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Part of the work was done while the authors were visiting the South China University of Science and Technology (SUSTech), Shenzhen, in 2018, and we are grateful (in particular to Professor Cai Heng Li) for the hospitality that we received.The authors would like to thank the Isaac Newton Institute for Mathematical Sciences, Cambridge, for support and hospitality during the programme Groups, representations and applications: new perspectives (supported by EPSRC grant no.EP/R014604/1), where further work on this paper was undertaken. In particular we acknowledge a Simons Fellowship (Cameron) and a Kirk Distinguished Visiting Fellowship (Praeger) during this programme. Schneider thanks the Centre for the Mathematics of Symmetry and Computation of The University of Western Australia and Australian Research Council Discovery Grant DP160102323 for hosting his visit in 2017 and acknowledges the support of the CNPq projects Produtividade em Pesquisa (project no.: 308212/2019-3) and Universal (project no.:421624/2018-3). Diagonal groups are one of the classes of finite primitive permutation groups occurring in the conclusion of the O'Nan-Scott theorem. Several of the other classes have been described as the automorphism groups of geometric or combinatorial structures such as affine spaces or Cartesian decompositions, but such structures for diagonal groups have not been studied in general. The main purpose of this paper is to describe and characterise such structures, which we call diagonal semilattices. Unlike the diagonal groups in the O'Nan-Scott theorem, which are defined over finite characteristically simple groups, our construction works over arbitrary groups, finite or infinite. A diagonal semilattice depends on a dimension m and a group T. For m=2, it is a Latin square, the Cayley table of T, though in fact any Latin square satisfies our combinatorial axioms. However, for m≥3, the group T emerges naturally and uniquely from the axioms. (The situation somewhat resembles projective geometry, where projective planes exist in great profusion but higher-dimensional structures are coordinatised by an algebraic object, a division ring.) A diagonal semilattice is contained in the partition lattice on a set Ω, and we provide an introduction to the calculus of partitions. Many of the concepts and constructions come from experimental design in statistics. We also determine when a diagonal group can be primitive, or quasiprimitive (these conditions turn out to be equivalent for diagonal groups). Associated with the diagonal semilattice is a graph, the diagonal graph, which has the same automorphism group as the diagonal semilattice except in four small cases with m

Prostate cancer (PCa) is the second most frequently diagnosed male cancer worldwide. Early diagnosis of PCa, response to therapy and prognosis still represent a challenge. Nearly 60% of PCa patients undergo radiation therapy (RT) which might cause side effects. In spite of numerous researches in this field, predictive biomarkers for radiation toxicity are still not elucidated. MicroRNAs as posttranscriptional regulators of gene expression are shown to be changed during and after irradiation. Manipulation with miRNA levels might be used to modulate response to RT-to reverse radioresistance-to induce radiosensitivity, or if needed, to reduce sensitivity to treatment to avoid side effects. In this review we have listed and described miRNAs involved in response to RT in PCa, and highlighted potential candidates for future biological tests predicting radiation response to RT, with the special focus on side effects of RT. Individual radiation response is a result of the interactions between physical characteristics of radiation treatment and biological background of each patient, and miRNA expression changes among others. According to described literature we concluded that let-7, miR-21, miR-34a, miR-146a, miR-155, and members of miR-17/92 cluster might be promising candidates for biological tests predicting radiosensitivity of PCa patients undergoing radiation treatment, and as future agents for modulation of radiation response. Predictive miRNA panels, especially for acute and late side effects of RT can serve as a starting point for decisions for individualized RT planning. We believe that this review might be one step closer to understanding molecular mechanisms underlying individual radiation response of patients with PCa.

At present, skin cancer is considered a widespread malignancy in human beings. Among diverse population types, Caucasian populations are much more prone to this malignancy in comparison to darker skin populations due to the lack of skin pigmentation. Skin cancer is divided into malignant and non-melanoma skin cancer, which is further categorized as basal and squamous cell carcinoma. Exposure to ultraviolet radiation, chemical carcinogen (polycyclic aromatic hydrocarbons, arsenic, tar, etc.), and viruses (herpes virus, human papillomavirus, and human T-cell leukemia virus type-1) are major contributing factors to skin cancer. There are distinct pathways available through which skin cancer develops, such as the JAK-STAT pathway, Akt pathway, MAPKs signaling pathway, Wnt signaling pathway, to name a few. Currently, several targeted treatments are available, such as monoclonal antibodies, which have dramatically changed the line of treatment of this disease but possess major therapeutic limitations. Thus, many phytochemicals have been evaluated either alone or in combination with the existing synthetic drugs to overcome their limitations and have been found to play a promising role in the prevention and treatment. In this review, a complete overview of skin cancer, starting from the signaling pathways involved, newer developed drugs with their targets and limitations, along with the emerging role of natural products alone or in combination as potent anticancer agents and their molecular mechanism involved has been discussed. Apart from this, various nano-cargos have also been mentioned here, which can play a significant role in the management and treatment of different types of skin cancer.

Glioblastoma Multiforme (GBM) is a poorly curable brain tumor because of its extremely invasive nature. Curcuminoids, as potential phytochemicals extracted from Curcuma Longa L., have been documented for their chemopreventive and antitumor activities against several types of malignancies. These compounds exert these effects via modulation of multiple signaling pathways and molecular targets at different stages of tumor progression, proliferation, and metastasis. In experimental studies, curcuminoids have demonstrated promising therapeutic benefits to overcome GBM. Curcuminoids have been shown to exert their anti-GBM effects through regulation of angiogenesis, apoptosis, autophagy, metastasis, invasion, as well as potential molecular targets, including receptor tyrosine kinases, Sonic Hedgehog, and NF-κB. This study reviews the observations regarding the impact of curcumin and its derivatives on GBM and the potential of translating the research findings into the clinic.

In this paper, energy-preserving time high-order average vector field (AVF) compact finite difference scheme is proposed and analyzed for solving two-dimensional nonlinear wave equations including the nonlinear sine-Gordon equation and the nonlinear Klein-Gordon equation. We first present the corresponding Hamiltonian system to the two-dimensional nonlinear wave equations, and further apply the compact finite difference (CFD) operator and AVF method to develop an energy conservative high-order scheme in two dimensions. The L p -norm boundedness of two-dimensional numerical solution is obtained from the energy conservation property, which plays an important role in the analysis of the scheme for the two-dimensional nonlinear wave equations in which the nonlinear term satisfies local Lipschitz continuity condition. We prove that the proposed scheme is energy conservative and uniquely solvable. Furthermore, optimal error estimate for the developed scheme is derived for the nonlinear sine-Gordon equation and the nonlinear Klein-Gordon equation in two dimensions. Numerical experiments are carried out to confirm the theoretical findings and to show the performance of the proposed method for simulating the propagation of nonlinear waves in layered media.

Autistic Spectrum Disorder (ASD) is a neurodevelopmental condition affecting approximately 1 out of 70 (range 1:59 – 1:89) children worldwide. It is characterized by a delay in cognitive capabilities, repetitive and restricted behaviors and deficit in communication and social interaction. Several factors seem to be associated with ASD development; its heterogeneous nature makes the diagnosis difficult and slow since it is essentially based on screening tools focused on stereotypical and repetitive behaviors, gait, facial emotion expression and speech assessments. Recently, artificial intelligence (AI) has been widely used to investigate ASD with the overall goal of simplifying and speeding up the diagnostic process as well as making earlier access to therapies possible. The aim of this review is to provide an overview of the state-of-the-art research in the ASD field, identifying and describing machine learning (ML) approaches in ASD literature that could be used by clinicians to improve diagnostic capability and treatment efficiency. A systematic search was conducted and the resulting articles were subdivided into several categories reflecting the different fields of study associated with ASD research. The existing literature has widely demonstrated the potential of ML in several types of ASD study analyses: behavior, gait, speech, facial emotion expression, neuroimaging, genetics, and metabolomics. Therefore, AI techniques are becoming increasingly implemented and accepted, so highlighting the power of ML approaches to extract and obtain knowledge from a large volume of data. This makes ML a promising tool for future ASD research and clinical endeavors suggesting possible avenues for improving ASD screening, diagnostic and therapeutic tools.

Current methods for diagnosing human disease are still incapable of rapidly and accurately screening for multiple diseases simultaneously on a large scale, and at an affordable price. MALDI-ToF mass spectrometry is an ultra-sensitive, ultra-fast, lowcost, high-throughput technology that has the potential to achieve this goal, allowing human phenotype characterization and thus phenomic screening for multiple disease states. In this review, we will discuss the main advances achieved so far, putting forward targeted applications of MALDI-ToF mass spectrometry in the service of human disease detection. This review focuses on the methodological workflow as MALDI-ToF data processing for phenomic analysis, using state-of-the-art bioinformatic pipelines and software tools. The role of mathematical modelling, machine learning, and artificial intelligence algorithms for disease screening are considered. Moreover, we present some previously developed tools for disease diagnostics and screening based on MALDI-ToF analysis. We discuss the remaining challenges that are ahead when implementing MALDI-ToF into clinical laboratories. Differentiating a standard profile from a single disease phenotype is challenging, but the potential to simultaneously run multiple algorithm screens for different disease phenotypes may only be limited by computing power once this initial hurdle is overcome. The ability to explore the full potential of human clinical phenomics may be closer than imagined; this review gives an insight into the benefits this technology may reap for the future of clinical diagnostics.

Artificial intelligence methods, in particular, machine learning, has been playing a pivotal role in drug development, from structural design to the clinical trial. This approach is harnessing the impact of computer-aided drug discovery due to large available data sets for drug candidates and its new and complex manner of information interpretation to identify patterns for the study scope. In the present review, recent applications related to drug discovery and therapies are assessed, and limitations and future perspectives are analyzed.

International audience; In the framework of the development of new passive safety systems for the second and third generations of nuclear reactors, the numerical simulations, involving complex turbulent two-phase flows around thin or massive inflow obstacles, are privileged tools to model, optimize and assess new design shapes. In order to match industrial demands, computational fluid dynamics tools must be the fastest, most accurate and most robust possible. To face this issue, we have chosen to solve the Navier-Stokes equations using a projection scheme for a mixture fluid coupled with an Immersed Boundary (IB) approach: the penalized direct forcing method - a technique whose characteristics inherit from both penalty and immersed boundary methods - adapted to infinitely thin obstacles and to a Finite Element (FE) formulation. Various IB conditions (slip, no-slip or Neumann) for the velocity on the IB can be managed by imposing Dirichlet values in the vicinity of the thin obstacles. To deal with these imposed Dirichlet velocities, we investigated two variants: one in which we use the obstacle velocity and another one in which we use linear interpolations based on discrete geometrical properties of the IB (barycenters and normal vectors) and the FE basis functions. This last variant is motivated by an increase of the accuracy/computation time ratio for coarse meshes. As a first step, concerning academic test cases for one-phase dilatable-fluid laminar flows, the results obtained via those two variants are in good agreement with analytical and experimental data. Moreover, when compared to each other, the linear interpolation variant increases the spatial order of convergence as expected. An industrial test case illustrates the advantages and drawbacks of this approach. In a shortcoming second step, to face two-phase turbulent fluid simulations, some methodology modifications will be considered such as adapting the projection scheme to low-compressible fluid and immersed wall-law boundary conditions.

International audience; The aim of this essay is to analyze Machiavellian strategies of reading and using Herodian (in particular the Latin version of Herodian, published by Poliziano in 1493) as a source of patterns of examples in The Prince and Discourses on Livy's Roman History.; Cette étude a pour but la réception de la version latine d'Hérodien, que Politien publia en 1493, dans le Prince et les Discours sur la première décade de Tite Live de Machiavel.

The COVID-19 (here specifically called Worldwide Acute Respiratory Syndrome, WARS) pandemic is surging worldwide. Unfortunately, no specific drug meets the urgent need to fight this pandemic, leading to thousands of deaths. The theory of host-directed therapies (HDTs) is viewed as the ideal means to rephrase the treatment of infectious diseases. However, related drugs based on this theory have not been identified. Previously, we realized that caffeine is the ligand of type 2 taste receptors (TAS2Rs), which play a critical role in host defense. Here, we gathered data on caffeine acting as an immunomodulator. Unexpectedly, we found that caffeine can fight WARS by acting on multiple organs, which may prevent the virus from entering the cell, stimulate the phagocytosis of macrophages, enhance breathing, and inhibit the cytokine storm. Thus, the immunoprotective effects of caffeine can improve the therapeutic outcomes in patients infected with coronavirus. Collectively, we report that caffeine, an FDA-approved, highly safe, inexpensive, and widely available drug, could be an excellent HDT for battling WARS.

Point-of-care (POC) testing decentralizes the diagnostic tests to the sites near the patient. Many POC tests rely on microfluidic platforms for sample-to-answer analysis. Compared to other microfluidic systems, magnetic digital microfluidics demonstrate compelling advantages for POC diagnostics. In this review, we have examined the capability of magnetic digital microfluidics-based POC diagnostic platforms. More importantly, we have categorized POC settings into three classes based on “where is the point”, “who to care” and “how to test”, and evaluated the suitability of magnetic digital microfluidics in various POC settings. Furthermore, we have addressed other technical issues associated with POC testing, such as controlled environment, sample-system interface, system integration and information connectivity. We hope this review would provide a guideline for the future development of magnetic digital microfluidics-based platforms for POC testing.

In this paper, an improved node moving technique was developed for adaptive solution of some flow problems. Collocated Discrete Least Squares Meshless (CDLSM) method was applied for simulation. This method is a truly meshless method and also enjoys the symmetric and positive-definite property. Then, an improved node moving technique was developed based on the spring analogy. In this mechanism, each node was assumed to be connected to its neighboring nodes via some virtual springs. Then, higher values of stiffness were allocated to the springs located at higher error areas. In the previously published works, the corresponding system of equations was assumed to be linear. In this work, first, it was shown that this assumption may lead to inaccurate results in some cases and then, an improved method was proposed considering the nonlinear nature of the system of equations. Some numerical examples were used to illustrate the ability of the proposed node moving technique for some simple examples and benchmark problems in the Computational Fluid Dynamics (CFD) context. The Results of the study showed a considerable improvement in comparison with those of previously published works.

Osteoarthritis (OA) is a degenerative disease of cartilage and bones, which results in severely compromised quality of life in the aged population. However, currently, no ideal treatment strategies have been developed to prevent OA progression. Cell therapies, such as chondrocyte and MSC transplantation, have been extensively tested and evaluated in clinical trials. Yet, to day, the clinical efficacy of articular injection of stem cells in OA has not been convincingly demonstrated. Recent studies have indicated that exosomes, one type of Extracellular Vesicles (EVs) play an important regulatory role in the pathogenesis of OA, suggesting the prospective therapeutic application of exosomes in OA treatment. In this review, we systematically summarized the paracrine effects of exosomes derived from MSCs and chondrocytes on cartilage regeneration, the use of exosomes as a delivery vehicle for OA treatment, the effectiveness of such treatments in OA animal models, and the future perspective of exosome-mediated drug delivery as a cell- free therapy of OA.

Paclitaxel (PTX) is the first natural plant-derived chemotherapeutic drug approved by the Food and Drug Administration. However, the clinical applications of PTX are limited by several drawbacks, such as its poor water solubility, rapid blood clearance, non-specific distribution, and adverse side effects. Nanocarriers have been used to improve drug delivery in recent years. However, they typically require a complicated synthesis process, have a low drug loading capacity, and when administered are associated with nanocarrier excipient-induced toxicity or immunogenicity. To overcome these issues, self-delivery nanomedicines (SDNs), wherein pure drug molecules directly self-assemble into nanomedicines, have been developed for improved drug delivery and enhanced antitumor efficacy. In this review, we comprehensively summarize the latest research on PTX-based SDNs for cancer therapy, including their main categories, therapeutic agents, morphologies, applicable cancer types, and properties. The self-assembly process to develop pure PTX nanodrugs is first discussed. Then, the emerging strategies of co-assembling PTX and other therapeutic agents for effective combination therapy, such as combination chemotherapy, chemo-photothermal therapy, chemo-photodynamic therapy, chemo-immunotherapy, and chemo-gene therapy, are presented. Finally, the limitations and future outlook of SDNs are discussed. Such rationally designed and unique nanoplatforms represent a promising new direction in efforts to develop highly efficient drug delivery systems for cancer therapy.

Prostate cancer is the fifth cause of tumor-related deaths in man worldwide. Due to its long latency period, this pathology represents an ideal type of disease for chemopreventive studies. Among the drugs considered thus far for the treatment of prostate cancer, the natural compound resveratrol emerged as very promising. Resveratrol is widely recognized as a chemopreventive agent and was shown to potentiate the antitumor activity of conventional chemotherapeutics in several tumors, including prostate cancer. Here, we overview the literature of the last five years and summarize the recent achievements of resveratrol and its derivatives as antimetastatic agents in prostate cancer. Moreover, drug combination studies as well as nanomedicine approaches proposed to improve resveratrol activity and to overcome delivery drawbacks are addressed. The last part of the review discusses the clinical trials containing resveratrol ongoing on cancer patients.

COVID-19 is an emerging outbreak similar to previous pandemics caused by Severe Acute Respiratory Syndrome (SARS) and Middle East Respiratory Syndrome (MERS). Till date, SARS-CoV-2 infection is still spreading, representing a major threat to public health, where several control measures are being practiced in order to culminate its spread. The research and development of new drugs require a lot of funding in addition to being a slow and costly process. As a result, new techniques have been proposed to streamline this process. The repositioning or repurposing of drugs represents an attractive strategy, presenting a promising way to introduce new drugs. Currently, numerous reused drugs are already available in the market and are in practice. In this review, it was observed that the antiviral drugs Entricitabine and Tenofovir display potential therapeutic efficacy in preclinical studies. Therefore, in silico analyses were considered a potential tool for predicting the effectiveness of drugs, mainly as an effective approach to encourage a complementary in vitro and in vivo antiviral evaluation.

Background: Diabetes Mellitus (DM) is a severe endocrine metabolic disease coupled with various long-term complications. A plethora of targets have been identified, however, with possible adverse effects. Therefore, researchers are in the perpetual quest for safe and more effective therapeutics. Natural products, particularly derived from plants, have proven to exert anti-diabetic effects via diverse mechanisms. Methods: An overview of DM pathogenesis and its associated micro- and macro-vascular complications is presented. Possible underlying mechanisms of herbal remedies in DM management are provided, highlighting some key therapeutic targets. The review also appraises the recent progress of herbal products in treating DM through regulating inflammation and gut microbiota. Finally, currently available pharmacological treatments are discussed. Results: The results show that numerous plants have proven to be promising sources of insulin secreting agents, α-glucosidase and α-amylase inhibitors. Among the non- conventional targets, inhibition of key enzymes such as lipase, cholinesterases and angiotensin converting enzyme has been directly and/or indirectly linked to DM and DM complications. For instance, hypericin, pseudohypericin and I3,II8-biapigenin isolated from Hypericum perforatum L., and palmatine and columbamine isolated from Dichocarpum auriculatum (Franch.) W. T. Wang & P. K have been found to be powerful lipase and cholinesterase inhibitors, respectively. Moreover, a number of plant-derived compounds such as feruloylated oligosaccharides from maize bran, baicalein and berberine are reported to mediate anti-diabetic property via modulation of gut microbiota. Conclusion: The information amassed in this review is anticipated to provide useful scientific baseline information to support advanced research in natural antidiabetic drug development.

In the current era, the market competition is becoming increasingly fierce, complicated and unpredictable. Based on the interaction of various factors, the probability of financial risks of listed companies is significantly improved. Because of its unique characteristics, the listed companies’ operating status affects the overall operation of China’s market economy and occupies a fundamental position in the national economic system. If listed companies have financial risks, it will cause great trauma to our economy. Based on the financial risk evaluation theory of listed companies, this paper analyzes the financial indicators of listed companies through random effect model, and puts forward the risk analysis and prediction index system of listed companies from theoretical and empirical angles, thus constructing a financial risk early warning model based on linear random effect model, and studying the financial risk early warning of listed companies with practical cases. The research results show that the financial risk early warning model of random effect model is feasible and effective, which can help listed companies to carry out financial risk early warning management and improve financial management level.

A deterministic model was formulated and employed in the analysis of the dynamics of tuberculosis with a keen emphasis on vaccination and drug resistance as the first line of treatment. It was assumed that some of the susceptible population were vaccinated but with temporal immunity. This is due to the fact that vaccines do not confer permanent immunity. Moreover, part of the infected individual after treatment grows resistance to the drug. Infective immigrants were also considered to be part of the population. The basic reproductive number for the model is estimated using the next-generation matrix method. The equilibrium points of the TB model and their local and global stability were determined. It was established that if the basic reproductive number was less than unity R 0 < 1 , then the disease free equilibrium is stable and unstable if R 0 > 1 . Furthermore, we investigated the optimal prevention, treatment, and vaccination as control measures for the disease. As the objective functional was optimised, there have been a significant reduction in the number of infections and an increase in the number of recovery. The best control measure in combating tuberculosis infections is prevention and vaccination of the susceptible population.

Drillstring vibration is known to cause failures of drilling equipment, including the drill bit. In particular, stick-slip vibration has been known for causing premature failure of the drill bit, hence resulting in reducing the rate of penetration. With dual reaming while drilling, cutting forces are acting on the drillstring due to the simultaneous contact of the reamer and the drill bit. Field studies have shown dramatic changes in the dynamics of the bottom hole assembly due to the dual cutting actions. This paper investigates the dynamics of bottom hole assembly for dual reaming and drilling operation, with emphasis on stick-slip vibrations due to the reamer and the bit contact with the formation. A coupled vibration model representing the drillstring was created to simulate the stick-slip vibrations caused by the bit and reamer interactions using the finite element approach. The numerical analysis showed an elevated stick-slip vibration due to the dual-action of the reamer and the bit. Sensitivity analysis indicated that the cutter aggressiveness for the bit and the reamer are the most significant parameters affecting stick-slip behavior.

The algebraic structures have many applications in coding theory, cryptography, and security networks. In this paper, the notion of hybrid subalgebras ofBCH-algebras is introduced and related properties are investigated. Moreover, some characterizations of hybrid subalgebras ofBCH-algebras are given. Furthermore, we state and prove some theorems in hybrid subalgebras ofBCH-algebras. The homomorphic images and inverse images of fuzzyBCH-subalgebras are studied and discussed.

Purpose The purpose of this paper is to study how budgetary constraints can have ethical implications on patient treatment options. Design/methodology/approach By applying a qualitative methodological approach (interviews) and participant observation, this paper studies the behaviour of surgeons in scenarios of financial restriction. Findings The empirical findings show that despite the conflict between the economy and the leges artis, surgeons maintain the ethical and deontological principles of their profession with fair rules of orientation. Practical implications The importance of this study can be realised by its continuity. One of the authors is already implicated on a wider research to investigate the influence of the economic scarcity of resources on general surgeons’ ethical behaviours. Social implications This paper is a contribution to understanding the rules that restrain the activities of surgeons. Politicians sometimes do not have a full understanding of the pressures that the medical profession faces in their day-to-day activities. Currently, with the addition of problems relate to COVID-19, politicians and populations seem to better understand the importance of the Serviço Nacional de Saúde (SNS), This paper hopes that this understanding will be not only a conjectural moment. Originality/value In conjunction with the economic recession that began in the first decade of this century, health institutions have long faced budgetary constraints that condition their material and human resources and correspondingly shape the scope of health professional activities. Until now, it has not been studied the impact of economic crises on the ethical behaviour of Portuguese surgeons. Therefore, this research is a first step and a useful contribution to understanding the rules that can restrain (or not) the ethical conduct of these health professionals.

Multiclass data classification, where the goal is to segment data into classes, is an important task in machine learning. However, the task is challenging due to reasons including the scarcity of labeled training data; in fact, most machine learning algorithms require a large amount of labeled examples to perform well. Moreover, the accuracy of a classifier can be dependent on the accuracy of the training labels which can be corrupted. In this paper, we present an efficient and unconditionally stable semi-supervised graph-based method for multiclass data classification which requires considerably less labeled training data to accurately classify a data set compared to current techniques, due to properties such as the embedding of data into a similarity graph. In particular, it performs very well and more accurately than current approaches in the common scenario of few labeled training elements. Morever, we show that the algorithm performs with good accuracy even with a large number of mislabeled examples and is also able to incorporate class size information. The proposed method uses a modified auction dynamics technique. Extensive experiments on benchmark datasets are performed and the results are compared to other methods.

In this paper, we found a common fixed point for several multivalued mappings on proximinal sets in regular modular metric space. Also, we introduced the notions of conjoint F-proximinal contraction as well as conjoint F-proximinal contraction of Hardy–Rogers-type for several multivalued mappings. Furthermore, we enhanced our results by giving an application in integral equations.

Tall structures, during their service lifetime, face many scenarios and are often prone to damages. Generally, static or dynamic measurements from the entire structure are used while formulating th...

The adaptation of well-known approaches to the assessment and identification of reserves for improving the productivity of managerial, administrative and other office work due to the changing format and technologies of its implementation as a result of digitalization is considered. A scientific approach to measuring and evaluating the productivity of intellectual labor is proposed based on the method of calculating productivity reserves as a value that depends on the time of organizational and qualification losses. The article shows the specifics of calculating such criteria for assessing labor productivity as possible and maximum productivity, the size of their reserves and the time loss factor for a particular employee or various organizational structures of managerial, administrative or office activities.

The article reviews the dynamics of statistical indicators of the development of the modern construction sector of the Tyumen region. The structure of the regional construction complex is described, indicating the key economic entities of the industry and the nomenclature of products of their own manufacture. The factors of the growth of construction in the region are identified based on the study of the dynamics of changes in prices and incomes in the Tyumen region in the analyzed period and the implemented support programs.

The purpose of this study is to introduce a new concept of the modular space, which is CΩ-modular space, and then some of the convex properties are discussed. We also study finding fixed-point in CΩ-modular space.

Price fluctuations at the producer and consumer levels determine the performance of the maize market in Gorontalo Province. This will affect the decisions and ability of the corn marketing agencies involved in responding to price changes. The slow response of marketing agencies to changes in maize prices indicates inefficient market conditions in terms of prices. In addition, differences in market power between marketing agencies indicate inefficiencies in marketing maize from an operational perspective. Therefore, this research was conducted with the aim of 1) analyzing market integration and transmission of maize prices between producers and consumers, 2) analyzing the distribution of marketing margins in the maize marketing channels in Gorontalo Province. The research data used is in the form of weekly price data at the producer and consumer levels for 148 weeks, from March 2016 to March 2019 and added data from direct interviews with the marketing actors involved, namely farmers as many as 30 people, while sampling of traders was carried out by snowball sampling technique. Data analysis used the Asymmetric Error Correction Model (AECM), marketing margin and farmer share for each channel. The results showed that the transmission of maize prices at the producer and consumer level is symmetrical in the long run indicating market integration. The smallest marketing margin and the largest farmer share are in Channel III, where marketing is done directly to an exporter. Corn marketing in Gorontalo Province shows price transmission and market integration as well as high farmer share value in each channel, so it can be concluded that marketing is efficient.

The advancement in electromagnetic metamaterials, which commenced three decades ago, experienced a rapid transformation into acoustic and elastic systems in the forms of phononic crystals and acoustic/elastic metamaterials. Since its early discovery, numerous wave phenomena alongside the possible engineering applications have been highlighted. The existing and emerging fields of metamaterials are far more extensive, ranging from optics to acoustic, and all the way to the elastic systems. Numerous fantastic dynamic properties in optics and acoustic/elastic systems have been reported to date, which cannot be found in naturally occurring materials. The present review tends to discuss the historical context, current progresses and possible future outcomes of metamaterials. The fascinating phenomena observed in optics/electromagnetic metamaterials have been explained and linked with acoustic and elastic counterparts. The idea of perfect lens that is governed by negative permittivity and negative permeability via left-handed materials with negative refractive index properties and the transformation optics for invisibility cloaks and optical rainbow effect alongside the hyperbolic metamaterials are reviewed and discussed. Furthermore, the associated transformation into acoustic and elastic focusing effects via graded index metamaterials, acoustic/elastic invisibility cloaks, transformational acoustics, and seismology and metawedges resembling optical rainbow effects and the likes are explained. The present state of the art has been examined and the physics involved in the governing of those peculiar wave mechanisms has been highlighted. Starting from photonic crystals, phononic crystals and acoustic metamaterials, the present state of the art research in some subfields of acoustic metamaterials has been outlined, such as metasurfaces, topological phononic crystals and seismic metamaterials, the three exciting and emerging research topics. The substantial challenges involved in these realms are characterised and the possible future outcome is further evaluated. This review article may assist researchers and engineers to grasp the idea of metamaterials in not only photonic and phononic crystal systems, but also the counterpart subfields.

An enhanced symbiotic organisms search (ESOS) algorithm is developed and presented. Modifications to the basic symbiotic organisms search algorithm are carried out in all three phases of the algorithm with the aim of balancing the exploitation and exploration capabilities of the algorithm. To verify validity and capability of the ESOS algorithm in solving general optimization problems, the CEC2014 set of 22 benchmark functions is first optimized and the results are compared with other metaheuristic algorithms. The ESOS algorithm is then used to optimize the sizing and shape of five benchmark trusses with multiple frequency constraints. The best (minimum) mass, mean mass, standard deviation of the mass, total number of function evaluations, and the values of frequency constraints are then compared with those of a number of other metaheuristic solutions available in the literature. It is shown that the proposed ESOS algorithm is generally more efficient in optimizing the shape and sizing of trusses with dynamic frequency constraints compared to other reported metaheuristic algorithms, including the basic symbiotic organisms search and its other recently proposed improved variants such as the improved symbiotic organisms search algorithm (ISOS) and modified symbiotic organisms search algorithm (MSOS).

CAPTCHA stands for Completely Automated Public Turing Test to Tell Computers and Human Apart. CAPTCHA is used for internet security. A few CAPTCHA schemes are available today like, text-based, audio-based, video/animation-based, puzzle based etc. In this paper, all these types are collaborating at single place to analyze. The main aim of this article is to present a literature to identify and recognize CAPTCHA, its types, the creation and breaking techniques. It is a systematic and complete analysis of all available CAPTCHA types. In this paper, 16 text-based CAPTCHA’s generation methods are discussed with usability and security ranges from 3 to 100 and 65 to 100%, respectively. The security and usability measures are not calculated/sustained using some known English schemes. Out of 16 reviewed CAPTCHAs, 12 are based on English language, 1 on Arabic language, 1 on Chinese language, 1 on Devanagari language and 1 on Gurumukhi script. The designs are made segment proof with overlapping random shapes, overlapping characters, clasping, different colors and different shades. For making recognition proof many techniques are used like image masking, local and global warping; broken characters, random rotation, arcs, jaws, etc. Approximately 50 schemes, especially based on the English language, are successfully broken with a success rate that ranges from 2 to 100%. The techniques that are used to break these schemes include shape context matching, distortion estimation, Log Gabor 2D filter, horizontal and vertical projection (for a segment the letters) are used. For recognition CNN, KNN, DNN and MCDNN are used. Almost 15 images-based CAPTCHAs are discussed that are designed with usability and security range 90–100 and 17–100%, respectively. Out of these 5 schemes are successfully broken with a success rate ranging between 7 and 100%. The K-NN and SVM are mostly used algorithms to recognize the images. Audio based CAPTCHAs (5 designs) are discussed with usability and security range from 68.5 to 100 and 100%, respectively. The broken rate of these audio schemes is also 45–75%. These schemes are broken with SVM and K-NN algorithms. The paper also discusses 4 popular video-based designs that provide usability and security that ranges from 75 to 100 and 98 to 100, respectively. These schemes are also compromised with broken rate 16–10% using SIFT, NN and simple OCR techniques. The paper can be a benchmark to precede any specific research to dive into any one of these types.

Numerical models are widely used for the prediction of stress and strains that the materials undergo in extrusion processes. These factors affect the quality of the extruded product as well as the speed of production process. Due to its meshless nature, the smooth particle hydrodynamics (SPH) method is capable of simulating processes where severe deformations occur. Therefore, it is highly suitable for modeling of extrusion processes. In this study, the SPH method is used for the simulation of extrusion processes for two different geometries of the die cross section. For all cases, a parametric analysis on friction was conducted and the influence on the strain fields as well as the extrusion forces was investigated. Also, in order to calibrate the models to predict the experimental forces, it was necessary to use different friction coefficients for the die and the container section. The SPH method modeled accurately the influence of each section’s frictional forces on the strain field and extrusion forces. Furthermore, the calibrated models were able to capture the experimental data with a 10% accuracy for a range of different values for the initial billet temperature and ram speed.

Background. Epilepsy is one of the most common neurological diseases globally. The unified concept about the role of hippocampus in the development of pharmacoresistant temporal lobe epilepsy is currently missing. Patients with pharmacoresistant temporal lobe epilepsy is often carried out by invasive electrocorticography to identify an epileptic focus. Registration of bioelectric activity of the hippocampus and comparison of data from the MRI pattern of the patient will determine the neurophysiological correlates of structural changes in hippocampus.Objective. The aim of the work was to determine the neurophysiological correlates of structural changes in the hippocampus in patients with focally caused temporal lobe epilepsy.Design and methods. The study was based on the analysis of the results of extraoperative invasive monitoring of the bioelectrical activity of the cortex and hippocampal complex, performed in 19 patients with focally caused drug-resistant epilepsy. The quantitative analysis included 34 tracks of hippocampal activity.Results. A distinctive feature of the bioelectrical activity of the hippocampal complex with its structural damage is the stable dominance of delta activity, which makes up 40–45 % of the total spectrum power. When the hippocampal complex is included in the epileptic system, high-index epileptiform activity is recorded. In the absence of structural damage to the hippocampal complex, the pattern is predominantly formed by the activity of theta and alpha frequency ranges. However, in the group of patients with the absence of a neuroimaging picture of sclerotic changes in the hippocampus, in 63 % of cases, a neurophysiological pattern of “prolapse” was recorded on the electrocorticogram. The spontaneous activity of the hippocampus had a low coherent relationship with the parameters of activity in the cortex of the ipsilateral and contralateral temporal lobes.Conclusions. The electrophysiological correlate of MR-positive structural changes in the hippocampal complex in drug-resistant epilepsy is the pattern of “loss of bioelectric activity”. Spontaneous hippocampal activity is generated independently of activity in the cortex of the ipsilateral and contralateral temporal lobes.

Розглянуто один з відомих методів класифікації об’єктів, в якому реалізовано критерій класифікації за максимумом дискримінантних функцій. Цей метод ефективно засто¬со-вується як класична обчислювальна модель, зокрема, у медицині при діагностуванні за-хворювань. Процес класифікації за цим методом можна реалізувати як просторово-роз-поділену обробку по стовпцях і рядках матриці у вигляді регулярних ітеративних алго¬ритмів. Це дозволяє відобразити їх на двовимірний систолічний масив матричного обчислювача у складі класифікатора об'єктів з подальшим розміщенням у ПЛІС. Запропонований матричний обчислю-вач функціонує в двох режимах і має низку специфічних властивостей, а саме виконання операції декремента одночасно над усіма елементами в кожному стовпці матриці обчислювача, а також використання сигналів ознаки нуля (обнуління) елементів в кожному рядку і кожному стовпці матриці як результатів обробки елементів дискримінантних функцій і для синхронізації самого процесу обробки. В подальшому за результатами обробки у матричному обчислювачі формують-ся вихідні сигнали класифікатора з визначенням конкретного класу об’єктів.

Retinex theory shows that the color of a scene perceived by human visual system is only dependent on the intrinsic reflectance and unrelated to the illumination. For images recorded by digital cameras, the intensities are the product of the scene reflectance and illumination. Image intensities are different when taken under different illuminations. The aim of image retinex problem is to decompose the reflectance from the image intensities. In this paper, we propose a variational model for image retinex problem. The model can be reduced to a linear complementarity problem and a modulus-based multigrid method is applied to solve it. A pointwise coupled modulus-based Gauss-Seidel iteration scheme is used as a smoother of the multigrid method. The smoothing factor of the smoother is analyzed by using the local Fourier analysis. Both theoretical analyses and numerical results show the efficiency of the proposed method for image retinex problem.

Evaluating the exact first derivative of a feedforward neural network (FFNN) output with respect to the input feature is pivotal for performing the sensitivity analysis of the trained neural network with respect to the inputs. In this paper, a novel method is presented that computes the analytical quality first derivative of a trained feedforward neural network output with respect to the input features without the need for backpropagation. To this end, the complex step derivative approximation is illustrated, and its implementation in the framework of the feedforward neural network is described. Artificial datasets are generated, and the efficacy of the proposed method for both regression and classification tasks is demonstrated. The results obtained for the regression task indicated that the proposed method is capable of obtaining analytical quality derivatives, and in the case of the classification task, the least relevant features could be identified.

Kidney disease is a serious health problem that burdens our healthcare system. It is crucial to find the accurate pathogenesis of various types of kidney disease to provide guidance for precise therapies for patients suffering from these diseases. However, the exact molecular mechanisms underlying these diseases have not been fully understood. Disturbance of calcium homeostasis in renal cells plays a fundamental role in the development of various types of kidney disease, such as primary glomerular disease, diabetic nephropathy, acute kidney injury and polycystic kidney disease, through promoting cell proliferation, stimulating extracellular matrix accumulation, aggravating podocyte injury, disrupting cellular energetics as well as dysregulating cell survival and death dynamics. As a result, preventing the disturbance of calcium homeostasis in specific renal cells (such as tubular cells, podocytes and mesangial cells) is becoming one of the most promising therapeutic strategies in the treatment of kidney disease. The endoplasmic reticulum and mitochondria are two vital organelles in this process. Calcium ions cycle between the endoplasmic reticulum and mitochondria at the conjugation of these two organelles known as the mitochondria-associated endoplasmic reticulum membrane, maintaining calcium homeostasis. The pharmacologic modulation of cellular calcium homeostasis can be viewed as a novel therapeutic method for renal diseases. Here, we will introduce calcium homeostasis under physiological conditions and the disturbance of calcium homeostasis in kidney diseases. We will focus on the calcium homeostasis regulation in renal cells (including tubular cells, podocytes and mesangial cells), especially in the mitochondria- associated endoplasmic reticulum membranes of these renal cells.

The aim of this paper is to analyse the potential impact of multiple current interventions in communities with limited resources in order to obtain optimal control strategies and provide a basis for future predictions of the most effective control measures against the spread of malaria. We developed a population-based model of malaria transmission dynamics to investigate the effectiveness of five different interventions. The model captured both the human and the mosquito compartments. The control interventions considered were: educational campaigns to mobilise people for diagnostic test and treatment and to sleep under bed nets; treatment through mass drug administration; indoor residual spraying(IRS) with insecticide to reduce malaria transmission; insecticide treated net (ITN) to reduce morbidity; and regular destruction of mosquito breeding sites to reduce the number of new mosquito and bites/contact at dusks and dawn. Analysis of the potential impact of the multiple control interventions were carried out and the optimal control strategies that minimized the number of infected human and mosquito and the cost of applying the various control interventions were determined.

Color image interpolation encompasses reconstructing parts of a video or an image based on information from the neighbor. Technique involves the restoration of noised photos and animation or image denoising. The Navier-Stokes (NS) technique has been widely investigated as an essential research by image restoration. These NS equations contribute spectacular results for producing an animation as they augment reality. They can boost real-time video games to be more sensible than ever. In this paper, we present the Incompressible NS approach (INS) for color image interpolating. The method per se is based on fluid flow concept to circulate directed lines from the peripheral into the area to be interpolated. The image intensity represents stream function in a computational flow of 2D fluid dynamics. The algorithm is implemented to carry on lines regarding gradient vectors at the edge of the interpolating region. It uses the improvement of powerful numerical analysis. It is also proven as an innovative idea for easing problems in image analytics as well as computer vision.

The sports industry is considered the industry with the most vitality, participation, and penetration in the 21st century. From the perspective of practice on a global scale, the demand for the sports market has gradually developed from low-level simplification to high-level multi-polarity, and sports consumption has also shifted from single sports goods to sports services and entertainment sports. The sport industry has become modern society containing emerging industries with large business opportunities. This article mainly uses literature, expert interviews, data collation, logical analysis, and other methods to conduct a comparatively systematic study on the development of the new form of the “Internet+” sports industry. In the study, by comparing the commodity types, organizational forms, and management of the traditional sports industry in terms of management and other aspects, the connotation of the new format of the “Internet+” sports industry is introduced in detail, which is divided into four industries: network broadcasting, smart software and hardware, vertical e-commerce, and e-sports. The research in this article shows that, after accounting, the total scale (total output) of the national sports industry in 2019 was 2,948.3 billion yuan, with a year-on-year added value of 1,124.8 billion yuan. In terms of growth, total output increased by 10.9% over 2018 and 11.6% year-on-year. Among them, sports activities integrated with the Internet have reached 58%. Finally, it will help the transformation and upgrading of Internet+sports and improve its own development level. Returning to the real economy and serving the real economy have become a consensus for the future development of Internet finance.

Very low Earth orbits (VLEOs) have an altitude of 100–450 km and have become a popular topic in space technology owing to their high application potential. However, spacecraft experience stronger a...

Internet of Things (IoT) is one of the recent innovations in Information Technology, which intends to interconnect the physical and digital worlds. It introduces a vision of smartness by enabling communication between objects and humans through the Internet. IoT has diverse applications in almost all sectors like Smart Health, Smart Transportation, and Smart Cities, etc. In healthcare applications, IoT eases communication between doctors and patients as the latter can be diagnosed remotely in emergency scenarios through body sensor networks and wearable sensors. However, using IoT in healthcare systems can lead to violation of the privacy of patients. Thus, security should be taken into consideration. Blockchain is one of the trending research topics nowadays and can be applied to the majority of IoT scenarios. Few major reasons for using the Blockchain in healthcare systems are its prominent features, i.e., Decentralization, Immutability, Security and Privacy, and Transparency. This paper’s main objective was to enhance the functionality of healthcare systems using emerging and innovative computer technologies like IoT and Blockchain. So, initially, a brief introduction to the basic concepts of IoT and Blockchain is provided. After this, the applicability of IoT and Blockchain in the medical sector is explored in three major areas—drug traceability, remote patient-monitoring, and medical record management. At last, the challenges of deploying IoT and Blockchain in healthcare systems are discussed.

The failure to achieve minimum design overlap between secant piles compromises the ability of a structure to perform as designed, resulting in water leakage or even ground collapse. To establish a more realistic simulation and provide guidelines for designing a safe and cost-effective secant-pile wall, a three-dimensional model of a secant pile, considering the geometric imperfections of the diameter and direction of the borehole, is introduced. An ultrasonic cross-hole test was performed during the construction of secant piles in a launching shaft in Beijing, China. Based on the test results, the statistical characteristics of the pile diameters and orientation parameters were obtained. By taking the pile diameter D, inclination angle β, and azimuth angle α as random variables, Monte Carlo simulations were performed to discuss the influence of different design parameters on the probability density functions of the overlap of secant piles. The obtained results show that the randomness of the inclination angle and pile diameter can be well described by a normal distribution, whereas the azimuth angle is more consistent with a uniform distribution. The integrity of the secant-pile wall can be overestimated without considering the uncertainty of geometric imperfections. The failure of the secant-pile wall increases substantially with increasing spatial variability in drilling inclination and diameter. A design flowchart for pile spacing under the target safety level is proposed to help engineers design a safe and economical pile wall.

With the rapid development of the Internet, text data has become one of the major formats of big data tourism and improves the quality and promotes the optimization and upgradation of tourism English talents. This paper proposes a model of tourism English talent resources based on data mining techniques using a big data framework. The characteristic distribution structure model is built to identify and blend the characteristics of tourism English talent resources. Connection feature mining and information fusion are combined to share data and schedule resources during the talent training process. Initially, the proposed research work uses a cloud storage system for developing intercultural communicative competence of tourism English talents. Next, the optimal scheduling design of tourism English talent training resource’s big data is carried out. Finally, the fuzzy clustering method deals with the adaptive clustering of tourism English talent resource distribution big data. The simulation findings show that the proposed method has high precision and big data computation efficiency. Moreover, it can successfully mentor the development of a new framework of tourism English talent training.

From the early days of pattern recognition, word spotting have been important test beds for studying how well machines can perform better decision making. In recent years, word spotting have made dramatic advances with state-of-the-art techniques reaching high level of performance in real life applications. This word spotting domain have driven research by providing suitable yet well-defined challenges for pattern recognition and document analysis practitioners. We continue in this direction by covering extensive literature and new challenges in this domain with comparison of previous work. In particular, we have covered recent deep learning technique role in word spotting and future scope of word spotting with deep learning. We believe writing suitable review of word spotting will not only be crucial for understanding of this field in today era, but also in broader collaborative efforts, especially those with artificial intelligence based tasks. To facilitate future research in word spotting, we have discussed word spotting from learning environment, including its framework design with components as query phase, preprocessing stages, segmentation, feature extraction, feature representation and matching process strategies. Further, deep learning working and use in word spotting architecture has been discussed. The study also include an experimental comparison for the research community to evaluate algorithmic advances along with benchmarked datasets, and future challenges in this field.

The natural environment is vital for the healthy growth of children. The absence of exposure to Nature in cities is an important cause of physical and psychological health problems in children. With the objective of a healthy growth environment for children, this paper states the active role of the urban waterfront space, analyses the relationship between children and urban waterfront space, summarises the characteristics of children’s outdoor activities, proposes the principle and methods of planning and designing a children-friendly urban waterfront space, and provides some improved design strategies.

Different numerical schemes are introduced to formulate and solve nonlinear vibration analysis of elastically supported multilayer composite plate problems. The type of elastic foundation is Winkle...

As an interdisciplinary between quantum computing and image processing, quantum image processing provides more possibilities for image processing due to the powerful parallel computing capabilities of quantum computers. In recent years, quantum image processing attracts more and more researcher’s attention. In order to allow researchers to better understand quantum image processing technology, we have reviewed relevant literature in recent years in the paper. First, the background and mathematical concepts of quantum computing are introduced. Then, the research progress of quantum image processing is sorted out and summarized in the fileds of quantum image representation, geometric transformation, image encryption, edge detection, image segmentation, filtering and compression. Finally, we have discussed the advantages and disadvantages of quantum image processing, and pointed out the potential future research.