Miracle-dz/UniFAR-FTUs huggingface dataset:
Evaluation of N-methylpyrrolidone and its oxidative products toxicity utilizing the Microtox assay

N-Methylpyrrolidone (NMP) is a cyclic nitrogen-containing organic chemical used to replace more volatile and toxic organic solvents in paint coating and cleaning applications. The Marine Corps Multi-Commodity Maintenance Center was concerned that the high NMP and organic levels in process water would upset treatment processes at the Industrial Process Water Plant (IWP). The NMP contaminated process water was oxidized by a semicontinuous advanced oxidation reactor to reduce the organic concentration. The oxidative byproducts of NMP were identified by GC/MS and tested for their toxicity. A toxicity test, utilizing the Microtox toxicity assay, revealed that methylsuccinimide was the most toxic identifiable product of NMP oxidation. The toxicity of the process water was reduced as methylsuccinimide and was further oxidized to succinimide and other amine products. The results indicate that NMP contaminated process water should be oxidized past the N-methylsuccinimide compound prior to standard industrial process water treatment procedures, so as to reduce toxicity concerns associated with NMP contaminated process water.
Fabrication and optimization of P3HT:PCBM organic photovoltaic devices

We explore the optimization of organic photovoltaic (OPV) devices based on a thin-film blend of the polymer poly(3-hexyl thiophene) [P3HT] and the fullerene derivative [6,6]-phenyl-C61-butyric acid methyl ester [PCBM]. In particular, we address the preparation of the PEDOT:PSS device anode, the composition, thickness and thermal treatment of the active semiconductor layer along with the effect of presence of self-assembled P3HT nanofibres. We also investigate the use of materials such as glycerol and oleic acid dispersed into the PEDOT:PSS and P3HT/PCBM layers respectively to improve the power conversion efficiency (PCE) of the device. Our optimization protocol results in the creation of OPV devices having power conversion efficiencies (PCEs) of up to 4.5%.
Preparation, characterization and photocatalytic performance of rutile TiO2 nanowire arrays via a novel hydrothermal process

The single crystal rutile TiO2 nanowire arrays with flat tetragonal crystallographic plane were successfully synthesized via a novel hydrothermal method. The prepared samples were characterized by X-ray diffraction (XRD) and scanning electron microscopy (SEM). The photocatalytic activities of the TiO2 nanowires were determined by degradation of methyl orange molecules in water under UV-light irradiation. The results indicated that the TiO2 nanowires were rutile phase. The reaction temperatures, pH values and time did not change the crystal structures, while the morphologies of nanowire arrays changed. The photocatalytic experiments revealed that different synthesis conditions can affect the photocatalytic activity of the sample under UV-light irradiation.
Research on Face Recognition Based on Gabor-LeNet Convolutional Neural Network Model

Aiming at the problems of slowing convergence speed, and low recognition accuracy in the face recognition training of the existing LeNet-5 convolutional neural network, an improved LeNet-5 convolutional neural network model is proposed and applied for face recognition. The main improvement is the use of Gabor filter to initialize the first convolutional layer and the activation function of Parametric Rectified Linear Unit (PReLU). The recognition accuracy of the improved model on ORL and GT face datasets has reached more than 98%. At the same time, the recognition accuracy rate on the face data set AR with occlusion has reached more than 90%, indicating that the improved model has strong robustness.
Interaction-aware risk assessment: focus on the lateral intention

To make the massive deployment of automated vehicles possible in complex urban environments, it is essential to provide them with the ability of making safe and useful decisions. To that end, it is necessary to improve their capability to infer the intentions of the surrounding vehicles and their associated collision risk for the ego-vehicle in complex driving scenes. This work shows the implementation and validation in simulation of a probabilistic approach to estimate the risk of driving under uncertain conditions, combining (i) intention estimations and (ii) the expected behaviour of vehicles according to the topology and the subsequent traffic rules of the considered driving scenario. Promising results in terms of success rate and prediction horizon have been obtained testing the proposed approach in driving situations where lateral intention estimation is relevant, namely in multi-lane roundabouts and highways.
Comparative evaluation of mechanical properties of three resin composite materials with different filler particle sizes

Aim: The aim of this study was to evaluate and compare the mechanical properties of three resin composites core materials with different filler particle sizes. Materials and methods: nanofilled (Filtek Z350 XT , 3M, ESPE USA), microhybrid (Te Econom, Ivoclar, Vivadent, India) and nanohybrid (Nexcomp, Meta biomed, korea) resin composites were used during this study. Mechanical properties regarding the compressive strength, diametral tensile strength and surface hardness were tested and results were collected and evaluated. Data were statistically analyzed using one-way Analysis of Variance and Tukey HSD test. Results: The nanofilled type revealed the highest significant compressive and diametral tensile strength values while the nanohybrid type showed the least significant hardness values among the results of the two types of composite resin for the three tests. Conclusions: after evaluation it was shown that the tested nanofilled resin composite type is more appropriate for use as core material compared to the other tested types.
Moods in Classical Paintings: An AI based Classification Approach

According to WHO, globally there are at least 2.2 billion people who have a vision impairment or blindness. While they are known as the disabled in our society, rarely have they benefited from the development of technology and introduction of new products. That is how I developed this idea of constructing a medium that would transfer visual information to audio forms, which would help this disadvantaged group gain access to “visual experience”. In order to achieve this goal, several essential problems should be solved: how can one establish a technological formula to convert visual in-put to audio output? Extracting visual features from paintings, I could set up my own data collection based on some core features, which can capture the mood of a painting. I then use Convolutional Neural Network to train the AI to link visual and audio in-formation. Loss functions are introduced to minimize the error of the product. After I obtain the mood of my pictures, I will use it to formulate a definitive sound, in the process of which the “mood” gets preserved, such as joy, sorrow, or any artistic theme that is shared both in visual and audio experience. Towards the end of this paper, several experiments will be introduced to test the efficacy of my project. The first part is on the functional efficacy of my AI system. The second part is about my product hypothesis: I will give to two distinct participants one pair of visual input and audial output. Then I will check whether they can recognize the exact theme inherent in the corresponding media. Their successful identifying the same “mood” testifies the success of the project goal of transferring visual experience to audio.
Numerical Investigation of Nonlinear Oscillations and Compression-Only Behavior of a Coated Microbubble Near an Elastic Wall

During the ultrasound imaging process, the ultrasound contrast agents (UCAs) are beating near the blood vessel wall. Therefore, the purpose of the present simulation study is to investigate the effect of the presence of an elastic wall on the radial and frequency acoustic response of a UCA microbubble oscillating in a nonlinear regime. For this reason, the numerical simulation of the dynamic behavior of a coated microbubble was performed using coding in MATLAB and a Rayleigh-Plesset equation modified by Doinikov. To study the nonlinear bubble oscillations, its compression-only behavior and the sub-harmonic nonlinear component are taken from a nonlinear shell model presented by Marmottant et al. Initially, coated bubble oscillations in two linear and nonlinear regimes were investigated for two types of shell models, and it was observed that presence of the elastic wall affects the bubble's compression-only behavior. Finally, due to the importance of the subharmonic component in the nonlinear oscillation of the coated bubble, the threshold of the appearance of subharmonic components for a coated bubble near an elastic wall was investigated using the Fast Fourier Transform (FFT) and compared with the oscillation in the infinite fluid.
Prediction of Cooling Energy Consumption Using a Neural Network on the Example of the Hotel Building

The purpose of this work is to determine internal and external factors affecting the cooling energy demand of a building. During the research, the impact of weather conditions and the level of hotel occupancy on cooling energy, which is necessary to obtain indoor comfort conditions, was analyzed. The subject of research is energy consumption in the Turówka hotel located in Wieliczka (southern Poland). In the article, the designer of neural networks was used in the Statistica statistical package. To design the network, a widely used multilayer perceptron model with an algorithm with backward error propagation was used. Based on the collected input and output data, various multilayer perceptron (MLP) networks were tested to determine the relationship most accurately reflecting actual energy consumption. Based on the results obtained, factors that significantly affect the consumption of thermal energy in the building were determined, and a predictive energy demand model for the analyzed object was presented. The result of the work is a forecast of cooling energy demand, which is particularly important in a hotel facility. The prepared predictive model will enable proper energy management in the facility, which will lead to reduced consumption and thus costs related to facility operation.
The Price of Privacy Control in Mobility Sharing

ABSTRACT One of the main features in mobility sharing applications is the exposure of personal data provided to the system. Transportation and location data can reveal personal habits, preferences, and behaviors, and riders could be keen not to share the exact location of their origin and/or destination. But what is the price of privacy in terms of decreased efficiency of a mobility sharing system? In this paper we address the privacy issues under this point of view, and show how location privacy-preserving techniques could affect the performance of mobility-sharing applications, in terms of both system efficiency and quality of service. To this extent, we first apply different data-masking techniques to anonymize geographical information, and then compare the performance of shareability network-based trip-matching algorithms for ride-sharing, applied to real data and to privacy-preserving data. The goal of the paper is to evaluate the performance of mobility-sharing, privacy-preserving systems, and to shed light on the trade-off between data privacy and its costs. The results show that the total traveled distance increase due to the introduction of data privacy could be bounded if users are willing to spend (or “pay”) for more time in order to share a trip, meaning that data location privacy affects both efficiency and quality of service.
Application of Extension Analytic Hierarchy Process in Spanning Tree

In view of the problem how to choose the minimum spanning tree.firstly, a minimum spanning tree was found by using broken circle. Secondly connected graph was reduced to obtain a reduced graph. Using broken circle method get all the minimum spanning tree, finally, it uses the extension analytic hierarchy process (EAHP), build the hierarchy, for all of the minimum spanning tree of outlier, aggregation coefficient, average path length, network sorting efficiency, optimal spanning tree, extension analytic hierarchy process provides the theoretical foundation for the screening of minimum spanning tree.
Effectiveness of Fascial Manipulation Method (FM®)

Background Fascial Manipulation® (FM®) is a manual method of treatment created by Luigi Stecco that centres on the muscular fascia. Fascia is a fibrous collagenous tissue which, with its wide strands, connects all the connective tissues in the body into a whole. Fascia can also cause restriction of motion of the organs as a result of an injury, inflammation or infection. FM® technique bases on identification and therapy of specific points, which are located at the point of convergence of the vectorial forces of the muscles involved in every movement. The aim of this literature review was to analyze publications on the use of FM® in treatment of pain and functional dysfunctions. Materials and methods Material for this literature review was found by two independent authors using PubMed, Cochrane library, ScienceDirect and PEDro databases. In order to identify appropriate studies, a combination of keywords ,,fascial manipulation”, „pain”, „dysfunction” was used. Finally, 10 publications were qualified for a literature review. Results In the analyzed ten publications, improvement after treatment, reduction of pain and better functional outcome in patients conditions were figured. Six authors had some similar conclusions which state that the positive results of treatment last over time. One study presented opposite findings and state that the treatment has not brought long-lasting effects. Conclusions Considering all the reports on this subject, it is suggested that FM® therapy is worth considering in treatment of pain disorders, orthopaedic dysfunctions and postural disabilities. Further research of the subject on whether the treatment results are sustainable is necessary.
Structure prediction and R115866 binding study of human CYP26A1: homology modelling, fold recognition, molecular docking and MD simulations

Two three-dimensional (3D) models of human cytochrome P450 26A1 (CYP26A1) were constructed using the programs Modeller and Sybyl-GeneFold, respectively. After refinement by molecular mechanics and molecular dynamics (MD) simulations, the two models were validated by structure analysis-validation online server. Subsequently, a flexible docking study was performed on the model constructed by GeneFold with the potent and specific inhibitor R115866 to examine the enzyme–inhibitor interactions. From the docking results, we can see R115866 interacts with amino acid residues at the active site by multiple hydrophobic interactions including the side chains of His111, Trp112, Ser115, Val116, Leu125, Ser126, Leu221, Phe222, Glu296, Phe299, Gly300, Glu303, Thr304, Pro371 and the cofactor heme. Trp112 and Thr304 form hydrogen bonds with R115866 and play important roles in stabilising the complex. This constructed CYP26A1 model may provide an opportunity to understand the action mode of the enzyme and could be useful in designing novel retinoic acid metabolism blocking agents (RAMBAs).
The Landscape and Gaps in Open Source Fairness Toolkits

With the surge in literature focusing on the assessment and mitigation of unfair outcomes in algorithms, several open source ‘fairness toolkits’ recently emerged to make such methods widely accessible. However, little studied are the differences in approach and capabilities of existing fairness toolkits, and their fit-for-purpose in commercial contexts. Towards this, this paper identifies the gaps between the existing open source fairness toolkit capabilities and the industry practitioners’ needs. Specifically, we undertake a comparative assessment of the strengths and weaknesses of six prominent open source fairness toolkits, and investigate the current landscape and gaps in fairness toolkits through an exploratory focus group, a semi-structured interview, and an anonymous survey of data science/machine learning (ML) practitioners. We identify several gaps between the toolkits’ capabilities and practitioner needs, highlighting areas requiring attention and future directions towards tooling that better support ‘fairness in practice. ’
The construction of complex multicomponent supramolecular systems via the combination of orthogonal self-assembly and the self-sorting approach

The realization of controllable and well-organized self-assembly within multicomponent supramolecular systems (MSSs) is still a great challenge. Herein, we present the construction of multicomponent supramolecular systems with high-level complexity through the combination of orthogonal self-assembly and the self-sorting approach. Driven by the orthogonality of metal–ligand coordination and host–guest interactions in the orthogonal self-assembly as well as directed by multiple molecular codes in the self-sorting process, five types of simple components (up to eighteen precursors) were successfully self-assembled into two novel tris[2]pseudorotaxanes in one pot through a highly selective manner, which were well-characterized by one-dimensional (1-D) and two-dimensional (2-D) multinuclear NMR as well as ESI-TOF-MS.
Bioorthogonally surface‐edited extracellular vesicles based on metabolic glycoengineering for CD44‐mediated targeting of inflammatory diseases

Abstract Extracellular vesicles (EVs) are essential mediators in intercellular communication that have emerged as natural therapeutic nanomedicines for the treatment of intractable diseases. Their therapeutic applications, however, have been limited by unpredictable in vivo biodistribution after systemic administration. To control the in vivo fate of EVs, their surfaces should be properly edited, depending on the target site of action. Herein, based on bioorthogonal copper‐free click chemistry (BCC), surface‐edited EVs were prepared by using metabolically glycoengineered cells. First, the exogenous azide group was generated on the cellular surface through metabolic glycoengineering (MGE) using the precursor. Next, PEGylated hyaluronic acid, capable of binding specifically to the CD44‐expressing cells, was labelled as the representative targeting moiety onto the cell surface by BCC. The surface‐edited EVs effectively accumulated into the target tissues of the animal models with rheumatoid arthritis and tumour, primarily owing to prolonged circulation in the bloodstream and the active targeting mechanism. Overall, these results suggest that BCC combined with MGE is highly useful as a simple and safe approach for the surface modification of EVs to modulate their in vivo fate.
Polyaniline/Fe composite nanofiber added softmagnetic carbonyl iron microsphere suspension and its magnetorheology

Fe0 nanoparticle-supported polyaniline (PANI) composite nanofibers were prepared as an additive for carbonyl iron (CI)-based magnetorheological (MR) fluids to improve the MR properties and dispersion stability. A simple chemical strategy was adopted to synthesize the PANI/Fe0 composite nanofibers. The synthesis method was based on the initial fabrication of interconnected PANI nanofibers by a rapidly mixed polymerization process using ferric chloride (FeCl3) as the oxidant and the subsequent reductive support of Fe0 nanoparticles onto the PANI nanofiber matrix using the polymerization by-products as a metal precursor. X-ray diffraction, Fourier transform infrared spectroscopy, and X-ray photoelectron spectroscopy showed that the fabricated nanofibers were composed of PANI and Fe0. Scanning and transmission electron microscopy imaging confirmed that the PANI/Fe0 composite nanofibers were 80–150 nm in diameter with an interconnected matrix. The MR fluid with the PANI/Fe0 composite nanofibers dispersed in mineral oil under an external magnetic field showed enhanced MR properties compared to that without an additive. Turbiscan data also confirmed the improved sedimentation properties of the MR fluid with an additive.
Explaining the Erosion of Relational Care Continuity: An Empirical Analysis of Primary Care in England

Relational continuity (RC) refers to an ongoing relationship between a patient and a clinician or clinical team beyond a specific service encounter or disease episode. As a defining characteristic of good medical practice, RC has been shown to confer many clinical and operational advantages and is desired by patients, clinicians, and policymakers alike. Yet despite its many benefits, RC in the primary care setting has been in sharp decline over the past decades, contributing to poorer health outcomes and lower efficiency as well as falling patient and provider satisfaction. Anecdotally, this downward trend has been attributed to a sustained increase in workload caused by a growing and aging population and to changes in the workforce composition caused by burnout and workload pressures. However, there is a dearth of evidence to support or contradict this impression, and the key factors that cause changes in RC are not well understood. As a result, little action is being taken to slow or reverse this trend. We fill this gap by empirically examining the main operational factors that can explain variation in RC both between practices and over time. To do so, we use a unique dataset of primary care consultations corresponding to '10% of England's population over ten years. Using a panel ARDL estimation approach, we show that workload and workforce factors have a significant influence on a primary care practice's ability to provide RC, explaining '36% of the residual within-practice variation after inclusion of all other controls. We also find that three factors alone can explain '45% of the decline in RC over the study period: increasing fragmentation of the workforce caused by i) primary care practitioners shifting to part-time work patterns and ii) greater dependence on temporary staff; a sustained increase in workload caused by iii) greater patient volumes without a proportionate increase in physician-hours. Of these, workforce factors appear to be relatively more important than workload factors, with increasing workforce fragmentation driving '33% of the total decline. We discuss the implications of these findings for patients and primary care practice managers, and we suggest strategies for maintaining levels of RC in the face of these industry trends.
An Hybrid Parallel Network Structure for Image Classification

The image classification task is to divide certain image target into the specific class which they belong to. The deep neural network simplifies image classification by sending input image data into the neural network to learn feature extraction and having the output results automatically. Existing network structures have been able to achieve or exceed human capabilities in image classification tasks. However, these image classification technologies still have problems such as over-fitting network model, insufficient generalization abilities and long convergence time. Interference caused by complex environments may also cause the performance degradation. This paper proposes a hybrid parallel neural network structure for this kind of artificial intelligence task. By adopting the K-parallel connection CNN structure and using different scales of RNN/CNN structure mixed-layer modes, the effects of image’s size on different objects and different shooting angles can be reduced in image classification. Experiments on the datasets show that the proposed structure will have the advantage of accuracy and computational efficiency both in the validation datasets and the test sets.