Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic pixel-level detection of tire defects based on a lightweight Transformer architecture

Efficient and accurate visual inspection of products is of great significance for intelligent manufacturing. In this paper, a visual inspection framework based on a lightweight Transformer is proposed for the pixel-level inspection of tire defects. A dual-path-Transformer feature encoder was proposed based on a hybrid architecture of convolutional neural network and Transformer, which is used to learn the local and global relationship of defect features. Moreover, multi-scale fusion Transformer (MFT) and spatial cross Transformer (SCT) were proposed, based on which a feature decoder was built. In the decoder, the MFT provides valuable spatial information for SCT, such that different levels of feature maps can refine the dependency of pixels through SCT. The proposed method is tested on the tire radiographic image dataset. Experimental results show that the proposed method reaches a detection accuracy of 98.57% and mIoU of 85.56%. Moreover, the method achieves the balance between accuracy and efficiency with satisfactory characterization ability for the defect geometric shape, and provides theoretical support for the future industrial deployment of Transformer.
Current Trends in Gelatin-Based Drug Delivery Systems

Gelatin is a highly versatile natural polymer, which is widely used in healthcare-related sectors due to its advantageous properties, such as biocompatibility, biodegradability, low-cost, and the availability of exposed chemical groups. In the biomedical field, gelatin is used also as a biomaterial for the development of drug delivery systems (DDSs) due to its applicability to several synthesis techniques. In this review, after a brief overview of its chemical and physical properties, the focus is placed on the commonly used techniques for the development of gelatin-based micro- or nano-sized DDSs. We highlight the potential of gelatin as a carrier of many types of bioactive compounds and its ability to tune and control select drugs’ release kinetics. The desolvation, nanoprecipitation, coacervation, emulsion, electrospray, and spray drying techniques are described from a methodological and mechanistic point of view, with a careful analysis of the effects of the main variable parameters on the DDSs’ properties. Lastly, the outcomes of preclinical and clinical studies involving gelatin-based DDSs are thoroughly discussed.
VRCT: A Viewport Reconstruction-Based 360° Video Caching Solution for Tile-Adaptive Streaming

360° video streaming demands higher bandwidth and lower latency than conventional videos. Some solutions employ tile-adaptive 360° video streaming and edge caching mechanisms to improve the quality of their content delivery. However, it is hard to cache large amounts of popular content with limited cache capacity. Reconstruction technologies, which have been widely adopted for images and conventional videos, can potentially reconstruct complete tile-based viewports from partial observation for 360° videos, thus further relieving the pressure on the caching. However, it is challenging to design a flexible and efficient caching solution that supports viewport reconstruction for 360° videos. In this paper, we propose a Viewport Reconstruction-based 360° video Caching solution for Tile-adaptive streaming (VRCT). To enhance viewers’ quality of experience (QoE), a QoE-driven reconstruction trigger scheme is designed to determine whether to perform reconstruction or not based on current cache information and network conditions. To make efficient use of the cache space and facilitate the viewport reconstruction, a heuristic-based solution, named aggregation-based cache replacement scheme, is proposed to improve the probability of viewport reconstruction by carefully selecting which tiles to be stored in the given limited space. Through comprehensive experiments with a real head movement dataset, we show that the proposed VRCT increases the cache hit ratio by up to 29%, reduces the backhaul usage by up to 44% and improves user QoE by up to 32% compared with other existing methods. In addition, experimental results show that our proposed cache replacement scheme facilitates viewport reconstruction and supports different video types.
Control of DC–DC Buck Converters Using Robust Composite Backstepping and Integral Terminal Sliding Mode Approaches

This article proposes a new composite scheme to design a robust voltage controller for dc–dc buck converters in which the composite structure combines the recursive backstepping approach with an integral terminal sliding mode controller (ITSMC). In this work, the proposed ITSMC is designed to overcome the one-degree limitation of existing ITSMCs and difficulties for the finite time convergence in standard sliding mode controllers (SSMCs) as it incorporates a quick reaching law. Moreover, the singularity problem in existing SSMCs is tackled using a modified integral sliding surface. The backstepping strategy is used to ensure the voltage monitoring with a higher degree of accuracy. The robustness of the proposed composite approach is ensured by capturing the variations in parameters, input voltage, and mismatch in the model; as all these factors are included in the dynamical model of the dc–dc buck converter based on which the proposed composite controller is designed. The robustness and stability of the proposed composite controller are simultaneously analyzed using the concept of the Lyapunov stability theory. The theoretical and analytical findings for the proposed composite robust backstepping ITSMC (RBITSMC) are further verified through extensive simulation studies in the MATLAB/Simulink platform. Performance comparisons of the proposed RBITSMC are also presented against an existing adaptive nonsingular fast terminal sliding mode controller. Finally, experimental results from the application of the proposed control strategy on a laboratory prototype are presented to justify its application on the practical system.
Chalcogenide Chip-Based Frequency Combs for Advanced Laser Spectroscopy

Optical frequency combs have revolutionized spectral measurement technologies, such as dual-comb spectrometers, for fast and accurate spectroscopic measurements over broad spectral ranges. Moreover, chip-scale frequency combs based on microresonators are revolutionizing laser spectroscopy due to their compact size, low cost, and low power consumption, which offer a promising solution for integrated optical sensing applications by replacing costly bulk optical sensing components. Developing new attractive photonic materials for integrated microcomb is critical to realizing chip-based microcombs with high efficiency, broad bandwidth, and low pumping power. In this tutorial, we present novel chalcogenide chip-based microcombs for broadband laser spectroscopy. The integrated chalcogenide microresonators on silicon wafers are achieved using a home-developed chalcogenide glass (Ge25Sb10S65) with ultra-wide transmittance window, high Kerr nonlinearity, and low thermo-optic coefficient. Two fabrication processes of high quality-factor integrated chalcogenide microresonators with different core-cladding structures operating in the telecom and mid-infrared (MIR) regions, respectively, are introduced. Combined high nonlinearity with lithographically controlled flexible dispersion engineering of the chalcogenide microresonators, we have realized the versatile Kerr microcombs generation and physical mechanism, including bright soliton microcomb, dark-pulse microcomb, Raman-Kerr microcomb, broadband Kerr microcomb and the future opportunity of the MIR Kerr comb. In addition, we review the most potent examples of dual-comb spectroscopy applications for molecular characterization and laser ranging with ultra-high sensitivity and high accuracy and discuss the challenges of realizing MIR microcombs with high performance and ultracompact footprint for laser spectroscopy.
Knockout of sws2a and sws2b in Medaka (Oryzias latipes) Reveals Their Roles in Regulating Vision-Guided Behavior and Eye Development

The medaka (Oryzias latipes) is an excellent vertebrate model for studying the development of the retina. Its genome database is complete, and the number of opsin genes is relatively small compared to zebrafish. Short wavelength sensitive 2 (sws2), a G-protein-coupled receptor expressed in the retina, has been lost in mammals, but its role in eye development in fish is still poorly understood. In this study, we established a sws2a and sws2b knockout medaka model by CRISPR/Cas9 technology. We discovered that medaka sws2a and sws2b are mainly expressed in the eyes and may be regulated by growth differentiation factor 6a (gdf6a). Compared with the WT, sws2a−/− and sws2b−/− mutant larvae displayed an increase in swimming speed during the changes from light to dark. We also observed that sws2a−/− and sws2b−/− larvae both swam faster than WT in the first 10 s of the 2 min light period. The enhanced vision-guided behavior in sws2a−/− and sws2b−/− medaka larvae may be related to the upregulation of phototransduction-related genes. Additionally, we also found that sws2b affects the expression of eye development genes, while sws2a is unaffected. Together, these findings indicate that sws2a and sws2b knockouts increase vision-guided behavior and phototransduction, but on the other hand, sws2b plays an important role in regulating eye development genes. This study provides data for further understanding of the role of sws2a and sws2b in medaka retina development.
Geosemantic Snapping for Sketch-Based Modeling: Optimization Details

The inter-primitive relationships of the model are specified by a set of geosemantic relationships G. For a relationship g ∈ G we define xg to be the set of parameters that participate in this relationship. For example, if g is an “orthogonal” relationship between two circles, then xg is a vector of six components: the normal vectors of both circles. For each g ∈ G we define a constraint equation ψg(xg) = 0. The constraint equations for each relationship are described in the paper. Note that the components of xg are a subset of ⋃
Starting flow in regular polygonal ducts

The starting flows in regular polygonal ducts of S = 3, 4, 5, 6, 8 sides are determined by the method of eigenfunction superposition. The necessary S-fold symmetric eigenfunctions and eigenvalues of the Helmholtz equation are found either exactly or by boundary point match. The results show the starting time is governed by the first eigenvalue.
Forward Kinematics Based Prediction for Bending Motion of Soft Pneumatic Actuators with Various Air Chambers

This study proposes a forward kinematic model for soft actuators that utilize pneumatic control to predict their bending motion, which is simulated using Ansys software. Firstly, a bending motion test is conducted with a 2-air chamber actuator to derive an equation that establishes the relationship between the bending angle and input pressure. Next, a serial model for the overall soft actuator is developed using forward kinematics with the DH method. The angle variables in the soft actuator are then replaced with an equation that relates the deformed angle and compressed air. Finally, the proposed serial model is used to predict the bending motion of 4-air and 6-air chamber actuators, and the results are compared to simulations and real experiments. The comparison shows that the proposed model could accurately predict the bending motion of the real actuators within an acceptable tolerance of 10%.
Determining Homogenous Transformation Matrix from DH Parameter Table using Deep Learning Techniques

One of the most popular ways of representing any robotic model mathematically is through Denavit-Hartenberg (DH) parameter table. And the most common way of finding a forward kinematics solution to any robotic model is by finding its homogenous transformation matrix, which is obtained from the DH parameter table by a certain set of steps or algorithms. In this research work, we have tried solving this problem in just a single step by deep learning method and thus finding forward kinematics of almost any kind of manipulator. This research work shows not just this problem but many more such complex problems which require a certain set of steps or algorithms that can be solved by deep learning techniques in a single step. The results obtained are very close to accurate and show the ability of deep learning techniques for solving different kinds of such problems.
Knowledge-Enhanced Causal Reinforcement Learning Model for Interactive Recommendation

Owing to its inherently dynamic nature and economical training cost, offline reinforcement learning (RL) is typically employed to implement an interactive recommender system (IRS). A crucial challenge in offline RL-based IRSs is the data sparsity issue, i.e., it is hard to mine user preferences well from the limited number of user-item interactions. In this article, we propose a knowledge-enhanced causal reinforcement learning model (KCRL) to mitigate data sparsity in IRSs. We make technical extensions to the offline RL framework in terms of the reward function and state representation. Specifically, we first propose a group preference-injected causal user model (GCUM) to learn user satisfaction (i.e., reward) estimation. We introduce beneficial group preference information, namely, the group effect, via causal inference to compensate for incomplete user interests extracted from sparse data. Then, we learn the RL recommendation policy with the reward given by the GCUM. We propose a knowledge-enhanced state encoder (KSE) to generate knowledge-enriched user state representations at each time step, which is assisted by a self-constructed user-item knowledge graph. Extensive experimental results on real-world datasets demonstrate that our model significantly outperforms the baselines.
ARM’s Progress on Improving Atmospheric Broadband Radiative Fluxes and Heating Rates

The geographical and vertical distribution of absorption of solar radiation and emission of longwave (LW) radiation in the atmosphere and at the surface are major drivers of the large-scale circulation and the hydrological cycle. Understanding and modeling the processes by which atmospheric gases, clouds, and aerosol particles affect the distribution of radiant energy in the atmosphere are essential to accurate simulations of Earth’s climate. Figure 20-1 shows the key elements of Earth’s energy budget (Trenberth et al. 2009). While satellites provide measurements of the global distribution of reflected and emitted broadband fluxes at the top of the atmosphere (TOA), less information is available on the radiative budget at the surface and the vertical distribution of absorption and emission in the atmosphere. Key goals of the Atmospheric Radiation Measurement (ARM) Program are to quantify the radiative energy balance profile in Earth’s atmosphere, to understand the physical processes controlling this balance, and to improve the representation of these processes in global climate models (GCMs; DOE 1990). To address these questions, the ARM site measurements were planned to provide a full characterization of the radiatively important properties of the atmospheric column, as well as measurements of the radiative fluxes themselves. The simultaneous measurement of surface broadband radiative fluxes (Michalsky and Long 2016, chapter 16) and cloud/aerosol properties (Shupe et al. 2016, chapter 19; McComisky and Ferrare 2016, chapter 21) enables both assessment of the overall impact of clouds and aerosol on the surface radiation budget as well as analysis of how those impacts vary with changes in cloud and aerosol properties. Additionally, comparison of measured broadband surface fluxes to those calculated using atmospheric properties measured or retrieved from other ARM instruments provides a test of howwell theARMmeasurements are able to characterize the full set of cloud, aerosol, atmospheric state, and surface properties in the vertical column. Such ‘‘radiative flux closure’’ exercises can identify weaknesses in measurements, retrievals, or radiative transfermodels and indicate under what conditions ARM measurements are representative of the large-scale atmospheric conditions. While spectral radiance and fluxes are important for understanding details of specific atmospheric processes that affect the radiative balance (Mlawer and Turner 2016, chapter 14), accurately measuring and modeling broadband radiative fluxes is critical since they represent the integrated impact of atmospheric processes on Earth’s energy budget. In this chapter, we describe the contributions that the ARM Program has made toward understanding and characterizing the processes that control broadband radiative fluxes and radiative heating Corresponding author address: Sally A. McFarlane, U.S. Department of Energy, Office of Biological and Environmental Research, 1000 Independence Ave. SW, Washington, DC 20585. E-mail: sally.anne. mcfarlane@gmail.com CHAPTER 20 MCFARLANE ET AL . 20.1
Hypermucoviscous Carbapenem-Resistant Klebsiella pneumoniae ST25 Infect Human Intestinal Epithelial Cells and Induce Moderate Inflammation

Klebsiella pneumoniae is an opportunistic pathogen that can produce moderate and severe infections in immunosuppressed hosts. In recent years, an increase in the isolation of hypermucoviscous carbapenem-resistant K. pneumoniae with sequence type 25 (ST25) in hospitals in Norwest Argentina was observed. This work aimed to study the virulence and inflammatory potential of two K. pneumoniae ST25 strains (LABACER01 and LABACER27) in the intestinal mucosa. The human intestinal Caco-2 cells were infected with the K. pneumoniae ST25 strains, and their adhesion and invasion rates and changes in the expression of tight junction and inflammatory factors genes were evaluated. ST25 strains were able to adhere and invade Caco-2 cells, reducing their viability. Furthermore, both strains reduced the expression of tight junction proteins (occludin, ZO-1, and claudin-5), altered permeability, and increased the expression of TGF-β and TLL1 and the inflammatory factors (COX-2, iNOS, MCP-1, IL-6, IL-8, and TNF-α) in Caco-2 cells. The inflammatory response induced by LABACER01 and LABACER27 was significantly lower than the one produced by LPS or other intestinal pathogens, including K. pneumoniae NTUH-K2044. No differences in virulence and inflammatory potential were found between LABACER01 and LABACER27. In line with these findings, no major differences between the strains were found when the comparative genomic analysis of virulence factors associated with intestinal infection/colonization was performed. This work is the first to demonstrate that hypermucoviscous carbapenem-resistant K. pneumoniae ST25 infects human intestinal epithelial cells and induces moderate inflammation.
No Soy De Aquí, Ni Soy De Allá: Second-Generation Latinx Youth Belonging Everywhere and Nowhere

ABSTRACT Belonging is a human need, one that is particularly difficult for children of immigrants to achieve. The Second-generation youth hangs in a balance between the culture of their parents and that of their friends and peers. Often these youth feel that they are not heard or seen by those around them, enhancing the feeling of not belonging in their communities, or even in their country. Therapeutic interventions could potentially create a “third space” in which they feel a sense of belonging. This paper explores the complexity in the experience of belonging in second-generation Latinx youth through a review of the literature, psychodynamic theories, a composite clinical case example and the reflections and implications to and from Second-generation Latina therapist.
Post-Wildfire Debris Flows in Montecito, California (USA): A Case Study and Empirically Based Debris Volume Estimation

Wildfires have a strong influence on various geotechnical and hydraulic properties of soils and sediments, which may become more vulnerable to landslides or debris flows. In the present study, a case investigation of the 2018 post-wildfire debris flows in Montecito, California, USA, was conducted, with a focus on the wildfire-affected areas and debris volume estimation. Significant debris were deposited around four major creeks, i.e., Montecito Creek, San Ysidro Creek, Buena Vista Creek, and Romero Creek in January, 2018, one month after the Thomas fire. Satellite images utilizing remote sensing techniques and geographic information system (GIS) data were analyzed to identify areas affected by the wildfire. Relevant data, including the slope, catchment area, and rainfall were used in two empirical models to estimate the debris volumes around the four creeks. As compared with field observation, each debris volume estimated with these empirical models was within the same order of magnitude. The debris volumes were generally underestimated when using the rainfall recorded at the Montecito Weather Station; the estimates considerably improved with the rainfall record from the Doulton Tunnel Station. The results showed that, overall, such empirical approaches are still of benefit for engineering practice, as they are capable of offering first-order approximations. The accuracy and availability of rainfall data are critical factors; the rainfall data in mountainous areas are generally higher than in the low lands, and consequently were more suitable for debris volume estimation in the present study, where the debris flows typically occurred in areas with steep slopes and at higher elevations.
Impact of environment performance on firm value: Evidence from Indonesia

Environmental policy is a way to mitigate the effects of climate change. However, the impact of complying with these policies on companies is still a matter of debate. This study examined the effect of environmental performance on firm value mediated by financial performance. This quantitative research used the purposive sampling method to select the sample. The sample comprised 95 non-financial companies with an observation period from 2017 to 2021. The panel data was tested using path analysis in SEM-STATA. The results show that financial performance partially mediates the effect of environmental performance on firm value. Environmental performance can also have a direct impact on firm value. The result indicates that investors consider factors other than financial performance when making investment decisions. The results also show that environmental performance positively impacts financial performance. Furthermore, financial performance positively impacts firm value. Investors optimally use the non-financial information provided by the company. One type of non-financial information used is environmental performance. Investors are aware of the importance of the company’s concern for the impact of its operations on the environment. Companies that display good environmental performance tend to have a higher level of sustainability, which is profitable for investors. However, implementing strict environmental policies carries the risk that companies will move to countries with looser environmental policies. Developing countries, in particular, still need to overcome many obstacles in the implementation of environmental policies.
Impact on Employee Engagement due to Covid-19 in India

Covid-19 is considered the most dreaded disease that has spread across all countries in the recent past. The research explores the impact on employees working in various companies in India. The researchers extracted data from various research journals, articles, and newspapers. This study presents an analysis of the impact on employee engagement due to Covid-19 in India and measures taken by many companies or industries in the field of healthcare and wellbeing. This research answers questions like how many industry sectors or companies have implemented employee engagement initiatives and how many employee engagement initiatives have been taken towards healthcare and wellbeing of the employees during the Covid-19 pandemic situation. Based on the theoretical underpinnings of Employee Engagement, the researchers explore the various engagement aspects in companies in India.