Information Theory Research Articles

Effect of extrusion temperature on microstructure and mechanical properties of ZTM631 magnesium alloy

Magnesium alloys are widely used in the fields of aviation, electronics, and automotive manufacturing due to their advantages of lightweight and high specific strength. Studying high-performance and low-cost deformed magnesium alloys to adapt to more application scenarios has important value. In the present work, the effect of extrusion temperature on the microstructure and mechanical properties of Mg-6Zn-3Sn-0.9Mn (ZTM631) alloy was investigated. Besides, the ultra-high cycle fatigue (VHCF) performance and fatigue failure mechanism of ZTM631 alloy was explored. Results show that extrusion temperature significantly affect the microstructure of ZTM631 alloys. Firstly, alloys extruded at 300℃ possess a relatively fine grain size, because the lower extrusion temperature prevent the grain growth. Secondly, phases precipitate at 300°C and 350°C with a large size (more than 1µm) are mainly α-Mn phase. while for the alloys extruded at 400°C, both α-Mn and Mg2Sn phases with the size larger than 1µm can be found in the matrix. Thirdly, alloys extruded at 400℃ exhibit the best mechanical properties, due to the combination of the grain refinement and precipitation strengthening. Fourthly, during the VHCF failure process, the crack preferentially generates at the densely distributed α-Mn phases for the alloy extruded at 300°C and 350°C, while the crack initiate near the Mg2Sn particles for the alloy extruded at 400°C. Also, undynamic recrystallized (un-DRXed) grains with severe deformation exist in the alloys extruded at 300℃ and 350℃, thus accelerating the crack initiation. This fundamental investigation provides theoretical information for developing high-performance Mg-Zn-Sn alloys with high strength and expanded their application fields.

Evolutionary tinkering enriches the hierarchical and nested structures in amino acid sequences

Genetic information often exhibits hierarchical and nested relationships, achieved through the reuse of repetitive subsequences such as duplicons and transposable elements, a concept termed “evolutionary tinkering” by François Jacob. Current bioinformatics tools often struggle to capture these, particularly the nested, relationships. To address this, we utilized ladderpath, an approach within the broader category of algorithmic information theory, introducing two key measures: order rate η for characterizing sequence pattern repetitions and regularities, and ladderpath-complexity κ for assessing hierarchical and nested richness. Our analysis of amino acid sequences revealed that humans have more sequences with higher κ values, and proteins with many intrinsically disordered regions exhibit increased η values. Additionally, it was found that extremely long sequences with low η are rare. We hypothesize that this arises from varied duplication and mutation frequencies across different evolutionary stages, which in turn suggests a zigzag pattern for the evolution of protein complexity. This is supported by simulations and studies of protein families such as ubiquitin and NBPF, implying species-specific or environment-influenced protein elongation strategies. The ladderpath approach offers a quantitative lens to understand evolutionary tinkering and reuse, shedding light on the generative aspects of biological structures. Published by the American Physical Society 2024

The Separability Problem in Molecular Quantum Systems: Information-Theoretic Framework for Atoms in Molecules.

Even though molecules are fundamentally quantum entities, the concept of a molecule retains certain classical attributes concerning its constituents. This includes the empirical separability of a molecule into its three-dimensional, rigid structure in Euclidean space, a framework often obtained through experimental methods like X-Ray crystallography. In this work, we delve into the mathematical implications of partitioning a molecule into its constituent parts using the widely recognized Atoms-In-Molecules (AIM) schemes, aiming to establish their validity within the framework of Information Theory concepts. We have uncovered information-theoretical justifications for employing some of the most prevalent AIM schemes in the field of Chemistry, including Hirshfeld (stockholder partitioning), Bader's (topological dissection), and the quantum approach (Hilbert's space definition). In the first approach we have applied the generalized principle of minimum relative entropy derived from the Sharma-Mittal two-parameter functional, avoiding the need for an arbitrary selection of reference promolecular atoms. Within the ambit of topological-information partitioning, we have demonstrated that the Fisher information of Bader's atoms conform to a comprehensive theory based on the Principle of Extreme Physical Information avoiding the need of employing the Schwinger's principle, which has been proven to be problematic. For the quantum approach we have presented information-theoretic justifications for conducting Löwdin symmetric transformations on the density matrix to form atomic Hilbert spaces generating orthonormal atomic orbitals with maximum occupancy for a given wavefunction.

SPILLOVER EFFECTS OF COVID-19 ON USA EDUCATION GROUP STOCKS

In this paper, we explore the price dynamics of 16 representative records of USA Education Group stocks, encompassing two non-overlapping periods (before, during, and after COVID-19). Based on information theory and cluster analysis techniques, our study provides insights into disorder, predictability, efficiency, similarity, and resilience/weakness, considering the most diverse financial stakeholders. Our findings contribute significantly to understanding price dynamics in the education sector, encompassing non-crisis and crisis periods, and provide valuable information for financial risk management, highlighting the importance of considering financial assets’ efficiency and resilience/weakness during economic turbulence, such as the COVID-19 crisis.

Perfect Codes over Non-Prime Power Alphabets: An Approach Based on Diophantine Equations

Perfect error-correcting codes allow for an optimal transmission of information while guaranteeing error correction. For this reason, proving their existence has been a classical problem in both pure mathematics and information theory. Indeed, the classification of the parameters of e-error correcting perfect codes over q-ary alphabets was a very active topic of research in the late 20th century. Consequently, all parameters of perfect e-error-correcting codes were found if e≥3, and it was conjectured that no perfect 2-error-correcting codes exist over any q-ary alphabet, where q>3. In the 1970s, this was proved for q a prime power, for q=2r3s and for only seven other values of q. Almost 50 years later, it is surprising to note that there have been no new results in this regard and the classification of 2-error-correcting codes over non-prime power alphabets remains an open problem. In this paper, we use techniques from the resolution of the generalised Ramanujan–Nagell equation and from modern computational number theory to show that perfect 2-error-correcting codes do not exist for 172 new values of q which are not prime powers, substantially increasing the values of q which are now classified. In addition, we prove that, for any fixed value of q, there can be at most finitely many perfect 2-error-correcting codes over an alphabet of size q.

The Effect of Flipped Classroom Model Applıcation on Academic Achievements and Attitudes of Secondary School Sixth-Grade Students Towards Social Studies Courses

With the development of technology in ourage, rapid change and development is observed in thefield of information technology. Thischange is alsoobserved in the field of educational Technologiesand beside this new teaching model sareemerged. One of them is the Flipped Classroom model. This teaching model is defined as a model that provides the opportunity to learn the theoretical information at home and practice it at school via homework. The flipped classroom model is integrated with the technology. The students can study as they want in terms of time duration or rate instead of just listening. They can view courses on their mobile devices when ever they want. This research examines‘whether the application of flipped classroom model have an effect on the secondary sixth grade students’ academic success in ‘Culture and Heritage’ unit’and‘what is their attitudes to wards social studies lessons? Qualita tivere search approach isused in the research. The method of the research is quasi-Test design with pretest-posttest control groups. The application was carried out in 5 week sand 20 lesson hours. The study group of there search consists of 25 students who are chosen viasimplerandom sampling method. The data collection tools of there search are academic achievement test and attitude scale. The dependent sample t-test and Mann Whitney U Test were used in quantitive ananalysis. According to the findings of the study, the students academic achievement in social studies lesson in creased significantly compared to the pre-test post-test results. There is no significant different cebet weenth e students' attitudes towards social studies lesson.

Digital citizenship education – training opportunities for foreign language teachers

ABSTRACT According to the Council of Europe, educators lack awareness on the topic of digital citizenship education and its importance to educate youngsters to move critically in a highly digitalized world. It is a core component of Digital Citizenship Education (DCE) to promote democratic competences, including critical reflection, the development of social and intercultural competences, critical thinking and media literacy. Bearing this in mind, a Teacher Training Package (TTP) was developed, aiming to promote professional development and qualification of pre- and in-service Foreign Language teachers as well as teacher trainers, as far as DCE is concerned, namely, pedagogical methodologies, practices and resources. Our study focuses on the Free Online Training Course for Self-guided Professional Development (part of the TTP), which directly approaches teachers’ professional knowledge. The analysis of the Teaching Units designed by the teachers and of their final course reflections suggest the relevance of the training course for the teachers’ professional development. Those who took part in the course highlighted the pertinence of the topic and underlined the relevance of having had contact with theoretical information and resources, which allowed them not only to reflect on their current practices but also to consider ways of introducing DCE in their lessons.

An empirical study on social network analysis for small residential communities in Gangwon State, South Korea

Social Network Analysis (SNA) provides a dynamic framework for examining interactions and connections within networks, elucidating how these relationships impact behaviors and outcomes. This study targeted small residential communities in Gangwon State, South Korea, to explore network formation theories and derive strategies for enhancing health promotion services in rural communities. Conducted in 12 small residential areas, the survey led to a network categorization model distinguishing networks as formal, informal, or non-existent. Key findings demonstrated that demographic and socio-economic factors, specifically age, income, living environment, leisure activities, and education level, significantly influence network formation. Importantly, age, environmental conditions, satisfaction with public transportation, and walking frequency were closely associated with the evolution of formal networks. These results highlight the importance of early community network assessments, which must consider distinct network traits to develop effective health promotion models. Utilizing SNA early in the assessment process can improve understanding of network dynamics and optimize the effectiveness of health interventions.

Information Theory, Living Systems, and Communication Engineering.

Mainstream research on information theory within the field of living systems involves the application of analytical tools to understand a broad range of life processes. This paper is dedicated to an opposite problem: it explores the information theory and communication engineering methods that have counterparts in the data transmission process by way of DNA structures and neural fibers. Considering the requirements of modern multimedia, transmission methods chosen by nature may be different, suboptimal, or even far from optimal. However, nature is known for rational resource usage, so its methods have a significant advantage: they are proven to be sustainable. Perhaps understanding the engineering aspects of methods of nature can inspire a design of alternative green, stable, and low-cost transmission.

Assessing the cascading impact of industry 4.0 disruption on supply chain analytics through the lens of dependency concepts

Nowadays, Industry 4.0 (I4.0) is regarded as one of the most sophisticated technologies that businesses need to have to stay competitive. I4.0 infrastructure system may be seen to function as a network of interdependent elements connected with multiple networks through various means. One such network is the supply chain which has an interrelation with Industry 4.0. Any disruption to Industry 4.0 will ripple through its entire supply chain and will lead to cascading effects. Thus, there is a need to examine how to minimize the disruption of I4.0 and make its dependent supply chain more functional and cost-saving. To that end, the aim of this research is threefold: (i) modeling the dependence between fourth industrial revolution and supply chain performance using the Bayesian network, (ii) capturing how a disruption in one network may propagate through the entire network and impact its overall robustness, (iii) conducting a series of advance analyses including belief propagation, sensitivity analysis, and information theory approach to validate the result and draw further insights. As a result of these analyses, it was found that technological factors and supplier quality are vital to the I4.0 disruption and performance of supply chain performance.

I.L. MAYAK AS A REVIEWER OF SCIENTIFIC LITERATURE ON THE HISTORY OF ANCIENT ROME

The subject of this study is a part of the scientific heritage of the Honored Professor of Moscow University, Iya Leonidovna Mayak (1922-2018), represented by her reviews on national and foreign monographs on Roman history, published in 1958-1999. The article deals with I.L. Mayak’s motives for writing the reviews, the methodological foundations of her analysis, her requirements for the source base and the historiographical rating of classical studies, their structure and the logic of presentation. It is noted that I.L. Mayak was opposed to dogmatism in the application of the theory of socio-economic formations as well as a hypercritical attitude to sources related to archaic Rome, the spread of which she considered to be the methodological consequence of Kantianism. It is shown that I.L. Mayak not only monitored the diffi cult process of rehabilitation of the ancient tradition as a source for studying the archaic Rome, but herself actively promoted this process, in particular by means of reviewing other people’s works. Th e study considers the requirements set out by I.L. Mayak for classical studies such as the need to provide a terminological analysis of sources data, to trace changes in the content of concepts over time, to show the possibility of various interpretations of the given facts. In the formulation of theoretical constructions I.L. Mayak considered essential to provide a concept against the background of the concepts introduced by predecessors, and if the problem was debatable, to indicate the view clearly and definitely. The analysis of reviews written by Iya Leonidovna allows us to conclude that they make it possible to expand our understanding of her academic views, especially on those problems of Roman history, which she did not directly examine in her monographs and articles, in particular, on the issue of preserving polis paradigm in the late Roma n Empire. Reviews by I.L. Mayak reflect her image memorable to many generations of historians: she was respectful to colleagues, however critical of their work, delicate in remarks, stringently academic in expressing her thoughts. The article shows the polemic sharpness and dialogic character of the reviews by I.L. Mayak.

The effect of emotional changes in composite reviews on consumers' information adoption from a dual perspective

PurposeWith the appearance of additional review functionality on e-commerce platforms emotional changes in composite reviews have become more diverse. How consumers process the emotional changes in composite reviews is an important concern for companies. This study investigates the impact of explores how changes in the emotional valence and emotional intensity of composite reviews on consumers' information adoption.Design/methodology/approachBased on emotion as social information theory, this study constructs a double mediation model of how the change in emotional valence of composite reviews affects consumers' adoption intention and examines the moderating effect of the dynamic change of emotional intensity. One field and three online experiments were conducted to test the proposed hypotheses.FindingsConsumers were more likely to adopt positive–negative composite reviews than negative–positive composite reviews. Compared to negative–positive composite reviews, positive–negative composite reviews led to higher perceived empathy and lower motivational suspicion, which, in turn, led to higher information adoption. Moreover, dynamic changes in emotional intensity played a moderating role in this effect. Interestingly, the amount of attribute difference changed the differences in perceived empathy and motivated consumer suspicion generated by the composite review when considering the reviewer’s attribute difference description.Originality/valueThe findings have important theoretical contributions that deepen business and consumer understanding of the impact of composite reviews and have practical implications for improving the management of composite reviews by businesses.

Exploring Simplicity Bias in 1D Dynamical Systems.

Arguments inspired by algorithmic information theory predict an inverse relation between the probability and complexity of output patterns in a wide range of input-output maps. This phenomenon is known as simplicity bias. By viewing the parameters of dynamical systems as inputs, and the resulting (digitised) trajectories as outputs, we study simplicity bias in the logistic map, Gauss map, sine map, Bernoulli map, and tent map. We find that the logistic map, Gauss map, and sine map all exhibit simplicity bias upon sampling of map initial values and parameter values, but the Bernoulli map and tent map do not. The simplicity bias upper bound on the output pattern probability is used to make a priori predictions regarding the probability of output patterns. In some cases, the predictions are surprisingly accurate, given that almost no details of the underlying dynamical systems are assumed. More generally, we argue that studying probability-complexity relationships may be a useful tool when studying patterns in dynamical systems.

Photoinduced Dehalogenation‐Based Direct In Situ Photolithography of CsPbBr3 Quantum Dots Micropatterns for Encryption and Anti‐Counterfeiting with High Capacity

Fluorescent lead halide perovskite quantum dots (LH PQDs) micropatterns hold great potential for photonic applications. However, current photolithography for LH PQDs micropatterning is hindered by their incompatibility with traditional photolithography methods, which involve development processes using numerous solvents and exhibit poor stability due to the ionic characteristics of LH PQDs. Herein, a direct in situ photolithography to fabricate CsPbBr3 PQDs micropatterns based on ultraviolet‐C light‐driven debromination is developed. Using this approach, fluorescent CsPbBr3 PQDs micropatterns with high theoretical information storage capacity (up to 10750205) can be achieved in a single step, without the need for tedious development processes. Furthermore, the fabricated CsPbBr3 PQDs micropatterns show high stability, remaining undamaged even after immersion in water for 6 months. The combination of excellent optical properties, development‐free process, high stability, and low cost makes the in situ photolithography strategy very promising for patterning LH PQDs toward photonic integrations.

Spatiotemporal Mechanisms of the Coexistence of Reintroduced Scimitar-Horned Oryx and Native Dorcas Gazelle in Sidi Toui National Park, Tunisia.

Examining the distribution patterns and spatiotemporal niche overlap of sympatric species is crucial for understanding core concepts in community ecology and for the effective management of multi-species habitats within shared landscapes. Using data from 26 camera-traps, recorded over two years (December 2020-November 2022), in Sidi Toui National Park (STNP), Tunisia, we investigate habitat use and activity patterns of the scimitar-horned oryx (n = 1865 captures) and dorcas gazelle (n = 1208 captures). Using information theory and multi-model inference methods, along with the Pianka index, we evaluated the habitat characteristics influencing species distribution and their spatial niche overlap. To delineate daily activity patterns, we applied kernel density estimation. Our findings indicate minimal spatial overlap and distinct environmental factors determining suitable habitats for each species. Furthermore, we found significant temporal niche overlaps, indicative of synchrony in daily activity patterns, with both species showing peak activity at dawn and dusk. Our results indicated that oryx and gazelle differ in at least one dimension of their ecological niche at the current density levels, which contributes to their long-term and stable coexistence in STNP.

Landauer Bound in the Context of Minimal Physical Principles: Meaning, Experimental Verification, Controversies and Perspectives

The physical roots, interpretation, controversies, and precise meaning of the Landauer principle are surveyed. The Landauer principle is a physical principle defining the lower theoretical limit of energy consumption necessary for computation. It states that an irreversible change in information stored in a computer, such as merging two computational paths, dissipates a minimum amount of heat kBTln2 per a bit of information to its surroundings. The Landauer principle is discussed in the context of fundamental physical limiting principles, such as the Abbe diffraction limit, the Margolus–Levitin limit, and the Bekenstein limit. Synthesis of the Landauer bound with the Abbe, Margolus–Levitin, and Bekenstein limits yields the minimal time of computation, which scales as τmin~hkBT. Decreasing the temperature of a thermal bath will decrease the energy consumption of a single computation, but in parallel, it will slow the computation. The Landauer principle bridges John Archibald Wheeler’s “it from bit” paradigm and thermodynamics. Experimental verifications of the Landauer principle are surveyed. The interrelation between thermodynamic and logical irreversibility is addressed. Generalization of the Landauer principle to quantum and non-equilibrium systems is addressed. The Landauer principle represents the powerful heuristic principle bridging physics, information theory, and computer engineering.

A window into the forest: post‐release behaviour of rehabilitated Bornean sun bears Helarctos malayanus euryspilus in Sabah, Malaysia

As the number of threatened wildlife species increases globally, captive rehabilitation and release of wild caught animals plays an increasingly significant role in conservation efforts, particularly where poaching threatens wild populations. Sun bear Helarctos malayanus populations are decreasing globally, with habitat loss and fragmentation and poaching as key threats to the species. This study aimed to assess post‐release behaviour of rehabilitated sun bears (n = 12) using GPS‐collar data to inform ongoing efforts to rehabilitate wild caught bears. We assessed spatial patterns of movement using kernel density estimates and utilisation distributions. We utilised an information theory (IT) and generalised linear modelling approach to assess temporal patterns of sun bear movement (n = 9). Post‐release home range estimates (mean = 56.68 km2, range:1.9–202.4) and behaviour varied widely between bears, with no significant difference in home range size between males (n = 3) and females (n = 6). Home ranges of most bears stabilised within the first month. Bears that were rehabilitated and released together did not spend a significant amount of time together following release. Bears were most active during daytime hours and most established a post‐release home range encompassing the riverside. The ultimate success of the releases is unknown, due to collar malfunction and damage, and the inaccessibility of the study terrain. We find that future releases of sun bears would benefit from soft‐release and in situ rehabilitation techniques. This study provides important insights into the complexity of bear reintroductions informing future rehabilitation and releases for threatened bear species.

Preparing Future Computer Science Teachers Through using Innovative Technologies

This article presents theoretical and practical information and recommendations on the use of innovative technologies in the training of future computer science teachers, in particular Collaborative Learning technology in teaching the science of programming basics, and the development of students’ digital and software-technological competencies. new approaches and issues are discussed.

Machine Learning and Information Theory Concepts towards an AI Mathematician

The current state of the art in artificial intelligence is impressive, especially in terms of mastery of language, but not so much in terms of mathematical reasoning. What could be missing? Can we learn something useful about that gap from how the brains of mathematicians go about their craft? This essay builds on the idea that current deep learning mostly succeeds at system 1 abilities—which correspond to our intuition and habitual behaviors—but still lacks something important regarding system 2 abilities—which include reasoning and robust uncertainty estimation. It takes an information-theoretical posture to ask questions about what constitutes an interesting mathematical statement, which could guide future work in crafting an AI mathematician. The focus is not on proving a given theorem but on discovering new and interesting conjectures. The central hypothesis is that a desirable body of theorems better summarizes the set of all provable statements, for example, by having a small description length while at the same time being close (in terms of number of derivation steps) to many provable statements.

An entropy-based study on the mutational landscape of SARS-CoV-2 in USA: Comparing different variants and revealing co-mutational behavior of proteins

COVID-19 emergency has pushed the international scientific community to use every resource to combat the spread of the virus, to understand its biology and predict its possible evolution in terms of new variants. Since the first SARS-CoV-2 virus nucleotide and amino acid sequences were made available, information theory was used to study how viral information content was changing over time and then trace the evolution of its mutational landscape. In this work we analyzed SARS-CoV-2 sequences collected mainly in the USA in a period from March 2020 until December 2022 and computed mutation profiles of viral proteins over time through an entropy-based approach using Shannon Entropy and Hellinger distance. This representation allows an at-a-glance view of the mutational landscape of viral proteins over time and can provide new insights on the evolution of the virus from different points of view. Non-structural proteins typically showed flat mutation profiles, characterized by a very low Average mutation Entropy, while accessory and structural proteins showed mostly non uniform and high mutation profiles, often coupled with the predominance of variants. Interestingly NSP2 protein, whose function is currently still debated, falls in the same branch of NSP14 and NSP10 in the phylogenetic tree of mutations constructed through correlations of mutation profiles, suggesting a co-evolution of those proteins and a possible functional link with each other. To the best of our knowledge this is the first study based on a massive amount of data (n = 107,939,973) that analyzes from an entropy point of view the mutational landscape of SARS-CoV-2 over time and depicts a mutational temporal profile of each protein of the virus.