Scientific Problems Research Articles

A comprehensive analysis of R's application in ecological research from 2008 to 2023

Abstract The field of ecology has been greatly enhanced by the integration of computational tools and statistical methods, with the programming language R emerging as a pivotal and flexible tool for ecological inquiry. As ecological research accelerates, understanding the prevalent trends and specific usage patterns of R in recent studies is crucial. Our investigation explored the use of R and its packages in 125,494 scholarly articles across 40 ecology journals from 2008 to 2023. A considerable number of articles, 52,658 (41.96%), designated R as their principal analytical tool, demonstrating a steady linear growth in its utilization from 10.31% in 2008 to 66.88% in 2023. Twelve R packages, including "lme4," "vegan," "nlme," "MuMIn," "ape," "ggplot2," "car," "mgcv," "MASS," "raster," "multcomp," and "lmerTest," each played a pivotal role in contributing to more than 1,000 scholarly articles. The highest usage rate of the lme4 package indicates that mixed-effect models have a particularly important role in ecological research, and the application of these models has helped ecologists solve many important scientific problems. Journal-specific package preferences aligned with their scientific domains, while the rise in the average number of R packages per article signals a trend towards more complex analytical methods in ecology. Our findings indicate a reciprocal relationship between the development of R and ecological research, underscoring the need for collaboration among quantitative ecologists, R developers, and ecologists to further advance both the language and the field.

Ecosystem assessment of semi-natural tidal flat in the Yangtze estuary by pressure-state-response model and fuzzy comprehensive evaluation method

With the development of hydraulic engineering such as harbor, channel and reclamation, more and more natural tidal flat turn into the semi-natural tidal flat (SNTF). How to evaluate ecosystem of semi-natural tidal flat is a scientific problem that has not been fully solved. The study tries to assess ecosystem health change of SNTF adjoining Hengsha East Shoal training dike in the Yangtze estuary. The study found SNTF area > 0m isobaths increased from 30.33 km2 in 2010–2014 to 46.66 km2 in 2014–2017 and to 56.84 km2 in 2017–2020, and the Hengsha Shoal region.0m, -2m and -5m isobaths increased from 174.78 km2, 303.82 km2, and 556.77 km2 in 2010–2014 to 233.34 km2, 365.59 km2, and 596.83 km2 in 2014–2017 and to 243.81 km2, 363.27 km2 and 567.45 km2 in 2017–2020. Using database of 3 pressure indicators, 16 state indicators and 3 response indicators, the study combined pressure-state-response (PSR) model and fuzzy comprehensive evaluation (FCE), constructed a systematic evaluation method of ecosystem health of semi-natural tidal flat. The evaluation result found the ecosystem health of SNTF of Hengsha East Shoal was rated as "Fair" level during 2010–2020, and the summation of comprehensive evaluation index (CEI) increased from 0.4009 in 2010–2014 to 0.4703 in 2014–2017 due to the rapid expansion of tidal flat vegetation, and then decreased to 0.4450 in 2017–2020 due to a reduction in vegetation area caused by erosion. The area of salt marsh vegetation plays an important role in the health of wetland ecosystems. The Hengsha East Shoal ecosystem is undergoing a quickly dynamic evolution processes, long-term series monitoring and further research for this area are necessary to guide its future development according to the tidal flat utilization patterns.

Hybrid Models of Atmospheric Block Columns of Primary Oil Refining Unit Under Conditions of Initial Information Deficiency

Hybrid Models of Atmospheric Block Columns of Primary Oil Refining Unit Under Conditions of Initial Information Deficiency

Rapid Automated Mapping of Clouds on Titan With Instance Segmentation

AbstractDespite widespread adoption of deep learning models to address a variety of computer vision tasks, planetary science has yet to see extensive utilization of such tools to address its unique problems. On Titan, the largest moon of Saturn, tracking seasonal trends and weather patterns of clouds provides crucial insights into one of the most complex climates in the Solar System, yet much of the available image data are still analyzed in a conventional way. In this work, we apply a Mask R‐CNN trained via transfer learning to perform instance segmentation of clouds in Titan images acquired by the Cassini spacecraft—a previously unexplored approach to a “big data” problem in planetary science. We demonstrate that an automated technique can provide quantitative measures for clouds, such as areas and centroids, that may otherwise be prohibitively time‐intensive to produce by human mapping. Furthermore, despite Titan‐specific challenges, our approach yields accuracy comparable to contemporary cloud identification studies on Earth and other worlds. We compare the efficiencies of human‐driven versus algorithmic approaches, showing that transfer learning provides speed‐ups that may open new horizons for data investigation for Titan. Moreover, we suggest that such approaches have broad potential for application to similar problems in planetary science where they are currently under‐utilized. Future planned missions to the planets and remote sensing initiatives for the Earth promise to provide a deluge of image data in the coming years that will benefit strongly from leveraging machine learning approaches to perform the analysis.

Creation of a stable vector vortex beam with dual fractional orbital angular momentum

Recently, vortex beams have been widely studied and applied because they carry orbital angular momentum (OAM). It is widely acknowledged in the scientific community that fractional OAM does not typically exhibit stable propagation; notably, the notion of achieving stable propagation with dual-fractional OAM within a single optical vortex has been deemed impracticable. Here, we address the scientific problem through the combined modulation of phase and polarization, resulting in the generation of a dual-fractional OAM vector vortex beam that can stably exist in free space. Applying this unique characteristic, we derive an integrated analytical model to calculate the focused electromagnetic fields and Poynting vector distributions based on Debye vector diffraction integral. Utilizing phase stitching technology, this research combines two fractional topological charges to investigate the properties of dual-fractional OAM optical vortices with diverse polarization conditions. Furthermore, the transmission characteristics of these optical vortices are meticulously analyzed. This work not only enriches the types of vortex beams but also provides a novel optical tool, potentially transformative for applications in optical communications, optical manipulation, and optical imaging.

Synergistic learning with multi-task DeepONet for efficient PDE problem solving.

Multi-task learning (MTL) is an inductive transfer mechanism designed to leverage useful information from multiple tasks to improve generalization performance compared to single-task learning. It has been extensively explored in traditional machine learning to address issues such as data sparsity and overfitting in neural networks. In this work, we apply MTL to problems in science and engineering governed by partial differential equations (PDEs). However, implementing MTL in this context is complex, as it requires task-specific modifications to accommodate various scenarios representing different physical processes. To this end, we present a multi-task deep operator network (MT-DeepONet) to learn solutions across various functional forms of source terms in a PDE and multiple geometries in a single concurrent training session. We introduce modifications in the branch network of the vanilla DeepONet to account for various functional forms of a parameterized coefficient in a PDE. Additionally, we handle parameterized geometries by introducing a binary mask in the branch network and incorporating it into the loss term to improve convergence and generalization to new geometry tasks. Our approach is demonstrated on three benchmark problems: (1) learning different functional forms of the source term in the Fisher equation; (2) learning multiple geometries in a 2D Darcy Flow problem and showcasing better transfer learning capabilities to new geometries; and (3) learning 3D parameterized geometries for a heat transfer problem and demonstrate the ability to predict on new but similar geometries. Our MT-DeepONet framework offers a novel approach to solving PDE problems in engineering and science under a unified umbrella based on synergistic learning that reduces the overall training cost for neural operators.

Honey bee as an alternative model invertebrate organism

Insects perfectly fit the flagship principle of animal research – 3R: to reduce (the number of animals), to replace (animals with alternative models) and to refine (methods). Bees have the most important advantages of a model organism: they cause minimal ethical controversy, they have a small and fully known genome, and they permit the use of many experimental techniques. Bees have a fully functional DNMT toolkit. Therefore, they are used as models in biomedical/genetic research, e.g. in research on the development of cancer or in the diagnostics of mental and neuroleptic diseases in humans. The reversion of aging processes in bees offers hope for progress in gerontology research. The cellular mechanisms of learning and memory coding, as well as the indicators of biochemical immunity parameters, are similar or analogous to those in humans, so bees may become useful in monitoring changes in behavior and metabolism. Bees are very well suited for studies on the dose of the substance applied to determine the lethal dose or the effect of a formula on life expectancy. Honeybees have proven to be an effective tool for studying the effects of a long-term consumption of stimulants, as well as for observing behavioral changes and developing addictions at the individual and social levels, as well as for investigating the effects of continuously delivering the same dose of a substance. The genomic and physiological flexibility of bees in dividing tasks among workers in a colony makes it possible to create a Single- Cohort Colony (SCC) in which peers compared perform different tasks. Moreover behavioral methods (e.g. Proboscis Extension Reflex – PER, Sting Extension Reflex – SER, free flying target discrimination tasks or the cap pushing response) make it possible to analyse changes occurring in honeybee brains during learning and remembering. Algorithms of actions are created based on the behavior of a colony or individual, e.g. Artificial Bee Colony Algorithm (ABCA). Honeybees are also model organisms for profiling the so-called intelligence of a swarm or collective intelligence. Additionally, they serve as models for guidance systems and aviation technologies. Bees have inspired important projects in robotics, such as B-droid, Robobee and The Green Brain Project. It has also been confirmed that the apian sense of smell can be used to detect explosive devices, such as TNT, or drugs (including heroin, cocaine, amphetamines and cannabis). This inconspicuous little insect can revolutionize the world of science and contribute to the solution of many scientific problems as a versatile model.

Data efficiency of classification strategies for chemical and materials design

We benchmark the performance of space-filling and active learning algorithms on classification problems in materials science, revealing trends in optimally data-efficient algorithms.

A novel digestive protease chymotrypsin-like serine contributes to anti-BmNPV activity in silkworm (Bombyxmori).

Serine proteases (SPs) are important proteases in the digestive system of lepidopteran insects. They play important roles in protein digestion, coagulation, signal transduction, hormone activation, inflammation and development. Blood-borne pyosis caused by Bombyx mori nuclear polyhedrosis virus (BmNPV) has caused serious harm to sericulture. At present, the scientific problems of BmNPV infection and silkworm resistance to BmNPV infection have been the focus of many scientists, but the molecular mechanism needs further research and exploration. Based on the results of label-free quantitative protein proteomics of the midgut digestive juice of different resistant strains in our laboratory, we successfully screened a differentially expressed candidate protein (DEP), B. mori chymotrypsin-like serine protease (BmCLSP), and comprehensively analyzed the biological characteristics and anti-BmNPV function of BmCLSP. The open reading frame (ORF) of BmCLSP is 891 bp, encoding 296 amino acid residues. The analysis of the domain structure showed that there was a signal peptide and a trypsin-like serine protease domain, Tryp_SPC, in the BmCLSP protein. Semi-quantitative and real-time fluorescence quantitative PCR analysis showed that the BmCLSP gene was highly expressed in the fifth instar larvae of silkworm, and specifically expressed in the midgut. The expression level of BmCLSP in the BmNPV resistant strain A35 was higher than that in the sensitive strain P50. Virus amplification analysis showed that the relative expression level of VP39 was significantly lower than that of the control group after infection of silkworm larvae and BmN cells with BmNPV treated with recombinant BmCLSP at an appropriate concentration. Furthermore, our overexpression of BmCLSP in BmN cells significantly inhibited the expansion of BmNPV. In summary, the results of this study indicate that BmCLSP has anti-BmNPV activity in silkworm, and can significantly inhibit the proliferation of BmNPV in silkworm. It offers a promising avenue for silkworm anti-virus breeding.

Transformation of Intelligence Technology Learning through the Inquiry-Scientific Problem Based Learning to Improve Critical Thinking Skills

This study examines the effectiveness of the Inquiry-Based Scientific Problem Learning (PBL) model in enhancing cadets’ critical thinking and learning outcomes at the Sekolah Tinggi Intelijen Negara (STIN). Addressing the evolving needs of the intelligence sector, which demands technical expertise and higher-order thinking, the research combines scientific inquiry with PBL to create a comprehensive instructional approach. A quasi-experimental design involved 50 fourth-semester cadets divided into experimental and control groups. Learning outcomes were evaluated through pre-test and post-test assessments, focusing on cognitive, affective, and psychomotor domains. Results showed that using the Inquiry-Based Scientific Problem Learning model, the experimental group achieved significant gains in all areas. Cadets in the experimental group demonstrated enhanced critical thinking and analytical skills in the cognitive domain, increased motivation, communication, and collaboration in the affective domain, and superior practical skills in the psychomotor domain. Statistical analyses, including N-gain, validated the model’s impact. These findings suggest that the Inquiry-Based Scientific Problem Learning model effectively develops intelligent professionals, bridging theory and practice while fostering technical and soft skills crucial in the field. This research supports the need for innovative and adaptive educational methods in specialized and dynamic environments like intelligence education.

Evaluation of the relationship between adversity quotient, professional identity, and perceived humanistic care of head nurses in Master’s Degrees in nursings

ABSTRACT Background: High-level nursing talents play an important role in solving nursing scientific problems and leading nursing innovation, so the construction of high-level nursing talents has become a necessary guarantee for the health strategy in the new era. Managers have high expectations for master’s degree in nursing, hoping that they will develop in all aspects of research, teaching, and professionalism, etc. However, the high expectations of managers have led to unclear job orientation, high clinical and scientific research pressure, and low recognition of nurses’ sense of professional value from the outside world, which have led to the lowering of master’s degree of professional identity and the tendency to leave the profession, which is not conducive to the stabilization of the nursing workforce. Purpose: To explore the influence of adversity quotient, perceived humanistic care of nurse leaders on professional identity of nursing masters, and to clarify the relationship model between the three. Methods: A convenience sampling method was used to conduct an online questionnaire survey on 160 master’s degree nursing students in Ningbo and Shanghai from July to September 2022 using the general information questionnaire, adversity quotient, career identity and perceived humanistic care of nurse leaders scale. Results: Adversity quotient, occupational identity and perceived humanistic care of nurse leaders were all at a moderate level. Adversity quotient (r = 0.430, P < 0.01), perceived humanistic care of nurse leaders (r = 0.443, P < 0.01) and occupational identity were positively correlated, and perceived humanistic care of nurse leaders could affect occupational identity through the mediating effect of adversity quotient, with the mediating effect accounting for 31.8%. Conclusion: Nursing managers should create a good organizational atmosphere, build a training system for adversity quotient courses, conduct regular assessment and humanistic care to improve the ability of nursing masters to face adversity and enhance the awareness of their own professional values.

Analytical Solutions of Time-Fractional Navier–Stokes Equations Employing Homotopy Perturbation–Laplace Transform Method

The aim of this article is to introduce analytical and approximate techniques to obtain the solution of time-fractional Navier–Stokes equations. This proposed technique consists is coupling the homotopy perturbation method (HPM) and Laplace transform (LT). The time-fractional derivative used is the Caputo–Hadamard fractional derivative (CHFD). The effectiveness of this method is demonstrated and validated through two test problems. The results show that the proposed method is robust, efficient, and easy to implement for both linear and nonlinear problems in science and engineering. Additionally, its computational efficiency requires less computation compared to other schemes.

РОЛЬ ДОПОЛНИТЕЛЬНОГО ОБРАЗОВАНИЯ В СТАНОВЛЕНИИ ПРОФЕССИОНАЛЬНОГО САМОСОЗНАНИЯ МОЛОДЕЖИ

Formation of professional self-awareness of future specialists in the process of their educational activity is one of the leading problems of modern psychological science. In modern Russia, the problem of formation of professional self-awareness of youth, organization of psychological and pedagogical conditions of its formation has become relevant in connection with the changed socio-economic conditions that arose in connection with the process of formation of a new civil society, modernization in the field of education, including professional, as well as with a significant need to transform the attitude of the subject of labor to his professional activity. The purpose of the study was to study the influence of additional education on the formation of professional self-awareness of youth. The study involved 130 students of state educational institutions of primary and secondary vocational education under the jurisdiction of the Committee on Education of St. Petersburg, included in the system of additional education, aged 17 to 22 years (including 15 boys and 15 girls), and 130 students of state educational institutions of primary and secondary vocational education under the jurisdiction of the Committee on Education of St. Petersburg, not included in the system of additional education, aged 17 to 22 years (including 15 boys and 15 girls). The following methods were used in the study: "Questionnaire for determining the level of professional orientation of students" (UPN) by T.D. Dubovitskaya; Methodology for the study of self-attitude (MIS) by S.R. Panteleev; Questionnaire "Stylish self-regulation of human behavior" by V.I. Morosanova, E.M. Konoz; Methodology for diagnosing socio-psychological attitudes of the individual in the motivational-needs sphere by O.F. Potemkina; Self-actualization test by E. Shostrom adapted by L. Ya. Gozman, M. V. Kroz, M. V. Latyn; “Personal Differential” (PD) method (version adapted at the Bekhterev Research Institute). To process the obtained data, methods of quantitative and qualitative data analysis were used (descriptive statistics methods, comparative analysis (Student’s t-test)). Before the comparative analysis, the empirical distribution was checked for normality using the Kolmogorov-Smirnov statistical normality test (with Liljefors correction). Statistical data processing was performed using the SPSS 23.0 Statistics 28.0.1 software package. The study found that students included in the system of additional education have a better developed cognitive component of professional self-awareness, realized in self-knowledge, an emotional component expressed in self-understanding as an evaluative and emotional attitude towards oneself, self-respect and self-acceptance, an operational component of professional self-awareness, as well as a motivational and target component realized in the desire for self-actualization. The results obtained allow us to conclude that additional education plays a positive role in developing professional self-awareness of young people. The system of additional education forms professional self-awareness, a sense of self-worth, and promotes the disclosure of personal and leadership potential, which will contribute to successful career building.

An Empirical Study of the Ideological Component in Russian Economic Science

Ideology plays a significant role in forming economic theories and models, although its influence is often underestimated or denied. In economics, the desire for objectivity prevails. However, historically, economic theories often bear the imprint of certain ideological attitudes. In this paper, the authors analyzed the ideological component of Russian economic science from 1992 to 2023. The total sample consisted of 134,124 scientific publications divided by year. As a hypothesis, we identified five economic ideologies: neoliberalism, socialism, dirigisme, special path and environmentalism. In this work, we classified economic scientific articles based on their ideological coloring using vector representations of texts (embeddings) obtained using the SciRus-tiny model and the LSTM neural network, which acts as a classifier. The accuracy of the model on cross-validation was 77.45%. The array of scientific articles was classified by ideology in the following proportions: dirigisme – 29.86%, environmentalism – 18.85%, special path – 16.60%, neoliberalism – 14.53%, socialism – 12.16% and 8.00% of articles remained unallocated. Our hypothesis was confirmed during the machine analysis of article texts since the smallest number of texts was undetermined during the classification. Further, the authors analyzed the representation of bigrams and trigrams. As a result, bigrams and trigrams found in each ideological group were determined, and the first ten were identified in prevalence for each ideological group separately. Using factor analysis (principal component analysis), we identified ten key micro-themes in each period (1992–1997, 1998– 2002, 2003–2007, 2008–2012, 2013–2017, 2018–2023). Based on the interpretation of the obtained factor loadings of the principal components in each group and the correlation of the bi- and trigrams identified for each ideology, we analyzed how different ideologies manifest themselves and what scientific problems they are associated with. The analysis showed that the most discussed issues in the articles were the problems of government regulation, the labor market, and industrial development throughout the entire period.

KİÇİKYAŞLI MƏKTƏBLİ ŞƏXSİYYƏTİNİN MİLLİ-MƏNƏVİ DƏYƏRLƏRİNİN FORMALAŞMASINDA AİLƏNİN TƏSİR İMKANLARI

Family is one of the main institutions that ensure interaction, integration and prioritization of interests and needs of personality and society. The first and main social institution for the formation of value orientations is the family. Under the influence of the processes taking place in modern society, cultural development has accelerated, new information technologies have begun to fundamentally change human thinking, and new economic relations have begun to form. These processes, in turn, have had an impact on the family, which is the most important component of social relations. The changes in the social system and public relations system were also reflected in family relations and national moral values. The current problems of modern science are to look at these problems from the perspective of the development of the strengthening of the moral foundations of the family.

THEORETICAL FOUNDATIONS FOR SUBSTANTIATING OF DESIGN SOLUTIONS FOR COMBAT VEHICLE ELEMENTS BASED ON THE ANALYSIS OF PHYSICAL AND MECHANICAL PROCESSES

Ensuring the world-class tactical and technical characteristics of armored vehicles is an extremely important and urgent problem in the field of national security and defense. This is due to the enormous potential of Ukraine's armored vehicle industry, which has not yet been realized in the design and development of the production of these vehicles for various reasons. One of the main problems is the lack of methods and models to substantiate the parameters of these vehicles and the properties of materials for the manufacture of the main components, primarily armor hulls and transmissions, at the stage of design research. This work involves developing the theoretical foundations and conducting research to determine the response of armored vehicle components to various factors and to determine the requirements for design solutions for their components. An extremely important factor is, for example, the prevention of critical deformations that occur in armored vehicles when exposed to munitions. Existing models do not allow for an adequate assessment of the degree of protection under the influence of various factors, which leads to fundamentally incorrect design decisions. Consequently, a new, relevant, and important scientific and technical problem arises - the study of processes and states of armored vehicles in combat use to improve their tactical and technical characteristics. The theoretical foundations of such research are described in this paper. Their significance is especially growing in the current situation in the presence of hostilities that require the manufacture and supply of military equipment with high tactical and technical characteristics. In turn, achieving the world level of these characteristics is impossible without a number of studies, in particular, based on the approach described in this paper, as a matter of priority. Research on this basis is aimed at scientific substantiation of advanced technical solutions for military equipment.

Developing a Sense of Community in the Activities of the University Library of Ukraine in Wartime (on the Example of the Scientific Library of USUST)

Objective. The authors believe that during the crisis of the highest level, associated with the threat to human life and health, the socio-cultural aspects of the activities of libraries of higher education institutions are important for different countries and require more detailed study. The purpose of the paper is to highlight the practices of developing a sense of community in the socio-cultural activities of a university library in Ukraine in wartime (on the example of the Scientific library of the Ukrainian State University of Science and Technologies). Methods. The article is based on self-reflection, direct experiences and crisis experience of the authors in organising and conducting library leisure activities for students at the Ukrainian State University of Science and Technologies (USUST) (Dnipro, Ukraine). The work uses a combination of research approaches and methods: to determine the state of development of the scientific problem, the author used a source search and analysis of relevant publications on the topic of the study; a comparative approach – to compare the similarities and differences in the main functions of university libraries in Ukraine and abroad; the method of observation – to determine the features of various library activities aimed at developing a sense of community among students. Results. The crisis experience of wartime librarians demonstrates the ability to quickly replace (despite narrow specialisation in peacetime) and unite colleagues, the ability to integrate cultural and social activities into a single process, simultaneous involvement in various types and forms of professional activities aimed at communities of students, teachers, researchers, and the public. The article presents a fairly wide landscape of leisure practices in the socio-cultural work of the university library of USUST in the frontline city of Dnipro, which allows students, teachers and their children to escape from the already everyday and familiar danger. Librarians help students to restore a sense of unity, cohesion, community, a sense of “I am not alone” and “together we are many”, even when they are often physically separated. Conclusions. Community has become a phenomenon that represents Ukrainian spirituality, humanity, dignity and resilience. The activities of university libraries aimed at developing community are especially important for those countries that are experiencing social and political transformations as a result of military conflicts.

A novel analytical iterative approach to time-fractional Vibration equation using Aboodh transform

Abstract The main aim of the work is to achieve the approximate analytical solution of a time-fractional vibration problem for large membranes via Aboodh transform iterative method (ATIM). The ATIM method is the combination of Daftardar-Gejji's New iterative method and Aboodh transform. The fractional derivative operator is considered in the sense of Liouville-Caputo. The approximate-analytical solutions of the proposed equation for multiple situations have been determined by utilizing the initial values, which has accelerated the quick convergence of the series solution. The proposed method performs extremely well in terms of simplicity and effectiveness. Using the fixed point theorem the existence and uniqueness of the proposed problem have been stated. The validity and applicability of the technique are shown for obtaining approximate analytical solutions for different particular cases which are presented numerically and graphically. Many science and engineering problems can be solved analytically and numerically using the proposed approach.

Graphene-Assisted Electron-Based Imaging of Individual Organic and Biological Macromolecules: Structure and Transient Dynamics.

Characterizing the structures, interactions, and dynamics of molecules in their native liquid state is a long-existing challenge in chemistry, molecular science, and biophysics with profound scientific significance. Advanced transmission electron microscopy (TEM)-based imaging techniques with the use of graphene emerged as promising tools, mainly due to their performance on spatial and temporal resolution. This review focuses on the various approaches to achieving high-resolution imaging of individual molecules and their transient interactions. We highlight the crucial role of graphene grids in cryogenic electron microscopy for achieving Ångstrom-level resolution for resolving molecular structures and the importance of graphene liquid cells in liquid-phase TEM for directly observing dynamics with subnanometer resolution at a frame rate of several frames per second, as well as the cross-talks of the two imaging modes. To understand the chemistry and physics encoded in these molecular movies, incorporating machine learning algorithms for image analysis provides a promising approach that further bolsters the resolution adventure. Besides reviewing the recent advances and methodologies in TEM imaging of individual molecules using graphene, this review also outlines future directions to improve these techniques and envision problems in molecular science, chemistry, and biology that could benefit from these experiments.

QUANTUM COMPUTING CHALLENGES: EXPLORING DECOHERENCE THROUGH THE LENS OF SHOR'S ALGORITHM

Quantum computing, with its transformative potential in solving complex problems, faces a critical challenge: coupling with the environment and the consequent decoherence of quantum systems. This article examines the state of the art on the subject, providing a theoretical basis for understanding how environmental interactions impact quantum algorithms. Quantum coherence, a fundamental element of quantum mechanics, allows quantum states to exist in superposition. However, interaction with the environment triggers decoherence, resulting in the loss of this coherence and affecting the accuracy of the results of quantum algorithms. The study investigates the mechanisms of environmental coupling, such as interactions with photons, fluctuations in magnetic fields, and other external factors. Additionally, the impact of decoherence on notable algorithms, such as Shor's algorithm, is explored, analyzing its implications for the efficiency in determining results. Contemporary mitigation strategies, including quantum error correction techniques applied to this algorithm, are also discussed. The results highlight that decoherence represents a significant obstacle to the practical application of quantum computing in scientific, organizational and social problems. In parallel, a promising set of tools to mitigate or avoid the effects of decoherence is being developed, advancing the viability of quantum computers as applicable technologies.