Miracle-dz/UniFAR-FTUs huggingface dataset:
Photoacoustic monitoring of corticosteroids’ vasoconstrictive effects in the skin

Corticosteroids are commonly used medications for dermatological diseases. The main mechanisms of corticosteroids are vasoconstriction and anti-inflammatory. In medical field, its effectiveness is determined based on the degree of skin whitening caused by vasoconstriction. In this study, we first quantitatively evaluate the vasoconstriction induced by corticosteroids using photoacoustic microscopy (PAM). We longitudinally monitor vascular density and observe vasoconstriction by corticosteroids. Further, the changes in vascular density are quantified in each skin layer. From these results, we believe that PAM could potentially be a useful evaluation tool to predict the effectiveness of the corticosteroids in dermatology.
Robust Sampling-Based Control of Mobile Manipulators for Interaction With Articulated Objects

In this article, we investigate and deploy sampling-based control techniques for the challenging task of the mobile manipulation of articulated objects. By their nature, manipulation tasks necessitate environment interactions, which require the handling of nondifferentiable switching contact dynamics. These dynamics represent a strong limitation for traditional gradient-based optimization methods, such as model-predictive control and differential dynamic programming, which often rely on heuristics for trajectory generation. Sampling-based techniques alleviate these constraints but do not ensure robots' stability and input/state constraints either. On the other hand, real-world applications in human environments require safety and robustness to unexpected events. For this reason, we propose a novel framework for safe robotic manipulation of movable articulated objects. The framework combines sampling-based control together with control barrier functions and passivity theory that, thanks to formal stability guarantees, enhance the safety and robustness of the method. We also provide the practical insights that enable robust deployment of stochastic control using a conventional central processing unit. We deploy the algorithm on a ten-degree-of-freedom mobile manipulator robot. Finally, we open source our generic and multithreaded implementation.
The Future of Extreme Weather Events - Advanced Machine Learning and Artificial Intelligence for Democratic Institution preparedness and enhanced National Food Security!

Extreme weather events have become the norm of our day-to-day lives! We hear long running heat waves, flash floods, extreme droughts, fires, and failed monsoons. These extreme weather events have an unprecedented impact on psychological and quality of life on the population, especially the poor and underprivileged will bear most of the impact in terms of loss of economic opportunities and un-sustained livelihood. The result is increased undernourishment and food insecurity. The preparedness of future democratic governments and public distributed systems rest in harnessing prognostic markers from data using advanced analytics from past weather events. The well-prepared governments apply multi-modal interdisciplinary governmental data systems to perpetually analyze and infuse departments to be ready to tackle any potential disruptions to the lives of citizens, especially poor and marginalized parts of the societies that could fall into vicious cycle of poverty-health-and-loss of economic opportunities. The paper proposes innovative Machine Learning Models to address food security concerns to the extreme weather events.
Predictive model of frailty onset using Bayesian network

In this study, we constructed a model for predicting the future frailty status of individuals based on information regarding their current lifestyle habits. In recent years, as the global population ages, the number of people certified for long-term care has increased. In an effort to prevent the need for long-term care, prediction of the onset of frailty, which is positioned as a preliminary stage of the caregiving state, is gaining attention. However, the onset of frailty takes a long time, and the incidence of frailty differs according to one’s health literacy. Therefore, a prediction is extremely difficult to achieve. In this study, we constructed a prediction model using a probabilistic latent semantic analysis and a Bayesian network. First, the probabilistic latent semantic analysis was conducted to classify individuals according to their health literacy. Second, we constructed a future frailty prediction model using a Bayesian network for each health literacy segment. As a result, we clarified the following: It is more appropriate to predict a future frailty by predicting the future lifestyle using information on the current lifestyle. In addition, more accurate prediction models can be constructed when individuals are divided based on their health literacy.
Reassessing cow comfort measures on Canadian dairy farms after a recommendation of improvements

Abstract In a previous study (Assessment 1), an on-farm assessment tool was used to establish a standard across 60 Quebec dairy farms using animal-, stall-, and management-based measures of cow comfort. The objective of the present follow-up study (Assessment 2) was to perform a reassessment using an identical cow comfort assessment tool on a subset of the original farms expected to benefit most from applying the recommended changes, and to determine the effects of the modifications on cow comfort. From the subsample of farms, 24 reported applying stall modifications based on recommendations from Assessment 1 with the aim of promoting cow comfort (Adopters), while the remaining 10 farms did not (Non-adopters). The assessment tool included 19 target areas, grouped into 9 critical areas based on measures of cow comfort that considered housing, feed-water, health, and welfare. The on-farm report of Assessment 1 was found to increase producer awareness of issues in cow comfort measures on farms, as shown by an improvement in stall design according to recommendations based on cow body dimensions and reduced injury prevalence. Routine reassessment of cow comfort measures may be important to use in supporting target achievement improvements as part of welfare improvement strategies.
Circulating Nitric Oxide and Metabolic Syndrome in Arab Children and Adolescents: A Case–Control Study

Background: Metabolic syndrome (MetS) represents a cluster of known cardiometabolic risk factors, which elevates the risk of type 2 diabetes mellitus (T2DM), atherosclerotic cardiovascular disease (CVD) and chronic kidney disease (CKD) in adults and, only recently, even in children and adolescents. Circulating nitric oxide (NOx) has been observed to influence MetS risk factors in adults, but this has been scarcely investigated in children. The aim of the present study was to determine whether circulating NOx levels correlate with known components of MetS in Arab children and adolescents. Methods: Anthropometrics, serum NOx, lipid profile and fasting glucose levels were measured in 740 Saudi Arabs aged 10–17 years (68.8% girls). The presence of MetS was screened using the criteria of de Ferranti et al. Results: Overall, serum NOx levels were significantly higher in MetS participants compared to non-MetS (25.7 µmol/L (10.1–46.7) versus 11.9 µmol/L (5.5–22.9), p < 0.001) even after adjustments for age, BMI and sex. With the exception of elevated blood pressure, higher circulating NOx significantly increased the odds for MetS and its components. Lastly, receiver operating characteristics (ROC) showed that NOx, as a diagnostic marker for MetS, had good sensitivity and was higher in boys than girls (all MetS participants: area under the curve (AUC) = 0.68, p < 0.001), (girls with MetS: AUC = 0.62, p = 0.002), (boys with MetS: AUC = 0.83, p < 0.001)). Conclusions: MetS and most of its components were significantly associated with circulating NOx levels in Arab adolescents and may be a promising diagnostic biomarker for MetS.
Intrusion Detection and Network Information Security Based on Deep Learning Algorithm in Urban Rail Transit Management System

The exploration of the intrusion detection effect of urban rail transit management system aims to further improve the safety performance of the traffic field in urban construction. Thus, the deep convolution neural network model AlexNet with more network layers and stronger learning ability is adopted and improved, to ensure the safe operation of urban rail transit. Meanwhile, the GRU (Gate Recurrent Unit) neural network is introduced into the improved AlexNet to build an intrusion detection model of urban rail transit management system. Finally, the model performance is verified through the collected data and simulation experiments. Through the comparative analysis of the model and other scholars’ models in related fields, the recognition accuracy of intrusion detection of the intrusion detection model reaches 96.00%, which is at least 1.55% higher than that of other neural network models. Besides, its training time is stable at about 55.05 seconds, and the test time is stable at about 22.17 seconds. Moreover, the analysis result of data transmission security performance indicates that the data message delivery rate of this model is more than 80%, the data message leakage rate and packet loss rate are less than 10%, and the average delay is basically stable at about 350 milliseconds. Therefore, the constructed model can achieve high data transmission security performance under the premise of ensuring prediction accuracy, which can provide experimental basis for improving the safety performance of rail transit systems in smart cities.
Nutritional, Physicochemical, Microstructural, Rheological, and Organoleptical Characteristics of Ice Cream Incorporating Adansonia digitata Pulp Flour

Ice cream’s appeal is unrivaled. Nonmilk and milk ingredients in ice cream formulas affect their nutritional value, structure, and organoleptical qualities. Seeking novel dietary ingredients instead of artificial flavoring compounds is vital for improving ice cream taste preference, adding antioxidants, and increasing nutritional value. The current study examines the feasibility of manufacturing a new flavored ice cream with excellent dietary value using Adansonia digitata L. (Baobab) fruit pulp (ADFP). The prepared ice cream’s physicochemical and microbiological quality, and rheological, microstructural, and organoleptic properties were investigated. Using ADFP instead of skim milk powder with a partial or complete replacement, five ice cream samples were produced and marked as IB-0, IB-25, IB-50, IB-75, and IB-100. Chemical characteristics were not noticeably impacted except protein and ash, which considerably decreased with increasing ADFP levels. Increasing ADFP in the samples increased titratable acidity and reduced pH. All ice cream samples were microbiologically acceptable with no pathogenic bacteria. By increasing ADFP in the samples, the daily values (%DV) of sodium, potassium, and magnesium were not considerably affected. Calcium reduced from 14.91% in IB-0 to 7.75% in IB-100. All microelements found in the study rose considerably as ADFP increased. Increasing ADFP levels significantly boosted antioxidant levels. The IB-100 sample had the highest total phenolic content (149.29 mg GAE 100 g−1), antioxidant activity (98.12 µmol of TE 100 g−1), total flavonoids (5.96 mg QE 100 g−1), and total flavanols (4.01 mg QE 100 g−1). The inclusion of ADFP had a beneficial effect on the color of the samples. It did not negatively affect the ice cream’s organoleptic acceptability as determined by organoleptic, rheological, and microstructural examinations. Interestingly, when skim milk powder was replaced with 50% and 75%, the hardness, viscosity, and aqueous phase separation were significantly improved. In conclusion, ADPF improves the nutritional value of ice cream and can be used as a natural coloring ingredient without affecting microstructural and rheological properties.
Artificial Hummingbird Algorithm Based Optimal Secondary Control for Islanded Microgrid

Microgrids based on power electronics are made up of several voltage source inverters (VSIs) that are harmony operated in parallel. This paper presents a two-level hierarchical control technique for parallel inverters. The virtual impedance-based droop control is used in the primary control level based on alpha/beta stationary reference frame for equal active and reactive power sharing. The voltage amplitude and frequency deviations resulted from the primary controller are corrected by the secondary control level (SCL). Control system parameters are tuned using Artificial Hummingbird Algorithm after comparing the performance with other two optimization algorithms which are Particle Swarm and Grey Wolf Optimization. The control system's performance in both steady-state and dynamic conditions is validated through simulation.
Predatory Performance of Chrysoperla carnea (Neuroptera: Chrysopidae) on Jacobiasca lybica (Hemiptera: Cicadellidae) under Laboratory Conditions

ABSTRACT Jacobiasca lybica (Bergevin & Zanon) (Hemiptera: Cicadellidae) is a polyphagous pest that damages grapevines in Europe and citrus fruits in Morocco. Management of this insect is based mainly on chemical control, but there is an urgent need to develop an alternative management tool, such as biological control. In this study, we evaluated the predation rate of the first-to third-instar Chrysoperla carnea (Stephens) (Neuroptera: Chrysopidae) on J. lybica under laboratory conditions. The predation rate of each lacewing instar was determined 24 h after placing an individual lacewing larva in a Petri dish containing a freshly detached grape leaf with 15 adult and five immature leafhoppers. The average predation rate by the third instar was 47.8%, which was significantly higher than that of the second instar (31.1%). The predation rate was not different between the first (21.7%) and second instars. The predation rates for leafhopper nymphs (46.7%) were significantly higher than for the adults (29.1%).
Controllable Connection of Fe2Se3 Double Chains and Fe(dien)2 Complexes for Organic–Inorganic Hybrid Ferrimagnet with a Large Coercivity

Organic–inorganic hybrid materials built by inorganic and organic building units have attracted intensive interest in the past decades due to unique chemical and physical properties. However, rare organic–inorganic hybrid materials show excellent permanent magnetic properties. Here, we develop a facile chemical solution method to bottom-up synthesize a new hybrid (Fe2Se3)2[Fe(dien)2]0.9. This hybrid phase with the space group P21/c (14) possesses a rodlike shape with a diameter of 100–2000 nm and a length of 5–50 µm. The hybrid rods are ferrimagnetic with a Curie temperature (TC) of 11 K. They show a high coercivity (HC) of 4.67 kOe and a saturation magnetization (MS) of 13.5 emu/g at 2 K. Compared with orthorhombic (FeSe2)2Fe(dien)2, the excellent magnetic performance of the hybrid rods is ascribed to the monoclinic hybrid structure built by Fe(dien)2 complexes and Fe2Se3 double chains. Our study provides guidance for connecting inorganic fragments of FeSe2 single chains, Fe2Se3 double chains or β-Fe3Se4 layers with Fe(dien)2 complexes for organic–inorganic hybrid phases with varied crystal structures and magnetic properties.
Sounding others’ sensations in interaction

ABSTRACT This study investigates the practice of “sounding for others,” wherein one person vocalizes to enact someone else’s putatively ongoing bodily sensation. We argue that it constitutes a collaborative way of performing sensorial experiences. Examples include producing cries with others’ strain or pain and parents sounding an mmm of gustatory pleasure on their infant’s behalf. Vocal sounds, their loudness, and duration are specifically deployed for instructing bodily experiences during novices’ real-time performance of various activities, such as tasting food for the first time or straining during a Pilates exercise. Vocalizations that are indexically tied to the body provide immediate displays of understanding and empathy that may be explicated further through lexicon. The existence of this practice challenges the conceptualization of communication as a transfer of information from an individual agent – even regarding assumedly individual body sensations – instead providing evidence of the joint nature of action and supporting dialogic theories of communication, including when language-marginal vocalizations are used.
3D printed multilayer dielectric elastomer actuators

In recent years, dielectric elastomer actuators (DEAs) have been widely used as flexible materials owing to their advantages of large strain, high efficiency, high energy density, and fast response in soft robots, grippers, and various artificial muscles. However, dielectric elastomers (DEs) are generally spin-coated and fabricated. The fabrication process is very complicated and cannot be realized quickly or in large quantities. In this study, we investigated a DEA that can be used for rapid fabrication via three-dimensional (3D) printing. This DE material can be rapidly cured using ultraviolet light and exhibits good mechanical and electrical properties. The material was modified based on CN 9021 (acrylate), and by adjusting the weight ratio of the diluent and crosslinker in the ink, viscosity and optimal mechanical properties that can be fabricated by 3D printing were obtained. The modified ink was then tested by printing it on a self-designed 3D printing platform, and the fabrication process was analyzed and improved to successfully produce DEA. The 3D printed single layer DEA can achieve a tip displacement of about 7.6 mm at the voltage of 2.5 KV. Comparing the driving performance of the DEA made by spin coating with that made by 3D printing, the DEA made by both fabrication methods showed the same performance. This 3D printing method greatly simplifies the DEA fabrication process and improves fabrication efficiency.
Environmental sound classification of western black-crowned gibbon habitat based on spectral subtraction and VGG16

Deep learning-based approaches have been widely used for environmental sound classification tasks in recent years due to the growth and development of this field, creating the technical groundwork for the research of environmental sound classification. However, the presence of noise in the ecological environment leads to a dramatic decrease in its recognition rate. The major goal of this study is to improve the classification accuracy of the model in the presence of noise interference. It does this by proposing an ecological environmental sound classification approach in a noisy background. The input audio is first noise-reduced by spectral subtraction, and then the Mel frequency cepstral parameter features are extracted and fed into the fine-tuned VGG16 model for classification training. The classification of five environmental sounds in the western black-crowned gibbon habitat of the Mourning Mountain was identified with an accuracy of 98.76%.
A new strategy for choosing the Chebyshev‐gegenbauer parameters in a reconstruction based on asymptotic analysis

Abstract The Gegenbauer reconstruction method, first proposed by Gottlieb et. al. in 1992, has been considered a useful technique for re‐expanding finite series polynomial approximations while simultaneously avoiding Gibbs artifacts. Since its introduction many studies have analyzed the method's strengths and weaknesses as well as suggesting several applications. However, until recently no attempts were made to optimize the reconstruction parameters, whose careful selection can make the difference between spectral accuracies and divergent error bounds. In this paper we propose asymptotic analysis as a method for locating the optimal Gegenbauer reconstruction parameters. Such parameters are useful to applications of this reconstruction method that either seek to bound the number of Gegenbauer expansion coefficients or to control compression ratios. We then illustrate the effectiveness of our approach with the results from some numerical experiments.
Tunable fiber ring laser absorption spectroscopic sensors for gas detection

Fiber-optic gas sensing techniques are commonly based on the recognition of a wide range of chemical species from characteristic absorption, fluorescence or Raman-scattering spectra signatures. By tuning over the vibrational lines of species in the path of laser beam, tunable diode laser gas sensors measure signal spectroscopic intensity, gas concentration, and other properties. However, they have limitations of bulk architecture, small change of signal on top of large background, and low sensitivity of direct absorption. Here we report the fabrication and optical measurements of tunable Er-doped fiber ring laser absorption spectroscopic sensor featuring a gas cell that is a segment of photonic crystal fiber (PCF) with a long-period grating (LPG) inscribed. The tunable laser beam is coupled into the cladding of the PCF by the LPG where the gas in air holes absorbs light. The light travels along the PCF cladding and reflects at the end of the fiber where a silver film is coated as a mirror at one end facet. The light propagates back within cladding and passes through the gas one more time thus increasing the interaction length. This light is finally recoupled into the fiber core for intensity measurement. The proposed fiber gas sensors have been experimentally used for ammonia (NH3) concentration detection. They show excellent sensitivity and selectivity, and are minimally affected by temperature and/or humidity changes. The sensors using PCF-LPG gas cell are simple to fabricate, cost-effective, and are deployed for a variety of applications not possible using conventional optical fibers.
Using Secondary Outcomes to Sharpen Inference in Randomized Experiments With Noncompliance

We develop new methods for analyzing randomized experiments with noncompliance and, by extension, instrumental variable settings, when the often controversial, but key, exclusion restriction assumption is violated. We show how existing large-sample bounds on intention-to-treat effects for the subpopulations of compliers, never-takers, and always-takers can be tightened by exploiting the joint distribution of the outcome of interest and a secondary outcome, for which the exclusion restriction is satisfied. The derived bounds can be used to detect violations of the exclusion restriction and the magnitude of these violations in instrumental variables settings. It is shown that the reduced width of the bounds depends on the strength of the association of the auxiliary variable with the primary outcome and the compliance status. We also show how the setup we consider offers new identifying assumptions of intention-to-treat effects. The role of the auxiliary information is shown in two examples of a real social job training experiment and a simulated medical randomized encouragement study. We also discuss issues of inference in finite samples and show how to conduct Bayesian analysis in our partial and point identified settings. Supplementary materials for this article are available online.
Robust two sided tolerance limits based on mml estimators

Robust estimators and are used in the two-sided tolerance limits. , These estimators are the ones proposed by Tiku (1967); they are referred to in the literature as the modified maximum likelihood (MML) estimators. , The constants k* needed to set. up 90% tolerance limits with p1 = p2 are tabled, The actual γ attained under a variety of nonnormal situations, using the MML estimators, has also been tabled. The efficacy of using the MML estimators in such instances is established.