Theoretical Progress Research Articles

Sex chromosome evolution: hallmarks and question marks.

Sex chromosomes are widespread in species with separate sexes. They have evolved many times independently, and display a truly remarkable diversity. New sequencing technologies and methodological developments have allowed the field of molecular evolution to explore this diversity in a large number of model and non-model organisms, broadening our vision on the mechanisms involved in their evolution. Diverse studies have allowed us to better capture the common evolutionary routes that shape sex chromosomes, however we still mostly fail to explain why sex chromosomes are so diverse. We review over half a century of theoretical and empirical work on sex chromosome evolution and highlight pending questions on their origins, turnovers, rearrangements, degeneration, dosage compensation, gene content, and rates of evolution. We also report recent theoretical progress on our understanding of the ultimate reasons for sex chromosomes' existence.

Macroscopic perspective on phase transition behavior of natural single-crystal graphite under different pressure environments

Comprehensive understanding of the direct transformation pathway from graphite to diamond under high temperature and high pressure has long been one of the fundamental goals in materials science. Despite considerable experimental and theoretical progress, current experimental studies have mainly focused on the local microstructural characterizations of recovered samples, which has certain limitations for high-temperature and high-pressure products, which often exhibit diversity. Here, we report on the pressure-induced phase transition behavior of natural single-crystal graphite under three distinct pressure-transmitting media from a macroscopic perspective using in situ two-dimensional Raman spectroscopy, scanning electron microscopy, and atomic force microscopy. The surface evolution process of graphite before and after the phase transition is captured, revealing that pressure-induced surface textures can impede the continuity of the phase transition process across the entire single crystal. Our results provide a fresh perspective for studying the phase transition behavior of graphite and greatly deepen our understanding of this behavior, which will be helpful in guiding further high-temperature and high-pressure syntheses of carbon allotropes.

Advances in Multiscale Modeling and Mechanical Properties Characterization of 3D‐Braided Composites

The 3D‐braided composites have been widely used in aerospace and other fields due to the excellent mechanical properties, designability, and resistance to delamination. Accurately analyzing and evaluating mechanical properties of braided composite structures is the key, which successfully designs related structural components. However, 3D‐braided composites have complex internal meso–microscopic structures, and directly establishing a solid structure model to predict mechanical behavior poses certain challenges. The multiscale research methods are a type of approach that studies the cross‐scale structural characteristics at macro–meso–microscopic scales and couples relevant scales into a whole. Its emergence provides the possibility for evaluating the overall structural performance of 3D‐braided composites. In this article, first, the multiscale analysis models of 3D‐multidirectional‐braided composites are reviewed, and then the theoretical and numerical research progress on the static and dynamic multiscale mechanical performance of regular components of 3D‐braided composites is summarized. In addition, due to the distortion and variation of geometric shapes of 3D‐braided composites on the meso–microscopic structure; therefore, also in this article, the research progress on multiscale mechanical performance characteristics of special‐shaped components is summarized. Finally, the key problems in the multiscale research of 3D‐braided composites are presented.

Revolutionizing Knowledge Synthesis: Introducing AI-Enhanced Collaborative Theory (AIECT)

In an era of unprecedented data development, standard literature review approaches can result in information overload and missed chances for theoretical innovation. This paper introduces AI-Enhanced Collaborative Theory (AIECT), a paradigm for dynamic collaboration between artificial intelligence (AI) systems and human researchers to improve knowledge synthesis and theoretical progress. AIECT focuses on the intersection of human intuition and AI capabilities, including essential components like dynamic co-creation, iterative feedback loops, contextual intelligence, and ethical frameworks. Empirical experiments show that AIECT can significantly increase the efficiency and depth of literature reviews and theory creation, showing its potential to revolutionize research processes and address information overload concerns. While limitations such as sample size and the need for additional validation are acknowledged, the findings indicate that AIECT represents a transformative approach to academic inquiry, encouraging innovative, ethical, and nuanced research techniques. By safely integrating AI, AIECT sets the way for advances in knowledge synthesis and provides a systematic foundation for future scholarly endeavors in various domains.

Active Inference in Psychology and Psychiatry: Progress to Date?

The free energy principle is a formal theory of adaptive self-organising systems that emerged from statistical thermodynamics, machine learning and theoretical neuroscience and has since been translated into biologically plausible 'process theories' of cognition and behaviour, which fall under the banner of 'active inference'. Despite the promise this theory holds for theorising, research and practical applications in psychology and psychiatry, its impact on these disciplines has only now begun to bear fruit. The aim of this treatment is to consider the extent to which active inference has informed theoretical progress in psychology, before exploring its contributions to our understanding and treatment of psychopathology. Despite facing persistent translational obstacles, progress suggests that active inference has the potential to become a new paradigm that promises to unite psychology's subdisciplines, while readily incorporating the traditionally competing paradigms of evolutionary and developmental psychology. To date, however, progress towards this end has been slow. Meanwhile, the main outstanding question is whether this theory will make a positive difference through applications in clinical psychology, and its sister discipline of psychiatry.

The Influence of Tourist–Environment Fit on Environmental Responsibility Behavior: A Moderated Mediation Model

Urban forest parks have emerged as significant tourist destinations thanks to economic expansion and the rise in free time. These parks offer tourists the opportunity to connect with nature. Existing studies have examined the value and importance of urban forest parks for tourists. However, there is a lack of research on the interaction between tourists and the natural environment and its influence on environmental responsibility. This study used structural equation modeling to explore the impact of tourist–environment fit on environmentally responsible behavior and the mediating role of natural empathy and awe in the impact of the above-mentioned relationship. In addition, the moderating role of tourists’ different cultural backgrounds (nationalities) was also analyzed. In this study, 509 valid questionnaires were collected from field surveys in April and May 2024 at the forest park of Lim City, Seoul, Republic of Korea. The data analysis indicates a notable and favorable impact of tourist–environment fit on visitors’ natural empathy, awe, and environmentally responsible behavior. Natural empathy and awe play a mediating role in the influence of tourist–environment fit on environmentally responsible behavior. It suggests that the fit between tourists and the recreational environment of urban forest parks promotes the emergence of empathy and awe and, thus, stimulates tourists’ environmentally responsible behavior. In addition, tourists’ cultural backgrounds play a moderating role in the influence of natural empathy and awe on environmentally responsible behavior. Compared to Korean tourists, Chinese tourists’ natural empathy and awe strongly influence environmentally responsible behavior compared to Korean tourists. This paper also explores the theoretical progress and practical observations that provide guidance and serve as references for the management and sustainable development of urban forest parks.

Enhancing PVT property predictions for black oil reservoirs through the application of supervised machine learning techniques

The study presents an innovative approach utilizing machine learning (ML) techniques to forecast essential characteristics of black oil reservoirs, namely the oil formation volume factor (Bo) and the solution gas-oil ratio (Rs). A suite of four established ML algorithms-Random Forest, Linear Regression, Ada Boost, and Gradient Boost were extensively conditioned using a dataset that includes 297 individual data points and evaluation is done using test data and a blind test of models. The study showcases the practical efficacy of ML in accurately predicting Bo and Rs, even with a relatively modest dataset. The potential of ML models to save time and costs by reducing the dependence on laborious laboratory measurements is underscored, presenting a transformative shift in reservoir engineering practices. Furthermore, the current research extends its impact by comparing ML predictions with various empirical correlations and actual values, demonstrating that ML models, particularly Gradient Boost, consistently outperform traditional correlations and it can be used to predict values in the undersaturated region too. It is observed from the blind test results that for the prediction of Bo Gradient boost and random forest models (R2 0.97, RMSE 0.01) is a preferable choice and for the prediction of Rs the Gradient boost model (R2 0.97, RMSE 22) can be preferred. The adaptability of ML models to integrate new data over time is a significant feature, ensuring continuous improvement in accuracy. This study adds to the expanding reservoir engineering knowledge base, highlighting the practical use of machine learning for accurately predicting crucial reservoir properties. Its significance goes beyond theoretical progress, providing tangible tools for reservoir engineers and geologists to improve decision-making in the management of oil and gas reservoirs.

Big data and supply chain crisis financial risk management: A bibliometric review

The presence of a crisis has consistently been an inherent aspect of the Supply Chain, mostly as a result of the substantial number of stakeholders involved and the intricate dynamics of their relationships. The objective of this study is to assess the potential of Big Data as a tool for planning risk management in Supply Chain crises. Specifically, it focuses on using computational analysis and modeling to quantitatively analyze financial risks. The “Web of Science—Elsevier” database was employed to fulfill the aims of this work by identifying relevant papers for the investigation. The data were inputted into VOS viewer, a software application used to construct and visualize bibliometric networks for subsequent research. Data processing indicates a significant rise in the quantity of publications and citations related to the topic over the past five years. Moreover, the study encompasses a wide variety of crisis types, with the COVID-19 pandemic being the most significant. Nevertheless, the cooperation among institutions is evidently limited. This has limited the theoretical progress of the field and may have contributed to the ambiguity in understanding the research issue.

Nonlinear physics of moiré superlattices.

Nonlinear physics is one of the most important research fields in modern physics and materials science. It offers an unprecedented paradigm for exploring many fascinating physical phenomena and realizing diverse cutting-edge applications inconceivable in the framework of linear processes. Here we review the recent theoretical and experimental progress concerning the nonlinear physics of synthetic quantum moiré superlattices. We focus on the emerging nonlinear electronic, optical and optoelectronic properties of moiré superlattices, including but not limited to the nonlinear anomalous Hall effect, dynamically twistable harmonic generation, nonlinear optical chirality, ultralow-power-threshold optical solitons and spontaneous photogalvanic effect. We also present our perspectives on the future opportunities and challenges in this rapidly progressing field, and highlight the implications for advances in both fundamental physics and technological innovations.

Fitting spatial stochastic frontier models in Stata

In this article, we introduce a new command, xtsfsp, for fitting spatial stochastic frontier models in Stata. Over the last decades, stochastic frontier models have seen important theoretical progress via the incorporation of various types of spatial components. Models that can account for spatial dependence and spillovers have been developed for efficiency and productivity analysis, drawing extensive attention from industry and academia. Because of the unavailability of the statistical packages, the empirical applications of the new stochastic frontier models appear to be lagging. The xtsfsp command provides a procedure for fitting spatial stochastic frontier models in the style of Orea and Álvarez (2019, Journal of Econometrics 213: 556-577) and Galli (2023, Spatial Economic Analysis 18: 239-258), enabling users to handle different sources of spatial dependence. In this article, we introduce spatial stochastic frontier models, describing the syntax and options of the new command and providing several examples to illustrate its usage.

Emerging Multifunctionality in 2D Ferroelectrics: A Theoretical Review of the Interplay With Magnetics, Valleytronics, Mechanics, and Optics

Abstract2D ferroelectric materials present promising applications in information storage, sensor technology, and optoelectronics through their coupling with magnetics/valleytronics, mechanics, and optics, respectively. The integration of 2D ferroelectrics with magnetism enhances data storage density in memory devices by enabling electric‐field‐controlled magnetic states. Ferroelectric‐valley coupling holds promise for high‐speed, low‐energy electronics by leveraging the electrical control of valley polarization. Ferroelectric‐strain coupling results in various polar topologies, with potential applications in high‐density data storage technologies and sensor devices. Moreover, the coupling between ferroelectrics and optics facilitates the development of nonlinear photonics based on ferroelectric materials. This review summarizes the latest theoretical progress in the coupling mechanisms, including the Dzyaloshinskii‐Moriya‐interaction‐induced magnetoelectric coupling, symmetry‐linked ferroelectric‐valley coupling, ferroelectric‐strain‐coupling‐generated polar topologies, and second‐harmonic generation through ferroelectric‐light interactions. The current challenges and future opportunities in harnessing the coupling in 2D ferroelectric materials for multifunctional applications are provided.

A Survey on Information Bottleneck.

This survey is for the remembrance of one of the creators of the information bottleneck theory, Prof. Naftali Tishby, passing away at the age of 68 on August, 2021. Information bottleneck (IB), a novel information theoretic approach for pattern analysis and representation learning, has gained widespread popularity since its birth in 1999. It provides an elegant balance between data compression and information preservation, and improves its prediction or representation ability accordingly. This survey summarizes both the theoretical progress and practical applications on IB over the past 20-plus years, where its basic theory, optimization, extensive models and task-oriented algorithms are systematically explored. Existing IB methods are roughly divided into two parts: traditional and deep IB, where the former contains the IBs optimized by traditional machine learning analysis techniques without involving any neural networks, and the latter includes the IBs involving the interpretation, optimization and improvement of deep neural works (DNNs). Specifically, based on the technique taxonomy, traditional IBs are further classified into three categories: Basic, Informative and Propagating IB; While the deep IBs, based on the taxonomy of problem settings, contain Debate: Understanding DNNs with IB, Optimizing DNNs Using IB, and DNN-based IB methods. Furthermore, some potential issues deserving future research are discussed. This survey attempts to draw a more complete picture of IB, from which the subsequent studies can benefit.

Multifold topological semimetals

The discovery of topological semimetals with multifold band crossings has opened up a new and exciting frontier in the field of topological physics. These materials exhibit large Chern numbers, leading to long double Fermi arcs on their surfaces, which are protected by either crystal symmetries or topological order. The impact of these multifold crossings extends beyond surface science, as they are not constrained by the Poincar classification of quasiparticles and only need to respect the crystal symmetry of one of the 1651 magnetic space groups. Consequently, we observe the emergence of free fermionic excitations in solid-state systems that have no high-energy counterparts, protected by non-symmorphic symmetries. In this work, we review the recent theoretical and experimental progress made in the field of multifold topological semimetals. We begin with the theoretical prediction of the so-called multifold fermions and discuss the subsequent discoveries of chiral and magnetic topological semimetals. Several experiments that have realized chiral semimetals in spectroscopic measurements are described, and we discuss the future prospects of this field. These exciting developments have the potential to deepen our understanding of the fundamental properties of quantum matter and inspire new technological applications in the future.

Numerical validation of scaling laws for stratified turbulence

Recent theoretical progress using multiscale asymptotic analysis has revealed various possible regimes of stratified turbulence. Notably, buoyancy transport can either be dominated by advection or diffusion, depending on the effective Péclet number of the flow. Two types of asymptotic models have been proposed, which yield measurably different predictions for the characteristic vertical velocity and length scale of the turbulent eddies in both diffusive and non-diffusive regimes. The first, termed a ‘single-scale model’, is designed to describe flow structures having large horizontal and small vertical scales, while the second, termed a ‘multiscale model’, additionally incorporates flow features with small horizontal scales, and reduces to the single-scale model in their absence. By comparing predicted vertical velocity scaling laws with direct numerical simulation data, we show that the multiscale model correctly captures the properties of strongly stratified turbulence within regions dominated by small-scale isotropic motions, whose volume fraction decreases as the stratification increases. Meanwhile its single-scale reduction accurately describes the more orderly, layer-like, quiescent flow outside those regions.

From the U.S. to Denmark and Beyond: It’s Time to Reimagine the Use of Control Variables in Observational Research

ABSTRACT Methodologists from a variety of social and behavioral sciences have called attention to misunderstandings, misclassifications, and misapplications of third variable “controls” in observational research. We are unaware, however, of a comprehensive discussion of these issues in the sexological literature. In this article, we attempt to detail several of the more important potential pitfalls within the context of a case study commentary on a recent Journal of Sex Research inquiry into Danish persons’ pornography use, sexual satisfaction, and sexual behavior. We emphasize that our own (still limited) edification came only through personal error and chart an optimistic path forward wherein the current state of practice can be transformed into theoretical progress and innovation.

The multiverse of universes: A tutorial to plan, execute and interpret multiverses analyses using the R package multiverse.

Even when guided by strong theories and sound methods, researchers must often choose a singular course of action from multiple viable alternatives. Regardless of the choice, it, along with all other choices made during the research process, individually and collectively affects study results, often in unpredictable ways. The inability to disentangle how much of an observed effect is attributable to the phenomenon of interest, and how much is attributable to what have come to be known as researcher degrees of freedom (RDF), slows theoretical progress and stymies practical implementation. However, if one could examine the results from a particular set of RDF (known as a universe) against a systematically and comprehensively determined background of alternative viable universes (known as a multiverse), then the effects of RDF can be directly examined to provide greater context and clarity to future researchers, and greater confidence in the recommendations to practitioners. This tutorial demonstrates a means to map result variability directly and efficiently, and empirically investigate RDF impact on conclusions via multiverse analysis. Using the R package multiverse, we outline best practices in planning, executing and interpreting of multiverse analyses.

IDH1-mutant metabolite D-2-hydroxyglutarate inhibits proliferation and sensitizes glioma to temozolomide via down-regulating ITGB4/PI3K/AKT

The heterogeneous molecular subtypes of gliomas demonstrate varied responses to chemotherapy and distinct prognostic outcomes. Gliomas with Isocitrate dehydrogenase 1 (IDH1) mutation are associated with better outcomes and are more responsive to temozolomide (TMZ) compared to those without IDH1 mutation. IDH1-mutant gliomas elevate D-2-hydroxyglutarate (D-2HG) levels, with potential dual effects on tumor progression. Limited research has explored the potential anti-glioma effects of D-2HG in combination with TMZ. Clinical data from over 2500 glioma patients in our study confirms that those with IDH1 mutations exhibit enhanced responsiveness to TMZ chemotherapy and a significantly better prognosis compared to IDH1 wild-type patients. In subsequent cellular experiments, we found that the IDH1-mutant metabolite D-2HG suppresses Integrin subunit beta 4 (ITGB4) expression, and down-regulate the phosphorylation levels of PI3K and AKT, ultimately inhibiting cell proliferation while promoting apoptosis, thereby improving glioma prognosis. Additionally, we have demonstrated the synergistic effect of D-2HG and TMZ in anti-glioma therapy involved inhibiting the proliferation of glioma cells and promoting apoptosis. Finally, by integrating data from the CGGA and TCGA databases, it was validated that ITGB4 expression was lower in IDH1-mutant gliomas, and patients with lower ITGB4 expression were associated with better prognosis. These findings indicate that ITGB4 may be a promising therapeutic target for gliomas and D-2HG inhibits proliferation and sensitizes glioma to temozolomide via down-regulating ITGB4/PI3K/AKT. These findings drive theoretical innovation and research progress in glioma therapy.

Research on the Theoretical Progression of General Secretary's Important Discourse on Women's Issues

The world is undergoing profound changes unseen in a century. The degree of globalization is deepening. While the development of women in various countries has made great progress, their development demands are still increasing. As socialism with Chinese characteristics has entered a new era, Chinese women are faced with new opportunities and new challenges. Under these circumstances, General Secretary Xi Jinping has made many speeches on women's issues on important occasions at home and abroad. His discourse is based on Marxist women's theory, the women's theory of successive generations of Communist Party leaders, and the excellent traditional Chinese women's thought. The three constitute the theoretical source of its discussion. Focusing on women's special needs, realizing individual equality of women's rights to development, building a family civilization with family education and morals, strengthening the reform and innovation of the construction of the three natures of women's federations, and promoting global women's development. These are the key nuclei of his important discourses. General Secretary Xi Jinping's important discourse on women's issues is rich in connotation, profound in thought, and has great practical significance. As a film with rich connotation, Disney 's live‐action Mulan film has attracted much attention. This article uses the literature data method and text analysis method to analyze the characteristics of the real‐life version of Mulan and its connection with post‐ colonial theory. By analyzing the cultural, historical and gender identity of the film, it explores its inherent cultural significance and profound connotation.

MPX3 van der Waals magnets under pressure (M = Mn, Ni, V, Fe, Co, Cd; X = S, Se)

van der Waals antiferromagnets with chemical formula MPX3 (M = V, Mn, Fe, Co, Ni, Cd; X = S, Se) are superb platforms for exploring the fundamental properties of complex chalcogenides, revealing their structure-property relations and unraveling the physics of confinement. Pressure is extremely effective as an external stimulus, able to tune properties and drive new states of matter. In this review, we summarize experimental and theoretical progress to date with special emphasis on the structural, magnetic, and optical properties of the MPX3 family of materials. Under compression, these compounds host inter-layer sliding and insulator-to-metal transitions accompanied by dramatic volume reduction and spin state collapse, piezochromism, possible polar metal and orbital Mott phases, as well as superconductivity. Some responses are already providing the basis for spintronic, magneto-optic, and thermoelectric devices. We propose that strain may drive similar functionality in these materials.

A new method to quantify the source contribution of mixed biodegraded oil based on pareto-optimized nonnegative matrix factorization and chord diagram visualization

The biogenic information of different components in crude oils with varying degrees of biodegradation is partially obscured, making traditional molecular indicators ineffective in determining the contributions of mixed sources. Previous studies have mainly focused on calculating mixed source proportion for normal oils, with few investigations involving biodegraded mixed oils. Developing a quantitative method for the mixed source contribution of biodegraded oils using machine learning (ML) algorithms is a significant attempt both to the exploration practice and theoretical progress. Taking the biomarker parameters resistant to biodegradation of 150 oil samples as input datasets, a new method called Pareto-Optimized NMF (PONMF) was proposed in this study, which combines multi-objective optimization (Pareto optimization) with non-negative matrix factorization (NMF) to analyze the relationship between biodegraded oils in the absence of source rock samples or original end-member (EM) oils while satisfying non-negativity constraints and additional constraint conditions. This method decomposes the matrix of crude oil samples into multiple end-members, each representing a possible input from a hydrocarbon source rock. Analysis of the end-member characteristics facilitates the deduction of potential hydrocarbon source layers and the traits of their depositional environments. This examination is supported by data derived from 15 prospective hydrocarbon source rock samples, which were employed to ascertain the actual geological significance of the end-members. The chord diagram depicts the visualization results of the overall relative contribution of source rock end members to crude oil in each area after the PONMF calculation results. The relative contribution of source rocks in different areas to the investigated crude oil samples is proportionate, and the arc length can reflect the contribution of the three sets of source rocks to a certain area. The findings indicate that the oils extracted from the stratified reservoirs in Eastern Chepaizi are primarily derived from the Permian source strata within the Shawan Depression, with a significant contribution ranging from 30% to 90% (EM2), and a lesser extent from the Carboniferous sources, contributing between 5% and 30% (EM1). Conversely, the provenance of oil in Western Chepaizi is overwhelmingly attributed to the Jurassic source interval located in the Sikeshu Depression, where EM3 accounts for over 85% of the source. Meanwhile, the oils in the Central Zone exhibit a composite origin, intermingling contributions from both EM1 and EM3. The advantage of this method is that it does not require direct access to hydrocarbon source rock samples or end-member oils but infers potential hydrocarbon source layers through the existing mixed-source oil samples. The PONMF method provides an effective tool for identifying the sources of biodegraded oils and can be used to study potential hydrocarbon source rock analysis for oils that have undergone biodegradation after hydrocarbon accumulation.