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This paper examines the impact of the COVID-19 pandemic on low-carbon consumption of dairy products through informational interventions. The empirical findings seek to enlighten developing countries' efforts in coping with climate change and potential dietary transitions.

A randomized controlled trial was designed to examine the effects of purpose-differentiated information interventions on individual dairy consumption. The experiment recruited and randomly assigned 1,002 college students into four groups to receive (or not) environmental or/and health information interventions.

The empirical analysis finds that health and combined information interventions have a positive impact on dairy consumption, while environmental information interventions' effect on dairy consumption is insignificant. In the context of the pandemic, health information interventions positively affected participants' perceptions and preferences for dairy products by delivering knowledge about their role in boosting immunity. However, environmental information interventions failed to do the same things as their insignificant effects on both perception and preference.

Macro-external shocks, such as public health events, may offset the impact of universal information interventions promoting pro-environmental behaviors. For a smooth dietary transition to achieve long-term environmental sustainability, diverse stakeholders must be included in more individualized interventions to guide daily consumption, especially in developing countries with large populations.

Considering the swift adoption of innovative sustainability practices in businesses to accomplish sustainable development goals (SDGs), research on corporate sustainability has increased significantly over the years. This research intends to analyze the published literature, emphasizing the existing, emerging and future research directions on achieving the SDGs through corporate sustainability.

This research analyzed the growing trends in corporate sustainability by incorporating 2,038 Scopus articles published between 1999 and 2022 using latent Dirichlet allocation (LDA) topic modeling, bibliometrics and qualitative content analysis techniques. The bibliometric data were analyzed using performance and science mapping. Thereafter, topic modeling and content analysis uncovered the topics included under the corporate sustainability umbrella.

The findings indicate that investigation into corporate sustainability has considerably increased from 2015 to date. Additionally, the majority of studies on corporate sustainability are from the United States of America, the United Kingdom and Germany. Besides, the USA has the most collaboration in terms of co-authorship. S. Schaltegger was considered the most productive author. However, P. Bansal was ranked as the top author based on a co-citation analysis of authors. Further, bibliometric data were evaluated to analyze leading publications, journals and institutions. Besides, keyword co-occurrence analysis, topic modeling and content analysis highlighted the theoretical underpinnings and new patterns and provided directions for further research.

This study demonstrates various existing and emerging themes in corporate sustainability, which have various repercussions for academicians and organizations. This research also examines the lagging themes in the current domain.

The purpose of this paper was to study the color change kinetics of lac dye in response to aldehydes, carbon dioxide and other food spoilage metabolites for its potential application in intelligent food packaging.

UV–Vis spectroscopy was used to study the color change of dye solution. Ratio of absorbance of dye solution at 528 nm (peak of ionized form) to absorbance at 488 nm (peak of unionized form) was used to study the color change. Color change kinetics was studied in terms of change in absorbance ratio (A₅₂₈/A₄₈₈) with time using zero and first-order reaction kinetics. Lac dye-based indicator was prepared to validate the result of study for monitoring quality of strawberries.

Lac dye was orange-red in acidic medium and purple in alkaline medium. Color change of dye in response to benzaldehyde followed zero-order reaction kinetics, whereas for carbon dioxide first-order model was found best. No color change of dye solution was observed for alcohols, ketones and sulfur compounds. In the validation part, the color of the indicator label changed from purple to orange when the strawberries spoiled.

The study expands application area for lac dye as sensing reagent in intelligent food packaging for spoilage or ripeness detection of fruits and vegetables.

This study aims to assess the efficacy of nano-alumina (nano-Al₂O₃) in improving the performance of epoxy adhesives used to assemble archaeological glass. The conservators face a significant problem in assembling this type of artifact. Therefore, the assembling process is considered one of the important stages that must be taken care of to preserve these artifacts from damage and loss.

To evaluate the stability of adhesives, the samples were subjected to artificial aging under varying environmental conditions. Some investigative techniques and mechanical testing were used in this study to evaluate the selected materials. It includes a transmission electron microscope, X-ray diffraction, visual assessment, digital microscope, scanning electron microscopy (SEM), color change and tensile strength test.

The visual evaluation and the digital microscope results showed that the epoxy/nano-Al₂O₃ greatly resisted artificial aging. Although slight yellowing was present, it did not significantly affect the general appearance of the samples. On the other hand, the pure epoxy sample showed cracks of different sizes on its surface due to aging, as evidenced by SEM examination. Furthermore, epoxy/nano-Al₂O₃ has a better tensile strength (11.27 MPa) and slight color change (ΔE = 2.06).

The main objective of the experimental study was to identify appropriate adhesive materials that possess key properties such as non-yellowing and improved tensile strength by conducting various tests and evaluations. Ultimately, the goal was to identify materials that could serve as effective adhesives for assembling the archaeological glass.

High levels of interior water vapour lead to condensation and black mould that in turn represent significant risks to residential properties and their occupants. Beliefs about window opening are good predictors of the degree to which householders will actually open windows to purge their homes of water vapour, including water vapour that they themselves generate. The present study tested if a short information-giving intervention could enhance householders’ beliefs that foster window opening as purge ventilation and, in turn, lead to greater window opening.

Data were collected from 242 UK householders with robust psychometrically sound measures embedded in an online self-report survey that also presented the intervention information.

The intervention led participants, and males in particular, to have significantly greater concerns about condensation and mould and significantly less concerns about heat loss costs arising from opening windows, and these altered beliefs in turn predicted a greater intention to open windows in the future.

By sharing simple information, surveyors and other building professionals can help householders take the simple step of opening their windows and so reduce the threats that condensation and mould present to themselves and their homes.

This is the first study to test (1) a time-based model that predicted the intervention would have a positive effect on specific window opening attitudes and that those new attitudes would in turn affect window opening intentions, and (2) if the intervention had different effects on men and women.

This paper aims to provide the security and privacy for Byzantine clients from different types of attacks.

In this paper, the authors use Federated Learning Algorithm Based On Matrix Mapping For Data Privacy over Edge Computing.

By using Softmax layer probability distribution for model byzantine tolerance can be increased from 40% to 45% in the blocking-convergence attack, and the edge backdoor attack can be stopped.

By using Softmax layer probability distribution for model the results of the tests, the aggregation method can protect at least 30% of Byzantine clients.

Although the quality of health-care infrastructure and equipment influences patient’s overall health-care experience, health-care infrastructure and equipment are not always managed and maintained with the attention required. This is due mainly to the complexity of health-care infrastructure and equipment and shortage of maintenance budget. This study aims to determine if patient’s satisfaction of core health-care business is mediated by the quality of health-care infrastructure and equipment.

This cross-sectional study comprises 622 adult patients at the Physician OPD and Polyclinic of Komfo Anokye Teaching hospital, Tamale Teaching hospital and Cape Coast Teaching hospital in Ghana. Structural equation model Smart PLS was used to analyse the data.

The study results showed that the quality of health-care infrastructure and equipment has a positive significant influence (mediation) on the relationship between health-care delivery and patient’s satisfaction as well as the relationship between adequacy of health-care resources and patient’s satisfaction. However, it was shown not to have a positive significant influence (mediation) on the relationship between quality of health-care personnel and patients’ satisfaction as well as health-care administrative process and patient’s satisfaction.

First, the study findings are centred on cross-sectional data, which capture the opinion of the patients at a specific time period instead of over a period of time. Consequently, in future, though difficult to achieve, a longitudinal study can be piloted to provide more insight. Second, the data was collected from only one country (Ghana); thus, the ability to generalise the results may be a challenge.

The implication of this study is that there is the need to prudently maintain hospital infrastructure and equipment in good working condition as it has a positive effect on patients’ satisfaction of their overall health-care experience.

Most studies have concentrated on patient’s health-care experience. This study extends the knowledge of patient’s health-care experience by determining the mediating role of quality of health-care infrastructure and equipment on the relationship between patient’s satisfaction and core health-care business. There are limited studies of such nature in Ghana. Therefore, this study will provide invaluable empirical data for the health-care sector of a developing African country.

Surface roughness has a serious impact on the fatigue strength, wear resistance and life of mechanical products. Realizing the evolution of surface quality through theoretical modeling takes a lot of effort. To predict the surface roughness of milling processing, this paper aims to construct a neural network based on deep learning and data augmentation.

This study proposes a method consisting of three steps. Firstly, the machine tool multisource data acquisition platform is established, which combines sensor monitoring with machine tool communication to collect processing signals. Secondly, the feature parameters are extracted to reduce the interference and improve the model generalization ability. Thirdly, for different expectations, the parameters of the deep belief network (DBN) model are optimized by the tent-SSA algorithm to achieve more accurate roughness classification and regression prediction.

The adaptive synthetic sampling (ADASYN) algorithm can improve the classification prediction accuracy of DBN from 80.67% to 94.23%. After the DBN parameters were optimized by Tent-SSA, the roughness prediction accuracy was significantly improved. For the classification model, the prediction accuracy is improved by 5.77% based on ADASYN optimization. For regression models, different objective functions can be set according to production requirements, such as root-mean-square error (RMSE) or MaxAE, and the error is reduced by more than 40% compared to the original model.

A roughness prediction model based on multiple monitoring signals is proposed, which reduces the dependence on the acquisition of environmental variables and enhances the model's applicability. Furthermore, with the ADASYN algorithm, the Tent-SSA intelligent optimization algorithm is introduced to optimize the hyperparameters of the DBN model and improve the optimization performance.

Employee voice is crucial for organizations to identify problems and make timely adjustments. However, promoting voice in organizations is challenging. This study aims to investigate how social media use (SMU) in the workplace affects employee voice by examining its intrinsic mechanisms and boundary conditions. Specifically, this study examines the mediating roles of social identifications and the moderating effects of job-social media fit on the relationship between SMU and social identifications.

This study conducted a survey of 348 employees in China.

First, SMU affects voice through social identifications. Second, distinct identifications have different effects on voice, such that organizational identification positively affects employee voice, while relational identification positively affects promotive voice and negatively affects prohibitive voice. Third, when social media is highly suitable for the job, the positive effect of work-related SMU on organizational identification is strengthened, while the positive effect of social-related SMU on organizational identification is weakened.

The results indicate that different identifications have distinct impacts on voice. Additionally, this study reveals a double-edged sword effect of SMU on voice through different social identifications. Further, job-social media fit moderates the relationship between SMU and social identifications. These findings have important implications for organizations adopting social media.

Path planning is an important part of UAV mission planning. The main purpose of this paper is to overcome the shortcomings of the standard particle swarm optimization (PSO) such as easy to fall into the local optimum, so that the improved PSO applied to the UAV path planning can enable the UAV to plan a better quality path.

Firstly, the adaptation function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself. Secondly, the standard PSO is improved, and the improved particle swarm optimization with multi-strategy fusion (MFIPSO) is proposed. The method introduces class sigmoid inertia weight, adaptively adjusts the learning factors and at the same time incorporates K-means clustering ideas and introduces the Cauchy perturbation factor. Finally, MFIPSO is applied to UAV path planning.

Simulation experiments are conducted in simple and complex scenarios, respectively, and the quality of the path is measured by the fitness value and straight line rate, and the experimental results show that MFIPSO enables the UAV to plan a path with better quality.

Aiming at the standard PSO is prone to problems such as premature convergence, MFIPSO is proposed, which introduces class sigmoid inertia weight and adaptively adjusts the learning factor, balancing the global search ability and local convergence ability of the algorithm. The idea of K-means clustering algorithm is also incorporated to reduce the complexity of the algorithm while maintaining the diversity of particle swarm. In addition, the Cauchy perturbation is used to avoid the algorithm from falling into local optimum. Finally, the adaptability function is formulated by comprehensively considering the performance constraints of the flight target as well as the UAV itself, which improves the accuracy of the evaluation model.