Miracle-dz/UniFAR-FTUs huggingface dataset:
Polymeric Composite of Magnetite Iron Oxide Nanoparticles and Their Application in Biomedicine: A Review

A broad spectrum of nanomaterials has been investigated for multiple purposes in recent years. Some of these studied materials are magnetics nanoparticles (MNPs). Iron oxide nanoparticles (IONPs) and superparamagnetic iron oxide nanoparticles (SPIONs) are MNPs that have received extensive attention because of their physicochemical and magnetic properties and their ease of combination with organic or inorganic compounds. Furthermore, the arresting of these MNPs into a cross-linked matrix known as hydrogel has attracted significant interest in the biomedical field. Commonly, MNPs act as a reinforcing material for the polymer matrix. In the present review, several methods, such as co-precipitation, polyol, hydrothermal, microemulsion, and sol-gel methods, are reported to synthesize magnetite nanoparticles with controllable physical and chemical properties that suit the required application. Due to the potential of magnetite-based nanocomposites, specifically in hydrogels, processing methods, including physical blending, in situ precipitation, and grafting methods, are introduced. Moreover, the most common characterization techniques employed to study MNPs and magnetic gel are discussed.
Mobility Infrastructures: Modern Visions, Affective Environments and the Problem of Car Parking

Abstract This paper builds upon recent research on mobility infrastructures to question the usefulness of the mobility/moorings binary and suggest that scholars should examine the practices of infrastructuring by which mobile subjects, affects and environments emerge. The paper outlines recent work on the affective qualities of mobility infrastructures before examining some of the diverse discourses, feelings and atmospheres which have gathered around the humble British car park over the past 50 years. It examines how the affective and experiential qualities of car parks and parking became commodified by car park designers, urban redevelopers and landscape architects, as new techniques and technologies were adopted to prevent wasteful ‘space-searching’ and design secure, safe, pleasant and aesthetically pleasing spaces. Finally, the paper examines some of the discourses and regulative practices which have gathered around the direct and indirect environmental impacts of parking provision and parking policies, from their influence on local ecology and hydrology, to the use of parking regulations and charges to try and reshape people’s mobility habits and reduce their environmental footprints.
FPGA, physics-based modeling of IGBT and PIN diode for hardware co-simulation of complex power electronic converters and systems

This paper presents the steps and the challenges for implementing analytical, physics-based models for the insulated gate bipolar transistor (IGBT) and the PIN diode in hardware and more specifically in field programmable gate arrays (FPGAs). The models can be utilised in hardware co-simulation of complex power electronic converters and entire power systems in order to reduce the simulation time without compromising the accuracy of results. Such a co-simulation allows reliable prediction of the system's performance as well as accurate investigation of the power devices' behaviour during operation. Ultimately, this will allow application-specific optimisation of the devices' structure, circuit topologies as well as enhancement of the control and/or protection schemes.
Momentum Iterative Gradient Sign Method Outperforms PGD Attacks

Adversarial examples are machine learning model inputs that an attacker has purposefully constructed to cause the model to make a mistake. A recent line of work focused on making adversarial training computationally efficient for deep learning models. Projected Gradient Descent (PGD) and Fast Gradient Sign Method (FGSM) are popular current techniques for generating adversarial examples efficiently. There is a tradeoff between these two in terms of robustness or training time. Among the adversarial defense techniques, adversarial training with the PGD is considered one of the most effective ways to achieve moderate adversarial robustness. However, PGD requires too much training time since it takes multiple iterations to generate perturbations. On the other hand, adversarial training with the FGSM takes much less training time since it takes one step to generate perturbations but fails to increase adversarial robustness. Our algorithm achieves better robustness to PGD adversarial training on CIFAR-10/100 datasets and is faster than PGD string adversarial training methods.
Joint Ordering and Selling Policy for Storable Agricultural Products in Contract Farming

With the farming mode widely promoted in many countries around the world, agricultural enterprises need to face risks from both supply and sales at the same time. Especially for storable agricultural products, it is also necessary to consider the potential profits and losses brought about by the sales of multi-period. This paper establishes a joint ordering and selling decision model under multiperiod random supply and demand. The model takes the maximum expected profit of agricultural enterprises as the goal, considers the constraints of production capacity and the risk preference coefficient of shortage, and makes decisions on the order and sales quantity in each cycle. The feasibility of the model was verified by taking China Yunnan Pu’er tea as a specific example. Finally, through numerical examples, the influence of important parameters on the joint ordering and selling strategy is as follows: order cost is the main factor affecting the order quantity. When the order cost increases to a certain level, the solving algorithm will make a decision that would rather be out of stock than to order. With the increase in selling price, inventory holding cost, and shortage cost, there will be an increasing trend of the sales quantity allocated to this period. Compared with other parameters, selling price and inventory holding cost have a more remarkable impact on sales quantity. In addition, enterprises can increase their expected profits of storable agricultural products by controlling the inventory holding cost, appropriately reducing the risk preference coefficient, and increasing the level of the selling price.
Controlled Synthesis of Fibre-reinforced Plastics Images from Segmentation Maps using Generative Adversarial Neural Networks

The replacement of traditional construction materials with lightweight fibre-reinforced plastics is an accepted way to reduce emissions. By automating quality assurance, errors in production can be detected earlier, avoiding follow-up costs and hard-to-recycle scrap. Deep learning based defect detection systems have shown promising results, but their prediction accuracy often suffers from scarce labelled data in production processes. Especially in the domain of fibre-reinforced plastics, the task remains challenging because of varying textile specific errors. In our work, we applied conditional generative adversarial networks combined with image-to-image translation methods to address data scarcity through generating synthetic images. By training a generative model on image-segmentation pairs, we produce realistic fibre images matching the given segmentation maps. Our model enables control over generated output images of arbitrary fibre shapes and structures, including gaps, ondulations, and folds as error classes. We evaluate our synthetic images based on GAN metrics, feature distribution and show that they are suitable as a data augmentation method to improve the error classification performance of deep neural networks. Thereby, we provide a solution for the manufacturing domain of fibre-reinforced plastics with scarce data, consequently contributing to an automated defect detection system that reduces resource-intensive scrap in the future.
Importance of Carbohydrate Quality: What Does It Mean and How to Measure It?

Abstract Dietary carbohydrates are our main source of energy. Traditionally, they are classified based on the polymer length between simple and complex carbohydrates, which does not necessarily reflect their impact on health. Simple sugars, such as fructose, glucose, and lactose, despite having a similar energy efficiency and caloric content, have very distinct metabolic effects, leading to increased risk for various chronic diseases when consumed in excess. In addition, beyond the absolute amount of carbohydrate consumed, recent data point out that the food form or processing level can modulate both the energy efficiency and the cardiometabolic risk associated with specific carbohydrates. To account for both of these aspects—the quality of carbohydrates as well as its food form—several metrics can be proposed to help identifying carbohydrate-rich food sources and distinguish between those that would favor the development of chronic diseases and those that may contribute to prevent these. This review summarizes the findings presented during the American Society of Nutrition Satellite symposium on carbohydrate quality, in which these different aspects were presented.
Isotropic multichannel total variation framework for joint reconstruction of multicontrast parallel MRI

Abstract. Purpose: To develop a synergistic image reconstruction framework that exploits multicontrast (MC), multicoil, and compressed sensing (CS) redundancies in magnetic resonance imaging (MRI). Approach: CS, MC acquisition, and parallel imaging (PI) have been individually well developed, but the combination of the three has not been equally well studied, much less the potential benefits of isotropy within such a setting. Inspired by total variation theory, we introduce an isotropic MC image regularizer and attain its full potential by integrating it into compressed MC multicoil MRI. A convex optimization problem is posed to model the new variational framework and a first-order algorithm is developed to solve the problem. Results: It turns out that the proposed isotropic regularizer outperforms many of the state-of-the-art reconstruction methods not only in terms of rotation-invariance preservation of symmetrical features, but also in suppressing noise or streaking artifacts, which are normally encountered in PI methods at aggressive undersampling rates. Moreover, the new framework significantly prevents intercontrast leakage of contrast-specific details, which seems to be a difficult situation to handle for some variational and low-rank MC reconstruction approaches. Conclusions: The new framework is a viable option for image reconstruction in fast protocols of MC parallel MRI, potentially reducing patient discomfort in otherwise long and time-consuming scans.
Compelling Questions in Research: Seeing What Everybody Has Seen and Thinking What Nobody Has Thought

This is the third editorial commentary in a three-part series that addresses introductions, implications, and interestingness. The first, focusing on the introduction design, is about what the beginning of a paper should look like. The second, focusing on practice implications, is about what the end of a paper should look like. The third discusses how to develop and fit attention-grabbing ideas into the academic conversation in the literature. We hope this editorial series provides inspiration and ideas about publishing papers that people want to engage with.
Novel Framework for Treatment Response Evaluation Using PSMA PET/CT in Patients with Metastatic Castration-Resistant Prostate Cancer (RECIP 1.0): An International Multicenter Study

Visual Abstract Our objective was to develop version 1.0 of a novel framework for response evaluation criteria in prostate-specific membrane antigen (PSMA) PET/CT (RECIP) and a composite response classification that combines responses by prostate-specific antigen (PSA) measurements and by RECIP 1.0 (PSA + RECIP). Methods: This was an international multicenter, retrospective study. One hundred twenty-four men with metastatic castration-specific prostate cancer (mCRPC) who underwent 177Lu-PSMA therapy and received PSMA PET/CT at baseline and at an interim time point of 12 wk were included. Pairs of baseline interim PET/CT scans were interpreted by consensus among 3 masked readers for appearance of new lesions. Tumor lesions were segmented, and total PSMA-positive tumor volume (PSMA-VOL) was obtained. Appearance of new lesions and changes in PSMA-VOL were combined to develop RECIP 1.0, which included classifications of complete response (RECIP-CR: absence of any PSMA-ligand uptake on interim PET/CT), partial response (RECIP-PR: decline ≥ 30% in PSMA-VOL and no appearance of new lesions), progressive disease (RECIP-PD: increase ≥ 20% in PSMA-VOL and appearance of new lesions), and stable disease (RECIP-SD: any condition but RECIP-PR or RECIP-PD). Changes in PSA levels at 12 wk by Prostate Cancer Working Group Criteria 3 were recorded. PSA + RECIP results were defined as response (PSA decline ≥ 50% or RECIP-PR/CR) or progression (PSA increase ≥ 25% or RECIP-PD). The study’s primary outcome measure was the prognostic value of RECIP 1.0 for overall survival (OS). The secondary outcome measure was the prognostic accuracy (C-index) of PSA + RECIP versus PSA responses. Results: Patients with RECIP-PD (n = 39; 8.3 mo) had a shorter OS than patients with stable disease (RECIP-SD) (n = 47; 13.1 mo; P < 0.001) or RECIP-PR (n = 38; 21.7 mo; P < 0.001). In identifying responders and progressors, PSA + RECIP had C-indices superior to those of PSA only: 0.65 versus 0.62 (P = 0.028) and 0.66 versus 0.63 (P = 0.044), respectively. Conclusion: PSMA PET/CT by RECIP 1.0 is prognostic for OS and can be used as a response biomarker to monitor early efficacy of 177Lu-PSMA in men with mCRPC. PSA + RECIP may be used as a novel composite endpoint in mCRPC clinical trial design.
Stochastic Resonance in Organic Electronic Devices

Stochastic Resonance (SR) is a phenomenon in which noise improves the performance of a system. With the addition of noise, a weak input signal to a nonlinear system, which may exceed its threshold, is transformed into an output signal. In the other words, noise-driven signal transfer is achieved. SR has been observed in nonlinear response systems, such as biological and artificial systems, and this review will focus mainly on examples of previous studies of mathematical models and experimental realization of SR using poly(hexylthiophene)-based organic field-effect transistors (OFETs). This phenomenon may contribute to signal processing with low energy consumption. However, the generation of SR requires a noise source. Therefore, the focus is on OFETs using materials such as organic materials with unstable electrical properties and critical elements due to unidirectional signal transmission, such as neural synapses. It has been reported that SR can be observed in OFETs by application of external noise. However, SR does not occur under conditions where the input signal exceeds the OFET threshold without external noise. Here, we present an example of a study that analyzes the behavior of SR in OFET systems and explain how SR can be made observable. At the same time, the role of internal noise in OFETs will be explained.
Auditory training strategies for adult users of cochlear implants

There has been growing interest recently in whether computer-based training can improve speech perception among users of cochlear implants (Fu et al., 2005; Oba et al., 2011; Ingvalson et al., 2013). This paper reports a series of experiments which first evaluated the effectiveness of different training strategies with normal-hearing participants who listened to noise-vocoded speech, before conducting a small-scale study with users of cochlear implants. Our vocoder studies revealed (1) that ‘High-Variability’ training led to greater generalisation to new talkers than training with a single talker, and (2) that word-and sentence-based training materials led to greater improvements than an approach based on phonemes in nonsense syllables. Informed by these findings, we evaluated the effectiveness of a computer-based training package that included word-and sentence-based tasks, with materials recorded by 20 talkers. We found good compliance with the training protocol, with 8 out of the 11 participants completing 15 hours of training as instructed. Following training, there was a significant improvement on a consonant test, but in general the improvements were small, highly variable, and not statistically significant. A large-scale randomised controlled trial is needed before we can be confident that computer-based auditory training is worthwhile for users of cochlear implants.
Novel tetrameric cell penetrating antimicrobial peptoids effective against mycobacteria and drug-resistant Staphylococcus aureus.

BACKGROUND Antimicrobial peptides (AMPs) are short, cationic, amphipathic molecules that have gained tremendous popularity as alternatives to traditional antibiotics due to their lower propensity to develop bacterial resistance. However, the clinical developability of AMPs remains impeded due to shortcomings such as proteolytic instability and poor penetration leading to low bioavailability. AIMS To improve the access of AMPs to cells and subsequent bacteria killing, we evaluated the cell-penetrating and antimicrobial properties of three novel libraries of synthetic peptoids using Minimum Inhibitory Concentration, killing efficacy and membrane permeabilization assays against mycobacteria and Staphylococcus aureus. In addition, we investigated cell selectivity using mammalian cells to assess peptoid toxicity. RESULTS We showed that short tetrameric Rhodamine B-labeled peptoids composed of a balance of aromatic and lipophilic residues have potent selective antimicrobial activity against a range of microorganisms. The most potent candidates were active against drug-resistant S. aureus isolates as well as mycobacterial strains, with cell penetrating capabilities reported in HeLa and RAW 264.7 macrophage cells. CONCLUSIONS These data suggest that peptoids with novel dual functionalities may potentially be an interesting class of therapeutics and/or molecular delivery agents for anti-infective purposes.
Underwater Acoustic Energy Fields Visualization Based on Technology of Pre-Integrated Volume Rendering

The acoustic environment of the ocean influences on naval battles,so how to keep the view and depict the underwater environment has an important meaning towards raising the directing personnel comprehension and applications of the environment.Visualization provides a valid means to resolve this problem.With applying the technology of OpenGL multi-textures and pre-integrated volume rendering,volume rendering in underwater acoustic energy field visualization gives a new method to study the underwater environment.During the discussion of the result of visualization,the defect of conventional volume rendering was overcome without increasing the number of clipping planes and the time of rending,and commanders were helped to get the structure of underwater acoustic energy fields and a hand for the assistant decision-making was given.
Classification of Fault Severity in Induction Machine Systems Based on Temporal Convolutions and Recurrent Networks

Detection and severity identification of mechanical and electrical faults by means of noninvasive methods such as electrical signatures of induction machine have attracted much attention in recent years. Since operating conditions of machines and severity of faults in incipient stages influence the amplitude of fault index in the fault detection process, diagnosing fault occurrence and severity can be more complicated. In this study, an efficient method for fault detection and classification in induction machine based on deep neural networks is introduced. The introduced method applies the long short-term memory (LSTM) and fully convolutional neural networks (FCNs) in a conjoined manner. The authors use the FCN architecture for feature extraction from the time-series signal and augment it with LSTM to improve classification performance. This structure has not been previously applied for fault severity detection in induction machine systems. The authors avoid manual feature engineering and, by eliminating the preprocessing phase, directly use time series of electrical signals for fault detection and classifications. The experimental results have been carried out in different fault severities and loads. The analysis of the results and comparison with other deep and classical methods show that the faulty cases can be separated based on severity and load levels with a high accuracy (98.92%), which shows that the adopted architecture is successful in automatically extracting discriminative features from the signal.
Anthocyanins: Traditional Uses, Structural and Functional Variations, Approaches to Increase Yields and Products’ Quality, Hepatoprotection, Liver Longevity, and Commercial Products

Anthocyanins are water-soluble, colored compounds of the flavonoid class, abundantly found in the fruits, leaves, roots, and other parts of the plants. The fruit berries are prime sources and exhibit different colors. The anthocyanins utility as traditional medicament for liver protection and cure, and importance as strongest plants-based anti-oxidants have conferred these plants products different biological activities. These activities include anti-inflammation, liver protective, analgesic, and anti-cancers, which have provided the anthocyanins an immense commercial value, and has impelled their chemistry, biological activity, isolation, and quality investigations as prime focus. Methods in extraction and production of anthocyanin-based products have assumed vital economic importance. Different extraction techniques in aquatic solvents mixtures, eutectic solvents, and other chemically reactive extractions including low acid concentrations-based extractions have been developed. The prophylactic and curative therapy roles of the anthocyanins, together with no reported toxicity has offered much-needed impetus and economic benefits to these classes of compounds which are commercially available. Information retrieval from various search engines, including the PubMed®, ScienceDirect®, Scopus®, and Google Scholar®, were used in the review preparation. This imparted an outlook on the anthocyanins occurrence, roles in plants, isolation-extraction, structures, biosynthetic as well as semi- and total-synthetic pathways, product quality and yields enhancements, including uses as part of traditional medicines, and uses in liver disorders, prophylactic and therapeutic applications in liver protection and longevity, liver cancer and hepatocellular carcinoma. The review also highlights the integrated approach to yields maximizations to meet the regular demands of the anthocyanins products, also as part of the extract-rich preparations together with a listing of marketed products available for human consumption as nutraceuticals/food supplements.