Miracle-dz/UniFAR-FTUs huggingface dataset:
On analyticity of the Lp ‐Stokes semigroup for some non‐Helmholtz domains

Consider the Stokes equations in a sector‐like C3 domain Ω⊂R2 . It is shown that the Stokes operator generates an analytic semigroup in Lσp(Ω) for p∈[2,∞) . This includes domains where the Lp ‐Helmholtz decomposition fails to hold. To show our result we interpolate results of the Stokes semigroup in VMO and L2 by constructing a suitable non‐Helmholtz projection to solenoidal spaces.
Fractional direct and inverse models of the dynamics of a human arm

When developing control architectures for physical human–robot interaction, it is often necessary to use a model of the human operator. This paper presents a comparative study of both direct and inverse models of the human arm at the elbow joint, relating the force at the hand with the arm angle and vice versa. Specifically, models of integer and fractional (commensurable and non-commensurable) nature are identified from the experiments. Likewise, for comparison purposes, neural networks models are also obtained. Taking into account their parameter variability, it is shown that fractional models are more adequate to describe human arm behaviour; they are simpler, more exact and with less parameter uncertainty.
Preparation of Sm-Fe-N hard magnetic nanopowder from micron-sized Sm-H and Fe mixed powder

Metastable TbCu7-type phases of Sm-Fe-N nanopowder were successfully prepared. The Sm-H fine powder prepared by a jet mill process and Fe mixed powder were used as a raw material for nanopowder preparation by the recently developed low oxygen induction thermal process. The particle size was around 100 nm. Most of the obtained nanopowder consists of a core/shell structure. The core is Fe and the shell is Sm or Sm-Fe alloy, as observed by microstructure analysis. The coercivity of nitrided nanopowder is only 0.16 T at 300 K, which is due to the existence of a large amount of Fe core in the nanoparticle. The thermal plasma process is therefore a unique process with potential for one-through synthesis of nanocomposite powders by controlling the iron core size.
Design and implementation of a Cooke triplet based wave-front coded super-resolution imaging system

Wave-front coding is a powerful technique that could be used to extend the DOF (depth of focus) of incoherent imaging system. It is the suitably designed phase mask that makes the system defocus invariant and it is the de-convolution algorithm that generates the clear image with large DOF. Compared with the traditional imaging system, the point spread function (PSF) in wave-front coded imaging system has quite a large support size and this characteristic makes wave-front coding be capable of realizing super-resolution imaging without replacing the current sensor with one of smaller pitch size. An amplification based single image super-resolution reconstruction procedure has been specifically designed for wave-front coded imaging system and its effectiveness has been demonstrated experimentally. A Cooke Triplet based wave-front coded imaging system is established. For a focal length of 50 mm and f-number 4.5, objects within the range [5 m, ∞] could be clearly imaged, which indicates a DOF extension ratio of approximately 20. At the same time, the proposed processing procedure could produce at least 3× resolution improvement, with the quality of the reconstructed super-resolution image approaching the diffraction limit.
Extracellular Synthesis of Fe-NPs by Bacteria/Biosurfactant, Kinetic Parameter and Reduction of Cr (VI) from Pollutant Samples

Hexavalent Cr is most toxic metal and hazardous compound worldwide. It is also considered to be cellular oxidizing pollutant causing damage, mutagenic and hematopoietic effects to living systems. Present work is focused on synthesis of zero valent iron nano particles (Fe-NPs) using Bacillus subtilis SHB 13, Achromobacter xylosoxidans SHB 204 and Surfactin and their application for Cr (VI) detoxification (100 ppm) in synthetic solutions and polluted sludge, sewage and soil samples about 90-100% reduction observed with above particles. Qualitative analysis, structural characterization of Fe-NPs, adsorption, and recovery of Cr (VI) were carried out using XRD, SEM and TEM. This work explains adsorption isotherm of Cr with iron nano particles (Fe- NPs), was best fitted with Langmuir model and it followed first order rate kinetic reaction. Microcosm studies were carried out using Fe-NPs in sludge, sewage and tannery samples and Cr (VI) was completely (100 %) removed within 72 h. In another microcosm experiment, bacteria were able to reduce (60-70 %) Cr (VI) from soils with and without moisture and glucose. In green house experiments both the bacteria were able to reduce Cr (VI) (65-90%) effectively from both polluted and non-polluted soil and there was increase in root length and shoot length of plant L. esculentum was observed and maximum plant biomass was observed with plants inoculated with SHB 204 and SHB 13.
Structural and Biological Features of G-Quadruplex Aptamers as Promising Inhibitors of the STAT3 Signaling Pathway

In this paper, we investigate the structural and biological features of G-quadruplex (G4) aptamers as promising antiproliferative compounds affecting the STAT3 signalling pathway. Targeting the STAT3 protein through high-affinity ligands to reduce its levels or activity in cancer has noteworthy therapeutic potential. T40214 (STAT) [(G3C)4] is a G4 aptamer that can influence STAT3 biological outcomes in an efficient manner in several cancer cells. To explore the effects of an extra cytidine in second position and/or of single site-specific replacements of loop residues in generating aptamers that can affect the STAT3 biochemical pathway, a series of STAT and STATB [GCG2(CG3)3C] analogues containing a thymidine residue instead of cytidines was prepared. NMR, CD, UV, and PAGE data suggested that all derivatives adopt dimeric G4 structures like that of unmodified T40214 endowed with higher thermal stability, keeping the resistance in biological environments substantially unchanged, as shown by the nuclease stability assay. The antiproliferative activity of these ODNs was tested on both human prostate (DU145) and breast (MDA-MB-231) cancer cells. All derivatives showed similar antiproliferative activities on both cell lines, revealing a marked inhibition of proliferation, particularly at 72 h at 30 µM. Transcriptomic analysis aimed to evaluate STAT’s and STATB’s influence on the expression of many genes in MDA-MB-231 cells, suggested their potential involvement in STAT3 pathway modulation, and thus their interference in different biological processes. These data provide new tools to affect an interesting biochemical pathway and to develop novel anticancer and anti-inflammatory drugs.
Multispectral image filtering method based on image fusion

This paper proposed a novel filter scheme by image fusion based on Nonsubsampled ContourletTransform(NSCT) for multispectral image. Firstly, an adaptive median filter is proposed which shows great advantage in speed and weak edge preserving. Secondly, the algorithm put bilateral filter and adaptive median filter on image respectively and gets two denoised images. Then perform NSCT multi-scale decomposition on the de-noised images and get detail sub-band and approximate sub-band. Thirdly, the detail sub-band and approximate sub-band are fused respectively. Finally, the object image is obtained by inverse NSCT. Simulation results show that the method has strong adaptability to deal with the textural images. And it can suppress noise effectively and preserve the image details. This algorithm has better filter performance than the Bilateral filter standard and median filter and theirs improved algorithms for different noise ratio.
Myrtenal and Myrtanal as Auxiliaries in the Synthesis of Some C, P-Stereogenic Hydroxyphosphine Oxides and Hydroxyphosphine-Boranes Possessing up to Four Contiguous Centers of Chirality

1,4- and 1,2-additons of secondary phosphine oxides to (1R)-myrtenal and (1S)-myrtanal were evaluated as potential routes to P,C-stereogenic phosphine oxides bearing additional hydroxyl or aldehyde functions. 1,4-Additions of racemic secondary phosphine oxides to (1R)-myrtenal were found to offer moderate to good stereoselectivity which shows some promise for utility in kinetic resolution processes, especially at lower conversions. In case of 1,2-additions making the process doubly asymmetric by using an enantiomerically pure secondary phosphine oxide as substrate turned out to be practical. The stereochemical course of the addition reactions under study is presented. The P-resolved 1,2-addition products were demonstrated to undergo facile reduction by BH3 at room temperature leading to the formation of the corresponding α-hydroxyphosphine-boranes with clean inversion of configuration at the P-centre. All P,C-stereogenic phosphine oxides and boranes that were isolated in the form of a single diastereoisomer were assigned their absolute configurations by means of X-ray crystallography and/or 2D NMR spectral techniques.
Microfinancing and Entrepreneurial Processes Among Females: A Role Congruity Theory Perspective

This research qualitatively investigates the activities that female entrepreneurs perform to become successful in their ventures with the support of microfinancing. Based on interviews with 34 female entrepreneurs from five regions of Bangladesh, this study sought to identify significant categories of start-up companies related to the development of entrepreneurship among rural women borrowers through microfinancing. Specifically, this study examined obstacles faced by females of Bangladesh and explored how they overcame those barriers and became successful entrepreneurs. A grounded theory approach was used to categorize critical obstructions and facilitating factors, and role congruity theory helped inform the nature of the emerging categories. Nine compelling themes emerged from the analysis, identifying vital factors influencing the nature and outcome of the small business ventures. From a role congruity theory perspective, the nine themes converge to suggests that microfinancing allows women to operate their businesses in a manner that enables them to maintain high congruity or fit with their family roles and other lifestyle requirements, contributing to successful business operations.
Theoretical Interpretations of the Eliassen-Palm Diagnostics of Wave-Mean Flow Interaction: Part I: Effects of the Lower Boundary@@@第1部:地表境界条件の効果

Etude, a l'aide d'un modele analytique simple, d'une violation des conditions necessaires et suffisantes etablies ici, en l'absence de l'amortissement moyen, pour la divergence du flux d'Eliassen-Palm. Cette violation due a l'effet de la limite inferieure, peut expliquer pourquoi certaines etudes de diagnostic d'observations trouvent que des tourbillons transitoires accelerent un courant d'Ouest dans la troposphere superieure, temperee, en l'absence d'un amortissement moyen, alors que la divergence transitoire du flux d'Eliassen-Palm soit ici d'Est. Il est suggere que le courant d'Ouest, dans la haute troposphere, peut etre soit renforce, soit reduit par ces tourbillons, suivant le degre d'amortissement moyen et d'echauffement radiatif
Convolutional Neural Network Outperforms Graph Neural Network on the Spatially Variant Graph Data

Applying machine learning algorithms to graph-structured data has garnered significant attention in recent years due to the prevalence of inherent graph structures in real-life datasets. However, the direct application of traditional deep learning algorithms, such as Convolutional Neural Networks (CNNs), is limited as they are designed for regular Euclidean data like 2D grids and 1D sequences. In contrast, graph-structured data are in a non-Euclidean form. Graph Neural Networks (GNNs) are specifically designed to handle non-Euclidean data and make predictions based on connectivity rather than spatial structure. Real-life graph data can be broadly categorized into two types: spatially-invariant graphs, where the link structure between nodes is independent of their spatial positions, and spatially-variant graphs, where node positions provide additional information about the graph’s properties. However, there is limited understanding of the effect of spatial variance on the performance of Graph Neural Networks. In this study, we aim to address this issue by comparing the performance of GNNs and CNNs on spatially-variant and spatially-invariant graph data. In the case of spatially-variant graphs, when represented as adjacency matrices, they can exhibit Euclidean-like spatial structure. Based on this distinction, we hypothesize that CNNs may outperform GNNs when working with spatially-variant graphs, while GNNs may excel on spatially-invariant graphs. To test this hypothesis, we compared the performance of CNNs and GNNs under two scenarios: (i) graphs in the training and test sets had the same connectivity pattern and spatial structure, and (ii) graphs in the training and test sets had the same connectivity pattern but different spatial structures. Our results confirmed that the presence of spatial structure in a graph allows for the effective use of CNNs, which may even outperform GNNs. Thus, our study contributes to the understanding of the effect of spatial graph structure on the performance of machine learning methods and allows for the selection of an appropriate algorithm based on the spatial properties of the real-life graph dataset.
Probabilistic choice between symmetric disparities in motion stereo matching for a lateral navigation system

Abstract. Two consecutive frames of a lateral navigation camera video sequence can be considered as an appropriate approximation to epipolar stereo. To overcome edge-aware inaccuracy caused by occlusion, we propose a model that matches the current frame to the next and to the previous ones. The positive disparity of matching to the previous frame has its symmetric negative disparity to the next frame. The proposed algorithm performs probabilistic choice for each matched pixel between the positive disparity and its symmetric disparity cost. A disparity map obtained by optimization over the cost volume composed of the proposed probabilistic choice is more accurate than the traditional left-to-right and right-to-left disparity maps cross-check. Also, our algorithm needs two times less computational operations per pixel than the cross-check technique. The effectiveness of our approach is demonstrated on synthetic data and real video sequences, with ground-truth value.
The Upcoming Future of Metal-Organic Frameworks: Challenges and Opportunities

Metal–organic frameworks (MOFs) belong to the group of porous and crystalline substances that enable the diversity of composition and structure beyond conventional solid-state materials. In the past few years’ MOFs have created enormous interest due to their potential applications in clean energy, becoming a storage medium for H2 and CH4. The luminescent metal-organic frameworks (LMOFs) are recognized for a broad range of applications in chemical sensing, energy-efficient lighting, removal of toxic substances from gases and liquids, bio-imaging, and related fields. The present review highlights the basic methods of synthesis, current challenges in the synthesis of novel MOF materials, various characterization methods, scope, and opportunities available for the future related to MOF research. The application of MOF materials in packaging and catalysis was also discussed.Keywords: Luminescent, Metal–organic frameworks (MOFs), Sensors, Storage energies, Supercapacitors.
Key management for encrypted storage on a grid

Abstract Data grids provide data owners with a tremendous volume of data storage capacity. Sensitive data stored on a grid is vulnerable to unauthorized disclosure because grids are an open environment. Encrypting data provides data with confidentiality in storage and in transit. However, if doing so, the main problems are how to store those cryptographic keys safely and how to allow the authorized users to have access to the cryptographic keys. In this paper, a scheme is proposed for data owners to store encrypted data and to share the data with authorized users in an efficient way.
In Memoriam Tadimeti Raja Rao (1930-2022): A Superb Plant Physiologist and Wonderful Mentor

We honour here Tadimeti Raja Rao (1930-2022), a wonderful human being – who was a friend to many and a superb plant physiologist of our time. His research began with discoveries in organic acid metabolism in plants, and on the effects of virus infection on nitrogen metabolism. Raja Rao (also spelled as ‘Rajarao’) followed this research by providing novel and practical information on the effects of several growth regulators (hormones) on the physiology of many plants, particularly sugarcane, guava, Indian gooseberry, and gladiolus, among others. During his later academic life, he gave unique and valuable guidance to many for the improvement of ‘crop production’ which was vital for plant biotechnology. We end this tribute to Raja Rao with Reminiscences from his family (Sarada, his wife, and Visweswara Rao and Srinivasa Rao, their sons); his friends from Allahabad (Rajni Govindjee and Raj Prasad), and two of the co-authors (Sheo Mohan Prasad and G. S. R. Murti#).
Large Eddy Simulations of large wind-turbine arrays in the atmospheric boundary layer

Large Eddy Simulations (LES) of arrays of wind turbines in the atmospheric turbulent boundary layer with many turbines often require simplifled models for the efiects of individual turbines, in order to avoid having to use very flne grid spacings near the individual (moving) turbine blades. This goal can be accomplished using the actuator disk model. This approach, however, raises several issues when implemented in the context of LES. In particular, the question is raised of which reference velocity should be used when parameterizing the induced forces: instantaneous versus time averaged, undisturbed velocity or the local one. Also, one may consider including tangential forces to represent the angular momentum in the wakes. In this paper we present several arguments to make the appropriate choices, and illustrate the efiects of these choices in LES of two wind turbine arrays, one with an aligned and one with a staggered arrangement. Predicted power outputs, as well as features of the predicted ∞ow flelds are analyzed.
DRL-based adaptive signal control for bus priority service under connected vehicle environment

Transit Signal Priority (TSP) strategy gives public transit vehicles privileges to pass through the intersection without stopping. Most previous studies have adopted the compulsory TSP strategy that considers to maximize the utility of public transportation, which is likely to reduce the efficiency of social vehicles. In this paper, we propose an Adaptive Transit Signal Priority (ATSP) model that considers the efficiency of both buses and social vehicles. This model has the Single Request Adaptive Transit Signal Priority (SR-ATSP) module and the Multi-Request Adaptive Transit Signal Priority (MR-ATSP) module. First, the intersection network is divided into grids based on the Discrete Traffic State Encoding (DTSE) idea to obtain the spatial information of vehicles. Then, in the SR-ATSP module, the Dueling Double Deep Q-learning Network (D3QN) algorithm is introduced to determine whether to implement the TSP strategy or not, considering the goal of minimizing the total passenger waiting time of buses and social vehicles. Based on the SR-ATSP, the MR-ATSP module introduces some rules to tackle the conflict from multiple priority requests of different buses. Simulation experiments based on an intersection in Nansha District, Guangzhou City are conducted on SUMO software. The results show that the proposed ATSP model can realize the priority treatment for of buses while reducing the waiting time of social vehicles by . It has superior performance for reducing the waiting time of buses and social vehicles than other widely-used TSP models.
Optimizations in Dynamic Origin Technique for Efficient Lane Detection for Autonomous Vehicles

— Driver assistance systems have started becoming a key differentiator in automotive space and all major automotive manufacturers have such systems with various capabilities and stages of implementation. The main building blocks of such systems are similar in nature and one of the major building blocks is road lane detection. Even though lane detection technology has been around for decades, it is still an ongoing area of research and there are still several improvements and optimizations that are possible. This paper offers an Optimized Dynamic Origin Technique (Optimized DOT) for lane detection. The proposed optimization algorithm of optimized DOT gives better results in performance and accuracy compared to other methods of lane detection. Analysis of proposed optimized DOT with various edge detection techniques, various threshold levels, various sample dataset and various lane detection methods were done and the results are discussed in this paper. The proposed optimized DOT lane detection average processing time increases by 9.21 % when compared to previous Dynamic Origin Technique (DOT) and 59.09 % compared to traditional hough transform.
The Challenges and Opportunities of AI-Assisted Writing: Developing AI Literacy for the AI Age

Generative AI may significantly disrupt the teaching and practice of business communication. This study of 343 communication instructors revealed a collective view that AI-assisted writing will be widely adopted in the workplace and will require significant changes to instruction. Key perceived challenges include less critical thinking and authenticity in writing. Key perceived benefits include more efficiency and better idea generation in writing. Students will need to develop AI literacy—composed of application, authenticity, accountability, and agency—to succeed in the workplace. Recommendations are provided for instructors and administrators to ensure the benefits of AI-assisted writing can outweigh the challenges.
Generalized minimizers of convex integral functionals and Pythagorean identities

Integral functionals based on convex normal integrands are minimized subject to nitely many moment constraints. The eective domain of the value function is described by a modication of the concept of convex core. The minimization is viewed as a primal problem and studied together with a dual one in the framework of convex duality. The minimizers and generalized minimizers are explicitly described whenever the primal value is nite, assuming a dual constraint qualication but not the primal constraint qualication. A generalized Pythagorean identity is presented using Bregman distance and a correction term.