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Autonomous robots are often deployed in applications to continually monitor changing environments such as supermarket floors or inventory monitoring, patient monitoring, and autonomous driving. With the increasing use of deep learning techniques in robotics, a large number of robot manufacturing companies have started adopting deep learning techniques to improve the monitoring performance of autonomous robots. The Robot Operating System (ROS) is a widely used middleware platform for building autonomous robot applications. However, the deployment of deep learning models to autonomous robots using ROS remains an unexplored area of research. Most recent research has focused on using deep learning techniques to solve specific problems (e.g., shopping assistant robots, autopilot systems, automatic annotation of 3D maps for safe flight). However, integrating the data collection hardware (e.g., sensors) and deep learning models within ROS is difficult and expensive in terms of computational power, time, and energy (battery). To address these challenges, we have developed EasyDLROS, a novel framework for robust deployment of pre-trained deep learning models on robots. Our framework is open-source, independent of the underlying deep learning framework, and easy to deploy. To test the performance of EasyDLROS, we deployed seven pre-trained deep learning models for hazard detection on supermarkets floors in a simulated environment and evaluated their performances. Experimental results show that our framework successfully deploys the deep learning models on ROS environment.

By making use of the concept of basic (or q-) calculus, many subclasses of analytic and symmetric q-starlike functions have been defined and studied from different viewpoints and perspectives. In this article, we introduce a new class of meromorphic multivalent close-to-convex functions with the help of a q-differential operator. Furthermore, we investigate some useful properties such as sufficiency criteria, coefficient estimates, distortion theorem, growth theorem, radius of starlikeness, and radius of convexity for this new subclass.

In the present investigation, our main aim is to give lower bounds for the ratio of some normalized q-Mittag-Leffler function and their sequences of partial sums. We consider various corollaries and consequences of our main results.

In this article, by utilizing the theory of quantum (or q-) calculus, we define a new subclass of analytic and multivalent (or p-valent) functions class Ap, where class Ap is invariant (or symmetric) under rotations. The well-known class of Janowski functions are used with the help of the principle of subordination between analytic functions in order to define this subclass of analytic and p-valent functions. This function class generalizes various other subclasses of analytic functions, not only in classical Geometric Function Theory setting, but also some q-analogue of analytic multivalent function classes. We study and investigate some interesting properties such as sufficiency criteria, coefficient bounds, distortion problem, growth theorem, radii of starlikeness and convexity for this newly-defined class. Other properties such as those involving convex combination are also discussed for these functions. In the concluding part of the article, we have finally given the well-demonstrated fact that the results presented in this article can be obtained for the (p,q)-variations, by making some straightforward simplification and will be an inconsequential exercise simply because the additional parameter p is obviously unnecessary.

Khan, Nazar, Fritsch, R. M., Sultan, Amir, Khan, Tahir (2021): ALLIUM (AMARYLLIDACEAE) SPECIES IN PAKISTAN: TWO NEW RECORDS AND A NEW SPECIES FROM ZHOB (BALOCHISTAN). Pakistan Journal of Botany 53 (5): 1-7, DOI: 10.30848/PJB2021-5(1)

Magnetohydrodynamics (MHD) is a very challenging problem which affects the stability of Poiseuille flow. Therefore, in this work we investigate the instability of an electrically conductive fluid between two parallel plates under the influence of a transverse magnetic field. We apply the Chebyshev collocation method to solve the generalised Orr–Summerfield equations to determine wave number, growth rates and spatial modes of the eigenmodes. To get the neutral curves of MHD instability, the QZ method is used. It is observed that the magnetic field has a stabilising effect on the flow and the stability increases as we increase the Hartmann number and for various wave numbers, magnetic field put down the growth of perturbation. It is concluded that effect of perturbations is little in span-wise direction for different Hartmann numbers that increase the critical values of Reynolds numbers.

Early detection and diagnosis of the SARS-CoV-2 virus which causes COVID-19 remains a difficult and time-sensitive process. Though progress has been made in the development and administration of tests, the time required to obtain a confident response can be prohibitive in areas of dense outbreaks. For these reasons, there has been great interest in adopting machine learning solutions to detect COVID-19 in prospective patients and aid clinicians in its localization. However, the adoption of deep learning approaches for these tasks is faced with multiple key challenges. In this work, we present a novel discriminative localization approach based on the computation of Class Activation Maps (CAMs) that addresses the problem of class overlap and imbalance within training data. In particular, we train a Convolutional Neural Network (CNN) on viral pneumonia and COVID-19 labels and compute the difference in scaled activations of the features in the last convolutional layer of the CNN for both classes. In doing so, we exploit the feature similarity between the two diseases for training purposes, thereby mitigating issues arising due to the scarcity of COVID-19 data. In models with overlapping classes, this can yield a neuron co-adaptation problem wherein the model is less confident in the outputs of similar classes, resulting in large CAMs with decreased localization certainty. Increasing the final mapping resolution of the last convolutional layer can reduce the overall area of such CAMs, but does not address class overlap, because CAMs computed for such classes exhibit significant spatial overlap, making it difficult to distinguish which spatial regions contribute the most to a specific target class. Our approach mitigates this by computing directed differences in scaled activations of pneumonia and COVID-19 CAMs, amplifying these differences, and projecting the results on to the original image in order to aid clinicians in the early detection of COVID-19. Specifically, our method clearly delineates spatial regions of an image that the classifier model considers as the most relevant for a given classification, even when standard CAMs for multiple classes within the model exhibit significant spatial overlap due to similarity in feature activations.

Compared to facial expression recognition, expression synthesis requires a very high-dimensional mapping. This problem exacerbates with increasing image sizes and limits existing expression synthesis approaches to relatively small images. We observe that facial expressions often constitute sparsely distributed and locally correlated changes from one expression to another. By exploiting this observation, the number of parameters in an expression synthesis model can be significantly reduced. Therefore, we propose a constrained version of ridge regression that exploits the local and sparse structure of facial expressions. We consider this model as masked regression for learning local receptive fields. In contrast to the existing approaches, our proposed model can be efficiently trained on larger image sizes. Experiments using three publicly available datasets demonstrate that our model is significantly better than $\ell_0, \ell_1$ and $\ell_2$-regression, SVD based approaches, and kernelized regression in terms of mean-squared-error, visual quality as well as computational and spatial complexities. The reduction in the number of parameters allows our method to generalize better even after training on smaller datasets. The proposed algorithm is also compared with state-of-the-art GANs including Pix2Pix, CycleGAN, StarGAN and GANimation. These GANs produce photo-realistic results as long as the testing and the training distributions are similar. In contrast, our results demonstrate significant generalization of the proposed algorithm over out-of-dataset human photographs, pencil sketches and even animal faces., IJCV Journal

Facial expression synthesis has achieved remarkable advances with the advent of Generative Adversarial Networks (GANs). However, GAN-based approaches mostly generate photo-realistic results as long as the testing data distribution is close to the training data distribution. The quality of GAN results significantly degrades when testing images are from a slightly different distribution. Moreover, recent work has shown that facial expressions can be synthesized by changing localized face regions. In this work, we propose a pixel-based facial expression synthesis method in which each output pixel observes only one input pixel. The proposed method achieves good generalization capability by leveraging only a few hundred training images. Experimental results demonstrate that the proposed method performs comparably well against state-of-the-art GANs on in-dataset images and significantly better on out-of-dataset images. In addition, the proposed model is two orders of magnitude smaller which makes it suitable for deployment on resource-constrained devices., ICPR 2020, 7 pages, 5 figures

In this article, by using the concept of the quantum (or q-) calculus and a general conic domain &Omega, k , q , we study a new subclass of normalized analytic functions with respect to symmetrical points in an open unit disk. We solve the Fekete-Szeg&ouml, type problems for this newly-defined subclass of analytic functions. We also discuss some applications of the main results by using a q-Bernardi integral operator.

By making use of the concept of basic (or q-) calculus, various families of q-extensions of starlike functions, which are associated with the Janowski functions in the open unit disk U, were introduced and studied from many different viewpoints and perspectives. In this paper, we first investigate the relationship between various known families of q-starlike functions which are associated with the Janowski functions. We then introduce and study a new subclass of q-starlike functions which involves the Janowski functions and is related with the conic domain. We also derive several properties of such families of q-starlike functions with negative coefficients including (for example) sufficient conditions, inclusion results and distortion theorems. In the last section on conclusion, we choose to point out the fact that the results for the q-analogues, which we consider in this article for 0 < q < 1, can easily (and possibly trivially) be translated into the corresponding results for the (p; q)-analogues (with 0 < q < p ?? 1) by applying some obvious parametric and argument variations, the additional parameter p being redundant.

Health is affected by the financial, environmental and work conditions. In this paper, we have proposed an algorithm to be used in a game so that it could behave intelligently for stress relieving and health care rather than in a traditional leisure. Our study explores the efficacy of intelligent games to keep the interest of a user in computer usage that helps in the reduction of stress. The games keep track of a user’s actions and decide intelligently which best next level should be provoked to the user for keeping his/her interest in the game. In several experiments it has been observed that after playing stress relief game, users become less distracted by rejection, help releasing their stress and anxiety.

The main aim of this work is to find some coefficient inequalities and sufficient condition for some subclasses of meromorphic starlike functions by using q-difference operator. Here we also define the extended Ruscheweyh differential operator for meromorphic functions by using q-difference operator. Several properties such as coefficient inequalities and Fekete-Szego functional of a family of functions are investigated.

In our present investigation, we first introduce several new subclasses of analytic and bi-univalent functions by using a certain $q$-integral operator in the open unit disk $$\mathbb{U}=\{z: z\in \mathbb{C} \quad \text{and} \quad \left \vert z\right \vert

The accurate calibration of powder diffraction data acquired from area detectors using calibration standards is a crucial step in the data reduction process to attain high-quality one-dimensional patterns. A novel algorithm has been developed for extracting Debye–Scherrer rings automatically using an approach based on computer vision and pattern recognition techniques. The presented technique requires no human intervention and, unlike previous approaches, makes no restrictive assumptions on the diffraction setup and/or rings. It can detect complete rings as well as portions of them, and works on several types of diffraction images with various degrees of ring graininess, textured diffraction patterns and detector tilt with respect to the incoming beam.

In this paper, we introduce two new subclasses of p-valent spirallike functions of order α:We prove necessary and suffcient conditions for these newly defined classes and also point out someknown consequences of our results.

Trust in predictions made by machine learning models is increased if the model generalizes well on previously unseen samples and when inference is accompanied by cogent explanations of the reasoning behind predictions. In the image classification domain, generalization can be assessed through accuracy, sensitivity, and specificity. Explainability can be assessed by how well the model localizes the object of interest within an image. However, both generalization and explainability through localization are degraded in scenarios with significant overlap between classes. We propose a method based on binary expert networks that enhances the explainability of image classifications through better localization by mitigating the model uncertainty induced by class overlap. Our technique performs discriminative localization on images that contain features with significant class overlap, without explicitly training for localization. Our method is particularly promising in real-world class overlap scenarios, such as COVID-19 and pneumonia, where expertly labeled data for localization is not readily available. This can be useful for early, rapid, and trustworthy screening for COVID-19., Comment: 7 pages, 6 figures

By using the idea of $q$-calculus, we generalize the conic domain and define various subclasses of analytic functions which map the open unit disk $$ \mathbb {U}= \{ z:z\in \mathbb {C} \text { and } \lvert z \rvert \lt 1 \} $$ onto this generalized conic domain. We study here some conspicuous results such as: coefficients estimates, sufficient condition and convolution properties for these newly defined classes. We consider various corollaries and consequences of our main results. Most of our results are connected with earlier works related to this field.

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous in recent years. This has led to the generation of large quantities of data in real-time, which is an appealing target for AI systems. However, deploying machine learning models on such end-devices is nearly impossible. A typical solution involves offloading data to external computing systems (such as cloud servers) for further processing but this worsens latency, leads to increased communication costs, and adds to privacy concerns. To address this issue, efforts have been made to place additional computing devices at the edge of the network, i.e close to the IoT devices where the data is generated. Deploying machine learning systems on such edge computing devices alleviates the above issues by allowing computations to be performed close to the data sources. This survey describes major research efforts where machine learning systems have been deployed at the edge of computer networks, focusing on the operational aspects including compression techniques, tools, frameworks, and hardware used in successful applications of intelligent edge systems., 35 pages, 4 figures; restructured text to combine ML/DL into a single section; updated tables/figures; added a new table summarizing major ML edge applications, fixed typos

In this paper, we have proposed to the mobile scientists about a social responsibility to take care about the health of the users by designing a computer-based mobile game so that it could behave intelligently and interactively for stress relieving and health care rather than the traditional leisure. Our study explores the efficacy of intelligent games to keep the interest of a user in computer and mobile usage that helps in the reduction of stress. This review paper highlights the need of social responsibility during the process of App development for mobile phone users.

By making use of the concept of basic (or q-) calculus, a subclass S ⁎ ( L , q ) of q-starlike functions, which is associated with the q-exponential function, is introduced here in the open unit disk U given by U = { z : z ∈ C and | z | 1 } . The main object of this article is to determine the upper bound of the third-order Hankel determinant H 3 ( 1 ) for functions belonging to the q-starlike function class S ⁎ ( L , q ) . For validity of our results, relevant connections with those in earlier works are also pointed out.

The aim of this paper is to introduce a new subclass of multivalent functions of complex order and to study some interesting properties such as coeffcient estimates, suffciency criteria, Fekete-Szego inequality, inclusion result and integral preserving property for this newly defined class.

The aim of present investigation was to explore dietary and health benefits of Artocarpus heterophyllus Lam (jackfruit). The nutritional profile of dried ripened jackfruit showed that it is a rich source of carbohydrates (13.08± 0.31%) as well as considerable amount of crude fiber (6.32±0.72 %), crude fat (5.63±0.18 %) and protein (1.48±0.11%) were found. The moisture value and ash content of ripened jackfruit pulp was 71.60±0.75% and 1.89±0.19%, respectively. Among the extracts of ripened jackfruit, in five different solvent systems, methanolic extract exhibited the highest total phenolic (239.87± 0.2 mg GAE/100g dry wt) and flavonoid content (109.94±2.16 mg QE /100g dry wt). Maximum ascorbic acid value (22.47±1.95 mg/100g of dry fruit) was observed in acetone extract of ripened jackfruit pulp. The antioxidant activity of extracts was assessed through DPPH radical scavenging assay. The acetone extract showed higher radical scavenging activity (89.31±0.78%) than remaining extracts. Results of present study enlighten that A. heterophyllus comprising significant nutritional value and high antioxidant potential may be utilized as functional food with towering therapeutic benefits.

In the present investigation, a new general subclass $M_{\alpha ,\beta }\left( \phi \right) $ of analytic functions is defined. Some subordination relations, inclusion relations, integral preserving properties, convolution properties and some other interesting properties are studied.

Current study was conducted at research area of Department of Entomology, Agriculture University, Peshawar, Khyber Pukthunkhwa during spring 2013. The purpose of study was to investigate the comparative resistance of tobacco cultivars and efficacy of Bifenthrin at various concentrations (0.08%, 0.125%, 0.166%, and 0.208% and control) against major insect pests of tobacco. The concentrations of insecticide differed significantly in term of controlling the cutworm, budworm and aphids infestation. The 0.166% and 0.08% concentration of Bifenthrin found most effective in controlling the cutworm and budworm infestation (1.2 and 8.2 percent, respectively). For checking the aphid population, the 0.208% concentration of Bifenthrin remained most suitable. The lowest infestation (29.1) of said insects was noticed on cultivar PVH- 2310. Cultivars behaved differently for the studied traits but not for insecticidal concentrations. For nicotine contents the response of cultivars was non- significant. Results regarding the percent budworm damage revealed that PVH2310 was the most resistant cultivar (7.7%) and 0.08% concentration of Bifenthrin showed most effective control (8.2%) seven days after the application of spray (5.0%).

Drought is a solemn environmental factor that causes great loss of yield in maize crop. Maize is highly sensitive to drought. There is need to develop drought tolerance maize genotypes to fulfill demand of feed for livestock and food for human. For this propose prescribed study was conducted to estimate genetic components for various physiological traits under normal and water tress conditions in the research area of the Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Pakistan. It was concluded that P1 (WFTMS) parent performed better under drought conditions for stomata frequency, stomata conductance, stomata size, cell membrane thermo stability, leaf water potential and excised leaf water loss while BC2 for stomata conductance, F1 for stomata size, F2 leaf water potential and BC1 for leaf temperature. Positive [h] dominance effects were recorded for cell membrane thermo stability, stomata frequency and leaf water potential while [d] additive effects for leaf water potential under normal conditions. It was reported that [i] additive × additive interaction were found for cell membrane thermo stability, stomata frequency and excised leaf water loss while negative for leaf water potential under normal conditions while under drought for cell membrane thermo stability, stomata frequency, stomata conductance and excised leaf water loss. It was suggested that the traits showed [d] additive and [i] additive × additive interaction may be used to fix the increase in the expression of traits in next generations and selection for the development of synthetic varieties for drought resistance may be helpful. The [h] dominance effects showed that the traits may be used for the development of hybrid. On the basis of genetic effects it was concluded that stomata frequency, stomata size, cell membrane thermo stability, leaf water potential and excised leaf water loss may be helpful for

The DNA damage response (DDR) alerts the immune system to the danger posed by DNA damage through the induction of damage-associated molecular pattern molecules, chemokines, and ligands for activating immune receptors such as lymphocyte function-associated antigen 1 (LFA-1), NKG2D, and DNAX accessory molecule 1 (DNAM-1). Here we provide evidence that OVA(257-264) -pulsed fibroblasts gain the ability to activate naive OT-I CD8(+) T cells in response to DNA damage. The ability of fibroblasts to activate OT-I CD8(+) T cells depended on the upregulation of ICAM-1 on fibroblasts and DNAM-1 expression of CD8(+) T cells. OVA(257-264) -pulsed fibroblasts were able to induce a protective T-cell response against B16-OVA cells in a DDR-dependent manner. Hence, the DDR may alert the immune system to the presence of potentially dangerous cells by upregulating the expression of ligands that can induce the activation of innate and adaptive immune cells.

In this paper, we use the concept of bounded Mocanu variation to introduce a new class of analytic functions, defined in the open unit disc, which unifies a number of classes previously studied such as those of functions with bounded radius rotation and bounded Mocanu variation. It also generalizes the concept of ?-spiral likeness in some sense. Some interesting properties of this class including inclusion results, arclength problems and a sufficient condition for univalency are studied.

Primary cells are derived directly from tissue and are thought to be more representative of the physiological state of cells in vivo than established cell lines. However, primary cell cultures usually have a finite life span and need to be frequently re-established. Fibroblasts are an easily accessible source of primary cells. Here, we discuss a simple and quick experimental procedure to establish primary fibroblast cultures from ears and tails of mice. The protocol can be used to establish primary fibroblast cultures from ears stored at RT for up to 10 days. When the protocol is carefully followed, contaminations are unlikely to occur despite the use of non-sterile tissue stored for extended time in some cases. Fibroblasts proliferate rapidly in culture and can be expanded to substantial numbers before undergoing replicative senescence.

Today drought is the worldwide problem that causes the reduction in the crop yield of plants. Maize (Zea mays L.) is greatly affected by drought and its production is declining day by day. However there are many techniques accessible today to achieve a better understanding of the multifarious network of plant responses to drought and to maneuver these responses. These techniques comprise the association mapping, mapping of quantitative trait loci (QTLs), studies of gene expression and genetic transformation. Selection based on performance in multi-environment trials (MET) has increased grain yield under drought through increased yield potential and kernel set, rapid silk exertion, and reduced barrenness, though at a lower rate than under optimal conditions. Knowledge of the physiology of drought tolerance has been used to dissect the trait into a series of key processes. This has been complemented by genetic dissection through the identification of QTL associated with these same traits. Both have been used to identify suitable organ- and temporal-specific promoters and structural genes. The present paper will overview both conventional breeding and QTL/MAS approaches towards improvement of maize yield in drought-prone environments and will briefly describe a QTL approach. Key Wards: Drought, QTL, Maize, zea mays, multi-environment trials, promoters, marker assisted selection, Pakistan.

The present study was conducted on two different locations, (1) fields of the Department of Plant Breeding and Genetics, University of Agriculture Faisalabad, Pakistan and (2) the field of Chaudhary Agriculture farms, Kasur, Pakistan, during the crop season of 2010-2011. Eighty chickpea genotypes (seventy lines and ten check varieties) were evaluated for genetic variability in morpho-physiological and quality traits in chickpea. Highest values of heritability and genetic advance were found for 100-seed weight, proteins, chlorophyll contents, pods per plant, leaf area and grain yield. Correlation studies showed that leaflets per leaf, chlorophyll contents, 100-seed weight and leaf area with pods per plant; proteins with 100-seed weight, proteins and biomass per plant with grains per plant were positive and significant at both genotypic and phenotypic levels at location 1. Leaflets per leaf, chlorophyll contents and proteins with grain yield; 100-seed weight and biomass per plant with seeds per pod were positive and highly significant at genotypic and phenotypic level for location 2. The higher value of genetic variability, genotypic and phenotypic correlation coefficients indicated that these traits can be used for selecting high yielding and better quality chickpea genotypes. Key words: Cicer arietinum, correlation, proteins, genetic variability, chickpea, PBG, UAF, Pakistan.

The translocation of some heavy metals, such as Hg, Cd, Cu, Co, Ni and Zn, as affected by organic pollutants, i.e. methanol, ethanol, propanol, formaldehyde, acetaldehyde, benzaldehyde, acetone, ethyl methyl ketone and cyclo-hexanone, was studied in an Indian red soil using soil thin layer chromatography. It was observed that an increase in the concentration of organic compounds in developer enhances the heavy metal mobility, except in the case of Cu and Hg which show a decreasing trend. The results are discussed in relation to the physico-chemical characteristics of the soil and adsorption/desorption phenomena.

An application of Bavistin (5 mg kilogram of soil) and Calixin (1.5 mg per kilogram of soil) significantly enhanced the microbial activity, as well as availability, of NH(4)(+)-N and P in the soil, while NO(2)(-)-NO(3)(-)-N and available K were found to decrease. During incubation, decreases in CO(2)-evolution were observed initially up to 21 days, with an increase up to 42 days, and, thereafter, a fall in both treatments.

An investigation was undertaken to study the mobilities of Cr, Ni, Pb, Ag and Cd through Indian Red Soil using soil thin layer chromatography. Different soil constituents such as organic matter, pH, anions of the soluble salts—i.e. Cl − , HCO 3 − , CO 3 −− , SO 4 −− , PO 4 −−− and MoO 4 −− —and some exchangeable cations were examined as mobility factors. It was found that the decomposition of soil organic matter greatly increased the mobility of heavy metals whereas a rise in pH of the soil water system caused a decrease. Initial addition of all anions apparently enhanced the mobility but high levels of added SO 4 −− , PO 4 −−− and MoO 4 −− caused a reduction. The mobility of heavy metals tested in soils saturated with different cations was found to be in the order H-soil > Na-soil > K-soil > Mg-soil > Ca-soil.