Scientific Problems Research Articles

Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence

The development of non-noble metal electrocatalysts for the Oxygen Evolution Reaction (OER) is advancing towards the use of multi-element materials. To reveal the complex correlations of multi-element OER electrocatalysts, we developed an iterative workflow combining high-throughput experiments and AI-generated content (AIGC) processes. An increased number of 909 (compared to 145 in previous literature) universal descriptors for inorganic materials science were constructed and used as Artificial Neural Network (ANN) input. A large number of statistical ensembles with each ANN individual ensemble having a reduced number of descriptors were integrated with a new Hierarchical Neural Network (HNN) algorithm. This algorithm addresses the longstanding challenge of balancing overwhelming descriptor numbers with insufficient datasets in traditional ANN approaches to materials science problems. As a result, the combination of AIGC and experimental validation significantly enhanced prediction accuracy, increase the R2 values from 0.7 to 0.98 for Tafel slopes.

An Exploration of Spatial Visualization Skills: Investigating Students' Use of 3D Models in Science Problems during Think-Aloud Interviews.

Effective spatial visualization and reasoning skills are often credited for students' success in science and engineering courses. However, students enrolled in these science courses are not always exposed to or trained properly on the best ways to utilize models to aid in their learning. Improving spatial visualization techniques with 3D models, such as molecular and DNA modeling kits, is often suggested to facilitate students' ability to conceptualize compounds in two and three dimensions. Here, we investigate what techniques students use to conceptualize 2D representations of various biomolecules with the use of 3D models by interviewing undergraduate students from various natural science and engineering disciplines in task-based, think-aloud sessions. After scoring and analyzing the participant data we explored some of the techniques used among successful scoring participants, including the use of informal models to transition between 2D and 3D. Additional techniques used by students who were able to successfully conceptualize 3D images included starting with smaller, granular details to inductively make conclusions when thinking between two and three dimensions. We find that (1) students who anchor their thinking in 3D models show a deeper level of understanding in initially solving science problems successfully, and (2) proper 3D model use and spatial visualization techniques may improve students' abilities to accurately visualize 2D and 3D representations of molecules in science courses. Our results demonstrate that implementing spatial visualization training to teach students how to effectively use 3D models may improve students' problem-solving techniques in science curricula.

Scientific libraries in the classification of the state libraries

Typology and classification of libraries is the most actual problem in library science. A great number of specialists have made attempts to solve it; however, the problem remains unresolved. The article discusses the issues of special terminology related to library differentiation. The author focuses on new tendencies in the development of the state library system and the fact that modern library science requires a revision of existing classifications of libraries. The classification proposed by the author is based on the social functions of libraries. The proposed classification allows for outlining the role of scientific libraries within the library system of the USSR.

A distributed memory parallel randomized Kaczmarz for sparse system of equations

AbstractKaczmarz algorithm is an iterative projection method for solving system of linear equations that arise in science and engineering problems in various application domains. In addition to classical Kaczmarz, there are randomized and parallel variants. The main challenge of the parallel implementation is the dependency of each Kaczmarz iteration on its predecessor. Because of this dependency, frequent communication is required which results in a substantial overhead. In this study, a new distributed parallel method that reduces the communication overhead is proposed. The proposed method partitions the problem so that the Kaczmarz iterations on different blocks are less dependent. A frequency parameter is introduced to see the effect of communication frequency on the performance. The communication overhead is also decreased by allowing communication between processes only if they have shared non‐zero columns. The experiments are performed using problems from various domains to compare the effects of different partitioning methods on the communication overhead and performance. Finally, parallel speedups of the proposed method on larger problems are presented.

Dual-Enhancement Electrochemiluminescence Device for Ultratrace Uranium Visualized Monitoring in Fish, Hair, and Nail Samples.

Uranium is a nuclear fuel but also a hazardous contaminant due to its radioactivity and chemical toxicity. To prevent and mitigate its potential threat, the accurate monitoring of ultratrace uranium (orders of magnitude of pg g-1) in practical environmental samples has become an important scientific problem. To meet this challenge, we developed an efficient electrochemiluminescence (ECL) UO22+ detection device by a novel dual-enhancement mechanism. In detail, poly[(9,9-dioctylfuor-enyl-2,7-diyl)-alt-co-(1,4-benzo-{2,1,3}-thiadiazole)] polymer dots (Pdots) are modified by the UO22+ DNA aptamer, and rhodamine B (RhB) is combined with dsDNA to quench the ECL signal via a resonance energy transfer (RET) process. UO22+ can cut off the DNA aptamer to release RhB, which generates an ECL enhancement process, and then, UO22+ continuously combines with the DNA chain, inducing another ECL enhancement by the RET process from UO22+ to Pdots. This device achieves an ultralow detection limit (12 pg L-1) and a wide linear range (113 pg L-1-11.3 mg L-1), which can successfully give accurate determination results to the ultratrace uranium in biosamples (<1 pg g-1) to monitor the uranium simulation of fish. This work presents an efficient strategy for ultratrace uranium determination in the environment, highlighting its significance in public health and environmental fields.

Information-bibliographical service of scientific and technical problem of „Application of protective coatings as well as protective and decorative metal coatings"

The article is devoted to the analysis of information models dealing with the problem of the application of protective coatings as well as protective and decorative metal coatings. Results of investigations reveal the subject structure of the problem, display the core of scientific and industrial journals, and define bibliographical manuals and databases. Based on the developed models, a structure for a problem-oriented data bank is suggested.

Management of Nutritional Failure in People with Severe ME/CFS: Review of the Case for Supplementing NICE Guideline NG206

In the United Kingdom, a small but steady stream of people diagnosed with ME/CFS (myalgic encephalomyelitis/chronic fatigue syndrome) have run into serious problems with nutrition because of difficulties with eating and drinking, and some have not survived. The clinical problem is extremely difficult, with little or no formal evidence on which to base diagnosis-specific recommendations for care, or information about prognosis. In several cases, however, the problem has apparently been compounded by a lack of provision of adequate services, misunderstandings, and conflict between health care professionals over diagnosis and approach to management. The following is a review of the clinical problem, including some suggestions for protocol content that might supplement NICE Guideline NG206. The main conclusion is that there is an urgent need for a consensus amongst professionals that focuses on practice based on reliable evidence rather than theory-laden diagnosis. The author is a physician with no direct involvement in ME/CFS care but with an interest in the clinical and scientific problems the condition poses.

Estimation of human age using machine learning on panoramic radiographs for Brazilian patients

This paper addresses a relevant problem in Forensic Sciences by integrating radiological techniques with advanced machine learning methodologies to create a non-invasive, efficient, and less examiner-dependent approach to age estimation. Our study includes a new dataset of 12,827 dental panoramic X-ray images representing the Brazilian population, covering an age range from 2.25 to 96.50 years. To analyze these exams, we employed a model adapted from InceptionV4, enhanced with data augmentation techniques. The proposed approach achieved robust and reliable results, with a Test Mean Absolute Error of 3.1 years and an R-squared value of 95.5%. Professional radiologists have validated that our model focuses on critical features for age assessment used in odontology, such as pulp chamber dimensions and stages of permanent teeth calcification. Importantly, the model also relies on anatomical information from the mandible, maxillary sinus, and vertebrae, which enables it to perform well even in edentulous cases. This study demonstrates the significant potential of machine learning to revolutionize age estimation in Forensic Science, offering a more accurate, efficient, and universally applicable solution.

Modelling the present and future scenario of urban green space vulnerability using PSR based AHP and MLP models in a Metropolitan city Kolkata Municipal Corporation

ABSTRACT Urban green spaces, despite their recognized importance for sustainable urban development, face significant challenges to their resilience and viability due to rapid urbanization and various associated pressures. The scientific problem addressed in this paper is the vulnerability of urban green spaces to various stressors, including proximity to settlement, land use and land cover changes, and proximity to roads, among others. Despite their importance, there is a lack of comprehensive assessments and frameworks to systematically evaluate and address this vulnerability. Therefore, this study aims to fill this gap by employing a multi-criteria decision-making framework, namely the Analytic Hierarchy Process (AHP), and the Pressure-State-Response (PSR) framework. By analyzing 10 indicators related to urban green space vulnerability through a pairwise comparison matrix, the study identifies critical factors influencing vulnerability and categorizes urban green spaces into different susceptibility levels. Additionally, the research extends its analysis to future scenario development for the years 2030 and 2040, using a Multi-Layer Perceptron (MLP) based Markov Chain model, incorporating various parameters such as distances from built-up areas, roads, rails, streams, slopes, and elevation. Through these methodologies, the study aims to provide valuable insights to urban planners, policymakers, and environmental practitioners, enabling them to make informed decisions to enhance the resilience and sustainability of urban green spaces and urban areas as a whole.

Physical Activity as a Means of Enhancing Students' Academic Performance

This publication addresses the issue of improving the educational system's performance by increasing the academic success of students in higher education institutions through physical activity. The relevance of this scientific problem is extremely high. In the current reality, ensuring a proper quality of education is of paramount importance for Ukraine. The educational system provides the state and society with qualified professional personnel. From the defense sector to healthcare, there is a need for well-prepared professionals. Given this situation, the issue of high-quality student training, who will meet the labor market demand in the future, is particularly pressing. It is also important to note the significant negative impact of the trend of a substantial number of skilled and able-bodied individuals leaving Ukraine. Furthermore, a large number of highly qualified professionals from various fields have been mobilized into the Defense Forces. The labor market currently experiences a shortage in a number of professions. These factors heighten the relevance of the discussed issue. It is also worth noting that at the time of this publication, the higher education system is undergoing reforms. The optimization of the network of higher education institutions is taking place, which may lead to various negative phenomena in the education system for an indefinite period, caused by changes in curricula, personnel, and administrative restructuring in educational institutions, among other factors. It is essential to consider this contemporary challenge. In this regard, developing methods to enhance students' academic performance through all available means is an important task for the educational system. Mass physical activity among students is one of the most effective methods within this problem area. In fact, the main goal of this publication is to demonstrate that systematic physical activity, given the mass involvement of student youth, has significant potential as a tool for improving academic performance in higher education institutions. Systematic physical activity helps combat the negative effects of stress, which adversely affects students' academic activities. Additionally, regular physical exertion has been observed to positively impact cognitive abilities, which is also significant in the context of this publication. However, it is noted that the outlined issue remains a field for extensive scientific and pedagogical work, as the direct impact of regular physical activity on students' academic success is not sufficiently studied, especially in the context of Ukrainian scientific research.

Valves materials for internal combustion engines

The main strategy for improving the efficiency of internal combustion engines is to increase the cylinder pressure, which inevitably leads to increased mechanical and thermal stress on key engine components, including the intake and exhaust valves. However, traditional materials used for engine valves today are being pushed to their limits. Thus, increasing the efficiency of the next generation internal combustion engines requires the development of new valve materials with a higher set of performance properties at elevated temperatures. The article discusses the operating conditions of engine valves and the requirements for valve materials. A comparative analysis of traditional Russian and foreign valve materials that are widely used today (martensitic and austenitic steels, nickel-based alloys) has been performed. It is noted that Russian standards for valve materials contain recommendations for the use of certain steels for engine valves, which have long lost their market positions abroad due to their inherent shortcomings. On the other hand, some foreign materials specially developed for exhaust valves of diesel engines are not produced by Russian industry. A review of new generation valve materials developed by leading foreign companies in this field to replace expensive nickel-based alloys was carried out. It is noted that the development of new domestic cost-effective valve materials capable of operating under conditions of higher temperatures and pressures in more aggressive environments is an important scientific and technical problem, the solution of which is necessary for the accelerated development of the Russian engine industry

Lessons of soviet psychology

The work is devoted to the analysis of the collection of works “Soviet psychology: the stage of the history of science and mentality” // Responsible. ed. A.L. Zhuravlev, D.V. Ushakov, A.V. Yurevich. Moscow: Publishing house “Institute of Psychology RAS”, 2024. The main ideas presented in 21 articles of this book are highlighted. By studying a retrospective analysis of the works, seven main lessons for modern psychology are formulated, allowing us to see the significance of the Soviet period for the development of science. The first lesson is that despite the ideological pressure and attempts to unify psychology, it implemented the principles of postmodernity and metamodernity, characteristic of modern methodology, with great anticipation. Many ideas and theories were ahead of their time and are relevant and are still being developed today. Lesson two is the formation of psychology as a recognized scientific discipline, which was institutionalized, organized, and acquired a qualifying status. The third lesson teaches us that the space of scientific psychology must be restored: the gaps are closed by the discovery of forgotten names, their achievements and their contribution to the development of scientific thought are analyzed. The development and solution of scientific problems in psychology is rooted in the Soviet period, determining the continuity of scientific knowledge — this is the fourth lesson. The fifth lesson shows us that applied psychology developed intensively during the Soviet period and many of its achievements remain relevant today. The sixth lesson follows from an analysis of the psychology of Soviet people. A real, balanced analysis points to the ambivalence of the characteristics of Soviet people, the limitations of their economic mentality, the presence of double morality, denunciations, and fear of power. Finally, the modern psychological community should not forget what price many scientists paid for their beliefs, what destruction of entire directions in psychology was experienced. The Soviet period of development of Russian psychology is more diverse and ambiguous than previously imagined. The completed project of analyzing the Soviet period of psychology from the perspective of modern science showed its high efficiency and promise. Filling the gaps in the history of Russian psychology is not only fundamental for science, but it is also a moral duty to the generation of Soviet scientists.

An information-theoretic framework for conditional causality analysis of brain networks.

Identifying directed network models for multivariate time series is a ubiquitous problem in data science. Granger causality measure (GCM) and conditional GCM (cGCM) are widely used methods for identifying directed connections between time series. Both GCM and cGCM have frequency-domain formulations to characterize the dependence of time series in the spectral domain. However, the original methods were developed using a heuristic approach without rigorous theoretical explanations. To overcome the limitation, the minimum-entropy (ME) estimation approach was introduced in our previous work (Ning & Rathi, 2018) to generalize GCM and cGCM with more rigorous frequency-domain formulations. In this work, this information-theoretic framework is further generalized with three formulations for conditional causality analysis using techniques in control theory, such as state-space representations and spectral factorizations. The three conditional causal measures are developed based on different ME estimation procedures that are motivated by equivalent formulations of the classical minimum mean squared error estimation method. The relationship between the three formulations of conditional causality measures is analyzed theoretically. Their performance is evaluated using simulations and real neuroimaging data to analyze brain networks. The results show that the proposed methods provide more accurate network structures than the original approach.

Feasibility study of a single-primer extension-based microhaplotype NGS system.

DNA degradation has been a thorny problem in forensic science. Shortening the amplicon length of the genetic markers improves the analysis of degraded DNA effectively. Microhaplotype (MH) has been proposed as a potential genetic marker that can be used for degraded DNA analysis. In the present study, a 146-plex MH-next-generation sequencing (NGS) system with an average Ae of 6.876 was constructed. Unlike other MH studies, a single-primer extension (SPE)-based NGS library preparation method was used to improve the detection of MH markers for degraded DNA. SPE employs a locus-specific and universal primer to amplify target fragments, reducing the necessity for complete fragment sequences. SPE might effectively mitigate the impact of degradation on amplification. However, SPE produces amplicons of varying lengths, posing challenges in allele calling for SPE-NGS data. To address this issue, this study proposed a flexible allele-calling strategy to improve amplicon detection. In addition, this study evaluated the forensic efficacy of the system using 12 low-template samples (from 1ng to 7.8pg), 10 mock-degraded DNA with various degrees of degradation, and 8 forensic casework samples. When the template is as low as 7.8pg, our system can accurately detect at least 37 loci and achieves a random match probability (RMP) of 10-30 using the complete allele-calling strategy. Eighty-two loci can be detected, and RMP can reach 10-54 using a flexible allele-calling strategy. After 150min of 98°C treatment, 36 loci can still be detected, and an RMP of 10-5 can be obtained using the flexible allele-calling strategy. Furthermore, the number of single nucleotide polymorphism detected at different DNA amounts and degradation levels suggests that the SPE method combined with a flexible allele-calling strategy is effective.

Investigating Lipase/Stain Interactions: Determining Interfacial Protein Conformation with Surface Spectroscopy.

Conventional bulk protein structure determination methods are not suitable for understanding the distinct and diverse interactions of proteins with interfaces. Notably, interfacial activation is a feature common to many lipases involving movement of a helical "lid" region upon contact with a hydrophobic surface to expose the catalytic site. Here we use the surface specificity of vibrational sum frequency generation spectroscopy (VSFG) spectroscopy to directly probe the conformation of Thermomyces lanuginosus lipase (TLL) at hydrophobic interfaces. The TLL-catalyzed reaction at the air/water interface is monitored by VSFG spectroscopy, showing loss of ester carbonyl modes and appearance of carboxylate stretching modes of the fatty acid products. Furthermore, comparison of experimental and calculated VSFG spectra of the amide I band of TLL allows us to discern the subtle structural changes involved with lid-opening at a hydrophobic surface. Finally, we report a likely orientation of this lid-open state, which interacts with the surface through a loop region away from the lid and active site. This experimental framework for probing protein structure and function at interfaces addresses a significant problem in protein science that is not only impeding the design of better enzymes for biotechnology applications but also drug discovery targeting membrane associated proteins.

A transdisciplinary approach to scientific problems: closer than ever to a real holistic perspective

A transdisciplinary approach to scientific problems: closer than ever to a real holistic perspective

Sudden Intrauterine Unexplained Death (SIUD) and Oxidative Stress: Placental Immunohistochemical Markers.

Intrauterine fetal death and perinatal death represent one of the most relevant medical scientific problems since, in many cases, even after extensive investigation, the causes remain unknown. The considerable increase in medical legal litigation in the obstetrical field that has witnessed in recent years, especially in cases of stillborn births, has simultaneously involved the figure of the forensic pathologist in scientific research aimed at clarifying the pathophysiological processes underlying stillbirth. our study aims to analyze cases of sudden intrauterine unexplained death syndrome (SIUD) to evaluate the role of oxidative stress in the complex pathogenetic process of stillbirth. In particular, the immunohistochemical expression of specific oxidative stress markers (NOX2, NT, iNOS, 8-HODG, IL-6) was evaluated in tissue samples of placentas of SIUDs belonging to the extensive case series (20 cases), collected from autopsy cases of the University of Ferrara and Politecnica delle Marche between 2017 and 2023. The study demonstrated the involvement of oxidative stress in intrauterine fetal deaths in the placenta of the cases examined. In SIUD, the most expressed oxidative stress markers were NOX2 and 8-HODG. The study contributes to investigating the role of oxidative stress in modulating different pathways in unexplained intrauterine fetal death (SIUD) tissues.

A relationship between two-dimensional and four-dimensional space-time by comparing generalized two-dimensional Yang–Mills theory and Maxwell construction

Some important problems in science do not have analytical solutions in four dimensions including QCD, but they are integrable in two dimensions. For many years, scientists have been trying to find a relation between two-dimensional and four-dimensional space-time to explain the real problem in four dimensions by accurately solving the appropriate model in two dimensions. In this paper, an interesting relation between gYM2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$gY M_2$$\\end{document} (generalized two-dimensional Yang–Mills) and Maxwell construction has been found, which can be a starting point for finding more relations between two-dimensional and four-dimensional space-time, so this paper can play an important role in the advancement of science. For this purpose, first, the large-N behavior of the quartic-cubic generalized two-dimensional Yang–Mills U(N) on a sphere is investigated for finite cubic couplings. It is shown that there are two phase transitions one of which is of third order, which is similar to previous papers, and the other one is of second order, which is a novel result. Second, gYM2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$gY M_2$$\\end{document} (for G(z)=zm+λzn;m=4,6;n<m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ G(z) = z^ m + \\lambda \\, z^n; m = 4, 6; n < m$$\\end{document}) and Maxwell construction are compared with each other and a relationship between two-dimensional space-time, which is integrable, and four-dimensional space-time is obtained.

A square-root speedup for finding the smallest eigenvalue

We describe a quantum algorithm for finding the smallest eigenvalue of a Hermitian matrix. This algorithm combines quantum phase estimation and quantum amplitude estimation to achieve a quadratic speedup with respect to the best classical algorithm in terms of matrix dimensionality, i.e. O~(N/ε) 9 9In this work O~ ignores terms that are polylogarithmic in N or 1/ε . black-box queries to an oracle encoding the matrix, where N is the matrix dimension and ɛ is the desired precision. In contrast, the best classical algorithm for the same task requires Ω(N)polylog(1/ε) queries. In addition, this algorithm allows the user to select any constant success probability. We also provide a similar algorithm with the same runtime that allows us to prepare a quantum state lying mostly in the matrix’s low-energy subspace. We implement simulations of both algorithms and demonstrate their application to problems in quantum chemistry and materials science.

Improving Molecular-Dynamics Simulations for Solid-Liquid Interfaces with Machine-Learning Interatomic Potentials.

Emerging developments in artificial intelligence have opened infinite possibilities for material simulation. Depending on the powerful fitting of machine learning algorithms to first-principles data, machine learning interatomic potentials (MLIPs) can effectively balance the accuracy and efficiency problems in molecular dynamics (MD) simulations, serving as powerful tools in various complex physicochemical systems. Consequently, this brings unprecedented enthusiasm for researchers to apply such novel technology in multiple fields to revisit the major scientific problems that have remained controversial owing to the limitations of previous computational methods. Herein, we introduce the evolution of MLIPs, provide valuable application examples for solid-liquid interfaces, and present current challenges. Driven by solving multitudinous difficulties in terms of the accuracy, efficiency, and versatility of MLIPs, this booming technique, combined with molecular simulation methods, will provide an underlying and valuable understanding of interdisciplinary scientific challenges, including materials, physics, and chemistry.