Miracle-dz/UniFAR-FTUs huggingface dataset:
Video spatio-temporal generative adversarial network for local action generation

Abstract. Generating action videos in future scenes based on static images can make computer vision systems to be better applied for video understanding and intelligent decision-making. However, current models pay more attention to the motion trend of the generated objects, and the processing effect on local details is not ideal. The local features of the generated video will have the problem of blurred frames and incoherent motion. This paper proposes a two-stage model, video spatio-temporal generative adversarial network (VSTGAN), which consists of two GAN networks, such as temporal network and spatial network (S-net). The model fully combines the advantages of CNNs, recurrent neural networks (RNNs), and GANs to decompose the complex spatiotemporal generation problem into temporal and spatial dimensions. Therefore, VSTGAN can focus on local features from the above dimensions respectively. In the temporal dimension, we propose an RNN unit, the convolutional attention unit (ConvAU), which uses the convolutional attention module to dynamically generate weights to update the hidden state. Thus, T-net uses the ConvAU to generate local dynamics. In the spatial dimension, S-net uses CNNs and attention modules to perform resolution reconstruction of the generated local dynamics for video generation. We build two small-sample datasets and validate our approach on these two new datasets and the KTH public dataset. The results show that our approach can effectively generate local details in future action videos and that the model performance on small-sample datasets is competitive with the state-of-the-art in video generation.
Data-Driven Robust Passivity-Based Controller Design for Grid-Connected Converters

Due to the integration of renewable-based distributed generation units (DGUs), energy storage systems (ESSs), and electric car charging stations, more and more power electronic converters are connected to the grid. In grids with a high penetration rate of these converters, high-current oscillation may happen, which can damage the grid equipment or cause a blackout. Passivity theory is used to design converter controllers in order to alleviate this problem in a decentralized fashion. However, the proposed methods are mostly model-based and are sensitive to parametric uncertainty. This article presents a robust data-driven frequency-domain grid-connected converter controller design with the possibility of defining robust passivity conditions on a performance channel. In addition to robust passivity, the controller design guarantees tracking and disturbance rejection performance. All performance and passivity conditions are written as constraints on closed-loop sensitivity functions and then changed to frequency-domain inequalities and convexified around an initial stabilizing controller. The controller design problem is converted to a convex optimization problem that can be solved efficiently using available solvers. The proposed method is validated by a power-hardware-in-the-loop (PHIL) experiment, including a switching inverter, real-time simulator, and $LCL$ filter.
Smart Fabric and E-Textile Sensor Technology for Wearables to Measure High Pressure

Smart fabric mymargin and electronic textile (e-textile)-based wearable pressure sensors have gained increasing interest in various applications. Fabric sensors for high-pressure measurement have not been well explored. This article presents the design and fabrication of a smart fabric pressure sensor (SFPS) and validates its general characteristics under high pressure. The designed sensor was composed of various material layers, with an active sensing layer made of semiconductive polymer composites (SCPCs). The SCPC layer’s width was modified in an iterative design process to improve the range, sensitivity, and linearity of the SFPS. A biasing circuit and a microcontroller-based data acquisition (DAQ) system were utilized to integrate the SFPS and read the sensor’s response. An experimental setup was built to repeatedly apply pressure to the SFPS from 4.63 to 300 kPa. Results show that within the specifications, on average, the proposed SFPS can linearly ( $r$ = −0.99) measure pressure from 4.63 to 74.08 kPa with a sensitivity of 0.0296 VkPa−1, and it has an accuracy of 97.72% and repeatability in terms of the standard error of the mean (SEM) of 0.25 kPa. These results encourage us to pursue further research on smart fabric and ${e}$ -textile-based wearable technologies that measure high pressure.
User-responsive diagnostic forecast evaluation approaches: Application to tropical cyclone predictions

The Hurricane Forecast Improvement Project1 (HFIP) was established by the U.S. National Oceanic and Atmospheric Administration (NOAA) in 2007 with a goal of improving tropical cyclone (TC) track and intensity predictions. A major focus of HFIP has been to increase the quality of guidance products for these parameters that are available to forecasters at the National Weather Service National Hurricane Center (NWS/NHC). One HFIP effort involved the demonstration of an operational decision process, named Stream 1.5, in which promising experimental versions of numerical weather prediction models were selected for TC forecast guidance. The selection occurred every year from 2010–2014 in the period preceding the hurricane season (defined as August through October), and was based on an extensive verification exercise of retrospective TC forecasts from candidate experimental models run over previous hurricane seasons. As part of this process, user-responsive verification questions were identified via discussions between NHC staff and forecast verification experts, with additional questions considered each year. A suite of statistically meaningful verification approaches consisting of traditional and innovative methods was developed to respond to these questions. Two examples of the application of the Stream 1.5 evaluations are presented, and the benefits of this approach are discussed. These benefits include the ability to provide information to forecasters and others that is relevant for their decision-making processes, via the selection of models that meet forecast quality standards and are meaningful for demonstration to forecasters in the subsequent hurricane season; clarification of user-responsive strengths and weaknesses of the selected models; and identification of paths to model improvement.
Interaction of heat transfer enhancement and fouling in operating heat exchangers

Recent increased focus on energy efficiency has contributed to ongoing research and industrial applications of heat transfer enhancement. Early work on efficiency of enhanced surfaces under clean conditions, Rabas 1989 [1] , and performance under fouling conditions, Panchal and Rabas 1999 and Watkinson 1991, [2,3] focused on interactions of enhancement and fouling. Interactions arise related to higher clean heat transfer coefficients, and higher wall shear stress of the enhanced surfaces, both of which should give rise to lower initial fouling rates according to popular models of the fouling process. On the Lab scale, enhancement was often achieved by static elements, such as wire wrapping on a heated rod in an annular flow (HTRI probe). Lab scale tests of necessity suffered from effects of relatively short duration of runs, and re-circulation of a batch of liquid with the danger of changing the fluid composition during experiment. Nevertheless, initial fouling rate results consistent with expected trends could be achieved for hydrocarbon fouling, and particulate fouling. There is a continued need to better represent and compare performance of plain and enhanced surfaces over prolonged operating periods in industrial heat exchangers, using commercial enhancement technologies. Example case studies are discussed which demand different methods of comparing performance of the enhanced unit compared to the plain tube unit. A daily averaged cost analysis is identified as a possible systematic approach on the evaluation of the economic benefit of the use of enhancement devices. This is illustrated for a case study example where tube insert is used as an enhancement option.
Modular Multilevel GaN Based Ultra-High Power Density Electric Power Conversion and Transmission on the Lunar Surface

NASA’s Watts on the Moon Challenge is seeking solutions to transfer at least 1.065 kW power from a 120 V dc source to a 24-32 V dc load over a 3-km distance under the same environmental conditions as the Lunar surface (i.e., 77 K temperature and 1 mTorr pressure). The selected solution from the author’s team proposed utilizing two modular multilevel Gallium Nitride (GaN) based isolated dc-dc converters to connect the 120 V dc source with the 24-32 V dc load bank via 1.5 kV rated dc transmission lines. The modular multilevel converters feature frequency multiplication, high step-down voltage ratio and low device voltage stress. In the converters, GaN gate injection transistor (GaN GIT) and GaN High-Electron-Mobility Transistor (GaN HEMT) devices are chosen as switching devices, due to the merits of lower power loss, radiation hardness and ability to work under cryogenic and vacuum conditions. In addition, LiFePO4 battery based energy storage with a power condition system is added in parallel with the load to provide uninterrupted power. Active heating units and passive multi-layer insulation are designed to manage the proper operation of the battery under cryogenic and vacuum conditions. Furthermore, the hardware design for the power converters has been validated at 1.5 kV/1 kW condition. The operation of submodules in the developed converter has been demonstrated at 77 K temperature and less than 10-6 Torr pressure. The efficiency of the designed converter can achieve 97.42%. In total, the proposed power conversion and transmission system has a total system mass of 103.2 kg, which is significantly lower than the 150 kg limit specified by NASA's requirement.
The use of N-of-1 trials to generate real-world evidence for optimal treatment of individuals and populations

Abstract Introduction: Ideally, real-world data (RWD) collected to generate real-world evidence (RWE) should lead to impact on the care and health of real-world patients. Deriving from care in which clinicians and patients try various treatments to inform therapeutic decisions, N-of-1 trials bring scientific methods to real-world practice. Methods: These single-patient crossover trials generate RWD and RWE by giving individual patients various treatments in a double-blinded way in sequential periods to determine the most effective treatment for a given patient. Results: This approach is most often used for patients with chronic, relatively stable conditions that provide the opportunity to make comparisons over multiple treatment periods, termed Type 1 N-of-1 trials. These are most helpful when there is heterogeneity of treatment effects among patients and no a priori best option. N-of-1 trials also can be done for patients with rare diseases, potentially testing only one treatment, to generate evidence for personalized treatment decisions, designated as Type 2 N-of-1 trials. With both types, in addition to informing individual’s treatments, when uniform protocols are used for multiple patients with the same condition, the data collected in the individual N-of-1 trials can be aggregated to provide RWD/RWE to inform more general use of the treatments. Thereby, N-of-1 trials can provide RWE for the care of individuals and for populations. Conclusions: To fulfill this potential, we believe N-of-1 trials should be built into our current healthcare ecosystem. To this end, we are building the needed infrastructure and engaging the stakeholders who should receive value from this approach.
Comments Analysis on Social Media: A Review

As the number of people using and participating in social media grows, academics become interest in studying this new media, specifically comment analysis, in order to comprehend public opinion and user behavior. However, there are no studies that map the development of comment analysis domain, which would be valuable for future research. To address the issue, we examine prior publications using PRISMA approach, and offer suggestions for further research. An investigation was conducted to locate pertinent publications published in databases between 2010 and 2022. On the basis of our examination of 115 relevant articles, we found that, within the scope of methodology, prior researches employ two methods (sentiment and content analysis) and three tools (human, software, and mixed coders), and the majority of them concentrate on gathering data from western countries, covering numerous platforms and topics. Based on these findings, we recommend that future research in comment analysis should synthesize methods and instruments. In addition, examine areas that have not been fully explore in terms of platforms (e.g., Instagram and Tiktok), topic (e.g., local government), and regions (e.g., eastern countries) that would be valuable in order to enhance the body of knowledge in this domain.
Patterns of Drug Resistance and Bacterial Pathogen Distribution in Patients with Urinary Tract Infections in the Jiaxing Region from 2020 to 2022

Background Urinary tract infections (UTIs) and the antibiotic resistance of pathogenic bacteria pose severe threats to public health in the current healthcare environment. Objective The purpose of this study was to assess the frequency distribution of bacterial pathogens causing UTIs as well as the characteristics of antibiotic susceptibility and resistance. Methods The retrospective study was conducted on 32,391 samples of midstream urine culture from January 1, 2020, to December 31, 2022, in Jiaxing. Bacteria were cultivated on blood agar and identified using MALDI-TOF, and their susceptibility to different antibiotics was assessed using the Kirby-Bauer disk diffusion method and drug sensitivity reaction cards. The SPSS 22 software was used for data analysis. Bivariate logistic regression was used to analyze the risk factors for multidrug resistance. Results The total number of positive growth samples was 5378 (16.6%), including 3206 females (59.6%) and 2172 males (40.4%). The four most common urinary pathogens were Escherichia coli (39.2%), Enterococcus faecalis (12.4%), Klebsiella pneumoniae (7.6%), and Enterococcus faecium (7.6%). As far as antibiotic resistance was concerned, Escherichia coli had a greater than 50% resistance rate to ampicillin (76.1%), ciprofloxacin (58.6%), and levofloxacin (51.2%). The multidrug resistance rate was high (41.8%). Low levels of resistance were seen to ertapenem (0.1%), imipenem (0.7%), meropenem (0.7%), piperacillin/tazobactam (0.7%), and nitrofurantoin (1.8%). Klebsiella pneumoniae was highly sensitive to ertapenem (100%). The resistance rates to nitrofurantoin, ceftriaxone, and ciprofloxacin were 37.4%, 37.1%, and 35.1%, respectively. Up to 41% of Escherichia coli strains and 26% of Klebsiella pneumoniae strains produced extended-spectrum lactamases (ESBL). Two species of enterococci were highly sensitive to tigecycline and linezolid (100%), and a small number of norvancomycin-resistant strains (0.2%/two strains) were found. Conclusion Escherichia coli and Enterococcus faecium were the most common urinary pathogens in this study. The isolated pathogens showed different sensitivity patterns. Antibiotics should be selected reasonably according to the sensitivity mode of pathogenic bacteria to effectively treat and prevent urinary tract infections.
Multichannel One-Dimensional Data Augmentation with Generative Adversarial Network

Data augmentation is one of the most important problems in deep learning. There have been many algorithms proposed to solve this problem, such as simple noise injection, the generative adversarial network (GAN), and diffusion models. However, to the best of our knowledge, these works mainly focused on computer vision-related tasks, and there have not been many proposed works for one-dimensional data. This paper proposes a GAN-based data augmentation for generating multichannel one-dimensional data given single-channel inputs. Our architecture consists of multiple discriminators that adapt deep convolution GAN (DCGAN) and patchGAN to extract the overall pattern of the multichannel generated data while also considering the local information of each channel. We conducted an experiment with website fingerprinting data. The result for the three channels’ data augmentation showed that our proposed model obtained FID scores of 0.005,0.017,0.051 for each channel, respectively, compared to 0.458,0.551,0.521 when using the vanilla GAN.
How Knowledge-intensive Organizations Thrive Under Dynamic Conditions: An Organizational Capability Perspective

Knowledge-intensive organizations primarily rely on knowledge and expertise as key strategic resources. In light of economic, social, and health-related crises in recent years, such organizations increasingly need to operate in dynamic environments. However, examinations on dynamic capabilities specifically in knowledge-intensive organizations remain scarce. This is remarkable given the role that knowledge holds as an economic resource in developed countries. To provide an explanation of how knowledge-intensive organizations can prevail among competitors under dynamic conditions, the authors integrate two literature streams in a knowledge-intensive context: the knowledge-based view and the dynamic capabilities approach. The knowledge-based view focuses on the nature of organizational knowledge as a critical resource and illustrates specific properties of knowledge in contrast to traditional means of labor such as capital. The dynamic capabilities approach on the other hand is about a firm's ability to integrate, build, and reconfigure internal and external resources and can be drawn on to explain organizational success through adaptation to dynamic contexts. In this conceptual study, the authors propose a research model linking knowledge processes to organizational performance through two different paths: (1) Operational capabilities permit organizations to make their living in the present and refer to efficiency. (2) Dynamic capabilities allow organizations to change their resource base and, therefore, enable their long-term survival in dynamic environments by focusing on effectiveness. Additionally, the authors hypothesize a moderating effect of environmental dynamics on the relationship between dynamic capabilities and performance. The study offers a comprehensive overview on the interplay between dynamic capabilities and the knowledge-based view, offering valuable insights for both researchers and practitioners in the field.
Pronósticos con Métodos Shrinkage utilizando una Gran Base de Datos

The aim of this paper is to address the problem of dimensionality in a large database for the Chilean economy. An alternative to deal with this problem is through Bayesian regression methods (shrinkage), and the other given by classical literature based on the use of principal components. In general, this work shows that the proposed Bayesian methods provide a good description of the Chilean data: economic activity indicator (IMACEC), inflation, and productive sectors such as commerce and industry, making forecasts many times with small predictive error and less volatile than other time series models. The main result is that these Bayesian methods are a good alternative for forecasting the most relevant series of the Chilean economy.
Spring Cold Bias of SST and Minimal Wind Mixing in the Equatorial Pacific Cold Tongue

Abstract The authors investigate the relationship between bias in simulated sea surface temperature (SST) in the equatorial eastern Pacific cold tongue during the boreal spring as simulated by an oceanic general circulation model (OGCM) and minimal wind mixing (MWM) at the surface. The cold bias of simulated SST is the greatest during the boreal spring, at approximately 3°C. A sensitivity experiment reducing MWM by one order of magnitude greatly alleviates cold biases, especially in March-April. The decrease in bias is primarily due to weakened vertical mixing, which preserves heat in the uppermost layer and results in warmer simulated SST. The reduction in vertical mixing also leads to a weak westward current in the upper layer, which further contributes to SST warming. These findings imply that there are large uncertainties about simple model parameters such as MWM at the oceanic surface.
Effects of Heated Drinking Water during the Cold Season on Serum Biochemistry, Ruminal Fermentation, Bacterial Community, and Metabolome of Beef Cattle

This study explored the effects of drinking heated water in the cold seasons on the serum metabolism, rumen microbial fermentation, and metabolome of beef cattle. Twelve fattening cattle (642 ± 14.6 kg) aged 21 to 22 months were randomly and equally divided into two groups based on body weight: one receiving room-temperature water (RTW; average 4.39 ± 2.55 °C) and the other heated water (HW; average 26.3 ± 1.70 °C). The HW group displayed a significant decrease in serum glucose (p < 0.01) and non-esterified fatty acid (p < 0.01), but increases in insulin (p = 0.04) and high-density lipoprotein (p = 0.03). The rumen fermentation parameters of the HW group showed substantial elevations in acetate (p = 0.04), propionate (p < 0.01), isobutyrate (p = 0.02), and total volatile fatty acids (p < 0.01). Distinct bacterial composition differences were found between RTW and HW at the operational taxonomic unit (OTU) level (R = 0.20, p = 0.01). Compared to RTW, the HW mainly had a higher relative abundance of Firmicutes (p = 0.07) at the phylum level and had a lower abundance of Prevotella (p < 0.01), norank_f_p-215-o5 (p = 0.03), and a higher abundance of NK4A214_group (p = 0.01) and Lachnospiraceae_NK3A20_group (p = 0.05) at the genus level. In addition, NK4A214_group and Lachnospiraceae_NK3A20_group were significantly positively correlated with the rumen propionate and isovalerate (r > 0.63, p < 0.05). Prevotella was negatively correlated with rumen propionate and total volatile fatty acids (r = −0.61, p < 0.05). In terms of the main differential metabolites, compared to the RTW group, the expression of Cynaroside A, N-acetyl-L-glutamic acid, N-acetyl-L-glutamate-5-semialdehyde, and Pantothenic acid was significantly upregulated in HW. The differentially regulated metabolic pathways were primarily enriched in nitrogen metabolism, arginine biosynthesis, and linoleic acid metabolism. Prevotella was significantly positively correlated with suberic acid and [6]-Gingerdiol 3,5-diacetate (r > 0.59, p < 0.05) and was negatively correlated with Pantothenic acid and isoleucyl-aspartate (r < −0.65, p < 0.05). NK4A214_group was positively correlated with L-Methionine and glycylproline (r > 0.57, p < 0.05). Overall, our research demonstrates the important relationship between drinking water temperature and metabolic and physiological responses in beef cattle. Heating drinking water during cold seasons plays a pivotal role in modulating internal energy processes. These findings underscore the potential benefits of using heated water as a strategic approach to optimize energy utilization in beef cattle during the cold seasons.
Pinaceae Pine Resins (Black Pine, Shore Pine, Rosin, and Baltic Amber) as Natural Dielectrics for Low Operating Voltage, Hysteresis‐Free, Organic Field Effect Transistors

Four pinaceae pine resins analyzed in this study: black pine, shore pine, Baltic amber, and rosin demonstrate excellent dielectric properties, outstanding film forming, and ease of processability from ethyl alcohol solutions. Their trap‐free nature allows fabrication of virtually hysteresis‐free organic field effect transistors operating in a low voltage window with excellent stability under bias stress. Such green constituents represent an excellent choice of materials for applications targeting biocompatibility and biodegradability of electronics and sensors, within the overall effort of sustainable electronics development and environmental friendliness.