Miracle-dz/UniFAR-FTUs huggingface dataset:
Highly reversible lithiation/delithiation in indium antimonide with hybrid buffering matrix

Sb‐based intermetallic materials have been extensively studied as electrodes for lithium‐ion batteries (LIBs) owing to the high discharge capacity and acceptable working voltage range. In this study, InSb nanocrystallites were homogeneously distributed and embedded into a combined matrix of amorphous carbon and rutile TiO2 by a facile two‐time ball‐milling process. The nanostructure of the composite exhibited a favorable synergistic effect of the three components (InSb: high‐capacity active material, TiO2: inorganic crystalline robust matrix, and C: carbonaceous amorphous conductive matrix), which not only supplied a buffering network to suppress the volume expansion of InSb during the Li+ ion intercalation/deintercalation process, but also enhanced the ionic/electronic conductivity of the anode material. Consequently, the InSb‐TiO2‐C anode delivered a long lifespan and remarkable rate performance. In addition, the anode showed a high reversible discharge capacity of 540 mAh g−1 even after 400 cycles at a high current rate of 500 mA g−1. Furthermore, the anode exhibited good capacity retention (87% at 2 A g−1 relative to the capacity at 0.1 A g−1). These results indicate the potential of InSb‐TiO2‐C nanocomposites for LIBs anode materials.
A Generic Indirect Deep Learning Approach for Multisensor Degradation Modeling

To monitor the degradation status of units and prevent unexpected failures in engineering systems, health index (HI)-based data fusion technologies have been rapidly developed by combining multiple sensor signals, which are helpful to understand the degradation processes of units and predict their remaining useful lifetime (RUL). Although promising, existing HI-based data fusion models for degradation modeling are still limited due to the restrictive assumptions made during the fusion or the degradation modeling processes, e.g., assuming the fusion model as a linear or kernel-based function from multiple sensor signals, or modeling the degradation process by a preselected basis function. Such assumptions are often invalid in industrial practice and may fail to accurately characterize the complicated relationships between multiple sensor signals and the underlying degradation process. To address the issue, this article proposes a generic indirect deep learning method that constructs an HI by combining multiple sensor signals to better characterize the degradation process. In particular, our innovative idea is to seamlessly integrate a deep neural network (DNN) and a long short term memory (LSTM) model to construct the HI by fusing multiple sensor signals and characterize the degradation process, which can be applied to the degradation modeling of various engineering systems. Domain knowledge including the concept of failure threshold and monotonicity of the degradation process is also considered to enhance the interpretability of the proposed method. For parameter estimation, we develop an indirect gradient descent (IGD) algorithm to train the proposed method. Simulation studies and a case study on the degradation of aircraft gas turbine engines are presented to validate the performance of the proposed method. Note to Practitioners—The article aims to develop a generic health index (HI)-based data fusion method for degradation modeling when multiple sensors are available to monitor the degradation status of a unit. Specifically, the developed method addresses two challenging questions in practice: 1) how to effectively combine multiple sensor signals to construct an HI that accurately characterizes the underlying degradation status and 2) how to flexibly model the degradation evolution based on the constructed HI. To implement this method in practice, four steps are included as follows: First, collecting multiple sensor signals and failure time of historical units. Second, constructing the HI and describing the underlying degradation process by training a deep neural network (DNN) model and a long short term memory (LSTM) model, respectively. Third, estimating model parameters using the proposed IGD algorithm. Fourth, constructing the HI of in-service units and predicting their remaining useful lifetime (RUL) using the constructed HI. The proposed method is expected to be able to characterize various degradation processes and be applied to the degradation modeling of different engineering systems.
Synthesis of CuxNi(1−x)O coral-like nanostructures and their application in the design of a reusable toxic heavy metal ion sensor based on an adsorption-mediated electrochemical technique

Nickel oxide, and non-stoichiometric and Cu-doped variants (NiO, Ni2O3 and CuxNi(1−x)O) possessing porous coral-like nanostructures, were prepared by a facile, low temperature, hydrothermal approach. Structural and morphological characterization was performed by X-ray diffractometry, X-ray photoelectron spectroscopy and FESEM imaging. The Cu-doped oxide was found to possess lattice strain which resulted in the induction of surface defects. The prepared materials were used as sensing materials for toxic Cr(VI) ions in aqueous medium. A novel sensing technique was proposed based on adsorption, which was found to be more effective in terms of reproducibility and reusability of the sensor than the conventional electrochemical sensing technique. The sensing mechanism was explained based on the phenomenon of adsorption. This makes the efficiency of the sensor surface-dependent rather than chemical reactivity-dependent, thereby making it a non-destructive sensing technique. The Cu-doped NiO nanostructure (10% Cu doping) was found to show maximum sensitivity (252.62 at 1 ppm) and high selectivity, together with a low response time (∼2 s), towards Cr(VI) ions relative to Cd(II), As(V) and Pb(II) ions. This was due to its enhanced surface properties compared with its un-doped variants. The effect of Cu doping on the nanostructure morphology and consequently on sensor efficiency was also studied. The limit of detection was found to be 1 ppm (1 mg L−1) of Cr(VI) ions in aqueous solution. This material, along with the technique proposed, can thus be advantageous for the cost-efficient monitoring of water quality and drinking water standards.
FAF1 blocks ferroptosis by inhibiting peroxidation of polyunsaturated fatty acids

Iron‐catalyzed peroxidation of polyunsaturated fatty acids (PUFAs) leads to ferroptosis. While scavenging reactions removing lipid peroxides such as that catalyzed by Gpx4 protect cells from ferroptosis, the mechanism through which cells prevent the initial physiological event in ferroptosis – the peroxidation of PUFAs, is not known. We previously identified Fas‐associated factor 1 (FAF1) as a protein directly interacting with PUFAs through its UAS domain. Here we report that the UAS domain of FAF1 and PUFAs form a globular structure that sequesters PUFAs into a hydrophobic core. This sequestration prevents PUFA peroxidation by limiting their access to iron. In the absence of FAF1‐mediated protection, cultured cells became sensitive to ferroptosis upon exposure to PUFAs, and mice developed hepatic injury upon consuming a diet enriched in arachidonic acid (AA), a PUFA. Our study suggests that the FAF1‐mediated sequestration of PUFAs from iron‐catalyzed peroxidation acts upstream of Gpx4 to protect cells from ferroptosis.
Railway stations as efficiency decision-making units: Input and output DEA model

It is known from the literature that efficiency is relative, since it applies to analyzed data only, and that it is identical only in application of two opposite models. In this paper we have revived two essentially different ways, by means of which for different data is reached the same efficiency: the input and the output model received using the DEA method (Data Envelopment Analysis). The information about efficiency is so necessary, however, not sufficient, because a new issue of selection of the most favorable data values or dilemma input vs. output DEA model is open. The solution of this simple theory is complex in practice, therefore, it is necessary to consider realization (difficulty and possibility) of targeted inputs (the input model) and targeted outputs (the output model). A very simple example of railway stations of the Passenger Transport Section Belgrade has shown that from an inefficient model to an efficient one lead: (I) reduction of the number of cashiers and the number of trains, in case of the input model, and (II) increase of the number of passengers and reduction of the number of trains, in case of the output model. In this way, the measures for improvement of efficiency fall within the domain of stations and operators, on one hand, and the market, on the other. Hence the recommendation for railway stations is that not only that they should be flexible themselves, in the context of the input model, but they should also keep up with the environment and fulfill the market conditions through a more effective service, which is the point of the output model.
Incidental thorax imaging findings in abdominal computed tomography: Results of a tertiary center

Background/Aim: Abdominal computed tomography (ACT) is a frequently used imaging modality. The large imaging area often results in the inclusion of lower sections of the thorax. It is known that some thoracic pathologies have symptoms that mimic upper abdominal pathologies. It is possible to detect many pathologies with careful examination of these levels. Previous studies have been conducted to detect incidental chest findings in imaging methods performed for emergency reasons such as trauma, but there is no study in the literature investigating incidental chest findings in ACT imaging. The aim of this study is to determine the incidental findings detected in thoracic sections included in abdominal computed tomography (CT) images and the prevalence of these findings in a tertiary center. Methods: This descriptive study includes 1133 patients who were admitted to Adiyaman Training and Research Hospital between 2017-2020 due to abdominal pain, diarrhea, vomiting, weight loss, constipation, and recurrent urinary tract infection, and underwent abdominal CT scanning. The necessary local ethics committee approval was obtained. Incidental findings in thoracic areas shown in the abdominal CT images included mediastinal findings, infectious findings, pulmonary lesions, pleural findings, lung parenchyma and pleural findings. Results: The mean age of the patients was 43.8 (18.7) years. Incidental findings were detected in 49.2% of the patients, the most common being those related to the lung parenchyma and the pleura (20.7%). The most common lung lesions were pulmonary nodules smaller than 1 cm (5%). In addition, 116 (10.2%) patients had infectious findings, among which the images of 31 (2.7%) suggested bacterial pneumonia factors, and 17 (1.5%) had interstitial pneumonia findings due to the SARS-CoV2 virus. Conclusion: According to the results of this study, evaluation of pathologies detected incidentally in the thorax in ACT sections may affect the treatment of patients. In addition, evaluation, follow-up, and early treatment of lung nodules that can be detected incidentally can prevent possible advanced stage malignancies. Although the symptoms and clinical statuses of the patients are very useful in evaluating the images, examination of every structure included in the imaging field may play a role in the early diagnosis and treatment of some pathologies.
Modeling Laser-Induced Incandescence of Soot Particles Produced In
A Premixed Ch4/O2/N2 Flat Flame

The present work deals with the modeling of an extensive set of laser-induced incandescence (LII) signals collected in an atmospheric CH4/O2/N2 premixed flat flame. To do so, a refined LII model built upon a comprehensive version of soot heatand massbalance equations has been used. This latter especially includes an absorption sub-model accounting for saturation of linear, singleand multi-photon absorption processes together with terms standing for soot oxidation and particle cooling by sublimation, conduction, radiation and thermionic emission. The comparison between the simulated and the experimentally-monitored data reported herein has been achieved based on LII fluence curves and time-decays obtained at different heights above the burner as well as on the basis of measured soot temperature profiles as a function of the laser fluence. In terms of results, the theoretical model implemented within this study turns out to correctly reproduce measured LII fluence curves and soot temperature profiles although it fails to correctly simulate LII time decays collected at high fluences. An analysis of the possible improvements to be operated to the so-implemented model eventually led to the conclusion that integrating an additional energy flux standing for soot annealing could allow obtaining improved agreements between theoretical and measured signals thus giving interesting insights for complementary works to be undertaken.
Performance Evaluation of RNN with Hyperbolic Secant in Gate Structure through Application of Parkinson’s Disease Detection

This paper studies a novel recurrent neural network (RNN) with hyperbolic secant (sech) in the gate for a specific medical application task of Parkinson’s disease (PD) detection. In detail, it focuses on the fact that patients with PD have motor speech disorders, by converting the voice data into black-and-white images of a recurrence plot (RP) at specific time intervals and constructing the detection model that combines RNN and convolutional neural network (CNN); the study evaluates the performance of the RNN with sech gate compared with long short-term memory (LSTM) and gated recurrent unit (GRU) with conventional gates. As a result, the proposed model obtained similar results to LSTM and GRU (an average accuracy of about 70%) with less hyperparameters, resulting in faster learning. In addition, in the framework of the RNN with sech in gate, the accuracy obtained by using tanh as the output activation function is higher than using the relu function. The proposed method will see more improvement by increasing the data in the future. More analysis on the input sound type, the RP image size, and the deep learning structures will be included in our future work for further improving the performance of PD detection from voice.
A New Spectrophotometer for Measuring the Spectral Reflectance of Gastric Mucous Membrane

AbstractA new spectrophotometer has been developed for measuring the spectral reflectance of the gastric mucous membrane. This spectrophotometer comprises an endoscope, spectrophotometer, optical multichannel analyzer, silicon photodiode linear image sensor with 1024 channels, and a microcomputer. With this equipment, it is possible to measure the spectral reflectance of gastric mucous membrane from 350 nm to 830 nm at 5-nm intervals, instantaneously.In this paper, the spectrophotometer is described, the accuracy is analyzed, and the results of spectral reflectance measurements of gastric mucous membrane for 26 patients are presented.
Entry Regulation and Competition - Evidence from Retail and Labor Markets of Pharmacists

We examine a deregulation of German pharmacists to assess its effects on retail and labor markets. From 2004 onward, the reform allowed pharmacists to expand their single-store firms and to open or acquire up to three affiliated stores. This partial deregulation of multi-store prohibition reduced the cost of firm expansion substantially and provides the basis for our analysis. We develop a theoretical model that suggests that the general limitation of the total store number per firm to four is excessively restrictive. Firms with high managerial efficiency will open more stores per firm and have higher labor demand. Our empirical analysis uses very rich information from the administrative panel data on the universe of pharmacies from 2002 to 2009 and their affiliated stores matched with survey data, which provide additional information on the characteristics of expanding firms before and after the reform. We find a sharp immediate increase in entry rates, which continues to be more than five-fold of its pre-reform level after five years for expanding firms. Expanding firms can double revenues but not profits after three years. We show that the increase of the number of employees by 50% after five years and the higher overall employment in the local markets, which increased by 40%, can be attributed to the deregulation.
Exploring Vertebrate Behavior at Mesocarnivore Carcass Sites: Implications for Non-Trophic Parasite Transmission

The spatiotemporal distribution of parasites in ecosystems is heterogeneous. High infection risk is often associated with aggregations of animals around attractive resources. Here, we explore the behavior of potential hosts of non-trophically transmitted parasites at mesocarnivore carcass sites. We used videos recorded by camera traps at 56 red fox (Vulpes vulpes) carcasses and 10 carcasses of other wild carnivore species in three areas of southeastern Spain. In general, scavenging species, especially wild canids, mustelids and viverrids that display rubbing behavior, were more exposed to infection risk at carnivore carcass sites than non-scavenging and domestic species. The red fox was the species that most frequently contacted carcasses and marked and rubbed carcass sites. Foxes contacted heterospecific carcasses more frequently and earlier than conspecific ones, and close contact was more frequently observed at heterospecific carcasses. Thus, foxes seemed to avoid contacting carcasses in those periods and at carcass types of maximum risk of acquiring parasites. Overall, our results suggest that infection risk at carnivore carcass sites may take place mainly for visitor-carcass contact or contact with feces and urine rather than direct contact between visitors. Moreover, contact events between scavengers and carnivore carcasses are far more frequent than consumption events, which indicates that scavenger behavior is more constrained by the risk of acquiring meat-borne parasites than non-trophically transmitted parasites. This study contributes to filling key gaps in understanding the role of carrion in the landscape of disgust, which may be especially relevant in the current global context of emerging and re-emerging pathogens.
Density Based Mode Within Radius and Its Application to EEG based Emotion Recognition

Statistical parameters such as center points are widely used to represent patterns in machine learning. There are three popular center points: mean, median, and mode. Mean and median are pervasively used as features to represent patterns, but mode is not because of its weakness. One of the major problems in the conventional definition of mode occurs when the distribution is multimodal with tied multiple maximum points. Hence, in this paper we propose a modified version of mode, which utilizes the density within a radius. The effectiveness of proposed mode is demonstrated in the emotion recognition application using the Electroencephalogram (EEG). An experimental result of amalgamating statistical parameters with a new modified parameter suggests the superiority of the proposed statistical parameter over the traditional measures.
Medially-invasive cholesteatoma

Cholesteatoma is life-threatening disease due to its intracranial complications. Some of cholesteatoma cases we define as aggressive cholesteatoma. In this category we include children cholesteatoma, medially-invasive cholesteatoma and petrous apex cholesteatoma which does not originate in the petrous apex. Another special form of cholesteatoma is petrous bone cholesteatoma. Retrospective study of 176 cholesteatoma operations (38.85%) from 453 consecutive middle ear surgeries for 4 years and 4 months in Trakia Park Hospital Stara Zagora, Bulgaria were rewieved. The same surgeon performs 106 Primary Cholesteatoma Surgery, 49 Revision Surgery and 21 Second-Look Surgery. In this 176 cholesteatoma cases the cholesteatoma matrix involve the otic capsule, petrous apex or skull base in 5 patients, who are defined as medially-invasive cholesteatoma (2.84%). In this medially-invasive cholesteatoma patients surgical resection with canal wall down operation remains the best treatment option.
The unique optical and physical properties of soft, transparent, stimulus-sensitive nanocomposite gels

A new type of polymer hydrogel with a unique organic (polymer)/inorganic (clay) network structure has been synthesized by the in-situ free-radical polymerization of N-isopropyl acrylamide (NIPA) in the presence of exfoliated clay platelets in an aqueous medium. The resulting nanocomposite hydrogels (NC gels) consisting of PNIPA and clay (hectorite) exhibit extraordinary optical, mechanical and swelling properties. NC gels also show a clear phase transition due to the coil-to-globule transition of the PNIPA chains. It was observed that the phase-transition temperature (lower critical solution temperature: LCST), defined as the onset temperature of a steep transmittance drop, shifts to a lower or higher temperature than that of pure water (≅ 34 °C) when conditions are altered. When an inorganic salt, such as NaCl, CaCl2 and AlCl3, was added to the surrounding water, the LCST of the NC gels generally shifted to a lower temperature, in a manner almost inversely proportional to the salt concentration. On the other hand, when the NC gels adsorbed cationic surfactant, e.g. hexadecyl trimethyl ammonium chloride, the LCST shifted toward a higher temperature, although the shift and its profile strongly depended on the adsorption conditions, such as the surfactant concentration and the adsorption time. Consequently, non-thermo-sensitive NC gel was obtained by using a surfactant aqueous solution with a concentration higher than the critical micelle concentration.