Search Results (190,313)

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article

Background/Objectives: Additive manufacturing (AM) technology has emerged as an innovative approach in dentistry. Recently, manufacturers have developed permanent resins engineered explicitly for the fabrication of definitive prostheses using AM techniques. This systematic review evaluated the mechanical and physical properties of 3D-printed permanent resins in comparison to milled resins and hybrid ceramics for the fabrication of indirect dental restorations. Methods: Three electronic databases—Scopus, Web of Science, and PubMed—were searched for English-language articles. Two independent researchers conducted study selection, data extraction, quality assessment, and the evaluation of the certainty of evidence. In vitro studies assessing the mechanical and physical properties of the permanent resins were included in this review. Results: A total of 1779 articles were identified through electronic databases. Following full-text screening and eligibility assessment, 13 studies published between 2023 and 2024 were included in this qualitative review. The investigated outcomes included physical properties (surface roughness, color changes, water sorption/solubility) and mechanical properties (flexural strength, elastic modulus, microhardness). Conclusions: Three-dimensionally printed permanent resins show promising potential for fabricating indirect dental restorations. However, the current evidence regarding their mechanical and physical properties remain limited and inconsistent, mainly due to variability in study methodologies. Full article

This study integrates the Theory of Planned Behavior and the Theory of Consumption Values through a mixed-methods approach (structured interview and structural equation model) to investigate cross-platform disposal behaviors for idle clothing on social media and second-hand platform ecosystems. The study reconstructs traditional theoretical variables: psychological motivation dimension (platform-enabled green attitude, social circle environmental demonstration, and cross-platform behavioral control) and perceived value dimension (functional integration value perception, socialized emotional empowerment, and community identity value). Key findings: Cross-platform behavioral control is the strongest predictor of behavioral intention. In the value dimension, emotional value has the strongest direct impact on disposal intentions, functional integration is key to enhancing behavioral control, and community identity value most significantly impacts the platform-enabled green attitude and the social circle environmental demonstration. Finally, proposing a governance framework of “technological empowerment–emotional resonance–identity motivation”, offering theoretical foundations for optimizing platform interoperability and formulating digital environmental policies. Full article

Insects and their bacterial endosymbionts form intricate ecological relationships, yet their role in host–pathogen interactions are not fully elucidated. The small brown planthopper (Laodelphax striatellus), a polyphagous pest of cereal crops, acts as a key vector for rice stripe virus (RSV), a significant threat to rice production. This study aimed to compare the endosymbiont community structures of nonviruliferous and RSV-viruliferous L. striatellus populations using 16S rRNA gene sequencing with high-throughput sequencing technology. Wolbachia was highly dominant in both groups; however, the prevalence of other endosymbionts, specifically Rickettsia and Burkholderia, differed markedly depending on RSV infection. Comprehensive microbial diversity and composition analyses revealed distinct community structures between nonviruliferous and RSV-viruliferous populations, highlighting potential interactions and implications for vector competence and virus transmission dynamics. These findings contribute to understanding virus-insect-endosymbiont dynamics and could inform strategies to mitigate viral spread by targeting symbiotic bacteria. Full article

Background/Objectives: Viral respiratory infections have a significant impact on global health and the economy. While vaccines are effective in preventing infection, they might not be available or sufficient when used alone and must be complemented by specific therapeutic strategies. The development of new antiviral agents is increasingly important due to the continual emergence of novel respiratory pathogens. Previously we identified bovine lactoferrin (bLf)-derived tetrapeptides and peptidomimetics that showed potent in vitro activity against the influenza A virus in the picomolar range. Methods: Inspired by these results, in this study, we evaluated the antiviral potential of these compounds against HCoV-229E, a human coronavirus that can cause severe disease in immunocompromised individuals, using a compound repositioning approach. Results: Functional studies revealed that SK(N-Me)HS (3) interferes with viral entry and replication, while compound SNKHS (5) primarily blocks infection in the early stages. Biophysical analyses confirmed the occurrence of high-affinity binding to the viral spike protein, and computational studies suggested that the compounds target a region involved in conformational changes necessary for membrane fusion. Conclusions: These findings highlight these compounds as promising candidates for coronavirus entry inhibition and underscore the value of compound repurposing in antiviral development. Full article

Backgrounds and aim: Protective effects of natural compounds have been suggested in the prevention and treatment of radiation-induced mucositis or bacterial infections. In this study, the protective effects of proanthocyanidin-rich grape seed extract (GSE) on bacterial Lipopolysaccharide (LPS) and radiation-induced epithelial barrier damage and Reactive Oxygen Species (ROS) production were investigated in an in vitro model. Methods: Human intestinal epithelial cells Caco-2, previously treated with LPS, GSE, or LPS + GSE, were irradiated with 10 Gy divided into five daily treatments. Epithelial barrier integrity and ROS production were measured before and after each treatment. Results: Irradiation, at different doses, significantly increased intestinal permeability and ROS production; pretreatment with GSE was able to significantly prevent the increased intestinal permeability (4.63 ± 0.76 vs. 15.04 ± 1.5; p < 0.05) and ROS production (12.9 ± 1.08 vs. 1048 ± 0.5; p < 0.0001) induced by irradiation treatment. When the cells were pretreated with LPS, the same results were observed: GSE cotreatment was responsible for preventing permeability alterations (5.36 ± 0.16 vs. 49.26 ± 0.82; p < 0.05) and ROS production (349 ± 1 vs. 7897.67 ± 1.53; p < 0.0001) induced by LPS exposure when added to the irradiation treatment. Conclusions: The results of the present investigation demonstrated, in an in vitro model, that GSE prevents the damage to intestinal permeability and the production of ROS that are induced by LPS and ionizing radiation, suggesting a potential protective effect of this extract on the intestinal mucosa during irradiation treatment. Full article

Civil aviation safety remains a critical concern globally, with continuous efforts aimed at reducing accidents and fatalities. This paper focuses on the comprehensive evaluation of civil aviation safety in the Slovak Republic over the past several years, with the main objective of identifying prevailing trends and key risk factors. A comprehensive analysis of 155 accidents and incidents was conducted based on selected operational parameters. Logistic regression was applied to identify potential causal factors influencing various levels of injury severity in aviation accidents. Moreover, the prediction model can also be used to predict the probability of specific injury severity for accidents with given parameter values. The results indicate a clear declining trend in the annual number of aviation safety events; however, the fatality rate has stagnated or slightly increased in recent years. Human error, particularly mistakes and intentional violations of procedures, was identified as the dominant causal factor across all sectors of civil aviation, including flight operations, airport management, maintenance, and air navigation services. Despite technological advancements and regulatory improvements, human-related failures persist as a major safety challenge. The findings highlight the critical need for targeted strategies to mitigate human error and enhance overall aviation safety in the Slovak Republic. Full article

Difficult, extreme operating conditions of parabolic antennas under precipitation and sub-zero temperatures require the creation of effective heating systems. The purpose of the research is to develop a multilayer coating containing two metal-ceramic layers, epoxy composite layers, carbon fabric, and an outer layer of basalt fabric, which allows for effective heating of the antenna, and to study the properties of this coating. The multilayer coating was formed on an aluminum base that was subjected to abrasive jet processing. The first and second metal-ceramic layers, Al₂O₃ + 5% Al, which were applied by high-speed multi-chamber cumulative detonation spraying (CDS), respectively, provide maximum adhesion strength to the aluminum base and high adhesion strength to the third layer of the epoxy composite containing Al₂O₃. On this not-yet-polymerized layer of epoxy composite containing Al₂O₃, a layer of carbon fabric (impregnated with epoxy resin) was formed, which serves as a resistive heating element. On top of this carbon fabric, a layer of epoxy composite containing Cr₂O₃ and SiO₂ was applied. Next, basalt fabric was applied to this still-not-yet-polymerized layer. Then, the resulting layered coating was compacted and dried. To study this multilayer coating, X-ray analysis, light and raster scanning microscopy, and transmission electron microscopy were used. The thickness of the coating layers and microhardness were measured on transverse microsections. The adhesion strength of the metal-ceramic coating layers to the aluminum base was determined by both bending testing and peeling using the adhesive method. It was established that CDS provides the formation of metal-ceramic layers with a maximum fraction of lamellae and a microhardness of 7900–10,520 MPa. In these metal-ceramic layers, a dispersed subgrain structure, a uniform distribution of nanoparticles, and a gradient-free level of dislocation density are observed. Such a structure prevents the formation of local concentrators of internal stresses, thereby increasing the level of dispersion and substructural strengthening of the metal-ceramic layers’ material. The formation of materials with a nanostructure increases their strength and crack resistance. The effectiveness of using aluminum, chromium, and silicon oxides as nanofillers in epoxy composite layers was demonstrated. The presence of structures near the surface of these nanofillers, which differ from the properties of the epoxy matrix in the coating, was established. Such zones, specifically the outer surface layers (OSL), significantly affect the properties of the epoxy composite. The results of industrial tests showed the high performance of the multilayer coating during antenna heating. Full article

In this study, we evaluated the spatial variability in droplet deposition in herbicide applications using a remotely piloted aircraft (RPA) in pasture areas. The investigation was conducted in a square grid (50.0 m × 50.0 m), with 121 sampling points, at two operational flight heights (3.0 and 4.0 m). Droplet deposition was quantified using the fluorescent dye rhodamine B, and the droplet spectrum was characterised using water-sensitive paper tags. Geostatistical analysis was implemented to characterise spatial dependence, complemented by multivariate statistical analysis. Droplet deposition ranged from 1.01 to 9.02 and 1.10–6.10 μL cm⁻² at 3.0 and 4.0 m flight heights, respectively, with the coefficients of variation between 19.72 and 23.06% for droplet spectrum parameters. All droplet spectrum parameters exhibited a moderate to strong spatial dependence (relative nugget effect ≤75%) and a predominance of adjustment to the exponential model, with spatial dependence indices ranging from 12.55 to 47.49% between the two flight heights. Significant positive correlations were observed between droplet deposition and droplet spectrum parameters (r = 0.60–0.79 at 3.0 m; r = 0.37–0.66 at 4.0 m), with the correlation magnitude decreasing as the operational flight height increased. Cross-validation indices demonstrated acceptable accuracy in spatial prediction, with a mean estimation error ranging from −0.030 to 0.044 and a root mean square error ranging from 0.81 to 2.25 across parameters and flight heights. Principal component analysis explained 99.14 and 85.72% of the total variation at 3.0 and 4.0 m flight heights, respectively. The methodological integration of geostatistics and multivariate statistics provides a comprehensive understanding of the spatial variability in droplet deposition, with relevant implications for the optimisation of phytosanitary applications performed using RPAs. Full article

To evaluate the efficiency of innovation development in China’s high-tech manufacturing industry, this paper constructs a two-stage dynamic network cross-efficiency model. This model divides innovation activities into two stages: technology research and development and achievement transformation and introduces a 2-year lag period in the technology research and development stage and a 1-year lag period in the achievement transformation stage. It proposes the overall efficiency and efficiency models for each stage. The model was applied to 30 provinces in China, and the results showed that most provinces have achieved relatively ideal results in the overall efficiency and achievement transformation stage of high-tech manufacturing, while the efficiency in the technology research and development stage is generally lower than that in the achievement transformation stage. It is recommended that enterprises increase their R&D investments, break through technological barriers, and optimize the innovation chain. Full article

Many commercial wearable sensor systems typically rely on a single continuous cardiorespiratory sensing modality, photoplethysmography (PPG), which suffers from inherent biases (i.e., differences in skin tone) and noise (e.g., motion and pressure artifacts). In this research, we present a wearable device that provides robust estimates of cardiorespiratory variables by combining three physiological signals from the upper arm: multiwavelength PPG, single-sided electrocardiography (SS-ECG), and bioimpedance plethysmography (BioZ), along with an inertial measurement unit (IMU) providing 3-axis accelerometry and gyroscope information. We evaluated the multimodal device on 16 subjects by its ability to estimate heart rate (HR) and breathing rate (BR) in the presence of various static and dynamic noise sources (e.g., skin tone and motion). We proposed a hierarchical approach that considers the subject’s skin tone and signal quality to select the optimal sensing modality for estimating HR and BR. Our results indicate that, when estimating HR, there is a trade-off between accuracy and robustness, with SS-ECG providing the highest accuracy (low mean absolute error; MAE) but low reliability (higher rates of sensor failure), and PPG/BioZ having lower accuracy but higher reliability. When estimating BR, we find that fusing estimates from multiple modalities via ensemble bagged tree regression outperforms single-modality estimates. These results indicate that multimodal approaches to cardiorespiratory monitoring can overcome the accuracy–robustness trade-off that occurs when using single-modality approaches. Full article

The rapidly changing dynamics of the digital age reshape the addiction relationship that high school students establish with technology. While smartphones remove boundaries in terms of communication and access to information, their usage triggers a source of anxiety and nomophobia. The increase in students’ anxiety levels because of their over-reliance on mobile phone use leads to significant behavioral changes in their mental health, academic performance, social interactions and financial dependency. This study examined the nomophobia levels of high school students according to selected socio-demographic indicators. Using the relational screening model, the multistage sampling technique was used to select a sample of 884 participants: 388 from Science High School and 496 from Anatolian High School (459 female, 425 male, Mage = 16.45 ± 1.14 year). Independent sample test and One-way ANOVA were applied. Depending on the homogeneity assumption of the data, Welch values were considered, and Tukey tests were applied as a second-level test from post hoc analyses. Comprehensive analyses of nomophobia levels revealed that young individuals’ attitudes towards digital technology differ significantly according to their demographic and behavioral characteristics. Variables such as gender, physical activity participation, grade level and duration of smartphone use are among the main factors affecting nomophobia levels. Female individuals and students who do not participate in physical activity exhibit higher nomophobia scores. Full article

Rising volumes of textile waste necessitate the development of more efficient recycling systems, with a primary focus on the optimization of sorting technologies. Near-infrared (NIR) spectroscopy is a state-of-the-art method for fiber identification; however, its accuracy in quantifying textile blends, particularly common polyester/cotton blend textiles, still requires refinement. This study explores the potential and limitations of NIR spectroscopy for quantifying cotton content in post-consumer textiles. A lab-scale NIR sorter and a handheld NIR spectrometer in complementary wavelength ranges were applied to a diverse range of post-consumer textile samples to test model accuracies. Results show that the commonly assumed 10% accuracy threshold in industrial sorting can be exceeded, especially when excluding textiles with <35% cotton content. Identifying and excluding the range of non-linearity significantly improved the model’s performance. The final models achieved an RMSEP of 6.6% and bias of −0.9% for the NIR sorter and an RMSEP of 3.1% and bias of −0.6% for the handheld NIR spectrometer. This study also assessed how textile characteristics—such as color, structure, product type, and alkaline treatment—affect spectral behavior and model accuracy, highlighting their importance for refining quantification when high-purity inputs are needed. By identifying current limitations and potential sources of errors, this study provides a foundation for improving NIR-based models. Full article

This study was conducted in the context of China’s latest “double carbon” policy. The objective of this study was twofold: firstly, to examine the characteristics of traditional Chinese residential skywell; and secondly, to investigate and develop climate-adaptive technologies for these structures. To this end, a study was conducted on the Huizhou skywell architecture in China. Firstly, we obtained a large amount of basic data, such as traditional buildings and skywell parameters in Huizhou through field research and on-site mapping. Second, we combined a large number of parameters and typical cases to analyze the data and determine a more scientifically typical model of Huizhou architecture and experimental coverage of skywell data. Different shapes and sizes of skywell were formed by changing the combination of skywell length and width, and the indoor wind environments of these skywell dwelling models were simulated one by one using PHOENIX (v2016) software. Finally, the data obtained from these simulations are analyzed using the variable control method, and the simulated indoor wind environments of skywells under different scale combinations in terms of skywell length, width, and length–width coupling effects are summarized and compared. The following conclusions were drawn: (1) The length of the skywell has a certain effect on all indoor wind environments, and the variation in indoor wind speed is smallest when the length of the skywell is 7.5 m. When selecting the design size of the skywell, the length of the skywell can be increased accordingly. (2) The width of the skywell for the indoor wind environment is divided into two cases, when the length of the skywell is less than 3.75 m, increasing the width of the skywell will make the indoor wind environment more unstable. However, when the length of the skywell is greater than 3.75 m, the width of the skywell is positively related to the comfort of the indoor wind environment. (3) The area of the skywell should not be too large, usually between 11.25 m² and 18.75 m². A slender skywell is more likely to provide a comfortable indoor wind environment. (4) After extensive evaluation, we found that the comfort of the indoor wind environment is high when the width of the skywell is 2 m. The optimum dimensions for the ventilation performance of the skywell are 7.5 m in length and 2 m in width. Full article

In the modern era, the use of blockchain technology has been growing rapidly, where Ethereum smart contracts play an important role in securing decentralized application systems. However, these smart contracts are also susceptible to a large number of vulnerabilities, which pose significant threats to intelligent systems and IoT applications, leading to data breaches and financial losses. Traditional detection techniques, such as manual analysis and static automated tools, suffer from high false positives and undetected security vulnerabilities. To address these problems, this paper proposes an Artificial Intelligence (AI)-based security framework that integrates Generative Adversarial Network (GAN)-based feature selection and deep learning techniques to classify and detect malware attacks on smart contract execution in the blockchain decentralized network. After an exhaustive pre-processing phase yielding a dataset of 40,000 malware and benign samples, the proposed model is evaluated and compared with related studies on the basis of a number of performance metrics including training accuracy, training loss, and classification metrics (accuracy, precision, recall, and F1-score). Our combined approach achieved a remarkable accuracy of 97.6%, demonstrating its effectiveness in detecting malware and protecting blockchain systems. Full article