Search Results (543)

Whilst there has been an increasing trend of women holding academic positions in European Higher Tertiary Institutions (HTIs), leadership positions are held predominantly by men. The study draws on radical feminism theory with which its methodology is aligned by investigating the perceptions of both genders. To that end, the study categorizes the impediments holding women back from breaking the glass ceiling into endogenous and exogenous factors. By doing so, the authors are in a better position to recommend the implementation of policies and procedures to address this inequality and navigate towards achieving sustainable gender equality. The research was conducted using an online survey questionnaire administered among all academic and administrative staff of universities in the Republic of Cyprus, the country with the highest glass ceiling in the EU. The authors found that the binary genders differ in their perceptions of what keeps women from breaking the glass ceiling and that this is attributable to exogenous factors, namely, (a) the walls created by male leaders, reinforcing a feeling of marginalization and mansplaining; and (b) family obligations enhancing women’s experiencing a lack of time and burnout. Furthermore, the exogenous factors and the extremely gendered higher echelons of HTIs underpin the endogenous factor of self-sabotage, making women feel they would rather avoid the toxic leadership environment with its lack of professional credit, a view supported by radical feminism theory. The authors suggest practical policy implications to rectify the gender imbalance in leadership in HTIs and suggest directions for future research. Full article

To prolong the service life of asphalt pavement and reduce its maintenance cost, a fiber Bragg grating (FBG) sensor encapsulated in carboxylated carbon nanotube (CNT-COOH)-modified gel material suitable for strain monitoring of asphalt pavement was developed. Through tensile and bending tests, the effects of carboxylated carbon nanotubes on the mechanical properties of gel materials under different dosages were evaluated and the optimal dosage of carbon nanotubes was determined. Infrared spectrometer and scanning electron microscopy were used to compare and analyze the infrared spectra and microstructure of carbon nanotubes before and after carboxyl functionalization and modified gel materials. The results show that the incorporation of CNTs-COOH increased the tensile strength, elongation at break, and tensile modulus of the gel material by 36.2%, 47%, and 17.2%, respectively, and increased the flexural strength, flexural modulus, and flexural strain by 89.7%, 7.5%, and 63.8%, respectively. Through infrared spectrum analysis, it was determined that carboxyl (COOH) and hydroxyl (OH) were successfully introduced on the surface of carbon nanotubes. By analyzing the microstructure, it can be seen that the carboxyl functionalization of CNTs improved the agglomeration of carbon nanotubes. The tensile section of the modified gel material is rougher than that of the pure epoxy resin, showing obvious plastic deformation, and the toughness is improved. According to the data from the calibration experiment, the strain and temperature sensitivity coefficients of the packaged sensor are 1.9864 pm/μm and 0.0383 nm/°C, respectively, which are 1.63 times and 3.61 times higher than those of the bare fiber grating. The results of an applicability study show that the internal structure strain of asphalt rutting specimen changed linearly with the external static load, and the fitting sensitivity is 0.0286 με/N. Combined with ANSYS finite element analysis, it is verified that the simulation analysis results are close to the measured data, which verifies the effectiveness and monitoring accuracy of the sensor. The dynamic load test results reflect the internal strain change trend of asphalt mixture under external rutting load, confirming that the encapsulated FBG sensor is suitable for the long-term monitoring of asphalt pavement strain. Full article

To address critical technical challenges in coalbed methane fracturing, including the uncontrollable release rate of conventional breaker agents and incomplete gel breaking, this study designs and fabricates an intelligent controlled-release breaker system based on paraffin-coated mesoporous silica nanoparticle carriers. Three types of mesoporous silica (MSN) carriers with distinct pore sizes are synthesized via the sol-gel method using CTAB, P123, and F127 as structure-directing agents, respectively. Following hydrophobic modification with octyltriethoxysilane, n-butanol breaker agents are loaded into the carriers, and a temperature-responsive controlled-release system is constructed via paraffin coating technology. The pore size distribution was analyzed by the BJH model, confirming that the average pore diameters of CTAB-MSNs, P123-MSNs, and F127-MSNs were 5.18 nm, 6.36 nm, and 6.40 nm, respectively. The BET specific surface areas were 686.08, 853.17, and 946.89 m²/g, exhibiting an increasing trend with the increase in pore size. Drug-loading performance studies reveal that at the optimal loading concentration of 30 mg/mL, the loading efficiencies of n-butanol on the three carriers reach 28.6%, 35.2%, and 38.9%, respectively. The release behavior study under simulated reservoir temperature conditions (85 °C) reveals that the paraffin-coated system exhibits a distinct three-stage release pattern: a lag phase (0–1 h) caused by paraffin encapsulation, a rapid release phase (1–8 h) induced by high-temperature concentration diffusion, and a sustained release phase (8–30 h) attributed to nano-mesoporous characteristics. This intelligent controlled-release breaker demonstrates excellent temporal compatibility with coalbed methane fracturing processes, providing a novel technical solution for the efficient and clean development of coalbed methane. Full article

Global warming and rapid urbanization have increased population exposure to heatwaves, with compound day- and night-time heatwaves (CDNH) posing greater health risks than individual heatwave events. Although air conditioning (AC) adaptation effectively mitigates heat-related impacts, its role in reducing CDNH exposure under climate change remains unknown. Using meteorological and socioeconomic data, this study quantified population exposure to CDNHs and the impacts that could be avoided through AC adaptation across China and its regional variations. Results show that CDNH exposure risks were particularly high in the middle–lower Yangtze–Huaihe Basin and south China, with an increasing trend observed over the period of 2001–2022. AC adaptation has reduced the exposure risk and its upward trend by 5.85% and 37.87%, respectively, with higher mitigating effects in urban areas. By breaking down the total exposure changes into climatic, demographic, and AC-driven changes, this study reveals that increased AC contributes 10.16% to exposure reduction, less than the effect of climate warming (59.80%) on the exposure increases. These findings demonstrate that expanding AC adaptation alone is insufficient to offset climate-driven increases in exposure, highlighting the urgent need for more effective adaptation measures to address climate change and thereby alleviate its adverse impacts on human beings. Full article

Tung oil, as dry oil, can quickly dry and polymerize into tough and glossy waterproof coatings, with a very high application value. Tung oil was used as a core material to prepare Tung oil microcapsules coated with chitosan–Arabic gum, and the preparation process of the microcapsules was optimized. The effect of adding a UV coating on the performance of the microcapsules was explored. Under the conditions of a core–wall mass ratio of 0.5:1.0, pH value of 3.5, mass ratio of chitosan to Arabic gum of 1.0:4.0, and spray drying temperature of 130 °C, Tung oil microcapsules coated with chitosan–Arabic gum had a higher yield and coverage rate, which were 32.85% and 33.20%, respectively. With the increase of the spray drying temperature during preparation, the roughness of the coating first increased and then decreased, the visible light transmittance decreased first and then increased, and the glossiness showed an overall downward trend. The self-repairing rate decreased gradually. When the microcapsules #11 were added to the UV topcoat at 5%, the coating can obtain excellent comprehensive properties; the roughness was 0.79 μm, elongation at break was 5.04%, visible light transmittance was 77.96%, gloss loss rate was 10.95%, and self-repairing rate was 20.47%. Full article

This study investigates the effects of repeated mechanical recycling on the structural, thermal, mechanical, and aesthetic properties of poly(butylene succinate) (PBS), a commercially available bio-based and biodegradable aliphatic polyester. PBS production scraps were subjected to five consecutive recycling cycles through semi-industrial extrusion compounding followed by injection molding to simulate realistic mechanical reprocessing conditions. Melt mass-flow rate (MFR) analysis revealed a progressive increase in melt fluidity. Initially, the trend of viscosity followed the melt flow rate; however, increasing the reprocessing number (up to 5) resulted in a partial recovery of viscosity, which was caused by chain branching mechanisms. The phenomenon was also confirmed by data of molecular weight evaluation. Differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) confirmed the thermal stability of the polymer, with minimal shifts in glass transition, crystallization, and degradation temperatures during the reprocessing cycles. Tensile tests revealed a slight reduction in strength and stiffness, but an increase in elongation at break, indicating improved ductility. Impact resistance declined moderately from 8.7 to 7.3 kJ/m² upon reprocessing; however, it exhibited a pronounced reduction to 1.8 kJ/m² at −50 °C, reflecting brittle behavior under sub-ambient conditions. Despite these variations, PBS maintained excellent color stability (ΔE < 1), ensuring aesthetic consistency while retaining good mechanical and thermal properties. Full article

The El Bola mafic–ultramafic intrusion (EBMU) in Egypt’s Northern Eastern Desert represents an example of Neoproterozoic post-collisional layered mafic–ultramafic magmatism in the Arabian–Nubian Shield (ANS). The intrusion is composed of pyroxenite, olivine gabbro, pyroxene gabbro, pyroxene–hornblende gabbro, and hornblende-gabbro, exhibiting adcumulate to heter-adcumulate textures. Mineralogical and geochemical analyses reveal a coherent trend of fractional crystallization. Compositions of whole rock and minerals indicate a parental magma of ferropicritic affinity, derived from partial melting of a hydrous, metasomatized spinel-bearing mantle source, likely modified by subduction-related fluids. Geothermobarometric calculations yield crystallization temperatures from ~1120 °C to ~800 °C and pressures from ~5.2 to ~3.1 kbar, while oxygen fugacity estimates suggest progressive oxidation (log fO₂ from −17.3 to −15.7) during differentiation. The EBMU displays Light Rare Earth element (LREE) enrichment, trace element patterns marked by Large Ion Lithophile Element (LILE) enrichment, Nb-Ta depletion and high LILE/HFSE (High Field Strength Elements) ratios, suggesting a mantle-derived source that remained largely unaffected by crustal contribution and was metasomatized by slab-derived fluids. Tectonic discrimination modeling suggests that EBMU magmatism was triggered by asthenospheric upwelling and slab break-off. Considering these findings alongside regional geologic features, we propose that the mafic–ultramafic intrusion from the ANS originated in a tectonic transition between subduction and collision (slab break-off) following the assembly of Gondwana. Full article

Against the backdrop of sustainable development and from a macro perspective, this paper focuses on the cultural and tourism industry, measures its sustainable development efficiency, analyzes influencing factors, and systematically explores improvement paths. Based on the theoretical perspective of sustainable development, this study has constructed an evaluation index system for measuring the sustainable development level of the cultural and tourism industry across four dimensions, as follows: cultural and tourism economic construction, cultural and tourism basic resources, social basic support, and ecological environment quality. The range entropy value was adopted to measure the sustainable development level of the cultural and tourism industry in 31 provinces of China from 2006 to 2023. The results show that the sustainable development level of China’s cultural and tourism industry is generally low, but shows an increasing trend. In terms of the annual growth rate of regional scores, the trend is as follows: North China (7.05%) > Central South (6.00%) > East China (5.97%) > Southwest (5.03%) > Northwest (4.56%) > Northeast (2.94%). This indicates considerable room for improvement in the future. Furthermore, this study used kernel density estimation to analyze the distribution dynamics and evolution trends of the sustainable development level of the cultural and tourism industry and its scores at all levels, revealing differences in development levels among provinces and regions. Finally, this study has innovatively adopted the fsQCA method to explore improvement paths for the sustainable development level of the cultural and tourism industry, and identified three implementation paths: “openness–human resources–consumption–environment-driven”, “human resources–consumption–environment-driven”, and “openness–environment-driven”. By constructing a multi-condition combination model, this study breaks through the limitations of traditional single-factor analysis and reveals multiple concurrent causal relationships in complex situations. This approach showcases the differentiated development models of each province under the interacting effects of multi-dimensional factors, and provides policymakers with a basis for precise policy implementation “tailored to local conditions and multi-dimensional collaboration”. Full article

During the dry machining of 6061 aluminum alloy, cemented carbide tools often suffer from severe wear and built-up edge (BUE) formation, which significantly shortens tool life. Inspired by the non-smooth surface structure of dung beetles, this study proposes an elliptical dimple–groove composite bionic micro-texture, applied to the rake face of cemented carbide tools to enhance their cutting performance. Four types of tools with different surface textures were designed: non-textured (NT), single-groove texture (PT), circular dimple–groove composite texture (AKGC), and elliptical dimple–groove composite texture (TYGC). The cutting performance of these tools was analyzed through three-dimensional finite element simulations using the Deform-3D (version 11.0, Scientific Forming Technologies Corporation, Columbus, OH, USA) software program. The results showed that, compared to the NT tool, the TYGC tool exhibited the best performance, with a reduction in the main cutting force of approximately 30%, decreased tool wear, and significantly improved chip-breaking behavior. Based on the simulation results, a response surface model was constructed to optimize key texture parameters, and the optimal texture configuration was obtained. In addition, a theoretical model was developed to reveal the mechanism by which the micro-texture reduces interfacial friction and temperature rises by shortening the effective contact length. To verify the accuracy of the simulation and theoretical analysis, cutting experiments were further conducted. The experimental results were consistent with the simulation trends, and the TYGC tool demonstrated superior performance in terms of cutting force reduction, smaller adhesion area, and more stable cutting behavior, validating both the simulation model and the proposed texture design. This study provides a theoretical foundation for the structural optimization of bionic micro-textured cutting tools and offers an in-depth exploration of their friction-reducing and wear-resistant mechanisms, showing promising potential for practical engineering applications. Full article

Symmetries and symmetry-breaking play significant roles in data security. Digital watermarking is widely employed in information security fields such as copyright protection and traceability. With the continuous advancement of technology, the research into and application of digital watermarking face numerous challenges. To gain a comprehensive understanding of the current research status and trends in the development of digital watermarking, this paper conducts a bibliometric analysis using the CiteSpace software, focusing on 8621 publications related to digital watermarking (watermark/watermarking) from the Web of Science (WOS) Core Collection database, spanning from 2004 to 2024. This study explores the research landscape and future trends in digital watermarking from various perspectives, including annual publication volume, keyword co-occurrence and burst detection, leading authors, research institutions, and publishing countries or regions. The results reveal a regional concentration of research efforts, with early research being primarily dominated by the United States, Taiwan, and South Korea, while recent years have seen a rapid rise in research from China and India. However, global academic collaboration remains relatively fragmented and lacks a well-integrated international research network. Keyword analysis indicates that research hotspots have expanded from traditional copyright protection to data integrity verification, multimedia watermarking, and the incorporation of intelligent technologies. Notably, the introduction of deep learning has propelled watermarking algorithms toward greater sophistication and intelligence. Using CiteSpace, this study is the first to systematically illustrate the dynamic evolution of digital watermarking research over the past 20 years, focusing on thematic trends and regional distributions. Unlike previous reviews that rely mainly on qualitative analyses, this study offers a quantitative and visualized perspective. These findings provide concrete references for the future development of more targeted research efforts. Full article

Ecosystem service flow (ESF) provides a new perspective for understanding the spatial transfer of ecosystem services across urban administrative boundaries, which is of significant importance for optimizing the regional ecological resource allocation. Taking the Yangtze River Delta (YRD) urban agglomeration as a case study, this study analyzed the spatiotemporal evolution characteristics of the ecosystem service value (ESV) and ESF in 41 cities of the region from 2000 to 2020, combining the modified equivalence factor method and the breaking-point model. It also revealed the regional division and evolution patterns of per area ESV and per capita GDP based on ESF in the YRD. The results showed that from 2000 to 2020, the overall ESV in the YRD exhibited a declining trend, with a spatial distribution showing higher values in the south and lower values in the north. Forest contributed over 50% of total ESV, while the value of hydrological regulation services consistently held the largest proportion and contributed the most significant growth. The overall decline in ESF was only 0.6%, with more than 70% of the flow occurring within provincial boundaries. Hangzhou, Taizhou (Zhejiang), and Chuzhou had the highest net outflows, while Jinhua, Changzhou, and Taizhou (Jiangsu) led in net inflows. The number of service-providing areas (SPAs) and service beneficiary areas (SBAs) remained relatively stable. Furthermore, a four-quadrant framework based on ESF, per area ESV, and per capita GDP was constructed, showing that the cities in the YRD mainly shifted between Quadrants I, II, and IV, with several cities transitioning from Quadrant III to II. Based on these findings, optimized management strategies for the coordinated economic-ecological development of the YRD are proposed. Full article

Background: The Bullwhip and Ripple effects are systemic phenomena that disrupt supply chain performance. However, research often neglects their connection to resilience. This article presents a hybrid literature review examining how both effects are addressed about supply chain resilience, focusing on methodological and conceptual trends. Methods: The review combines thematic analysis of studies from Web of Science and ScienceDirect (2000–2023) with bibliometric trend modeling using Long Short-Term Memory neural networks to detect nonlinear patterns and disciplinary dynamics. Results: While 64.7% of the reviewed works explicitly link the Bullwhip Effect or Ripple Effect to resilience, only 11.7% of those focused on the Bullwhip Effect offer models with clear practical use. A structural break in 2019 marks a notable rise in research connecting these effects to resilience. Nonlinear modeling dominates (88.23%) through network theory and system dynamics. Social, Engineering and Business Sciences drive Bullwhip-related studies, while Economics, Computer Science, and Social Sciences lead Ripple-related research. Business, Energy, and Social Sciences strongly influence the integration of the Ripple Effect into supply chains. A modeling typology is proposed, and neural network techniques uncover key bibliometric patterns. Conclusions: The review highlights limited practical application and calls for more adaptive, integrative research approaches. Full article

The aim of the homogenization of climatic time series is to remove non-climatic biases from the observed data, which are caused by technical or environmental changes during the period of observations. This bias removal is generally more successful for long-term trends and annual means than for monthly and daily values. The homogenization of probability distribution (HPD) may improve data accuracy even for daily data when the signal-to-noise ratio favors its application. HPD can be performed by quantile matching or spatial interpolations, but both of them have drawbacks. This study presents a new algorithm which helps to increase homogenization accuracy in all temporal and spatial scales. The new method is similar to quantile matching, but section mean values of the probability distribution function (PDF) are compared instead of individual daily values. The input dataset of the algorithm is identical with the homogenization results for section means of the studied time series. The algorithm decides about statistical significance for each break detected during the homogenization of the section means, and skips the insignificant breaks. Correction terms for removing the inhomogeneity biases of PDF are calculated jointly by a Benova-like equation system, a low pass filter is used for smoothing the prime results, and the mean value of the input time series between two consecutive detected breaks is preserved for each of such sections. This initial version does not deal with seasonal variations either during HPD or in other steps of the homogenization. The method has been tested connecting HPD to ACMANTv5.3, and using overall 8 wind speed and relative humidity datasets of the benchmark of European project INDECIS. The results show 4 to 12 percent RMSE reduction by HPD in all temporal scales, except for the extreme tails where a part of the results are weaker. Full article

Various fields within biological and psychological inquiry recognize the significance of exploring long-range temporal correlations to study phenomena. However, these fields face challenges during this transition, primarily stemming from the impracticality of acquiring the considerably longer time series demanded by canonical methods. The Bayesian Hurst–Kolmogorov (HK) method estimates the Hurst exponents of time series—quantifying the strength of long-range temporal correlations or “fractality”—more accurately than the canonical detrended fluctuation analysis (DFA), especially when the time series is short. Therefore, the systematic application of the HK method has been encouraged to assess the strength of long-range temporal correlations in empirical time series in behavioral sciences. However, the Bayesian foundation of the HK method fuels reservations about its performance when artifacts corrupt time series. Here, we compare the HK method’s and DFA’s performance in estimating the Hurst exponents of synthetic long-range correlated time series in the presence of additive white Gaussian noise, fractional Gaussian noise, short-range correlations, and various periodic and non-periodic trends. These artifacts can affect the accuracy and variability of the Hurst exponent and, therefore, the interpretation and generalizability of behavioral research findings. We show that the HK method outperforms DFA in most contexts—while both processes break down for anti-persistent time series, the HK method continues to provide reasonably accurate H values for persistent time series as short as $N=64$ samples. Not only can the HK method detect long-range temporal correlations accurately, show minimal dispersion around the central tendency, and not be affected by the time series length, but it is also more immune to artifacts than DFA. This information becomes particularly valuable in favor of choosing the HK method over DFA, especially when acquiring a longer time series proves challenging due to methodological constraints, such as in studies involving psychological phenomena that rely on self-reports. Moreover, it holds significance when the researcher foreknows that the empirical time series may be susceptible to contamination from these processes. Full article

Mathematical models and methods serve as fundamental tools for studying ice-related phenomena in the Yellow River. River ice is driven and constrained by hydrometeorological and geographical conditions, creating a complex system. Regarding the Yellow River, there are some uncertainties that manifest in unique features in this context, including ice–water–sediment mixed transport processes and the distribution of sediment both within the ice and on its surface. These distinctive characteristics are considered to different degrees across different scales. Mathematical models for Yellow River ice developed over the past few decades not only encompass models for the large-scale deterministic evolution of river ice formation and melting, but also uncertainty parameter schemes for deterministic mathematical models reflecting the Yellow River’s particular ice-related characteristics. Moreover, there are modern mathematical results quantitatively describing these characteristics with uncertainty, allowing for a better understanding of the unique ice phenomena in the Yellow River. This review summarizes (a) universal equations established according to thermodynamic and hydrodynamic principles in river ice mathematical models, as well as (b) uncertainty sources caused by the river’s characteristics, ice properties, and hydrometeorological conditions, embedded in parametric schemes reflecting the Yellow River’s ice. The intractable uncertainty-related problems in space–sky–ground telemetric image segmentation and the current status of mathematical processing methods are reviewed. In particular, the current status and difficulties faced by various mathematical models in terms of predicting the freeze-up and break-up times, the formation of ice jams and dams, and the early warning of ice disasters are presented. This review discusses the prospects related to the uncertainties in research results regarding the simulation and prediction of Yellow River ice while also exploring potential future trends in research related to mathematical methods for uncertain problems. Full article