Possibility Of Application Research Articles

Cantilever configurations in vibration-based piezoelectric energy harvesting: a comprehensive review on beam shapes and multi-beam formations

Abstract Vibration-based energy harvesting technology is a well-established research area that has attracted tremendous interest over the last decade. This interest is primarily owing to its extension into a wide range of engineering domains, particularly in microelectromechanical systems. The cantilever beam is the most common and widely used model for vibration-based energy harvester, driven by two key factors: (a) simplicity in design, and (b) high output power density. Numerous studies over the years have focused on optimizing the cantilever beam design to increase output power capacity and/or widen the frequency bandwidth of the harvester. While researchers have proposed a plethora of cantilever beam configurations for specific purposes (e.g. low-frequency harvesting, multi-directional frequency harvesting, etc), there is a notable lack of detailed literature on the types and configurations of cantilever beams. This gap hinders researchers from gaining a comprehensive understanding of the cantilever beams already introduced. Following the need, in this article a comprehensive review is made to list the types of cantilever beams proposed by the researchers over the years. This review covers the working principles of piezoelectric energy harvesting, analyses existing solutions geared towards increasing power output and widening working frequency, and discusses diverse configurations including single and multiple beam setups. The listed beams are categorized based on their structural shape and organization such that it can be helpful for a reader to anticipate which cantilever beam design can be suitable for a specific need. Power output capacity and operating frequency for every beam design are also presented in a tabular form, under each beam category. This would enable the researchers to tailor their designs for specific applications, enhance material efficiency, drive innovation, and open new application possibilities.

Neuroendocrine tumors of the stomach and pancreas. Possibilities of application of radiomics. Problems and solutions.

Background. Radiomics is promising tool contributing to the diagnosis and treatment of neuroendocrine neoplasms of various localizations. This method is often used for differential diagnosis of gastrointestinal neoplasms from other neoplasms of this localization.Aim. To demonstrate difficulties in imaging of gastrointestinal neuroendocrine tumors, as well as possibilities of radiomics in differential diagnosis.Materials and methods. Data of 12 patients with morphologically confirmed gastric neoplasms (6 - with neuroendocrine tumor, 6 - with adenocarcinoma) and data of 22 patients with morphologically confirmed pancreatic neoplasms (11 - with neuroendocrine tumor, 11 - with adenocarcinoma) who underwent CT examination of abdominal cavity organs with intravenous contrast before treatment at the Russian Scientific Center of Roentgenoradiology were used in this study. Radiomic features were extracted from the area of manually segmented gastric and pancreatic tumors in the native phase of the CT scans. Processing of the features and statistical analysis were performed using Microsoft Office Excel and free open-source software development environment for the R programming language - R-Studio.Results. Typical and atypical imaging features of gastric and pancreatic neuroendocrine tumors, peculiarities of contrast, localization and structure of neoplasms were demonstrated on the examples of CT scans. There were 15 radiomic features that were statistically significantly different in patients with neuroendocrine tumors and gastric adenocarcinomas. In the group of patients with neuroendocrine tumors and pancreatic adenocarcinomas, statistically significant differences were observed in 14 radiomic features.Conclusion. Neuroendocrine tumors of the stomach and pancreas are rare neoplasms that are not only clinically silent in most cases, but also difficult to visualize due to their small size and poor contrast characteristics. Texture analysis is a promising tool for the differential diagnosis of gastrointestinal neoplasms from other neoplasms of this localization, especially considering the difficulties of biopsy.

Exploring the Therapeutic Potential of Theobroma cacao L.: Insights from In Vitro, In Vivo, and Nanoparticle Studies on Anti-Inflammatory and Anticancer Effects

Theobroma cacao L., commonly known as cocoa, has been an integral part of human culture and diet for thousands of years. However, recent scientific research has highlighted its potential therapeutic properties, particularly in the treatment of cancer and inflammatory diseases. This comprehensive review analyzes the anti-inflammatory and anticancer effects of Theobroma cacao extracts combined with nanoparticles using in vitro and in vivo studies. Its diverse biological activity can be attributed to its rich phytochemical profile, including polyphenols, flavonoids, and alkaloids. In vitro studies have found that cocoa extracts, alone or in combination with nanoparticles, inhibit cancer cell proliferation, induce apoptosis and modulate key signaling pathways in various cancer cell lines. The extracts have also been found to reduce tumor growth and enhance the efficacy of conventional chemotherapeutic agents, potentially reducing their side effects, in vivo. Its anti-inflammatory properties are based on its ability to modulate inflammatory mediators, inhibit NF-κB signaling, and regulate macrophage polarization. These effects have been observed in both cellular and animal models of inflammation. This review opens up new possibilities for future research and therapeutic applications, highlighting the potential of Theobroma cacao as a valuable complementary approach in the treatment and prevention of cancer and inflammatory diseases.

Digital Technologies in the National Cadastre System of Uzbekistan: Issues of Legal Regulation

Objective: to critically analyze the state of national legislation of Uzbekistan in terms of legal regulation of digitalization and the use of artificial intelligence in the cadastral sphere.Methods: the research is based on such methods of scientific cognition as formal-legal and comparative-legal analysis, induction and deduction.Results: the provisions that regulate digitalization and the use of artificial intelligence in the cadastral sphere were analyzed, legal gaps were identified. It was determined that the practical application of artificial intelligence technologies outpaces its legal regulation. The shortcomings of legal regulation in this sphere were noted (lacking legal definition of the legal status of artificial intelligence in the national legislation; regulation of business entities’ participation in the management of artificial intelligence, etc.). The said shortcomings hinder its full application and harmonization with traditional sources of cadastral information. The author substantiated the need for universal digitization of the national cadastre and predicts the possibility of wider application of artificial intelligence in the natural-resource cadastral system. It is argued that the existing system in its current state may lead to wrong decisions and cadastral errors, hence, it is necessary to improve the legal regulation of cadastre.Scientific novelty: for the first time the results of the national cadastre digitization were assessed. Forecasts were given about the possibility of using artificial intelligence in this area, subject to further improvement of legal regulation. The latter is fundamentally important for reforming the cadastral system, since the technological basis of this system does not fully meet the needs of the digital economy.Practical significance: it is due to the lack of legal regulation of the artificial intelligence concept and legal status in the national legislation, as well as a unified approach to the cadastral system digitalization. Modern technologies are actively used in practice, but lack a sufficient legal basis. The main conclusions, proposals and recommendations of the study can be a basis for further improvement of the legal framework of Uzbekistan in terms of the application of artificial intelligence technologies.

Advances in the study of DNA-metal complexes in biomedical applications

DNA-metal composites are emerging as emerging materials for biomedical applications due to their unique DNA and metal nanoparticle properties. DNA is a multifunctional macromolecule that enables targeted drug delivery through aptamer selection and gene silencing through nuclease activity. These properties make it highly selective and targeted in therapy. While conventional chemotherapy faces significant drawbacks, including poor targeting and severe side effects, DNA-metal composites with their excellent targeting and therapeutic efficiencies can significantly enhance therapeutic efficacy. This paper first describes the structural properties, synthesis methods of DNA-metal composites and their therapeutic applications. The combination of DNA and metal nanoparticles provides a novel approach to improve the effectiveness of anticancer and antimicrobial therapies, addressing key limitations in current therapeutic strategies. Innovative applications of DNA-metal composites in the anticancer and antimicrobial fields are then explored, highlighting their multiple synthesis and application possibilities through interactions with metal ions or particles. DNA-metal composites address several of the limitations of conventional chemotherapeutic and antimicrobial drugs, providing better targeting and resolving the issue of drug resistance. DNA-metal composites in biomedicine show DNA-metal composites show great promise in the biomedical field, and future research will continue to promote their application and development in clinical therapy.

Rapid preparation and mechanism investigation of covalent organic framework membranes by 3D printing based on electrostatic spraying

Covalent organic framework (COF) has great advantages in the field of membrane separation, but the efficient and convenient preparation of COF membranes is still a challenge. Herein, we propose a novel method with green solvent for the preparation of COF membranes based on electrostatic spraying, which can be successfully prepared within 10 min, forming the TpPa COF with a pore size of 1.8 nm. Molecular dynamics simulations demonstrate that electrostatic spraying enhances the reaction rate by lowering the energy barrier and increasing the movement of reacting monomers. Atomization of the solvent in electrostatic spraying causes the rapid volatilization of the solvent, resulting in supersaturation of the generated TpPa, precipitation and crystallization, and promotes the formation of TpPa COF structure. The optimal COF membrane for the separation of Congo red wastewater is prepared by adjusting the voltage, concentration, and spraying time, with selectivity of Na2SO4 for Congo Red attaining 118 and water permeance reaching 70.2 L m-2 h-1 bar-1. This work not only elaborates the membrane preparation mechanism in the electrostatic spraying process, but also greatly shortens the time of COF membrane preparation compared to the traditional method (2–3 days), which contributes to the possibility for practical application of COF membranes.

Electropolymerization of a New Diketopyrrollopyrrole Derivative into Inherent Chiral Polymer Films

Electropolymerization is a convenient way to obtain conducting polymers (CPs) directly adhered to an electrode surface. CPs are well-known for their various application fields in photovoltaic cells, chemical sensors, and electronics. By implementing chirality into a CP, the application possibilities will spread further onto chiral sensors or optoelectronics. In this work, we introduce a new inherently chiral polymer based on a macrocyclic 3,4-ethylenedioxythiophene-diketopyrrolopyrrole-3,4-ethylenedioxythiophene triad (EDOT-DPP-EDOT) fused by 1,4-phenylene groups, which was prepared via oxidative electropolymerization directly on the electrode surface. The investigation of the chiroptical properties was performed by circular dichroism spectroscopy in the solid state. The enantiomeric pure polymer films obtained showed dissymmetry factors of up to −2.71 × 10−4, whereby linear dichroism contributions can be widely excluded.

Choice of an environmentally friendly method of producing cellulose from flax biomass (<i>Linum usitatissimum linaceae</i>) grown in different regions of the Volga region

The experimental assessment of prospects of receiving cellulose material on the basis of use of vegetable raw materials – straw of a flax-dolgunetc is carried out. Results of researches showed a possibility of application for extraction of cellulose of the water solutions containing hydrogen peroxide, hydroxide and sodium bisulfite in the conditions of high-temperature influence within 2 hours with receiving the technical cellulose suitable for production of cardboard and packaging materials. For purification of technical cellulose, it was carried out additional processing by solution of hydrogen peroxide and acetic acid in similar parametrical conditions that allowed to receive a target product with high extent of maintenance of the main substance, at insignificant amount of the accompanying impurity. The presented results show a possibility of the solution of questions of environmental safety at the organization of process of receiving cellulose on the basis of renewable vegetable raw materials with use of the chemical reagents which are not containing compounds of sulfur.

Contemporary and prospective use of azathioprine (AZA) in viral, rheumatic, and dermatological disorders: a review of pharmacogenomic and nanotechnology applications.

Azathioprine (AZA) has been extensively used for immunomodulatory effects in autoimmune disorders and transplantation. This article is proposed to review the contemporary and prospective use of AZA in viral, rheumatic, and dermatological disorders. The primary objective is to draw attention to possible developments in regards to AZA application in recent years, with an emphasis on the use of pharmacogenomics and nanotechnology to improve its efficacy in practice. This study reveals that AZA, having the active metabolites 6-mercaptopurine (6-MP) and 6-thioguanine (6-TG), may be useful in the treatment of systemic lupus erythematosus (SLE), pemphigus vulgaris, and psoriasis. Pharmacogenomic testing of thiopurine methyltransferase (TPMT) and Nudix hydrolase 15 (NUDT15) genotypes minimizes the occurrence of myelosuppression. Furthermore, new formulations of AZA using biocompatible polymers and nanoparticles for drug delivery were reported to improve its efficacy and lower systemic toxicity. This paper aims to establish the multifunctional nature of AZA in modern medicine, thus emphasizing its potential for other applications. Through the combination of pharmacogenomic analysis along with nanotechnology application, AZA makes the promise of enhancing patients' treatment efficacy and extending the stock of medical information available. These advancements offer new possibilities for application of precision medicine and improvements in the use of AZA therapy.

An evaluate of ORR catalysts in fuel cell: Current Advances and Applications

Abstract. This thesis presents a comprehensive review of fuel cell technology, with a specific emphasis at the latest trends and programs of oxygen reduction response (ORR) catalysts. The examine starts offevolved with the aid of using evaluating the benefits and drawbacks of hydrogen fuel cells with different alternative power sources, which include sun power and secondary batteries, highlighting the ability of fuel cells to update traditional inner combustion engines because of their high power density and environmental benefits. The thesis then explores the essential running ideas of fuel cells and examines their operational mechanisms in numerous chemical environments. In the context of ORR catalysts, the assessment evaluates quite a number catalysts, which include precious metal catalysts, non-precious metal catalysts, and single-atom catalysts, that specialize in techniques to decorate their activity and stability. Finally, the thesis discusses the demanding situations and destiny possibilities for the industrial application of ORR catalysts in hydrogen fuel cells.

Enhancement of the photocatalytic efficiency of ZnO utilizing luminescent Y2O3 particles

Abstract This study explored the superior photocatalytic performance of the nanoscale zinc oxide-yttrium oxide (ZnO-Y2O3) based composite over ZnO nanoparticles (NPs). We investigated this by following the photocatalytic degradation efficiency of methylene blue (MB). The sol–gel synthesized Y2O3 and ZnO NPs were characterized by x-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The spectral behaviour and photocatalytic efficiency of the proposed composite were investigated by UV–vis spectrophotometry, steady-state and time-resolved photoluminescence (PL) measurements, respectively. The results indicated the successful formation of Y2O3 and ZnO nanoparticles with desirable structural properties. The photocatalytic degradation efficiency of the MB solution was evaluated for different concentrations of the counterparts of the ZnO-Y2O3 composite. In particular, the ZnO-5Y2O3-based composite showed superior photocatalytic activity after 150 min of UV irradiation, achieving 98.4% degradation of the MB solution compared to the 77% degradation achieved by pure ZnO. Although Y2O3 alone does not exhibit photocatalytic activity, its combination with ZnO significantly enhances the photocatalytic performance of ZnO. This improvement was attributed to the luminescence properties of Y2O3. By elucidating this unique mechanism, the performance of photocatalytic materials can be significantly enhanced. In our study, the improvement of the photodegradation rate constant (kapp) of ZnO from 0.009 min−1 to 0.0242 min−1 demonstrated the promising photocatalytic efficiency of the ZnO-5Y2O3 based composite, opening up exciting possibilities for further applications in environmental remediation and other fields.

Advancements in Data Augmentation and Transfer Learning: A Comprehensive Survey to Address Data Scarcity Challenges

Abstract: Deep Learning (DL) models have demonstrated remarkable proficiency in image classification and recognition tasks, surpassing human capabilities. The observed enhancement in performance can be attributed to the utilization of extensive datasets. Nevertheless, DL models have huge data requirements. Widening the learning capability of such models from limited samples even today remains a challenge, given the intrinsic constraints of small datasets. The trifecta of challenges, encompassing limited labeled datasets, privacy, poor generalization performance, and the costliness of annotations, further compounds the difficulty in achieving robust model performance. Overcoming the challenge of expanding the learning capabilities of Deep Learning models with limited sample sizes remains a pressing concern even today. To address this critical issue, our study conducts a meticulous examination of established methodologies, such as Data Augmentation and Transfer Learning, which offer promising solutions to data scarcity dilemmas. Data Augmentation, a powerful technique, amplifies the size of small datasets through a diverse array of strategies. These encompass geometric transformations, kernel filter manipulations, neural style transfer amalgamation, random erasing, Generative Adversarial Networks, augmentations in feature space, and adversarial and meta- learning training paradigms. : Furthermore, Transfer Learning emerges as a crucial tool, leveraging pre-trained models to facilitate knowledge transfer between models or enabling the retraining of models on analogous datasets. Through our comprehensive investigation, we provide profound insights into how the synergistic application of these two techniques can significantly enhance the performance of classification tasks, effectively magnifying scarce datasets. This augmentation in data availability not only addresses the immediate challenges posed by limited datasets but also unlocks the full potential of working with Big Data in a new era of possibilities in DL applications.

Advancing Photodynamic Therapy with Nano-Conjugated Hypocrellin: Mechanisms and Clinical Applications.

Hypocrellin-based photodynamic therapy (PDT) is developing as a viable cancer therapeutic option, especially when enhanced by nanoconjugation. This review investigates the methods by which nano-conjugated hypocrellin enhances therapeutic efficacy and precision when targeting cancer cells. These nanoconjugates encapsulate or covalently bind hypocrellin photosensitizers (PSs), allowing them to accumulate preferentially in malignancies. When activated by light, the nanoconjugates produce singlet oxygen and other reactive oxygen species (ROS), resulting in oxidative stress that selectively destroys cancer cells while protecting healthy tissues. We look at how they can be used to treat a variety of cancers. Clinical and preclinical studies show that they have advantages such as increased water solubility, improved tumor penetration, longer circulation times, and tailored delivery, all of which contribute to fewer off-target effects and overall toxicity. Ongoing research focuses on improving these nanoconjugates for better tumor targeting, drug release kinetics, and overcoming biological obstacles. Furthermore, the incorporation of developing technologies such as stimuli-responsive nanocarriers and combination therapies opens exciting opportunities for enhancing hypocrellin-based PDT. In conclusion, the combination of hypocrellin and nanotechnology constitutes a significant approach to cancer treatment, increasing the efficacy and safety of PDT. Future research will seek to create conjugates including hypocrellin, herceptin, and gold nanoparticles to induce apoptosis in human breast cancer cells in vitro, opening possibilities for therapeutic applications.

Delay-aware resource allocation for partial computation offloading in mobile edge cloud computing

Mobile Edge Cloud Computing (MECC), as a promising partial computing offloading solution, has provided new possibilities for compute-intensive and delay-sensitive mobile applications, which can simultaneously leverage edge computing and cloud services. However, designing resource allocation strategies for MECC faces an extremely challenging problem of simultaneously satisfying the end-to-end latency requirements and minimum resource allocation of multiple mobile applications. To address this issue, we comprehensively consider the randomness of computing request arrivals, service time, and dynamic computing resources. We model the MECC network as a two-level tandem queue consisting of two sequential computing processing queues, each with multiple servers. We apply a deep reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG) to learn the computing speed adjustment strategy for the tandem queue. This strategy ensures the end-to-end latency requirements of multiple mobile applications while preventing overuse of the total computing resources of edge servers and cloud servers. Numerous simulation experiments demonstrate that our approach is significantly superior to other methods in dynamic network environments.

Releasing Charge Transport Barriers at Low Bias in Perpendicularly Aligned 2D Halide Perovskite

2D halide perovskites have garnered significant interest due to the great potential in optoelectronic devices. The 2D Perovskites samples are structured as n layers of [MX6]4− octahedral sheets inducing strong quantum confinement effects. Here, we have synthesized a series of 2D (BA)2MAn-1PbnI3n+1 and its 3D counterpart by controlling the particles alignment to the substrate. We have performed a comprehensive study involving electrical transport and photoconductivity effect as a function of the particle's organization: parallel, perpendicular, and disordered orientation to the electrodes. We have observed that for intermediary value of n, the 2D layered particles are aligned perpendicular to the electrode and parallel to the transport electrical current. A distinct and complex S-shape behavior in V-i curves is disclosed, suggesting two electrical resistance regimes. The sharp increase in resistance suggests the presence of electronic barriers for all samples, which is suppressed for columnar arrays of n = 2 and 3 under illumination. We suggest that this linear behavior in the photoconductivity effect is close related to the absence of electrical barriers at the interface of the particles due to perpendicular alignment to the electrodes. The absence of interfaces in a more columnar array facilitates ionic conductivity enhancing photoelectric effect. Our findings reveal distinct differences in charge transport for perpendicularly aligned 2D samples, opening up possibilities for important applications at low bias.

Programable shape recovery properties and snap-through instabilities of viscoelastic and shape-memory NS metamaterials

The combination of smart material and mechanical metamaterial has the potential to bring novel properties and additional functionalities, which, however, has been ignored for a long time. This work numerically, experimentally, and analytically examines the negative stiffness (NS) metamaterials based on shape memory polymers (SMPs) with a focus on the tunable and temperature-dependent properties induced by the interaction of material and geometry nonlinearity. A universal discrete model of a series of shape-memory NS metamaterial is developed by separating the bending effect and stretching effect, where the viscoelastic material is described by the generalized Maxwell-Wiechert model, and an SMP constitutive model is proposed based on multiplicative decomposition of the deformation gradient. The mechanical and shape-memory properties influenced by temperature, geometry, material and loading rate are experimentally observed through relaxation tests and cyclic compression tests. Then the discrete model and finite element analysis (FEA) is adopted to examine the novel mechanical responses in a wide range of parameters. Results show that viscoelasticity and loading rate play a significant role in mechanical responses, and tunable load-capacity and stability can be achieved for shape-memory metamaterial. A phase diagram of different stabilities is constructed showing strictly monotonic, S-shaped, and snapping responses. Additionally, repeated compression and recovery performance proves the possibility of reusable applications. This work provides new opportunities for functional metamaterial to obtain programmable shape recovery and mechanical responses.

Unlocking the potential of copper-reinforced fibre metal laminates: The influence of stacking sequences

Fibre-Metal Laminates (FMLs) are a class of advanced hybrid materials consisting of a metal layer and fibre-reinforced polymers that give an excellent combination of mechanical properties. The present study deals with the development and characterization of copper-based FMLs developed through the hand layup method by considering multiple stacking sequences for improved mechanical properties and by incorporating carbon nanotubes. Extensive tensile, flexural, fatigue, and wear tests were carried out on the FMLs. SEM analysis was also done to investigate the fracture. According to the test results, in the Type III stacking sequence where optimum 0.5% CNT content is used, the best overall tensile strength together with good flexibility, fatigue resistance, and wear resistance could be obtained. The Type III stacking sequence exhibited a maximum tensile strength of 117.85 MPa, flexural strength of 168.0 MPa, and fatigue life of 22,00,000 cycles at 50 MPa stress amplitude. Further SEM analysis has shown that enhancement in microstructural integrity actually occurred with the inclusion of CNT, especially in fiber-matrix bonding improvement and reduction of voids. The results obtained in this work indicate the suitable possibility for application in high-performance aerospace and automotive industries of copper-based FMLs. This study highlights optimization issues related to both material composition and stacking sequence toward the attainment of superior mechanical properties in FMLs.

Bio-based epoxidized cardanol sorbate as an interface-localized compatibilizer for enhancing poly(lactic acid) (PLA) and poly(3-hydroxybutyric acid-co-4-hydroxybutyric acid) (P34HB) blend

PLA is a bio-based polyester with limited barrier and flexible properties. Adding poly(3-hydroxybutyrate-co-3-hydroxyvalerate) (P34HB), a biodegradable polyester known for its outstanding barrier properties, was the strategy employed to boost PLA performance. However, the blend’s immiscibility necessitates compatibilizers to bolster interfacial adhesion and dispersion. This study designed a novel bio-based compatibilizer, epoxidized cardanol sorbate (ECD-SA), to improve the compatibility of the blend. The molecular design of ECD-SA has been optimized to minimize steric hindrance, thereby enhancing interfacial reactions more effectively. Two commercial compatibilizers, specifically ADR 4468 and NC-514, were selected for comparison. Theoretical model was employed to calculate dispersion preferences of three different compatibilizers in the blend. A systematic investigation was carried out to analyze the effects of compatibilizers on the structure and properties of PLA/P34HB blends. The findings suggest that ECD-SA functions as an interface-localized compatibilizer in PLA/P34HB blends, enhancing their compatibility and performance significantly. Specifically, the incorporation of 2 phr (parts per hundred resin) ECD-SA into the PLA/P34HB film resulted in a marked increase in tensile strength from 35.6 MPa and 23.5 MPa to 48.6 MPa and 36 MPa in the longitudinal and transverse directions, respectively, and an increase in elongation at break from 31.9 % and 9.2 % to 104.8 % and 48.3 %, respectively. This underscores its significant potential as a compatibilizer for PLA/P34HB, pointing towards extensive application possibilities in present-day packaging industries.

An ingenious electrochemical system based on naphthalenediimide derivatives for ultrasensitive immunosensing of alpha-fetoprotein

It is crucial to develop highly efficient electrochemistry systems for sensitive detection of tumour markers. In this work, naphthalenediimide derivatives with electrochemical application potential were successfully synthesized and characterized. Electrochemistry and calculation of density functional theory (DFT) showed that 2,7-bis(4-(dimethylamino)phenyl)benzo[lmn] [3,8]phenanthroline-1,3,6,8(2H,7H)-tetraone (NDI-1) was an ideal candidate for electrochemical probe construction. Subsequently, based on the cyclic catalytic effect between NDI-1 and K2S2O8, a satisfying composite of GO/NDI-1/AuNPs was prepared and used to construct electrochemical probe for the design of ingenious sandwiched electrochemical immunosensor. Taking alpha-fetoprotein (AFP) as the model target biomarker, the designed immunosensor showed good detection performance for AFP, which exhibited wide range of linear response (10 fg/mL - 10 ng/mL), low detection limit (3.3 fg/mL). Moreover, the proposed immunosensor has been successfully applied to AFP detection in human serum, which provides the possibility for clinical applications. The designed electrochemical system provides a new electrochemical probe for the construction of immunosensors and may be extended to the electroanalysis of other biomolecules with recognition units.

Effective use of the tools of probability theory and mathematical statistics in solving the problems of the professional orientation of physicists and mathematicians: an interdisciplinary approach

ABSTRACT The use of methods of mathematical statistics for teaching students carries the task of determining mathematical abilities, formalizing the choice of a professional orientation and the potential choice of solutions. In higher education, the use of the mathematical apparatus allows students to form the ability to multiple choice and assess the conditions of the choice. These include not only the choice of training courses, but also the areas of knowledge in which each student can fulfil themself. For mathematicians, this is especially true, since in most cases mathematics is used to solve purely applied problems. The novelty of the study is determined by the fact that the methods and tools of probability theory cannot be applied exclusively on an individual level. To determine the effectiveness of the methods used, it is necessary to determine the possibilities of group application of the used methodological apparatus and identify areas of assistance to the student in choosing a professional orientation. In terms of the use of ubiquitous distance education, the authors demonstrate the possibility of group work with mathematical support through the use of clusters and network techniques, when each individual is the host of the training programme.