Physiopathological Insights into Atrial Fibrillation Onset through Heart Rate Variability Correlations

Atrial fibrillation (AF) is the most common cardiac arrhythmia and is associated with increased morbidity and mortality. Early prediction of AF episodes remains a clinical challenge. This study aimed to generate physiopathological hypotheses for AF onset by analyzing correlations among heart rate variability (HRV) parameters in patients monitored via long-term Holter ECG. We utilized the IRIDIA-AF database, comprising 1319 paroxysmal AF episodes from 872 patients. An XGBoost machine learning model was developed to predict AF onset within 24 h using short- and long-term HRV features, fragmentation indices, and non-linear metrics extracted during sinus rhythm. Model interpretation was performed using SHapley Additive exPlanations (SHAP) values, and dimensionality reduction techniques were applied for data visualization. The model achieved an area under the receiver operating characteristic curve of 0.919 and an area under the precision-recall curve of 0.919, with high accuracy, sensitivity, and specificity. Key predictive features included short-term vagal activity, HRV fragmentation indices, and non-linear parameters, highlighting the role of the autonomic nervous system in AF initiation. Our findings suggest that distinct physiological profiles, detectable via HRV, may underlie AF susceptibility and could inform personalized monitoring and prevention strategies.

Vocabulary of Chinese Origin in the Language of Russian Residents of Harbin in the First Half of the 20th Century

The purpose of the article is to study the functioning of lexical units of Chinese origin in the speech of representatives of the Far Eastern emigration. The language of everyday communication is the first to respond to socio-cultural, ethnocultural, ethno-religious processes occurring in society. At present, when the culture of Far Eastern emigration in its close interaction with Chinese culture has become a fact of history, the reconstruction of the processes of intercultural communication between Russians and Chinese in Harbin causes great difficulties. This explains the relevance of studying the Chinese influence on the language of Russian emigrants who found refuge in Harbin in the first half of the 20th century. The novelty of the work is due to the lack of comprehensive studies dealing with Chinese borrowings in the everyday language of ordinary Harbin residents. An appeal to the memories and oral histories of Harbin residents allows us to trace how lexemes borrowed from the Chinese language and continuing to live in the linguistic consciousness of people who grew up in Harbin. The methodology of this article is based on historical-cultural, functional, linguocultural, and lexical-semantic approaches, as well as interviewing. The work uses materials from the authors’ field research among Harbin residents. Based on the results of the study, the authors conclude that although most Russians living in Harbin in the first half of the 20th century did not speak Chinese, Chinese borrowings were a constant part of their lives. This is especially true for various lacunae related to everyday realities, cooking, traditional culture, etc. Harbin residents organically assimilated such lexical units and preserved them in their speech for decades—even outside China. Of course, this testifies to close ethnocultural contacts between Russians and Chinese in Manchuria.

The Thermal Impact of Various Pavement Materials on Outdoor Temperature in a Temperate Four-Season Climate: A Case Study of Arak City, Iran

Urban heat and oasis effects significantly alter urban microclimates due to anthropogenic heat emissions and the thermal properties of urban surfaces. This study aims to quantitatively assess the thermal effects of different pavement types on outdoor temperatures across seasonal extremes in a temperate four-season climate. Conducted in Arak city, Iran, on 22 July and 22 January 2023, this research investigates both warm and cold seasons to provide a comprehensive understanding of pavement influence on urban microclimates throughout the year. Using the ENVI-met 5.0.3 modeling software, an environmental meteorology tool for simulating urban microclimates, the thermal performance of commonly used asphalt pavement was compared with alternative materials such as basalt and light-colored concrete on Dr. Hesabi Street. Simulation results reveal that basalt and light-colored concrete pavements reduce summer cooling loads by up to 3.49 degrees Celsius (°C), enhancing pedestrian thermal comfort, but simultaneously increase winter heating demands by 1.04 °C. This balance presents an optimal scenario to minimize adverse climate effects across seasons. The findings offer valuable insights for sustainable urban planning, promoting resilient city design strategies that mitigate heat and oasis effects in diverse climates. This study contributes actionable recommendations for urban planners seeking to balance thermal performance in temperate climates with seasonal variability.

Initiation of Surface Processes by Resonance IR Laser Excitation—State and Perspectives

A possibility to initiate surface reactions by resonant IR laser radiation has been studied. Several systems have been tried, including those with linkage isomerism, such as CO bound to cations in zeolites, decomposition of adsorbed unstable molecules like ozone or HN₃, reactions of vibrationally excited molecules with coadsorbed species, or the effect of resonance excitation of hydroazide acid HN₃ upon its ability to induce the protonation of dimethylpyridine adsorbed on silanol groups of silica. In almost all the experiments, the changes caused by irradiation were weak, and isotopic selectivity was rather poor. The choice of systems and possible ways to improve their characteristics are discussed as well as the perspectives of their usage for isotope separation or other practical tasks.

Reflections on Synthetic Biology in the Manufacturing of Chemicals and Fuels

Statistical Analysis of GSR Particles Morphometry Using the CART Method

According to ASTM E1588-20, gunshot residue (GSR) particles can be unequivocally identified through chemical and morphometric analysis using scanning electron microscopy coupled with energy-dispersive X-ray spectroscopy (SEM-EDS), the gold standard technique for GSR detection. Recent studies have reported the presence of characteristic GSR particles—containing lead (Pb), barium (Ba), and antimony (Sb)—on vehicle occupants exposed to airbag deployment, underscoring the need for complementary analytical approaches. While elemental composition remains the primary criterion for GSR identification, morphometric analysis enhances the ability to differentiate GSR from other environmental particles. Furthermore, detailed characterization of GSR particle morphology may assist in determining the type of firearm used in a shooting incident. This study systematically analyzed characteristic GSR particles originating from four Brazilian-manufactured ammunition, establishing an initial framework for differentiating between two classes of firearms (short and long) based on morphometric features using the Classification and Regression Tree (CART) method. CART is well-suited for scenarios where interpretability and ease of implementation are priorities. Two short firearms—Taurus G2C pistol (0.40 caliber) and Glock G23 pistol (9 mm caliber) and two long firearms—Colt M16A2 rifle (5.56 mm caliber) and IMBEL FAL rifle (7.62 mm caliber) were tested: Ammunition types included CBC 0.40 S&W CSCV 160 gr, CBC 9 mm copper bullet (batch BNC10), CBC 5.56 mm AXO46 (batch A0142946), and CBC 7.62 × 51 mm Common. Morphometric analysis revealed distinct variations in characteristic GSR particle profiles across different ammunition calibers. A new four-category classification system for characteristic GSR particles was developed, with 57% identified as regular spheroids. Using CART analysis, a statistical model achieved 76% accuracy in distinguishing between short and long firearms based on morphometric parameters, particularly circularity and Feret diameter. Further research with expanded datasets and alternative predictive methods is recommended to enhance model performance and generalizability. These findings reinforce the potential of morphometric classification as a complementary tool in forensic ballistics.

Hybrid Encoder–Decoder Model for Ultra-Short-Term Prediction of Wind Farm Power

The ability to ensure safe and economic operation of power grids is challenging because of the large-scale integration of wind power as a result of its intermittent and fluctuating nature. Accurate wind power prediction is critical to overcome these concerns. This study proposed a novel hybrid encoder–decoder model by combining bidirectional gated recurrent unit, multi-head attention mechanism, and ensemble technique for multi-step ultra-short-term power prediction of wind farms. The bidirectional gated recurrent unit accurately details the complex temporal dependency of input sequence information in the encoder and outputs the encoded vector. To focus on features that contribute more to the output, two types of multi-head attention mechanism, including self-attention and cross-attention, were used in the decoder to decode the encoded vector and obtain the forecast wind power sequence. Furthermore, an ensemble technique was used to integrate forecast results from various individual predictors, which reduced the uncertainty of individual prediction results and improved predictive accuracy. The input data included historical information from the wind farm and future information from numerical weather prediction. The forecast model was validated using actual data, and results showed that it achieved superior accuracy and stability compared with other existing models in four multi-step prediction scenarios (1-, 2-, 3-, and 4-h prediction).

Expanding Private Capital Investment in Universities to Biodiversity Conservation

Biodiversity is essential for human well-being, and serves as the green engine for education, science, technology and health. China’s prosperous private entrepreneurs have established hundreds of private colleges and universities with several newcomers positioned as world-class research universities. Unfortunately, biodiversity conservation education and researches appear overlooked in these institutions. Universities, particularly the top-tier universities, serve as critical hubs where talent, knowledge, and technology concentrate. Private capital’s flexible management framework and rapid response to emerging academic disciplines enable universities, enterprises, and markets to collaborate effectively in developing tools and equipment for biodiversity assessment and monitoring. Expanding huge private capital investment in universities to biodiversity conservation could spur broader investment in ecological products in the future, while would also offer an opportunity for the universities to achieve their ambitions.

Feature Selection Technique Using Multiple Linear Regression for Accurate Electricity Demand Forecasting

The rising power demand, driven by population growth, technological innovations, and the advent of smart cities, necessitates precise forecasting to ensure efficient energy distribution and align supply with demand. This paper presents a novel methodology for predicting short-term power consumption through machine learning approaches, specifically employing multiple linear regression for feature selection. In this study, two models are implemented and compared: Support Vector Regression (SVR) and Long-Short-Term Memory (LSTM). Exploratory data analysis was used to discover the relationships and associations between variables. It reveals that temperature, humidity, time of day, and season are major determinants of electricity use. The results indicate that the LSTM model surpasses Support Vector Regression (SVR) in terms of accuracy and precision. By incorporating multiple linear regression (MLR) for feature selection, the performance of both models improved, with precision gains of 29.1% for SVR and 18.19% for LSTM. Removing extraneous elements, such as wind speed and diffuse solar radiation, enhanced the models’ efficiency and interpretability, allowing for a focus on the most significant factors. The study’s findings underscore the need to optimize feature selection to enhance forecast accuracy and streamline models. This method provides critical insights for enhancing energy management strategies and facilitating sustainable power distribution in light of rising global energy demand.

The Recyclability of Wind Turbine Blade Material, Manufacturing Process, and Recycling Technology

As wind energy continues to be deployed at a significantly increasing rate, the number of decommissioned wind turbines is expected to increase accordingly. To improve material efficiency, a large amount of waste requires appropriate identification and recycling, particularly the composite materials used in wind turbine blades (WTB). This study focuses on two life cycle stages, manufacturing and the decommissioning stage, which contribute most to the waste generation of WTB. This study investigates the material efficiency factors in WTB and organises fragmental information in manufacturing waste management, focusing on the recycling factor and quantifying the recyclability of wind turbine blade material regarding the different recycling technologies. This study fills the gap in existing research by evaluating recycling methods for specified carbon fibre-reinforced polymers (CFRPs) and glass fibre-reinforced polymers (GFRPs) using a revised recyclability index. Additionally, innovative sustainable materials and recent composite recycling studies have also been incorporated into the quantification and evaluation to update the current progress. The current source of WTB post-production waste, the corresponding disposal method, and opportunities were also reviewed and identified. The findings quantified recyclability and revealed that the recyclability of WTB materials varies significantly depending on the specific composite type and the recycling method employed. Furthermore, the calculated recyclability, combined with other factors such as global warming potential (GWP), cost, and technology readiness level (TRL), is discussed, along with the potential for improving material efficiency by selecting future material recycling technology and effective manufacturing waste management.