Regeneration and characterization of spent bleaching earth: recycling in the corn oil bleaching process

The spent bleaching earth (SBE) is a solid waste from the edible oil refining industry which generates soil contamination was successfully recycled after deoiling through an extraction process using different organic solvents, followed by heat treatment. In the current study, the effects of factors, such as solvent to (SBE) ratio [1:1–5:1], temperature [20–40 °C], and stirring time [30–60 min] on the efficiency of extracted oil were investigated by maceration method. Characterization analyses (SEM, XRD, XRFA and TGA) were carried out to compare the characteristics of samples. The best oil extraction efficiency was obtained at the highest level of solvent to (SBE) ratio (MR = 5) at 30 °C temperature and at the 45 minutes stirring time this condition led to 72.82% oil extraction yield. The corn oil bleaching efficiency using the SBE treated at optimal condition and heated at 400 °C was improved to 84.75%

An assessment of an urban protected area through a space syntax approach: the case of Ordu, Turkiye

Traditional architecture, preserved and integrated into urban life, has the potential to become a point of attraction for cities and contribute to their image. The aim of this study is to analyse the configurational characteristics of the urban conservation area and its immediate surroundings, which constitute the historical core of the city of Ordu, and the general condition of the registered architectural examples in the area. The evaluations of the spatial configuration are based on the analyses of connectivity, global and local integration, which are the basic parameters of the spatial sequence method. As a result of the study, it can be seen that the two main traffic arteries of the area, Sıtkı Can Street and Dr Osman Hilmi Memecan Street, have the highest sequence values. Although it can be seen that Sıtkı Can Street, one of these traffic arteries, is more advantageous than other streets in terms of the registered civil architecture stock in the area, it is important that restoration processes should be implemented as soon as possible in order to ensure structural and historical continuity and not to lose the registered civil architecture examples located in different parts of the study area

Effects of the COVID-19 crisis on work-life balance, mental health, and perceived health status among Hungarian defense employees: a cross-sectional study

The COVID-19 pandemic impacted profoundly on the mental and physical health of defense personnel, due to their involvement in the enforcement of COVID-19 measures and confined work environments. This cross-sectional study assessed the effects of the pandemic on work-life balance, mental health, and perceived health status among Hungarian defense employees. Data was collected from 300 employees of a Hungarian defense company using an online questionnaire that included demographics, work-related observations, mental health scales (DASS), and perceived health status (SF-12). Data was analyzed using descriptive and inferential statistics. The results indicated that work type, flexibility, and workload remained stable before, during, and after the pandemic. Compensation satisfaction (p = 0.025) showed a slight increase post-COVID-19, while organizational support did not significantly change (p > 0.05). Work-life balance significantly decreased during the pandemic (p = 0.012), and the mental health indicators stress (p = 0.005), anxiety (p < 0.001), and depression (p < 0.001) increased significantly. Reliability analysis (Cronbach’s alpha) demonstrated good internal consistency across the scales. These findings underscore the significant negative impact of the COVID-19 pandemic on the mental health of defense employees, reinforcing the need for sustained support mechanisms to promote both physical and mental well-being in this workforce

A novel hybrid model for predicting the bearing capacity of piles

Due to the uncertainty of soil condition and pile design characteristics, it is always a challenge for geotechnical engineers to accurately determine the bearing capacity of piles. The main objective of this study is to propose a hybrid model coupling least squares support vector machine (LSSVM) with an improved particle swarm optimization (IPSO) algorithm for the prediction of bearing capacity of piles. The improved PSO algorithm was used to optimize the LSSVM hyperparameters. The performance of the IPSO-LSSVM model was compared with seven artificial intelligence models, namely adaptive neuro-fuzzy inference system (ANFIS), M5 model tree (M5MT), multivariate adaptive regression splines (MARS), gene expression programming (GEP), random forest (RF), regression tree (RT) and a stacked ensemble model. Six statistical indices (e.g., coefficient of determination (R2), mean absolute error (MAE), root mean squared error (RMSE), relative root mean squared error (RRMSE), BIAS and discrepancy ratio (DR)) were used to evaluate the performance of the models. The R2, MAE, RMSE, RRMSE and BIAS values of the IPSO-LSSVM model were 1, 4.27 kN, 6.164 kN, 0.005 and 0, respectively, for the training datasets and 0.9977, 22 kN, 36.03 kN, 0.0275 and –11, respectively, for the testing datasets. Compared with the ANFIS, MARS, GEP, M5MT, RF, RT and the stacked ensemble models, the proposed IPSO-LSSVM model shows high accuracy and robustness on the test datasets. In addition, the sensitivity, uncertainty, reliability and resilience of the IPSO-LSSVM model were also analyzed in this study. First published online 22 October 202

Do emotional strategies work? Evidence from rumor clarification announcement

Financial markets are filled with rumors because of information asymmetry. Although issuing clarification announcements is the most straightforward approach for organizations, previous research has mostly focused on analyzing the influence of rumors and the heterogeneity of their clarification statements on the efficacy of rumor management. This study investigates how mood elements influence the effectiveness of 335 rumor clarification statements in China\u27s A-share market from 2019 to 2023. By employing textual sentiment analysis, event study method, and fixed-effects regression models, the primary results indicate that rumors vary in their characteristics and have diverse effects on stock price volatility. Furthermore, we find that clarification announcements effectively restore stock values, though their influence on negative rumors is somewhat restricted. Announcements with a positive mood greatly improve the effectiveness of clarification, particularly when addressing favorable rumors. The level of transparency and the characteristics of the firm\u27s information influence the impact of sentiment. Furthermore, the positive impact of sentiment is more noticeable in firms that are extremely transparent or not owned by the state

A relative measurement and monitoring of OTTV of glass facade passive building in tropical climate

Efficient and productive buildings are vital to sustainable cities, significantly contributing to Sustainable Development Goals (SDGs) 3, 7, 11, and 12. Urban areas are responsible for 80% of global energy consumption, with buildings accounting for 40%. Achieving a healthy and comfortable indoor thermal envelope depends on various factors including building function, location, layout design, openings, and materials. The building facade, particularly glass facades, is a significant contributor to both energy performance and occupant comfort. Despite their importance, few studies focus on real-time measurement and monitoring of the Overall Thermal Transfer Value (OTTV) of passive buildings, especially with glass facades. Glass allows natural light and heat exchange, impacting the overall energy performance and quality of indoor environments. This study investigates the real-time impact of temperature variations on the OTTV of glass facade passive buildings from 8 am to 5 pm, focusing on Malaysia’s tropical climate. The study’s findings revealed that the OTTV varies from 42.642 W/m2 at 11:30 am to 80.341 W/m2 at 10:30 am. The study contributes to the body of knowledge by providing valuable insights regarding the dynamic thermal behaviour of the passive building envelope. Specifically, it demonstrates how OTTV varies with changing climatic conditions such as temperature fluctuations and solar radiation

Smart tourism economics: introducing a technology-driven competitive advantages framework

Recently, in the Hospitality 5.0 era, the Smart Tourism industry has experienced disruptions due to the widespread integration of cutting-edge technologies (Artificial Intelligence, Internet of Things, Robots, Big Data, and others) enabling the overall tourist experience enhancement. This study aims to identify the main clusters of technologies adopted and to highlight the competitive advantages resulting from their implementation in Smart Tourism. A meta-analysis was conducted on a final WoS sample of 60 papers published between 2015 and 2023. Four synoptic tables showcase the technologies, the processes they facilitate, the competitive advantages, and the smart destinations where they have been implemented. The originality of this research consists of the 70 competitive advantages identified across the industry, leveraged in designing the CECoR map (Customer Experience, Costs, Revenues) and the CAdSTT framework (Competitive Advantage-driven Smart Tourism Technologies). Guidelines for supporting managers in planning and initiating projects aiming at integrating technology to increase organisational value were proposed

Artificial intelligence as applied to classifying epoxy composites for aircraft

The problem of classification of epoxy composites used for the manufacture of aircraft structures is solved by machine learning methods: neural network, reinforced trees and random forests. Classification metrics were obtained for each method used. Parameters such as precision, recall, F1 score and support were determined. The neural network classifier demonstrated the highest results. Boosted trees and random forests showed slightly lower results than the neural network method. At the same time, the classification metrics were high enough in each case. Therefore, machine learning methods effectively classify epoxy composites. The results obtained are in good agreement with the experimental ones. The prediction accuracy score obtained using each method was greater than 0.88

Assessment of emergency landing options: case study of Riga flight information region

The aim of this research, titled “Assessment of Emergency Landing Options: Case Study of Riga Flight Information Region” is to explore and analyze the feasibility of emergency landings within the Riga FIR and evaluate the necessity of an additional runway. The theoretical section of the study covers various types of aviation accidents and emergency landing procedures. The empirical part includes a thorough analysis of meteorological aerodrome reports (METAR) to assess whether weather conditions are conducive to safe landings at Lielvarde military airfield. Based on the findings, conclusions are drawn regarding the need for an additional runway. The study also examines other potential airfields, not listed in the Republic of Latvia’s Aeronautical Information Publications, which could serve as emergency landing sites. To enhance pilots’ situational awareness, digital maps have been developed to display these alternative airfields

Human factors considerations for critical maintenance tasks and their effect on the transition to digital documentation: an exploratory expert survey

Digitised maintenance documentation will soon be the norm in aviation. Failure to correctly perform maintenance tasks may lead to aviation safety hazardous events. This article explores the views of aviation maintenance subject matter experts on errors affecting critical maintenance tasks and how views can inform transition to digitised documentation. This exploratory study offers a fresh view on human factors’ implications around critical maintenance tasks and their relation to digital documentation. A cross-sectional design method was utilised. Anonymous responses were collected with a mixed-methods questionnaire from convenience sample of participants from different aircraft maintenance and continuing airworthiness management organisations. Expert opinions of 25 aircraft maintenance and technical services engineers were recorded. All participants had personal experience with maintenance errors, where human factors attributed to these errors. They highlighted the lack of human factors’ awareness and the need to strengthen their contributory role in critical maintenance tasks. Participants’ views appeared divided in terms of challenges associated with digital documentation utilisation. Positive features emerged, such as critical maintenance tasks or duplicate/independent inspections’ highlighting, notes and warnings’ higher visibility, up-to-date documentation availability and better connectivity among activities. Negative themes concentrated on the tactile nature of paper and on the additional technology knowledge requirements