Improving performance and promoting sustainability in women's handball are key objectives to maximize the potential of female players and ensure the long-term viability of the sport. In this context, training with Olympic movements and their derivatives improves the development of strength, power, and speed, which are determinants of performance in team sports. The aim of this study was to determine if training with Olympic movements produces significant improvements in jumping, throwing, sprinting, and change of direction performance in women handball players. Twenty-one female handball players participated in the study (10 for the control group and 11 for the intervention group). Age ranged from 15 to 17 years. All participants performed four assessment tests (Abalakov Test, throw test, 20-meter Sprint and V-Cut Test) to determine jump height, throwing speed, running speed, and change of direction ability. Measurements were carried out before and after the intervention. For six weeks, the control group performed the strength work established by the club twice a week while the intervention group additionally performed training with Olympic movements. Significant differences (p <0.05) were found between the pre and post measurement of the control group and the intervention group in jump height, throwing speed and running speed, being higher in the intervention group. For the change of direction, no significant differences were found. Between groups, significant differences were observed at the end of the intervention for jump height and running speed. The conclusion of this study was that, by training with Olympic movements, in addition to regular training, could produce greater improvements in jumping performance, throwing speed and running speed in female handball players.

The ratio of males to females with ASD is generally quoted as 4:1 though it is believed that there are biases preventing females being diagnosed and that the true ratio is lower. These biases have not been clearly identified or quantified. Starting with a clinical dataset of 1711 children <18 years old four different methods were employed in an inductive study to identify and quantify the biases and calculate the proportion of females missed. A mathematical model was constructed to compare the findings with current published data. The true male to female ratio appears to be 3:4. Eighty per cent of females remain undiagnosed at age 18 which has serious consequences for the mental health of young women.

Industrial buildings have numerous energy-losing equipment, such as engines, ovens, boilers, and heat exchangers. Energy losses are related to inefficient energy use and lousy work conditions for the people inside the buildings. This work is devoted to the recovery of lost energy from industrial buildings. Firstly, the residual energy of the building is extracted to be used to warm water. Consequently, the work conditions of the people inside industrial buildings can be improved by maintaining the adequate temperature. The recovery energy is done by the multipurpose heat pump system (HP system). The working fluid used in the HP system is R134a, which is a traditional and cheap working fluid. The thermophysical properties of R134a are obtained through the PC-SAFT equation of state. This work presents a performance mapping based on the intercepted areas framework to evaluate which working conditions are the optimal operating variables. The latter depends on several key parameters, such as compressor work, heat delivery, heat absorbed, and exergetic efficiency. The results show that the optimal work conditions are found at different condenser and evaporator temperatures, and that may be limited by what the designer considers a good performance of the heat pump system.

Dynamic crossflow filtration (DCF) is the state-of-the-art technology for solid-liquid separation from viscous and sensitive feed streams in the food and biopharma industry. Up to now, the potential of industrial processes is often not fully exploited because fixed recipes are usually applied to run the processes. Therefore, a digital twin has been developed to optimize an industrial brownfield DCF plant. The core of the digital twin is a mechanistic-empirical process model combining fundamental filtration laws with process expert knowledge. The effect of variation of selected process and model parameters on plant productivity has been assessed using a model-based design-of-experiments approach and a regression metamodel has been trained with the data. A cyclic program that bidirectionally communicates with the DCF asset serves as frame of the digital twin. It monitors the process dynamics membrane torque and transmembrane pressure and feeds back the optimum permeate flow rate setpoint to the physical asset in almost real-time during process runs. The presented digital twin framework is a simple example how an industrial established process can be controlled by a hybrid model-based algorithm. With a digital process dynamics model at hand, the presented metamodel optimization approach can be easily transferred to other (bio)chemical processes.

Tourism gentrification presents issues of privatization of public space, the transformation of public services to cater to tourists, the erosion of community social bonds, and the commodification of regional consumption, which has far-reaching impacts on the well-being of local residents and a desire to relocate. Thus, tourism gentrification in traditional industrial areas is defined as a process driven by the reconfiguration of spatial functions and the transformation of service provisions, resulting in displacement. This study employs Q methodology aims to understand the residents’ perceptions of tourism gentrification with a specific focus on the 798 Industrial Art Zone in Beijing, China. The results reveal four dimensions that form residents’ stress: neighboring environment, community attachment, economic interest, and cultural identity. Based on the stress threshold theory, this study further proposes a framework to explain the decision-making processes of residents in relation to their intent to relocate due to stress from interactions with migrants and visitors and changes in the place functions of industrial areas and communities. The findings offer valuable theoretical knowledge surrounding tourism gentrification from residents’ perspectives and suggest practical guidance for policymakers and destination managers in reducing residents’ stress and promoting sustainable development of industrial tourism.

In hydraulic systems, energy dissipation can be significant. The pressure drops that can occur in the hydraulic circuit, influenced by the adopted drive architecture, result in an absorbed power often significantly greater than that required by the mechanical system. In this paper, a comparative study of energy efficiency among five drive common architectures in industrial hydraulic axes is carried out. The analysis is applied to a hydraulic blanking press with variable speed and force, a fairly frequent industrial system, e.g. in the production of semi-finished brass products. Standard, regenerative, high-low, variable displacement pump and variable speed drive for a fixed displacement pump configurations have been analysed and compared. An adequate and optimized sizing of the various components of the system has been carried out in each case and subsequently the energy consumption has been estimated for a load cycle common to all the considered cases. The results show that the choice of power generation architecture of the hydraulic system has a very significant impact on energy efficiency and consequently operating costs and carbon footprint. The performed quantification of the potential energy efficiency of the considered drive architectures can be very useful in guiding energy-conscious choices.

The subject of study in the article is the method of industrial equipment vibration diagnostics using Allan variance. The goal is to increase the precision and accuracy of industrial equipment's vibration diagnostics processes by developing and implementing IoT-oriented solutions based on intelligent sensors and actuators per the IEEE 1451.0-2007 standard. Tasks: justify the feasibility of using platform-oriented technologies for vibration diagnostics of industrial equipment and choose a cloud service for the implementation of the platform; develop software and hardware solutions of the IoT platform for vibration diagnostics of industrial equipment; calibrate the vibration diagnostics system and check the measurement precision and accuracy. The methods used are the microservice approach, multilevel architecture, and assessing equipment state-based Allan variance. We obtained the following results. The architecture of the IoT system for vibration diagnostics of industrial equipment developed and presented in the article is three-level. The level of autonomous sensors provides readings of vibration acceleration indicators and transmits data to the Hub level, which is implemented based on a BeagleBone single-board microcomputer through the BLE digital wireless data transmission channel. BeagleBone computing power provides work with artificial intelligence algorithms. At the third level of the server platform, the tasks of diagnosing and predicting the condition of the equipment are solved, for which the Dictionary Learning algorithm implemented in the Python programming language is applied. Verifying the accelerometer calibration method for vibration diagnostics of industrial equipment was performed using a unique stand. Correct operation of the entire system is confirmed by the coincidence of expected and measured results. In the next step, we plan the development of additional microservices that will provide the possibility of using time series analysis methods and modern artificial intelligence technologies for complex diagnostics and forecasting of the equipment state.

New materials are obtained by transforming fly ash wastes into a valuable composite, with tandem adsorption and photodegradation properties. Mild hydrothermal synthesis, from titanium dioxide, Platinum nanoparticles and zeolite materials obtained from a waste, fly ash, as support, was involved in the composite preparation. The Platinum nanoparticles extended the photocatalytic activity of the composite in Visible range (Eg = 2.1 eV). The efficiency of tandem adsorption and photocatalytic activity of the new composite were evaluated to 80.70% for Bemacid Blau and 93.89% for Bemacid Rot, after 360 min, the irradiation time, with H2O2 addition.

Digital enabled manufacturing systems require high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; However, this presence of workers in the shared workspace with robots decreases the productivity as the robot is not aware about the human position and intention which leads to concerns about the human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (Cobots). The main idea here is, to significantly enhance safety by a combination of recognition of human actions using visual perception and at the same time interpreting physical human-robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if a physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection which in combination, are expected to lead to the enhancement of human safety and an increase of the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human–robot collaboration (HRC) in industrial automation.

Using qRT-PCR analysis, the LcIL-8 expression level indicated broad expression in most detected tissues, with the most predominant expression in whole blood at 6 to 24 h after infection with S. iniae at concentrations of 105 colony-forming unit (CFU)/fish (p &lt; 0.05). After immersion with F. covae, the LcIL-8 transcript was upregulated in the gills, liver and intestine, with the highest expression level found in the gills. However, LcIL-8 was downregulated in all tested tissues at 48 and 96 h after infection with the two pathogenic strains, indicating that Lc-IL8 has a short half-life during the early immune responses to pathogens. MIC analysis of rLcIL-8 protein against S. iniae was 10.42 ± 3.61 µg/mL. Furthermore, the functional analyses clearly demonstrated that 10 and 100 µg of rLcIL-8 protein efficiently enhanced the phagocytic activity of Asian seabass phagocytes in vitro (p &lt; 0.05). Additionally, in vivo injection of S. iniae following the rLcIL-8 protein indicated that 50 and 100 µg of rLc-IL-8 were highly effective in protecting fish from the tested pathogen (p &lt; 0.001). These results indicate that rLcIL-8 plays a crucial role in the defense mechanisms against bacterial infections in the target fish.

Childhood overweight/obesity (OV/OB) is a major public health problem, of greater concern when accompanied with comorbidities such as hypertension and insulin resistance leading to metabolically unhealthy obesity (MUO). Aetiologic associations between meal patterns, OV/OB risk and MUO are limited. The aim of this systematic review was to explore associations between meal patterns and the risk of childhood OV/OB and MUO. Longitudinal studies and randomised controlled trials from PUBMED and Scopus published between January 2013 - April 2024 were retrieved. Twenty-eight studies were included, all of which reported on OV/OB risk; none on MUO risk. Regular consumption of breakfast and family meals and avoiding dining while watching TV may be protective factors against childhood OV/OB, whereas meal skipping (primarily breakfast) may be a detrimental factor. Mixed effects of meal frequency on OV/OB risk were observed; no effects of frequency of lunch or of fast-food consumption and of meals served at school were found. There was insufficient evidence to support the role of other patterns (meal timing, eating in other social contexts). Meals were mainly participant-identified, leading to increased heterogeneity. Research focusing on childhood MUO and improved methodological approach (e.g. harmonised definitions) regarding the assessment of meal patterns are highly warranted.

Design plays a critical role and creates a positive user experience, establishing a solid brand identity and staying competitive in the digital world. This research investigates the impact of user data collecting and analysis in improving user experience. The Data-driven User interface design can create an optimized and relevant UI with individual preferences, which leads to increasing user engagement and usability. The proposal explores methodologies to implement data collection and user research for tailoring digital products.

This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.

This article presents a unique set of voltage and current data from a public building and acquired using a hybrid measurement solution that combines Python and Grafana. The transversal purpose consists of contributing to the community with a vision of the quality of the supply more oriented to the monitoring of the state of the network, providing a more realistic vision, which allows a better understanding, and the adoption of the best decisions to achieve the efficient energy management and thus optimize the operation and maintenance of power systems. The work focuses on higher order statistical estimators that, combined with exploratory data analysis techniques, improve the characterization of the shape of the stress signal. These techniques and data, together with the acquisition and monitoring system, present a unique combination in the line of low-cost measurement solutions. It also incorporates the underlying benefit of the contribution to industrial benchmarking. The paper also includes a computational comparison between Python and LabVIEW to elicit the performance of the measurement solution.

The study is aimed at developing a resilient benchmarking system for small hotels in Ukraine, designed to ensure their survival and growth amid global disruptions and local crises. Given the severe challenges associated with the COVID-19 pandemic and military actions, the resilience of the regional tourism business becomes particularly relevant. The methods used in the study, including factor and cluster analysis, the method of taxonomy, and dendrograms, have enabled the development of development programs for two clusters of hotels: those located in areas with increased military risk and those in relatively safe territories. The taxonomic analysis revealed significant differences in managerial practices and operational efficiency, largely determined by the geographic location of the hotels. Hotels in active combat zones experience a 40% reduction in tourist flow and financial instability, while hotels in safe areas demonstrate a 30% higher level of customer satisfaction. The application of advanced security systems and modern marketing techniques has led to a 40% reduction in incidents.

In recent years, governments applied intelligent transportation system (ITS) technique to provide several convenience services (e.g., garbage truck app) for residents. This study proposes a garbage truck fleet management system (GTFMS) and data feature selection and data clustering methods for travel time prediction. A GTFMS includes mobile devices (MD), on-board units, fleet management server, and data analysis server (DAS). When user uses MD to request the arrival time of garbage truck, DAS can perform the procedure of data feature selection and data clustering methods to analyses travel time of garbage truck. The proposed methods can cluster the records of travel time and reduce variation for the improvement of travel time prediction. After predicting travel time and arrival time, the predicted information can be sent to user’s MD. In experimental environment, the results showed that the accuracies of previous method and proposed method are 16.73% and 85.97%, respectively. Therefore, the proposed data feature selection and data clustering methods can be used to predict stop-to-stop travel time of garbage truck.

This research aims to investigate and conduct the impact of earnings management practices on the performance of the banking sector in Jordan. Data were collected from 24 main commercial banks from (2015 – 2021). The study examine how earnings management practices affect banking sector performance using debt ratios and equity ratios based on firm size and debt ratios, and measure the impact of earnings management on it using regression analysis. The result of the study reveal that a positive and statistically significant association between equity per share (EPS) and return on investment (ROI) with earnings management practices, also, earnings management techniques are positively and significantly impacted by return on equity (ROE), the hypothesis that returns on investment (ROI) encourage business managers to employ earnings management strategies. Also, the study show that dependent variables of earnings management is measured as a debt ratio and equity ratio. Variables that are independent or explanatory of earnings management are considered independent variables.

The regions with the best economy have a greater capacity to develop low-income or social impact housing, thus contributing to the reduction of poverty, and therefore, to the fulfillment of the Sustainable Development Goals. This is observed in fewer people living in extreme poverty and with fewer unmet basic needs. The present article analyzes the correlation between development by the main economic sectors in the different regions (departments) of Colombia, and the offer of low-income housing. The valid relationship found is between the economic condition of the regions (GDP) and non-social housing (more expensive commercial value) (Spearman´s Rho: 0.9). That means that there is an imbalance between regional economic capacity and the low-income housing offer because their economic potential allows them to have less of a demanding population, that is, living in poverty. This correlation is higher with activities that are mostly developed in an urban environment, such as manufacturing, construction, real estate, and finance and insurance. On the contrary, the correlation is lower with industries such as mining and agriculture, which mostly operate in rural areas. The analysis for low-income housing and economic sectors GDP yields low correlations, but are especially dismissible for more rural industries, such as mining and agriculture. The analysis shows the change of trend in the correlations for the year 2021, the beginning of the post-pandemic economic recovery.

The concept of public financial accountability, often associated with account-rendering, lacks a clear and comprehensive definition in the realm of accounting literature. Disentanglement the true essence of a phenomenon is essential, as formal definitions can only provide a superficial understanding. This study seeks to delve into the core principles of public financial accountability and their implications for financial reporting in the public sector. Applying a qualitative approach, data was gathered through in-depth interviews with 25 Nigerian scholars, professionals, and public affairs experts. The analysis reveals that the essence of public financial accountability lies in upholding citisens' trust in public officials, ensuring the responsible management of public financial resources for the greater public good, and effectively communicating financial decisions, actions, and outcomes to the public through a transparent reporting mechanism. This study sheds light on the fundamental nature of financial accountability in the public sector, enhancing our understanding of its significance in governance and financial reporting.

The ultimate goal of this paper was to examine the degree of elasticity between two variables, namely, agricultural credit and agricultural growth, in Angola in the period 2003–2022. Time series data were fitted into the ARDL test using various econometric techniques such as the ADF stationarity test, Granger causality and the ordinary least squares method as well as a vector error correction model (VECM) to analyze the relationship between agricultural credit and agricultural economic growth, showing a causal relationship. Both the impacts through elasticities and the optimal point existing in this relationship were estimated. It was concluded that the impact of agricultural credit on agricultural GDP was 14.41%. Granger causality shows signs of a positive linkage between agricultural credit and agricultural GDP. However, there is a causal relationship between agricultural credit and agricultural GDP, in a unidirectional aspect. This result is consistent with most of the earlier studies reviewed in the literature, confirming that credit-oriented monetary policies can boost economic growth and, consequently, development in Angola. It is important for agricultural credit systems to be designed in a way that ensures equitable access, fair interest rates, and appropriate risk management mechanisms. Additionally, monitoring and evaluation mechanisms should be in place to assess the environmental and social impacts of credit programs on agricultural sustainability. It is worth noting that this is a first-of-a-kind study on the matter of the Angolan credit experience, specifically for the agricultural sector. Angola is still searching for a sustainable credit model that could be used as a catalyzer to boost growth and contribute to economic development.

The building sector of most tropical countries still use predominantly primary biomass as the principal fuel. This has adverse effects like CO2 emission and deforestation and is associated with issues like poverty, ill-health, and low standard of living. Therefore, energy policies try to improve on the efficiency of firewood and charcoal end-use technologies, to palliate the negative effects. In this research, the global change assessment model (GCAM) is used, to investigate the impact of efficiency improvement on the energy consumption pattern of the building sector of developing countries. The aim of the study is to provide empirical data that would better inform policymakers on the effects of modernizing these primary fuels. The study developed three scenarios with different levels of efficiency improvements. The results show that efficiency improvement rather increases primary biomass consumption and CO2 emission. However, there is a fall in the consumption of traditional biomass in the second half of the modelling period. The increase in biomass-based fuels consumption was seen to be linked to their affordability. Therefore, policymakers need not only elaborate policies that improve biomass efficiency, but also introduce and motivate other clean cooking fuels like butane, biogas, and electricity.

The pharmaceutical industry is facing the pressure of a global economy, loss of value in local markets and the highly intense innovation that characterizes this sector. This has a heavy impact, particularly in smaller economies. With this investigation, we intend to identify the determinants of internationalization as levers for sustainability in the pharmaceutical export sector of a small economy. Data was collected from a sample representing 63% of the total universe, Portuguese pharmaceutical organizations with exporting activity. Contextualization of the sector and a bibliographic review were previously carried out, which laid the groundwork for the empirical framework. This study revealed a deeply internationalized sector conditioned by a few shortcomings, namely a certain lack of sustainable competitive advantages, relatively low investment in research and development (R&D), insufficient innovation in internationalization strategies as well as scarce institutional support. Our findings may help pave the way for a more complete understanding of the dynamics of internationalization in highly competitive sectors.

In this study, a dynamic mixture copula is used to estimate the marginal expected shortfall in the South African insurance sector. While other studies assumed nonlinear dependence to be static over time, our model capture time-varying nonlinear dependence between institutions and the market. In order to capture time-varying nonlinear dependence, the generalized autoregressive score (GAS) is used to model the dynamic copula parameters. Furthermore, our study implements a ranking that expresses to what degree individual insurers are systemically important in South Africa. We use daily stock return of five South African insurers listed in the Johannesburg Stock Exchange (JSE) from November 13, 2007 to June 15, 2020. We find that Sanlam and Discovery contribute the most to systemic risk, while Santam is found to be the least contributor to the overall systemic risk in the South African insurance sector. Our findings would be of paramount importance for the South African regulators as they would be informed that not only banks are systemically important, but some insurers also are systemically important financial institutions. Hence, stricter regulation of these institutions in the form of higher capital and loss absorbency requirements could be required based on the individual business activities undertaken by the company.

The potential of generalized machine learning models developed for crop water estimation was examined in the current study. Extreme Gradient Boosting (XGBoost), Gradient Boosting Machine (GBM), and Random Forest (RF) are three ensembled machine learning models that were developed using all of the data from a single location from 1976 to 2017 and then immediately applied at eleven different locations without the need for any local calibration. For the test period of January 2018 to June 2020, the model's capacity to estimate the numerical values of crop water requirement (Pen-man-Monteith (PM) ETo values) was assessed. In comparison to the GBM and RF models, the XGBoost model outperformed them both marginally and significantly. The estimate's weighted standard error was smaller than 0.85 mm/day, and the model's effectiveness varied from 96% to 99% across various locations. The model's strong performance was indicated by the decreased noise-to-signal ratio. A real-time water management system at the regional level can be seamlessly linked with this type of model due to its accuracy in estimating crop water requirements and its capacity to generalize.

Accurate forecasting of crop yields holds paramount importance in guiding decision-making processes related to breeding efforts. This study focused on the application of multi-sensor data fusion and machine learning algorithms based on unmanned aerial vehicles (UAVs) in wheat yield prediction. Five machine learning (ML) algorithms namely random forest (RF), partial least squares (PLS), ridge regression (RR), K-Nearest Neighbor (KNN) and eXtreme Gradient Boosting Decision Tree (XGboost) were utilized for multi-sensor data fusion, and three ensemble methods including the second-level ensemble methods (stacking and feature-weighted) and the third-level ensemble method (simple average) for wheat yield prediction. The 270 wheat hybrids were used as planting materials under full and limited irrigation treatments. A cost-effective multi-sensor UAV platform, equipped with red–green–blue (RGB), multispectral (MS), and thermal infrared (TIR) sensors, was utilized to gather remote sensing data. The results revealed that the XGboost algorithm exhibited outstanding performance in multi-sensor data fusion, with the RGB+MS+Texture+TIR combination demonstrating the highest fusion performance (R2=0.660, RMSE= 0.754). Compared with the single ML model, the employment of three ensemble methods significantly enhanced the prediction accuracy of wheat yield. Notably, the third-layer simple average ensemble method demonstrated superior performance (R2 = 0.733, RMSE= 0.668 t ha-1). It significantly outperformed both the second-layer ensemble methods of Stacking (R2= 0.668, RMSE= 0.673 t ha-1) and feature-weighted (R2= 0.667, RMSE= 0.674 t ha-1), thereby exhibiting superior predictive capabilities. This finding demonstrated that the third-layer ensemble method not only augments the predictive ability of the model but also fine-tuned the accuracy of wheat yield prediction through the employment of simple average ensemble learning. Consequently, it offers a novel perspective for crop yield prediction and breeding selection.

This research aims to review the educational and research operations of Mahasarakham University and propose development directions for the university to meet sustainable development criteria for ranking as a green university. This involves gathering data and analyzing the results of operations over the past 3 years, then using this analysis to synthesize lessons learned and develop guidelines for sustainable development in the coming years. The study found that the university's policies and strategies implemented following the annual performance evaluation criteria align with the green university assessment criteria for education and research. This has resulted in minor improvements in curriculum for sustainability. However, community service projects for sustainability have increased by 89.10%, while funding for sustainable research and academic publications related to sustainability has decreased. In summary, the projects under the university's development strategy adequately support and drive suitable development activities. Nevertheless, the university must continuously review its operations to adapt to changing contexts, budget constraints, and evolving competition and develop each year sustainably.

Common prosperity is the core goal of rural revitalization, and sustainable growth of farmers' income is an inevitable requirement. This study uses CLDS2016-2020 village and family level questionnaire data to reveal how the degree of village organization affects the sustainable growth of farmers' income, thus providing reference and decision-making for the local government to promote the increase of farmers' income and narrow the gap from the village level. The results show that the degree of village organization can significantly affect farmers' income, and the analysis results of the degree of formal organization are consistent with them. Specifically, for every 1% increase in the degree of village organization, the per capita household income will increase by 32.4%, among which the wage income will increase by 22.6%, the operational income will increase by 5%, the property income will increase by 0.8%, and the transfer income will increase by 4.2%. The estimation results through instrumental variables are consistent, and the results are robust. At the same time, improving the degree of village organization can also significantly increase the proportion of farmers' non-agricultural income and narrow the income gap among farmers. On the impact of heterogeneity, it is found that the geographical distance between villages and county and township governments, as well as the political identity and education level of farmers will affect the role of the degree of organization. The results of mechanism analysis show that improving the degree of village organization is mainly through promoting the circulation of agricultural land, socialized service of agricultural machinery and industrial and commercial capital to the countryside to increase farmers' income. The research revealed that the modernization of rural governance should be accelerated, and the degree of village organization should be improved, especially the degree of formal organization, so as to build a benign trust and cooperation relationship between the two village committees and the villagers, and implement policies and measures to improve the income of farmers according to local conditions. At the same time, strengthen the publicity and education in party member to improve the proportion of rural party member and the education level of farmers.

The high reliable correlations between CBR and DCP in general proposed are presented in this paper. We analyzed the test data and correlations from many researchers to find the best equation for prediction the CBR from DCP test. The findings show that all correlations from existing paper are based on local material and cannot apply to soil in a different location in general purpose. New correlations between CBR and DCP were proposed and validated in this paper for general uses. The validation confirmed that these new correlations can predict well with higher R2.

The transport sector is heavily relying on the use of fossil fuels that are causing major environmental concerns. Solutions relying on the direct or indirect use of electricity, through e-fuels production, are emerging to power the transport sector. To ensure environmental benefits over this transition, an accurate estimation of the impact of the use of electricity is needed. This work presents a temporal analysis of carbon intensity of e-fuels using grid electricity in countries members of the European Network of Transmission System Operators (ENTSO-E). It also provides an estimation of the potential load factor for to produce low-carbon e-fuels according to the European Union legislative framework. This was achieved, building on top of the existing EcoDynElec tool, by developing EcoDynElec_xr a python tool enabling, with an hourly time resolution, the calculation, visualisation and the analysis of the historical time-series of electricity mix from the ENTSO-E. The results highlight that, in 2023, a minority of European countries were reaching low carbon intensity of electricity that enables the use of grid electricity for green electrolytic hydrogen production. The methodological hypothesis to consider the consumed electricity mix instead of the production mix, and the considered time step, are of paramount importance and drastically impact the potential load factor of green hydrogen production. The developed tool and calculated data are released under an open-source license to ensure transparency, results reproducibility, reuse on newer data, or for other purposes.

Advanced Hepatocellular carcinoma (HCC) is no longer a terminal illness. This change was mainly attributed to the development of new treatments including tyrosine-kinase inhibitors (TKIs), vascular endothelial growth factor (VEGF) inhibitors and immune checkpoint inhibitors (ICPIs) but the financial toxicity of treating advanced HCC is of a major concern specially in low middle-income countries (LMICs) where the patients are still battling for their most basic rights. Most of advanced HCC patients in LMICs have very limited accessibility to the new treatments including ICPIs. Searching for out of the box solutions to improve access to treatments -mainly ICPIs- is an utmost necessity for LMICs advanced HCC patients.

The creation of a Europe-wide electricity market combined with the increased intermittency of supply from renewable sources calls for an investigation into the risk of aggregate peak demand. This paper makes use of a risk model to assess differences in time-use data from residential end-users in five different European electricity markets. Drawing on the Multinational Time-Use Survey database, it assesses risk in relation to the probability of electrical appliance use within households for five European countries. Findings highlight in which countries and for which activities the risk of aggregate peak demand is higher and link smart home solutions (automated load control, dynamic pricing and smart appliances) to different levels of peak demand risk.

Candida albicans, a prominent constituent of the human microbiota, poses a significant health threat due to its opportunistic pathogenicity and adept biofilm-forming capabilities. This research delves into the antifungal attributes and biofilm inhibition potential of Tea Tree Oil (Melaleuca alternifolia) cultivated in Vietnam. Renowned for its bioactive compounds, including terpinen-4-ol, 1,8-cineole, and terpinolene, the essential oil of Melaleuca alternifolia, commonly referred to as tea tree oil (TTO), is scrutinized for its impact on Candida albicans. The study meticulously determines the Minimum Inhibitory Concentration (MIC) and Minimum Fungicidal Concentration (MFC) of TTO against Candida albicans in both planktonic and biofilm states. Additionally, it assesses the biofilm-forming capacity of Candida albicans and establishes the Minimum Biofilm Inhibitory Concentration (MBIC) and Minimum Biofilm Eradication Concentration (MBEC) of TTO. Employing a liquid culture dilution method within a defined concentration range of 0.1 μL/mL to 10 μL/mL, the experimental approach adheres to established protocols. The outcomes reveal a noteworthy MIC and MIC₈₀ of 0.1 μL/mL and 2 μL/mL, respectively, and an MFC of 10 μL/mL for TTO against Candida albicans. Furthermore, TTO exhibits an MBIC of 2 μL/mL, showcasing its efficacy in inhibiting biofilm formation. However, the MBEC of TTO remains uncertain, possibly due to insufficient concentration tested, which may not effectively disrupt over 50% of the pre-formed biofilm mass within 24 hours. These results underscore the promising potential of TTO as a potent antifungal agent and biofilm inhibitor against Candida albicans, offering valuable insights for the innovation of novel therapeutic strategies in the realm of fungal infections.

(1) Background: A wide literature based on loading and unloading bending tests has been carried out over the half past century for assessing the force released by superelastic wires. The aim of this investigation is not only to characterize a broadly described superelastic NiTi and a novel CuNiTi archwire, but also to provide novel insights into the load vs deflection profiles. A harmonic scenario for superelastic wires will be shown as temperature is kept constant at 37°C. However, a completely different picture will be highlighted as a temporary heating step up to 50°C is introduced within the force vs deflection profiles. (2) Methods: A variety of NiTi and a novel CuNiTi round and rectangular cross-section archwires have been investigated. Static 3-point bending tests up to a deflection level of 3.0 mm have been carried out for detecting the unloading force plateau levels occurring at 37°C. A hold deflection step of 5 min at deflection levels occurring along the unloading plateau extension is introduced. The effect of a temporary heating up to 50°C on the force plateau level released by the investigated wires is analyzed. (3) Results: Mechanical profiles suggest that the plateau force levels detected at 37°C are consistent with those broadly reported in the literature, but they only represent a single block puzzle of the force scenario that a superelastic wire releases during the orthodontic treatment. (4) Conclusions: Once a superelastic wire is bent and clinically applied, the effect of a temporary heating up to 50°C (e.g. hot food or drink ingestion) produces a significant plateau force increase whose magnitude depends on the deflection level. The highest force increase of about 50% and 40% have been detected at deflection levels located at the beginning of the unloading plateau extension for NiTi and CuNiTi wires, respectively.

Total-body PET/CT scanners provide increased sensitivity, enabling the adjustment of imaging parameters by reducing injected activity or shortening acquisition time. This study aimed to evaluate the limitations of reduced [18F]FDG activity doses on image quality, lesion detectabil-ity, and quantification of lesion uptake in the Biograph Vision Quadra, as well as to assess the benefits of the recently introduced ultra-high sensitivity mode in a clinical setting. A number of 26 patients who underwent [18F]FDG-PET/CT (3.0 MBq/kg, 5 min. scan time) were included in this analysis. PET raw data was rebinned for shorter frame durations to simulate 5 min. scans with lower activities in high sensitivity (HS) and ultra-high sensitivity (UHS) modes. Image quality, noise, and lesion detectability (n=82) were assessed using a 5-point Likert scale. The co-efficient of variation (CoV), signal-to-noise ratio (SNR), tumor-to-background ratio (TBR), and standardized uptake values (SUV) including SUVmean, SUVmax, and SUVpeak were evaluated. Sub-jective image ratings were generally superior in UHS compared to HS mode. At 0.5 MBq/kg, le-sion detectability decreased to 95% (HS) and 98% (UHS). SNR was comparable at 1.0 MBq/kg in HS (5.7±0.6) and 0.5 MBq/kg in UHS (5.5±0.5). With lower doses, there were negligible reductions in SUVmean and SUVpeak, whereas SUVmax increased steadily. Reducing [18F]FDG activity to 1.0 MBq/kg (HS/UHS) in a total-body PET/CT provides diagnostic image quality without statistical-ly significant changes in uptake parameters. UHS mode improves image quality, noise, and le-sion detectability compared to HS mode.

Many university female students are concerned about their bodies. Body image perception has become a public health issue globally. This study aimed to explore factors contributing to body image dissatisfaction among female students at the University of Venda. The study was qualitative in nature and employed exploratory research design. A sample of 10 female students enrolled at the University of Venda were identified using convenience sampling method. A pre-tested, semi-structured interview guide was used to collect data and thematic content analysis technique was used to analyse the collected data. The findings of the study showed that body comparison, societal beauty standards, social media, and body shaming by family and friends were the main factors contributing to student’s body image dissatisfaction. The findings further revealed that lack of self-confidence, stress, avoidance, anxiety and depressive symptoms were the challenges faced by students with body image dissatisfaction. Acceptance. Self-care, and healthy diet were identified as coping strategies to help deal with the challenges of student’s body image dissatisfaction. Conclusively, students should be encouraged to seek professional help timeously, to help navigate their body image concerns to avoid decline in their daily functioning.

The intranasal route of drug administration is characterized by high bioavailability and is considered promising for rapid delivery of drugs with systemic action to the central nervous system, bypassing the blood-brain barrier. This is particularly important for the use of neuroprotective drugs in the treatment of brain tissue damage in infants caused by the effects of intrauterine hypoxia. Creation of new dosage forms for neonatology with the use of software and mathematical technologies in pharmaceutical development allows to create cerebroprotective drugs with controlled pharmaco-technological properties, thus reducing time and resources for necessary research. We developed a formulation of a new nasal gel formulation with Angiolin using Box-Benken experiment design for the therapy of prenatal CNS damage. It was found that the consistency properties of the nasal gel were significantly influenced by the gelling agent and mucoadhesive component - sodium salt of carboxymethylcellulose. We have optimized the composition of nasal gel formulation with Angiolin using the formed models and relationships between the factors. The optimized composition of the obtained nasal gel has satisfactory parameters of thixotropic properties. The developed 1% gel for neuroprotection with Angiolin meets all the requirements for harmlessness and safety to drug forms of this group - low toxicity, absence of local irritant and allergic effect.

The application of OpenAI's advanced artificial intelligence language model, ChatGPT, in education has garnered widespread attention from the academic community. The openness and intelligence features of ChatGPT demonstrate great potential and value in supporting students' learning, daily life, and communication. This study, grounded in the theoretical frameworks of Innovation Diffusion Theory (IDT) and the Technology Acceptance Model (TAM), conducted a systematic empirical analysis through a questionnaire survey of 677 university students who had used or were currently using ChatGPT. The results indicate that the six perceived attributes of innovation significantly influence university students' behavioral intention to use ChatGPT, while attitudes towards ChatGPT, perceived usefulness, and perceived ease of use indirectly influence students' behavioral intention to continue using ChatGPT. Based on the findings of this study, we propose an extended Technology Acceptance Model (TAM) that incorporates key elements of Innovation Diffusion Theory (IDT) to optimize the application of advanced technologies, such as ChatGPT, in education, thereby improving students' learning efficiency and behavioral performance. These findings not only provide important insights into the complex relationships between college students’ perceptions of ChatdGPT technology and predictors of behavioral intentions, but can also help relevant education departments, higher education institutions, and university teachers to effectively plan and evaluate the use of ChatGPT as a learning tool.

ABSTRACT"In the realm of the industrial complex, a pressing concern lies in optimizing resource utilization and enhancing energy efficiency. This critical issue finds strong support from a variety of government programs, strategies, and regulatory documents. To effectively tackle this challenge and revolutionize petrochemical production, it is imperative to establish and develop innovative mechanisms. One such mechanism involves harnessing the power of automation and advanced Industry 4.0 technologies to streamline production processes. By doing so, we can unlock the full potential of these systems, driving operational efficiency, conserving valuable reserves, and propelling innovation within the petrochemical industry."The article delves into an in-depth analysis of the prominent trends in the application of the "Industry 4.0" concept within the petrochemical industry. It sheds light on the various factors that impact the efficiency of organizing production systems. Additionally, the article identifies the primary avenues that drive the advancement and enhancement of production organization practices. Moreover, the article explores the fundamental tools employed for process improvements and highlights the significance of process automation in petrochemical plants. By examining these key aspects, it aims to provide valuable insights and recommendations for leveraging Industry 4.0 technologies to optimize production processes in the petrochemical industry..The study focuses on examining and evaluating various systems and technologies that contribute to the optimization of production processes within the petrochemical industry. Specifically, it delves into the implementation and benefits of the just-in-time system, the 5S system for workplace rationalization, as well as different information systems including ERP systems and CALS-technologies. By exploring these systems and technologies, the article aims to identify the advantages of automating production processes within the petrochemical complex. This analysis will unveil the potential benefits such as improved efficiency, reduced waste, enhanced productivity, streamlined operations, and increased overall competitiveness. Through this research, we aim to shed light on the value of these automation solutions and their impact on driving the petrochemical industry towards greater success.

The concept of supply chain leadership strategy has garnered increasing interest from policymakers and experts, because it is among the most dependable practices to sustainable innovation and competitiveness for large firms across developing economies. Effective implementation of supply chain leadership strategic actions at each level in the company can create sustainable innovative and competitiveness. The study collected statistical data using an online Survey- Monkey questionnaire from 46 of top 100 JSE-listed companies with the intention to discover supply chain leadership strategic actions that drive sustainable innovation and competitiveness in the company. The findings expose a significant empirical insight for companies to understand and leverage supply chain leadership strategic actions to influence sustainable innovative position in a developing economy. These results suggest that firms operating within emerging markets should embrace and acknowledge the importance of sustainable innovation in business practices in the provision of goods and services. This study therefore delivers major contributions to the literature to assist in planning sustainable innovation and supply chain practices in a developing economy to survive in a competitive global environment. The paper also offers a vigorous awareness and knowledge of the common characteristics and strategies for supply chain management that needs to be prioritized by the JSE-listed companies.

Education on Innovation and Entrepreneurship (I&E) has increased in the last two decades, specially, through MOOCs. Lately, these reusable online alternatives have tended to be revalorized by HEIs into blended learning activities, posing new challenges for instructors, specially, on how to bridge prior knowledge with in-class activities. Adopting a discursive approach to knowledge, our proposal aims to meet this challenge by identifying student’s ‘representations’, i.e., patterned constructions on disciplinary knowledge. Representations can be found across different cohorts and thus further complemented by instructors. To test this assumption and build our proposal, we analysed student’s representations in two observations. We mapped students’ representations over key I&E definitions (e.g., ‘start-up’) and, to know how prior knowledge may be complemented by instructors, we identified students’ alignment with expert disciplinary knowledge. Firstly, we found that the two cohorts tended to express representations by turning attention to several dimensions, e.g., referring to different types of features or finalities associated with concepts. Secondly, the disciplinary alignment description revealed that students tended to focus on the same components present in experts’ definitions, but with a greater level of generality. Our results have been packaged into a proposal that aims to help instructors scale their blended activities.

Innovation in general education governance is one of the development trends not only in Vietnam but also in the world. This is also an important measure to change and improve the quality of education, especially expand autonomy of high schools and universities. The paper focuses on some main contents: overview of general education; the experiences of some countries in the implementation of the school administration model towards the school autonomy model so that give lessons for Vietnam’s education can be learned in the context of development conditions nowadays. The results in this work would be used to classify the schools into the various groups. The data also analysts on decision-making capability, on what we called an “index of school autonomy”, expressed the possible level of school-level decision-making.

Abstract: The conundrum of Latin American countries in the green transition unveils new opportunities and old structural problems. There is a clear advantage in the energy grid for Argentina, Brazil, and Mexico, coming mainly from hydropower, solar, and wind energy. In 2022, Brazil had the largest solar photovoltaic energy generation capacity in Latin America and the Caribbean, with over 24 gigawatts. Mexico ranked second, with some 9 gigawatts, and Chile ranked third, with a capacity of around 6 gigawatts. The window of opportunity for the industrialized Latin American countries resides chiefly in greening the natural resources industries and agriculture and husbandry, mainly for Brazil and Argentina. Latin America faces the dual challenge of promoting renewable energy and upgrading its value chains while managing the socioeconomic dynamics inherent in its developmental stages. It poses an enormous challenge for Latin American countries because of the phenomenon of "time compression" (Whittaker et al., 2020). Developing country's innovation and technology-intensive segments coexist with informal labor and low-skilled workers. Far from disappearing with economic growth, such coexistence becomes reinforced through an increasingly wide array of temporary and insecure jobs. Time compression condenses different stages of industrial and economic development within the same space and time, directly affecting the indicators related to the reduction of environmental impacts. Instead of locking in one path for this transition, Latin American countries had better not cling to panaceas but consider the threats, the risks, and the need to experiment with multiple paths and solutions simultaneously. However, this change implies the formation of a genuine developmental and environmental coalition guided by rigorous strategic considerations not captured by immediate interests.

The increasing focus on sustainability and the adoption of green practices across industries underscore the necessity of equipping future professionals with competencies in implementing sustainable solutions. This study explores the perceptions of former students from two departments within the technological sector towards sustainability practices. A cross-sectional online survey was carried out among alumni from two programs at the University of Thessaly, a university recognized for its commitment to sustainability and green initiatives as a member of the INVEST European University alliance. The survey questionnaire included background inquiries and 16 statements designed to gauge alumni perspectives towards sustainability practices. Specifically, it aims to assess their views through self-assessment and compare differences between the two alumni groups. Responses were collected on a 5-point Likert scale ranging from strongly disagree to strongly agree. A total of 300 graduates from the Computer Science and Telecommunications as well as Electrical Engineering undergraduate programs were encouraged to take part in the survey, achieving a response rate of 69%. Of those who responded, 61% (n = 126) represented the Computer Science and Telecommunications group, while 39% (n = 81) were from the Electrical Engineering program. Our findings indicate a generally positive attitude towards sustainability, with statistically significant differences in perspectives on the implementation of sustainable technologies and stakeholder engagement. These results underscore the necessity of integrating sustainability education deeply within the curricula of technological programs to cultivate a workforce capable of addressing future environmental challenges. This study illuminates the critical role of educational frameworks in shaping the sustainability competencies of technology professionals.

The IPCC’s sixth assessment report projects 15% to 43% (44 EJ/y – 310 EJ/y) of global primary energy to be generated by biomass in 2050 across multiple GHG mitigation scenarios. That report also emphasises the importance of electrification to meet GHG reduction targets. With increased reliance on electric power, and increased appeal to biomass, bioenergy for electricity is expected to play a major role in future energy markets. What makes the bioenergy solution more attractive is its reported reasonable Energy Return on Investment (EROI). However, generation at large scale is projected to be greatly dependent on crops and plantations. This shifts the GHG emissions concern to be concerns over land use and other emissions integrated in the bioenergy lifecycle. It is therefore vital to know whether the potential of electricity generation from biomass outweighs environmental impact of bioenergy. This paper evaluates the potential of biomass electricity mainly generated from short rotation woody crops combustion in generating green energy. This is done using the “Green EROI (EROIg)” quantification methodology, which indicates the net energy generated to society after investing in ecosystem maintenance energy (ESME). ESME is a non-monetary weighting mechanism of an entity’s different lifecycle environmental impacts. This study found that the EROIg of bioelectricity is marginally larger than unity when converted to its primary equivalent form (EROIg-PE) which indicates that the technology is somewhat energetically viable if its production was to be green. Three design options were proposed to improve bioenergy’s EROIg performance, these include adding 20% waste wood in the combustion mix, staggered harvesting and plantation to achieve annual harvest and pelletizing wood. This approach appeared to improve the EROIg especially for pelletizing, due to its simultaneous reduction in storage and transport costs, making the production energetically and environmentally viable even at a 1 : 1 secondary : primary ratio with an EROIg of 1.11 and an EROIg-PE of 3.17. We conclude with the discussion of the multiple indirect advantages of growing crops that can be used for energy generation, and a discussion on how this technique can be used alongside others to help them generate cleaner energy while facing the current global climate, biodiversity and waste issues.

The global environmental impact of plastic waste is significant, with only 9% being recycled, causing pollution and harming the environment and humans. Due to increased traffic, limited funding, and dwindling natural resources, Jordan's road network is deteriorating rapidly. Pavement performance can be improved through high-quality materials and sustainable construction practices. The research investigates using Polyethylene Terephthalate as a polymer additive in asphalt mixtures to enhance their properties. Basalt and limestone mixtures were applied to asphalt mixtures. The optimal binder content for the control mixture was 4.8% for basalt and 4.93% for limestone. When modified with 10% PET, the basalt mixture showed slightly better stability than the control mix, while higher PET proportions led to reduced strength. PET-modified mixtures consistently displayed higher flows and bulk densities, with a more pronounced impact on the basalt mixture. PET increased the air-void ratio in basalt but had minimal effect on VMA. PET offers economic and environmental advantages, saving 8.4% of the original bitumen cost. As a result, limestone mixture properties, which are inferior to basalt mixture properties, improved significantly compared to basalt mixture properties. PET has the potential to create sustainable and high-performing asphalt mixtures, providing valuable insights for road construction and environmental management.

There is an overall scientific consensus that public space and, mainly, space with a high ecological index – most notably experienced in green areas – positively affects individual and collective well-being and urban dwellers’ physical and mental health. However, the ‘reality check’ indicates difficulties in translating the assumed benefits of green space into implementable interventions in urban environments. To examine such an ‘implementation gap’ on the case of Zurich seems valid, given that the ‘green urban agenda’ debate has been embedded in numerous Swiss policies (from the federal to the municipal levels). In narrow terms, the research first focuses on Zurich’s district 9 consisting of two neighborhoods (Altstetten and Albisrieden) – the area under ongoing densification yet with a variety of green spaces, to then elucidate four green space clusters and their 400-meter catchment areas in the mentioned district. The mixed-method approach has been applied at three analytical levels: 1) documentary analysis of multi-scale national policy toolkits, to identify the critical tenets associated with the ‘green urban agenda’, 2) assessment of green spaces in Zurich’s district 9 based on online available quantitative data and on-site observation, to determine their provision, types, size and mutual connectivity, and 3) analysis of four green space clusters within the mentioned district through on-site observation, to identify green space attributes and main activities. Through critical mutual examination of the identified ‘green urban agenda’ principles in the national policies, on the one hand, and green space attributes, on the other, the research findings reveal the extent of the convergence and/or divergence between the ‘green urban agenda’ policy trends and their implementation in practice.

The quickest and easiest way to avoid greenhouse gas (GHG) emissions is to purchase renewable electricity and offset the remaining emissions. However, the industrial sector’s electricity needs already exceed renewable electricity generation. Moreover, electricity accounts for only one third of the industry’s energy needs. Simultaneously, the advance of sectoral coupling and the decarbonisation of industrial processes, as well as the desire to rapidly decrease dependence on fossil fuels, are creating significant additional demand for renewable energy. Neither existing nor planned generation and transmission infrastructure will suffice to meet the expected short-term demand. Based on survey data from the German Industry Energy Efficiency Index, this article therefore examines the share of GHG savings that companies intend to achieve on- and off-site. Understanding how much additional generation and transmission capacity is needed by the industry to decarbonise and by when is crucial to identify and address the extent of excess demand. On average, companies plan to avoid 22 % of their 2019 emissions by 2025 and 27 % by 2030, primarily through on-site measures. In combination with the extrapolation of the entire industry’s needs for off-site capacity, the data calls for a rapid expansion of planning authority and green generation capacities.

(1) Background: The 2002 Budapest Open Access Initiative recommended on self-archiving of scientific articles in open repositories as the “green road” to open access. Twenty years later, only one part of the researchers deposits their publications in open repositories; moreover, one part of the repositories’ content is not based on self-archived deposits but on mediated nonfaculty contributions. The purpose of the paper is to provide more empirical evidence on this situation and to assess the impact on the future of the green road. (2) Methods: We analyzed the contributions on the French national HAL repository from more than 1,000 laboratories affiliated to the ten most important French research universities, with a focus on 2020, representing 14,023 contributor accounts and 166,939 deposits. (3) Results: We identified seven different types of contributor accounts, including deposits from nonfaculty staff and import flows from other platforms. Mediated nonfaculty contribution accounts for at least 48% of the deposits. We also identified difference between institutions and disciplines. (4) Conclusions: Our empirical results reveal a transformation of open repositories from self-archiving and direct scientific communication towards research information management. Repositories like HAL are somewhere in the middle of the process. The paper describes data quality as the main issue and major challenge of this transformation.

The paper is an investigation on the impact of financial markets on the volatility of green bonds credit risk component, measured by the option-adjusted spread/swap curve (OAS) of the Global Bloomberg Barclays MSCI Green Bond Index, for both the non and pandemic periods. For these purpose, after observing the dynamic joint correlations between all the variables through a DCC-GARCH, we adopt GARCH(1,1) and EGARCH(1,1) models, putting the OAS as dependent variable. Our main results show that the conditional variance parameters are significant and persistent in both times, testifying the overall impact of the other markets on the OAS. In more detail, we highlight that the gamma in the two EGARCH models is positive: so the “green” credit risk volatility is more sensitive to positive shocks than negative ones. With reference to the conditional mean, we note that if during the non pandemic time only the stock market is significant, during the pandemic also conventional bonds and gold are impacting. To the best of our knowledge this is the first study that analyzes the specific credit risk component of green bond yields: we deem our findings useful to observe the change of green bonds creditworthiness in a complex market context.

The focus of this paper is on supply chain strategy formulation. A conceptual theory approach is used for investigating and identifying the relationship between multiple elements, dimensions, forces and factors that influence and affect the supply chain strategy formulation in Greenfield context, specific to the slate mining industry. The research study involved secondary data review and series of 20 qualitative interviews, followed by 2 group discussions, one with mining and transportation experts external to the supply chain and one group discussion with supply chain internal experts. Through critical analysis, a number of problems emerge and the process of addressing these problems, results in a new framework for evaluating the relationship between business and supply chain strategy, specific to Greenfield project and integration design context.

Solar green roofs, namely rooftops functionalized with properly selected living vegetation and photovoltaic modules, achieve an ideal symbiotic relationship in which promotion of biodiversity and onsite renewable energy production are both enhanced whereas the roof provides a wide range of environmental, health, aesthetic and economic benefits. This study provides a unified outlook of this eminent sustainable technology at the dawn of its uptake across the world, especially in polluted urban areas.

A paper impregnated with silver nanoparticles (AgNPs) has been prepared. For the preparation of the substrates, aqueous suspensions of pulp fines, a side product from the paper production, have been mixed with Ag nanoparticles (AgNP) suspensions. The nanoparticle synthesis thereof was carried out via laser ablation of pure Ag in water. After the sheet formation process, the leaching of the AgNPs was determined to be low while the sheets exhibited antimicrobial activity towards E. Coli.

Diatom microalgae are the most outstanding natural source of porous silica. Diatom cell is enclosed in 3-D ordered nanopatterned silica cell wall, called frustule. The unique properties of diatoms frustule, including high specific surface area, thermal stability, biocompatibility, tailorable surface chemistry, make them really promising for biomedical applications. Moreover, diatoms are easy to cultivate in artificial environment and there is a huge availability of diatom frustules as fossil material (diatomite) in several areas of the world. For all these reasons, diatoms are an intriguing alternative to synthetic materials for the development of low-cost drug delivery systems. This review article focuses on the possible use of diatoms derived silica as drug carrier systems. The functionalization strategies of diatom micro-/nanoparticles for improving their biophysical properties, such as cellular internalization and drug loading/release kinetics, are described. In addition, the realization of hybrid diatom-based devices with advanced properties for theranostics and targeted or augmented drug delivery applications, are also discussed.

Korenaga and coworkers present evidence to suggest that 4.3 billion years ago the Earth’s mantle was dry and water filled the ocean to twice its present volume.[2] CO₂ was constantly exhaled during the mafic to ultramafic volcanic activity associated with magmatic plumes that produced the thick, dense and relatively stable oceanic crust. In that setting two distinct major types of sub-marine hydrothermal vents were active: ~400 °C acidic springs whose effluents bore vast quantities of iron into the ocean, and ~120 °C, highly alkaline and reduced vents exhaling from the cooler, serpentinizing crust at some distance from the heads of the plumes. When encountering the alkaline effluents, the iron from the plume head vents precipitated out forming mounds likely surrounded by voluminous exhalative deposits similar to the banded iron formations known from the Archean. These mounds and the surrounding sediments likely comprising nanocrysts of the variable valence FeII/FeIII oxyhydroxide, green rust. The precipitation of green rust, along with subsidiary iron sulfides and minor concentrations of Ni, Co and Mo in the environment at the alkaline springs may have established both the key bio-syntonic disequilibria, and the means to properly make use of them – those needed to drive the essential inanimate-to-animate transitions that launched life. In the submarine alkaline vent model for the emergence of life specifically it is first suggested that the redox-flexible green rust microcrysts spontaneously formed precipitated barriers to the complete mixing of carbonic ocean and alkaline hydrothermal fluids, barriers that created and maintained steep ionic disequilibria; and second, that the hydrous interlayers of green rust acted as 'engines' that were powered by those ionic disequilibria and drove essential endergonic reactions. There, aided by sulfides and trace elements acting as catalytic promoters and electron transfer agents, nitrate could be reduced to ammonia and carbon dioxide to formate, while methane may have been oxidized to methyl and formyl groups. Acetate and higher carboxylic acids could then have been produced from these C1 molecules and aminated to amino acids, and thence oligomerized to offer peptide nests to phosphate and iron sulfides and secreted to form primitive amyloid-bounded structures, leading conceivably to protocells.

Chronic gastro-abdominal pain with altered bowel habits are associated with pathologies including gastroenteritis, autoimmune and inflammatory bowel disease, and irritable bowel syndrome (IBS). In IBS, diagnostic evidence of infection or inflammation are absent, yet symptoms nonetheless include chronic abdominal pain and alterations of stool frequency and consistency, with most common subtypes including diarrhea-predominant (IBS-D) or constipation predominant (IBS-C). [1, 2] IBS is a common clinical complaint in westernized nations, with females more frequently diagnosed than males [3]. Obesity is also associated with increased likelihood of chronic pain [4], and is associated with intestinal dysbiosis, and systemic inflammatory signatures [5]. Improving personalized medicine therefore requires patient stratification based on a combination of biological factors contributing to the individual’s symptom and biomarker spectrum. The “buffy coat” method isolates leukocytes and PBMCs from whole blood, comprises the immune cell population in peripheral blood. These cells are relatively inactive but are primed to respond to generalized and localized immune activation signals; they are under active investigation as diagnostic biomarkers [6]. Collection of buffy coat is a standard method in clinical laboratories; detection of immune activation via RNA expression biomarkers will be informative of differential biological response, and be tractable to collect in clinical settings. Results of differential expression, pathway analysis, and cell-type abundance analysis from buffy coat RNA, is presented here using variables for BMI, Gender, and IBS-subtype from publicly available Nanostring RNA expression and phenotypic data from the NCBI GEO database.

This paper explores the latest developments in electrolysis technology, a key player in the transition to sustainable energy systems. Electrolysis, despite currently contributing a small share to global hydrogen production, holds immense potential for producing green hydrogen. The study delves into the efficiency of electrolysis systems, emphasizing ongoing efforts to enhance energy conversion rates. It investigates the impact of high-temperature electrolysis on reducing electricity consumption, thus making the process more efficient. The paper discusses the various challenges in the research on water electrolysis and underscores the critical role electrolysis plays in integrating renewable energy sources. The study emphasizes the need for continuous advancements in electrolysis technology to bridge existing gaps, making a compelling case for its pivotal role in the green hydrogen revolution.

This paper analyses the EU’s policies for energy and climate, using Börzel's (2002) theoretical framework on Europeanisation, and examines member states’ Green Deal responses, strategies, and compliance. As expressed in their final NECPs, although member states’ responses vary, most of the critical components were partially addressed, while the others were largely addressed. We observe a considerable variation in Member State’s strategies. Member States classified as foot-dragging beforehand are fence-sitting now, while those previously categorised as fence-sitting are now either foot-dragging or pace-setting. The root cause of these classification changes for the member states within the EU can be traced back to their internal environments in which the involved stakeholders each have a different response pace regarding environment, climate, and energy. We present and analyse our theoretical context, discuss the EU’s energy policies and the NECPs, examine member states’ responses and compliance with this new framework, and propose several challenges.

Wasted Green tuff powder produced by cutting Towada stone is recycled as environmental cleaning material. The optimum temperature for green tuff powder calcination to reduce the hydroxyl radical produced in hydrogen peroxide decomposition with ultraviolet light (UV) and no light. The green tuff calcined at 800 °C shows the large decomposition rate of hydrogen peroxide with no UV light when measured by using ESR. With UV light, the optimum temperature for calcinating the green tuff powder in order to reduce hydroxyl radical is also 800 °C. Next, the powder calcined at 800 °C is used to produce the tile by compression and heating, and then the formaldehyde adsorption rate was measured. The green tuff powder calcined at 800 °C showed a high adsorption rate, similar to that of the activated carbon. The tiles formed at 40 MPa and heated st 1100 °C were the strongest and also adsorbed formaldehyde. The adsorbed formaldehyde on the green tuff tile and powder might have a possibility to decompose by photocatalytic.

Nepal, primarily an agricultural country, heavily relies on agricultural residue and fuelwood for daily energy requirements. In 2022, total energy consumption was 640 PJ, with traditional sources accounting for 64.17%, and fuelwood comprising 58.53% of total fuel consumption. The estimated potential supply of agricultural residue is 26 million tonnes, yielding about 442 million GJ of energy in 2021. Biomass trading often emphasizes volume or weight, necessitating a rapid and non-destructive assessment of energy properties for mutual benefit, aiding in the identification, management, and utilization of biomass sources. In this study, 200 biomass samples were collected in two batches (126 and 74 samples) from various locations in Nepal. Using Partial Least Squares Regression (PLSR), a model was developed correlating higher heating value (HHV) from a bomb calorimeter and spectral data from Fourier Transform near-infrared spectroscopy (FT-NIRS) (3595 – 12,489 cm-1) sensor. PLSR models incorporated raw spectra, eight preprocessing techniques, the multi-preprocessing five-range method, and a genetic algorithm. Outliers in the first batch were identified, and the first batch divided into an 80% calibration set and a 20% validation set, while the second batch was designated as an unknown sample set. The optimum PLSR model, utilizing first derivative preprocessing, improved accuracy by 6.77%, with coefficients of determination in the calibration set, validation set, and unknown set as 0.9694, 0.9578, and 0.8089, respectively. Root mean square errors were 132.4790 J/g, 189.4800 J/g, and 360.8845 J/g for the calibration set (RMSEC), validation set (RMSEP), and unknown set (RMSEUN), respectively. The prediction to deviation ratio (RPD) for the validation set and unknown set was 4.9 and 2.4, respectively. The cross validation model of combined sample data of every sets showed the R2CV of 0.95 and RPDCV of 4.6 indicating the model could serve as a reliable and swift non-destructive alternative for evaluating biomass HHV using NIRS and ready for updating for industrial use. However, incorporating a larger number of representative samples is crucial to enhance accuracy and develop a more comprehensive global model for predicting biomass HHV.

This paper examines the transformation of the Ukrainian stock market by analyzing daily returns from the PFTS stock index spanning 1995-2022. Segmented into sub-periods, it tests the hypothesis that the market's efficiency has grown over time. Various data properties, including persistence, volatility, normality, and resistance to anomalies, are explored using different statistical techniques and methods. Volatility, indicated by standard deviation, exhibits a general decreasing trend, suggesting a shift towards a more efficient market. Returns show no significant differences across sub-periods, except for 2005-2007. R/S analysis indicates the decrease of persistence in the market: a trend towards increased efficiency. Normality tests support a normal distribution of daily returns throughout sub-periods. An analysis of anomalies reveals the market's resilience to day-of-the-week effects, with no specific trends observed. The results suggest the absence of serious changes in data properties in the Ukrainian stock market over the time, means evolutionary processes over 25 years can’t be clearly observed. Reforms and changes have had limited impact, justifying continued reform for the market's evolution.

Interest in developing novel crop protectants has gained attention in the recent decade due to the hazardous effects of synthetic pesticides on humans and the environment. The citrus industry worldwide is threatened by the D. citri, the primary vector of phlo-em-limited bacterium (HLB) or citrus greening. However, there is no cure for citrus greening available. Diaphorina citri management largely depends on synthetic insecticides, but their massive application leads to resistance in pest populations. Therefore, alternative pest management strategies are needed. Our results indicated fewer D. citri adults settled on plants treated with ABEO than on control 48 h after re-lease. The psyllid fed on citrus leaves treated with ABEO significantly secreted less honeydew than the control. The essential oil showed potent ovicidal activity against the D. citri eggs with LC50 5.88 mg/mL. Furthermore, we also explore the fitness of D. citri on ABEO-treated and untreated Citrus × sinensis by using two-sex life table tools. The results indicated that the intrinsic rate of increase (r) was higher on untreated seedlings (0.10 d−1) than those treated with a sub-lethal dose of ABEO (0.07 d−1). Similarly, the net re-productive rate (R0) was higher for untreated seedlings (14.21 offspring) than those treated (6.405 offspring). Notably, the ABEO were safer against Aphis melifera, with LC50 ranging from 31.05 to 55.86 mg/mL, which is relatively higher than the LC50 24.40 mg/mL values against D. citri. Our results indicate that ABEO exhibits toxic and behavioral effects on D. citri that could be useful for managing this pest.

Here we describe the genome of an Echovirus 7 isolate of South-East Asian ancestry recovered from a child with acute flaccid paralysis (AFP) in Nigeria. The genome has 7,295nt and an open reading frame with 2,195 amino acids.

In recent years, the widespread introduction of bike-sharing systems in China has had a profound impact on the daily lives of Chinese citizens and the development of the urban transport system. This article attempts to analyse the impact of this phenomenon on sustainability. The gradual improvement of related monitoring measures has facilitated the maturation of the bike-sharing industry from the initial stage of uncontrolled growth to the current stage of standardised management. By tracing the global development of bike-sharing systems with a special focus on China, this study sheds light on the platformisation of bicycles and its multiple impacts on technical, environmental, cultural, economic and social sustainability. Furthermore, the study provides a comprehensive analysis of the transformation of bicycles in China and highlights the diverse impacts of platform-based bike sharing on various facets of Chinese society. The development of different bike-sharing systems in China is a unique and crucial case to interpret the current situation of bike-sharing and imagine future scenarios. In contrast to the prevailing and uniform approach derived from the experiences of Northern European countries, the massive and widespread experimentation with different bike-sharing schemes in China reveals not only potentials and aspects of sustainability, innovation and urban and social regeneration, but also some hidden shadows similar to those in small-scale contexts such as Northern Europe. Furthermore, the study emphasises the crucial role of sustainable development principles in addressing urban challenges specific to China.

Assembly theory provides a promising framework to explain the complexity of systems such as molecular structures and the origins of life, with broad applicability across various disciplines. In this study, we explore and consolidate different aspects of assembly theory by introducing a simplified toy model to simulate the autocatalytic formation of complex structures. This model abstracts the molecular formation process, focusing on the probabilistic control of catalysis rather than the intricate interactions found in organic chemistry. We establish a connection between probabilistic catalysis events and key principles of assembly theory, particularly the probability of a possible construction path in the formation of a complex object, and examine how the assembly of complex objects is impacted by the presence of autocatalysis. Our findings suggest that autocatalysis tends to favor longer consecutive construction sequences in environments with low catalysis probability, while this bias diminishes in environments with higher catalysis probabilities, highlighting the significant influence of environmental factors on the assembly of complex structures.

Corrugated boards are used for packaging because of their high strength-to-weight ratio, recyclability, and biodegradability. They have an orthotropic sandwich structure with unique characteristics for each direction owing to their flute shape. In this study, the bending behavior was qualitatively analyzed for various variables through an FE-based simulation, and the possibility of an alternative test method for the four-point bending test on a corrugated board was examined through a similarity analysis with the experimental results. The cross-machine direction (CD) bending stiffness through finite element analysis (FEA) simulation was closely related to the thickness of the corrugated board. In AB-flute-double-wall (AB/F-DW), the difference in CD bending stiffness based on the bending direction was approximately 17.2% in the FEA simulation and approximately 10.5% in the experiment. However, the differences in the bending behavior (bending force vs. deflection plot) and bending stiffness based on the phase shift between the two flutes constituting BB-flute-double-wall (BB/F-DW) were insignificant. Overall, the CD bending behavior of the corrugated board was simulated relatively well through FEA simulation. However, it was impossible to simulate the machine-direction (MD) bending behavior through FE-based simulation because of a decrease in convergence and a large error caused by the variability of the contact condition of the modeled test specimen with a non-uniform MD cross-section.

This paper investigates generalized Bayesian (GB) inference in the context of a one-parameter Pareto distribution. The study focuses on the derivation of generalized Bayesian estimators (GBE) and generalized empirical Bayesian estimators (GEBE) for the parameters of the Pareto distribution using type II censored data. In addition, the study investigates a one-sample prediction approach to examine generalized Bayesian prediction (GBP) and generalized empirical Bayesian prediction (GEBP). To evaluate the performance of these estimation and prediction methods, Monte Carlo simulations were performed comparing GBE with GEBE and GBP with GEBP for different parameter values and learning rate parameters (LRP).

Fatigue performance and self-repairing activity of asphalt binders are two properties that highly influence the fatigue cracking response of asphalt pavement. There are still numerous gaps of knowledge to fill linked with these two characteristics. For instance, current parameters fail to fully accommodate these two bitumen phenomena. This study aims to propose a new procedure to address this issue utilizing the linear amplitude sweep (LAS) test, LAS with rest period (RP) (LASH) test, and simplified viscoelastic continuum damage (S-VECD) model. This research work used four different types of asphalt binders: neat asphalt (NA), self-healing thermoplastic polyurethane (STPU)-modified bitumen (STPB), self-healing poly (dimethyl siloxane) crosslinked with urea bond (IPA1w)-modified bitumen (IPAB), and styrene–butadiene–styrene (SBS)-modified bitumen (SBSB). Before the testing process, all the materials were subjected to short-term and long-term aging. The new procedure showed a superior capacity to analyze and accommodate all bitumen fatigue performances and self-repairing activities compared to the current method. Another finding proved that asphalt binders with higher self-restoration behavior failed to show better fatigue performance. A breakthrough finding demonstrated that asphalt binder fatigue response augmented when the RP was applied at a higher damage intensity (S) value. STPBs and IPABs reached their highest increments of fatigue response, containing 1.0% of STPU and 0.5% of IPA1w, respectively. Those augmentations were 207.54% and 232.64%, respectively.

Background: Both hypertension and carotid atherosclerosis are independent risk factors for cardiovascular disease. Understanding their combined impact could enhance risk assessment and management strategies. Hypothesis: We hypothesized that the synergistic effects of hypertension and carotid plaques would increase the risk of both cardiovascular events and all-cause mortality. Methods: A follow-up study was conducted at the Preventive Cardiology department of Vilnius University Hospital Santaros klinikos between 2012 and 2021. The study recruited participants aged 40-60 who did not have overt CVD and were part of the Lithuanian High Cardiovascular Risk primary preventive program. The study collected demographic and clinical data, including an ultrasound assessment of carotid plaque. The participants were monitored for 4-10 years for CVD events and all-cause mortality. Results: Among 6,138 participants, 954 (16%) experienced cardiovascular disease (CVD) events. The presence of carotid plaque on both sides was significantly associated with CVD events, myocardial infarction, and all-cause mortality. However, the combination of hypertension and carotid plaque did not significantly increase the risk for CVD events, stroke, or all-cause mortality. Conclusion: The combination of hypertension and carotid plaque did not significantly increase the risk of CVD events or all-cause mortality. CV events depend on the spread of the atherosclerosis process in the carotid vessels.

This article investigates robust optimality and duality for a class of nonsmooth multiobjective programming problems with vanishing constraints under data uncertainty (in short, UNMPVC) via convexificators. Using the properties of convexificators, we introduce generalized standard Abadie constraint qualification (in short, GS-ACQ) for the considered problem UNMPVC. Moreover, we introduce generalized robust version of nonsmooth stationary conditions, namely, weakly stationary point (in short, RW-Stationary), T-stationary point (in short, RT-Stationary), M-stationary point (in short, RM-Stationary) and S-stationary point (in short, RS-Stationary) for UNMPVC. By employing GS-ACQ, we establish that the RS-Stationary is the necessary first-order optimality condition for a local Pareto solution of UNMPVC. Moreover, under generalized convexity assumptions, we establish sufficient optimality criteria for UNMPVC. Furthermore, we formulate the Wolfe-type (in short, WRD) and Mond-Weir-type (in short, MWRD) robust dual models corresponding to the primal problem UNMPVC.

In the whole world, including Mexico, the rising prevalence of diabetes poses significant challenges to healthcare systems, with a notable impact on hospital admissions even being an ambulatory care sensitive condition, that means, hospital admissions are avoidable. Traditional healthcare methodologies have been instrumental in managing diabetes and preventing complications, yet they often face limitations such as merging, cleansing, and outlier analysis of health data to identify and address diseases effectively. This paper aims to address this gap by conducting a comprehensive comparison of the methodologies used in healthcare and data science for this purpose. This work uses hospital diabetes discharge records from 2010 to 2023, a total of 36,665,793 records, which belong to medical units of the Ministry of Health of Mexico. In this work, we seek to provide the arguments why as a data scientist it is mandatory to learn the field of knowledge of the problem and its implications if this is not done, and therefore to disclose insights that can help in policy decisions and reduce the burden of avoidable hospitalizations. The approach is mainly based on the standardization or adjust rates by sex and age groups. This study provides the foundations for a new way of data scientist must deal with health data.

Carbapenem antibiotics are the drug of choice for treating multidrug-resistant bacterial infections. Metallo-beta-lactamases (MBLs) are carbapenemase capable of hydrolyzing nearly all therapeutically available beta-lactam antibiotics. Consequently, a need to assess the frequency and phenotypic resistance phenomena of two MBL genes in diarrheal and urinary tract infections (UTIs). Samples were collected through a cross-sectional study, with MBLs genes detected via PCR. Two hundred twenty eight diarrheal bacteria were isolated from 240 samples. The most predominant pathogens were Escherichia coli (32%) and Klebsiella spp. (7%). Phenotypic resistance to amoxicillin-clavulanic acid, aztreonam, cefuroxime, cefixime, cefepime, imipenem, meropenem, gentamicin, netilmicin, and amikacin was 50.4%, 65.6%, 66.8%, 80.5%, 54.4%, 41.6%, 25.7%, 41.2%, 37.2%, and 42.9%, respectively.  Total 142 UTI pathogens were obtained from 150 urine samples, with Klebsiella spp. (39%) and Escherichia coli (24%) are the major pathogens. Phenotypic resistance to amoxycillin-clavulanic acid, aztreonam, cefuroxime, cefixime, cefepime, imipenem, meropenem, gentamicin, netilmicin, and amikacin was 93.7%, 75.0%, 91.5%, 93.7%, 88.0%, 72.5%, 13.6%, 44.4%, 71.1%, and 43%, respectively.  Twenty four diarrheal isolates carried either blaNDM-1 or blaVIM genes; the overall MBL gene prevalence was 10.5%. Thirty six UTI pathogens carried either blaNDM-1 or blaVIM genes (25.4%). Seven isolates carried both blaNDM-1 and blaVIM genes.  MBL genes exhibited a strong association with phenotypic carbapenem and other beta-lactam antibiotic resistance. Resistance to carbapenems requires active surveillance and stewardship.

Levels of trace element pollution in surface soil can be estimated using soil analyses and leaching tests. These methods may reveal different results due to the effect of soil properties, such as grain size and mineral composition, on elemental availability. Therefore, this study advocates an alternative method for monitoring and assessment of trace element pollution in surface soil using terricolous fruticose lichens. Lichens growing at abandoned mine sites and unpolluted areas in southwest Japan and their substrata were analyzed using inductively coupled plasma-mass spectrometry and X-ray fluorescence spectrometry to clarify the relationships between Cu, Zn, As, and Pb concentrations in lichens and soils, including their absorption properties. Concentrations of these elements in the lichens were positively correlated with those in the soils regardless of lichen species, location, habitat, or conditions of soils. The analyzed lichens had neither competitive nor antagonistic properties in their elemental absorption, which made them good biomonitors of trace element pollution in surface soil. The distribution maps of average Cu, Zn, As, and Pb concentrations at each sampling region detected almost all of the Cu, Zn, and As pollution of the soils. Therefore, lichens could be used in practical applications to monitor Cu, Zn, and As pollution in surface soils.

In an era of relentless technological change, accelerating the pace of innovation has become a critical imperative for organizations seeking to maintain a competitive edge and drive meaningful societal progress. Yet the complex, multifaceted innovation process - from ideation to commercialization - is often plagued by bottlenecks and challenges that can significantly delay the time-to-market for groundbreaking new products, services, and business models. This pioneering article represents an outstanding contribution to the burgeoning field of innovationology, offering a comprehensive, evidence-based playbook for shortening the innovation lifecycle. Drawing on an extensive review of over 180 cutting-edge academic sources, the paper maps the key stages of the innovation process - idea generation, concept development, prototyping, testing, and launch - and illuminates cutting-edge strategies that innovators can deploy to overcome common barriers and drive transformative innovations to market more quickly. From advanced idea generation techniques like design thinking and AI-powered creativity to innovative approaches to rapid prototyping, virtual experimentation, and data-driven commercialization, this seminal work equips scholars, practitioners, and policymakers with a transformative, globally-relevant toolkit for accelerating the innovation lifecycle. By synthesizing insights from a diverse array of industries and disciplines, the article establishes a new benchmark for innovationology research and empowers organizations to cultivate the conditions needed to translate visionary ideas into game-changing realities with unprecedented speed and efficiency. As the world grapples with ever-more complex, interconnected challenges, the ability to rapidly develop and scale breakthrough innovations has never been more crucial. This paradigm-shifting contribution to the field of innovationology represents a vital resource for driving the bold, disruptive changes needed to shape a more sustainable, equitable future.

This study investigates the adoption of emerging technologies in procurement within supply chain management, focusing on the transformative potential of artificial intelligence (AI) and blockchain. Through a qualitative approach, involving semi-structured interviews with procurement professionals and technology experts, the study explores how these technologies enhance operational efficiency, optimize inventory management, and improve supply chain transparency and trust. AI-driven predictive analytics were found to significantly enhance demand forecasting, inventory optimization, and supplier performance evaluation, fostering data-driven decision-making and proactive risk management. Blockchain technology ensures secure and transparent transactions, facilitating compliance with ethical sourcing standards and reducing risks associated with counterfeit goods. Despite these benefits, organizations face substantial challenges in adopting these technologies, including data security concerns, integration complexities with existing IT infrastructure, and skills shortages in AI and blockchain expertise. Leadership commitment and organizational readiness are identified as crucial enablers for overcoming these barriers and driving successful technology adoption. The study also highlights the strategic importance of supplier relationship management, emphasizing how emerging technologies enhance real-time collaboration, performance insights, and contractual transparency. By addressing these challenges and leveraging technological innovations strategically, organizations can achieve sustainable growth, resilience, and competitive advantage in an increasingly digital and interconnected global economy. The findings contribute to advancing knowledge on procurement innovation and offer valuable insights for organizations seeking to navigate the complexities of technology-driven procurement practices. Future research directions may explore the long-term impact of technology adoption on supply chain dynamics, sustainability initiatives, and organizational performance.

This study aims to analyse whether Chinese companies' R&amp;D investment affects management innovation performance. Also, this dissertation defines management innovation as diversification strategies such as technology and industry diversification and analyzes the impact of these diversification strategies. In particular, this paper analyzes whether R&amp;D investment leads to technology and industry diversification and whether technology diversification creates a mediating effect in the process of R&amp;D investment leading to industry diversification. The analysis results can be summarized as follows: First, R&amp;D investment showed a significant positive relationship with industry diversification. Second, R&amp;D investment shows a significantly positive relationship with technology diversification. Third, technology and industry diversification showed a positive relationship. Fourth, this study examined the mediating role of technology diversification, and it was confirmed that technology diversification plays a mediating role in the effect of R&amp;D investment on industry diversification. R&amp;D investment and business innovation play a key role in the sustainable development of enterprises, and the findings of this paper have important reference value for enterprises' R&amp;D investment decision-making. The results of this study provide the following implications that Chinese companies improve business innovation performance by increasing their R&amp;D investment, promoting innovative R&amp;D, and improving technology diversification and industry diversification strategies.

With the disruption of digital technologies, customers have emerged as co-producers in order to reduce costs and enhance productivity. Customer-driven business logic recognizes customer capabilities and sustainable innovation as key factors in the performance of customized manufacturing enterprises. The potential relationship between customers and producers has become a new area of inquiry. Existing research rarely delves into the impact of customer predictive ability on the process of customized production activities, particularly in relation to sustainable innovation, which remains inadequately characterized. Ships serve as a typical representation of customized enterprises. This study explores the underlying drivers of sustainable innovation through customer demand orientation by examining 20-year historical patterns in the global shipping and shipbuilding markets, considering the environmental dependency and temporal correlation characteristics of the shipbuilding market (producers) and the shipping market (consumers). By employing time series and panel data in a machine learning algorithm, specifically the random forest model, the study reveals a strong and statistically significant correlation between new ship deliveries and the Baltic Dry Index (BDI), with larger value ships having a more pronounced impact on the consumer market. The correlation analysis confirms that these two variables, in combination, can comprehensively reflect customer predictive ability and serve as crucial decision criteria for customer investment in new ship production. Furthermore, based on principal component analysis of customer predictive ability and ship innovation levels, as well as Granger causality tests, the study demonstrates that customer predictive ability is a Granger cause of sustainable innovation in customized production. Customer predictive ability influences sustainable innovation in customized enterprises to varying degrees. This research provides valuable insights for shipbuilding companies in terms of engaging in sustainable innovation in international markets and understanding the value of international market customers.

In this age of global warming, academics and policymakers are increasingly concerned about firm environmental sustainability success. Therefore, this study aims to investigate whether Environmental, Social and Governance (ESG) performance impacts sustainability performance through the mediating effect of firm innovation. To this end, Structural Equation Modeling (SEM) was deployed to analyze data collected from the employees of manufacturing industries in an emerging economy like Bangladesh. The results revealed that ESG performance significantly enhances the innovation and sustainability performance of manufacturing industries, indicating that the higher the ESG performance of a firm, the greater its innovation and sustainability performance. Furthermore, the results confirmed that firm innovation performance fully mediates the relationship between ESG initiatives and sustainability performance. The findings of this study provide policymakers and industry authorities with valuable insight into the role of ESG and innovation performance in improving sustainability performance. Specifically, the study sheds knowledge on how firm ESG initiatives and innovation performance impact sustainability performance in the manufacturing sector of an emerging economy like Bangladesh.

In the development and growth of family businesses, succession is an unsolvable problem, which is also a popular focus of academic research. For a family firm, succession may be a strategic decision but also a long-term and intricate "footrace." It will have a significant impact on the long-term viability of a family firm if it is not handled appropriately. This study mainly explores the influences of family business owners' intergenerational succession intention on their family firms’ innovation strategy in China. In addition, this study further examines the moderating role of the institutional environment in the above relationship. Therefore, the data in this article comes from a survey of 271 family businesses in eight different regions of China. Also, this paper can aid the smooth transition of intergenerational transmission of small and medium-sized family businesses in addition to the untroubled development of technological innovation activities. Specifically, the institutional environment plays a negative moderating role in the relationship between family succession, radical succession, technological innovation, and a positive regulating role in the relationship between single equity succession and technological innovation.

Taking capital, manpower, and natural resources as inputs, regional GDP as expected output, and industrial pollution as undesired output, this study measures the ecological efficiency of various regions in China through the Global-Malmquist model. The results show a trend of an initial sharp decline in ecological efficiency followed by a gradual increase in the time dimension, but there is no significant correlation in the spatial dimension. Using the gravity model to quantify the attractiveness of the regions’ capital and human resources for collaborative innovation, it estimates the impact of collaborative innovation on eco-efficiency through the system-Generalized Method of Moments (GMM) model. The results show that technological innovation capital in other regions has a negative “U” relationship with local ecological efficiency, while scientific and technological innovation human resources have a positive “U” relationship. In addition, government financial support in science and technology and the ecological efficiency of the previous period serve as promoting factors of the current local ecological efficiency, while the introduction of foreign technological innovation is likely to inhibit improvements in ecological efficiency. Based on these findings, this study puts forward corresponding policy recommendations for local governments to advance their development agendas alongside their environmental priorities in line with their specific circumstances.

Preparing high school students for engineering disciplines is crucial for sustainable scientific and technological developments in the USA. This paper discusses a pre-college program, which not only exposes students to various engineering disciplines but also enables them to consider engineering as the profession. The four-week long “Engineering Innovation (EI)” course is offered every year to high school students by the center of outreach, Johns Hopkins University. EI program is designed to develop problem-solving skills through extensive hands-on engineering experiments. A team consisting of an instructor, generally a PhD in Engineering, and a teaching fellow, generally a high school science teacher, closely work with students to pedagogically inculcate basics of core engineering disciplines such as civil, mechanical, electrical, materials, and chemical engineering. EI values independent problem-solving skills and simultaneously promote the team spirit among students. A number of crucial engineering aspects such as professional ethics, communications, technical writing, and understanding of common engineering principles are inculcated among high school students via well-designed individual and group activities. This paper discusses the model of EI program and its impact on students learning and their preparation for the engineering career.

Direct-to-consumer genetic testing companies aim to predict the risks of complex diseases using proprietary algorithms. Companies keep algorithms as trade secrets for competitive advantage, but a market that thrives on the premise that customers can make their own decisions about genetic testing should respect customer autonomy and informed decision making and maximize opportunities for transparency. The algorithm itself is only one piece of the information that is deemed essential for understanding how prediction algorithms are developed and evaluated. Companies should be encouraged to disclose everything else, including the expected risk distribution of the algorithm when applied in the population, using a benchmark DNA dataset. A standardized presentation of information and risk distributions allows customers to compare test offers and scientists to verify whether the undisclosed algorithms could be valid. A new model of oversight in which stakeholders collaboratively keep a check on the commercial market is needed.

The Genotype-Phenotype Working Group was established in November 2021 as part of the AgBioData Consortium (https://www.agbiodata.org) with the goal of identifying current challenges in annotating and integrating large-scale genotype and phenotype data. Over the course of the year, the members of this working group identified different types of data sets, explored experimental platforms and methods for data generation, and examined how these data are annotated including the metadata requirements. We conducted a thorough review of publicly funded repositories for raw and processed data for each data type. We also examined several secondary databases and knowledgebases that enable the integration of heterogeneous data types in the context of the Genome Browser, Pathway Networks and tissue-specific gene expression. The review revealed a need for additional infrastructural support, standards, and tools to connect Genotype to Phenotype data and enhance data interoperability for knowledge synthesis and to foster translational research.

Green peppers (Capsicum annuum L.) is a fruit vegetable with great culinary versatility, and present important nutritional properties for human health. Water deficit negatively affects nutritional quality of green peppers fruits. This study aimed to investigate the influence of zinc oxide nanoparticles (ZnONPs), associated with plant growth-promoting bacteria (PGPB), on the post-harvest quality nutritional of green peppers subjected to water deficit. In an open-field ex-periment, two irrigation levels (50 and 100% of crop evapotranspiration - Etc), four treatments composed by combination of ZnONPs, zinc sulfate (ZnSO4) and PGPB (T1 = ZnSO4, T2 = foliar ZnONPs, T3 = foliar ZnONPs + PGPB, T4 = ZnSO4 via soil + PGPB via soil) and a control treatment (Check) were tested. Water deficit or water deficit mitigation treatments did not interfere with the physical-chemical parameters (except vitamin C content) and physical color parameters (except L*) of green pepper. The water deficit reduced the levels of Ca, Mg, P, K, Mn, Fe, B, and Zn in the fruits. The foliar application of ZnONPs in association with PGPB provided better nutritional quality of green pepper in terms of vitamin C and mineral nutrients. Under water deficit, ZnONPs or ZnSO4 via foliar, associated or not with PGPB, increased the levels of Ca, P, Mg, and Fe in the fruits. Applying Zn via foliar as ZnONPs or ZnSO4 mitigated the negative effects of water deficit on the quality of pepper fruits that was enhanced by the Bacillus subtilis and B. amyloliquefaciens inoculation.

A sustainable green one pot procedure for the synthesis of dihydropyrimidinones (DHPMs) derivatives by a three-component reaction of β-ketoester derivatives, aldehyde and urea or thiourea over the alkali-treated H-ZSM-5 zeolite under ball-milling has been developed. The product was isolated by simple washing of the crude reaction residue with ethyl acetate followed by evaporation of solvent. The hierachical zeolite catalyst (MFI27_6) showed high yield (86–96%) of dihydropyrimidinones (DHPMs), in very short time (10-30 min). The catalyst is recycled for subsequent reactions in four runs without appreciable loss of activity and high yields of products provide efficient protocol for Biginelli reactions.

In the evolving digital landscape, the pervasive influence of Artificial Intelligence (AI) on social media platforms reveals a compelling paradox: the capability to provide personalized experiences juxtaposed with inherent biases reminiscent of human imperfections. Such biases prompt rigorous contemplation on matters of fairness, equity, and societal ramifications, and penetrate the foundational essence of AI. Within this intricate context, the research ventures into novel domains by examining the potential of quantum computing as a viable remedy for bias in artificial intelligence. The conceptual framework of the Quantum Sentinel is presented - an innovative approach that employs quantum principles for the detection and scrutiny of biases in AI algorithms. Furthermore, the study poses and investigates the question of whether the integration of advanced quantum computing to address AI bias is seen as an excessive measure or a requisite advancement commensurate with the intricacy of the issue. By intertwining quantum mechanics, AI bias, and the philosophical considerations they induce, this research fosters a discourse on the journey toward ethical AI, thus establishing a foundation for an ethically conscious and balanced digital environment.

"Hydrogen is an extremely promising energy carrier, poised to become a commercially viable product in the coming years. However, there are significant hurdles that need to be addressed. One such challenge is the high energy consumption during the electrolysis process, which is used to produce hydrogen. Furthermore, the co-production of CO and CO2 during steam hydrocarbon reforming adds to the complexity. Finding innovative solutions to these issues is crucial for the widespread adoption of hydrogen as a clean and efficient energy source." Simultaneously, various industries have the capability to generate hydrogen as a by-product, each with differing levels of purity. For instance, sodium and chlorine production plants have the potential to produce high-purity hydrogen. On the other hand, digester and sewage gas can yield low-purity hydrogen. In both instances, there is an economic incentive to utilize this hydrogen for energy production or to inject it into existing natural gas pipelines A brief estimation of the technical potential of utilizing hydrogen from various sources in Nigeria, specifically for fuel cell usage, has been conducted. Moreover, resource potential visualization maps have been generated to help visualize the availability and distribution of these hydrogen sources throughout the country.

In the pursuit of China’s dual carbon goals, identifying spatio-temporal changes in industrial carbon emission efficiency and their influencing factors in cities at different stages of development is the key to effective formulation of countermeasures to promote the low-carbon transformation of Chinese national industry and achieve high-quality economic development. In this study, we used balanced panel data of 270 Chinese cities from 2005 to 2020 as a research object: (1) to show spatio-temporal evolution patterns in urban industrial carbon emission efficiency; (2) to analyze the aggregation characteristics of industrial carbon emission efficiency in Chinese cities using Global Moran's I statistics; and (3) to use the hierarchical regression model for panel data to assess the non-linear impact of the digital economy on the industrial carbon emission efficiency of cities. The results show the following: (1) the industrial carbon emission efficiency of Chinese cities exhibited an upward trend from 2005 to 2020, with a spatial distribution pattern of high in the south and low in the north; (2) China's urban industrial carbon emission efficiency is characterized by significant spatial autocorrelation, with increasing and stabilizing correlation, and a relatively fixed pattern of spatial agglomeration; (3) there is a significant inverted-U-shaped relationship between the digital economy and the industrial carbon emission efficiency of cities. The digital economy increases carbon emissions and inhibits industrial carbon-emission efficiency in the early stages of development, but inhibits carbon emissions and promotes industrial carbon emission efficiency in mature developmental stages. Therefore, cities at all levels should reduce pollution and carbon emissions from high-energy-consuming and high-polluting enterprises, gradually reduce carbon-intensive industries, and accelerate the digital transformation and upgrading of enterprises. Western, central and eastern regions especially should seek to promote the sharing of innovation resources, strengthen exchanges and interactions relating to scientific and technological innovation, and jointly explore coordinated development routes for the digital economy.

The Industrial Internet of Things (IIoT) paradigm is a key research area derived from the Internet of Things (IoT). The emergence of IIoT has enabled a revolution in manufacturing and production, through the employment of various embedded sensing devices connected with each other by an IoT network, along with a collection of enabling technologies such as artificial intelligence (AI) and edge/fog computing. One of the unrivaled characteristics of IIoT is the inter-connectivity provided to industries; however, this characteristic might open the door for cyber-criminals to launch various attacks. In fact, one of the major challenges hindering the prevalent adoption of the IIoT paradigm is IoT security. Inevitably, an increasing number of research proposals have been introduced over the last decade to overcome these security concerns. To obtain an overview of this research area, conducting a literature survey of the published research is necessary, eliciting the various security requirements and their considerations. This paper provides a literature survey of IIoT security, focused on the period from 2017 to 2023. We identify IIoT security threats and classify them into three categories, based on the IIoT layer they exploit to launch these attacks. Additionally, we characterize the security requirements that these attacks violate. Finally, we highlight how emerging technologies, such as AI and edge/fog computing, can be adopted to address security concerns and enhance IIoT security.

The catalytically active vanadium-containing system on γ-Al2O3 was studied using a wide range of physical and chemical methods depending on the synthesis conditions. It is shown that the vanadium-containing system includes several complexes with different thermal stability and catalytic activity. Low-active complexes are destroyed with the formation of more active ones based on V2O5 oxide as the temperature of heat treatment increases. It can be assumed that the V2O5 oxide has the decisive role in its catalytic activity. It was concluded that the vanadium-containing catalytic system on aluminum oxide, in the studied temperature range, is thermal stable and shows high activity not only in the reduction of nitrogen oxides but also in the oxidation of hydrocarbons (even of the most difficult ones, such as oxidizable methane). These properties of the system make it quite promising in the field of application for purification of exhaust gases of motor transport and industrial enterprises from environmentally harmful components as well as for understanding the mechanism of the action of catalysts in these processes, which is very important for solving the problems of decarbonization and achieving carbon neutrality.

Pineapple is a highly demanded fruit in international markets, thanks to its unique appearance and flavor, high fiber content, vitamins, folic acid, and minerals. It makes the pineapple production and processing market a significant source of income for producing countries, such as Costa Rica. Nowadays, its processing produces a large amount of waste with negative consequences for the environment. However, pineapple waste is an essential source of cellulose, hemicellulose, lignin, and other high-value products like enzymes (bromelain). These by-products can be obtained by pineapple waste biorefinery, generating an additional source of export goods and foreign currency, framing pineapple processing in the concept of the circular economy. This review discusses how incorporating biorefinery in the pineapple production processes can contribute to the post-COVID 19 economy in Costa Rica. Pineapple production in Costa Rica is explored, and the contamination of generated residues is delineated. Furthermore, the primary processes for by-product extraction via biorefinery, their general characteristics and applications in the medical field, and their contribution to the circular economy are presented.

Very well into the dawn of the fourth industrial revolution (industry 4.0), we hardly distinguish between what is artificial and what is natural (e.g. man-made virus and natural virus). Thus, the level of discombobulation among people, companies or countries is indeed unprecedented. The fact that industry 4.0 is explosively disrupting or retrofitting each and every industrial sector, makes industry 4.0 the famous buzzword amongst researchers today. However, the insight of industry 4.0 disruption in the industrial sectors remains ill-defined in both academic and non-academic literature. The present study aimed at identifying industry 4.0 neologisms, understanding the industry 4.0 disruption and illustrating the disruptive technologies convergence in the major industrial sectors. A total of 99 neologisms of industry 4.0 were identified. Industry 4.0 disruption in Education industry (Education 4.0), Energy industry (Energy 4.0), Agriculture industry (Agriculture 4.0), Healthcare industry (Healthcare 4.0), and Logistics industry (Logistics 4.0) are described. The convergence of 12 disruptive technologies including 3D printing, Artificial intelligence, Augmented reality, Big Data, Blockchain, Cloud computing, Drones, Internet of things, Nanotechnology, Robotics, Simulation and Synthetic biology in agriculture, healthcare and logistics industries are illustrated.

Exoskeleton robots are a rising technology in industrial contexts to assist humans in onerous applications. Mechanical and control design solutions are intensively investigated to achieve a high performance human-robot collaboration (e.g., transparency, ergonomics, safety, etc.). However, the most of the investigated solutions involve high-cost hardware, complex design solutions and standard actuation. In the presented work, an industrial exoskeleton for lifting and transportation of heavy parts is proposed. A low-cost mechanical design solution is proposed, exploiting compliant actuation at the shoulder joint to increase safety and transparency in human-robot cooperation. A hierarchic model-based controller is then proposed (including the modeling of the compliant actuator) to actively assist the human while executing the task. An inner optimal controller is proposed for trajectory tracking, while an outer fuzzy logic controller is proposed to online deform the task trajectory on the basis of the human’s intention of motion. A gain scheduler is also designed to calculate the optimal control gains on the basis of the performed trajectory. Simulations have been performed in order to validate the performance of the proposed device, showing promising results. The prototype is under realization.

The RECONNECT project tackles the fragmentation in Ireland's public healthcare systems, aiming to improve service planning and delivery. It integrates complex systems within the Health Service Executive (HSE) for chronic disease management, prioritizes data privacy, and supports future digital resource integration. The methodology includes: (1) structural integration via a Federated Database design to maintain system autonomy while ensuring privacy; (2) semantic integration through a Record Linkage module to enable integration without individual identifiers; and (3) adoption of the HL7-FHIR framework for high interoperability, aligning with the national electronic health record (EHR) and the Integrated Information Service (IIS). The RECONNECT system is unique in Ireland, featuring a generic architecture for loosely coupled systems, adherence to HL7-FHIR for interoperability, efficient reuse of digital assets, and a robust privacy layer. A demonstration system using synthetic data from the Hospital Inpatient Enquiry (HIPE) and Chronic Disease Management (CDM) systems is implemented on Neo4j for visualization.

This research investigates the alignment of soft power and institutional skills in Montenegro's journey towards accession to the European Union (EU), with a particular focus on the environmental sector. An online survey targeting individuals employed in state institutions directly engaged in negotiation processes, notably the Ministry of Sustainable Development and Tourism and the Agency for Nature and Environmental Protection, was conducted. The research conducted an online survey, distributed before and after the summer recess to accommodate the transition of power following parliamentary elections, aimed at assessing the effectiveness of current personnel and identifying areas for improvement in staffing and negotiation strategies within Montenegro's environmental sector. Employing diverse methodologies, the survey's analysis delved into the demographic, social, and professional backgrounds of respondents. It explored their roles within institutions, involvement in environmental negotiations, and possession of relevant skills and expertise. Furthermore, respondents' knowledge of environmental issues, legislation, and challenges facing the country was assessed to gauge institutional capacity for environmental governance. Demographic data, including gender, age, education and regional origin, were collected to understand gender-specific attitudes and regional disparities in environmental perspectives. The sample of 84 individuals, comprising executives and employees from both institutions, provided insights into the age structure and regional diversity of personnel involved in negotiation tasks for Chapter 27. The selection of the Ministry of Sustainable Development and Tourism and the Agency for Nature and Environmental Protection reflects their pivotal roles in shaping Montenegro's environmental policies and addressing climate change challenges. This study aims to illuminate the dynamics of environmental governance within Montenegro's state administration, contributing to the country's path towards EU accession. The research findings highlight the critical need for Montenegro to prioritize strategic initiatives in personnel management, skill development, and institutional capacity-building within its environmental sector. The implications of this research extend beyond academia to inform policymaking and societal action, emphasizing the urgency for Montenegro to bolster its environmental sector capabilities, fostering both EU alignment and sustainable governance practices for the benefit of present and future generations.

The present study aimed to clarify the relationship between brand awareness and word-of-mouth communication in the fast-food sector, Lambayeque Region, 2023. By adopting a quantitative approach, statistical tools were utilized to measure variables and address hypotheses and objec-tives. The study was correlational in nature, as it demonstrated the relationship between brand awareness and word-of-mouth communication. It was also applied in scope, disseminating learning based on the research that will assist the studied area. Furthermore, this study employed a nonexperimental, cross-sectional design because the variables were only analyzed in the workplace environment at a single point in time and were not manipulated. According to the results, it is evident that consumers will easily recall their favorite brands, provided these brands meet their expectations are deemed significant, and the information on social networks is updated, detailed, useful, and valuable. Moreover, they are likely to recommend them if they recognize and have a positive valuation of the brand, sharing favorable information about it on their social networks and with their peers. The study concludes that virtual interactivity and the quality of information are linked to the generation of brand awareness, which in turn was shown to be a predictor of eWOM.

Environmental pollution management combined with food safety represent two of the main important challenges of the last decades. Soil and water contamination. have historically threatened food safety. As ubiquitous pollutants, microplastics (MPs) have attracted increasing attention over the last few years, due to their impact on the socio-ecological system. These particles can affect the balance of terrestrial, aquatic and aerial ecosystems. Their negative impacts are intensified when they adsorb and carry toxic chemicals. They can circulate through organisms and accumulate in the human food chain. Physiological dysfunctions in living beings continue to be reported, both in terrestrial and aquatic ecosystems. The question that remains is on what scale this might be affecting the global production of food. The present article addresses issues related to the food industry, including agriculture, livestock, and fish farming. In doing so it seeks to provide subsidies for the evaluation of the real impact of MP dispersion through the different world environments and the resulting effects on global food production.

Malaysian healthcare institutions still use ineffective paper-based vaccination systems to manage childhood immunization schedules. This may lead to missed appointments, incomplete vaccinations, and outbreaks of preventable diseases among infants. To address this issue, we proposed a text messaging vaccination reminder and recall system named Virtual Health Connect (VHC) to simplify and accelerate the immunization administration for nurses that may result in improving the completion and timeliness of immunizations among infants. Considering the narrow research about the acceptance of these systems in the healthcare sector, we examined factors influencing nurses’ attitude and intention to use VHC using the extended technology acceptance model (TAM). The novelty of the conceptual model is proposing new predictors of attitude, namely, perceived compatibility and perceived privacy and security issues. We conducted a survey among 121 nurses in Malaysian government hospitals and clinics to test the model. We analyzed the collected data using partial least squares-structural equation modeling (PLS-SEM) to examine the significant factors influencing nurses’ attitude and intention to use VHC. Moreover, we applied artificial neural network (ANN) to determine the most significant factors of acceptance with higher accuracy. Therefore, we could offer more accurate insights to decision-makers in the healthcare sector for the advancement of health services. Our results highlighted that the compatibility of VHC with the current work setting of nurses developed their positive perspectives toward the system. Moreover, the nurses felt optimistic about the system when considering it to be useful and easy to use in the workplace. Finally, their attitude toward using VHC played a pivotal role in raising their intention. Based on the ANN models, we also found that perceived compatibility was the most significant factor influencing nurses' attitude towards using VHC, followed by perceived ease of use and perceived usefulness.

The energy transition requires integration of different energy carriers, including electricity, heat, and transport sectors. Energy modeling methods and tools are essential to provide a clear insight into the energy transition. However, the methodologies often overlook the details of small-scale energy systems. The study states an innovative approach to facilitate sub-national energy systems with 100% renewable penetration and sectoral integration. An optimization model, OSeEM-SN, is developed under the Oemof framework. The model is validated using the case study of Schleswig-Holstein. The study assumes three scenarios representing 25%, 50%, and 100% of the total available biomass potentials. OSeEM-SN reaches feasible solutions without additional offshore wind investment, indicating that they can be reserved for supplying other states’ energy demand. The annual investment cost varies between 1.02 bn – 1.44 bn €/yr for the three scenarios. The electricity generation decreases by 17%, indicating that with high biomass-based combined heat and power plants, the curtailment from other renewable plants can be decreased. Ground source heat pumps dominate the heat mix; however, their installation decreases by 28% as the biomass penetrates fully into the energy mix. The validation confirms OSeEM-SN as a beneficial tool to examine different scenarios for sub-national energy systems.

The tourism sector in general and the hotelier in particular face the challenge of managing appropriate measures to deal with the covid-19 pandemic. In this sense it is useful to know what the measures are most demanded by the clientele. This research, by analyzing the variance (ANOVA) of a factor concluded that women are more demanding than men in terms of the level of demand in relation to the measures to be taken in hotels. This, as a practical application, allows to know more accurately the expectations, demands and demands of the client segmented by sex, in order to satisfy and improve the tourist experiences.

The tourism sector in general and the hotelier in particular face the challenge of managing appropriate measures to deal with the covid-19 pandemic. In this sense it is useful to know what the measures are most demanded by the clientele. This research, by analyzing the variance (ANOVA) of a factor concluded that women are more demanding than men in terms of the level of demand in relation to the measures to be taken in hotels. This, as a practical application, allows to know more accurately the expectations, demands and demands of the client segmented by sex, in order to satisfy and improve the tourist experiences.