bluuebunny/crossref_metadata_2025 huggingface dataset:
Magnetic domains and the process of technical magnetization
Authors: Sharipov Mirzo , Ergasheva Nilufar , Raupova Iroda , Fayzieva Zarifa , Zokirova Zarkhal
Published: February 2025

This article presents the results of the formation of a domain structure and the process of technical magnetization of weak ferromagnets. It has been determined that for a given film thickness, the characteristic dimensions of the domain structure depend on the energy density of domain boundaries and the magnetic dipole energy density.
Experimental studies of the influence of temperature on the process of polystyrene processing in a supercritical water environment
Authors: Gilmutdinov Ilnur
Published: February 2025

This paper discusses a hydrothermal method for processing polystyrene in a supercritical water environment. This depolymerization process is carried out in a closed reactor where water serves as a solvent, catalyst and reactant. When approaching the critical point, water changes its properties. These significant changes allow supercritical water to cause rapid, selective reactions to convert polystyrene into low molecular weight hydrocarbons. In this work, experimental studies were carried out on the processing of polystyrene in supercritical water at temperatures T = 375 - 425 0C, a volume of water equal to V = 200 ml, for a time τ = 10 - 120 minutes. Hydrothermal processing of polystyrene in supercritical water leads to the formation of a liquid phase, which was analyzed with a Crystal 9000 gas chromatography-mass spectrometer. The analysis results indicate the content of styrene, ethylbenzene, benzene, toluene, and naphthalene in the liquid phase. It was found that at temperature T=375 0C and time τ=10- 15 minutes, a significant amount of styrene is formed. This work also examined the mechanism of depolymerization of polystyrene in supercritical water.
Investigation of the radiation parameters of an oscillating charge in an ion system
Authors: Shulaev Nikolay , Efimova Galina
Published: February 2025

A solution to the problem of determining the frequency spectrum and intensity of electromagnetic radiation generated by an oscillating charge in a dipole-quadrupole system has been obtained. The analysis of the obtained dependences for the oscillation frequencies reveals that knowing the maximum and minimum frequencies of the oscillating charge radiation spectrum allows for the calculation of molecular parameters. This analysis can be applied to study the electromagnetic radiation spectra of atoms and molecules in an excited state, as well as the parameters of the crystal lattice.
Diagnostics of the PEO-coating growth on a zirconium alloy by the acoustic spectrum
Authors: Aubakirova Veta , Farrakhov Ruzil , Parfenov Evgeny
Published: February 2025

Plasma-electrolytic oxidation is accompanied by microdischarges, which determine the main mechanism of coating growth. These microdischarges, along with electrolyte boiling, generate acoustic vibrations that are closely related to the coating growth process. In this paper, authors investigated the acoustic characteristics and surface properties of a zirconium alloy during plasma-electrolytic oxidation, specifically focusing on the constant voltage mode. The study identified informative ranges within the acoustic spectrum that corresponded to two distinct stages of microdischarge development. Furthermore, regression models were created to establish a strong correlation between the increase in coating thickness and changes in current density and peak amplitudes within the acoustic spectrum. The acoustic spectrum ranges of 4000-5000 Hz and 17000-18000 Hz were found to be the most informative for coating thickness diagnostic.
Contextual bandits to increase user prediction accuracy in movie recommendation system
Authors: Chen Yizhe
Published: February 2025

Cold-start problems are inevitable phenomena where recommendation systems fail to accurately predict users’ favour and cause the loss of new users. The typical Multi-Armed Bandit (MAB) models are widely adopted as recommendation systems to solve cold-start problems, but standard MAB takes much more recommendation trials than new user’s tolerance. This study adopts Contextual Multi-Armed Bandit (CMAB) to alleviate such situations and compares the performance of CMAB and typical MAB models at an early stage of the cold phase. Overall, CMAB generated better results in 15 trials in terms of cumulative regret and discounted cumulative gain. The optimal number of groups is 10, which alleviates cold-start problems efficiently, and sustains the efficiency of the off-line recommendation system under collaborative filtering. This paper suggests a possible selection of CMAB for recommendation systems to alleviate the cold start problem and estimates the tuned parameters for the MovieLens dataset. The evaluation metric in this paper provides a possible method of analyzing the general performance of a hybrid recommendation system, instead of adopting multiple evaluation metrics respectively, these metrics also provide estimates of the optimal value of parameters.
Comparative Investigation of Machine Learning and Deep Learning Approaches for Air Quality Prediction
Authors: Zhang Borui
Published: February 2025

Air pollution is a critical environmental issue with significant impacts on human health and ecosystems, exacerbated by urbanization and industrialization, leading to increased emissions. Forecasting air quality accurately is crucial for risk mitigation and policy direction. Recent advancements in deep learning have enhanced prediction capabilities by automatically extracting features and managing complex data. This paper compares machine learning and deep learning approaches in air quality forecasting, highlighting their strengths and weaknesses. Machine learning offers easier interpretability with limited data but struggles with complex data relationships. Deep learning captures nonlinear patterns more effectively but lacks interpretability and requires more data. Challenges in the field of air quality forecasting include feature selection, model interpretability, and applicability across regions. Future directions involve introducing feedback mechanisms, interpretability methods, and transfer learning to improve model performance and generalization. This review provides valuable insights into existing methodologies and guides future research for effective air quality management.
Minecraft’s Implementation and Impact on Education
Authors: Liu Qihao
Published: February 2025

Minecraft is a popular sandbox video game that offers freedom of creation and collaboration. Its unique qualities are benefiting education. This paper aims to analyze the impact of Minecraft on education and its current application status. The study analyzed features of Minecraft that encourage players’ creativity or help improve their skills before stating a number of detailed examples to connect the game and education through the concept of gamification. The study found that aspects like multiplayer mode, creating and destroying blocks, and ease of use contribute to the unique advantages of Minecraft in education. This research will help inform educators about how they can use the game to improve the efficiency and quality of the specific subject they teach. Future research can also be based on this study to find more advanced methods and use cases as well as judge other games’ potential to benefit education.
Application and Effectiveness of BERT in Question and Answer Modelling
Authors: Weng Haofeng
Published: February 2025

In the field of Artificial Intelligence, chat Question and Answer (Q&A) systems represent a core application that simulates human dialogue capabilities. Given the quick development of natural language processing (NLP) technology, chat Q&A models according to Bidirectional Encoder Representations from Transformers (BERT) have emerged as a significant research focus. The BERT model, known for its deep bi-directional representations, enhances Q&A systems with a level of semantic comprehension that previous models struggled to achieve. This review aims to explore recent research progress in BERT-based chat Q&A models and analyze key issues and challenges encountered in their practical applications. This paper begins by introducing the background and principles of the BERT model, highlighting its importance in natural language processing. Then, the paper reviews in detail the key techniques and approaches of BERT-based chat Q&A models. The purpose of this thorough summary is to provide readers a clear grasp of BERT's role in chat Q&A systems, and the challenges it faces, and ultimately advance research and application development in this domain.
Research progress on Chinese and English text error correction
Authors: Wang Yao
Published: February 2025

Text error correction is an essential task in natural language processing (NLP) that focuses on automatically identifying and correcting errors in written text. With the increasing amount of digital text in both Chinese and English, errors such as typos, grammatical mistakes, and contextual ambiguities have become more prominent, affecting readability and communication. Over the years, various models and methodologies have been developed to tackle these challenges, evolving from traditional rule- based systems to more advanced statistical and machine learning-based approaches. This paper presents an overview of the current research status in Chinese and English text error corrections. By analyzing several papers involving Chinese and English error correction models, the article points out the types of errors such as spelling, grammatical and semantic errors included in text error correction and their basic elements. It discusses the advantages and limitations of the latest approaches including rule-based models, statistical and deep learning methods. The development potential of deep learning and big modeling techniques in the field of text error correction is shown. These findings help to advance automated error correction systems and their facilitation in the real world.
Rumor Propagation and Detection Techniques in Social Networks
Authors: Qiu Zichang
Published: February 2025

Social networks have become the core platform for information exchange in the new era, and their uniqueness is reflected in the extensive interconnections brought about by complex ties and technological progress. These characteristics jointly shape social networks and have a profound impact on the development of the new era. With the popularity of the Internet, the problem of online rumors has become increasingly prominent, bringing many negative effects on society. Due to the fast speed of information dissemination, extensive user participation, difficulty in verification, and significant group effects, online rumors can quickly spread in social networks and have an impact. Therefore, strengthening the supervision of network information is crucial to maintaining justice and order in cyberspace. The four leading rumor detection techniques are currently covered in this article, along with an introduction to the fundamentals of rumor detection. Prospects seem to offer fresh approaches to rumor detection in light of the potential difficulties facing societal development. Analyze the experimental data of different detection methods and explore the advantages and disadvantages of each technique. It is hoped that through in-depth research, information dissemination can be guaranteed, contributing to creating a safe and harmonious cyberspace.
Predicting Stroke Risk Based on an Optimized Machine Learning Model
Authors: Liu Felix
Published: February 2025

Stroke risk prediction is critical for identifying at-risk populations in healthcare as well as for early diagnosis and resource optimization in stroke management. This study's main goal was to create a reliable stroke prediction model by analyzing a publically accessible dataset containing 4981 records and 11 variables using machine learning techniques. Initial data preprocessing consisted of converting categorical variables to numerical values and addressing class imbalance through methods such as class weighting and the Synthetic Minority Oversampling Technique (SMOTE). Three basic models - logistic regression, decision trees, and random forests - were implemented to establish base performance. To improve prediction accuracy and address imbalances, optimized ensemble models were built using stacking and hyperparameter tuning through grid search and cross-validation. This paper evaluates the performance of the model based on composite indicators. While the base model demonstrated high accuracy and recall, the optimized model had a superior performance with an AUC score of 0.94, showing that the capacity to distinguish between stroke and non-stroke cases has significantly improved. By offering a useful predictive tool that can precisely estimate the risk of stroke and direct creative ways to early intervention, this study advances the field of healthcare analytics.
Machine Learning Approaches for Predicting Bank Customer Subscription: A Comparative Analysis
Authors: Zhao Zibo
Published: February 2025

As people enter the big data era, the traditional banking industry faces huge competitive pressure from new Internet financial products, which requires the traditional banking industry to use data mining and machine learning methods to optimize marketing strategies. Based on the bank marketing data set, this paper explores the effectiveness of several machine learning methods in predicting potential customers, including random forest, k-nearest neighbor (KNN) algorithm, and logistic regression, and conducts comparative analysis before and after data oversampling. Experiments show that the synthetic minority oversampling technique can effectively strengthen the model's capacity to recognize minority samples, but it may cause overfitting. Among them, the random forest has the best overall performance; logistic regression is limited by its linear assumption and performs slightly worse; the KNN algorithm is sensitive to noise and unbalanced data and has poor results. Future research can explore combining different sampling methods or using model integration to improve performance. This study provides important reference significance for the banking industry in accurately positioning potential customers.
Research of Pedestrian Detection Methods with Anchor Frame Based on Deep Learning
Authors: Yan Tao
Published: February 2025

Pedestrian detection technology has been one of the hotspots for target detection tasks. Deep learning-based theories and techniques perform exceptionally well in the pedestrian detection domain, and several general-purpose target detectors are continuously utilized in the target detection domain. Deep learning-based pedestrian detection techniques are examined in this research, and anchor frame-based pedestrian detection techniques are separated, contrasted, and examined in light of the anchor frames. The occlusion problem and the scale change problem are one of the main causes of omission and false detection problems in practical applications of pedestrian detection. This paper first presents the conventional pedestrian detection techniques, then concentrates on the R-CNN detector in the anchor frame-based two-stage pedestrian detection algorithm. When addressing the scale change problem, the enhanced Faster R-CNN algorithm, which is based on R-CNN, greatly minimizes redundant computation and enhances recognition accuracy. The YOLOv3 model in the YOLO model exhibits notable modifications in the overall architecture of pedestrian identification in the single-stage pedestrian recognition method using anchor frames, greatly improving its capacity to handle scale changes and occlusion issues.
Guided Diffusion: Balancing Fidelity and Diversity in Dataset Augmentation with EfficientNet and Gaussian Noise
Authors: Ouyang Tianyi
Published: February 2025

The denoising diffusion probabilistic model (DDPM) has recently attracted massive attention due to its better capability of synthesizing high-quality and diverse synthetic data than generative adversarial network (GAN), paving the way for its application in data augmentation scenarios. However, balancing fidelity and diversity remains a challenge. To address the problem, a novel architecture is proposed, incorporating EfficientNet to extract features from the original dataset and fuse them with those of noise samples, guiding the denoising process and ensuring fidelity between synthetic samples and the original data. Additionally, random Gaussian noise is introduced to the UNet bottleneck output at each timestep to enhance diversity. A pre-trained CNN classification network follows to ensure label consistency between the reference and the synthetic images. The approach is evaluated through experiments on lung cancer prediction using a chest CT-scan dataset, achieving a 13.6% improvement in classification accuracy over baseline methods, 9.8% over the traditional cropping and rotation approach, and 4.1% over the GAN-based approach. These results validate the effectiveness of the proposed method for dataset augmentation.
A Medical Image Semantics Segmentation Method Based on Image Pre-processing and Image Transformer
Authors: Li Zhaopeng
Published: February 2025

Semantics segmentation is a task aiming at classifying each pixel of an image into a category. Because of its ability to comprehend and interpret the context of images, it plays a vital role in the medical area. Existing models in this field pay more attention to editing U-Net’s structure and convolutional layers. In this paper, an image pre-processing technique to enrich the potential of images and an image transformer involved in network TrUNet are proposed to tackle these issues. In particular, the histogram equalisation method was introduced to improve the quality of each image, and image augmentation methods including flipping and rotation were used to increase the size of the image dataset. Then TrUNet cooperates vision transformer to U-Net to improve the model’s ability to recognize global context. Extensive experiments show that this method outperforms U-Net on the DRIVE dataset. Its validation loss curve is smoother and decreases more decently. Its accuracy increases more significantly in early epochs and is higher than that of U-Net. It has higher precision and F1-score compared with U-Net.
Improve image repair efficiency and quality based on Diffusion-GANs model
Authors: Huang Jingyi , Qin Xingsheng , Yang Haopeng
Published: February 2025

With the rapid development of modern science and technology, deep learning and artificial intelligence have greatly promoted the progress of computer vision, among which image restoration is a key field of computer vision, aiming to repair images that are damaged or missing important parts of the main body. Although traditional interpolation and region filling techniques are effective in some environments, they often have difficulty handling complex scenes that require image restoration in today's world. In contrast, modern methods such as GANs and Diffusion models have significantly improved the quality and reliability of restoration. However, GANs are hindered by problems such as instability and mode collapse, and although diffusion models can generate high-quality images, they are computationally demanding. To address these challenges, this review explores the hybrid diffusion GANs framework and focuses on the basic conceptual restoration principles of the above three models and compares the performance of the GANs and Diffusion model and other traditional models by comparing indicators such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM). Furthermore, although Diffusion-GANs model is mainly applied to image generation, we discuss their great potential for image inpainting, providing new possibilities for future improvements in image restoration and generation.
Evaluating the EVENODD Code: Principles, Applications, and Future Prospects in Data Storage Systems
Authors: Deng Jinxiang
Published: February 2025

In modern data storage and transmission, ensuring data integrity and reliability is critical due to potential losses or corruption caused by channel instability and system errors. Check codes have been developed to address these issues, allowing recovery of the original data even when errors occur. This paper provides a comprehensive analysis of the EVENODD code, a widely used parity code in error detection and correction applications. The fundamental principle of the EVENODD code relies on adding a binary check bit to ensure that the count of ones in the data string is either even or odd, depending on the desired configuration. Its implementation in Redundant Array of Independent Disks level 6 architecture highlights the code’s ability to improve data reliability by incorporating dual parity, enhancing fault tolerance in distributed systems. The advantages and limitations of EVENODD, such as its efficiency in single-bit error detection but inability to correct multi-bit errors, are examined. Additionally, comparisons are made with similar codes, including Longitudinal Redundancy Check and Cyclic Redundancy Check, to showcase their respective strengths and use cases. The paper discusses the EVENODD code’s industrial applications, particularly in satellite remote sensing and library databases, where data integrity is paramount. Future directions include optimizing the code's performance and cost-effectiveness for large- scale data storage and transmission environments, promoting secure and reliable information systems.
A novel metaheuristic algorithm with applications in parameter estimation and engineering problems
Authors: Liu Huiran , Wang Zheng , Fang Zhiming
Published: February 2025

This paper develops a metaheuristic, the Natural Selection Optimization Algorithm (NSOA), taking inspiration from the natural selection and the survival, growth, and reproduction of the fittest. The algorithm uses two instruments: Normal distribution and Sigmoid function. The Normal distribution generates random numbers such that the individuals in the initial population obey a Normal distribution, which can place search agents appropriately. The Sigmoid function controls the search process of the NSOA, aiding the convergence of the algorithm. To appreciate the global search ability of the NSOA, we test the NSOA against 52 benchmark functions, a hydrogeologic parameter estimation problem, and three engineering problems. Moreover, this paper compares the NSOA with six other algorithms under the same experimental configuration. Our results show that, for most functions, the NSOA converges and extracts the optimal value faster than the other established algorithms, pointing to the novelty and practical efficacy of the NSOA.
Decision and Coordination of Sustainable Supply Chain Considering CSR Implementation and Channel Leadership
Authors: Liu Shan , Duan Caiquan , Qiao Jinyou , Sun Yan
Published: February 2025

To achieve the ultimate objective of multi-dimensional sustainable development of the supply chain system, this paper examines the decision and coordination of the sustainable supply chain (SSC) from the dimensions of environment, society, and governance. Based on a dual-channel collecting structure, this study constructs six supply chain decision models considering two corporate social responsibility (CSR) implementers and three channel leadership structures. The findings reveal that in the SSC with a channel leader, it is most beneficial for the game follower to implement CSR from the perspective of maximising the total benefit to the supply chain and social welfare. The selection of the optimal channel leadership hinges on the trade-off between the collecting cooperation fees and the degree of the member's CSR implementation. Finally, this research proposes a revenue-responsibility sharing contract to achieve the perfect coordination of the SSC. Our results can function as guidelines for establishing an environmental-friendly SSC system.
Arthrose in neuem Licht: Aktuelle Erkenntnisse und
Perspektiven
Authors: Toomey Clodagh M. , Roos Ewa M.
Published: February 2025

Arthrose wird aktuell als eine entzündliche und biomechanische Erkrankung des
gesamten Gelenks angesehen, die mehrere Gewebe betrifft und bei der ein Auf und
Ab der Schmerzen sehr häufig sind. Dabei ist es wichtig, sowohl die
strukturellen Veränderungen („Disease") als auch das subjektive Erleben der
Patient*innen („Illness“) zu verstehen. Die Behandlungspyramide macht deutlich,
dass jeder Person, die wegen Gelenkschmerzen Unterstützung sucht, Training,
Gewichtsmanagement und Edukation angeboten werden sollten.
Aromatherapie-Praxis
Authors: Häringer Erwin
Published: February 2025

Es gibt verschiedene pharmakodynamische Duftwirkungen, die durch chemische Eigenschaften der Wirkstoffe, persönliche Erfahrungen und emotionale Assoziationen beeinflusst werden, wobei eine klare Dosis-Wirkungs-Beziehung besteht. Die Aromatherapie erfordert jedoch eine individuelle Anpassung und Berücksichtigung von Faktoren wie Vertrauen und Therapieadhärenz, da die Wirksamkeit von Düften stark variieren kann und nicht nur auf den chemischen Eigenschaften basiert, sondern auch auf semantischen und lustbezogenen Mechanismen.
A comparison of field and genetic methods for identifying feral cat predation events during wildlife translocations
Authors: Ryan-Schofield Ned L. , Bannister Hannah L. , Ross Alexandra , Moseby Katherine E.
Published: February 2025

Translocation programs are commonly undertaken to conserve threatened species, but loss of animals to predation after release remains a significant challenge. Feral cats (Felis catus) are a major predator of many native Australian mammals and a significant contributor to translocation failure in this group. However, identifying cat predation as the cause of mortality in the field is challenging and can hinder or delay the implementation of appropriate management actions. We compared field and genetic data collected from 92 cat predation events that occurred during native mammal translocations in South Australia. Our study found that necropsy and genetic evidence obtained through DNA analysis were the most reliable methods for identifying feral cat predation. Field evidence such as cat tracks, scats or collar bite marks were recorded infrequently and inconsistently. Relying solely on field evidence will significantly underestimate predation by feral cats on translocated species and even DNA evidence was found to underestimate impact. We demonstrated the value of intensively monitoring translocated animals post-release so as to facilitate prompt collection of multiple lines of evidence such as necropsy, field evidence and reliable DNA samples. An absence of field evidence should not be used to discount cat predation as a potential cause of mortality in translocation programs.
Temporal variation in the chemical composition of Nassella trichotoma under a defoliation gradient
Authors: García Andrés , Distel Roberto A. , Loydi Alejandro
Published: February 2025

Fluctuations in the chemical composition of invasive species can identify windows where grazing implementation can be maximised for improved weed management. Our goal was to determine the chemical composition of Nassella trichotoma (Nees) Hack. ex Arechav., an aggressive invasive grass species, after being subjected to either high or low defoliation across a defoliation intensity gradient and leaves were harvested over the growing season. All samples were analysed for protein content (PC) and neutral detergent fibre (NDF), acid detergent fibre (ADF) and acid detergent lignin (ADL) content. PC was generally higher, and NDF and ADF were higher in defoliated compared with undefoliated plants. An induced anti-herbivore defence may explain the increase in fibre content in response to defoliation, which would accentuate the low nutritional quality of N. trichotoma and, thus, its avoidance by cattle. Therefore, efforts should focus on preventing N. trichotoma leaf tissues from losing nutritional quality by integrating control measures, such as grazing regrowing shoots after disturbances that cause severe defoliation (e.g. burning) from early autumn to early spring. This approach could effectively reduce N. trichotoma competitiveness, preventing its persistence and spread.
La calidad del texto turístico promocional de ofertas alojativas en línea
Authors: Trujillo Karina Socorro
Published: February 2025

Si la industria turística es uno de los elementos clave de la economía española, parece lógico que se gestione adecuadamente no solo en términos de creación de infraestructuras, protección de espacios naturales y conservación del patrimonio cultural, sino también en lo que respecta a la creación de material promocional y su correspondiente traducción a diferentes idiomas (Déniz Suárez, 2015). Por ello, ante la necesidad de mejorar la calidad de los textos turísticos (Kelly, 2000; Bugnot 2006; Durán Muñoz, 2012), este trabajo se enfoca al análisis de errores identificados en textos turísticos promocionales de ofertas alojativas en línea de Canarias y redactados en español conforme a la tipología de errores establecida por Cruces Collado (2001).
Las visitas virtuales a bodegas y su discurso
Authors: Janodet Francisco Luque , Jurado Manuela Álvarez
Published: February 2025

El enoturismo digital es una práctica desarrollada en los últimos años que permite, entre otros, presentar los atractivos de
una bodega mediante una serie de visitas virtuales. En la presente contribución analizaremos las características de estas visitas a partir
de una serie de páginas webs de bodegas que ofertan esta actividad. Posteriormente, nos detendremos en las características lingüísticas
presentes en este discurso, para poder extraer conclusiones de cara a su traducción en el par de lenguas francés-español. Del análisis
realizado se extrae la convivencia de recursos persuasivos y de terminología vitivinícola en ambos idiomas que, unidos a esta modalidad
online, pueden suponer una serie de desafíos al traductor.
Análisis contrastivo alemán-español aplicado a la traducción de textos del enoturismo
Authors: Almansa Isidoro Ramírez
Published: February 2025

El presente trabajo se centra en la traducción DE-ES de textos enoturísticos con el objetivo de categorizar, en primer lugar,
las características de la lengua y el texto del enoturismo en alemán y, en segundo lugar, determinar cuáles son las semejanzas y
divergencias con las características en español y establecer cómo estas afectar a su traducción. Para ello, nos serviremos de la
lingüística de corpus al compilar un corpus textual compuesto por textos enoturísticos escritos originalmente en lengua alemana. Dicho
corpus está formado por diferentes géneros textuales pertenecientes al campo enoturístico y cumplen en todo momento los requisitos
exigidos para ser considerado un corpus de calidad (Torruella y Llisterri, 1999; Parodi, 2010). Una vez compilado, se someterá dicho corpus a un exhaustivo análisis en el que se
examinarán sus rasgos lingüísticos y textuales. De esta forma, podremos establecer una categorización de las características lingüísticas
y textuales de los textos enoturísticos en alemán. Posteriormente, mediante un análisis contrastivo compararemos esta categorización en
alemán con la contraparte en español — propuesta elaborada por Pascual-Cabrerizo (2006, 2016) — y determinaremos cuáles son sus semejanzas y divergencias y cómo estas afectan al proceso de
traducción.
Research approaches and methods in second language French
Authors: Howard Martin
Published: February 2025

Following on the individual chapter contributions across different areas of SLA inquiry, this
concluding chapter to the volume offers some synthesis reflections more generally on approaches and methods that
characterise the study of L2 French. The presentation considers the specific research questions overriding the volume
as a whole, looking at the main methodological trends of the past as opportunities to inform possible future
methodological directions in the language going forward.
Two cases of dissimilation in Palestinian Arabic and their theoretical implications
Authors: Davis Stuart , Abo Mokh Noor
Published: February 2025

In-depth analyses of specific cases of consonantal dissimilation are rare in the literature on
Arabic phonology. This chapter examines two instances of consonantal dissimilation that occur in two different
Palestinian varieties: the depharyngealization of emphatic fricatives when occurring before back velars in a rural
variety and the dissimilation of a prefixal glottal stop to [h] when it precedes a root glottal stop in an urban
variety. Our examination focuses on the detailed descriptive patterns and offers an analysis referencing feature
geometry and rule ordering. Theoretical implications concern the general nature of dissimilation (Bennett 2013; Payne 2017) and the role
of abstract features in Arabic dialectal phonology especially regarding the historic uvular
*
q that can merge with [k] in rural Palestinian and with [ʔ] in urban Palestinian
Arabic while still having a phonology that is distinct from the sounds with which they merge.
Enhancing Hemmaberg Pilgrimage Route as a Heritage Destination
Authors: Kailos Pavlos , Vanneste Dominique
Published: February 2025

This case study examines the Hemmaberg Pilgrimage route, situated within the UNESCO Global Geopark of Karavanke/Karawanken, spanning the Austria-Slovenia border. The broader area of the Geopark faces economic and demographic challenges. Despite its richness in geological diversity and cultural heritage, the lack of awareness of the area’s tourism potential and absence of a solid tourism plan hinder its path towards sustainable tourism success. Through a series of interviews with local stakeholders and experts in heritage and culture, as well as the use of secondary data, this case study outlines how merely preserving heritage value is not enough to ensure regeneration in this rural area, especially due to its remoteness. Via a specific model used for tourism management, it is established that the Hemmaberg route has great potential to attract tourists but must enhance various aspects of Hardware, Software and Orgware to become a successful pilgrimage route. These aspects include improving accessibility, tourist appeal and experiences while involving multiple stakeholders in heritage preservation and promotion. As the cave of worship of the patron saint of the area, St. Rosalia, was recently restored after a rockfall in 2015, now is the perfect time to chart a path towards sustainable tourism success for this pilgrimage site.

Information

© The Authors 2025
Development of extensible, durable and linear core-spun yarn sensor for smart textile applications
Authors: Xie Juan , Zhao Jing , Huang Gang , Qian Yao , Guo Changsheng , Liu Pengbi , Lin Guichun , Yuan Yayi
Published: February 2025

In recent years, fiber- and fabric-based strain sensors have demonstrated potential for achieving high sensitivity and large working sensing ranges. However, they are associated with difficulties due to unsatisfactory repeatability and poor wearing comfort. Here, we report a novel design of a flexible and extensible strain sensor with a core-sheath structure, where a conductive blended fiber assembles wrap around a flexible Spandex core filament. Compared to our previous conductive blended yarn, the elastic core-spun conductive yarn in this study enables an increased strain sensing range from 3% to approximately 16%, while guarantees excellent stability and durability during 100 stretching cycles and high linearity (goodness-of-fit > 0.97). Due to these enhanced performance attributes, the prepared elastic yarn strain sensor effectively recognizes gesture language when being fixed on a glove and presents remarkable advantages in detecting and monitoring joint activities (finger, elbow, knee, and neck).
Carrier transport and electronic defects in gallium oxide studied by photoconductivity techniques
Authors: Reynolds Steve , Nicol David , Smith Matthew , Nandi Arpit , Vanjali Sai Charan , Kuball Martin , Massabuau Fabien
Published: February 2025

Two complementary localised density of states spectroscopies, Modulated Photo-Conductivity (MPC) and the Constant Photocurrent Method (CPM) are applied to Ga2O3 thin films and are shown to be sensitive to carrier traps above and below the Fermi level, respectively. These techniques measure the film directly, without requiring a Schottky or p-n junction, which may offer advantages over conventional techniques in the study of high-resistivity or semi-insulating materials. The benefits of a higher-resolution MPC analysis are demonstrated.
Metallic Glass from the Point of View of Molecular Entropy Theory
Authors: Solunov Hristo Al.
Published: February 2025

The molecular entropy theory describes the glass transition as a cooperative molecular process. In theories’ equations, all thermodynamic and kinetic quantities of glass formation are expressed using molecular parameters. Two key molecular parameters are the size of the cooperative cluster and the average potential barriers per kinetic unit within the cluster. Equations has been established to measure the temperature dependence of both parameters The product of these two parameters defines the temperature dependence of the activation energy, which governs relaxation times, viscosity, and diffusion in glass formation. We found that, for all glass-forming substances, including metallic glass, the glass transition temperature is strictly proportional to the activation energy of the cooperative cluster at this temperature. Furthermore, we demonstrate that the size of the cooperative cluster at the glass transition temperature serves as molecular basis of the dynamic fragility (steepness) index.
Engineering the mechanical and X-ray attenuation properties of gelatine composite hydrogels with a potential for tissue mimicking materials
Authors: Tankovski Georgi , Georgieva Veselina , Traikova Nikoleta , Exner Ginka
Published: February 2025

The ability to engineer the properties of gelatine composite hydrogels with low-budget fillers, as potential tissue-mimicking materials in computed tomography, CT, is demonstrated. Composites with different distilled water/glycerol ratios, gelatine concentration of 0.08 g/ml and two fillers (collagen and eggshells powder, 0.08 g/ml) were prepared. Hounsfield units were measured as a function of the tube voltage and compared to biological tissues. The mechanical performance was also investigated. The results indicate that the composites successfully mimic liver, heart and muscle tissues in CT as well as trabecular bones. The mechanical performance allows the use of all composites as free standing tissue mimicking materials and they show a potential to be used as multimodal materials in CT and Ultrasound elastography for heart tissue replacement.
Stability of stochastic T-S fuzzy systems via dynamic event-triggered control
Authors: Zhang Yali , Liu Linna , Pan Chenglong , Zhou Shuaibo
Published: February 2025

This paper aims to examine the practical input-to-state stability (p-ISS) of stochastic T-S fuzzy systems (T-SFS) with parameter uncertainty by employing dynamic event-triggered control (DETC). In order to reduce the frequency of triggering events and the number of messages transmitted, we propose a DETC scheme, which introduces the waiting time to avoid Zeno behaviour and further saves communication resources. Additionally, within the DETC approach, LMI-based criteria are formulated to guarantee p-ISS for nonlinear stochastic T-SFS.
Control method of global interconnection of substation GIS equipment under repeated deduction
Authors: Li Dawei , Chen Jian , Lin Zhiyong , Zhao Shaojie , Guo Chunlan
Published: February 2025

To realize the overall control of substation GIS equipment from the overall perspective, research on the global interconnection control method of substation GIS equipment under repeated deduction is proposed. Considering that the core goal of the control of substation GIS equipment is to ensure that the health status of the substation reaches the optimal level, the twin assessment index system of substation health status is constructed, and based on this, a global interconnection model of substation GIS equipment is designed based on introducing FIS technology. In the control phase, the inner loop control and outer loop control are adopted to adjust the control parameters and output the optimal control in combination with the health status output of the whole area interconnection model of the substation GIS equipment. In the test results, the active power output of the design method is significantly higher than that of the control group and shows a higher stability.
Electricity demand forecasting in Zhangjiajie under the background of “dual carbon”
Authors: Ren Lang , Li Jiaxi , Zeng Ting , Wen Ming
Published: February 2025

Electricity demand forecasting is an important foundation for guiding energy planning. The proposal of the “dual carbon” policy has an impact on economic development, energy structure, and electricity demand, which traditional research has not considered. This work proposes a method for predicting electricity demand under the “dual carbon” background. Firstly, the main influencing factors of electricity demand under a “dual carbon” background are analyzed. Afterwards, the relationship between economy, energy, and carbon emissions is analyzed, and based on macro development trends, major economic indicators such as industrial structure are predicted. On this basis, combined with policy constraints such as energy consumption and carbon emission intensity, and considering the energy consumption structure, a method for predicting the total electricity consumption of the whole society under the background of “dual carbon” is proposed. The maximum load forecasting method is proposed based on the analysis of the maximum utilization hours of the load. The proposed method is applied to Zhangjiajie, providing methodological guidance for the analysis of the city’s economic development, the prediction of electricity consumption for the whole society, and the prediction of maximum load.
Two-tier optimal dispatch strategy for a park distribution network with electric vehicles with dynamic tariffs
Authors: Wang Junwei , Li Jianguo
Published: February 2025

In this paper, the optimal scheduling problem of active distribution networks after electric vehicle (EV) access is investigated, and a two-layer optimal scheduling strategy for an EV-containing park distribution network with dynamic tariffs is proposed. The upper-layer model takes the minimisation of EV charging and discharging cost and load variance as the objectives, and updates the charging and discharging states with the tariff, and particle swarm solves the optimal charging and discharging power curves and inputs them into the lower-layer model; the lower-layer distribution model takes the economic cost of the distribution network, the three-phase imbalance, the load variance, and the discard rate as the objectives, and adopts the multi-objective dung-beetle algorithm for solving the problem. The dynamic tariff of each phase is input into the upper layer model, and the up and down cycles are iterated until relatively stable. The results show that the two-layer optimal dispatch model with EVs proposed in this paper can not only optimise the charging and discharging cost and load difference of EVs in the upper layer, but also improve the four optimisation objectives of the lower distribution network, taking into account the optimisation of both the upper and lower layers, and the simulation results validate the effectiveness of the scheduling strategy.
Analysis of the influence of airflow wind angle and wind speed on the flow field distribution characteristics near double I-string composite insulators in transmission lines
Authors: Li Yanru , Liu Kang , Wang Yihu
Published: February 2025

In strong wind environments, changes in airflow patterns can affect the degree of deflection of insulator skirts. Deflection or tearing of composite insulator skirts can reduce their insulation performance. This article takes the double I-string composite insulator of the 330 kV transmission line as the research object. The wind angles are set as 0°, 16°, 30°, 60°, and 90°. The wind speeds are set as 5m/s, 10m/s, 20m/s, 30m/s, and 40m/s. The simulation results show that under the 0° windward angle condition, the upstream insulator completely blocks the airflow. The flow state near the double I-string changes with the variation of airflow velocity. The flow states on the high, medium, and low voltage sides of the upstream and downstream insulators are different. In future research, it may be considered to analyze the impact of airflow on the long-term performance of insulators and provide a reference for the optimization of insulator umbrella skirt structures.
Dynamically emergent correlations in bosons via quantum resetting
Authors: Kulkarni Manas , Majumdar Satya N , Sabhapandit Sanjib
Published: February 2025

We study the nonequilibrium stationary state (NESS) induced by quantum resetting of a system of $N$ noninteracting bosons in a harmonic trap. Our protocol consists of preparing initially the system in the ground state of a harmonic oscillator centered at $+a$, followed by a rapid quench where the center is shifted to $-a$ and the system is allowed to evolve unitarily up to a random Poissonian time $\tau$ distributed via $r\, e^{-r \tau}$. Then the trap center is reset to $+a$ again and the system is assumed to cool instantaneously to the initial ground state. The system is again allowed to evolve unitarily in the trap centered at $-a$ up to a random time, and the procedure is repeated. Under repeated resetting, the system reaches a NESS where the positions of bosons get {\em strongly correlated} due to simultaneous resetting induced by the trap. We fully characterize the steady state by analytically computing several physical observablessuch as the average density, extreme value statistics, order and gap statistics, and also the distribution of the number of particles in a region $[-L,L]$, known as the full counting statistics (FCS). In particular, we show that in the large $N$ limit, the scaling function describing the FCS exhibits a striking feature: it is supported over a nontrivial finite interval, and moreover is discontinuous at an interior point of the support. Our results are supported by numerical simulations. This is a rare example of a strongly correlated quantum many-body NESS where various observables can be exactly computed.
Essential Concepts and Measures in Organizational Network Analysis
Authors: Emery Cécile , Halgin Daniel S , Li Zexi (Flavia) , Zhao Xiwen
Published: February 2025

This chapter introduces the foundational concepts necessary for understanding organizational networks and their analysis, as explored in the book. This chapter delves into key network concepts such as nodes and ties, elucidating how social connections form the fabric of organizations. Through this introduction to social network analysis, readers gain insights into graph representation and statistical measures for analyzing network properties. The chapter outlines the practical utility of these tools in uncovering hidden patterns and extracting valuable insights from organizational networks. By mastering these fundamental principles, readers can navigate the complexities of social structures and interpret subsequent chapters effectively.