Facile fabrication of amorphous Al/Fe based metal-organic framework as effective heterogeneous fenton catalyst for environmental remediation.

This study presents a facile preparation and durable amorphous Fe and Al-based MOF nanoplate (AlFe-BTC MOFs) catalyst with notable stability in Fenton reactions. Rigorous characterization using XRD, HR-TEM, and BET confirms the amorphous nature of the synthesized AlFe-BTC MOFs, revealing mesopores (3.4 nm diameter), a substantial surface area (232 m2/g), and a pore volume of 0.69 cc/g. XPS analysis delineates distinct Al2p and Fe2p binding energy values, signifying specific chemical bonding. FE-SEM elemental mapping elucidates the distinctive distribution of Fe and Al within the framework of AlFe-BTC MOFs. In catalytic activity testing, the amorphous AlFe-BTC MOFs exhibited outstanding performance, achieving complete degradation of Methylene blue (MB) dye and 78% TOC removal over 45 min of treatment under mild reaction conditions. The catalyst's durability was assessed, revealing about 75% TOC removal and complete dye decomposition over five successive recycles, with less than 1 mg/L of Fe and Al leaching. UV-Vis spectra revealed the destruction of MB dye over multiple recycling studies. Based on this finding, the amorphous AlFe-BTC MOF nanoplates emerge as a promising solution for efficient dye removal from industrial wastewater, underscoring their potential in advanced environmental remediation processes.

Occupational accidents in Slovak Military Forests and Estates: incidence, timing, and trends over 10 years.

Introduction: Forestry provides a wide range of employment opportunities worldwide and is seen as one of the high-risk industries in terms of occupational accidents. Objectives: The submitted study analyzed the injury rate in the Military Forests and Estates of the Slovak Republic (62.6 thousand ha) between 2013 and 2022. Methods: The data analyses included regression and correlation analyses, χ2 tests to analyze the relationships between studied variables, and incidence rates. Results: During the observed period, employees suffered 26 occupational accidents, of which 19.2% were light, 57.7% were registered, 23.1% were severe, and 0% were fatal. For every 1 million m3 of harvested timber, 7.7 accidents occurred. The incidence rate during the observed period was 672.1/100,000 employees. The highest proportion of accidents was in the age group 51-60 years and in employees with the lowest length of work experience <5 years. Regarding time, the highest proportion of occupational accidents occurred between 8:01 and 10:00 AM (53.8%) and day-wise on Thursdays (46.2%). The highest proportion of accidents occurred among forest workers (65.3%) during pruning and silviculture activities (42.3%). The most common injury site was forest stands (65.3%). Superficial injuries (34.6%) were the most common, mainly affecting the lower limbs (50%). The most frequent material agents causing the accidents were work and transport areas as sources of worker fall (38.5%), and the most frequent reason for an accident to occur was the lack of personal requirements for proper work performance (92.4%), whereas only (3.8%) of accidents occurred due to the use of forbidden or hazardous working procedures. Conclusion: The presented study identified the most vulnerable worker groups and provided an overview of the overall injury rate at the state forest company in Slovakia. The documentation can be incorporated into the safety strategies of forest enterprises.

Intelligence level evaluation and influencing factors analysis of equipment manufacturing industry in the Yangtze River Delta.

The Yangtze River Delta (YRD) bears the vital task of driving the growth of China's equipment manufacturing industry (EMI) intelligence as an advanced region. Fostering the transformation and upgrading of the EMI in the YRD and constructing a modern production mode is vital to developing and reforming China's manufacturing industry. This paper uses industrial robot data to assess the level of intelligence (LoI) in the EMI from 2016 to 2019. The OLS (ordinary least squares) model is used for the measurements, and the MQ (the modified contribution index) is used to estimate the degree of contribution from a host of variables. It is identified that the LoI is on the rise. However, excluding railways, aerospace, shipbuilding, and other transportation equipment manufacturing, the LoI is significantly higher than in other subsectors. It is also identified that technological innovation ability, human capital density, and enterprise cost pressure govern the industry's LoI. Moreover, while there is a difference in the main influencing factors in LoI within different industries, R&D investment, technological innovation ability, and enterprise cost pressure have the most significant impact across most equipment manufacturing sub-industries.

Digital economy, innovation factor allocation and industrial structure transformation-A case study of the Yangtze River Delta city cluster in China.

The attainment of regional high-quality development necessitates the critical role of the digital economy in facilitating the transformation of industrial structures. This study intends to investigate the effect of the digital economy on industrial structure transformation from the perspective of innovation factor allocation using a panel dataset of 41 cities in the Yangtze River Delta region for the period from 2011 to 2020. This paper considers four dimensions to measure the level of industrial structure transformation i.e. industrial structure servitization, industrial structure upgradation, service industry structure upgradation and industrial interaction level. The results of the study suggest that the digital economy can significantly improve industrial structure transformation. The results remain consistent even after several robustness checks. Further, the analysis of the mechanism of action shows that the digital economy can promote industrial structure transformation by optimizing the innovation factor allocation. The study provides several policy implications for the digital economy and its role in the promotion of industrial structure transformation.

Microorganism-mediated biodegradation for effective management and/or removal of micro-plastics from the environment: a comprehensive review.

Micro- plastics (MPs) pose significant global threats, requiring an environment-friendly mode of decomposition. Microbial-mediated biodegradation and biodeterioration of micro-plastics (MPs) have been widely known for their cost-effectiveness, and environment-friendly techniques for removing MPs. MPs resistance to various biocidal microbes has also been reported by various studies. The biocidal resistance degree of biodegradability and/or microbiological susceptibility of MPs can be determined by defacement, structural deformation, erosion, degree of plasticizer degradation, metabolization, and/or solubilization of MPs. The degradation of microplastics involves microbial organisms like bacteria, mold, yeast, algae, and associated enzymes. Analytical and microbiological techniques monitor microplastic biodegradation, but no microbial organism can eliminate microplastics. MPs can pose environmental risks to aquatic and human life. Micro-plastic biodegradation involves fragmentation, assimilation, and mineralization, influenced by abiotic and biotic factors. Environmental factors and pre-treatment agents can naturally degrade large polymers or induce bio-fragmentation, which may impact their efficiency. A clear understanding of MPs pollution and the microbial degradation process is crucial for mitigating its effects. The study aimed to identify deteriogenic microorganism species that contribute to the biodegradation of micro-plastics (MPs). This knowledge is crucial for designing novel biodeterioration and biodegradation formulations, both lab-scale and industrial, that exhibit MPs-cidal actions, potentially predicting MPs-free aquatic and atmospheric environments. The study emphasizes the urgent need for global cooperation, research advancements, and public involvement to reduce micro-plastic contamination through policy proposals and improved waste management practices.

A review of experimental Assessment Processes of material resistance to marine and freshwater biofouling.

Biofouling presents hazards to a variety of freshwater and marine underwater infrastructures and is one of the direct causes of species invasion. These negative impacts provide a unified goal for both industry practitioners and researchers: the development of novel antifouling materials to prevent the adhesion of biofouling. The prohibition of tributyltin (TBT) by the International Maritime Organization (IMO) in 2001 propelled the research and development of new antifouling materials. However, the evaluation process and framework for these materials remain incomplete and unsystematic. This mini-review starts with the classification and principles of new antifouling materials, discussing and summarizing the methods for assessing their biofouling resistance. The paper also compiles the relevant regulations and environmental requirements from different countries necessary for developing new antifouling materials with commercial potential. It concludes by highlighting the current challenges in antifouling material development and future outlooks. Systematic evaluation of newly developed antifouling materials can lead to the emergence of more genuinely applicable solutions, transitioning from merely laboratory products to materials that can be effectively used in real-world applications.

Green supply chain management practices and triple bottom line performance: Insights from an emerging economy with a mediating and moderating model.

The current study explores the dynamics of business practices in Pakistan's Small and Medium Enterprises (SMEs). This research focusing on how "green supply chain management" (GSCM), Industry 4.0 technologies (IND), total quality management (TQM) and sustainable performance (SP) are interconnected. The study collected data from 382 SMEs owners and managers and data analysis was conducted utilizing by SPSS and SmartPLS. The results indicate that employ GSCM pattern has constructive impact on SMEs overall performance in terms of their social and financial aspects. Interestingly IND act as intermediaries among GSCM and sustainable performance underscoring their potential to translate sustainability efforts into outcomes. Moreover, our survey reveals that TQM have a crucial function in enriching the relationship between Industry 4.0 technologies and sustainable performance by executing as a moderator. It further highlighting the value of adopting a quality focused approach to maximize the causal factor of advancements on sustainability output. The findings of this survey research offering insights for practitioners, SMEs, and policy makers alike by highlighting the significance of integrating practices such as GSCM, IND and effective quality management to enhance SME efficiency. These outcomes further contribute to an understanding of the mechanics at play, within SMEs while offering guidance for organizations maneuvering the changing landscape of sustainable business practices and IND.

Improving new energy subsidy efficiency considering learning effect: A case study on wind power.

Subsidy policies are instrumental in driving the development of new energy. However, the effective allocation of new energy subsidies over time is challenging given fiscal constraints. This study addresses this challenge by considering the learning effect associated with the new energy industry. A two-stage dynamic programming model is proposed to capture the investment decision-making process of companies under new energy subsidy policies and government subsidy setups. Theoretical findings suggest that company investment decisions in new energy are influenced by a guiding principle: The subsidy rate should be negatively correlated with the variation rate of production scale increment (VRPSI). We calibrate this investment decision principle using wind power data from 14 countries. According to this principle, excessive subsidy rates may result in a low VRPSI, thereby diminishing future investment profitability in the new energy industry and leading to subsidy inefficiency. Upon investigating the efficiency of annual subsidy allocation, we find that the subsidy rates were potentially set too high in 2014, 2016, and 2017. Furthermore, the government should exercise caution regarding an inefficient subsidy pattern whereby companies invest in new energy only when the subsidy rate exceeds a certain threshold, neglecting traditional power sources. It is crucial to note that although this study uses wind power industry data for calibration and simulation, the theoretical model can be broadly applied to other new energy industries and emerging industries with increasing marginal net profit.

Coupling development of sports industry and tourism industry based on internet of things.

This study investigates the positive coupling between the sports industry and tourism, exploring the ways to promote their interconnection. Under state guidance, the integration of sports industry services is facilitated to attract sports culture and tourism fairs, leveraging regional economic development advantages to enhance the industrial market appeal. The emerging leisure consumption mode of sports tourism injects vitality into the economy, fostering the core sports service industry. The coupling of the education and tourism sectors is strategically aligned with long-term national policies. Using IoT technology, this paper employs a grey relational analysis to assess the coupling between the sports industry and tourism, revealing a significant correlation. Experimental results demonstrate a positive coupling trend, likened to conjoined twins with a natural material basis and technical support. This coupling not only aligns with industry trends but also resonates with the "environmental protection era," "green era," and "ecological era," marking a pivotal aspect of industrial development. The study contributes valuable insights into the symbiotic relationship between the sports and tourism industries, emphasizing their interconnectedness and the positive implications for economic and environmental sustainability.

Does the opening of producer services promote the wage growth of the downstream manufacturing industry?-Empirical evidence from Chinese manufacturing listed companies.

Based on the vertical connection between upstream and downstream industries, a unique theoretical model is constructed to analyse the impact mechanism of the opening of producer services on downstream manufacturing wage growth. The empirical tests are carried out using the data of China's manufacturing listed companies from 1999 to 2020. Our findings indicate that the opening of producer services has an inverted-U-shaped impact on downstream manufacturing wage growth, and the average level of the opening of producer services in the sample period is lower than the corresponding threshold. Overall, it is in the stage of promoting the wage growth of the downstream manufacturing industry. The opening of producer services mainly affects the wage growth of the downstream manufacturing industry through two channels: labour productivity and labour income share. The results of heterogeneity analysis show that the wages of capital and technology-intensive and low-competitive manufacturing industries are relatively strongly promoted by the opening of producer services. Therefore, promoting the orderly opening of producer services and strengthening the technological links between industries will help promote the wage growth of downstream manufacturing industries.

Understanding and including 'pink-collar' workers in employment-based travel demand models.

The segmentation of commuters into either blue or white-collar workers remains is still common in urban transport models. Internationally, models have started to use more elaborate segmentations, more reflective of changes in labour markets, such as increased female participation. Finding appropriate labour market segmentations for commute trip modelling remains a challenge. This paper harnesses a data-driven approach using unsupervised clustering-applied to 2017-20 South East Queensland Travel Survey (SEQTS) data. Commuter types are grouped by occupational, industry, and socio-demographic variables (i.e., gender, age, household size, household vehicle ownership and worker skill score). The results show that at a large number of clusters (i.e., k = 8) a highly distinct set of commuter types can be observed. But model run times tend to require a much smaller number of market segments. When only three clusters are formed (k = 3) a market segmentation emerges with one female-dominated type ('pink collar'), one male-dominated type ('blue collar') and one with both genders almost equally involved ('white collar'). There are nuances as to which workers are included in each segment, and differences in travel behaviours across the three types. 'Pink collar' workers are mostly comprised of female clerical and administrative workers, community and personal service workers and sales workers. They have the shortest median commutes for both private motorised and active transport modes. The approach and methods should assist transport planners to derive more accurate and robust market segmentations for use in large urban transport models, and, better predict the value of alternative transport projects and policies for all types of commuters.

Effects of environmental regulation on adjustment of agricultural industrial structure--An empirical study based on panel data of 31 provinces in China.

Using the panel data of 31 provinces in China between 2000 and 2018, this study theoretically and empirically analyses the impact of environmental regulations on the adjustment of the agriculture industrial structure from the perspectives of rationalisation and optimisation. Overall, variability in the impact of environmental regulation on the adjustment of the agricultural industrial structure is identified: a negative influence on the rationalisation of agricultural industrial structure and a positive influence on the optimisation of agricultural industrial structure. The impact of environmental regulations on the adjustment of agricultural industrial structure also reflects "large country characteristics": environmental regulations are more significant at the medium and low industrial structure levels. Environmental regulation significantly impacts the rationalisation of agricultural industrial structure in central and western regions and its optimisation in eastern and central regions. Through the panel threshold model test, this paper further finds that the effect of environmental regulations on the adjustment of agricultural industrial structure is not invariant. However, there is a non-linear relationship with significant threshold characteristics. Based on the above results, some countermeasures are suggested.

The multi-strategy hybrid forecasting base on SSA-VMD-WST for complex system.

In view of the strong randomness and non-stationarity of complex system, this study suggests a hybrid multi-strategy prediction technique based on optimized hybrid denoising and deep learning. Firstly, the Sparrow search algorithm (SSA) is used to optimize Variational mode decomposition (VMD) which can decompose the original signal into several Intrinsic mode functions (IMF). Secondly, calculating the Pearson correlation coefficient (PCC) between each IMF component and the original signal, the subsequences with low correlation are eliminated, and the remaining subsequence are denoised by Wavelet soft threshold (WST) method to obtain effective signals. Thirdly, on the basis of the above data noise reduction and reconstruction, our proposal combines Convolutional neural network (CNN) and Bidirectional short-term memory (BiLSTM) model, which is used to analyze the evolution trend of real time sequence data. Finally, we applied the CNN-BiLSTM-SSA-VMD-WST to predict the real time sequence data together with the other methods in order to prove it's effectiveness. The results show that SNR and CC of the SSA-VMD-WST are the largest (the values are 20.2383 and 0.9342). The performance of the CNN-BiLSTM-SSA-VMD-WST are the best, MAE and RMSE are the smallest (which are 0.150 and 0.188), the goodness of fit R2 is the highest(its value is 0.9364). In contrast with other methods, CNN-BiLSTM-SSA-VMD-WST method is more suitable for denoising and prediction of real time series data than the traditional and singular deep learning methods. The proposed method may provide a reliable way for related prediction in various industries.

Comparative stability analysis of Indonesian banks: Markov Switching-Dynamic Regression for Islamic and conventional sectors.

The banking industry necessitates implementing an early warning system to effectively identify the factors that impact bank managers and enable them to make informed decisions, thereby mitigating systemic risk. Identifying factors that influence banks in times of stability and crisis is crucial, as it ultimately contributes to developing an improved early warning system. This study undertakes a comparative analysis of the stability of Indonesian Islamic and conventional banking across distinct economic regimes-crisis and stability. We analyze monthly banking data from December 2007 to November 2022 using the Markov Switching Dynamic Regression technique. The study focuses on conducting a comparative analysis between Islamic banks, represented by Islamic Commercial Bank (ICB) and Islamic Rural Bank (IRB), and conventional banks, represented by the Conventional Commercial Bank (CCB) and Conventional Rural Bank (CRB). The findings reveal that both Islamic and conventional banks exhibit a higher probability of being in a stable regime than a crisis regime. Notably, Islamic banks demonstrate a greater propensity to remain in a stable regime than their conventional counterparts. However, in a crisis regime, the likelihood of recovery for Sharia-compliant institutions is lower than for conventional banks. Furthermore, our analysis indicates that larger banks exhibit higher stability than their smaller counterparts regarding assets and size. This study pioneers a comprehensive comparison of the Z-score, employed as a proxy for stability, between two distinct classifications of Indonesian banks: Sharia (ICB and IRB) and conventional (CCB and CRB). The result is expected to improve our awareness of the elements that affect the stability of Islamic and conventional banking in Indonesia, leading to a deeper comprehension of their dynamics.

The impact of COVID-19 pandemic on the world's major economies: based on a multi-country and multi-sector CGE model.

Objective: To quantitatively assess the impact of COVID-19 pandemic on public health, as well as its economic and social consequences in major economies, which is an international public health concern. The objective is to provide a scientific basis for policy interventions. Subject and methods: This study utilizes a multi-country, multi-sector CGE-COVID-19 model to analyze the repercussions of the pandemic in 2022. The re-search focuses on quantifying the effects of COVID-19 on the macroeconomy and various industry sectors within six economies: the United States, China, the EU, the United Kingdom, Japan, and South Korea. Results: The COVID-19 pandemic shock had the most significant impact on China and the EU, followed by notable effects observed in the United States and the United Kingdom. In contrast, South Korea and Japan experienced relatively minimal effects. The reduction in output caused by the pandemic has affected major economies in multiple sectors, including real industries such as forestry and fisheries, and the services such as hotels and restaurants. Conclusion: The overall negative macroeconomic impact of the epidemic on major economies has been significant. Strategic interventions encompassing initiatives like augmenting capital supply, diminishing corporate taxes and fees, offering individual subsidies, and nurturing international cooperation held the potential to mitigate the detrimental economic consequences and enhance the global-economic amid the pan-demic. Consequently, this study contributes to the advancement of global anti-epidemic policies targeting economic recovery. Moreover, using the CGE-COVID-19 model has enriched the exploration of general equilibrium models in PHEIC events.

Does the "Belt and Road Initiative" benefit the environment? Insight from analysis of intra-industry trade in environment goods.

The expansion of the Belt and Road Initiative (BRI) has raised a wide range of concerns about its environmental impact. Therefore, from the perspective of environmental impacts, this study used the two-way fixed effect staggered differences in differences (TWFE Staggered DID) method to examine the impact of the BRI on the Environment Goods (EGs) intra-industry trade (IIT) between China and other Belt and Road (B&R) countries, including a sample of 191 countries, covering the period from 2010 to 2019 for eliminating the impact of COVID-19 and the financial crisis in 2008 and 2009. Because only 135 countries signed a Memorandum of Understanding between 2010 and 2019, this study treated these B&R countries as the study group, and the other 73 countries (non-B&R countries) as the control group. This study described EGs using the 54 6-digit code Environment Goods in Harmonized Commodity Description and Coding System listed in the "APEC LIST OF ENVIRONMENT GOODS" published by the Asia-Pacific Economic Cooperation in 2012, and used the intra-industry trade index proposed by Grubel and Lloyd in 1971 to measuring dependent variable. The research results indicated that the BRI has significantly promoted bilateral EGs IIT. The mechanism test implied that, in addition to direct impacts, the BRI also has indirect impacts by boosting the energy restructuring of B&R countries. These results prove that the BRI has positive impacts on the environment. The heterogeneity test showed that there is a heterogeneous impact depending on the type of IIT, product categorization, B&R countries' income levels, and geographic environment. This study not only gives theoretical and empirical evidence of the positive environmental impacts of the BRI, but also provides practical guidance for the development of EGS IIT between China and B&R countries, thereby contributing to global carbon emissions reduction and environmental governance to some degree.

Gold Nanoparticles (AuNPs)-Toxicity, Safety and Green Synthesis: A Critical Review.

In recent years, the extensive exploration of Gold Nanoparticles (AuNPs) has captivated the scientific community due to their versatile applications across various industries. With sizes typically ranging from 1 to 100 nm, AuNPs have emerged as promising entities for innovative technologies. This article comprehensively reviews recent advancements in AuNPs research, encompassing synthesis methodologies, diverse applications, and crucial insights into their toxicological profiles. Synthesis techniques for AuNPs span physical, chemical, and biological routes, focusing on eco-friendly "green synthesis" approaches. A critical examination of physical and chemical methods reveals their limitations, including high costs and the potential toxicity associated with using chemicals. Moreover, this article investigates the biosafety implications of AuNPs, shedding light on their potential toxic effects on cellular, tissue, and organ levels. By synthesizing key findings, this review underscores the pressing need for a thorough understanding of AuNPs toxicities, providing essential insights for safety assessment and advancing green toxicology principles.

Employee Grievance Redressal and Corporate Ethics: Lessons from the Boeing 737-MAX Crashes.

Two Boeing 737-MAX passenger planes crashed in October 2018 and March 2019, suspending all 737-MAX aircraft. The crashes put Boeing's corporate practices and culture under the spotlight. The main objective of this paper is to use the case of Boeing to highlight the importance of efficient employee grievance redressal mechanisms and an independent external regulator. The methodology adopted is a qualitative analysis of statements of various whistleblowers and Boeing and the Federal Aviation Administration (FAA) stakeholders. It suggests that employee feedback flowing up the chain of command should be more flexible and dealt with more seriousness. It recommends that companies adopt a cooling-off period or a lifetime restriction for employees who have gone through the revolving door between regulators and the industry. The Boeing 737-MAX case, which emphasizes the ethical obligations of the job, can offer value to engineers, engineering educators, managers, ombudsmen, and human resource professionals.