Miracle-dz/UniFAR-FTUs huggingface dataset:
Influences of boundary layer phenomena and meteorology on ambient air quality status of an urban area in eastern India

Monitoring and analyses of various air pollutants like particulate matter (PM 10 ), sulphur dioxide (SO 2 ), nitrogen dioxide (NO 2 ), ozone (O 3 ), carbon monoxide (CO) and ammonia (NH 3 ), as well as meteorological parameters (temperature, humidity, wind speed and direction) have been performed for the period between April and May, 2014 at three different sites (an area with high vehicular density, a residential area and an industrial area) of an urban area in eastern India. The hourly average concentration of surface ozone is found maximum at the industrial site (70.085 µg/m 3 ) followed by the residential site (39.067 µg/m 3 ) and the site with high vehicular density (25.936 µg/m 3 ). The site with high vehicular density shows the maximum 24 h average concentration of NO 2 (86.112 µg/m 3 ) and NH 3 (109.673 µg/m 3 ). Maximum daily (24 h) average concentration of PM 10 (392.127 µg/m 3 ) and SO 2 (174.069 µg/m 3 ), and highest hourly average of CO (3.846 mg/m 3 ) at the industrial site can be attributed to emissions from the surrounding industries. Computation of air quality indices (AQI) show that both site I (a traffic congested area with AQI = 53.85) and site III (an industrial area with AQI = 71.68) fall under a moderately polluted category whereas site II (a residential area with AQI = 22.69) is found to be clean. This detailed study reveals that variations in the concentration of different air pollutants manifest the combined effect of emission sources and patterns, planetary boundary layer height and meteorology.
Recent advances in deep-learning-enhanced photoacoustic imaging

Abstract. Photoacoustic imaging (PAI), recognized as a promising biomedical imaging modality for preclinical and clinical studies, uniquely combines the advantages of optical and ultrasound imaging. Despite PAI’s great potential to provide valuable biological information, its wide application has been hindered by technical limitations, such as hardware restrictions or lack of the biometric information required for image reconstruction. We first analyze the limitations of PAI and categorize them by seven key challenges: limited detection, low-dosage light delivery, inaccurate quantification, limited numerical reconstruction, tissue heterogeneity, imperfect image segmentation/classification, and others. Then, because deep learning (DL) has increasingly demonstrated its ability to overcome the physical limitations of imaging modalities, we review DL studies from the past five years that address each of the seven challenges in PAI. Finally, we discuss the promise of future research directions in DL-enhanced PAI.
Rice and Barnyardgrass: Water Stress and Initial Establishment

The present work aimed to evaluate the effect of different water stress levels on the development of both rice and barnyardgrass. The study was established into greenhouse, in completely randomized design, in factorial scheme 2 × 5, with four replications. Factor “A” comprised the plant species (rice cv. BRS Querencia, or the weed Echinochloa crusgalli), and factor “B” comprised the water stress levels: (T1) continuous flood (CF); (T2) 0 kPa (saturated soil); (T3) 10 kPa; (T4) 40 kPa; and (T5) 100 kPa. Plant emergence was assessed every day; plant length was measured 30 days after planting. At the end of the experiment, the number of leaves per plant, root length and shoot and root dry mass were determined. Our data supply evidence that under moderate water stress, up to approximately 40 kPa, rice tends to perform better than barnyardgrass in the initial stage of crop growth, when under equivalent plant density. However, barnyardgrass is usually present in much higher plant density than crop plants in fields traditionally cropped with rice. Thus, efficient control of barnyardgrass should be accomplished to avoid damage to rice crop by competition for environmental resources.
Sample partition and grouped sparse representation

Abstract Recently, conventional representation-based classification (RBC) methods demonstrate promising performance in image recognition. However, conventional RBCs only use a kind of deviations between the test sample and the linear combination of training samples of each class to perform classification. In many cases, a single kind of deviations corresponding to each class cannot effectively reflect the difference between the test sample and reconstructed sample of each class. Moreover, in practical applications, limited training samples are not able to reflect the possible changes of the image sufficiently. In this paper, we propose a novel scheme to tackle the above-mentioned problems. Specifically, we first use the original training samples to generate corresponding mirror samples. Thus, the original sample set and its mirror counterpart are treated as two separate training groups. Secondly, we perform collaborative representation classification on these two groups from which each class leads to two kinds of deviations, respectively. Finally, we fuse two kinds of deviations of each class and their correlation coefficient to classify the test sample. The correlation coefficient is defined for two kinds of deviations of each class. Experimental results on four databases show the proposed scheme can improve the recognition rate in image-based recognition.
Thermo-hydraulic rating of a gasketed-plate heat exchanger for methanol heating

The use of plate heat exchangers is desirable since they have a simple compact construction, are reliable, and very efficient at small temperature difference between fluids. In the present work the thermo-hydraulic rating of a proposed gasketed-plate heat exchanger was carried out, in order to heat a methanol stream. A safety factor of 1.62, a percent over surface of 3.91% and a cleanliness factor of 0.962 was obtained. The calculated values of the pressure drops for both the hot water and methanol streams were 47,199.05 Pa and 78,414.61 Pa, respectively, while the pumping power required for those both streams were 4.81 kW and 11.38 kW, respectively. The proposed gasketed-plate heat exchanger can be used for the requested heat transfer service since the percent over surface is lower than 25% and the calculated pressure drop of both streams are below the maximum permissible pressure drop established by the process.
Movements Recognition in the Human Body Based on Deep Learning Strategies

These days, the study of human body movements for the purpose of emotion identification is an absolutely necessary component of social communication. Several different contexts call for the implementation of non-verbal communication strategies such as gestures, eye movements, facial expressions, and body language. Among them, emotion detection based on body movements. It can also identify the emotions of a person even if they are too far away from the camera. Other studies have shown that body language can express emotional states more effectively than words can. In this research study, an emotional state is determined by the human motion of the entire body. The architecture of a deep convolution neural network is used, and multiple parameter settings are considered. Both the University of York's emotion dataset, which includes 15 different kinds of emotions, and dataset of GEMEP corpus, which includes five emotions, can be used to assess the proposed system. The results of the experiments demonstrated that the proposed system has a higher degree of recognition accuracy.
Study on mode mixing problem of empirical mode decomposition

Abstract. After defined the basic concept of mode or intrinsic mode, EMD as a data-driven adaptive decomposition method, its purpose is to decompose arbitrary non-stationary and nonlinear signal into different intrinsic mode functions (IMFs) on the time series. However, in some cases, the EMD method often cannot get ideal decomposition results which can result mode mixing problem. once mode mixing phenomenon appear, mode mixing will affect the subsequent decomposition components, the time-frequency distribution of subsequent IMFs are confusion, eventually the EMD decomposition process lose physical meaning. The EMD mode mixing problem directly affect its application in various fields. This paper discuss the basic cause of mode mixing, the results show that: the jumping change of the decomposed signal result in mode mixing, mode mixing caused by amplitude and frequency relationship of the signal.
Can we beat overfitting?—A closer look at Cloarec's PLS algorithm

Random noise has been addressed as a cause of overfitting in partial least squares regression. A previous study pinpointed that one of the sources of overfitting resides in the calculation of scores due to the accumulation of noise in the diagonal of the variance‐covariance matrix, and a modified partial least squares regression was proposed with the removal of this diagonal prior to the score calculation. Here, a further modification of the NIPALS algorithm is proposed, with the same ability to overcome overfitting due to noise, but algebraically more similar to the original NIPALS. The results indicate that it is possible to get more reliable auto‐prediction R2 with a cross‐validation performance close to that of the original NIPALS algorithm.
A New Heuristic Algorithm for Drawing Binary Trees within Arbitrary Polygons Based on Center of Gravity

Graphs have enormous usage in software engineering, network and electrical engineering. In fact graphs drawing is a geometrically representation of information. Among graphs, trees are concentrated because of their ability in hierarchal extension as well as processing VLSI circuit. Many algorithms have been proposed for drawing binary trees within polygons. However these algorithms generate binary trees with edge intersections and bends. Likewise, they have limitations in drawing binary trees in any arbitrary polygon with uniform distribution. The proposed algorithm which is based on calculating center of gravity in polygons draws a binary tree inside any kinds of polygon. The results which have been implemented with Microsoft Visual Basic reveal that the proposed heuristic algorithm had no edge intersection and no bend. In addition, not only the symmetry of drawing has been improved but also the computational complexity becomes less.
Uncertain DC-DC Zeta Converter Control in Convex Polytope Model Based on LMI Approach

A dc-dc zeta converter is a switch mode dc-dc converter that can either step-up or step-down dc input voltage. In order to regulate the dc output voltage, a control subsystem needs to be deployed for the dc-dc zeta converter. This paper presents the dc-dc zeta converter control. Unlike conventional dc-dc zeta converter control which produces a controller based on the nominal value model, we propose a convex polytope model of the dc-dc zeta converter which takes into account parameter uncertainty. A linear matrix inequality (LMI) is formulated based on the linear quadratic regulator (LQR) problem to find the state-feedback controller for the convex polytope model. Simulation results are presented to compare the control performance between the conventional LQR and the proposed LMI based controller on the dc-dc zeta converter. Furthermore, the reduction technique of the convex polytope is proposed and its effect is investigated.
Design and Simulation of Active and Reactive Power Control of Double-Fed Induction Generator for Wind Energy Conversion

Wind energy plays an increasingly important role in the world because it is friendly to the environment. During the last decades, the concept of a variable-speed wind turbine has been receiving increasing attention due to the fact that it is more controllable and efficient, and has good power quality. As the demand of controllability of variable speed wind turbines increases, it is therefore important and necessary to investigate the modeling for wind turbine-generator systems that are capable of accurately simulating the behavior of each component in the wind turbine-generator systems. Therefore, this thesis will provide detailed models of a grid-connected wind turbine system equipped with a doubly-fed induction generator (DFIG). In order to control the power flowing from the rotor of the DFIG and the power network, a control law is synthesized using PI controller. The performance is compared in terms of power reference tracking. The power reference tracking was smooth and overshoot was negligible at the transient points.
Microemulsion-Based Keratin–Chitosan Gel for Improvement of Skin Permeation/Retention and Activity of Curcumin

Curcumin (Cur) is a kind of polyphenol with a variety of topical pharmacological properties including antioxidant, analgesic and anti-inflammatory activities. However, its low water solubility and poor skin bioavailability limit its effectiveness. In the current study, we aimed to develop microemulsion-based keratin–chitosan gel for the improvement of the topical activity of Cur. The curcumin-loaded microemulsion (CME) was formulated and then loaded into the keratin–chitosan (KCS) gel to form the CME-KCS gel. The formulated CME-KCS gel was evaluated for its characterization, in vitro release, in vitro skin permeation and in vivo activity. The results showed that the developed CME-KCS gel had an orange-yellow and gel-like appearance. The particle size and zeta potential of the CME-KCS gel were 186.45 ± 0.75 nm and 9.42 ± 0.86 mV, respectively. The CME-KCS gel showed desirable viscoelasticity, spreadability, bioadhesion and controlled drug release, which was suitable for topical application. The in vitro skin permeation and retention study showed that the CME-KCS gel had better in vitro skin penetration than the Cur solution and achieved maximum skin drug retention (3.75 ± 0.24 μg/cm2). In vivo experimental results confirmed that the CME-KCS gel was more effective than curcumin-loaded microemulsion (Cur-ME) in analgesic and anti-inflammatory activities. In addition, the CME-KCS gel did not cause any erythema or edema based on a mice skin irritation test. These findings indicated that the developed CME-KCS gel could improve the skin penetration and retention of Cur and could become a promising formulation for topical delivery to treat local diseases.
How Can We Promote Smartphone Leasing via a Buyback Program?

Leasing is an important sustainable PSS model of recycling smartphones, and they have emerged as a crucial component of retailers’ business evolution in recent times. Using hybrid selling–leasing transformations, retailers not only provide selling services but also leasing services, which not only increases revenue sources but also triggers internal competition. Due to this, retailers are reluctant to promote smartphone leasing. How can we enhance retailers’ motivation to promote smartphone leasing? This paper aims to answer this question by exploring the potential of a manufacturer’s buyback program and analyzing three price decision models: pure selling (S), hybrid selling–leasing without a buyback program (SL), and hybrid selling–leasing with a buyback program (HSL). The results show that (1) when consumers’ acceptance of leasing is moderate, retailers can benefit from hybrid selling–leasing transformation. (2) If the manufacturer chooses to buy back used leasing smartphones from the retailer, it is advisable to set a high buyback price that is at least equal to their residual value. (3) The buyback program can increase consumers’ leasing demand and manufacturer’s profits, as well as decrease the environmental impact of the supply chain system. More importantly, it has the potential to drive retailers to conduct hybrid selling–leasing transformation and can establish a positive correlation between retailers’ profits and consumers’ acceptance of leasing. This means that buyback programs can promote smartphone leasing and can be beneficial for smartphone recycling and urban sustainable development.
Maximum response perturbation-based control of virus infection model with time-delays

Abstract A new method for constructing the multi-modal impacts on the immune systemin the chronic phase of viral infection, based on mathematical models formulated with delay-differential equations is proposed. The so called, optimal disturbances, widely used in the aerodynamic stability theory for mathematical models without delays are constructed for perturbing the steady states of the dynamical system for maximizing the perturbation-induced response. The concept of optimal disturbances is generalized on the systems with delayed argument. An algorithm for computing the optimal disturbances is developed for such systems. The elaborated computational technology is tested on a system of four nonlinear delay-differential equations which represents the model of experimental infection in mice caused by lymphocytic choriomeningitis virus. The steady-state perturbations resulting in a maximum responsewere computed with the proposed algorithm for two types of steady states characterized by a low and a high levels of viral load. The possibility of correction of the infection dynamics and the restoration of virus-specific lymphocyte functioning of the immune system by perturbing the steady states is demonstrated.
3D Integral Invariant Signatures And Their Application on Face Recognition

FENG, SHUO. 3D Integral Invariant Signatures And Their Application on Face Recognition. (Under the direction of Professor Hamid Krim). Curves are important features in computer vision and pattern recognition, and their classification under a variety of transformations, such as Euclidean, affine or projective, poses a great challenge. Invariant features of these curves turn out to be crutial to simplifying any classification procedure. This, as a result, has recently led to a renewed research interest in transformation invariants. In this thesis, new explicit formulae for integral invariants for curves in 3D with respect to the special and the full affine groups are presented.The development of the 3D integral invariant are based on an inductive approach in terms of Euclidean invariants. For the first time, a clear geometric interpretation of both 2D and 3D integral invariants is presented. Since integration attenuates the effects of noise, integral invariants have advantages in computer vision applications. We use integral invariants to construct global and local signatures that characterize curves up to the special affine transformations, subsequently extended to the full affine group. Global Signatures are independent of parameterization, and Local Signatures are independent of both parameterizationa and initial point selection. We analyze the robustness of these invariants in their application to the problem of classification of noisy spatial curves extracted as characteristics from a 3D object. Our investigation of 2D and 3D integral invariants and signatures, originally motivated by Biometrics applications, are successfully implemented and applied to face recognition to eliminate the effects of pose and facial expression. A high recognition performance rate of 95% is achieved in the test with a large face data set. 3D Integral Invariant Signatures And Their Application on Face Recognition
Extracting Vibration Severity Time Histories from Epicyclic Gearboxes

Monitoring epicyclic gearboxes in vital power transition situations is still a challenge. In this paper, we discuss these challenges with long time vibration measurements through two industrial examples. The first are the two gearboxes in the front axle of a load haul dumper (LHD) from Pyhäsalmi mine and the second a two stage gearbox from Kelukoski water power station (WPS). The LHD was monitored almost continuously for nearly two years until its breakdown. The data from WPS was intermittent from a five month period. We discuss how to find stable conditions for comparable measurements in these cases. For this we utilise a tacho signal from the cardan axle of the LHD and power measurements from the WPS. It is found that in both cases second derivatives of acceleration signals, called snap, respond more quickly to changes in vibration severity. In the LHD case we get clear trends for increasing norms of snap signals. The trends are extracted with nonparametric regression. The shorter measurement period of the WPS makes it impossible to say if its changes are only seasonal. Spectral analysis shows increase in high frequency vibration with time in both cases but provides almost no help for detailed diagnostics.
Redesenho do trabalho de aproximação: via de acesso ao engajamento no trabalho?

Work engagement is a source of competitive advantage for organizations and can be promoted by job crafting. We investigated the influence of three types of approach crafting on work engagement among Brazilian workers. The results indicated that approach crafting showed positive and moderate relationships with work engagement. Network analysis indicated that approach resources crafting (behavioral) had a direct positive influence, and approach demands crafting (behavioral) had an indirect influence on work engagement. Approach resources crafting (cognitive) presented the strongest relationship with work engagement. We concluded that the behavioral and cognitive efforts to approach the positive aspects of work and modify resources and demands influence work engagement. We suggest that managers should foster approach crafting for healthy engagement of professionals with their work.
Bioactive Phenolic Compounds from Apples during Simulated In Vitro Gastrointestinal Digestion: Kinetics of Their Release

Bioactive phenolic compounds in their natural form show beneficial effects on the gastrointestinal system. The kinetics of their release are important for understanding those effects. The aim was to study the kinetics of the release of phenolic compounds from apples during in vitro simulated gastrointestinal digestion by using modified equations of first- and second-order kinetics. 35% and 67% of total phenolic compounds were released in the gastric phase, and 26% and 27% in the intestinal phase (peel and flesh, respectively). Intensive release of anthocyanins, flavan-3-ols, dihydrochalcones, phenolic acids, and flavonols occurred in the first 10 min of gastric digestion. In intestinal digestion, flavan-3-ols and anthocyanins were not identified; the dihydrochalcone amount decreased, while phenolic acids and flavonols showed stability. Concentrations at the endpoint of the release (c∞) were determined with kinetic equations fitted well to the experimental data (cexp) (r2 = 0.9973 and 0.9946 for first- and second-order). The half-life for released phenolic subgroups in gastric digestion was up to 3.5 (first-order kinetics) or 2.5 min (second-order), or in intestinal digestion up to 20.9 (first-order) or 32.3 min (second-order). Modified equations reported here for the first time fit well with the experimental data.
The Minimum Length Scale for Evaluating QG Omega Using High-Resolution Model Data

AbstractThe minimum length scale to investigate quasigeostrophic (QG) vertical motion within a mesoscale operational model is determined using simulations of 28 baroclinic systems from the North American Mesoscale Forecast System (NAM) model. Two upscaling methods are tested to find the optimal QG characteristic length. The box method takes an average of each field before performing finite-differencing calculations. The cross method samples the data at increasing distances between finite-difference calculations. The traditional QG omega equation is evaluated with each upscaling technique and found to be reliable between 800 and 200 hPa. The minimum QG length scale is found to be L = 140 km considering correlations of QG omega back to operational model values, which are for both methods on an “extended” QG omega. The box method performs marginally better than the cross method due to a larger reduction of QG forcing in higher-order wavenumbers, but at the appropriate length scale, both methods have indistin.