Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparative Study of the Durability Behaviors of Epoxy- and Polyurethane-Based CFRP Plates Subjected to the Combined Effects of Sustained Bending and Water/Seawater Immersion

In many applications, carbon fiber reinforced polymer (CFRP) composite materials suffer from the combined effects of sustained bending and immersion. In the present study, pultruded epoxy- and polyurethane (PU)-based CFRP plates were studied for their long-term performances, subjected to the combined effects of water/seawater immersion and sustained bending (0%, 30%, and 58% of the ultimate strain). The water uptake and the evolution of the mechanical properties were investigated. In addition, the service lives of the CFRPs were predicted using the Arrhenius method. Generally, the sustained bending led to a decrease in the water uptake, and reduced the mechanical properties. A diffusion model, dividing the cross-section of CFRPs into the “less resin area” and “rich resin area”, was proposed to elucidate the variation of water uptake and mechanical properties. Compared to epoxy-based CFRPs, although PU-based CFRPs possessed a significantly higher water uptake, they exhibited better long-term performances in terms of mechanical properties.
Minimizing Freshwater Consumption in the Wash-Off Step in Textile Reactive Dyeing by Catalytic Ozonation with Carbon Aerogel Hosted Bimetallic Catalyst

In textile reactive dyeing, dyed fabrics have to be rinsed in the wash-off step several times to improve colorfastness. Thus, the multiple rinsing processes drastically increase the freshwater consumption and meanwhile generate massive waste rinsing effluents. This paper addresses an innovative alternative to recycle the waste effluents to minimize freshwater consumption in the wash-off step. Accordingly, catalytic ozonation with a highly effective catalyst has been applied to remedy the waste rinsing effluents for recycling. The carbon aerogel (CA) hosted bimetallic hybrid material (Ag–Fe2O3@CA) was fabricated and used as the catalyst in the degradation of residual dyes in the waste rinsing effluents by ozonation treatments. The results indicate the participation of Ag–Fe2O3@CA had strikingly enhanced the removal percentage of chemical oxidation demand by 30%. In addition, it has been validated that waste effluents had been successfully reclaimed after catalytic ozonation with Ag–Fe2O3@CA. They could be additionally reused to reduce freshwater consumption in the wash-off step, but without sacrificing the color quality of corresponding fabrics in terms of color difference and colorfastness. This study may be the first to report the feasibility of catalytic ozonation in minimization of freshwater consumption in the wash-off step in textile reactive dyeing.
Recent Advances in RAFT Polymerization: Novel Initiation Mechanisms and Optoelectronic Applications

Reversible addition-fragmentation chain transfer (RAFT) is considered to be one of most famous reversible deactivation radical polymerization protocols. Benefiting from its living or controlled polymerization process, complex polymeric architectures with controlled molecular weight, low dispersity, as well as various functionality have been constructed, which could be applied in wide fields, including materials, biology, and electrology. Under the continuous research improvement, main achievements have focused on the development of new RAFT techniques, containing fancy initiation methods (e.g., photo, metal, enzyme, redox and acid), sulfur-free RAFT system and their applications in many fields. This review summarizes the current advances in major bright spot of novel RAFT techniques as well as their potential applications in the optoelectronic field, especially in the past a few years.
Polymer Translocation Across a Corrugated Channel: Fick–Jacobs Approximation Extended Beyond the Mean First-Passage Time

Polymer translocation across a corrugated channel is a paradigmatic stochastic process encountered in diverse systems. The instance of time when a polymer first arrives to some prescribed location defines an important characteristic time-scale for various phenomena, which are triggered or controlled by such an event. Here we discuss the translocation dynamics of a Gaussian polymer in a periodically-corrugated channel using an appropriately generalized Fick–Jacobs approach. Our main aim is to probe an effective broadness of the first-passage time distribution (FPTD), by determining the so-called coefficient of variation γ of the FPTD, defined as the ratio of the standard deviation versus the mean first-passage time (MFPT). We present a systematic analysis of γ as a function of a variety of system’s parameters. We show that γ never significantly drops below 1 and, in fact, can attain very large values, implying that the MFPT alone cannot characterize the first-passage statistics of the translocation process exhaustively well.
Thiolated Chitosan Masked Polymeric Microspheres with Incorporated Mesocellular Silica Foam (MCF) for Intranasal Delivery of Paliperidone

In this study, mesocellular silica foam (MCF) was used to encapsulate paliperidone, an antipsychotic drug used in patients suffering from bipolar disorder. MCF with the drug adsorbed was further encapsulated into poly(lactic acid) (PLA) and poly(lactide-co-glycolide) (PLGA) 75/25 w/w microspheres and these have been coated with thiolated chitosan. As found by TEM analysis, thiolated chitosan formed a thin layer on the polymeric microspheres’ surface and was used in order to enhance their mucoadhesiveness. These microspheres aimed at the intranasal delivery of paliperidone. The DSC and XRD studies showed that paliperidone was encapsulated in amorphous form inside the MCF silica and for this reason its dissolution profile was enhanced compared to the neat drug. In coated microspheres, thiolated chitosan reduced the initial burst effect of the paliperidone dissolution profile and in all cases sustained release formulations have been prepared. The release mechanism was also theoretically studied and three kinetic models were proposed and successfully fitted for a dissolution profile of prepared formulations to be found.
A Combined XRD, Solvatochromic, and Cyclic Voltammetric Study of Poly (3,4-Ethylenedioxythiophene) Doped with Sulfonated Polyarylethersulfones: Towards New Conducting Polymers

Despite the poor solubility in organic solvents, poly (3,4-ethylenedioxythiophene) (PEDOT) is one of the most successful conducting polymers. To improve PEDOT conductivity, the dopants commonly used are molecules/polymers carrying sulfonic functionalities. In addition to these species, sulfonated polyarylethersulfone (SPAES), obtained via homogeneous synthesis with different degrees of sulfonation (DS), can be used thanks to both the tight control over the DS and the charge separation present in SPAES structure. Here, PEDOTs having enhanced solubility in the chosen reaction solvents (N,N-dimethylformamide, dimethylacetamide, dimethyl sulfoxide, and N-methyl-2-pyrrolidone) were synthesized via a high-concentration solvent-based emulsion polymerization with very low amounts of SPAES as dopant (1% w/w with respect to EDOT monomer), characterized by different DS. The influence of solvents and of the adopted doping agent was studied on PEDOT_SPAESs analyzing (i) the chemical structure, comparing via X-ray diffraction (XRD) the crystalline structures of undoped and commercial PEDOTs with PEDOT_SPAES’ amorphous structure; (ii) solvatochromic behavior, observing UV absorption wavelength variation as solvents and SPAES’ DS change; and (iii) electrochemical properties: voltammetric peak heights of PEDOT_SPAES cast onto glassy carbon electrodes differ for each solvent and in general are better than the ones obtained for neat SPAES, PEDOTs, and glassy carbon.
Near-Field Electrospun Piezoelectric Fibers as Sound-Sensing Elements

A novel integration of three-dimensional (3D) architectures of near-field electrospun polyvinylidene fluoride (PVDF) nano-micro fibers (NMFs) is applied to an intelligent self-powered sound-sensing element (ISSE). Using 3D architecture with greatly enhanced piezoelectric output, the sound wave energy can be harvested under a sound pressure of 120+ dB SPL of electrical signal about 0.25 V. Furthermore, the simple throat vibrations such as hum, cough and swallow with different intensity or frequency can be distinguishably detected. Finally, the developed ultrathin ISSE of near-field electrospun piezoelectric fibers has the advantage of direct—write fabrication on highly flexible substrates and low cost. The proposed technique demonstrates the advancement of existing electrospinning technologies in new practical applications of sensing purposes such as voice control, wearable electronics, implantable human wireless technology.
Chitosan-Modified PLGA Nanoparticles for Control-Released Drug Delivery

Poly (lactic-co-glycolic acid) nanoparticles (PLGA NPs) are well recognized as an ideal drug delivery carrier for their biocompatibility and biodegradability. In order to overcome the disadvantage of drug burst release, chitosan (CS) was used to modify the PLGA nanoparticles. In this work, CS-PLGA nanoparticles with different ratio of CS to PLGA were prepared using high-gravity rotating packed bed (RPB). With the increase of amount of CS, the particle size increased from 132.8 ± 1.5 nm to 172.7 ± 3.2 nm, zeta potential increased from −20.8 ± 1.1 mV to 25.6 ± 0.6 mV, and drug encapsulation efficiency increased from 65.8% to 87.1%. The initial burst release of PLGA NPs reduced after being modified by CS, and the cumulative release was 66.9%, 41.9%, 23.8%, and 14.3%, after 2 h, respectively. The drug release of CS-modified PLGA NPs was faster at pH5.5 than that at pH 7.4. The cellular uptake of CS-modified PLGA NPs increased compared with PLGA NPs, while cell viability was reduced. In conclusion, these results indicated that CS-modified, PTX-loaded PLGA NPs have the advantages of sustained drug release and enhanced drug toxicity, suggesting that CS-modified NPs can be used as carriers of anticancer drugs.
An Empirical Study of AI Population Dynamics with Million-agent Reinforcement Learning

In this paper, we conduct an empirical study on discovering the ordered collective dynamics obtained by a population of artificial intelligence (AI) agents. Our intention is to put AI agents into a simulated natural context, and then to understand their induced dynamics at the population level. In particular, we aim to verify if the principles developed in the real world could also be used in understanding an artificially-created intelligent population. To achieve this, we simulate a large-scale predator-prey world, where the laws of the world are designed by only the findings or logical equivalence that have been discovered in nature. We endow the agents with the intelligence based on deep reinforcement learning, and scale the population size up to millions. Our results show that the population dynamics of AI agents, driven only by each agent's individual self interest, reveals an ordered pattern that is similar to the Lotka-Volterra model studied in population biology. We further discover the emergent behaviors of collective adaptations in studying how the agents' grouping behaviors will change with the environmental resources. Both of the two findings could be explained by the self-organization theory in nature.
Evaluating Representation Learning of Code Changes for Predicting Patch Correctness in Program Repair

A large body of the literature of automated program repair develops approaches where patches are generated to be validated against an oracle (e.g., a test suite). Because such an oracle can be imperfect, the generated patches, although validated by the oracle, may actually be incorrect. While the state of the art explore research directions that require dynamic information or that rely on manually-crafted heuristics, we study the benefit of learning code representations in order to learn deep features that may encode the properties of patch correctness. Our empirical work mainly investigates different representation learning approaches for code changes to derive embeddings that are amenable to similarity computations. We report on findings based on embeddings produced by pre-trained and re-trained neural networks. Experimental results demonstrate the potential of embeddings to empower learning algorithms in reasoning about patch correctness: a machine learning predictor with BERT transformer-based embeddings associated with logistic regression yielded an AUC value of about 0.8 in the prediction of patch correctness on a deduplicated dataset of 1000 labeled patches. Our investigations show that learned representations can lead to reasonable performance when comparing against the state-of-the-art, PATCH-SIM, which relies on dynamic information. These representations may further be complementary to features that were carefully (manually) engineered in the literature.
Characterizing Text Complexity with Core Vocabulary Distributional Patterns: Corpus-based Approach

In this paper, we report a corpus study aimed at testing the hypothesis that bigram distributional information is related to text complexity. We explored a corpus of Russian textbooks on Social studies for middle and high school to examine how the number of bigrams of the core vocabulary correlates with reading levels of texts within the grade range 5 – 11. The corpus contains 45380 sentences from 14 textbooks, written by two independent groups of authors. Each word in the corpus has a part-of-speech tag derived by TreeTagger. Due to the nature of the domain, we focus our study on a single, but high-frequency pattern: ‘chelovek’ (a man) + verb. The findings are particularly relevant for text complexity theory as they are consistent with the previous results of corpus investigations on a correlation of text complexity with a number of text features.
Hyperband: Bandit-Based Configuration Evaluation for Hyperparameter Optimization

Performance of machine learning algorithms depends critically on identifying a good set of hyperparameters. While recent approaches use Bayesian Optimization to adaptively select configurations, we focus on speeding up random search through adaptive resource allocation. We present HYPERBAND, a novel algorithm for hyperparameter optimization that is simple, flexible, and theoretically sound. HYPERBAND is a principled early-stoppping method that adaptively allocates a predefined resource, e.g., iterations, data samples or number of features, to randomly sampled configurations. We compare HYPERBAND with state-of-the-art Bayesian Optimization methods on several hyperparameter optimization problems. We observe that HYPERBAND can provide over an order of magnitude speedups over competitors on a variety of neural network and kernel-based learning problems.
To Share or Not to Share? How Emotional Judgments Drive Online Political Expression in High-Risk Contexts

Previous scholarship on networked authoritarianism has examined an array of repressive legal and political strategies employed by regimes to constrain online political expression. How the tension between citizens’ desires to engage in online political expression and the possible dire consequences of doing so is resolved, however, is understudied. We address this lacuna by drawing upon concepts from risk and decision-making research and examining how the emotional and cognitive components of risk and decision-making shape citizens’ online political expression. Employing a three-wave panel survey of Turkish internet users collected over 8 months, our fixed-effects regression analyzes show that anticipatory emotions drive expressive behavior, but that risk assessment does not. Furthermore, the influence of negative emotions on online expression is moderated by individuals’ degree of regime opposition. We discuss the importance of understanding the psychological mechanisms by which networked authoritarian contexts influences citizens’ decisions to engage in contentious online speech.
Deep Nets Don't Learn via Memorization

We use empirical methods to argue that deep neural networks (DNNs) do not achieve their performance by memorizing training data in spite of overlyexpressive model architectures. Instead, they learn a simple available hypothesis that fits the finite data samples. In support of this view, we establish that there are qualitative differences when learning noise vs. natural datasets, showing: (1) more capacity is needed to fit noise, (2) time to convergence is longer for random labels, but shorter for random inputs, and (3) that DNNs trained on real data examples learn simpler functions than when trained with noise data, as measured by the sharpness of the loss function at convergence. Finally, we demonstrate that for appropriately tuned explicit regularization, e.g. dropout, we can degrade DNN training performance on noise datasets without compromising generalization on real data.
Enhancing Employee Job satisfaction via E-learning: The Mediating Role of an Organizational Learning Culture

ABSTRACT Although e-learning systems have been widely employed to develop employee learning at a workplace, discerning what influences the interaction between employee acceptance of an e-learning system within an organizational learning culture (OLC) and the resultant impact on job attitudes remains under-researched and requires further investigation. This study examined the relationship between employee perceptions of an e-learning system, the existing OLC, and job satisfaction. Structural equation modeling was applied to analyze data collected from 297 employees of a telecommunications company. The results suggest that employee acceptance of e-learning is a positive predictor of the OLC and employee job satisfaction. OLC mediates the relationship between the use and acceptance of e-learning by employees and their job satisfaction. Recommendations and implications are subsequently provided in terms of the future for human resource development researches and practices.
Hyper-Scalable JSQ with Sparse Feedback

Load balancing algorithms play a vital role in enhancing performance in data centers and cloud networks. Due to the massive size of these systems, scalability challenges, and especially the communication overhead associated with load balancing mechanisms, have emerged as major concerns. Motivated by these issues, we introduce and analyze a novel class of load balancing schemes where the various servers provide occasional queue updates to guide the load assignment. We show that the proposed schemes strongly outperform JSQ( d ) strategies with comparable communication overhead per job, and can achieve a vanishing waiting time in the many-server limit with just one message per job, just like the popular JIQ scheme. The proposed schemes are particularly geared however towards the sparse feedback regime with less than one message per job, where they outperform corresponding sparsified JIQ versions. We investigate fluid limits for synchronous updates as well as asynchronous exponential update intervals. The fixed point of the fluid limit is identified in the latter case, and used to derive the queue length distribution. We also demonstrate that in the ultra-low feedback regime the mean stationary waiting time tends to a constant in the synchronous case, but grows without bound in the asynchronous case.
Optimizing the Energy Harvesting Cycle of a Dissipative Dielectric Elastomer Generator for Performance Improvement

This paper optimizes the energy harvesting cycle of dissipative dielectric elastomer generators (DEGs) to explore possible approaches for improving the energy harvesting performance. By utilizing the developed theoretical framework, the dissipative performance of the DEG with a constant voltage cycle is analyzed, which shows good agreement with the existing experimental data. On this basis, we design a novel energy harvesting cycle and a corresponding energy harvesting circuit in which a transfer capacitor is utilized to store the charge transferred from the DEG. Then, the energy conversion performance of the DEG with the novel energy harvesting cycle is investigated. The results indicate that both the energy density and conversion efficiency are improved by choosing a high voltage during the discharging process and that as the R-C time constant increases, the enhancement effect of the voltage increases and then approaches to the saturation. In addition, there is an optimal transfer capacitor that can maximize energy density or conversion efficiency, and the optimal transfer capacitor increases with the increase in the R-C time constant. These results and methods are expected to guide the optimal design and assessment of DEGs.
Fabrication of Novel Ball-Like Polystyrene Films Containing Schiff Base Microspheres as Photostabilizers

Polystyrene films containing a low concentration of three highly aromatic Schiff bases were prepared using the casting method. The polystyrene films were irradiated with ultraviolet light (300 h). The polystyrene infrared spectra, weight loss, molecular weight reduction and the surface morphology were examined upon irradiation. The Schiff bases acted as photostabilizers and reduced the photodegradation of polystyrene films to a significant level in comparison to the blank film. The images recorded of the surface of the miscible polystyrene/Schiff base blends showed novel ball-like microspheres with a diameter of 3.4–4.3 µm. The Schiff bases were able to endow excellent protection to polystyrene against ultraviolet irradiation.
Assistive visual content creation tools via multimodal correlation analysis

Visual imagery is ubiquitous in society and can take various formats: from 2D sketches and photographs to photorealistic 3D renderings and animations. The creation processes for each of these mediums have their own unique challenges and methodologies that artists need to overcome and master. For example, for an artist to depict a 3D scene in a 2D drawing they need to understand foreshortening effects to position and scale objects accurately on the page; or, when modeling 3D scenes, artists need to understand how light interacts with objects and materials, to achieve a desired appearance. Many of these tasks can be complex, time-consuming, and repetitive for content creators. The goal of this thesis is to develop tools to alleviate artists from some of these issues and to assist them in the creation process. The key hypothesis is that understanding the relationships between multiple signals present in the scene being created enables such assistive tools. This thesis proposes three assistive tools. First, we present an image degradation model for depth-augmented image editing to help evaluate the quality of the image manipulation. Second, we address the problem of teaching novices to draw objects accurately by automatically generating easy-to-follow sketching tutorials for arbitrary 3D objects. Finally, we propose a method to automatically transfer 2D parametric user edits made to rendered 3D scenes to global variations of the original scene.
Degradation of Polyacrylate in the Outdoor Agricultural Soil Measured by FTIR-PAS and LIBS

Recently, polyacrylates (PA) have been applied in coated controlled-release fertilizer (CRF), but the impacts of the soil on the degradation of PA have not been evaluated. In this study, an outdoor agriculture soil buried test was carried out for 12 months to investigate the degradation of PA films. The residual degraded films were taken regularly from the soil and analyzed by SEM, Fourier transform infrared photoacoustic spectroscopy (FTIR-PAS) and laser-induced breakdown spectroscopy (LIBS). The concentration of C–H and C=O molecular groups of PA were decreased, and the element concentrations of C, O, K, Si of PA were increased under the degradation process. The surface of PA became rough and the degradation of PA occurred on the surface layer. Principal component analysis (PCA) showed that soil invaded PA. The results indicated that PA were environmentally friendly when applied to CRF. FTIR-PAS and LIBS were advanced in the in-situ surface analysis of the degradation process of the polymer.