Scientific Domains Research Articles

A Review of Salt Tectonics in Romania's Transylvanian Basin and Implications for Energy Transition

Salt, pivotal in societal evolution and vital in scientific and economic domains, notably in chemical and hydrocarbon industries, gains relevance amid the climate crisis. As the shift from fossil fuels to sustainable energy sources becomes imperative, salt deposits can advance the energy transition, notably by large-scale hydrogen storage in solution-mined caverns. Other promising applications include carbon capture utilisation and storage and geothermal energy. Due to its potential role in energy transition and the required novel geotechnical solutions, research focusing on salt's contribution remains an emerging field. The Transylvanian Basin (Romania), provides opportunities for comprehensive multiscale observations, including surface and subsurface data, offering insights into salt deformation. While studies on salt tectonics in the basin exist, it is generally understudied and the focus on energy transition applications is sparse. This paper aims to review the current state of salt tectonics in the Transylvanian Basin, emphasising internal deformation complexities, impurity distributions, and potential implications for energy transition industries. By shedding light on this significant yet underexplored aspect, this work seeks to stimulate further research interest and prompt, comprehensive investigations into salt-related energy transition applications.

Darcy-Forchheimer magnetohydrodynamic flow of non-Newtonian Reiner-Rivlin hybrid nanofluid bounded by double-revolving disks

The applications of fluid flow and heat transfer between coaxial double-revolving disks are diverse and can be found in various engineering and scientific domains. In general, the utilization of engine oil ( EO) mixed with hybrid nanoparticles serves various purposes in engine cooling, such as maintaining viscosity-temperature properties, preventing corrosion, and achieving other cooling-related objectives. This investigation focuses on the Darcy-Forchheimer flow of a non-Newtonian Reiner-Rivlin hybrid nanofluid confined between double-revolving disks. The hybrid nanoparticles used include cadmium telluride ( CdTe) and titanium dioxide ( TiO2), combined with the base fluid engine oil. The dimensional flow equations are converted into dimensionless forms using suitable similarity transformations. Subsequently, a semi-analytical methodology known as the homotopy analysis method ( HAM) is utilized to tackle this problem. Graphs are used to determine the effects of the various flow parameters of the hybrid nanofluids on the velocities, temperature, skin friction coefficient, and the Nusselt number. After validating the findings against previous research, an attractive agreement was observed. Some major outcomes from this present problem are that axial, radial, and temperature profiles increase with the rise of the cross-viscosity parameter while the tangential velocity decreases. Radial velocity increased, and tangential velocity diminished with an enhanced magnetic field parameter. Radial velocity is increased for higher values of the dimensionless forced parameter. For higher values of the porosity parameter, the temperature profile improved. Also, the exploration of the Nusselt number and skin friction for the disks, incorporating the effects of a magnetic field and porous medium, reveals that skin friction for both disks increase with the rising Reynolds number and volume fraction of the cadmium telluride nanoparticles.

New Estimates of the Potential Schrödinger Equation

The Schrödinger equation, a fundamental construct in quantum mechanics, plays a pivotal role in understanding the properties and behaviors of quantum systems across various scientific fields, including but not limited to physics, economics, and geophysics. This research paper delves into the exploration of novel invariants associated with the Schrödinger equation, uncovering previously unrecognized symmetries and properties that open new pathways for theoretical and applied scientific exploration. The investigation reveals that the energy of bound states remains invariant under transformations of the coordinate system, highlighting a universal symmetry embedded within the equation. This discovery, coupled with an analysis of the variation in scattering amplitudes’ phase with different coordinate systems, not only enriches the theoretical framework of quantum mechanics but also has significant practical implications across various domains such as fluid dynamics, material science, and medical imaging. Furthermore, by applying the principles of the Poincaré-Riemann-Hilbert boundary-value problem, the study offers a novel approach to estimate potentials within the Schrödinger equation, advancing the three-dimensional inverse problem of quantum scattering theory. This methodological innovation allows for a more profound understanding of the unitary scattering operator, facilitating enhanced modeling of complex systems and phenomena. The implications of these findings extend beyond theoretical physics, offering transformative insights and applications in areas ranging from fluid dynamics, where it aids in refining models for the Navier-Stokes equations, to seismic exploration, tomography, and ultrasound imaging, where it enhances phase selection and interpretation of measurement results. In essence, this research not only contributes to a deeper understanding of the Schrödinger equation’s fundamental properties but also paves the way for interdisciplinary advancements, demonstrating the profound impact of theoretical discoveries on practical applications in diverse scientific and technological domains.

Advocating individual-based profiles of elite athletes to capture the multifactorial nature of elite sports performance

Elite athletes are high-performance outliers within their specific sports. Even though science seeks to understand the nature of expertise and elite performance, much knowledge remains compartmentalized within subdisciplines. Despite this multidimensionality being acknowledged, an interdisciplinary approach to understanding elite athletes is still rare. This paper synthesizes insights across scientific domains in order to describe the population and individual characteristics of elite athletes. We analyzed diagnostic data from approximately 300 German squad athletes across eight different sports (e.g., gymnastics, volleyball, ice hockey, 3 × 3 basketball etc., agefemale = 18.95 ± 4.84 years, agemale = 19.32 ± 4.19 years) with expertise values ranging from 2 (low expertise) to 16 (high expertise). Data covered muscular strength, lower-body dynamics, muscle-power genetics, blood micronutrients, basic cognitive function, mental health, social support, and training conditions. Results of logistic regressions identified basic cognitive function (B = 0.89) and well-balanced blood micronutrients (B = 1.22) as critical factors distinguishing elite athletes. Additionally, multiple linear regressions suggested that lower-body dynamics (ß = 0.72) is related to increasing expertise values. We examined interactions between determinants of elite performance, and found that social support is positively associated with mental health and training conditions, whereas muscular strength correlates with lower-body dynamics. Focusing on top elite athletes in contrast to semi-elite athletes, we found higher within-group similarities in basic cognitive function and blood micronutrients. Findings indicate the need for a systemic, individualized, and comprehensive model using individual-based profiles.

Beyond The Basics: A Detailed Survey of Advanced Python Applications and Innovations

Abstract: Python is the general purpose, high-level, scriptable, interpreted, object oriented and easy to learn language was developed by Guido van Rossum. Python is a popular programming language because of its simplicity, ease of use, open-source licensing, and accessibility. It provides various libraries and frameworks for learning and real world programming. Python’s versatility and active community make it an ideal language for Machine Learning, Data Science, Artificial Intelligence, Networking, Game Development, Web Development and various other domains. In this paper, we will provide an introduction to the main Python programming software tools used for Web development, Data science, Machine learning techniques, Educational Development, etc. In this paper will first introduce Python as a language, and give introduction about Web Development, Data science, Machine learning, Educational Development & Financial Analysis and then describe packages that are popular in that sector

Enhancing emerging technology discovery in nanomedicine by integrating innovative sentences using BERT and NLDA

Abstract Purpose Nanomedicine has significant potential to revolutionize biomedicine and healthcare through innovations in diagnostics, therapeutics, and regenerative medicine. This study aims to develop a novel framework that integrates advanced natural language processing, noise-free topic modeling, and multidimensional bibliometrics to systematically identify emerging nanomedicine technology topics from scientific literature. Design/methodology/approach The framework involves collecting full-text articles from PubMed Central and nanomedicine-related metrics from the Web of Science for the period 2013–2023. A fine-tuned BERT model is employed to extract key informative sentences. Noiseless Latent Dirichlet Allocation (NLDA) is applied to model interpretable topics from the cleaned corpus. Additionally, we develop and apply metrics for novelty, innovation, growth, impact, and intensity to quantify the emergence of novel technological topics. Findings By applying this methodology to nanomedical publications, we identify an increasing emphasis on research aligned with global health priorities, particularly inflammation and biomaterial interactions in disease research. This methodology provides deeper insights through full-text analysis and leading to a more robust discovery of emerging technologies. Research limitations One limitation of this study is its reliance on the existing scientific literature, which may introduce publication biases and language constraints. Additionally, manual annotation of the dataset, while thorough, is subject to subjectivity and can be time-consuming. Future research could address these limitations by incorporating more diverse data sources, and automating the annotation process. Practical implications The methodology presented can be adapted to explore emerging technologies in other scientific domains. It allows for tailored assessment criteria based on specific contexts and objectives, enabling more precise analysis and decision-making in various fields. Originality/value This study offers a comprehensive framework for identifying emerging technologies in nanomedicine, combining theoretical insights and practical applications. Its potential for adaptation across scientific disciplines enhances its value for future research and decision-making in technology discovery.

Advances in Light Promoted Transformation of N-Heterocyclic Carbene-Ligated Boryl Radical

Organoboron compounds are integral to modern life, with extensive applications in synthesis, materials science, medicine, and various other domains closely linked to human endeavors. NHC-BH3, noted for its stability, ease of synthesis, and high reactivity as a boryl radical precursor, has emerged as a key focus in boryl radical chemistry. Recently, the visible-light-induced single electron transfer (SET) and hydrogen atom transfer (HAT) processes have garnered considerable interest, presenting innovative strategies for generating boryl radicals from NHC-BH3. In the context of this review, our focus is on the synthesis of C-B and X-B bonds under visible light irradiation, facilitated by NHC-BH3. Furthermore, we explored the role of NHC-BH3 as a hydrogen donor or halogen atom transfer reagent in the construction of C-C bond.

Hyperspectral unmixing for Raman spectroscopy via physics-constrained autoencoders

Raman spectroscopy is widely used across scientific domains to characterize the chemical composition of samples in a nondestructive, label-free manner. Many applications entail the unmixing of signals from mixtures of molecular species to identify the individual components present and their proportions, yet conventional methods for chemometrics often struggle with complex mixture scenarios encountered in practice. Here, we develop hyperspectral unmixing algorithms based on autoencoder neural networks, and we systematically validate them using both synthetic and experimental benchmark datasets created in-house. Our results demonstrate that unmixing autoencoders provide improved accuracy, robustness, and efficiency compared to standard unmixing methods. We also showcase the applicability of autoencoders to complex biological settings by showing improved biochemical characterization of volumetric Raman imaging data from a monocytic cell.

A Comparative Study on ERNIE Bot 4.0 Turbo and ChatGPT 4o’s Performance in Evaluating First-Year Undergraduate Persuasive Essays

This study compares the performance of two prominent AI language models, ERNIE Bot 4.0 Turbo and ChatGPT 4o, in evaluating first-year undergraduate persuasive essays within the social sciences domain. Drawing from the Louvain Corpus of Native English Essays, a comprehensive collection of academic writings by British and American university students, this study aims to examine the models’ capabilities in assessing the grammatical correctness, vocabulary usage, coherence, content depth, and writing style of the essays. This study adopts a structured evaluation framework based on IELTS writing criteria to assess the models’ performance. A 40 persuasive essays from the Louvain Corpus were evaluated by both AI models and compared with human raters’ evaluations to ensure validity. The findings reveal distinct differences in the assessment styles of the two models. ChatGPT 4o exhibits a more critical approach, pinpointing areas for improvement, such as lack of argument development, coherence issues, and grammatical errors. Conversely, ERNIE Bot 4.0 Turbo offers a more balanced assessment, acknowledging essays’ strengths and suggesting improvement areas. Notably, ERNIE Bot’s evaluation highlights potential biases in AI-based assessment systems, particularly in its unequal emphasis on viewpoints. This comparative examination offers valuable perspectives on the advantages and constraints of AI models in assessing scholarly compositions, underscoring the significance of amalgamating varied AI functionalities to establish more all-inclusive and efficient feedback systems for learners. By understanding these differences, researchers and educators can better utilize AI-assisted essay evaluation systems to enhance student learning experiences.

On the Use of Image Analysis for Hematocrit Evaluation in Dried Blood Spots

Dried blood spots (DBSs) are formed by collecting a small sample of blood on specialized filter paper and allowing it to dry naturally. Various domains of life sciences and drug research extensively use DBSs as a sampling technique. The “Hematocrit (Ht) effect” affects assay bias, and several strategies have been put forth to deal with it, including the correction of quantified concentrations using an appropriate correction factor. The approach was previously applied, following the utilization of an image processing algorithm developed in Matlab® to derive a reliable equation correlating DBS areas to Ht% values. The present work looks more closely at the application of image analysis to the evaluation of Ht in DBS samples. Utilizing image analysis software, DBS samples with known Ht values were processed. Preparation of cards has followed a previously developed protocol for the appropriate formation of uniform area DBSs, irrespective of Ht. The resulting areas showed close resemblance to the respective theoretical areas calculated by applying the correlation equation. Following that, the equation was utilized to determine the Ht values for each sample, and a comprehensive comparison of measured versus calculated Ht was carried out using various statistical approaches for method comparison. The results demonstrated a strong correlation, suggesting the method’s viability in estimating Ht for unknown DBS samples.

A vulnerable soil environment study in karst areas: a bibliometric analysis

Karst landforms are widely distributed around the world, and karst rocky desertification has occurred on a large scale in many countries and regions, causing significant adverse impacts on local natural environments and societies. The improvement and rational use of karst soil is a key aspect of rocky desertification governance. Karst soil science studies are of great value in karst regions and are essential for controlling karst rocky desertification and ecological restoration. In order to understand the research hotspots and the development directions in the field of vulnerable karst soil environment, we undertook bibliometrics citation analysis on 1913 contributions to the literature written in the range from 2001 to 2019 based on the “Web of Science” core collection citation index database. Hopefully, this work will help to set up a scientific foundation for further studies. Using CiteSpace visualization software, we analyzed the distribution of disciplinary categories, reference co-citation clusters, and keyword clusters in the literature. The results show the basic characteristics and evolution of the literature related to karst pedology. We then recognized the main intellectual bases in the domain of karst soil science. This study also revealed the research hotspots and trends in this field. Through a bibliometrics citation analysis of research on karst vulnerable soil environment, the present study provides a quantitative and objective understanding of development directions that have emerged in this field over the past 19 years, offering a reference for future research.

9.X.2. Activating the ASPHER-WHO Public Health Core Curriculum: Interprofessional Skills

Abstract The updated ASPHER-WHO Public Health Core Curriculum builds on ASPHER’s previous work on Competencies for Public Health Professionals (2006-2020), reflects the changing needs of our population and planet and public health professionals. The key objectives of this workshop are to: - Share the updated ASPHER core curriculum for public health; - Illustrate examples of public health challenges that young professionals have championed for inclusion; - Share the Faculty of Public Health (UK)’s work on Fair training as a case study on operationalising public health leadership on equality, diversity and inclusion and demonstrating the core competencies in action. A curricular content survey of member schools and programmes yielded responses from 60 schools, with > 500 submissions of detailed. subject curricula. These have been compiled into 36 Subject Areas in four Domains, namely: (1) Core Subject Areas in Public Health: (2) Subject-specific Areas in Public Health;(3) Core Cross-curricular Subject Areas; and (4) Core Interdisciplinary Professional Skills in PH. The development process was guided and grounded by the expertise and passion of young public health professionals. They are helping us make the core curriculum fit for purpose for the near future and provide them with the skills required to be a competent member of a supportive public health team in an ever changing world. This Workshop will profile selected Subject Areas from each Domain using real life challenges facing WHO European Region to show how the core curriculum integrates research, theory and practice to help build those essential interdisciplinary skills. The examples will focus on Economics in Public Health (Domain 1); Public Health and Criminal Justice; (Domain 2); Health Literacy and Infodemiology, Mental Health and Wellbeing (Domain 3); and Integrative Learning in Action; Communication Skills; Advocacy, Negotiation; Collaborations and Partnerships; and Leadership, Management and Implementation Science (Domain 4). Discussions for each of the Subject Areas will be led by an expert in the area. We will also illustrate an example of these competencies in action, how they are helping shine a light on the challenges we face in creating that supportive team and environment and how the potential for collaboration helps us to capitalise on the expertise available in some schools and programmes for the benefit of all. Key messages • Learning interdisciplinary skills are essential for effective public health analysis, advocacy and action. • Collaboration across countries, schools, professions and generations can create transformational change. Speakers/Panelists Mary Codd University College Dublin, Dublin, Ireland Karl F Conyard University College Dublin, Dublin, Ireland Jenny Houston University of Edinburgh, Edinburgh, UK Samia Latif Faculty of Public Health, London, UK Olalekan Popoola ASPHER, UCD, Dublin, Ireland

Experimental investigation on the electrical and hydrogen-production performance of a novel PEM electrolyzer driven by CdTe PV with a tracking system

Addressing the challenges of intermittent and variable energy supply, solar hydrogen production via electrolysis has garnered significant attention due to its unique advantages, emerging as a hot topic in the scientific research domain. However, the application of this technology is currently mainly related to the non-tracking system, which undergoes a rapid change in solar irradiation during the day. To expand the matching relationship of solar irradiation, PV characteristics, and electrolyzer, this paper proposes the development of a Proton Exchange Membrane (PEM) electrolyzer driven by Cadmium Telluride (CdTe) photovoltaic (PV) modules with a tracking system for hydrogen production. An experimental platform was set up in Chengdu and the performance tests were carried out for different operating modes during summer. The experimental results demonstrate that the average electrical and hydrogen production efficiencies of the system are 13.11% and 7.88%, respectively, on a sunny day, with a total of 566.7 L of hydrogen produced within 7 h of operation. Furthermore, the solar irradiation received by the tracking surface of the system is significantly enhanced by 10.29% in comparison to the horizontal surface. On a cloudy day with an electrolyzer inlet water temperature of 45 °C, the system has the potential to achieve a hydrogen production efficiency of 11.15 %. At a constant current density of 1.0 A/cm2, the efficiency of the electrolysis process increased by 2.07% and 1.25% for each 10 °C increase in the temperature of the electrolyzer, having an inlet temperature of 35 °C. This study offers a novel approach for a PV-PEM system with a higher hydrogen production efficiency while also establishing a robust foundation for technology optimization and broader implementation.

Exploration of Chemical Space through Automated Reasoning

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user‐specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen’s outputs are fully interpretable and provably correct. Users interact with Comgen through a high‐level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end‐to‐end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation.

Exploration of Chemical Space through Automated Reasoning.

The vast size of composition space poses a significant challenge for materials chemistry: exhaustive enumeration of potentially interesting compositions is typically infeasible, hindering assessment of important criteria ranging from novelty and stability to cost and performance. We report a tool, Comgen, for the efficient exploration of composition space, which makes use of logical methods from computer science used for proving theorems. We demonstrate how these techniques, which have not previously been applied to materials discovery, can enable reasoning about scientific domain knowledge provided by human experts. Comgen accepts a variety of user-specified criteria, converts these into an abstract form, and utilises a powerful automated reasoning algorithm to identify compositions that satisfy these user requirements, or prove that the requirements cannot be simultaneously satisfied. In contrast to machine learning techniques, explicitly reasoning about domain knowledge, rather than making inferences from data, ensures that Comgen's outputs are fully interpretable and provably correct. Users interact with Comgen through a high-level Python interface. We illustrate use of the tool with several case studies focused on the search for new ionic conductors. Further, we demonstrate the integration of Comgen into an end-to-end automated workflow to propose and evaluate candidate compositions quantitatively, prior to experimental investigation.

Unveiling the Potential of Protein-Based Sustainable Antibacterial Materials.

The surge in bacterial growth and the escalating resistance against a multitude of antibiotic drugs have burgeoned into an alarming global threat, necessitating urgent and innovative interventions. In response to this peril, scientists have embarked on the development of advanced biocompatible antibacterial materials, aiming to counteract not only bacterial infections but also the pervasive issue of food spoilage resulting from microbial proliferation. Protein-based biopolymers and their meticulously engineered composites are at the forefront of this endeavor. Their potential in combating this severe global concern presents an approach that intersects the domains of biomedicine and environmental science. The present review article delves into the intricate extraction processes employed to derive various proteins from their natural sources, unraveling the complex biochemical pathways that underpin their antibacterial properties. Expanding on the foundational knowledge, the review also provides a comprehensive synthesis of functionalized proteins modified to enhance their antibacterial efficacy, unveiling a realm of possibilities for tailoring solutions to specific biomedical and environmental applications. The present review navigates through their antibacterial applications; from wound dressings to packaging materials with inherent antibacterial properties, the potential applications underscore the versatility and adaptability of these materials. Moreover, this comprehensive review serves as a valuable roadmap, guiding future research endeavors in reshaping the landscape of natural antibacterial materials on a global scale.

Progressive analytical techniques utilized for the detection of contaminants attributed to food safety and security

Food analysis and authentication are very crucial to assure safety, quality, and integrity of food and food-related products. Analysis of food products not only ensures the quality of food but also gives information about composition, physical characteristics, flavor, shelf-life, safety, microstructure, and processibility. Consequently, food is now regarded as an inexpensive means of preventing disease while also serving as a source of energy. Typically, a wide range of distinct chemical components make up the chemical composition of food. Furthermore, during processing or storage, food is always susceptible to structural alterations. Therefore, to assess the nutritional value and the quality of food products, food components must be analyzed and quantified. Food analysis mainly includes the preparation, examination, and identification of major food components, such as dietary fiber, phenolic compounds, carbohydrates, proteins, amino acids, peptides, enzymes, phospholipids, pesticides, alcohols, fat-soluble and water-soluble vitamins, organic acids, organic bases, bittering agents, pigments, and aroma compounds, as well as minor components including preservatives, antioxidants, colorants. Therefore, there are several advanced analytical techniques, including spectroscopy, mass spectrometry, chromatography, NMR, HSI, MALDI-TOF, RT-PCR, biosensors, etc. have been vital in the food science domain due to their high selectivity and sensitivity. This review emphasizes implementing various analytical techniques to analyze food safety and security.

Analysis of multi-term arbitrary order implicit differential equations with variable type delay

Due to their capacity to simulate intricate dynamic systems containing memory effects and non-local interactions, fractional differential equations have attracted a great deal of attention lately. This study examines multi-term fractional differential equations with variable type delay with the goal of illuminating their complex dynamics and analytical characteristics. The introduction to fractional calculus and the justification for its use in many scientific and technical domains sets the stage for the remainder of the essay. It describes the importance of including variable type delay in differential equations and then applying it to model more sophisticated and realistic behaviours of real-world phenomena. The research study then presents the mathematical formulation of variable type delay and multi-term fractional differential equations. The system’s novelty stems from its unique combination of variable delay, generalized multi terms fractional differential operators (n and m), and integral implicit parameters, and studying the stability of the the newly formulated system as compared to the work in the existing literature. While the variable type delay is introduced as a function of time to describe instances where the delay is not constant, the fractional order derivatives are generated using the Caputo approach. The existence, uniqueness, and stability of solutions are the main topics of the theoretical analysis of the suggested differential equations. In order to establish important mathematical features, the inquiry makes use of spectral techniques, and fixed-point theorems. The study finishes by summarizing the major discoveries and outlining potential future research avenues in this developing field. It highlights the potential contribution of multi-term fractional differential equations with variable type delay to improving the control and design of complex systems. Overall, this study adds to the growing body of knowledge in the field of fractional calculus and provides insightful information about the investigation of multi-term fractional differential equations with variable type delay, making it pertinent for academics and practitioners from a variety of fields.

Developmental and validation of a novel small and high-efficient panel of microhaplotypes for forensic genetics by the next generation sequencing

BackgroundIn the domain of forensic science, the application of kinship identification and mixture deconvolution techniques are of critical importance, providing robust scientific evidence for the resolution of complex cases. Microhaplotypes, as the emerging class of genetic markers, have been widely studied in forensics due to their high polymorphisms and excellent stability.Results and discussionIn this research, a novel and high-efficient panel integrating 33 microhaplotype loci along with a sex-determining locus was developed by the next generation sequencing technology. In addition, we also assessed its forensic utility and delved into its capacity for kinship analysis and mixture deconvolution. The average effective number of alleles (Ae) of the 33 microhaplotype loci in the Guizhou Han population was 6.06, and the Ae values of 30 loci were greater than 5. The cumulative power of discrimination and cumulative power of exclusion values of the novel panel in the Guizhou Han population were 1-5.6 × 10− 43 and 1-1.6 × 10− 15, respectively. In the simulated kinship analysis, the panel could effectively distinguish between parent-child, full-sibling, half-sibling, grandfather-grandson, aunt-nephew and unrelated individuals, but uncertainty rates clearly increased when distinguishing between first cousins and unrelated individuals. For the mixtures, the novel panel had demonstrated excellent performance in estimating the number of contributors of mixtures with 1 to 5 contributors in combination with the machine learning methods.ConclusionsIn summary, we have developed a small and high-efficient panel for forensic genetics, which could provide novel insights into forensic complex kinships testing and mixture deconvolution.

Two-Dimensional Hydration and Triple-Interlayer Lattice Structures in Sulfate-Intercalated Graphene Oxide Nanosheets

Sulfate anions (SO42−) are pivotal in various scientific and industrial domains, including mineralogy, biology, and materials science. While extensive research has elucidated sulfate hydration in bulk solids, liquids, and gaseous clusters, a significant gap persists in understanding sulfate interactions within two-dimensional materials, particularly graphene oxide (GO) nanosheets. This study investigates the intricate hydration phenomena and novel triple-interlayer lattice configurations that emerge from sulfate intercalation in GO nanosheets. Utilizing a straightforward methodology for obtaining precise X-ray measurements of confined nanospaces, we analyzed the temperature-dependent behavior and structural characteristics of these systems. Our findings reveal how sulfate ions modulate interlayer spacing, the dynamics of GO layers, and phase transitions. This research offers an atomic-scale understanding of hybrid hydration behaviors within confined SO4-H2O nano-environments, advancing our knowledge of sulfate interactions in two-dimensional materials.