Beef carcass aging, which enhances tenderness and flavor through proteolysis, is traditionally costly and slow, requiring long-term storage at temperatures near 0 °C. To reduce energy consumption, a new technique using moderate cooling room temperatures was tested. Six carcasses of Holstein bulls were used. From each carcass, two shoulders were processed in different ways: one was refrigerated at 8 °C (W), and after spraying with a solution with calcium chloride and sodium chloride, was coated with sodium alginate. The other shoulder was stored at 2 ± 1 °C as a cold control (C). After five days of aging, the shoulders were dissected, and two muscles (Caput longum triceps brachii and Supraspinatus) were subjected to physico-chemical analysis, microbiological safety assessment, and sensory testing. The remaining samples of both muscles were stored in domestic conditions for an additional 5 days at various temperatures (2, 4, 8 °C), where the same physic-chemical and sensory tests were conducted. The results showed that moderate aging temperature improved meat quality, significantly reducing the shear force (p = 0.001) and increasing sarcomere length, the myofibrillar fragmentation index, and sensory tenderness (p = 0.042, p = 0.039, and p = 0.027, respectively). However, domestic storage post-dissection should not exceed 4 °C to prevent rapid lipid oxidation, as observed at 8 °C for both muscles (p < 0.001). Mild aging temperature maintained legal safety standards, enhanced certain meat qualities, and promoted enzymatic activity similar to traditional dry aging while reducing high energy consumption.

Air pollution is a growing concern in rural areas where agricultural production can be reduced by it. This article analyses data obtained as part of a research project. The aim of this study is to understand the influence of atmospheric pressure, air temperature, air relative humidity, longitude and latitude of the location, and indoor and outdoor environment on local rural workplace diversity of air pollutants such as carbon monoxide (CO) and suspended particulate matter (SPM), as well as the contribution of these variables to changes in such air pollutants. The focus is on four topics: motivation, innovation and creativity, leadership, and social responsibility. Furthermore, this study developed an artificial neural network (ANN) model to predict CO and SPM concentrations in the air based on data collected from the mentioned inputs. The related sensors were assembled on an Arduino Mega 2560 board to form a field-portable device to detect air pollutants and meteorological parameters. The sensors included an MQ7 sensor for CO concentration measurement, a Sharp GP2Y1010AU0F dust sensor for SPM concentration measurement, a DHT11 sensor for air temperature and air relative humidity measurement, and a BMP180 sensor for air pressure measurements. The longitude and latitude of the location were measured using a smartphone. Measurements were conducted from 20 December 2021 to 16 July 2022. Results showed that the overall average outdoor CO and SPM concentrations were 10.97 ppm and 231.14 μg/m3 air, respectively. The overall average indoor concentrations were 12.21 ppm and 233.91 μg/m3 air for CO and SPM, respectively. Results showed that the ANN model demonstrated acceptable performance in predicting CO and SPM in both the training and testing phases, exhibiting a coefficient of determination (R2) of 0.575, a root mean square error (RMSE) of 1.490 ppm, and a mean absolute error (MAE) of 0.994 ppm for CO concentrations when applying the testing dataset. For SPM concentrations, the R2, RMSE, and MAE using the test dataset were 0.497, 30.301 μg/m3 air, and 23.889 μg/m3 air, respectively. The most influential input variable was air pressure, with contribution rates of 22.88% and 22.82% in predicting CO and SPM concentrations, respectively. The acceptable performance of the developed ANN model provides potential advances in air quality management and agricultural planning, enabling a more accurate and informed decision-making process regarding air pollution. The results of short-term estimation of CO and SPM concentrations suggest that the accuracy of the ANN model needs to be improved through more comprehensive data collection or advanced machine learning algorithms to improve the prediction results of these two air pollutants. Moreover, as even lower cost devices can predict CO and SPM concentrations, this study could lead to the development some kind of virtual sensor, as other air pollutants can be estimated from measurements of particulate matters.

Background: Growing environmental challenges necessitate the implementation of sustainable practices across various sectors, including sports club management. The aim of this study was to investigate environmental management practices in martial arts clubs in Poznan, Poland, with a particular focus on energy and water conservation, waste management, and the promotion of sustainable mobility. Materials and Methods: A qualitative study was conducted with a group of fourteen martial arts clubs operating in Poznan. A semi-structured interview method was employed with club owners and managers, asking about their approaches to reducing energy and water consumption, waste sorting, waste reduction, and promoting sustainable transportation. Data analysis involved thematic analysis, where the practices adopted by individual clubs were compared and qualitatively assessed based on recurring themes. Results: The results indicate significant variability in approaches among the surveyed clubs. Only one club implements comprehensive solutions for energy and water conservation and four take moderate actions, while nine do not pay much attention to these issues. Similarly, only some clubs actively engage in waste sorting and waste reduction efforts, while others do not see the need for such actions. In terms of promoting sustainable transportation, some clubs encourage the use of bicycles and public transport, but the majority do not take any action in this regard. Conclusion: The study reveals that most martial arts clubs in Poznan do not prioritize sustainable environmental management practices, citing other priorities such as athlete comfort and organizational needs. Implementing more systematic pro-ecological actions in these facilities requires greater awareness and support in terms of knowledge and financial resources. These findings provide practical insights for martial arts clubs in Poznan, suggesting that by adopting more comprehensive sustainability practices, they can improve their environmental impact while enhancing their community engagement and organizational reputation.

The authors would like to make the following corrections to the published paper [...]

Air pollution can induce diseases and increase the risks of death, and it also has close links with climate change. Carbonaceous matter is an important component of aerosols, but studies quantifying the source apportionment of carbonaceous compositions in different-sized aerosols from a stable carbon isotopic perspective remain scarce. In this study, fine (particulate size < 2.5 μm) and coarse (particulate size 2.5~10 μm) particles were collected from December 2021 to February 2022 (winter) and from June to August 2022 (summer) in the tropical city of Haikou; the concentrations of water-soluble inorganic ions (WSIIs) and total carbonaceous matter (TC) and the stable carbon isotope of TC (δ13C-TC) values in both fine and coarse particles were analyzed. Higher concentrations of TC, SO42−, NO3−, and NH4+ but lower δ13C-TC values in fine particles than those in coarse particles in both winter and summer indicated that combustion-related emissions dominate fine particulate TC sources. The δ13C-TC values coupled with the stable isotope mixing model in R (SIAR) results showed that combustion-related emissions contributed 77.5% and 76.6% to the TC of fine particles in winter and summer, respectively. Additionally, the lowest δ13C-TC values were observed in summertime fine particles; plant physiological activity was identified as an important source of fine particulate TC in summer and contributed 12.4% to fine particulate TC. For coarse particles, higher δ13C-TC values and Ca2+ and Na+ concentrations but lower TC concentrations implied significant contributions from natural emissions (29.2% in winter and 44.3% in summer) to coarse particulate TC. This study underscores that instead of fossil fuels and biomass, clean energy can decrease 45–78% of aerosol TC at Haikou. In addition, our results also provide a dataset for making environmental policy and optimizing the energy structure, which further favors the sustainable development of air quality.

China attaches great importance to land use and ecological civilization; hence, clarifying the relationship of land use on ecological resilience is crucial for urban development. The aim of this paper is to study the impact of land-use carbon efficiency on ecological resilience and the moderating role played by different environmental regulatory policies between the two, with the aim of providing a research basis and decision-making reference for the country’s ecological high-quality development by proposing suggestions for different subjects based on the results of this study. Taking 30 provinces and cities in mainland China from 2009 to 2022 as samples, the authors constructed an indicator system to measure their ecological resilience using the entropy method, measured their land-use carbon efficiency using the super SBM, and verified the mechanism of land-use carbon efficiency on ecological resilience by using the bidirectional fixed-effects model. Robustness and endogeneity tests confirmed the validity of the regression results. The following is a summary of this study’s findings: (1) Land-use carbon efficiency can enhance ecological resilience through various mechanisms such as scale promotion, structural upgrading, and technological progress. (2) Regional research shows that different regions have distinct effects of land-use carbon efficiency on ecological resilience. The northeastern region shows a non-significant inhibitory effect, whereas the eastern, middle, and western regions show varying degrees of promotion effects. Land-use carbon efficiency contributes to increased ecological resilience in resource-based and non-resource-based provinces, with resource-based provinces witnessing a greater increase in ecological resilience. The effects of land-use carbon efficiency on different aspects of ecological resilience are diverse, with ecosystem resistance and recovery being empowered. However, the precise mechanism through which ecosystem adaptability influences ecological resilience remains unclear. (3) Moreover, there is variation in the moderating impact of environmental legislation. Command-and-control environmental regulation impedes the positive impact of land-use carbon efficiency, and market-incentive environmental regulation strengthens their relationship, while spontaneous-participation environmental regulation does not significantly enhance their connection. It provides a new theoretical perspective for the study of ecological resilience, deepens the understanding of ecological resilience, and provides theoretical support for enhancing the resilience of ecosystems.

This study aims to evaluate the potential of using ChatGPT at the primary school level from the teachers’ perspective within a sustainability framework. The research was conducted as a qualitative case study involving 40 primary school teachers in Turkey during the 2023–2024 academic year, all of whom had no prior experience with ChatGPT. Data collection tools included semi-structured interview forms and researcher diaries developed by the researchers. The data obtained were analysed using content analysis. The findings indicate that most primary school teachers believe ChatGPT is suitable for primary education and can contribute to Sustainable Development Goal (SDG) 4. Additionally, teachers noted that ChatGPT enriches the teaching process and is user-friendly. These findings suggest potential contributions to SDG 4.1 and SDG 4.2. However, concerns were raised regarding ChatGPT’s potential to provide false information, which may negatively impact SDG 4.7. The study also identified that ChatGPT is particularly suitable for mathematics, Turkish, and English courses. This study’s main contribution is that it shows how ChatGPT can help sustainable practices in primary education by getting teachers more involved and meeting specific curriculum needs. This gives us useful information for incorporating AI tools into education that is in line with SDG 4. It is recommended that training programs about ChatGPT and similar AI-supported tools be organised for teachers and parents.

A better understanding of climate change impact on dry/wet conditions is crucial for agricultural planning and the use of renewable energy, in terms of sustainable development and preservation of natural resources for future generations. The objective of this study was to investigate the impact of climate change on temporal fluctuations of dry/wet conditions in Serbia on multiple temporal scales through multifractal analysis of the standardized precipitation evapotranspiration index (SPEI). We used the well-known method of multifractal detrended fluctuation analysis (MFDFA), which is suitable for the analysis of scaling properties of nonstationary temporal series. The complexity of the underlying stochastic process was evaluated through the parameters of the multifractal spectrum: position of maximum α0 (persistence), spectrum width W (degree of multifractality) and skew parameter r dominance of large/small fluctuations). MFDFA was applied on SPEI time series for the accumulation time scale of 1, 3, 6 and 12 months that were calculated using the high-resolution meteorological gridded dataset E-OBS for the period from 1961 to 2020. The impact of climate change was investigated by comparing two standard climatic periods (1961–1990 and 1991–2020). We found that all the SPEI series show multifractal properties with the dominant contribution of small fluctuations. The short and medium dry/wet conditions described by SPEI-1, SPEI-3, and SPEI-6 are persistent (0.5<α0<1); stronger persistence is found at higher accumulation time scales, while the SPEI-12 time series is antipersistent (0<α0−1<0.5). The degree of multifractality increases from SPEI-1 to SPEI-6 and decreases for SPEI-12. In the second period, the SPEI-1, SPEI-3, and SPEI-6 series become more persistent with weaker multifractality, indicating that short and medium dry/wet conditions (which are related to soil moisture and crop stress) become easier to predict, while SPEI-12 changed toward a more random regime and stronger multifractality in the eastern and central parts of the country, indicating that long-term dry/wet conditions (related to streamflow, reservoir levels, and groundwater levels) become more difficult for modeling and prediction. These results indicate that the complexity of dry/wet conditions, in this case described by the multifractal properties of the SPEI temporal series, is affected by climate change.

Shared bikes are widely used in Chinese cities as a green and healthy solution to address the First/Last Mile issue in public transit access. However, usage declines in cold regions during winter due to harsh weather conditions. While climate factors cannot be changed, enhancing the built environment can promote green travel even in winter. This study uses data from Shenyang, China, to investigate how built environment attributes impact the travel satisfaction of shared bike users who utilize bikes as a First/Last Mile solution to access public transit in winter cities. By employing machine learning algorithms combined with Asymmetric Impact-Performance Analysis (AIPA) and grounded theory, we systematically identify the key attributes and rank them based on their asymmetric impact and urgency of improvement. The analysis revealed 19 key attributes, 17 of which are related to the built environment, underscoring its profound influence on travel satisfaction. Notably, factors such as the profile design of cycling paths and safety facilities along routes were identified as high priorities for improvement due to their significant potential to enhance satisfaction. Meanwhile, features like barrier-free access along paths and street greenery offer substantial opportunities for improvement with more modest efforts. Our research provides critical insights into the nuanced relationship between built environment features and travel satisfaction for First/Last Mile shared bike users. By highlighting priority improvements, we offer urban planners and policymakers a framework for creating livable, sustainable environments that support green travel even in harsh winter conditions.

This study seeks to explore the relationship between science teachers’ computational thinking skills, collaborative learning attitudes, and their creativity in the context of sustainable education. The study adopted an explanatory sequential design, which is one of the designs used in mixed-method research. A total of 369 science teachers were included in the quantitative phase of the study. Quantitative data were collected using three different scales. These scales included the “Computational Thinking Scale”, “Online Cooperative Learning Attitude Scale (OCLAS)”, and “Creative Self-Efficacy Scale”. Structural Equation Modelling (SEM), confirmatory factor analysis, and path analysis were conducted to analyze the quantitative data. The qualitative phase of the study consisted of nine science teachers. Data were collected with a semi-structured interview form by considering the scores obtained from the scales. Qualitative data were analyzed through descriptive analysis. It was found that science teachers’ computational thinking skills and collaborative learning attitudes significantly predicted their creativity within the framework of sustainable education. As a result of the interviews conducted, it was concluded that science teachers lacked computational thinking skills. It is critical to provide teachers with guidance on how to integrate computational thinking skills into their subject areas. Science teachers’ knowledge of computational thinking skills can be enhanced, and computational thinking skills can be included in all teacher education programs.