Search Results (3,884)

Social network platforms have a big impact on the development of companies by influencing clients’ behaviors and sentiments, which directly affect corporate reputations. Analyzing this feedback has become an essential component of business intelligence, supporting the improvement of long-term marketing strategies on a larger scale. The implementation of powerful sentiment analysis models requires a comprehensive and in-depth examination of each stage of the process. In this study, we present a new comparative approach for several feature extraction techniques, including TF-IDF, Word2Vec, FastText, and BERT embeddings. These methods are applied to three multilingual datasets collected from hotel review platforms in the tourism sector in English, French, and Arabic languages. Those datasets were preprocessed through cleaning, normalization, labeling, and balancing before being trained on various machine learning and deep learning algorithms. The effectiveness of each feature extraction method was evaluated using metrics such as accuracy, F1-score, precision, recall, ROC AUC curve, and a new metric that measures the execution time for generating word representations. Our extensive experiments demonstrate significant and excellent results, achieving accuracy rates of approximately 99% for the English dataset, 94% for the Arabic dataset, and 89% for the French dataset. These findings confirm the important impact of vectorization techniques on the performance of sentiment analysis models. They also highlight the important relationship between balanced datasets, effective feature extraction methods, and the choice of classification algorithms. So, this study aims to simplify the selection of feature extraction methods and appropriate classifiers for each language, thereby contributing to advancements in sentiment analysis. Full article

Urban heat island circulation (UHIC) determines the wind and thermal environments in urban areas. For Loess Tableland valley towns, the evolution characteristics of the UHIC over this negative terrain are not well understood, and therefore, it is important to investigate the evolution characteristics. A city-scale computational fluid dynamics (CSCFD) model is used, and simulation results are validated by the water tank experiment. The evolution process over such negative terrain can be divided into transient and quasi-steady stages, and in the transient stage, the airflow pattern evolves from thermal convection to city-scale closed circulation, while that in the quasi-steady stage is only city-scale closed circulation. In order to further reveal the characteristics of city-scale closed circulation, the sensitivities of different factors influencing the start time, outflow time, mixing height and heat island intensity are analyzed, and the most significant factors influencing these four parameters are urban heat flux, slope height, slope height, and potential temperature lapse rate, respectively. Finally, the dimensionless mixing height and heat island intensity for the valley town increase by 56.80% and 128.68%, respectively, compared to those for the flat city. This study provides guidance for the location and layout of built-up areas in the valley towns. Full article

Creating a comfortable microclimate in the premises of buildings is currently becoming one of the priorities in the field of architecture, construction and engineering systems. The increased attention from the scientific community to this topic is due not only to the desire to ensure healthy and favorable conditions for human life but also to the need for the rational use of energy resources. This area is becoming particularly relevant in the context of global challenges related to climate change, rising energy costs and increased environmental requirements. Practice shows that any technical solutions to ensure comfortable temperature, humidity and air exchange in rooms should be closely linked to the concept of energy efficiency. This allows one not only to reduce operating costs but also to significantly reduce greenhouse gas emissions, thereby contributing to sustainable development and environmental safety. In this connection, this study presents a parametric assessment of the influence of climatic and geometric factors on the aerodynamic characteristics of the air cavity, which affect the heat exchange process in the ventilated layer of curtain wall systems. The assessment was carried out using a combined analytical calculation method that provides averaged thermophysical parameters, such as mean air velocity ($V_s$), average internal surface temperature ($t_{in.s}^{av}$), and convective heat transfer coefficient (${\alpha }_s$) within the air cavity. This study resulted in empirical average values, demonstrating that the air velocity within the cavity significantly depends on atmospheric pressure and façade height difference. For instance, a 10-fold increase in façade height leads to a 4.4-fold increase in air velocity. Furthermore, a three-fold variation in local resistance coefficients results in up to a two-fold change in airflow velocity. The cavity thickness, depending on atmospheric pressure, was also found to affect airflow velocity by up to 25%. Similar patterns were observed under ambient temperatures of +20 °C, +30 °C, and +40 °C. The analysis confirmed that airflow velocity is directly affected by cavity height, while the impact of solar radiation is negligible. However, based on the outcomes of the analytical model, it was concluded that the method does not adequately account for the effects of solar radiation and vertical temperature gradients on airflow within ventilated façades. This highlights the need for further full-scale experimental investigations under hot climate conditions in South Kazakhstan. The findings are expected to be applicable internationally to regions with comparable climatic characteristics. Ultimately, a correct understanding of thermophysical processes in such structures will support the advancement of trends such as Lightweight Design, Functionally Graded Design, and Value Engineering in the development of curtain wall systems, through the optimized selection of façade configurations, accounting for temperature loads under specific climatic and design conditions. Full article

Precipitation, as a key input in the water cycle, directly influences the formation and change process of runoff. Meanwhile, the return runoff intuitively reflects the available quantity of water resources in a river basin. An in-depth analysis of the evolution laws and response relationships between precipitation and return runoff over a long time scale serves as an important support for exploring the evolution of hydrometeorological conditions and provides an accurate basis for the scientific planning and management of water resources. Taking Lanzhou Station on the upper Yellow River as a typical case, this study proposes the VSSL (LSTM Fusion Method Optimized by SSA with VMD Decomposition) deep learning precipitation element series extension method and the SSVR (SVR Fusion Method Optimized by SSA) machine learning runoff element series extension method. These methods achieve a reasonable extension of the missing data and construct 100-year precipitation and return runoff series from 1921 to 2020. The research results showed that the performance of machine learning and deep learning methods in the precipitation and return runoff test sets is better than that of traditional statistical methods, and the fitting effect of return runoff is better than that of precipitation. The 100-year precipitation and return runoff series of Lanzhou Station from 1921 to 2020 show a non-significant upward trend at a rate of 0.26 mm/a and 0.42 × 10⁸ m³/a, respectively. There is no significant mutation point in precipitation, while the mutation point of return runoff occurred in 1991. The 100-year precipitation series of Lanzhou Station has four time-scale alternations of dry and wet periods, with main periods of 60 years, 20 years, 12 years, and 6 years, respectively. The 100-year return runoff series has three time-scale alternations of dry and wet periods, with main periods of 60 years, 34 years, and 26 years, respectively. During the period from 1940 to 2000, an approximately 50-year cycle, precipitation and runoff not only have strong common-change energy and significant interaction, but also have a fixed phase difference. Precipitation changes precede runoff, and runoff responds after a fixed time interval. Full article

Muscle–joint flexibility is defined as the ability of a muscle to stretch in a controlled manner, allowing a wide range of movement at the joints. While numerous methodologies exist for improving flexibility, few studies have investigated the role of athletes’ perceptual processes and awareness related to their own body and movement control during such training. In this pilot study, we explored how two different training protocols—static and dynamic stretching (control group, CON) and multi-joint resistance training (experimental group, EXP)—influence both flexibility and psychophysical awareness, understood as a multidimensional construct involving perceived flexibility improvements, self-assessed control over exercise execution, and cognitive-emotional responses such as engagement, motivation, and satisfaction during physical effort. The study involved 24 male amateur track-and-field athletes (mean age 23 ± 2.5 years), randomized into two equal groups. Over 12 weeks, both groups trained three times per week. Flexibility was assessed using the Sit and Reach Test at three time points (pre-, mid-, and post-intervention). A 2 × 3 mixed ANOVA revealed a significant group × time interaction (F = 20.17, p < 0.001), with the EXP group showing greater improvements than the CON group. In the EXP group, Sit and Reach scores increased from pre = 28.55 cm (SD = 4.91) to mid = 29.39 cm (SD = 4.67) and post = 29.48 cm (SD = 4.91), with a significant difference between pre and post (p = 0.01; d = 0.35). The CON group showed minimal changes, with scores of pre = 28.66 cm (SD = 4.92), mid = 28.76 cm (SD = 5.03), and post = 28.84 cm (SD = 5.10), and no significant difference between pre and post (p = 0.20; d = 0.04). Psychophysical awareness was assessed using a custom questionnaire structured on a 5-point Likert scale, with items addressing perception of flexibility, motor control, and exercise-related bodily sensations. The questionnaire showed excellent internal consistency (Cronbach’s α = 0.92). Within the EXP group, psychophysical awareness increased significantly (from 3.50 to 4.17; p = 0.01; d = 0.38), while no significant change occurred in the CON group (p = 0.16). Post-hoc power analysis confirmed small to moderate effect sizes within the EXP group, although between-group differences lacked sufficient statistical power. These results suggest that resistance training may improve flexibility and concurrently enhance athletes’ psychophysical self-awareness more effectively than traditional stretching. Such findings offer practical implications for coaches seeking to optimize flexibility training by integrating alternative methods that promote both physical and perceptual adaptations. Full article

The hydrological response of a watershed is strongly influenced by the spatiotemporal dynamics of rainfall. Rainfall events of similar magnitude can produce markedly different flood processes due to variations in the spatiotemporal patterns of rainfall, posing significant challenges for flood forecasting under complex rainfall scenarios. Traditional methods typically rely on high-resolution or synthetic rainfall data to characterize the scale, direction and velocity of rainstorms, in order to analyze their impact on the flood process. These studies have shown that storms traveling along the main river channel tend to exert the greatest impact on flood processes. Therefore, tracking the movement of the rainfall center along the flow direction, especially when only rain gauge data are available, can reduce model complexity while maintaining forecast accuracy and improving model applicability. This study proposes a machine learning-based time-variable instantaneous unit hydrograph that integrates rainfall spatiotemporal dynamics using quantitative spatial indicators. To overcome limitations of traditional variable unit hydrograph methods, a pre-training and fine-tuning strategy is employed to link the unit hydrograph S-curve with rainfall spatial distribution. First, synthetic pre-training data were used to enable the machine learning model to learn the shape of the S-curve and its general pattern of variation with rainfall spatial distribution. Then, real flood data were employed to learn the actual runoff routing characteristics of the study area. The improved model allows the unit hydrograph to adapt dynamically to rainfall evolution during the flood event, effectively capturing hydrological responses under varying spatiotemporal patterns. The case study shows that the improved model exhibits superior performance across all runoff routing metrics under spatiotemporal rainfall variability. The improved model increased the simulation qualified rate for historical flood events, with significant rainfall center movement during the event from 63% to 90%. This study deepens the understanding of how rainfall dynamics influence watershed response and enhances hourly-scale flood forecasting, providing support for disaster early warning with strong theoretical and practical significance. Full article

This study investigates the influence of process parameters on the fabrication and mechanical performance of Scalmalloy^® lattice structures produced via laser powder bed fusion (PBF-LB) and their mechanical responses at different cell size. A full-factorial design of experiments was employed to evaluate the effect of scan speed, hatch distance, and downskin power on internal porosity and dimensional accuracy. Regression models revealed significant relationships, with optimised parameters identified at a scan speed of 700 mm/s, hatch distance of 0.13 mm, and downskin power of 80 W. Mechanical characterisation through tensile tests of bulk samples and compression tests of lattice structures highlighted the strengthening effects of the heat treatment. Experimental data on quasi-elastic gradient and yield strength were compared to predictions from the Ashby–Gibson model, revealing a partial agreement but noticeable deviations attributed to cell geometry and manufacturing defects. The scaling laws observed differed from the classical model, particularly in the yield strength exponent, indicating the need for empirical models tailored to metallic lattices. This work provides key insights into the optimisation of PBF-LB parameters for Scalmalloy^® and underlines the complex interplay between process parameters, structural design, and mechanical behaviour. Full article

The tandem tilt-wing UAV features an advanced aerodynamic layout design and is regarded as a solution for small-scale urban air mobility. However, the tandem wing configuration exhibits complex aerodynamic interactions between the front and rear wings during cruise flight and the wing tilt transition process. The objective of this paper is to investigate the aerodynamic coupling characteristics between the front and rear wings of the tandem tilt-wing UAV under level flight and tilt transition conditions while also assessing the influence of the propellers on the aircraft’s aerodynamic performance. Through CFD numerical analysis, the aerodynamic characteristics of various aircraft components are examined at different angles of attack and wing tilt angles, and the underlying reasons for the observed differences and variations are explored. The results indicate that, during level flight, the aerodynamic interference between the wings is primarily dominated by the detrimental influence of the front wing on the rear wing. During the tilt transition process, mutual interactions between the front and rear wings occur as wing tilt angle changes, leading to more drastic variations in lift coefficients and increased control difficulty. However, the propeller’s effect contributes to smoother changes in lift and drag, thereby enhancing aircraft stability. Full article

Based on the theoretical framework of systems thinking, this study investigates the mechanism of systems thinking in promoting entrepreneurial persistence among technology entrepreneurs in China’s digital economy development. From a dynamic complex systems perspective, 409 technology entrepreneurs from China, were measured using the systems thinking scale, the psychological ownership scale, the resource bricolage scale, and the entrepreneurial persistence scale. Systems thinking among technology entrepreneurs has been found to enhance entrepreneurial persistence significantly. Psychological ownership of technology entrepreneurs partially mediates the process of systems thinking influencing entrepreneurial persistence. Resource bricolage positively moderates the systems thinking process, influencing entrepreneurial persistence among technology entrepreneurs. This study innovatively introduces systems thinking into the field of technology entrepreneurship, reveals the relationship between systems thinking and entrepreneurial persistence of technology entrepreneurs, and provides theoretical guidance for Chinese technology entrepreneurs to enhance entrepreneurial persistence through systems thinking. Full article

Background: Preparing patients for surgery considers assessing the patient’s somatic health, for example by the American Society of Anesthesiology (ASA) scale or the Revised Cardiac Risk Index (RCRI), known as the Lee index. This process usually ignores mental functioning (personality and anxiety), which is known to influence health. The purpose of this study is to analyze the existence of a relationship between personality traits (the Big Five model and trait-anxiety) and anesthesia scales (ASA scale, Lee index) used for the preoperative evaluation of patients. Methods: The study group comprised 102 patients (59 women, 43 men) scheduled for hip replacement surgery. Patients completed two psychological questionnaires: the NEO-FFI (NEO Five Factors Inventory) and the X-2 STAI (State-Trait Anxiety Inventory) sheet. Next, the presence and possible strength of the relationship between personality traits and demographic and medical variables were analyzed using Spearman’s rho rank correlation coefficient. Results: Patients with a high severity of trait anxiety are classified higher on the ASA scale (rs = 0.359; p < 0.001). Neuroticism, defined according to the Big Five model, significantly correlates with scales of preoperative patient assessment: the ASA classification (rs = 0.264; p < 0.001) and the Lee index (rs = 0.202; p = 0.044). A hierarchical regression model was created to test the possibility of predicting ASA scores based on personality. It explained more than 34% of the variance and was a good fit to the data (p < 0.05). The controlled variables of age and gender accounted for more than 23% of the variance. Personality indicators (trait anxiety, neuroticism) additionally accounted for slightly more than 11% of the variance. Trait anxiety (Beta = 0.293) proved to be a better predictor than neuroticism (Beta = 0.054). Conclusions: These results indicate that inclusion of personality screening in the preoperative patient evaluation might help to introduce a more individualized approach to patients, which could result in better surgical outcomes. Full article

This article presents an initial effort to systematize two interrelated research fronts within ecological economics (EE): ecological microeconomics and ecological macroeconomics. In response to the field’s transdisciplinary and plural nature—attributes that, while enriching, may limit its political influence—the article proposes a conceptual delineation of these two domains as a means to strengthen EE’s analytical identity and facilitate dialogue with other economic approaches. Ecological microeconomics focuses on the material and energy intensity of economic activity, the complementarity of natural capital in production processes, and the redesign of consumption and firm behavior under ecological constraints. Ecological macroeconomics, in turn, centers on the biophysical limits to growth, the concept of sustainable and optimal scale, and the integration of environmental variables into macroeconomic indicators and policy frameworks. The article argues that both fronts, despite their distinct emphases, are united by the need for long-term structural change and a normative commitment to sustainability. Together, they offer a coherent basis for rethinking prosperity within the ecological boundaries of the Earth system. Full article

Mycelium-bonded composites (MBCs), or myco-composites, represent a novel engineered material that combines natural lignocellulosic substrates with a fungal matrix. As a sustainable alternative to plastics, MBCs are gaining increasing interest; however, their large-scale industrial adoption remains limited, partly due to low social acceptance resulting from their unattractive appearance. Laser engraving provides a promising method for fabricating intricate patterns and functional surfaces on MBCs, minimizing tool wear, material loss, and environmental impact, while enhancing esthetic and engineering properties. This study investigates the influence of CO₂ laser parameters on the material removal rate during the engraving of myco-composites, focusing on the effects of variable laser power, beam defocus, and head feed rate on engraving outcomes. The results demonstrate that laser power and beam focus significantly impact material removal in mycelium-bonded composites. Specifically, increasing the laser power results in greater material removal, which is more pronounced when the beam is focused due to higher energy density. In contrast, a beam defocused by 1 mm produces less intense material removal. These findings highlight the critical role of beam focus—surpassing the influence of power alone—in determining engraving quality, particularly on irregular or uneven surfaces. Moreover, reducing the laser head feed rate at a constant power level increases the material removal rate linearly; however, it also results in excessive charring and localized overheating, revealing the low thermal tolerance of myco-composites. These insights are essential for optimizing laser processing techniques to fully realize the potential of mycelium-bonded composites as sustainable engineering materials, simultaneously maintaining their appearance and functional properties. Full article

For high-growth firms, designing and implementing strategies to ensure the long-term sustainability of business models is a key priority. Although these strategies are carefully planned to achieve specific outcomes, these firms also encounter contextual factors inherent to entrepreneurship, as well as the potential negative consequences of operating as small- and medium-sized enterprises (SMEs). Consequently, they adapt emergent outcomes to secure positive scaling-up processes. A comprehensive analysis of 69 studies from 1978 to 2023 revealed that 34.8% used sales as the main indicator of high-growth outcomes, 18.8% considered employment to be the most important outcome, and 37.7% incorporated both. The assessment period for these studies spanned three to seven consecutive years. A subsequent review of the existing literature yielded 56 potential new outcomes, emphasising the existence of a diverse array of concepts and metrics with which to assess high-growth performance. The study confirmed sales and positive profits arising during the planning process as strategic outcomes. However, it was also demonstrated that geographical expansion and innovation become emergent outcomes in critical situations. The research also identified that external factors, including an adverse public environment, business context difficulties, and a favourable business environment, may influence the effect of the firm’s high growth. Full article

Eco-migration (EM) constitutes a specialized form of migration aimed at enhancing living environments and alleviating ecological pressure. Nevertheless, large-scale external migration has intensified habitat fragmentation (HF) in resettlement areas. This paper takes the Shule River Resettlement Project (SRRP) as a case, based on the China Land Cover Dataset (CLCD) data of the resettlement area from 1996 to 2020, using the Landscape Pattern Index (LPI) and the land use transfer matrix (LTM) to clearly define the stages of migration and the types of resettlement areas and to quantitative explore how EM affects HF. The results show that (1) EM accelerates the transformation of natural habitats (NHs) to artificial habitats (AHs) and shows the characteristics of sudden changes in the initial stage (1996–2002), with stability in the middle stage (2002–2006) and late stage (2007–2010) and dramatic changes in the post-migration stage (2011–2020). In IS, MS, LS, and PS, AH increased by 26.145 km², 21.573 km², 22.656 km², and 16.983 km², respectively, while NH changed by 73.116 km², −21.575 km², −22.655 km², −121.82 km², and −213.454 km², respectively. The more dispersed the resettlement areas are the more obvious the expansion of AH will be, indicating that the resettlement methods for migrants have a significant effect on habitat changes. (2) During the resettlement process, the total number of plaques (NP), edge density (ED), diversity (SHDI), and dominance index (SHEI) all continued to increase, while the contagion index (C) and aggregation index (AI) continued to decline, indicating that the habitat is transforming towards fragmentation, diversification, and complexity. Compared with large-scale migration bases (LMBs), both small-scale migration bases (SMBs), and scattered migration settlement points (SMSPs) exhibit a higher degree of HF, which reflects how the scale of migration influences the extent of habitat fragmentation. While NHs are experiencing increasing fragmentation, AHs tend to show a decreasing trend in fragmentation. Ecological migrants play a dual role: they contribute to the alteration and fragmentation of natural habitat patterns, while simultaneously promoting the formation and continuity of artificial habitat structures. This study offers valuable practical insights and cautionary lessons for the resettlement of ecological migrants. Full article

Interoceptive impairments are increasingly recognized as psychopathology processes underlying emotional disorders. However, their relationship with depression remains inconclusive. Alexithymia may influence the association between interoception and depressive symptoms. This study aimed to examine the role of alexithymia between interoception and depression. Eighty-eight depressed patients (DEPs) and fifty healthy controls (HCs) were recruited. Interoceptive sensibility was assessed using the Multidimensional Assessment of Interoceptive Awareness, and interoceptive accuracy and interoceptive awareness were evaluated through heartbeat counting tasks. Alexithymia was measured with the Toronto Alexithymia Scale. In the DEP group, depressive symptoms were assessed using the Hamilton Depression Scale. In DEPs, none of the three dimensions of interoception were associated with depressive symptoms. The alexithymic depressed patients exhibited lower interoceptive sensibility than their non-alexithymic counterparts, while the latter did not differ from the HC group. Moreover, alexithymia mediated the link between interoceptive sensibility and depressive symptoms. These results suggested that impaired interoceptive sensibility may primarily contribute to alexithymia, which, in turn, leads to depression. This highlights the importance of addressing alexithymia in therapeutic interventions aimed at improving the interoceptive process in depressed individuals. Full article