Miracle-dz/UniFAR-FTUs huggingface dataset:
COMPARISONS OF THE USE OF DESICCATORS WITH OR WITHOUT VACUUM FOR WATER SORPTION AND GLASS TRANSITION STUDIES

Water sorption properties of food solids are often determined gravimetrically using samples stored over various salt solutions. In such studies desiccators with or without vacuum can be used. However, the apparent equilibrium water contents may differ depending on the technique used. The objectives of the present study were to investigate the resultant isotherms when desiccators with or without vacuum were used for freeze-dried wheat dough, gluten, denatured gluten, native, and gelatinized starch and to determine the glass transition temperatures for gluten, denatured gluten, and gelatinized starch using Differential Scanning Calorimetry (DSC). Using vacuum desiccators, all five materials were dehydrated over P2O5 until a constant weight was achieved. Using desiccators without vacuum, gluten and native starch were dried in vacuum for 3 days at 30 °C. Triplicate samples were rehumidified in desiccators with or without vacuum over salt solutions ranging from aw 0.113 to 0.843 at 24–25 °C. The BET and GAB sorption isotherm models were fitted to two water sorption data. Equilibrium water contents were achieved after 2–3 days of storage using vacuum desiccators, while equilibrium time was 2–3 weeks using desiccators without vacuum over the whole range of relative humidity (RH) for all materials. The GAB sorption isotherm for all materials showed clearly that water contents were higher at the high water activities (>0.6) and lower at the low water activities (<0.4) using vacuum desiccators, because of a probable difference in humidity between the external atmosphere and inside the desiccators with or without vacuum.
Effects of Temperature on Structural Properties of Hydrated Montmorillonite: Experimental Study and Molecular Dynamics Simulation

Montmorillonite (MMT) is highly sensitive to environmental changes and therefore plays a key role in the structural evolution of rocks and soils and even damage and disasters. The effects of important environmental factors (the temperature and water content) on MMT structural properties require in-depth study. The structure and morphology of sodium montmorillonite (Na-MMT) and its thermal products (micro-nanoparticles) were characterized by X-ray diffraction (XRD), Fourier transform infrared (FTIR) spectroscopy, and scanning electron microscopy (SEM). A molecular dynamics (MD) simulation was performed to investigate how temperature (below the failure temperature of the Na-MMT crystal layer) affects the structural properties of hydrated MMT. (1) The laboratory results showed that increasing the temperature significantly affected water molecules, and the particle aggregates exhibited inhomogeneous thermal expansion. The interlayer structure collapsed at 500–700°C. (2) In the simulation, the pull-off force inhibited interactions among oxides, crystal layers on both sides of the sample, and the bonding structure of water molecules, thus exposing the stress on the bonding body for analysis. The MMT ultimate stresses in the X, Y, and Z directions all trended downward with increasing water content and temperature. (3) Environmentally induced damage was most likely to occur in the Z direction. Increasing the number of interlayer water molecules increased the layer spacing and considerably weakened van der Waals forces, such that the roles of the electrostatic force and the interlayer hydrogen bond network gradually became significant. The most significant impact of increasing the temperature was reflected in the hydrogen bonding network, resulting in the destruction of the interlayer water bridge, the gradual failure of the layered bonding structure, and the formation or development of cracks. This improved understanding of the structural properties of MMT aggregates under environmental change advances research on the evolutionary behaviour of nano-, micro-, and macrostructures of rocks and soils.
Assessment of the Contribution of Inter-Territorial Interaction in the Development of the National Economy

Determination of the main indicators of economic growth is a key research question. In the course of solving this problem, many scientists agree that one of the drivers of economic development is interregional interaction. This article contains the results of a quantitative assessment of the role of interregional interaction for the Russian economy. The study is based on a modification of the Solow economic growth model which includes the level factor of interregional interaction. For a quantitative assessment of the effects of interregional interaction on economic development, a set of factors was determined that make it possible to assess the advantage that localization in a particular region can give to economic agents. The selected indicators have a direct impact on the potential and intensity of interregional interaction. The database for assessing the coefficients of the models and calculating the index of interregional interaction was formed on the basis of data from the website of the Federal State Statistics Service for the period from 2010 to 2018 for 83 regions of Russia. To select the best model of economic growth, considering interregional interaction, the authors evaluated different types of models: fixed effects model, time fixed effects model, random effects model, and time random effects model. The thesis about the importance of interregional interaction as one of the most important factors of production in modern Russia has found its empirical confirmation: the share of interregional interaction in ensuring the economic growth of Russian regions in the years 2015–2018 averaged 33%. The developed model of economic growth and the consideration of interregional interaction is universal and can be applied to various administrative-territorial units.
Methane generation from the digestion of whey in a two-stage system

Abstract A two‐phase anaerobic process consisting of an acid and a methane phase in series was studied for the conversion of whey to methane. The acid phase was studied at retention times of 8, 16 and 24 h at pH 4 and 5. Retention time of the methane phase was 72 h and the pH was not adjusted during the study. Samples from gas collection systems were analyzed for methane and carbon dioxide composition. Samples from effluent lines of the acid and methane phases were analyzed for suspended solids, volatile solids, volatile acids and Chemical Oxygen Demand (COD) concentrations. It was found that a 24 h retention time of the acid phase at pH 5 was the most effective treatment of whey for producing methane and reducing the pollutional load of the final effluent in the methane phase. This set of experimental conditions produced the largest amount of short chain volatile acids in the acid phase, creating a desirable substrate for the methane phase and for the methane generating microorganisms. The effluent from .
Study of Non-Periodic Pinhole Array Filter for Decreasing High-Order Noise for Compact Holographic Display

The non-periodic pinhole array filtering of a spatial light modulator (SLM) is proposed for filtering the high-order noise and DC noise of a holographic display. Conventionally, DC and high-order noise sources are filtered by a 4f filtering system. Because the 4f filtering system requires a long optical path length, noise filtering is a stumbling block when attempting to realize a compact holographic display. By contrast, the proposed method simply uses a thin filter fabricated by photolithography. In order to verify this concept, we confirmed the feasibility of the filter with a numerical simulation and with a custom-made non-periodic pinhole array filter used in a practical experiment. The proposed method was shown to have the potential to be used in applications ranging from compact wearable devices to table-top holographic displays.
Predicting users' continued engagement in online health communities from the quantity and quality of received support

Online health communities (OHCs) have been major resources for people with similar health concerns to interact with each other. They offer easily accessible platforms for users to seek, receive, and provide supports by posting. Taking the advantage of text mining and machine learning techniques, we identified social support type(s) in each post and a new user's support needs in an OHC. We examined a user's first‐time support‐seeking experience by measuring both quantity and quality of received support. Our results revealed that the amount and match of received support are positive and significant predictors of new users' continued engagement. Our outcomes can provide insight for designing and managing a sustainable OHC by retaining users.
Biochemical responses of mycorrhizal-inoculated Lamiaceae (Lavender, Rosemary and Thyme) plants to drought: a field study

ABSTRACT Mycorrhizal symbiosis may help plants to compensate the water deficit-induced macronutrients, secondary metabolites and osmotic adjustments as the basis of crop production. The present investigation was undertaken to evaluate the physiological responses of the lavender (Lavandula officinalis L.), rosemary (Rosmarinus officinalis L.) and thyme (Thymus vulgaris) host plants to inoculation with mycorrhizal fungi (Funnelliformis mosseae, Rhizophagus irregularis compared with non-inoculated control treatment) in different irrigation levels (irrigation at 75% field capacity, 50% field capacity and rainfed). The 2-year (2015–2016) factorial experiment was conducted based on a randomized complete block design with three replications at Urmia University. The results showed that the stress treatments promoted the generation of osmotic adjustment (total soluble sugars and proline) in non-inoculated plants more than plants inoculated with fungi species. Nutrient (nitrogen, phosphorous and potassium) concentrations and leaf chlorophyll, reduced by water-limiting irrigation, were enhanced with mycorrhizal inoculation. Colonized plants were better adapted and had greater relative water content under stressful conditions due to higher absorption sites for water and nutrients. Also, stress treatments stimulated the phenylalanine ammonia-lyase, DPPH radical-scavenging activity and, the total phenolic and flavonoids content in non-inoculated plants more than that inoculated with two fungi species.
Fuzzy Simheuristics: Solving Optimization Problems under Stochastic and Uncertainty Scenarios

Simheuristics combine metaheuristics with simulation in order to solve the optimization problems with stochastic elements. This paper introduces the concept of fuzzy simheuristics, which extends the simheuristics approach by making use of fuzzy techniques, thus allowing us to tackle optimization problems under a more general scenario, which includes uncertainty elements of both stochastic and non-stochastic nature. After reviewing the related work, the paper discusses, in detail, how the optimization, simulation, and fuzzy components can be efficiently integrated. In order to illustrate the potential of fuzzy simheuristics, we consider the team orienteering problem (TOP) under an uncertainty scenario, and perform a series of computational experiments. The obtained results show that our proposed approach is not only able to generate competitive solutions for the deterministic version of the TOP, but, more importantly, it can effectively solve more realistic TOP versions, including stochastic and other uncertainty elements.
Sturdy goals coverage for power harvesting Wi-Fi detector coterie

Power harvesting Wi-Fi detectors Networks structure the dream of web of things frameworks. Vitality reaping hubs might be sent deliberately to screen at least one targets along with a valuable resource. Be that as it may, as these hubs depend on surrounding vitality assets along with sun oriented, they experience irregular power appearances. Subsequently, they may debilitate their collected force simultaneously as observing an objective. To this quit, network administrators require a tough arrangement that secure all goals are observed continually throughout whatever time length with a minded possibility. On this paper, we reckon three novel solid protection prerequisites; each essential safeguard with chance), where is the opportunity of fiascos. To start with, sensor hubs should now not go through more than their absolute collected vitality throughout T time allotments. Second, the power use of each detector hub need to no longer surpass the quality collected in each opening. Third the power consumption of sensor hubs should not surpass the power collected up to the current opening. We figure threat controlled stochastic applications that consolidate those necessities and cure them utilizing the example basic Approximation (SAA) technique. We affirm through tremendous reenactment examines that our applications are fit for figuring detector hubs actuation occasions that meet a given inclusion disappointment likelihood.
Measurement and Correlation of the Solid−Liquid−Gas Equilibria for the Carbon Dioxide + S-(+)-Ibuprofen and Carbon Dioxide + RS-(±)-Ibuprofen Systems

The pressure p−temperature T projections of solid−liquid−gas (S−L−G) three-phase coexistence lines for the carbon dioxide + S-(+)-ibuprofen system and the carbon dioxide + RS-(±)-ibuprofen system were measured by the first melting point method in which the initial appearance of the liquid phase was observed. The experimental p−T projections of the S−L−G lines for the two systems were quite different from each other. The experimental p−T projections of the S−L−G lines were also correlated by the Peng−Robinson equation of state with a binary interaction parameter introduced into an attraction term. The present model gave good correlation results for all of the experimental S−L−G lines with maximum average absolute relative deviations of 0.14% for the carbon dioxide + S-(+)-ibuprofen system and 0.90% for the carbon dioxide + RS-(±)-ibuprofen system.
Time for relative velocity optimal time approach in internet of vehicle communication

In the present era of Internet of Vehicles recommending or developing an optimal routing protocol and its architecture has the potential to reduce the overhead is considered to be the biggest task to attain the scalability and adaptability. This paper propose an architecture that meets the network standard and evolves as per the defined software for driving the “Internet of Vehicles” over dynamic networks that partitions the data and control planes in the proposed protocol. Based on speed of vehicle and distance between nodes we proposed Time for Relative Velocity (TRV) road aware system that enhances the performance over infrastructure oriented vehicular network that partitions roads into distinct segments based on multi hop method for exploring the cellular networks and their relay controlled messages with minimal latency. We also propose a mathematical model to implement dynamic vehicle topology by estimating various paths and selecting the shortest one and further suggest the same to the controller. And the results attained in this paper denote the performance obtained by implementing the proposed protocol which consumes very little routing overhead and almost negligible failure rate.
Lexical activation in late bilinguals: effects of phonological neighbourhood on spoken word production

ABSTRACT This research explores patterns of lexical activation of both languages of a bilingual when producing spoken words. Specifically, it investigates the influence of within- and cross-language measures of phonological neighbourhood, on the English picture naming performance of a diverse group of French (L1) – English (L2) late bilinguals. A novel phonological neighbourhood density measure was used that took into account the phonological and lexical constraints of this sample of late bilinguals. Results showed that both within- and cross-language neighbours influenced picture naming performance and that the length of exposure to L2 and familiarity of the target modulated these effects, with both facilitatory and inhibitory effects observed. Additionally, the previously well-attested cognate facilitation effect decreased with L2 language exposure. These results support interactive models of word production, within a language non-specific view of lexical access, and show the dynamic nature of a bilingual’s lexical connections.
Learning Deep Features for Stain-free Live-dead Human Breast Cancer Cell Classification

Automated cell classification in cancer biology is a challenging topic in computer vision and machine learning research. Breast cancer is the most common malignancy in women that usually involves phenotypically diverse populations of breast cancer cells and an heterogeneous stroma. In recent years, automated microscopy technologies are allowing the study of live cells over extended periods of time, simplifying the task of compiling large image databases. For instance, there have been several studies oriented towards building machine learning systems capable of automatically classifying images of different cell types (i.e. motor neurons, stem cells). In this work we were interested in classifying breast cancer cells as live or dead, based on a set of automatically retrieved morphological characteristics using image processing techniques. Our hypothesis is that live-dead classification can be performed without any staining and using only bright-field images as input. To our knowledge, there is no previous work attempting this task on in vitro studies of breast cancer cells, nor is there a dataset available to explore solutions related to this issue. We tackled this problem using the JIMT-1 breast cancer cell line that grows as an adherent monolayer. First, a vast image set composed by JIMT-1 human breast cancer cells that had been exposed to a chemotherapeutic drug treatment (doxorubicin and paclitaxel) or vehicle control was compiled. Next, several state-of-the-art classifiers were trained based on convolutional neural networks (CNN) to perform supervised classification using labels obtained from fluorescence microscopy images associated with each bright-field image. Model performances were evaluated and compared on a large number of bright-field images. The best model reached an AUC = 0.941 for classifying breast cancer cells without treatment. Furthermore, it reached AUC = 0.978 when classifying breast cancer cells under drug treatment. Our results highlight the potential of machine learning and computational image analysis to build new diagnosis tools that benefit the biomedical field by reducing cost, time, and stimulating work reproducibility. More importantly, we analyzed the way our classifiers clusterize bright-field images in the learned high-dimensional embedding and linked these groups to salient visual characteristics in live-dead cell biology observed by trained experts.
Kilohertz Macromolecular Crystallography Using an EIGER Detector at Low X-ray Fluxes

Time-resolved in-house macromolecular crystallography is primarily limited by the capabilities of the in-house X-ray sources. These sources can only provide a time-averaged structure of the macromolecules. A significant effort has been made in the development of in-house laser-driven ultrafast X-ray sources, with one of the goals as realizing the visualization of the structural dynamics of macromolecules at a very short timescale within the laboratory-scale infrastructure. Most of such in-house ultrafast X-ray sources are operated at high repetition rates and usually deliver very low flux. Therefore, the necessity of a detector that can operate at the repetition rate of the laser and perform extremely well under low flux conditions is essential. Here, we present experimental results demonstrating the usability of the hybrid-pixel detectors, such as Eiger X 1M, and provide experimental proof that they can be successfully operated to collect macromolecular crystallographic data up to a detector frame rate of 3 kHz from synchrotron sources. Our results also show that the data reduction and structural analysis are successful at such high frame rates and fluxes as low as 108 photons/s, which is comparable to the values expected from a typical laser-driven X-ray source.
Randomized Trial of Hymovis® versus Synvisc® on Matrix Metalloproteinases in Knee Osteoarthritis

LEVEL OF EVIDENCE: 1B SUMMARY Background. This study aims to evaluate the effects of HYADD 4-G, a not chemically cross-linked HA (Hyaluronic Acid) derivative, and hylan G-F 20, a cross-linked HA product, on synovial matrix metalloproteinases and cytokines in patients suffering from knee osteoarthritis. Methods. 31 patients were randomized to receive HYADD 4-G or hylan G-F 20. Synovial fluid was collected before and one week after the first injection. Activity of MMP-3, MMP-2, MMP-13, IL-6 and other cytokines were measured. Changes in synovial fluid neutrophils and lymphocytes were analyzed. The VAS, the WOMAC questionnaire, and a Physician Global Assessment (PhGA VAS) were recorded. Results. A trend towards a greater decrease in MMP-3 activity was observed in the Hymovis® group. Active MMP-2 and MMP-13 decreased in both groups. IL-6 levels also decreased significantly in both groups. Median change in neutrophils from baseline was significantly different between the treatment groups. No differences between the treatment groups were observed for the WOMAC, VAS and PhGA VAS scores. No serious adverse events were reported. Conclusions. These findings demonstrate that intra-articular HA injections in patients with knee osteoarthritis tend to protect cartilage structural integrity reducing the activity of key proteolytic enzymes implicated in cartilage degradation as well as inflammatory mediators.
Towards Automatic Depression Detection: A BiLSTM/1D CNN-Based Model

Depression is a global mental health problem, the worst cases of which can lead to self-injury or suicide. An automatic depression detection system is of great help in facilitating clinical diagnosis and early intervention of depression. In this work, we propose a new automatic depression detection method utilizing speech signals and linguistic content from patient interviews. Specifically, the proposed method consists of three components, which include a Bidirectional Long Short-Term Memory (BiLSTM) network with an attention layer to deal with linguistic content, a One-Dimensional Convolutional Neural Network (1D CNN) to deal with speech signals, and a fully connected network integrating the outputs of the previous two models to assess the depressive state. Evaluated on two publicly available datasets, our method achieves state-of-the-art performance compared with the existing methods. In addition, our method utilizes audio and text features simultaneously. Therefore, it can get rid of the misleading information provided by the patients. As a conclusion, our method can automatically evaluate the depression state and does not require an expert to conduct the psychological evaluation on site. Our method greatly improves the detection accuracy, as well as the efficiency.