella enterica (NTS) has been confirmed in Indonesia by this study. We confirmed the virulence genes and antimicrobial susceptibilities of clinical NTS (n = 50) isolated from chicken meat in Indonesia and also detected antimicrobial resistance genes. Of 50 strains, 30 (60%) were non-susceptible to nalidixic acid (NA) and all of them had amino acid mutations in gyrA. Among 27 tetracycline (TC) non-susceptible strains, 22 (81.5%) had tetA and/or tetB. The non-susceptibility rates to ampicillin, gentamicin or kanamycin were lower than that of NA or TC, but the prevalence of blaTEM or aadA was high. Non-susceptible strains showed a high prevalence of virulence genes compared with the susceptible strains (tcfA, p = 0.014; cdtB, p < 0.001; sfbA, p < 0.001; fimA, p = 0.002). S. Schwarzengrund was the most prevalent serotype (23 strains, 46%) and the most frequently detected as multi-antimicrobial resistant. The prevalence of virulence genes in S. Schwarzengrund was significantly higher than other serotypes in hlyE (p = 0.011) and phoP/Q (p = 0.011) in addition to the genes above. In conclusion, NTS strains isolated from Indonesian chicken had a high resistance to antibiotics and many virulence factors. In particular, S. Schwarzengrund strains were most frequently detected as multi-antimicrobial resistant and had a high prevalence of virulence genes.
The stability against sliding analysis of a reservoir dam

According to the dam safety appraisal requirement,the dam slope stability was analyzed.The dam slope stability in various construction situations were checking calculated with software and Sweden slip circular method.Combined with SL 274-2001 Rolled-Earth Dam Design Code,the calculation achievements were analyzed,and it concluded that the stability against sliding safety coefficient satisfied code requirements.
The Side-Effects of the COVID-19 Pandemic: Increased BMI z-Score in Children with Overweight and Obesity in a Personalised Lifestyle Intervention One Year after the Start of the Pandemic in The Netherlands

Background: Early research showed weight gain in children during the COVID-19 pandemic. Objective: To compare changes in BMI z-score of children with overweight and obesity in a personalised lifestyle intervention before and during the pandemic. Methods: Changes in BMI z-score half a year (T6) and twelve months (T12) after the first lockdown were included for 71 children in the ‘2020 during COVID’ group and compared to 48 children in the ‘2019 before COVID’ group, using a marginal model for repeated measures (model 1). Model 2 corrected for lifestyle intervention characteristics, and model 3 corrected additionally for family characteristics. Results: The mean difference in BMI z-score change was significantly different at T12 (+0.07 in 2020 versus −0.09 in 2019, p = 0.022). Model 3 showed significant differences in BMI z-score change at both T6 (+0.15, p = 0.024) and T12 (+0.18, p = 0.016). This model also defined ‘having a mother with obesity’ (+0.13, p = 0.019) and the frequency of no-show consultations (+0.41 per missed consultation per month, p = 0.025) as related factors. Conclusions: Lifestyle intervention in children with overweight and obesity is less successful in decreasing BMI z-score during the COVID-pandemic. Identified risk factors for less success could contribute to identifying children with higher risks for, and possibly prevent, BMI z-score increase.
Optimized outdoor parking system for smart cities using advanced saliency detection method and hybrid features extraction model

As a new concept in urban development, smart cities are characterized primarily by their mobility. To solve these problems, it became necessary to develop an intelligent system. Using the Advanced Saliency Detection Method and an Efficient Features Extraction Model, the proposed work is aimed at detecting vacant outdoor parking lots. Experimental work has been conducted using the publicly available “PKLot” dataset, which consists of 695,899 segmented images. Under different weather conditions, the images were taken from three different camera locations in two different parking lots in Brazil, including sunny, cloudy, and rainy days. The experimental results mentioned that the hybrid feature extraction model enhanced the performance of parking detection systems. Using three different datasets, PUCPR, UFPR04, and UFPR05, we obtain an accuracy of 99.93%, 99.89%, and 99.87%. This is clear that the hybrid feature extraction model with the PUCPR dataset has produced the highest accuracy.
A Novel Inflammation-Related Gene Signature for Overall Survival Prediction and Comprehensive Analysis in Pediatric Patients with Wilms Tumor

Wilms tumor (WT) is a common pediatric renal cancer, with a poor prognosis and high-risk recurrence in some patients. The inflammatory microenvironment is gradually gaining attention in WT. In this study, novel inflammation-related signatures and prognostic model were explored and integrated using bioinformatics analysis. The mRNA profile of pediatric patients with WT and inflammation-related genes (IRGs) were acquired from Therapeutically Available Research to Generate Effective Treatments (TARGET) and Gene Set Enrichment Analysis (GSEA) databases, respectively. Then, a novel prognostic model founded on 7-IRGs signature (BICC1, CSPP1, KRT8, MYCN, NELFA, NXN, and RNF113A) was established by the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression to stratify pediatric patients with WT into high- and low-risk groups successfully. And a stable performance of the prognostic risk model was verified in predicting overall survival (OS) by receiver-operating characteristic (ROC) curves, Kaplan-Meier (KM) curves, and independent prognostic analysis (p < 0.05). In addition, a novel nomogram integrating risk scores with good robustness was developed and validated by C-index, ROC, and calibration plots. The potential function and pathway were explored via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and GSEA, with mainly inflammation and immune-related biological processes. The higher-risk scores, the lower immune infiltration, as shown in the single-sample GSEA (ssGSEA) and tumor microenvironment (TME) analysis. The drug sensitivity analysis showed that regulating 7-IRGs signature has a significant correlation with the chemotherapy drugs of WT patients. In summary, this study defined a prognostic risk model and nomogram based on 7-IRGs signature, which may provide novel insights into clinical prognosis and inflammatory study in WT patients. Besides, enhancing immune infiltration based on inflammatory response and regulating 7-IRGs signature are beneficial to ameliorating the efficacy in WT patients.
Recent Trends and Future Prospects of Neural Recording Circuits and Systems: A Tutorial Brief

Recent years have seen fast advances in neural recording circuits and systems as they offer a promising way to investigate real-time brain monitoring and the closed-loop modulation of psychological disorders and neurodegenerative diseases. In this context, this tutorial brief presents a concise overview of concepts and design methodologies of neural recording, highlighting neural signal characteristics, system-level specifications and architectures, circuit-level implementation, and noise reduction techniques. Future trends and challenges of neural recording are finally discussed.
Development of non-dispersive infrared metheane gas transducer

Under the circumstances of coal mining applications,one of the problems on metheane gas sensor technique based on infrared absorption is how to eliminate the bad measurement accuracy due to infrared source power supply jitter,thermal radiation,and interference of other gas,water vapor,coal dust etc.Also,extremely small output singals for infrared detector was another defect of the method.So,a digital narrowband filter and a double-beam non-dispersive infrared technique were discussed.Also,a new kind of transducer architecture and a thermal compensation method based on Lambert-Beer Law were proposed for coal mining applications.As a result,an infrared metheane transducer was developed successfully.Its measurement error is ±0.1%,and its respond time is less than 20 s.Its other features and functions all achieve the criterions for coal mining applications.
The detection of bacterial exometabolites in marine dissolved organic matter through ultrahigh‐resolution mass spectrometry

Bacteria play a key role in sustaining the chemodiversity of marine dissolved organic matter (DOM), yet there is limited direct evidence of a major contribution of bacterial exometabolites to the DOM pool. This study tests whether molecular formulae of intact exometabolites can be detected in natural DOM via untargeted Fourier‐transform ion cyclotron resonance mass spectrometry (FT‐ICR‐MS). We analyzed a series of quantitative mixtures of solid‐phase extracted DOM from the deep ocean, of a natural microbial community and selected model strains of marine bacteria. Under standard instrument settings (200 broadband scans, mass range 92–1000 Da), 77% of molecular formulae were shared between the mesocosm and marine DOM. However, there was < 10% overlap between pure bacterial exometabolome with marine DOM, and in mixing ratios closest to mimicking natural environments (1% bacterial DOM, 99% marine DOM), only 4% of the unique bacterial exometabolites remained detectable. Further experiments with the bacterial exometabolome DOM mixtures using enhanced instrument settings resulted in increased detection of the exometabolites at low concentrations. At 1000 and 10,000 accumulated scans, 23% and 29% of the unique molecular formulae were detectable at low concentrations, respectively. Moreover, windowing a specific mass range encompassing a representative fraction of exometabolites tripled the number of unique detected formulae at low concentrations. Routine FT‐ICR‐MS settings are thus not always sufficient to distinguish bacterial exometabolome patterns from a seawater DOM background. To observe these patterns at higher sensitivity, we recommend a high scan number coupled with windowing a characteristic region of the molecular fingerprint.
Laser Structuring and DLC Coating of Elastomers for High Performance Applications

Even though hard, low friction coatings such as diamond like carbon (DLC) would be beneficial for the performance and longevity of rubber seals, a crucial challenge remains. The elastic mismatch of rubber substrate and DLC coating prevents a fracture free coating application. In this work, a nature inspired approach is applied to render the stiff coating flexible and resilient to delamination at the same time by direct patterning. Rubber substrates were laser structured with tile patterns and subsequently DLC coated. Tensile and tribology tests were performed on structured and unstructured samples. Unstructured DLC coatings showed a crack pattern induced by the coating process, which was further fragmented by tensile stress. Coatings with tile patterns did not experience a further fragmentation under load. During continuous tribological loading, less heterogenous damage is produced for tile structured samples. The findings are ascribed to the relief of induced coating stress by the tile structure, meaning a more resilient coating.
Getting to Grade 10 in Vietnam: does an employment boom discourage schooling?

ABSTRACT Blue-collar employment growth increases schooling opportunities by raising incomes, but also reduces incentives for some students to advance beyond compulsory education. These contradictory influences may help to explain relatively slow and uneven growth of progression to upper-secondary schooling in Vietnam, which has experienced a foreign investment boom in mainly low-skill manufacturing industries. We use data on participation rates and scores in an upper-secondary school entrance exam to analyze variation due to demand-side and supply-side factors. The data come from less advanced provinces and so illuminate the challenges of deepening educational development at the extensive margin, especially among ethnic minority populations.
Weather, Land and Crops in the Indus Village Model: A Simulation Framework for Crop Dynamics under Environmental Variability and Climate Change in the Indus Civilisation

The start and end of the urban phase of the Indus civilization (IC; c. 2500 to 1900 BC) are often linked with climate change, specifically regarding trends in the intensity of summer and winter precipitation and its effect on the productivity of local food economies. The Indus Village is a modular agent-based model designed as a heuristic “sandbox” to investigate how IC farmers could cope with diverse and changing environments and how climate change could impact the local and regional food production levels required for maintaining urban centers. The complete model includes dedicated submodels about weather, topography, soil properties, crop dynamics, food storage and exchange, nutrition, demography, and farming decision-making. In this paper, however, we focus on presenting the parts required for generating crop dynamics, including the submodels involved (weather, soil water, land, and crop models) and how they are combined progressively to form two integrated models (land water and land crop models). Furthermore, we describe and discuss the results of six simulation experiments, which highlight the roles of seasonality, topography, and crop diversity in understanding the potential impact of environmental variability, including climate change, in IC food economies. We conclude by discussing a broader consideration of risk and risk mitigation strategies in ancient agriculture and potential implications to the sustainability of the IC urban centres.
Delay-Tolerance SLNR Precoding to Mitigate Inter-Cell Asynchronous Interference

The different propagation times due to the distinct distance between the BSs and MSs result in intercell asynchronous interference,which severely destroy system performance. How to mitigate inter-cell asynchronous interference by making use of SLNR (Signal to Leakage Noise Ratio) precoding is the key to this paper. A framework of Co-BS (Cooperative Base Station Systems) was developed and the delay between the cells was analyzed. A delay-tolerance SLNR precoding was brought forward in LTE (Long Term Evolution) system to suppress inter-cell asynchronous interference. And this method can also reduce the complication of terminal. The numerical delay-tolerance SLNR precoding simulation results are presented based on cooperative base station LTE systems,the conclusions show that it can mitigate inter-cell asynchronous interference. And the method can reach a good performance under different antenna configuration compared with receive algorithm. The performance is affected by channel estimation error,but it can still be accepted.
Research on Co-simulation Using ADAMS and MATLAB for Vibration Isolation Station of Micro-manufacture

The method of co-simulation using ADAMS and MATLAB was introduced and the simulation model of vibration isolation station of micro-manufacture was built using it.The results indicate that the vibration acceleration of this station is reduced using mixed vibration isolation and the working performance is improved and can meet the requirement.The system dynamic equation establishment and transfer function computation can be avoided using this method.This supplies a new way for research the dynamic feature of complex system.
HSETA: A Heterogeneous and Sparse Data Learning Hybrid Framework for Estimating Time of Arrival

The estimated time of arrival (ETA) plays a vital role in intelligent transportation systems and has been widely used as a basic service in ride-hailing platforms. Obtaining a precise ETA is a challenging task due to the complexity of the real-world geographic and traffic environments. Previous works suffer from heterogeneous sparse data learning and multiple-correlation extraction issues. Therefore, this paper presents a hybrid deep learning framework (HSETA) to estimate the vehicle travel time from massive data. First, we encode heterogeneous data to represent various features in different respects. Then, we develop an ensemble factorization machine block (EFMB) structure combined with gated recurrent unit (GRU) and multilayer perceptron (MLP) to extract information from sparse and dense features. Next, the multiple-correlation learning block (MCLB) structure that we propose is utilized to aggregate information based on multiple correlations. Finally, the travel time can be estimated by simple regression. Our extensive evaluations on two real-world datasets show that HSETA significantly outperforms all baselines. Our PyTorch implementation of HSETA and sample data are available at https://github.com/LouisChenki/HSETA
Magnetic Analysis of the Farm Transport Vehicle Electromagnetic Brake System Based on the Software Ansoft

As the working environment of the farm transport vehicle is complex,brake failure easily appears when the road condition becomes sandy and wading.For this reason,the traditional pneumatic or hydraulic brake system of the farm transport vehicles should be refit,and electromagnetic brake system will be feasible.According to Maxwell's equations and braking torque system structure,the brake torque formula is deduced,magnetic field analysis is carried out through applying the software Ansoft Maxwell 2D/3D.The results show that the braking torque(Tb) generated by electromagnetic braking system is 242N.m,the brake system can be used for the farm transport vehicles,whose total weight is between 2 ton and 3 ton,it can effectively function as auxiliary brake.