Search Results (5,312)

This pilot study investigated an automated pilot plant for removing microplastics (MPs) from industrial wastewater that are generated during packaging production. MP removal is based on organosilane-induced agglomeration-fixation (clump & skim technology) followed by separation. The wastewater had high MP loads (1725 ± 377 mg/L; 673 ± 183 million particles/L) and an average COD of 7570 ± 1339 mg/L. Over 25 continuous test runs, the system achieved consistent performance, removing an average of 97.4% of MPs by mass and 99.1% by particle count, while reducing the COD by 78.8%. Projected over a year, this equates to preventing 1.7 tons of MPs and 6 tons of COD from entering the sewage system. Turbidity and photometric TSS measurements proved useful for process control. The approach supports water reuse—with water savings up to 80%—and allows recovery of agglomerates for recycling and reuse. Targeting pollutant removal upstream at the source provides multiple financial and environmental benefits, including lower overall energy demands, higher removal efficiencies, and process water reuse. This provides financial and environmental incentives for industries to implement sustainable solutions for pollutants and microplastic removal. Full article

In this study, we investigate the return propagation mechanism, hedging effectiveness, and portfolio performance across several common agricultural commodities, crude oil, and S&P 500 index, ranging from July 2000 to June 2024 by using a time-varying parameter vector autoregression (TVP-VAR) connectedness approach and three common multiple assets portfolio optimization strategies. The empirical results show that, the total connectedness peaked during the 2008 global financial crisis, followed by the European debt crisis and the COVID-19 pandemic, while it remained relatively lower at the onset of the Russia-Ukraine conflict. In the transmission mechanism, commodities and S&P 500 index exhibit distinct and dynamic characteristics as transmitters or receivers. Portfolio analysis reveals that, with exception of the COVID-19 pandemic, all three dynamic portfolios outperform the S&P 500 benchmark across major global crises. Additionally, the minimum correlation and minimum connectedness strategies are superior than transitional minimum variance method in most scenarios. Our findings have implications for policymakers in preventing systemic risk, for investors in managing portfolio risk, and for farmers and agribusiness enterprises in enhancing economic benefits. Full article

Under the global context of climate governance and sustainable development, low-carbon energy transition has become a strategic imperative. As a critical force in resource allocation, the financial system’s impact on energy transition has attracted extensive academic attention. This paper presents the first comprehensive literature review on energy transition research in the context of financial development. We develop a “Financial Functions-Energy Transition Dynamics” analytical framework to comprehensively examine the theoretical and empirical evidence regarding the relationship between financial development (covering both traditional finance and emerging finance) and energy transition. The understanding of financial development’s impact on energy transition has progressed from linear to nonlinear perspectives. Early research identified a simple linear promoting effect, whereas current studies reveal distinctly nonlinear and multidimensional effects, dynamically driven by three fundamental factors: economy, technology, and resources. Emerging finance has become a crucial driver of transition through technological innovation, risk diversification, and improved capital allocation efficiency. Notable disagreements persist in the existing literature on conceptual frameworks, measurement approaches, and empirical findings. By synthesizing cutting-edge empirical evidence, we identify three critical future research directions: (1) dynamic coupling mechanisms, (2) heterogeneity of financial instruments, and (3) stage-dependent evolutionary pathways. Our study provides a theoretical foundation for understanding the complex finance-energy transition relationship and informs policy-making and interdisciplinary research. Full article

Pimelea is one of the highly toxic plants in Australia, particularly affecting cattle. It contains simplexin, a potent toxin that can cause Pimelea poisoning (St. George Disease) in livestock. A survey was conducted to assess the current impact of Pimelea on livestock production, pasture systems, and financial losses among agricultural producers. In addition, information was also sought about the environmental conditions that facilitate its growth and the effectiveness of existing management strategies. The survey responses were obtained from producers affected by Pimelea across nine different Local Government Areas, through three States, viz., Queensland, New South Wales, and South Australia. Pimelea was reported to significantly affect animal production, with 97% of producers surveyed acknowledging its detrimental effects. Among livestock, cattle were the most severely affected (94%), when compared to sheep (13%), goats (3%), and horses (3%). The presence of Pimelea was mostly observed in spring (65%) and winter (48%), although 29% of respondents indicated that it could be present all year-round under favorable rainfall conditions. Germination was associated with light to moderate rainfall (52%), while only 24% linked it to heavy rainfall. Pimelea simplex F. Muell. was the most frequently encountered species (71%), followed by Pimelea trichostachya Lindl. (26%). Infestations were reported to occur annually by 47% of producers, with 41% noting occurrences every 2 to 5 years. Financially, producers estimated average annual losses of AUD 67,000, with 50% reporting an average of 26 cattle deaths per year, reaching up to 105 deaths in severe years. Some producers were spending up to AUD 2100 per annum to manage Pimelea. While chemical and physical controls were commonly employed, integrating competitive pastures and alternative livestock, such as sheep and goats, was considered as a potential management strategy. This study reiterates the need for further research on sustainable pasture management practices to reduce Pimelea-related risks to livestock and agricultural production systems. Full article

The work presents a comprehensive analysis and costing of sugar beet cultivation in 2020–2022, for individual farms of the Lublin region. About 120 farms were analyzed. Based on this analysis, the criteria for a model farm were determined and adopted for the calculation of sugar beet production costs. ARIMA process modeling was performed, based on which forecasts were determined for several selected parameters. Customs tariffs introduced by the USA have a drastic impact on the economy. The effects of the COVID19 pandemic may also have a significant impact on the current market situation. Forecasting in the current geopolitical situation is very difficult because of the lack of stationarity of parameters. The financial result obtained by growers is mainly influenced by indirect costs absorbing 61.31% of total costs in 2020. In 2021 and 2022, indirect costs were 61.16% and 59.61% of production income, respectively. Among this group of costs, the largest share is accounted for by the costs of sowing services, sugar beet harvesting, and soil liming amounting from 14.27% to 15.92%. During the analyzed period, sugar beet cultivation remained profitable, with a production profitability index of 1.31 in 2020 and 2021, and 1.10 in 2022. The unit cost of production increased every year. In 2020, it was 14.27% and in 2021, it increased to 15.19%. The unit cost of production in 2022 was the highest, at 23.41%. Sugar beet cultivation is one of the profitable activities in agricultural production, but it is characterized by high production costs, which increased during the years analyzed (2020 to 2022), topping out at 90.87% of total revenue. The information and data presented in this study will be used in the development of a farmer-oriented application and will support the creation of an expert system for sugar beet growers. Cost forecasting will enable farmers to plan their production more effectively. Full article

This study assesses the quality of financial reporting in ten Central and Eastern European countries using a methodology based on natural language processing (NLP) techniques. 570 annual reports of companies listed on the main index on the stock exchanges of 10 Central and Eastern European (CEE) countries, over the period 2019–2023, were evaluated to determine the degree of convergence of the following four measurable qualitative characteristics: relevance, exact representation, comparability and understandability. The main objective is to identify consistency in the quality of accounting information based on the application of an international financial reporting framework. The applied methodology eliminates subjective variability by implementing a standardized scoring system, aligned with the criteria developed by NiCE, using libraries such as spaCy and NLTK for term extraction, respective sentiment analysis and word frequency evaluation. The results reveal significant heterogeneity in all characteristics examined, with statistical tests confirming substantial differences between countries. The investigation of relevance revealed partial convergence, with three dimensions achieving complete uniformity, while the exact representation showed the highest variability. The assessment of comparability showed a significant difference between countries’ extreme values, and in terms of comprehensibility a formalistic approach was evident, with technical dimensions outweighing user-oriented aspects. The overall quality index varied significantly across countries, with a notable average deterioration in 2023, indicating structural vulnerabilities in financial reporting systems. These findings support initial hypotheses on the lack of homogeneity in the quality of financial reporting in the selected region, despite the implementation of international standards. Full article

In recent years, growing concerns over environmental degradation and deepening awareness of the necessity of sustainable development have propelled green and low-carbon energy transition into a focal issue for both academia and policymakers. By decomposing energy transition into the transformation of energy structure and the upgrading of energy efficiency, this study investigates the impact and mechanisms of green finance on energy transition across 30 provinces (municipalities and autonomous regions) in China, with the exception of Tibet. In addition, the impact of climate change is incorporated into the analytical framework. Empirical results demonstrate that green finance development significantly accelerates energy transition, a conclusion robust to rigorous validation. Analysis of the mechanism shows that green finance promotes energy transition through the facilitation of technological innovation and the upgrade of industrial structures. Moreover, empirical evidence reveals that climate change undermines the promotional influence of sustainable finance on energy system transformation. The magnitude of this suppression varies nonlinearly across provincial jurisdictions with differing energy transition progress. Regional heterogeneity analyses further uncover marked discrepancies in climate–finance interactions, demonstrating amplified effects in coastal economic hubs, underdeveloped western provinces, and regions with mature eco-financial markets. According to these findings, actionable policy suggestions are put forward to strengthen green finance and accelerate energy transition. Full article

Using waste biomass as a raw material for the combined production of electricity and heat offers corresponding energy, economic, environmental and resource efficiency benefits. The study examines both the performance of a system for combined energy production based on the Organic Rankine Cycle (ORC) utilizing wood biomass and the market interest in its deployment within Bulgaria. Its objective is to propose a technically and economically viable solution for the recovery of waste biomass through the combined production of electricity and heat while simultaneously assessing the readiness of industrial and municipal sectors to adopt such systems. The cogeneration plant incorporates an ORC module enhanced with three additional economizers that capture residual heat from flue gases. Operating on 2 t/h of biomass, the system delivers 1156 kW of electric power and 3660 kW of thermal energy, recovering an additional 2664 kW of heat. The overall energy efficiency reaches 85%, with projected annual revenues exceeding EUR 600,000 and a reduction in carbon dioxide emissions of over 5800 t/yr. These indicators can be achieved through optimal installation and operation. When operating at a reduced load, however, the specific fuel consumption increases and the overall efficiency of the installation decreases. The marketing survey results indicate that 75% of respondents express interest in adopting such technologies, contingent upon the availability of financial incentives. The strongest demand is observed for systems with capacities up to 1000 kW. However, significant barriers remain, including high initial investment costs and uneven access to raw materials. The findings confirm that the developed system offers a technologically robust, environmentally efficient and market-relevant solution, aligned with the goals of energy independence, sustainability and the transition to a low-carbon economy. Full article

The level of participation and performance of water user associations (WUAs) in drained and irrigated areas is influenced by many factors. This paper aims to identify the main challenges to the functioning and performance of these associations in Poland and Ukraine using the methodology of international comparative analysis. We examined legal, organizational, and financial framework of WUAs performance in Poland and Ukraine based on selected case study areas. The results of the study indicate that creation of WUAs in both countries can be assessed as beneficial for sustainable water development in general. However, it is found that the actions intended to bring benefits can actually exacerbate the problem of drought and water shortages. Research shows that the lack of complete documentation on the layout of the drainage networks plays a huge constraint factor that can lead to problems with controlling the reconstruction of drainage networks and significant deterioration of water relations. Another significant problem is the restriction of the scope of WUA activities in Poland to those types of actions subsidized by the state, while lacking financial resources for other necessary activities. Full article

This study investigates the predictive performance of financial indicators in forecasting stock prices within the automotive sector using an adaptive neuro-fuzzy inference system (ANFIS). In light of the growing complexity of global financial markets and the increasing demand for automated, data-driven forecasting models, this research aims to identify those financial ratios that most accurately reflect price dynamics in this specific industry. The model incorporates four widely used financial indicators, return on assets (ROA), return on equity (ROE), earnings per share (EPS), and profit margin (PM), as inputs. The analysis is based on real financial and market data from automotive companies, and model performance was assessed using RMSE, nRMSE, and confidence intervals. The results indicate that the full model, including all four indicators, achieved the highest accuracy and prediction stability, while the exclusion of ROA or ROE significantly deteriorated model performance. These findings challenge the weak-form efficiency hypothesis and underscore the relevance of firm-level fundamentals in stock price formation. This study’s sector-specific approach highlights the importance of tailoring predictive models to industry characteristics, offering implications for both financial modeling and investment strategies. Future research directions include expanding the indicator set, increasing the sample size, and testing the model across additional industry domains. Full article

Ujimqin sheep, known for its distinctive multi-vertebrae phenotypes (T13L7, T14L6, and T14L7) and economic value, has garnered significant attention. However, conventional phenotypic detection methods suffer from low efficiency and high costs. In this study, based on a key SNP locus (ABCD4 gene, Chr7:89393414, C > T) identified through a genome-wide association study (GWAS), a TaqMan-MGB (minor groove binder) genotyping system was developed. the objective was to establish a high-throughput and efficient molecular marker-assisted selection (MAS) tool. Specific primers and dual fluorescent probes were designed to optimize the reaction system. Standard plasmids were adopted to validate genotyping accuracy. A total of 152 Ujimqin sheep were subjected to TaqMan-MGB genotyping, digital radiography (DR) imaging, and Sanger sequencing. the results showed complete concordance between TaqMan-MGB and Sanger sequencing, with an overall agreement rate of 83.6% with DR imaging. For individuals with T/T genotypes (127/139), the detection accuracy reached 91.4%. This method demonstrated high specificity, simplicity, and cost-efficiency, significantly reducing the time and financial burden associated with traditional imaging-based approaches. the findings indicate that the TaqMan-MGB technique can accurately identify the T/T genotype at the SNP site and its strong association with the multi-vertebrae phenotypes, offering an effective and reliable tool for molecular breeding of Ujimqin sheep. Full article

Background: Social and financial services are essential for the inclusion and well-being of people with disabilities (PWDs), who often rely on family caregivers to access these systems. In Saudi Arabia, where disability inclusion is a strategic goal under Vision 2030, understanding caregiver experiences is crucial to identifying service gaps and improving accessibility. Objectives: This study aimed to explore caregivers’ perspectives on awareness, perceived barriers, and accessibility of social and financial services for PWDs in Saudi Arabia. The analysis is grounded in Andersen’s Behavioural Model of Health Service Use and the WHO’s International Classification of Functioning, Disability and Health (ICF) framework. Methods: A cross-sectional survey was conducted with 3353 caregivers of PWDs attending specialised day schools. The survey collected data on demographic characteristics, service awareness, utilisation, and perceived obstacles. Exploratory Factor Analysis (EFA) identified latent constructs, and Structural Equation Modelling (SEM) was used to test relationships between awareness, barriers, and accessibility. Results: Findings reveal that over 70% of caregivers lacked awareness of available services, and only about 3% had accessed them. Key challenges included technological barriers, complex procedures, and non-functional or unclear service provider platforms. Both User Barriers and Service Barriers were negatively associated with Awareness and Accessibility. Awareness, in turn, significantly predicted perceived Accessibility. Caregiver demographics, such as age, education, gender, and geographic location, also influenced awareness and service use. Conclusions: There is a pressing need for targeted awareness campaigns, accessible digital service platforms, and simplified service processes tailored to diverse caregiver profiles. Inclusive communication, decentralised outreach, and policy reforms are necessary to enhance service access and promote the societal inclusion of PWDs in alignment with Saudi Arabia’s Vision 2030. Full article

In this paper a new state model is introduced, an adaptative state model in a binary temporal representation (ASMBRT) as well as its application in constructing an algorithmic trading system. The presented model uses the binary temporal representation, which allows for a precise analysis of exchange rates without losing any informative value of the data. The basis of the model is the trajectory analysis for the ensuing changes in price quotations and dependencies between the duration of each change. The main advantage of the model is to eliminate the threshold analysis, used in existing state models. This solution allows for a more accurate identification of investor behavior patterns, which translates into a reduction of investment risk. In order to verify obtained results in practice, the paper presents a concept of creating an algorithmic trading system and an analysis of its financial effectiveness for the exchange rate most popular among investors, namely EUR/USD. Full article

Cardiovascular disease (CVD) remains Europe’s leading cause of mortality, responsible for >45% of deaths. Beyond established risk factors (hypertension, diabetes, dyslipidaemia, smoking, obesity), psychosocial elements—depression, anxiety, financial stress, personality traits, and trauma—significantly influence CVD development and progression. Behavioural Cardiology addresses this connection by systematically incorporating psychosocial factors into prevention and rehabilitation protocols. This review examines the HEARTBEAT model, developed by Greece’s first Behavioural Cardiology Unit, which aligns with current European guidelines. The model serves dual purposes: primary prevention (targeting at-risk individuals) and secondary prevention (treating established CVD patients). It is a personalised medicine approach that integrates psychosocial profiling with traditional risk assessment, utilising tailored evaluation tools, caregiver input, and multidisciplinary collaboration to address personality traits, emotional states, socioeconomic circumstances, and cultural contexts. The model emphasises three critical implementation aspects: (1) digital health integration, (2) cost-effectiveness analysis, and (3) healthcare system adaptability. Compared to international approaches, it highlights research gaps in psychosocial interventions and advocates for culturally sensitive adaptations, particularly in resource-limited settings. Special consideration is given to older populations requiring tailored care strategies. Ultimately, Behavioural Cardiology represents a transformative systems-based approach bridging psychology, lifestyle medicine, and cardiovascular treatment. This integration may prove pivotal for optimising chronic disease management through personalised interventions that address both biological and psychosocial determinants of cardiovascular health. Full article

Population aging poses significant challenges to the sustainability of pension systems. This study presents an integrated methodological approach that uniquely combines actuarial life-cycle modeling with agent-based simulation to assess the potential of Reverse Mortgage Loans (RMLs) as a dual lever for enhancing retiree welfare and supporting pension system resilience under demographic and financial uncertainty. We explore Reverse Mortgage Loans (RMLs) as a potential financial instrument to support retirees while alleviating pressure on public pensions. Unlike prior research that treats individual decisions or policy outcomes in isolation, our hybrid model explicitly captures feedback loops between household-level behavior and system-wide financial stability. To test our hypothesis that RMLs can improve individual consumption outcomes and bolster systemic solvency, we develop a hybrid model combining actuarial techniques and agent-based simulations, incorporating stochastic housing prices, longevity risk, regulatory capital requirements, and demographic shifts. This dual-framework enables a structured investigation of how micro-level financial decisions propagate through market dynamics, influencing solvency, pricing, and adoption trends. Our central hypothesis is that reverse mortgages, when actuarially calibrated and macroprudentially regulated, enhance individual financial well-being while preserving long-run solvency at the system level. Simulation results indicate that RMLs can improve consumption smoothing, raise expected utility for retirees, and contribute to long-term fiscal sustainability. Moreover, we introduce a dynamic regulatory mechanism that adjusts capital buffers based on evolving market and demographic conditions, enhancing system resilience. Our simulation design supports multi-scenario testing of financial robustness and policy outcomes, providing a transparent tool for stress-testing RML adoption at scale. These findings suggest that, when well-regulated, RMLs can serve as a viable supplement to traditional retirement financing. Rather than offering prescriptive guidance, this framework provides insights to policymakers, financial institutions, and regulators seeking to integrate RMLs into broader pension strategies. Full article