With technological transformations such as big data, blockchain, artificial intelligence, and cloud computing, digital techniques are penetrating the field of finance. Digital finance is a resource-saving and environmentally friendly innovative financial service. It shows great green attributes and can drive the flow of financial resources towards environmentally friendly enterprises, thereby promoting green and low-carbon development. This paper investigates the effects of digital finance on green and low-carbon cyclic development and the mechanism. First, the level of green and low carbon cyclic development in China is estimated from multiple aspects based on the panel data of 31 provinces by using the spatio-temporal range entropy weight method. Then, the working mechanism of digital finance on green and low carbon cyclic development is revealed by performing empirical tests based on panel regression models, mediating effect models, and threshold models. Finally, the effects of digital finance and conventional variables on green and low-carbon cyclic development are investigated by using a random forest model and a CatBoost model in based on the machine learning. The results show that digital finance has significant positive effects on green and low-carbon cyclic development, and technological innovation plays a key role in the effects of digital finance on green and low-carbon cyclic development; meanwhile, the driving effect of digital finance on green and low carbon cyclic development shows nonlinear features with increasing “marginal effect”; besides, both digital finance and conventional factors have significant impacts on green and low carbon cyclic development. This study provides an empirical basis and path reference for digital finance to achieve “carbon peak, carbon neutralization” in China.

Dendrobium okinawense is an endangered epiphytic orchid, and there has been no scientific report so far on its propagation. Protocorm is mass of cell, and protocorm-like bodies (PLBs) look alike protocorm produced by vegetative explants in vitro. Regeneration of PLBs is the most efficient technique for the orchids micro-propagation. We used different light emitting diodes (LEDs) for the efficient PLB organogenesis of D. okinawense. PLBs regenartion under green and red LED surpassed respectively 81.1% and 71.6% in numbers, and respectively 80.8% and 57.8% in fresh weight over white fluorescent light. We manipulated the culture media by different concentrations of PCIB and HMI. PLBs organogenesis promoted by low concentration, it increased respectively 35.9% and 19.3% over control by 0.01 mg/L PCIB and 0.01 ml/L HMI in numbers. Green LED and PCIB independently produced mostly similar numbers of new PLBs. Interestingly, culture media with 0.01 mg/L of PCIB further increased 8.5% of the numbers of PLBs under green LED, whereas the culture media with 0.01 mg/L HMI reduced the number of PLBs under green LED. PLBs culture under green LED with very low concentrations of PCIB manipulated culture media can significantly increase their organogenesis of D. okinawense.

Background/ObjectivesMental health and substance use disorders (MHD, SUD) affect cardiac allograft and VAD recipients and impact their quality of life and compliance. Limited research on MHDs and SUDs in transplant and MCS candidates and recipients. This study compares the incidence of MHDs and SUDs in transplant list, VAD, and post-transplant patients with that in heart failure patients. MethodsStudy cohorts were derived from the TriNetX using ICD-10 codes. Differences in incidence were examined using the log-rank test. Adults with mental health disorders before the window of time were excluded. Propensity score matching was performed using TriNetX to balance demographic and medical comorbidities. Statistical significance was set at p<0.05. All comparisons were made between propensity-matched cohorts. ResultsTransplant waitlist patients showed a statistically significant increase in the incidence of anxiety, panic disorder, adjustment disorder, depression, mood disorder, alcohol use disorder, and eating disorder. Post-transplant patients showed a statistically significant increase in the incidence of depression and opioid use compared to the heart failure population. VAD patients showed a statistically significant increase in the incidence of depression and a statistically significant decrease in panic disorder and anxiety. ConclusionsThe results pave the pathway for further investigations on prevention and coping strategies because deterioration of mental health will significantly impact compliance with medications as well as patient survival and quality of life. The use of opioids for pain management in the early postoperative period should be further investigated to assess its impact on long-term substance use and addiction. .

The present study discusses how adopting cryptos affects financial inclusion in developing economies. Primary constructs like Financial Inclusion (FI), Perceived Economic Empowerment (PEE), Trust in Financial Institutions (TFI), User Satisfaction (US), and Cryptocurrency Adoption (CA) were tested through Structural Equation Modeling (SEM). The results indicated that CA significantly and positively influenced FI, US, TFI, and PEE. These relationships extend to the interaction effects: US, TFI, and PEE all positively related to FI. It is a reflection of cryptocurrencies as an opportunity to redress most of the afflictions characteristic of traditional finance systems and to promote financial inclusion and economic empowerment in developing countries. Future research should also investigate whether digital literacy and regulatory environments support cryptocurrency access.

The Grand Ethiopian Renaissance Dam (GERD), formerly known as the Millennium Dam, is currently under construction and has been filling at a fast rate without sufficient known analysis on possible impacts on the body of the structure. The filling of GERD not only has an impact on the Blue Nile Basin hydrology, water storages and flow but also pose massive risks in case of collapse. Rosaries Dam located in Sudan at only 116 km downstream of GERD, along with the 20 million Sudanese benefiting from that dam, would be seriously threatened in case of the collapse of GERD. In this study, through the analysis of Sentinal-1 satellite imagery we show concerning deformation patterns associated with different sections of the GERD’s Main Dam (structure RCC Dam type) and the Saddle Dam (Embankment Dam type). We processed 109 descending mode scenes from Sentinel-1 SAR imagery, from December 2016 to July 2021, using the Differential Synthetic Aperture Radar Interferometry technique to demonstrate the deformation trends of both - the GERD’s Main and Saddle Dams. The time-series generated from the analysis clearly indicates different displacement trends at various sections of the GERD as well as the Saddle Dam. Results of the multi temporal data analysis on and around the project area show inconsistent subsidence at the extremities of the GERD Main Dam, especially the west side of the dam where we recorded varying displacements in the range of 10 mm to 90 mm at the crest of the dam. We conducted the current analysis after masking the images with a coherence value of 0.9 and hence, the subsequent results are extremely reliable and accurate. Further decomposition of the subsiding rate has revealed higher vertical displacement over the west side of the GERD’s Main Dam as compared to the east side. The local geological structures consisting of weak zones under the GERD’s accompanying Saddle Dam adds further instability to its structure. We identified seven critical nodes on the Saddle Dam that match the tectonic faults lying underneath it, and which display a varying degree of vertical displacements. In fact, the nodes located next to each other displayed varying displacement trends: one or more nodes displayed subsidence since 2017 while the other node in the same section displayed uplift. The geological weak zones underneath and the weight of the Saddle Dam itself may somewhat explain this inconsistency and the non-uniform vertical displacements. For the most affected cells, we observed a total displacement value of ~90 mm during the whole study period (~20 mm/year) for the Main Dam while the value of the total displacement for the Saddle dam is ~380 mm during the same period (~85 mm/year). Analysis through CoastSat tool also suggested a non-uniformity in trends of surface water-edge at the two extremities of the Main Dam.

Air pollution includes particle-bound Polycyclic Aromatic hydrocarbons (PAHs) which eventually reach the placenta triggering adverse perinatal outcomes by long-term exposure. Late-ly, air pollution has increased over the Metropolitan Area of Medellin-Colombia (MAMC) but its effects on pregnancy are still unknown. In this research, we made a real-time analysis of airborne total PAHs using a photoelectric sensor for residential places influenced by industrial and traffic sources contrasting southern and northern MAMC during the second peak of the bimodal ten-dency for PM2.5 emissions in this region. Additionally, we analyzed individual PAHs by GC/MS coupled to pressurized hot water extraction methodology. Data was applied in an Inhalation In-take Model to assess pregnancy exposure. The average concentration of PAHs over southern MAMC was three times higher than over northern MAMC where the abortion rate has been 1.4 times higher presented in database. Previous research found that PAHs act as an Endo-crine-Disrupting Chemical (EDC) during pregnancy and even heavy congeners could reside in umbilical cord blood. Finally, the annual series of abortion rates in the MAMC showed a signifi-cant correlation with the annual average levels of PM2.5 which are associate to PAHs. Although this significant correlation does not imply causality, our results suggest an important connection between both variables. This latter finding opens a gap in deeply understanding how regions with high PAHs-convergence influences abortion rates in MAMC.

China’s rural e-commerce has been developing rapidly. Taobao Villages are combination of e-commerce and rural industries. When rural e-commerce coverage evolves from Taobao village to Taobao town, the scale of industrial clusters has been expanding synchronously. This paper investigates flower and seedling industrial cluster in Xinhe Town, Yanji Town and Miaotou Town of Shuyang County, China, and conducts the econometric analysis of the expansive determinants of flower and seedling industrial cluster of Taobao Villages. An effective sample of 263 was obtained through a face-to-face survey of e-merchants of flower and seedling in the Shuyang County of Jiangsu Province. Bases on the structural equation modeling, series of test results show that the data can be used to calculate the path regression. The outcomes shows that creation of e-merchants of flower and seedling, integration of e-commerce platform, supply chain friendliness, involvement of e-commerce service providers, and governmental policy guidance are driving expansion of flower and seedling industrial cluster together, moreover, the five forces interact with each other. This implies that expansion of flower and seedling industrial cluster is a systematic process, each stakeholder needs to pay attention to the role of other forces, and five forces achieve a balanced situation in the cluster.

Agriculture is the backbone of Kenya’s economy, supporting up to 80% of the rural livelihoods. Kenya’s export horticulture is currently the leading Agriculture subsector in Kenya has evolved from small-holder farming to agro-industrial large-scale export farming dominated by multinational companies. It is regarded as an agro-industrial food system based on the economies of scale producing for mass markets outside of the production area. Much of the food consumed from this food system has undergone multiple transformations and been subject to a host of formal and informal insitutions (rules, regulations, standards, norms and values). An Anthropological study of export horticulture in Northwest Mount Kenya was carried out utilizing qualitative data collection methods in Northwest Mount Kenya region. Data was coded and analysed thematically based on grounded theory approach. The study described the institutional settings of export horticulture from an emic perspective as changing and defining the operations of the food system access and management of common pool resources, namely water and land. With the agro-industrial food system competing for these scarce resources in a semi-arid zone, there is potential for conflict and also reduced production and overall benefits to the different actors in the study area.

The recent outbreak of a new Coronavirus has developed into a global pandemic with about 10,5 million reported cases and over 500,000 deaths worldwide. Our prospective paper reports an updated analysis of the impact that this pandemic had on the Italian agri-food sector during the national lockdown and discusses why and how this unprecedented economic crisis could be a turning point to deal with the overall sustainability of food and agricultural systems in the frame of the forthcoming European Green Deal. Its introductory part includes a wide-ranging examination of the first quarter of pandemic emergency, with a specific focus on the primary production, to be understood as agriculture (i.e. crops and livestock, and their food products), fisheries and forestry. The effect on the typical food and wine exports, and the local environment tourism segments is also taken into account in this analysis, because of their old and deep roots into the cultural and historical heritage of the country. The subsequent part of the paper is centered on strategic lines and research networks for an efficient socio-economic and territorial restart, and a faster transition to sustainability in the frame of a circular bio-economy. Particular emphasis is given to the urgent need of investments in research and development concerning agriculture, in terms of not only a fruitful penetration of the agro-tech for a next-generation agri-food era, but also a deeper attention to the natural and environmental resources, including forestry. As for the rest of Europe, Italy demands actions to expand knowledge and strengthen research applied to technology transfer for innovation activities aimed at providing solutions for a climate neutral and resilient society, in reference to primary production to ensure food security and nutrition quality. Our expectation is that science and culture return to play a central role in national society, as their main actors are capable of making a pivotal contribution to renew and restart the whole primary sector and agri-food industry, addressing also social and environmental issues, and so accelerating the transition to sustainability.

The versatile one-pot green synthesis of a highly concentrated and stable colloidal dispersion of AgNPs was carried out using the self-assembled tannic acid without using any other hazardous chemicals. Tannic acid (Plant-based polyphenol) was used as a reducing and stabilizing agent for silver nitrate in a mild alkaline condition. The synthesized AgNPs were characterized for their concentration, capping, size distribution, and shape. The experimental results confirmed the successful synthesis of nearly spherical and highly concentrated (2281 ppm) AgNPs, capped with poly-tannic acid (AgNPs-PTA). The average particle size of AgNPs-PTA was found 9.90 ± 1.60 nm. The colloidal dispersion of synthesized nanoparticles was observed stable for more than 15 months in the ambient environment (25 oC, 65 % relative humidity). The synthesized AgNPs-PTA showed an effective antimicrobial activity against Staphylococcus Aureus Escherichia coli. Ag-PTA also exhibited enhanced catalytic properties. It reduces 4-nitrophenol into 4-aminophenol in the presence of NaBH4 with a normalized rate constant (Knor = K/m) of 615.04 mL·s-1·mg-1. Furthermore, AgNPs-PTA were stable for more than 15 months under ambient conditions. The unique core-shell structure and ease of synthesis render the synthesized nanoparticles superior to others, with potential for large-scale applications, especially in the field of catalysis and biomedical.

Generic text embeddings have demonstrated considerable success across a multitude of applications. However, these embeddings are typically derived by modeling the co-occurrence patterns within text-only corpora, which can limit their ability to generalize effectively across diverse contexts. In this study, we investigate methodologies that incorporate visual information into textual representations to overcome these limitations. Through extensive ablation studies, we introduce a novel and straightforward architecture named VisualText Fusion Network (VTFN). This architecture not only surpasses existing multimodal approaches on a range of well-established benchmark datasets but also achieves state-of-the-art performance on image-related textual datasets while utilizing significantly less training data. Our findings underscore the potential of integrating visual modalities to substantially enhance the robustness and applicability of text embeddings, paving the way for more nuanced and contextually rich semantic representations.

Serious crime modelling typically needs to be undertaken securely behind a firewall where police knowledge and capabilities can remain undisclosed. Data informing an ongoing incident is often sparse, with a large proportion of relevant data only coming to light after the incident culminates or after police intervene - by which point it is too late to make use of the data to aid real-time decision making for the incident in question. Much of the data that is available to police to support real-time decision making is highly confidential so cannot be shared with academics, and is therefore missing to them. In this paper, we describe the development of a formal protocol where a graphical model is used as a framework for securely translating a model designed by an academic team to a model for use by a police team. We then show, for the first time, how libraries of these models can be built and used for real-time decision support to circumvent the challenges of data missingness and tardiness seen in such a secure environment. The parallel development described by this protocol ensures that any sensitive information collected by police, and missing to academics, remains secured behind a firewall. The protocol nevertheless guides police so that they are able to combine the typically incomplete data streams that are open source with their more sensitive information in a formal and justifiable way. We illustrate the application of this protocol by describing how a new entry - a suspected vehicle attack - can be embedded into such a police library of criminal plots.

Background: Acute ischemic stroke treatment has been thoroughly studied to identify strategies to reduce treatment times. However, many centers still do not meet the benchmark time metrics. Additionally, smaller centers often face longer treatment times, yet studies focus primarily on larger, more advanced centers. Objectives: The aim of this study is to analyze existing literature to understand the strategies implemented in primary and comprehensive stroke centers to reduce their treatment times, and categorize the studies based on methods used to improve their processes Results: Three main categories of improvements were identified in the literature: 1) standardization of processes, 2) resource management, and 3) data collection. Both primary and comprehensive stroke centers were able to reduce treatment times through standardization of processes. However, challenges such as variations in resources between hospitals and difficulties in integrating data collection software into workflow were highlighted. Additionally, many strategies to optimize resource management and data collection were conducted in comprehensive stroke centers, which can benefit primary stroke centers. Conclusions: Many existing strategies to improve treatment times are viable for both primary and comprehensive stroke centers. However, while data collection is recognized as crucial for process improvement, challenges persist in integrating data collection methods into clinical workflow. Proposed solutions include the development of easy-to-use software tailored to clinician needs.

The rapid technological progress has ushered in a new era of human-computer interaction, where the distinction between the physical and virtual realms is becoming increasingly blurred. This research paper explores the profound and multifaceted intersection of Human-Data Interaction (HDI) and Data Virtualization (DV), examining how emerging technologies can significantly enhance the exploration, comprehension, and utilization of complex, multidimensional data sets. Informed by the insights gleaned from prior research in this domain , the present study delves into the potential of DV techniques to improve HDI, with a particular focus on three experimental investigations conducted within the realms of education, healthcare, and retail. The findings reveal the benefits and potential challenges associated with the implementation of DV in these diverse contexts, offering valuable guidance for the design and development of future HDI systems. Drawing upon a diverse array of authoritative sources, this paper presents a holistic, forward-looking perspective on the future of HDI, underscoring the critical role that DV will play in shaping the next generation of human-computer interfaces and facilitating a deeper, more intuitive understanding of the digital world. Furthermore, the paper presents a preliminary framework for integrating HDI principles into standard design practices. This framework outlines key considerations and guidelines to help designers and developers incorporate HDI techniques more effectively into the development of data-driven applications and interfaces.The proposed framework outlines key considerations for enhancing data accessibility and comprehension, empowering users to exercise greater control over their data, and cultivating transparent dialogues between data providers and end-users. By establishing this conceptual foundation, the paper aims to facilitate the seamless integration of HDI principles into standard design practices, ultimately leading to more intuitive, user-centric, and ethically-grounded approaches to data interaction and utilization.

In this short communication, a concept has been presented to model geographical data to predict future temperature of Tabuk, region. Machine learning has been applied to the weather station data to develop a prediction model. The preliminary results are promising and encouraging and are envisaging to further this research towards the determination of unknown temperature rise in the region. This is important to mention here, that the problem has been formulated as a Regression problem, NOT as a classification problem. Hence, applying Convolutional neural networks is not possible, due to the non-existence of classes or converting the temperature values to classes does not make any sense. Hence, this is defined as a regression problem which achieved encouraging desirable results.

Cycle times at workstations in offsite construction factories fluctuate widely due to various influencing factors. Consequently, relying on average rates, such as length per unit of time, for estimating cycle times proves to be inaccurate, often leading to significant deviations between production schedules and actual operations. To address this issue, this study proposes an estimation system that leverages machine-learning-based prediction, statistical methods, 3D simulation, and computer vision to predict cycle times at the workstation level. Testing of the system on a semi-automated wood-wall framing workstation in a panelized construction factory shows that it reduces the mean absolute error and sum of errors by approximately 36% and 68%, respectively, compared to the fixed rate method. The results also highlight the efficacy of using computer vision data for training machine-learning models for cycle time estimation, the importance of identifying and understanding the factors influencing cycle times, and the impact of random delays on the accuracy of cycle time estimation systems.

Sensor fusion algorithms and models have been widely used in recent times. Although research evidence has informed the use of sensor fusion models in diverse applications, there is room for improvement, especially in home-based health monitoring applications which require less supervision and technical knowledge of users. The present work compares data mining-based fusion software packages such as RapidMiner Studio, Anaconda, Weka, and Orange, and proposes a data fusion framework suitable for in-home applications. 574 privacy-friendly (binary) images and 1,722 datasets gleaned from thermal and Radar sensing solutions respectively, were fused using the software packages on instances of homogeneous and heterogeneous data aggregation. Experimental results indicated that the proposed fusion framework achieved an average Classification Accuracy of 84.7% and 95.7% on homogeneous and heterogeneous datasets respectively, with the help of data mining and machine learning models such as Naïve Bayes, Decision Tree, Neural Network, Random Forest, Stochastic Gradient Descent, Support Vector Machine, K-Nearest Neighbours and CN2 induction. Further evaluation of the sensor data fusion framework based on cross validation of features indicated average values of 94.4% for Classification Accuracy, 95.7% Precision and 96.4% for Recall.

Google Trends data can be informative for infectious disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we tested the ability of Google Trends search data to predict monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010-2021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms as Lyme disease. We built mixed negative binomial models based on a training dataset (2010-2016) and tested the models on a test dataset (2017-2021). A model was built for each search term and monthly lags of search terms were included as predictors. The highest performing models had high predictive ability, indicated by low Root Mean Squared Errors (RMSEs) and close association between observed and predicted case counts. The highest performing model was for the search term “Summer Flu”, which indicates low specificity of some of the terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data.

This paper focuses on a joint model to analyse longitudinal proportion and survival data. After transforming the longitudinal proportional data by a logit function, we adopt a partially linear mixed-effect model for it, in which nonlinear function of time is fitted via using B-splines technique and a centered Dirichlet Process Mixture Model (CDPMM) is specified for a general distribution of random effects. The survival data is assumed to a Cox proportional hazard model, the sharing random effects joint model is developed for the two types of data. Combining the Gibbs sampler and the Metropolis-Hastings algorithm, we propose a Bayesian Lasso (BLasso) method to simultaneously estimate unknown parameters and select important covariates. Simulation studies are conducted to investigate the finite sample performance of the proposed methods. An example from the MA.5 research experiment is used to illustrate the proposed methodologies.

Object classification is a crucial task in deep learning, which involves the identification and categorization of objects in images or videos. Although humans can easily recognize common objects, such as cars, animals, or plants, performing this task on a large scale can be time-consuming and error-prone. Therefore, automating this process using neural networks can save time and effort while achieving higher accuracy. Our study focuses on the classification step of human chromosome karyotyping, an important medical procedure that helps diagnose genetic disorders. Traditionally, this task is performed manually by expert cytologists, which is a time-consuming process that requires specialized medical skills. Therefore, automating it through deep learning can be immensely useful. To accomplish this, we implemented and adapted existing preprocessing and data augmentation techniques to prepare the chromosome images for classification. We used ResNet-50 convolutional neural networks and an ensemble approach to classify the chromosomes, obtaining state-of-the-art performance in the tested dataset.

According to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world’s elderly people. Day by day the number of Alzheimer’s patients is raising. Considering the increasing rate and the dangers, Alzheimer’s disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer’s diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data. To solve these issues, this paper proposes a novel explainable Alzheimer’s disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, Freesurfer MRI segmentation data, and psychological data. For Alzheimer’s disease vs cognitively normal prediction, the random forest classifier provides 100% accuracy. Furthermore, Alzheimer’s disease and non-Alzheimer’s dementia should be classified properly because their symptoms are similar. To the best of our knowledge, we are the first to present a three-class classification on Alzheimer’s disease vs cognitively normal vs non-Alzheimer’s dementia and achieved 99.86% accuracy using an ensemble model. Besides, a novel Alzheimer’s patient management architecture is also proposed in this work..

Next generation sequencing and metabolomics have become very cost and work efficient and are integrated into an ever growing number of life science research projects. Typically, well established software pipelines provide quantitative data informing about gene expression or concentrations of metabolites from the raw data. This data needs to be visualized and further analyzed in order to support scientific hypothesis building and identification of underlying biological patterns. Some tools exist, but require installation or manual programming. We developed “Gene Expression Plotter” (GXP), an RNA-Seq and Metabolomics data visualization and analysis tool entirely running in the user’s web browsers, thus not needing any custom installation, manual programming or upload of confidential data to third party servers. GXP enables the user to generate interactive plots, visually summarize genetic or metabolic responses in scientific sketches (Mapman), carry out cluster and principal component analysis, and conduct overrepresentation analyses. GXP can be used to publish research data along with interactive plots and results of analyses carried out with it. GXP is freely available on GitHub: https://github.com/usadellab/GeneExpressionPlots

Scientists and astronomers have attached Scientists and astronomers have attached great importance to the task of discovering new exoplanets, even more so if they are in the habitable zone. To date, more than 4300 exoplanets have been confirmed by NASA, using various discovery techniques, including planetary transits, in addition to the use of various databases provided by space and ground-based telescopes. This article proposes the development of a deep learning system for detecting planetary transits in Kepler Telescope lightcurves. The approach is based on related work from the literature and enhanced to validation with real lightcurves. A CNN classification model is trained from a mixture of real and synthetic data, and validated only with real data and different from those used in the training stage. The best ratio of synthetic data is determined by the perform of an optimisation technique and a sensitivity analysis. The precision, accuracy and true positive rate of the best model obtained are determined and compared with other similar works. The results demonstrate that the use of synthetic data on the training stage can improve the transit detection performance on real light curves.

Metabolomics deals with multiple and complex chemical reactions within living organisms and how these are influenced by external or internal perturbations. It lies at the heart of omics profiling technologies not only as the underlying biochemical layer that reflects information expressed by the genome, the transcriptome and the proteome, but also as the closest layer to the phenome. The combination of metabolomics data with the information available from genomics, transcriptomics, and proteomics offers unprecedented possibilities to enhance current understanding of biological functions, elucidate their underlying mechanisms and uncover hidden associations between omics variables. As a result, a vast array of computational tools have been developed to assist with integrative analysis of metabolomics data with different omics. Here, we review and propose five criteria – hypothesis, data types, strategies, study design and study focus – to classify statistical multi-omics data integration approaches into state-of-the-art classes under which all existing statistical methods fall. The purpose of this review is to look at various aspects that lead the choice of the statistical integrative analysis pipeline in terms of the different classes. We will draw a particular attention to metabolomics and genomics data to assist those new to this field in the choice of the integrative analysis pipeline.

This paper investigated building data from multispectral and single-photon Lidar systems. The multispectral datasets from the individual channels and fused channels were explored. The multispectral and single-photon Lidar data were compared across multiple aspects: the data acquisition geometry, number of echoes, intensity, density, resolution, data defects, noise level, and the absolute and relative accuracy. In addition, we explored the performance of the multispectral and single-photon data for roof plane detection for eight complex/stylish buildings to investigate the suitability of these data for 3D building reconstruction. The building data from the single-photon and multispectral Lidar systems were evaluated with respect to the reference building vector data with an accuracy of better than 5 cm. The advantages and disadvantages of both technologies and their applications in the urban building environment are discussed.

Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

Many models have been proposed to explain the intergalactic redshift using different observational data and different criteria for the goodness-of-fit of a model to the data. The purpose of this paper is to examine several suggested models using the same supernovae Ia data and gamma-ray burst (GRB) data with the same goodness-of-fit criterion and weigh them against the standard $\text{Λ}$ CDM model. We have used the redshift – distance modulus ( $z-\mu$ ) data for 580 supernovae Ia with $0.015\le z\le 1.414$ to determine the parameters for each model, and then use these model parameter to see how each model fits the sole SNe Ia data at $z=1.914$ and the GRB data up to $z=8.1$ . For the goodness-of-fit criterion, we have used the chi-square probability determined from the weighted least square sum through non-linear regression fit to the data relative to the values predicted by each model. We find that the standard ΛCDM model gives the highest chi-square probability in all cases albeit with a rather small margin over the next best model - the recently introduced nonadiabatic Einstein de Sitter model. We have made ( $z-\mu$ ) projections up to $z=1096$ for the best four models. The best two models differ in $\mu$ only by 0.328 at $z=1096$ , a tiny fraction of the measurement errors that are in the high redshift datasets.

Data collection and analysis are becoming more and more important in a variety of application domains as long as the novel technologies advance. At the same time, we are experiencing a growing need for human-machine interaction with expert systems pushing research through new knowledge representation models and interaction paradigms. In particular, in the last years eHealth - that indicates all the health-care practices supported by electronic elaboration and remote communications - calls for the availability of smart environment and big computational resources. The aim of this paper is to introduce the HOLMeS (Health On-Line Medical Suggestions) framework. The introduced system proposes to change the eHealth paradigm where a trained machine learning algorithm, deployed on a cluster-computing environment, provides medical suggestion via both chat-bot and web-app modules. The chat-bot, based on deep learning approaches, is able to overcome the limitation of biased interaction between users and software, exhibiting a human-like behavior. Results demonstrate the effectiveness of the machine learning algorithms showing 74.65% of Area Under ROC Curve (AUC) when first-level features are used to assess the occurrence of different prevention pathways. When disease-specific features are added, HOLMeS shows 86.78% of AUC achieving a more specific prevention pathway evaluation.

Authentication systems based on biometrics characteristics and data represents one of the most important trend in the evolution of our world. In the near future, biometrics systems will be everywhere in the society, such as government, education, smart cities, banks etc. Due to its uniqueness characteristic, biometrics systems will become also vulnerable, privacy being one of the most important challenge. The classic cryptographic primitives are not sufficient to assure a strong level of secureness for privacy. The following work paper represents an effort to present the main cryptographic techniques and algorithms that can give us the possibility to raise a certain level of secureness for privacy. We will show their own challenges (strengths and weaknesses). We will demonstrate how we can use the most common and well-known techniques and algorithms in order to get a maximum efficiency and a high level in assuring the integrity of the biometrics data.

Expensive cameras meant for research applications are usually characterized by the manufacturers and detailed specifications [1] are available for them. Suppliers of inexpensive cameras usually do not provide such detailed information about their cameras. This data set provides the acquisition speed and noise characteristics acquired from a monochrome 1.2 megapixel CMOS camera, the QHY5L-II M [2]. The source code provided along with this data set [3] can also be used to acquire similar data for other QHY cameras. This enables the use of such cost-effective cameras for other scientific applications in other fields, beyond the designed use in Astronomy.

Satellite remote sensing is a powerful tool to map flooded areas. In the last years, the availability of free satellite data sensibly increased in terms of type and frequency, allowing producing flood maps at low cost around the World. In this work, we propose a semi-automatic method for flood mapping, based only on free satellite images and open-source software. As case studies, we selected three flood events recently occurred in Spain and Italy. Multispectral satellite data acquired by MODIS, Proba-V, Landsat, Sentinel-2 and SAR data collected by Sentinel-1 were used to detect flooded areas using different methodologies (e.g., MNDWI; SAR backscattering variation; Supervised classification). Then, we improved and manually refined the automatic mapping using free ancillary data like DEM based water depth model and available ground truth data. For the areas affected by major floods, we also validated and compared the produced flood maps with official maps made by river authorities. We calculated flood detection performance (flood ratio) for the different datasets we used. The results show that it is necessary to take into account different factors for the choice of best satellite data, among these, the time of satellite pass with respect to the flood peak is the most important one. SAR data showed good results only for co-flood acquisitions, whereas multispectral images allowed detecting flooded areas also with the post-flood acquisition. With the support of ancillary data, it was possible to produce reliable geomorphological based flood maps in the study areas.

We present a new method for assessing the strength of fuzzy rules with respect to a dataset, based on the measures of the greatest energy and smallest entropy of a fuzzy relation. Considering a fuzzy automaton (relation) in which A is the input fuzzy set and B the output fuzzy set, the fuzzy relation R1 with greatest energy provides information about the greatest strength of the input-output and the fuzzy relation R2 with the smallest entropy provides information about uncertainty of the relationship input-output. We consider a new index of the fuzziness of the input-output based over R1 and R2. In our method this index is calculated for each pair of input and output fuzzy sets in a fuzzy rule. A threshold value is set for choosing the most relevant fuzzy rules with respect to the data.

The impact of the climate crisis on urban environments and life within them is evident. In the face of challenges like heatwaves, floodings and other extreme events, pollution, and declining quality of life in cities, there is a growing demand for the preservation and expansion of urban green spaces, often driven by citizen-led transformations. This paper examines 100 urban greening projects initiated or supported by citizens globally, categorizing them according to the type of greenery, the stakeholders involved, the mode of implementation and the use of smart technologies incorporated. We notice variations in green endeavors based on the stakeholders spearheading them, however most of entirely citizen-led initiatives aimed at the creation of urban farms and food growing, demonstrating the pressuring needs for food securing and self-determination in communities. More than half of the assessed initiatives that managed to scale up and multiply had public authorities providing a framework or a type of support for their development or an NGO or other organization providing expertise and mobilize citizens at various stages. In terms of technological use, we find mostly websites and social media platforms easing participatory endeavors and knowledge sharing of best practices, accelerating scaling efforts, while there is a low integration of more advanced digital technologies, which if used, could enable real-time monitoring of green spaces, inform evidence-based decision-making, and streamline processes in scaling up green initiatives.

The European Green Deal is the policy established by the EU against global environmental problems. However, it is closely concerned with the countries carrying import and export activities with the EU. Because it is planned to impose an obligation to declare greenhouse gas emission inventories of products imported into the EU, the EU will soon begin to assess the CBAM on imports from carbon-intensive industries. This regulation is expected to affect Turkey-EU trade relations. One of the industries that Turkey has export relations with the EU is the building materials industry. For this reason, it will have had necessary to provide sustainability criteria for the export of building materials. However, there is no database in Turkey where the inventory data of building materials is declared. In Turkey, within the scope of EU Green Deal action plans, it is aimed to create different structures for environmental information of building materials in the medium and long term. In light of such information, within the scope of this study it is aimed to create reference information for building material databases to be developed in Turkey. The study recommends weight and normalization reference values following the life cycle assessment methodology defined by ISO 14040 and ISO 14044 international standards. Twelve environmental impact categories accepted in the literature, including greenhouse gas emissions, have been considered as the environmental impacts of building materials. Semi-structured interviews were held with twenty-one industry stakeholders in Tur-key to determine the weight reference values. The results obtained from the semi-structured interviews were combined using the AHP method. An extensive research study was conducted on Turkey’s national inventory data to determine normalization reference values. Environmental impact calculations were carried out for different building materials in a case study to present the importance of regional adaptation of the determined reference values. The recommended reference information may be used in greenhouse gas emission declarations under the EU Green Deal and other potential environmental impact declarations from building materials.

Rapid increase in urbanization and industrialization due to increase in human population has led to increase in global warming resulting to climate change that affects the ecosystem. Unusual natural disasters such as cyclones, storms, floods, weather fluctuations, rising seal levels, transitional ecosystems and coastal disruptions are major adverse effects of climate change. There are huge worrisome concerns and uncertainties about the future of the planet earth due to climate change caused by high carbon emissions. Global aggressive policies in control of Carbon-dioxide (CO2) emissions from several combustion mechanisms have paved way for alternative/ clean energy sources. Green hydrogen is a promising means of clean energy sources for commercial power generation stations and other crucial systems such as hydrogen fuel cell vehicles (HFCVs) , motors, drives, backup power for buildings, and also serving as a source of electrical energy for remote locations. The goal of this study is to review the current level of Sub-Saharan African nations’ preparedness to adopt green hydrogen as a way to mitigate local energy supply issues and strengthen their individual economies.

The digital economy promotes economic development, industrial upgrading, and environmental protection. This article calculates the green total factor productivity (GTFP) based on the SBM-DDF model and uses the entropy method and principal component analysis to calculate the digital economy index. We use panel data from 282 Chinese cities to test the driving effect of the digital economy on green total factor productivity. The study results show that the digital economy significantly increases GTFP, and the heterogeneity of this impact is further explored. We also explored the mechanism of the digital economy in promoting green. The digital economy can indirectly increase industrial production efficiency by promoting innovation in green technologies.

The feasibility of replacing the toxic chlorobenzene antisolvent with the environmentally friendly anisole in the fabrication of planar triple cation perovskite solar cells was explored. The successful integration of anisole not only ensures comparable device performance but also contributes to the development of more sustainable and green fabrication processes for next-generation photovoltaic technologies. Nevertheless, to ensure the possibility to achieve well-functioning unencapsulated devices, at any time of the year, with changed temperature and relative humidity, several batches with different cesium concentration were realized. We found that 10% of CsI in the precursor solution makes devices with low hysteresis index and sustained performance stability over a 90-day period both with cholorobenzene and anisole antisolvent. However the use of anisole as a green antisolvent ensures enhanced performance.

This paper presents the design and experimental results of a long cavity length Nd: YAG laser with large stable zone for water jet guided laser (WJGL) applications. The design is based on the light transmission matrix and resonator stability conditions, aiming to achieve a large stable zone and a short cut-off thermal focal length (CTFL). A folded concave resonator is researched to enhance the cavity length, and the influence of the tun-able cavity arm length on the oscillating beam in the resonator and in the YAG crystal is theoretically studied. Moreover, the effects of the output mirror curvature and the cavity arm length on the range of the stable area and the cut-off thermal focal length are also investigated. Experimental results show that a stable green laser output is obtained after second harmonic generation (SHG), with a pulse width ranging from 43ns to 143ns within the laser operating frequency range of 5-20kHz. At an operation frequency of 10kHz, the output power is 21.33W, and the instability of the output power within 400 minutes is 0.88%. The laser source achieves a maximum power of 25.7W at 20kHz, and the maximum single pulse energy reaches 2.7mJ at 6kHz. Finally, this is used as the laser source to couple with a water jet with a diameter of 100 microns, achieving a lossless water conductivity transmission over 60mm length with a peak power density of 0.742GW/cm2. These results demonstrate the suitability of the designed laser source for WJGL technology research.

The development of sustainability, the improvement of the quality of life, and the protection of future generations have become of prime importance in light of climate changes and carbon emissions. Universities are at the top of institutions that should participate in creating an environmentally friendly infrastructure. This study thus aimed to explore the extent to which Saudi universities achieve sustainability in the light of the Saudi Green Initiative from the faculty members’ perspective. Surely, this investigation would produce a proposal to promote sustainability in Saudi universities. A questionnaire consisting of five dimensions, namely academic sustainability, community partnership, resource management, planning and administration, and innovation and leadership was developed and administered to 120 faculty members from five Saudi universities. The participants’ ratings of Saudi universities’ achievement of sustainability dimensions ranged from high to moderate. Based on the results, a proposal was offered to take sustainability in Saudi universities to a higher level.

This study examined the efficacy of essential oil (EO) obtained from the Australian native plants, lemon myrtle (Backhousia citriodora) (LM) to inhibit Penicillium digitatum by in vitro agar diffusion and vapor assay, and in inoculated oranges. Gas chromatograph/mass spectrometry analysis revealed the main constituent of LM EO as citral. Pure citral was also included in the experiment for com-parison. LM EO at 1, 2, 3, 4 and 5 µL disc-1 in the in vitro tests significantly inhibited fungal growth compared to the non-treated control. Moreover, LM EO at 4 and 5 µL disc-1 in both the assays completely inhibited fungal growth similar to its main component of citral. Inoculated oranges dipped in 1000 μL L-1 LM EO solutions for 5, 10, 15 and 30 sec showed significantly lower fungal wounds compared to control. While higher dipping times generated slight rind injury, a 5 and 10 sec dip did not cause any injury to the fruit rind. The quality assessments and sensory evaluations also revealed that the addition of LM EO did not adversely affect the quality and taste of the oranges. These findings suggest LM EO as an alternative to synthetic fungicides to inhibit wastage in citrus during storage.

Australia has recently declared its commitment to a circular economy. Policy and initiatives to date have focused on recycling and waste management and research to date has highlighted the need for more ambitious policy, clearer definitions, collaboration, and consensus on goals. There are also calls from some government and non-business sectors for more inclusive circular models, including Doughnut Economics. In a context of competing mainstream circular economy and inclusive circular society discourses, circular intermediary organizations and their representatives are key to achieving change. Compared to the green growth business narrative of policy and industry media, intermediary representatives are aware of the diversity of challenges and solutions for Australia. Based on semi-structured interviews with twenty representatives of circular intermediaries in Australia and thematic discourse analysis, this study finds circular economy, circular society and de-growth discourses informing themes about government, business, growth, consumers, society, and policy present and future. The study concludes with recommendations for a more holistic policy and practice beyond the current circularity for circularity’s sake.

The Voltammetric and polarographic methods are proposed to determine the short time corro-sion rates of brass in 1N HCl solution. It should be noted that it is also possible to determine the corrosion rates of each species present in the solution, simultaneously like Cu (II) and Zn (II) in brass. Samples extracted from bark of Babul (ESBB) is used as corrosion inhibitors. The corrosion inhibition efficiency is 96% at 24h after the test started. These results are compared with the av-erage corrosion rates obtained by using gravimetric (PIT) and some electrochemical methods i.e. galvanostatic and potentiodynamic polarization measurements.

One main objective of this study was to increase the utilization of the raw material in the rose (Rosa damascena Mill.) essential oil industry by application of membrane technologies. A new ultrafiltration-based process for green recovery of polyphenols from distilled (de-aromatized) rose petals, the primary by-product in essential oil production, was created. The process includes enzyme-assisted extraction and subsequent membrane separation for partial concentration. Following a green membrane technology strategy, the newly developed process proposes an environmentally-friendly alternative to the conventional processing, comprising organic solvent ex-traction and adsorber resin purification. The results showed that the permeate flux decreased with volume reduction ratio’s rise and increased with the transmembrane pressure and feed flow rate rises. At the beginning of the process the highest flux was with UF1-PAN membrane, but at the end of the process with UF10-PAN membrane. Total polyphenols of the retentates in-creased by 27-39% and 26-67% during ultrafiltration with UF1-PAN and UF10-PAN membrane, respectively, with the highest value obtained for UF10-PAN membrane at VRR 6. The highest concentration factor and rejection of total solids, total polyphenols, redox-active antioxidants and radical scavenging antioxidants were obtained at VRR 6 with UF10-PAN membrane. This membrane and concentration level at transmembrane pressure of 0.4 MPa and feed flow rate of 330 dm3/h are recommended to be used for application of ultrafiltration of rose-petals by-product for potential use in the food industry.

Green Stormwater Infrastructure (GSI), a sustainable engineering design approach for managing urban stormwater runoff, has long been recommended as an alternative to conventional conveyance-based stormwater management strategies to mitigate the adverse impact of sprawling urbanization. Hydrological and hydraulic simulations of small-scale GSI measures in densely urbanized micro watersheds require high-resolution spatial databases of urban land use, stormwater structures, and topography. This study presents a highly resolved Storm Water Management Model developed under considerable spatial data constraints. It evaluates the cumulative effect of the implementation of dispersed, retrofitted, small-scale GSI measures in a heavily urbanized micro watershed of Costa Rica. Our methodology includes a high-resolution digital elevation model based on Google Earth information, whose accuracy was sufficient to determine flow patterns and slopes, as well as to approximate the subsurface stormwater structures. The model produced satisfactory results in event-based calibration and validation, which ensured the reliability of the data collection procedure. Simulating the implementation of GSI shows that dispersed, retrofitted, small-scale measures could significantly reduce impermeable surface runoff (peak runoff reduction up to 40%) during frequent, less intense storm events and delay peak surface runoff 5-10 minutes. The presented approach can benefit stormwater practitioners and modelers conducting small scale hydrological simulation under spatial data constraint.

This article presents the copper ions adsorption process using an activated carbon from winemaking wastes. The pH, temperature, activated carbon amount and initial copper concentration were varied based on a full factorial 2^k experimental design. Kinetic and thermodynamic studies were also carried out. The adsorption kinetics was found follow a pseudo-second-order model. The adsorption data fit better to the Langmuir isotherm. The ANOVA demonstrated that both pH of the solution and activated carbon dosage had the greatest influence on copper adsorption. The activation energy was -32 kJ·mol^-1 suggesting that the copper adsorption is a physic-sorption process. The best fit to a linear correlation was the moving boundary equation that controls the kinetics for the adsorption copper ions onto the activated carbon. The X-ray photoelectron spectroscopy (XPS) results reveal the existence of different copper species (Cu²⁺, Cu⁺ and or Cu⁰) on the surface of the carbonaceous adsorbent after the adsorption, which could suggest a simultaneous reduction process.

This review focuses on the Just Energy Transition (JET) policies of the BRICS member countries, with the aim of finding lessons and possible adoption of some of the key energy policy aspects utilized in other countries for benchmarking for the South African context. Considerations of the present stage of JET in South Africa and the energy-source mix supporting the electricity sector, cognisant of the lifespan and condition of power plants. Analysis of the energy mix revealed that all nations were energy dependent on electricity which was produced by predominantly fossil fuelled power plants with high GHG (concentrating on high CO2 emissions). It was concluded that some of the learnings and lessons learnt from the BRICS countries might be incorporated into a South African energy plan for the transition with options and guidance for formulation of policies. This study will attract responsible environmentally conscious audience that has a commitment and drive to combat global warming and curbing climate change.

Child and adolescent sexual abuse (CSA) is an international public health problem. Despite the importance of the CSA, there is no consensus definition, and the lack of consensus is related to difficulties in conducting prevalence studies, as well as research in other areas. To establish a consensual definition, legal aspects such as the age of sexual consent and the difference in age or power between victim and aggressor, aspects related to sexual freedom and sexual indemnity must be considered. Therefore, the main goal of this research was to analyze the age of sexual consent in the legal systems of Spanish-speaking countries and to examine whether the Romeo and Juliet clause is established. To achieve the proposed aims, we employed the legal interpretation method, and we analyzed the current Criminal Codes of the 21 Spanish-speaking countries. From the results, it is found that the age of sexual consent varies between countries, establishing valid sexual consent between 13 and 18 years. In addition, only six countries have the Romeo and Juliet clause that protects sexual freedom in adolescents. Finally, we discussed the lack of consensus on the age of sexual consent and the limitations presented by the Romeo and Juliet clause.

The aim of this study is to identify the effective institutional factors on the exploitation of entrepreneurial opportunities of the sports tourism industry in developing countries. This research is a qualitative study, the systematic method of Strauss and Corbin (1990) has been used to analyze the data. Interviewing is the main method of collecting data in this research, which semi-structured interviews were done with 45 members of the research community. According to Shane and Venkataraman individual-opportunity nexus framework (2000), interview questions were drafted and simultaneously with data collection from interviews, data analysis was performed. Based on the research results, 75 indicators affecting the exploitation of entrepreneurship opportunities in sports tourism were identified. Our research findings show that the necessary institutional arrangements in regulatory/legal/administrative dimensions (rule of law, government policies), normative/cultural (social norms, values and beliefs), cognitive/educational (promotion of elite knowledge, promotion of social knowledge) and guidance measures/supporter (public sector support, private sector support, complementary attraction and information technology) can improve the rate of entrepreneurial behavior in this area by increasing the ability and willingness of entrepreneurs to take advantage of entrepreneurial opportunities in the field of sports tourism. Finally, according to the research results, it can be suggested that for the growth of the sports tourism industry in developing countries, The existence of a legal, normative, supportive and educational environment will affect the ability and desire of market participants to identify and exploit the entrepreneurial opportunities of this emerging industry.

This article has explored whether Aid for Trade (AfT) flows that accrue to recipient-countries depend on the latter's level of export product concentration. The analysis covers a sample of 133 countries over the period 2002-2017. The findings indicate that least developed countries (LDCs) receive higher AfT flows when they experience a rise in the level of export product concentration, while NonLDCs enjoy higher AfT flows when they diversify export products. Interestingly, higher amounts of AfT accrue to countries that diversify their export product basket towards manufacturing products, although different result patterns appear for the components of manufactured exports.

This study investigates the optimal energy mix for industrial complexes by integrating renewable energy systems, decarbonization strategies, and sector coupling technologies, using actual load data from the Balan Industrial Complex in Korea. Five different energy scenarios were analyzed, ranging from a conventional base case (Scenario 1) to progressively more advanced configurations incorporating renewable sources (Scenarios 2 to 5). The findings reveal that Scenario 5, which combines sector coupling, hydrogen-based systems, and decarbonization technologies, is the most cost-effective and environmentally sustainable, achieving the lowest Net Present Cost (NPC) and significantly reducing CO2 emissions. To enhance the study’s relevance, electricity prices and CO2 emission costs from Korea, the United States, and Germany were considered, revealing that regional electricity tariffs and carbon pricing mechanisms greatly influence the economic feasibility of the proposed energy systems. Despite the higher initial investment for renewable-based setups, Scenario 5 proved to be the most economically viable over the long term when factoring in operational costs and emission penalties, underlining the potential for renewable integration to achieve both cost savings and environmental benefits. These results provide valuable insights for policymakers and industry leaders aiming to adopt sustainable, cost-effective energy solutions, highlighting the necessity of tailored strategies to optimize energy systems for industrial applications. This research thus offers a meaningful contribution to the field of sustainable energy transitions, suggesting that sector coupling and renewable integration hold significant promise for enhancing the efficiency and sustainability of energy systems in diverse industrial areas.

Aluminium dross is the industrial waste from aluminium refining industry and accordance with toxic waste materials, it’s also recognized as a by-product of aluminium production, has historically posed significant environmental challenges due to its toxic and reactive nature. However, recent advancements in technology have revealed the potential of transforming aluminium dross into a valuable source of white hydrogen. This review paper explores the methodologies, benefits, and challenges associated with this transformation. By analysing current research and industrial practices, our review highlighted the potential of this innovative approach to mitigate waste and contribute to sustainable energy solutions. In addition, the utilization of Aluminium dross can be considered as innovative and advancement in sustainable waste management strategy instead of direct disposal, and hot springs use advances in direct utilization. Our experimental work achieved white hydrogen maximum flowrate at 600 sec. with 120ºC.

ABSTRACT Heavy metals have been classified as dangerous environmental pollutants. Interest in feasible ways to reduce heavy metals pollution is a noble goal. Adsorption is an effective method based on its effectiveness and low- cost. Nowadays, low-cost bio-sorbents have been utilized to replace costly expensive adsorbents. Rich cellulose materials are effective heavy metal adsorbents, and their performance can be enhanced via chemical modification. The present work reviews the steady progress in the utilization of agriculture by-product wastes as heavy metal bio-sorbent. The unique advantages of these by-products such as renewability, abundance, simple processing, and low cost, justifier this work. The results confirm that the chemically treated agro-wastes exhibit high- removal efficiency (reaches 95% in most cases). The adsorption isotherm studies indicate that the concentration of the metal ions has a significant effect on the adsorption process. The Freundlich model provides a physical adsorption model of the metal ions on the bio-sorbent surface. Langmuir isotherm offers an estimate of the effect of different factors on the efficiency of the process. The adsorption kinetics studies offer an idea of reaction pathways including the adsorption mechanism and the efficiency of the metal ion removal. Applying the pseudo-first-order and pseudo-second-order models describes the adsorption kinetics. This information can be utilized in developing effective strategies to remove heavy metal ions using chemically treated agro-waste. The data confirmed that chemical modification is a significant parameter in the efficiency of metal ion removal. The feasibility and effectiveness of using these cellulosic by-products as bio-sorbent should be given attention and encouraged.

Valorization of excess sludge through the recovery of high-value products, such as biopolymers, could be a crucial step to implement circular economy principles in wastewater treatment plant (WWTP). In this frame, the present study was aimed at evaluating the simultaneous production of polyhydroxyalkanoates (PHA) and extracellular polymeric substances (EPS), obtainable from the treatment of an agro-industrial wastewater. Two biological systems, one implementing aerobic granular sludge (AGS) and the other a conventional activated sludge operating as a sequencing batch reactor (SBR) were operated for 204 and 186 days, respectively. Both the systems involved a three-stage process for mixed microbial culture enrichment and biopolymers accumulation. The maximum biopolymers accumulation capacity was close to 0.60 mg gVSS^-1 in the AGS when the enrichment reactor was operated at 3 kgCODm^-3d^-1, whereas in the SBR it was slightly more than half (0.35 mg gVSS^-1). Biopolymers extracted from the AGS were mainly constituted by EPS (>70%), which percentage increased with the organic loading rate applied in the enrichment reactor up to 95%. In contrast, SBR enabled to obtain a higher PHA production (50% of the biopolymers). Results suggested that organic carbon was mainly channeled toward metabolic pathways for extracellular storing in AGS, likely as a consequence of metabolic stressors (e.g., hydraulic selection pressure, shear forces) applied for promoting aerobic granulation.

The outdoor sculpture of the first Portuguese king, D. Afonso Henriques (~1109 – 1185 AD), placed in Guimarães (North Portugal) is one of the most emblematic national sculptures. Created in 1887 by António Soares dos Reis, it possesses a remarkable symbolic value in the presumed birthplace of the king. In addition to the artistic and heritage importance of the monument, it is one of the few sculptures cast by a Portuguese industrial foundry in the 19th-century. This study obtained data on the sculpture's elemental composition and corrosion products, gathering important historical and technical information. For this purpose, a multi-analytical approach consisting of X-ray fluorescence (XRF), X-ray diffraction (XRD), optical microscopy (OM) and scanning electron microscopy (SEM-EDS) was carried out to characterise the bulk metal and corrosion layers. The data revealed a ternary alloy of Cu, Sn, Zn with Pb, Fe, As, Bi and Mn as minor elements. The alloy matches that of other sculptures cast in that period. In terms of corrosion, it is characterised by the presence of oxides. These results represent the first step for applying an appropriate conservation strategy for bronze sculptures with similar characteristics.

This paper investigated the effectiveness of natural wetlands (Phragmites australis) along Wadi Zomer in reducing the organic and inorganic pollution loads from diverse industrial discharges including occasional emergency discharges from Nablus West Sewage Treatment Plant (NWSTP), Palestine. We monitored physical and chemical parameters at four selective sampling stations (S1-S4) along Wadi Zomer with a length of 5 km downstream of NWSTP to assess the purification capacity of Wadi Zomer treatment wetlands (water, sediment, and vegetation) with Phragmites australis in pollution loads reduction. The results showed that S2 (0+0.5 km) and S3 (0+3.0 km) reflected an increase in pollution loads due to illicit industrial discharge and sewer overflow discharge from NWSTP during emergency conditions. BOD values varied significantly along the sampling sites from 6.64 mg/l (S1) to 437.10 mg/l (S3). The BOD at S1 and S2 in water samples were below the Palestinian Water Standard (PWS) compared to S3 and S4 with 437.1 and 333.9 mg/l, respectively. Water samples from all sites (S1-S4) showed a decreasing tendency in heavy metals concentrations (Fe>Cu>Zn>Cr >Ni) and were below the PWS limits, sediment samples followed the same decrease pattern for Zn, Cr, and Ni content with Wadi Zomer flow course. The concentration of Fe (6687 mg/kg) and Cu (1384.7 mg/kg) were highest in the sediment samples (S1-S4); this might be due to non-point sources of pollution. The research demonstrated that phytoremediation is a sustainable nature-based technology for the restoration of heavily polluted surface water bodies in Palestine.

Iron is a critical yet limited nutrient for microbial growth. To scavenge iron, most microbes produce siderophores—diverse small molecules with high iron affinities. Different siderophores are specifically recognized and uptaken by corresponding recognizers, enabling targeted interventions and intriguing cheater-producer dynamics. We propose constructing a comprehensive iron interaction network, or "iron-net," across the microbial world. Such a network offers the potential for precise manipulation of the microbiota, with conceivable applications in medicine, agriculture, and industry, as well as advancing microbial ecology and evolution theories. Previously, our successful construction of an iron-net in the Pseudomonas genus demonstrated the feasibility of co-evolution-inspired digital siderophore typing. Enhanced by machine learning techniques and expanding sequencing data, forging such an iron-net calls for multidisciplinary collaborations, and holds significant promise in addressing critical challenges in microbial communities.

The burden of bacterial bloodstream infections (BSIs) is rapidly increasing in Africa including Rwanda. This is a retrospective study investigates the diversity, distribution, and antimicrobial susceptibility profiles of BSIs bacteria in three tertiary referral hospitals in Rwanda between 2020 and 2022. A total of 1,532 blood culture tests were performed for visiting patients. Overall, the proportions of Gram-negative and Gram-positive bacteria were 48.2% and 51.8, respectively. Staphylococcus aureus was the predominant species accounting for 25% of all Gram-positive BSIs species and Klebsiella species representing 41% of all Gram-negative BSIs species. Antimicrobial susceptibility testing revealed that Amikacin exhibited the highest activity against Enterobacter spp., Serratia spp. and Escherichia coli in &gt;92% of cases and Klebsiella spp. in 75.7%. Meropenem and Imipenem were highly efficacious to Salmonella spp. (100% susceptibility), Enterobacter spp. (96.2% and 91.7%, respectively), and Escherichia coli (94.7% and 95.5%, respectively). The susceptibility of Enterococcus spp., S. aureus, and Streptococcus spp. to Vancomycin was 100%, 99.5% and 97.1%, respectively. Klebsiella spp. was highly sensitive to Colistin (98.7%), Polymyxin B (85.6%), Imipenem (84.9%), and Meropenem (78.5%). We recommend strengthening the implementation of integrated transdisciplinary and multisectoral One Health including AMR-stewardship for the surveillance, prevention, and control of AMR in Rwanda.

Optical communication systems pose significant challenges, including the effects of nonlinear noises. The nonlinearities, including Kerr-induced phase noise, become more problematic in m-QAM as the orden of the format increases becoming a highly densed set of data-symbols and therefore, requiring advanced signal processing for successful separation of symbols at the demodulation stage. Machine learning techniques have recently been applied to improve signal integrity in such scenarios. This paper explores the application of a spectral clustering algorithm adapted to deal with data streaming to mitigate nonlinear noise in long-haul optical channels dominated by nonlinear phase noise, offering a promising solution to a pressing issue. We demonstrate that the spectral clustering algorithm outperforms the k-means algorithm in the face of nonlinear phase noise in -90, -100, and -110 dBc/Hz scenarios at 1 MHz in a simulated 10 GHz symbol rate channel.

Our study essentially concerns the dynamic behavior of an SIRS epidemic model in discrete time. Two equilibrium points are obtained; one is disease-free while the other is endemic. We are interested in the endemic fixed point as well as its asymptotic stability. Depending on the parameters which are estimated using the data from US Department of health and SIRS modelling with optimization, two Flip and Transcritical bifurcations appear. We illustrate their diagrams, as well as their bifurcation curves using the method of Carcasses \cite{carcasses1993determination,carcasses1995singularities} for the Flip bifurcation and by an implicit function deduced from such an equation for the Transcritical bifurcation. We use the scanning of the parametric plane to have a global view of the behavior of the model and to highlight the zones of stability of the existing singularities. A superposition of the bifurcation curves with the parametric plane can show the overlap of the curves with the boundaries of the stability domains, which confirms the smooth running of the simulation and its correspondence with the theory, we finish this article with constrained optimal control applied to infection rate and recruitment rate for an SIRS discrete epidemic model. Pontryagin's maximum principle is used to determine these optimal controls. Finally using COVID-19 data in the USA, we obtain results that demonstrate the effectiveness of the proposed control strategy to mitigate the spread of the pandemic.

Ceratonia siliqua L. is a medicinal plant that has long been used in traditional Moroccan medicine to treat many diseases. This study aimed to assess the impact of the stages of the immature phase of carob pulp (M1, M2, M3, M4, and M5) on phytochemical composition, antioxidant activity, and antidiabetic activity of Ceratonia siliqua L. The identification of the phenolic profile by HPLC-UV/MS-MS and the study of the antidiabetic effect by in silico, in vitro, and in vivo studies were carried out for extracts with a high content of phenolic compounds from immature wild carob pulp from the communes of Timoulit (TM), Bin Elouidane (AW), and Ouaouizerth (TG) in the province of Azilal in the Beni Mellal-Khenifra region. The results revealed a gradual increase in total sugar content over the pulp's ripening period, reaching a value of 2134 ±56.23 mg GE/100 g fresh matter (FM) for TG. The three locations showed peak values for total polyphenol content (TPC), total flavonoid content (TFC), and total condensed tannin (TCT) at the M2 stage. AW has the highest concentrations of TPC (3819 ± 226.4 mg GAE/100 g FM), TFC (1034 ± 57.08 mg QE/100 g FM), and TCT (1472 ± 28.46 mg CE/100 g FM). The DPPH assay (7892 ± 296.1 mg TE/100 g FM) and the FRAP assay (278.2 ± 7.85 mg TE/100 g FM) both demonstrate that the TG zone is a highly potent antioxidant zone. In contrast, the AW site exhibits a markedly elevated value of 725.4 ± 103.6 mg TE/100 g FM in the ABTS assay. HPLC-UV-MS/MS analysis showed that the methanolic extracts of immature carob pulp (MEICP) from the three areas contained several different chemical compounds. The most prevalent were 3-O-p-coumaroyl-5-O-caffeoylquinic acid, quercetin 3-methyl ether, gallic acid, and galloylquinic acid. Immature carob pulp extract. (ICPE) from AW showed the strongest in vitro inhibition of pancreatic α-amylase (IC50 = 0.405 µg/mL) and TG extracts were most potent against intestinal α-glucosidase (IC50 = 0.063 µg/mL). In vivo, AW, TG, and TM extracts significantly reduced postprandial glycemia in rats, with AW having the greatest effect. These results highlight the antidiabetic potential of ICPE. The 3-O-p-Coumaroyl-5-O-caffeoylquinic acid showed a better affinity for α-amylase compared to acarbose and interacted significantly with several amino acid residues of the enzyme. Similarly, this molecule and 3,4-Dicaffeoylquinic acid demonstrated a strong affinity for α-glucosidase, suggesting their potential as natural inhibitors of enzymes involved in carbohydrate metabolism. Most of the compounds are not substrates of P-glycoprotein and exhibit high intestinal absorption. Furthermore, the majority of these compounds do not act as inhibitors or substrates of CYP450 enzymes, reinforcing their suitability for development as oral medications. These results underscore the potential of immature carob pulp as a promising antidiabetic agent.

The world is currently experiencing the fourth industrial revolution driven by the newest wave of digitisation in the manufacturing sector. The term Industry 4.0 (I4.0) represents at the same time: a paradigm shift in industrial production, a generic designation for sets of strategic initiatives to boost national industries, a technical term to relate to new emerging business assets, processes and services, and a brand to mark a very particular historical and social period. I4.0 is also referred to as Industrie 4.0 the New Industrial France, the Industrial Internet, the Fourth Industrial Revolution and the digital economy. These terms are used interchangeably in this text. The aim of this article is to discuss major developments in this space in relation to the integration of new developments of IoT and cyber physical systems in the digital economy, to better understand cyber risks and economic value and risk impact. The objective of the paper is to map the current evolution and its associated cyber risks for the digital economy sector and to discuss the future developments in the Industrial Internet of Things and Industry 4.0.

In the era of Big Data and the Internet of Things (IoT), ensuring the security and privacy of patient data within distributed health systems is paramount. This research delves into the challenges posed by these advancements and explores viable solutions, focusing notably on blockchain technology, data encryption methods, and access control mechanisms. Employing a comprehensive literature review approach, the study synthesizes current research findings, complemented by analyses of existing frameworks and case studies from healthcare organizations, offering practical insights. Interviews with cybersecurity experts and healthcare IT professionals provide nuanced perspectives on current security issues and emerging trends. Through qualitative and quantitative data analyses, the study generates recommendations aimed at enhancing patient data security. These include the adoption of new frameworks and innovative technologies to fortify healthcare data systems. By addressing these critical aspects, this research aims to significantly contribute to the advancement of a more secure and efficient healthcare ecosystem in today's digital landscape.

(1) Background: The analysis of athletic performance has always aroused great interest from sport scientist. This study utilized machine learning methods to build predictive models using a comprehensive CrossFit (CF) dataset, aiming to reveal valuable insights into the factors influencing performance and emerging trends.; (2) Methods: The study used Random Forest (RF) and Multiple Linear Regression (MLR) models to predict performance in four key weightlifting exercises within CF: clean &amp; jerk, snatch, back squat, and deadlift. Performance was evaluated using R-squared (R2) values and Mean Squared Error (MSE). Feature importance analysis was conducted using RF, XGBoost, and AdaBoost models.; (3) Results: The RF model excelled in deadlift performance prediction (R2 = 0.80), while the MLR model demonstrated remarkable accuracy in clean &amp; jerk (R2 = 0.93). Across exercises, clean &amp; jerk consistently emerged as a crucial predictor. The feature importance analysis revealed intricate relationships among exercises, with gender significantly impacting deadlift performance.; (4) Conclusions: This research advances our understanding of performance prediction in CF through machine learning techniques. It provides actionable insights for practitioners, optimize performance, and demonstrates the potential for future advancements in data-driven sports analytics.

Atrial fibrillation (AF) is an increasingly recognized comorbidity in patients with can-cer. Indeed, cancer patients have a significantly higher incidence of AF than that ob-served in the general population. A reciprocal relationship between these two diseases has been observed, as much as some assume AF as a marker for occult cancer screen-ing, especially in older adults. The pathophysiological mechanisms are many and var-ied, including the underlying pro-inflammatory state, specific treatments (chemo and radiotherapy) and surgery. The therapeutic management of patients with cancer and AF involves the same rhythm and frequency control strategies as the general popula-tion; however, the numerous interactions with chemotherapeutics, which lead to a sig-nificant increase in side effects, as well as the extreme fragility of the patient should be considered. Anticoagulant therapy is also a complex challenge to address, as bleeding and stroke risk scores have not been fully assessed in this subpopulation. Furthermore, in large studies establishing the efficacy of direct oral anticoagulants (DOACs), cancer patients have been underrepresented. In this review, we elaborate on mechanisms linking AF to cancer patients with a particular focus on therapeutic challenges in this population.

Atrial fibrillation (AF) is an increasingly recognized comorbidity in patients with cancer. Indeed, cancer patients have a significantly higher incidence of AF than that observed in the general population. A reciprocal relationship between these two diseases has been observed, as much as some assume AF as a marker for occult cancer screening, especially in older adults. The pathophysiological mechanisms are many and varied, including the underlying pro-inflammatory state, specific treatments (chemo and radiotherapy) and surgery. The therapeutic management of patients with cancer and AF involves the same rhythm and frequency control strategies as the general population; however, the numerous interactions with chemotherapeutics, which lead to a significant increase in side effects, as well as the extreme fragility of the patient should be considered. Anticoagulant therapy is also a complex challenge to address, as bleeding and stroke risk scores have not been fully assessed in this subpopulation. Furthermore, in large studies establishing the efficacy of direct oral anticoagulants (DOACs), cancer patients have been underrepresented. In this review, we elaborate on mechanisms linking AF to cancer patients with a particular focus on therapeutic challenges in this population.

Wetlands benefits can be summarized but are not limited to their ability to store floodwaters and improve water quality, providing habitats for wildlife and supporting biodiversity, as well as aesthetic values. Over the past few decades, remote sensing and geographical information technologies has proven to be a useful and frequent applications in monitoring and mapping wetlands. Combining both optical and microwave satellite data can give significant information about the biophysical characteristics of wetlands and wetlands` vegetation. Also, fusing data from different sensors, such as radar and optical remote sensing data, can increase the wetland classification accuracy. In this paper we investigate the ability of fusion two fine spatial resolution satellite data, Sentinel-2 and the Synthetic Aperture Radar Satellite, Sentinel-1, for mapping wetlands. As a study area in this paper, Balikdami wetland located in the Anatolian part of Turkey has been selected. Both Sentinel-1 and Sentinel-2 images require pre-processing before their use. After the pre-processing, several vegetation indices calculated from the Sentinel-2 bands were included in the data set. Furthermore, an object-based classification was performed. For the accuracy assessment of the obtained results, number of random points were added over the study area. In addition, the results were compared with data from Unmanned Aerial Vehicle collected on the same data of the overpass of the Sentinel-2, and three days before the overpass of Sentinel-1 satellite. The accuracy assessment showed that the results significant and satisfying in the wetland classification using both multispectral and microwave data. The statistical results of the fusion of the optical and radar data showed high wetland mapping accuracy, with an overall classification accuracy of approximately 90% in the object-based classification. Compared with the high resolution UAV data, the classification results give promising results for mapping and monitoring not just wetlands, but also the sub-classes of the study area. For future research, multi-temporal image use and terrain data collection are recommended.

This study provides a unique procedure for validating and reconstructing temperature and precipitation data. Although developed from data in Middle Italy, the validation method is intended to be universal, subject to appropriate calibration according to the climate zones analysed. This~research is an attempt to create shared applicative procedures that are most of the time only theorized or included in some software without a clear definition of the methods. The purpose is to detect most types of errors according to the procedures for data validation prescribed by the World Meteorological Organization, defining practical operations for each of the five types of data controls: gross error checking, internal consistency check, tolerance test, temporal consistency, and~spatial consistency. Temperature and~precipitation data over the period 1931--2014 were investigated. The~outcomes of this process have led to the removal of 375 records (0.02%) of temperature data from 40 weather stations and 1286 records (1.67%) of precipitation data from 118 weather stations, and 171 data points reconstructed. In conclusion, this work contributes to the development of standardized methodologies to validate climate data and provides an innovative procedure to reconstruct missing data in the absence of reliable reference time series.

Graphs are largely used in computer science to model relations, or associations, among entities the compose complex systems. More recently, they found a broad field of application in bioinformatics and medical informatics supporting modelling, analysis of many systems. The applications span from representing interactions among molecules within cells, to model the functions of the brains. In order to support research, algorithms from graph theory that are able to extract knowledge from network should be coupled to efficient visualisation techniques. Although the importance of such topic, we retain that many challenges should be faced in the futures, such as automated pathway and network layout to better match biologists’ needs, the developing of a standard able to give more and better choices to represent nodes, edges finally, the developing of automated mechanism able to improve the network navigation methods that help users manage large and complex networks with the goal to improve the usability.

This data descriptor summarizes the process applied and data gathered from 50 publications/papers reporting on the use of photography generated by tourists, tour operators and members of the public, with a particular focus on the crowdsourcing of photographs through online platforms and social networking sites (SNSs) as a method of research for wildlife conservation and ecotourism. The papers were collected in a systematic literature review to inform a pilot study of the feasibility of using SNSs to crowdsource georeferenced photographs of endangered Bornean Pygmy Elephants (Elephas maximus borneensis) taken by ecotourists along the Lower Kinabatangan River region of Sabah, Malaysia. Papers were sourced using the Murdoch University Findit online-search tool to search over 100 databases, including Proquest, Scopus and Web of Science. The criteria for a paper to be included in the review (and shared via the dataset attached to this this data descriptor) were that it was peer-reviewed, published in English, between 1997 and the 31 December 2017, had the full text accessible online and reported on a study or studies that utilized photographs that tourists, tour operators and/or members of the public generated and shared via SNSs or online platforms.

With the development of Internet, big data and artificial intelligence technology, personal information protection has become the focus of social attention. The legal boundary of data protection has become increasingly prominent, involving the collection, storage, use, transmission and disclosure of personal information. This paper aims to explore the legal boundary of data protection, analyze the legal basis, legal principles and legal provisions of personal information protection, and put forward corresponding legal suggestions, in order to provide reference for the improvement of China's data protection legal system.

With an ever increasing amount of research data available, it becomes constantly more important to possess data literacy skills to benefit from this valuable resource. An integrative course was developed to teach students the fundamentals of data literacy through an engaging genome sequencing project. Each cohort of students performed planning of the experiment, DNA extraction, nanopore sequencing, genome sequence assembly, prediction of genes in the assembled sequence, and assignment of functional annotation terms to predicted genes. Students learned how to communicate science through writing a protocol in the form of a scientific paper, providing comments during a peer-review process, and presenting their findings as part of an international symposium. Many students enjoyed the opportunity to own a project and to work towards a meaningful objective.

Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue they offer the most promising prospects for the future of spatial machine learning.

Modern scientific research particularly radio astronomi- cal spectroscopic observations cannot be imagined without appropriate software suite. That is necessary because of two reasons 1) data are in digital form and 2) data processing is a complex multi-step process. In this paper created in VIRAC data processing suite for a single radio telescope, space maser observations are presented. Software Defined Radio (SDR) backend data processing is described. Implementation of the frequency switching algorithm, acquisition of observation data and their format for storing are discussed. Multiple ways to display the observation results are highlighted.

A lack of data reproducibility (“reproducibility crisis”) is debated across many scientific and medical disciplines. A systematic analysis of the available evidence on the reliability of scientific data revealed that, although the existence of a reproducibility crisis is widely perceived, conclusive data on the scale of the problem are lacking. Most importantly we found that, although the debate is largely focused on methodological issues, researcher conduct defines research standards and in turn data reliability. The availability of reliable methods itself does not guarantee good practice. Moreover, research is typically characterised by a lack of established methods due to its immanent novelty. Despite the crucial importance of researcher conduct, research and conclusive data on the determinants of researcher behaviour are missing. In conclusion, meta-research is urgently needed that establishes an understanding of the factors that determine researcher behaviour. This knowledge can then be used to implement and iteratively improve measures, which incentivise researchers to apply the highest standards resulting in high quality data.

Based on the deployment of global innovation value chain, the article analyzes the existing independent innovation capability of Guangzhou, and analyzes in detail from innovative talents, innovation system, innovation culture and innovation environment. The role of the central city of Guangzhou has led to research on issues such as the common development of cities in Guangdong and the Pearl River Delta. Using the combination of qualitative analysis and quantitative analysis, we can find out the shortcomings of Guangzhou's independent innovation capability, explore the causes of problems, and seek solutions to the main problems.

The Hennovation project, an EU H2020 funded thematic network, aimed to explore the potential value of practice-led multi-actor innovation networks within the laying hen industry. The project proposed that husbandry solutions can be practice-led and effectively supported to achieve durable gains in sustainability and animal welfare. It encouraged a move away from the traditional model of science providing solutions for practice, towards a collaborative approach where expertise from science and practice were equally valued. During the 32-month project, the team facilitated 19 multi-actor networks in 5 countries through 6 critical steps in the innovation process: problem identification, generation of ideas, planning, small scale trials, implementation and sharing with others. The networks included farmers, processors, veterinarians, technical advisors, market representatives and scientists. The interaction between the farmers and the other network actors, including scientists, was essential for farmer innovation. New relationships emerged between the scientists and farmers, based on experimental learning and the co-production of knowledge for improving laying hen welfare. The project demonstrated that a practice-led approach can be a major stimulus for innovation with several networks generating novel ideas and testing them in their commercial context. The Hennovation innovation networks not only contributed to bridging the science-practice gap by application of existing scientific solutions in practice but more so by jointly finding new solutions. Successful multi-actor, practice-led innovation networks appeared to depend upon the following key factors: active participation from relevant actors, professional facilitation, moderate resource support and access to relevant expertise. Farmers and processors involved in the project were often very enthusiastic about the approach, committing significant time to the network’s activities. It is suggested that the agricultural research community and funding agencies should place greater value on practice-led multi-actor innovation networks alongside technology and advisor focused initiatives to improve animal welfare and embed best practices.

This study introduces a novel and sustainable method of producing graphene from coconut shells and investigates its application in Graphene, Ni/Graphene, and Zn/Graphene electrodes for advanced energy storage devices. The graphene was synthesized scalably using a pyrolysis and impregnation technique, with its successful synthesis verified by X-ray diffraction (XRD), energy-dispersive X-ray spectroscopy (EDX), and electrical conductivity measurements characterizations. The study highlights the enhanced performance of Zn/Graphene electrodes, which outperform both pure graphene and Ni/Graphene variants. This superior performance is attributed to the smaller particle size of Zn (mean = 2.356 µm) compared to Ni (mean = 3.09 µm), and Zn’s more favorable electron configuration for electron transfer. These findings demonstrate the potential of bio-derived graphene composites as efficient, high-performance electrodes, paving the way for more sustainable and cost-effective energy storage solutions.

In recent years, the abundance of streaming data has posed significant challenges for traditional machine learning algorithms due to the dynamic nature and high volume of data. To address these challenges, this paper presents an adaptive supervised learning framework designed for data streams, leveraging the power of reproducing kernel Hilbert spaces (RKHS) while incorporating a data sparsity constraint. The proposed framework aims to adaptively update the model to efficiently handle the continuous arrival of streaming data while ensuring accurate and reliable predictions. The primary objective is to exploit the RKHS framework, which provides a rich mathematical structure for learning in high-dimensional feature spaces. By utilizing RKHS, the model can capture complex patterns and nonlinear relationships in the streaming data. Furthermore, the framework incorporates a data sparsity constraint to address the issue of limited resources and computational efficiency. The constraint promotes the selection of a subset of relevant features, reducing the dimensionality of the problem and enhancing the scalability of the learning algorithm. This constraint not only improves computational efficiency but also mitigates the effects of noisy or irrelevant features, leading to more robust and accurate predictions. To achieve adaptability, the proposed framework employs an online learning approach that incrementally updates the model parameters as new data arrives. This allows the model to adapt to concept drift and changing data distributions, ensuring its relevance and effectiveness over time. The adaptation process is guided by a mechanism that balances the exploitation of current knowledge with the exploration of new information, enabling the model to gradually evolve and refine its predictions. Experimental evaluations on various benchmark datasets demonstrate the efficacy of the proposed framework in handling streaming data. The results indicate that the adaptive supervised learning approach in RKHS with a data sparsity constraint outperforms traditional batch learning methods and exhibits superior accuracy, scalability, and adaptability in dynamic data stream scenarios. Overall, this research contributes to the development of adaptive supervised learning methods for data streams, highlighting the effectiveness of RKHS and the significance of incorporating a data sparsity constraint. The proposed framework offers a promising solution to handle the challenges posed by streaming data, paving the way for real-time, efficient, and accurate learning in diverse application domains.

While machine learning (ML) has been quite successful in the field of structural health monitoring (SHM), its practical implementation has been limited. This is because ML model training requires data containing a variety of distinct damaged instances captured from a real structure and the experimental generation of such data is challenging. One way to tackle this issue is by generating training data through numerical simulations. However, simulated data cannot capture the bias and variance of experimental uncertainty. To overcome this problem, this work proposes a deep learning-based domain transformation method for transforming simulated data to the experimental domain. Use of this technique has been demonstrated for debonding location and size prediction of stiffened panels using a vibration-based method. The results are satisfactory for both, debonding location and size prediction. This domain transformation method can be used in any field where experimental data for training machine learning models is scarce.

This study investigates the land surface temperature (LST) distribution from thermal infrared data for analyzing the characteristics of surface coverage using the Vegetation-Impervious-Soil (VIS) approach. A set of ten images, obtained from Landsat-5 Thematic Mapper, between 2001 and 2010, were used to study the urban environmental conditions of 47 neighborhoods of Porto Alegre city, Brazil. Porto Alegre has had the smallest population growth rate of all 27 state capitals in the last two decades in Brazil, with an increase of 11.55% in inhabitants from 1,263 million in 1991 to 1,409 million in 2010. We applied the environmental Kuznets curve (EKC) theory in order to test the influence of the economically-related scenario on the spatial nature of social-environmental arrangement of the city at neighborhood scale. Our results suggest that the economically-related scenario exerts a non-negligible influence on the physically driven characteristics of the urban environmental conditions as predicted by EKC theory. The linear inverse correlation R2 between household income (HI) and LST is 0.36 and has shown to be comparable to all other studied variables. Future research may investigate the relation between other economically-related indicators to specific land surface characteristics.

Objective: To study the changes in urine metabolism in female water polo players before and after high-intensity training by using ultra-high performance liquid chromatography-mass spectrometry, and to explore the biometabolic characteristics of urine after training and competition. Methods: Twelve young female water polo players (except goalkeepers) from Shanxi Province were selected. A 4-week formal training was started after one week of acclimatization according to experimental requirements. Urine samples (5 ml) were collected before formal training, early morning after 4 weeks of training, and immediately after 4 weeks of training matches, and labeled as T1, T2, and T3, respectively. The samples were tested by LC-MS after pre-treatment. XCMS, SIMCA-P 14.1, and SPSS16.0 were used to process the data and identify differential metabolites. Results: On comparing the immediate post-competition period with the pre-training period (T3 vs T1), 24 differential metabolites involved in 16 metabolic pathways were identified, among which niacin and niacinamide metabolism and purine metabolism were potential post-competition urinary metabolic pathways in the untrained state of the athletes. On comparing the immediate post-competition period with the post-training period (T3 vs T2), 10 metabolites involved in 3 metabolic pathways were identified, among which niacin and niacinamide metabolism was a potential target urinary metabolic pathway for the athletes after training. Niacinamide, 1-methylnicotinamide, 2-pyridone, L-Gln, AMP, and Hx were involved in two metabolic pathways before and after the training. Conclusion: Differential changes in urine after water polo games are due changes in the metabolic pathways of niacin and niacinamide.

Food composition data in the EMR countries are often lacking, obsolete or unreliable. The study aims to provide reliable and up-to-date nutrient data of selected food products consumed in Oman in order to evaluate consistency of nutrition labelling and update the current used food composition database. Contents of total fat, fatty acids, carbohydrates, total nitrogen, sugars and sodium were chemically analyzed in 221 foods and beverages purchased in 2022 and label and corresponding laboratory values were compared using the tolerance levels of the European Union. Food items were classified according their nutritional composition, the extent of processing and coded according FoodEx2 system. Results indicate that the nutrient values stated on a label align with the values obtained in the laboratory with the exception of a 6.3% discrepancy in TFAs content, where the reported values are higher than the appropriate reference values. The category with the highest frequency (71.5%) consisted of ultra-processed foods. In terms of inconsistencies in the nutrition claims, 5.1% of food products with claims did not comply with the statement “sugar free” or “low salt”. These findings underscore the significance of vigilant monitoring of nutritional labels as a means to implement measures that promote the well-being of consumers.

There are several challenges on how to attain energy efficiency while maintaining balance among factors affecting energy consumption such as power rating and temperature setpoints of cooling units, room temperature and humidity, and indoor air quality (IAQ). A real-time energy and IAQ monitoring system were installed in an educational building to profile the operational power consumption of inverter-based air condition unit (ACU) installed in each room. Polynomial curve and neural fitting regression analysis were applied to the real-time power consumption, indoor temperature and humidity, and carbon dioxide (CO2) level data. The derived models were able to provide the stabilizing indoor thermal and air conditions of the room to reach steady state ACU power consumption. These collected data and calculated parameters can be used to define rules in automating control of cooling appliances for an efficient energy utilization. The regression approaches, using real-time data, have determined the influence of indoor heat conditions and carbon dioxide levels on ACU power consumption. These parameters were utilized to establish consistent values for temperature, humidity, and carbon dioxide levels under stable settings of inverter-based air conditioning units.

In data-scarce regions, prudent planning and precise decision-making for sustainable development especially in agriculture remains a challenging task due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use land cover (LULC) classes and crop identification. Applying Remote Sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/ algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resources management. The (this)study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/ classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The MODIS (Moderate Resolution Imaging Spectroradiometer) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with unsupervised classification technique to get LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as a key input for agricultural mechanization planning and resources management. The accuracy of the LULC map was 78%, assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced techniques of processing, which will make it more useful and applicable for regional agriculture and environmental management.

Despite increased protection globally, climate change and other anthropogenic factors have caused a significant decline in seagrass cover. Identifying the specific causes of this decline is paramount if they are to be addressed. Therefore, we identified the causes of long-term change in seagrass/submerged aquatic vegetation (SAV) percentage cover and extent in the Bluefields Bay Special Fish Conservation Area (BBSFCA), a marine protected area on Jamaica’s southern coast. We used two random forest regression (RFr) models that included 2013 hydroacoustic survey SAV percentage cover data (dependent variable), and auxiliary data, reflectance data from level 2 processed 2013 Landsat 7 and 8 images and image band texture statistics maps (mean and variance) as predictors to generate 24 SAV percentage cover maps for the period 1984 – 2021 (37 years) using data from Landsat 4-5, 7 and 8 images., from which benthic features maps (SAV present, absent and coral reef) were created. Rainfall and map data were used to determine if SAV extent/area (km2) and average percentage cover and annual rainfall changed significantly over time and to evaluate the influence of rainfall. Rainfall impact on the overall spatial patterns of SAV loss, gain, and percentage cover change was assessed using a pixel-based regression. Finally, the most important spatial pattern predictors (two rainfall proxies (distance and direction from river mouth), benthic topography, depth, and hurricane exposure (a measure of hurricane disturbance)) of SAV loss, gain, and percentage cover change during 23 successive 1-to-4-year periods were identified using spatial Bayesian INLA generalized linear mixed models. SAV area/extent was largely stable with &gt; 70% mean percentage cover for multiple years. However, Hurricane Ivan (in 2004) caused a significant decline in SAV area/extent (by 1.62 km2, or a 13%) during 2002 – 2006 and a second hurricane (Dean) in 2007 delayed recovery until 2015. Additionally, rainfall declined significantly by &gt;1000 mm since 1901, and mean monthly rainfall positively influenced SAV percentage cover change. The pixel-based regression highlighted areas where mean monthly rainfall had a positive overall effect on SAV cover percentage change (across the entire bay) and gain (closer to the mouth of a river). The most frequently selected important spatial pattern predictors were the two rainfall proxies (areas closer to the river mouth were more likely to experience SAV loss and gain) and depth, with shallow areas generally having a higher probability of SAV loss and gain. Three hurricanes had significant but different impacts depending on their distance from the southern coastline. Hurricane Gilbert, which made landfall in 1988, resulted in higher SAV percentage cover loss in 1987 - 1988. Benthic locations with a northwestern/northern facing aspect (the predominant direction of Ivan’s leading edge wind bands) experienced higher SAV losses during 2002 – 2006. Although some locations impacted by Ivan and not by Dean recovered during 2006 – 2008, exposure to Ivan explained percentage cover loss during 2006 – 2008 and average exposure to (the cumulative impact of) Ivan and Dean (both with tracks close to the southern coastline) explained SAV loss during 2013 – 2015. Therefore, despite historic lows in annual rainfall, overall, higher rainfall was beneficial, multiple hurricanes impacted the site, and despite two hurricanes in three years SAV recovered within a decade. Hurricanes and a further reduction in rainfall may pose a serious threat to SAV persistence in the future.

This paper proposes a concise literature review aimed at identifying the current body of knowledge on the adoption of Social Networks in Crisis Management. The major input is a structured research question based on the initial reading around the topic. Before the recent pan-demic, most literature has focused on local crises, with a relatively low number of exceptions. Additionally, self-organizing system are spontaneous established between people who are af-fected by a crisis. Among the identified challenges, there is the need to integrate official commu-nication by emergency agencies with citizen-generated contents in a contest of credibility and trustworthiness. In certain cases, it has been reported a lack of specific competence, knowledge, and expertise, as well as a lack of sufficient policies and guidelines in the use of Social Networks. Those challenges need to be framed by considering the classic difficulties to provide timely and accurate information, to deal with fake news, unverified or misleading information, and infor-mation overload. Bridging major gaps though advanced analytics and AI-based technology is expected to provide a key contribution to establish and safely enable in practice an effective and efficient communication, to contrast dissonant mental models, which are often fostered by Social Networks, and to enable a shared situational awareness.

Green leaf volatiles (GLVs) are important in giving grape a fresh and green aroma. But the changes in GLVs during the phenological development of grapevines are not well known. This study analysed the GLVs and transcription levels of associated biosynthetic genes in six grape species from the Loess Plateau region at five stages of maturation. Thirteen GLVs were detected, showing unique patterns for each grape type at various growth phases. The primary components in six grapes were (E)-2-Hexenal, (E)-2-Hexen-1-ol, and Hexanal. With the exception of Cabernet Franc in 2019, the overall GLV contents of the six types generally increased during growth and development, peaking or stabilizing at harvest. And Sauvignon Blanc, Cabernet Gernischt, and Cabernet Sauvignon exhibited higher total contents among the varieties. Correlation analysis revealed a strong positive correlation between the levels of Hexanal, 1-Hexanol, (E)-2-Hexen-1-ol, (Z)-3-Hexenyl acetate, Nonanal, and (E, E)-2,6-Nonadienal and the expression of VvHPL and VvAAT genes in the LOX-HPL pathway. Specifically, VvHPL emerges as a potential candidate gene responsible for species-specific differences in GLV compounds. Comprehending the changing patterns in the biosynthesis and accumulation of GLVs offers viticulturists and enologists the opportunity to devise targeted strategies for improving the aromatic profile of grapes and wines.

As we know, the economy of science is to achieve optimal gains using scarce resources. Similarly, a problem where economies of the world are trying to resolve it is the issue of how to achieve an optimal economic contribution from limited tourism resources. According to the fact that global studies and statistics indicate that good governance is considered as the most important factor in developing countries and is considered as a strong governance of the development and as Information and Communication Technology (ICT) is effective in the interactions and economic and social exchanges of any society, it is the link between the tourism industry and the technology under the form of a robust tool and utilitarian in the tourism process called E-tourism can play a significant role. Therefore, the aim of this study was to investigate the effect of network Readiness Index and good governance on income from tourism industry. In this study, data and information about 14 selected countries were collected from Southwest Asia in the annual period of 2016-2011 in the form of panel data and the proposed model was used and estimated by the generalized torques system method. The estimated results of the model indicate that both hypotheses have a positive and significant impact on the amount of Network Readiness Index in the income of tourism industry, as well as the positive and significant effect of the index the amount of income from the tourism industry is confirmed, and their impact has been positive and significant, as well as exchange rate variables, revenue and commercial openness, also have a positive and significant effect on revenues of the tourism industry.

Hepatocellular carcinoma (HCC) early diagnosis is challenging. Moreover, for patients with al-pha-fetoprotein (AFP)-negative HCC, this challenge is augmented. MicroRNAs (miRs) profile may serve as potential HCC molecular markers. We aimed to assess plasma homo sabines (hsa)-miR-21-5p, hsa-miR-155-5p, hsa-miR-192-5p, hsa-miR-199a-5p expression levels, as panel of biomarkers for HCC in chronic hepatitis C virus (CHCV) patients with liver cirrhosis (LC), es-pecially AFP-negative HCC cases, a step toward non-protein coding (nc) RNA precision medicine. Subjects and Methods: 79 patients enrolled with CHCV infection with LC, subclassified into LC group without HCC (n=40) and LC with HCC (n=39) in comparison with 15 apparently healthy control subjects. Real-time quantitative PCR was used to measure plasma hsa-miR-21-5p, hsa-miR-155-5p, hsa-miR-192-5p, and hsa-miR-199a-5p. Results: Plasma hsa-miR-21-5p and hsa-miR-155-5p demonstrated significant upregulation, while hsa-miR-199a-5p demonstrated significant downregulation in the HCC group (n=39) when compared to LC group (n=40). Hsa-miR-21-5p expression was positively correlated with serum AFP, insulin, and insulin re-sistance (r=0.5, p<0.001, r=0.334, p=0.01, and r=0.303, p=0.02, respectively). According to the ROC curves, for differentiating HCC from LC, combining AFP with each of hsa-miR-21-5p, hsa-miR-155-5p, and miR199a-5p improved the diagnostic sensitivity to 87%, 82%, and 84%, re-spectively, vs 69% for AFP alone, with an acceptable specificity of 77.5%, 77.5%, and 80%, respec-tively, and AUC= 0.89, 0.85, and 0.90, respectively vs. 0.85 for AFP alone. Hsa-miR-21-5p/hsa-miR-199a-5p and hsa-miR-155-5p/hsa-miR-199a-5p ratios discriminated HCC from LC at AUC=0.76 and 0.71, respectively, with sensitivities=94% and 92%, and specifici-ties=48% and 53%, respectively. Upregulation of plasma hsa-miR-21-5p was considered as an independent risk factor for HCC development [OR=1.198(1.063–1.329), p=0.002]. Conclusion: Combining each of hsa-miR-21-5p, hsa-miR-155-5p, and hsa-miR-199a-5p with AFP made possi-ble to identify HCC development in LC patients’ cohort with higher sensitivity than using AFP alone. Hsa-miR-21-5p/hsa-miR-199a-5p and hsa-miR-155-5p/hsa-miR-199a-5p ratios are poten-tial HCC molecular markers for AFP-negative HCC patients. Hsa-miR-21-5p was linked to insu-lin metabolism in HCC patients’ group as well as being an upregulated independent risk factor for the emergence of HCC from LC in CHCV patients.

The study provide insights into the technological characteristics of green practices adoption and the potential benefits they offer in terms of socio-economic development, environmental and public health. The study adopted a cross-sectional research design. A structured questionnaire was developed to collect quantitative data from the participants. The questionnaire include closed-ended questions, Likert scale items, and demographic questions. Both stratified and simple random sampling techniques were employed to select the study participants (government officials, employees, managers, and community members). A total of 321 copies of questionnaires were duly filled and returned. The collected data were analyze using descriptive statistics, such as frequencies, percentages, mean, and standard deviation, were used to summarize the data and provide an overview of the key variables related to green practices adoption. The findings indicate that the manufacturing company in Ogun State, Nigeria, has shown moderate commitment to adopting green practices. While there is room for improvement in certain areas, such as waste-to-energy technologies and green building practices. Additionally, green practices have positively influenced employee well-being and community health, while aligning with the company's commitment to safeguarding the environment and public health.

Marine macroalgae have garnered significant attention in the field of cosmeceutical research due to their rich abundance of bioactive compounds. These compounds offer remarkable skin benefits without inducing any adverse effects on human health, such as cytotoxicity, reproductive toxicity, genotoxicity, mutagenicity, or carcinogenicity. Among the various bioactive compounds found in brown algae, phenolic compounds exhibit diverse chemical structures and are present in high concentrations. In addition to phenolic compounds, brown algae also contain terpenoids, bro-mophenols, mycosporine amino acids (MAAs), and flavonoids, with the well-studied poly-phenol compound, phlorotannin, being particularly prominent. Marine macroalgae further possess an array of pigments derived from their natural pigmentation, including chlorophylls, carotenoids (such as fucoxanthin and β-carotene), phycobiliproteins (such as phycoerythrin and phycocyanin), and melanin. These pigments have been extensively investigated for their potential cos-meceutical applications. The phenolic compounds and pigments derived from marine macroalgae have been thoroughly studied for their beneficial effects on the skin, including skin whitening, moisturizing, photoprotection, anti-aging, anti-wrinkle, anti-melanogenic, and anti-oxidant properties. This comprehensive review aims to explore the extraction, characterization, and skin cosmetic effects of phenolic compounds and pigments derived from marine macroalgae, as documented in the existing literature, thereby enhancing our understanding of their therapeutic potential.

In this study, the effect of extraction conditions on the total phenolic content of clove buds was investigated. Clove buds were extracted using a deep eutectic solvents like mixtures choline chloride: lactic acid in the 1:2 molar ratio. Additionally, the HPLC-DAD method was used to determine the main components of the clove extracts, including eugenol, eugenol acetate, and β-caryophyllene. The conditions used in the extraction with choline chloride: lactic acid (molar ratio 1:2) included extraction temperature (t = 40 – 80°C), water addition (5.6 – 40%), and extraction time (30 – 90 min). Determination of the phenolic compounds was done by the Folin-Ciocalteu method. The optimum operating conditions for the total phenolic content were identified at 77°C, 30 min, and a water addition of 40%. Based on the results, the highest amount of eugenol (307.26 ± 8,44 mg/g dry raw material) was determined in clove extracts which were extracted under condition of 60°C, 22.8%, and 30 min.

This study was conducted to propose the Green Economic Development Plan for Manufacturing S.M.E.s based on financial performance and operations thru Green Initiatives. The descriptive survey method used to gather data to determine the green initiative's implementation of small and medium (S.M.E.s) manufacturing enterprises. Green initiatives use as a basis for crafting a green economic development plan. Document analysis was also employed to obtain data from any available printed materials and records provided by the respondents. Such methods of gathering information used to validate data gathered from local and foreign-related literature. The observation also employed to survey the assets owned and validate any green initiatives practiced, including their implementation. The study's findings show that among the green initiatives implemented by SMMEs, which resulted in the reduction of total costs and expenses, were the proper disposal and segregation of waste materials, water management by recycling wastewater and using water-efficient equipment, natural resources, and raw materials management. SMMEs should encourage active participation and support of suppliers and customers in achieving G.E.D.'s objectives by developing incentive schemes.Furthermore, SMMEs should continue to benchmark with G.E.D. Practitioners are operating locally and abroad to adopt best greening strategies and regularly network with concerned government agencies for continuous updating on G.E.D. Initiatives that may benefit the firm. Further research may be conducted on green initiatives implemented by small and medium enterprises in other industry sectors.

Poly lactic acid (PLA) has made inroads for commercial market segment with lot many unique characteristics such as tenacity, low flame rate, moisture regain percentage, loss of ignition percentage, heat of combustion, UV resistance, Elastic recovery and higher melting point allowed it to be the fastest moving material in today's commercial market. An attempt has been made to test the feasibility and biocompatibility aspect of PLA with cement mix. The basic strength and physical test results were carried out and published in an article, to the continuation of the work, micro-structural study was conducted to evaluate the elemental characteristics. Thermo gravimetric analysis revealed that PLA either in granular form or filament will hold good for the inclusion into construction applications, provided degradation aspects are to be looked out for improvisation. From DSC it was found that PLA in filament form is the best inclusion material for construction application, however the tenacity of fibers has to checked, as currently available filaments in market does not have high tenacity value. From EDX reports, 30% inclusion of PLA as replacement for fine aggregate has constituent members as Calcium carbonate(CaCO3), Silica(SiO2) and Wollastonite (CaK) resulted in best composition among the rest. FESEM images revealed that, proper gradation in size, rough surface of PLA granular form or filament form will definitely enhance the mechanical/physical or even chemical behavior of PLA.

The processing of Mexican limes generates great amounts of peel as a byproduct. Lime peel is mainly rich in the flavonoid hesperidin, whose bioactivity is oriented mainly to cardiovascular diseases and cancer. The purpose of this work was to develop a green process for the extraction and purification of hesperidin from Mexican lime peel. The extraction of hesperidin was investigated on a laboratory scale by varying the solvent composition and the solid-to-solvent ratio. The best conditions (solid-to-solvent ratio of 0.33 g/mL and 60% ethanol) were used for the extraction of hesperidin in a pilot scale (Volume = 20 L). The kinetics of the extraction was studied to find the maximum hesperidin concentration at 100 min. The concentrated extract had a hesperidin content of 0.303 mg/mL. Next, a purification process using adsorption resins was assessed. Through static tests, it was determined that higher adsorption efficiencies were achieved with the EXA-118 resin and diluted extract (4:6 ratio with 10% DMSO). Finally, the adsorption of hesperidin from the diluted extract (hesperidin concentration of 0.109 mg/mL) was carried out at 25 °C in a column packed with 80 mL of EXA-118 resin. The mean recovery efficiency of hesperidin from the extract was almost 90%.

Vitamin B2, also known as riboflavin (RF) is an essential micronutrient for human health and must be obtained from dietary sources. Plants biosynthesize riboflavin and are important dietary sources of vitamin B2 for humans. Our present study reports sensitive detection of Vitamin B2 in widely consumed for tea infusions, namely black, green, sage and rosemary tea infusions, by a capillary electrophoresis method combined with laser induced fluorescence detection. Moreover, the correlation between Vitamin B2 contents of tea plants with their total phenolics (TPs) and antioxidant capacity are evaluated in this study. Whereas green teas have the highest TPs and antioxidant capacity, the highest RF contents are in sage infusions. The RF contents range between 0.34 and 10.36 µg/g for all tea samples studied. Comparing RF contents of tea samples found in this study to the RF contents of known RF sources, tea infusions are proposed as important dietary sources of Vitamin B2.

Our study examines how dengue incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales. We used the incidence of dengue in Ibagué, Colombia, from 2013 to 2018, to examine the associations with climate, socioeconomic and demographic factors from the census and satellite imagery at various levels of local spatial aggregation, including Manzanas, Secciones, Sectores, and Comunas. Our findings show a significant effect of spatial variables at finer levels of aggregation, showing varying degrees of correlation with dengue incidence. Temporal variables such as temperature and precipitation displayed consistent patterns across all spatial levels, with notable variations in Relative Risk (RR). Our study employs Geographically Weighted Regression (GWR) to identify relevant socioeconomic and demographic predictors. Then, these predictors were integrated into hierarchical models implemented in Integrated Nested Laplace Approximation (INLA) at each spatial level to assess spatiotemporal interactions. We comprehensively analyzed the three distinct models developed for each level: spatial, temporal, and spatiotemporal. A comparative evaluation of the models reveals that while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling and highlight the potential for targeted public health interventions based on localized risk factor analysis. Notably, the intermediate level Secciones emerged as the most informative, balancing spatial heterogeneity and case distribution density, thereby providing a robust framework for understanding the spatial determinants of dengue.

Certain species of Mucorales have been identified as causative agents of mucormycosis, a rare yet often lethal fungal infection. Notably, these fungi exhibit intrinsic resistance to common azole drugs, which target lipids. Given the pivotal role of lipids in drug resistance and their contribution to the innate resistance to azoles, this study provides a comprehensive overview of key lipid classes, including sphingolipids (SLs), glycerophospholipids(GPLs), and sterols, in Rhizopus delemar 99-880, a well-characterized reference strain among Mucorales. Through high-throughput analysis involving the identification and quantification of intermediates, we have elucidated the biosynthetic pathways of SLs, PGLs, and sterols. Notably, the acidic SLs were not found probably because the acidic branch of the SL biosynthesis pathway terminates at α-hydroxy phytoceramides as evident by their high abundance. Intermediates in the neutral SL pathway incorporated higher levels of 16:0 fatty acid compared to other pathogenic fungi. A strikingly high phosphatidylethanolamine (PE)/phosphatidylcholine (PC) ratio was observed among GPLs. Ergosterol remains the major sterol, similar to other fungi, and our analysis confirms the existence of alternate ergosterol biosynthesis pathways. The total lipidomic profile of R. delemar 99-880 offers insights into its lipid metabolism and potential implications for studying pathogenesis and drug resistance mechanisms.

A standard method assessing postural control is the measurement in standing position. However, the implementation is usually only possible to a limited extent. Recording of postural control directly on the trunk, in seated position, could provide an alternative diagnostic method for quantifying neuromuscular control. Comparison of the measurements of Center of Pressure (CoP) in standing and seated position was performed on 66 healthy adult subjects. Reliability of measurements in seated position was tested on 23 subjects. In addition, the extension force of all test subjects was recorded. The assessments of CoP fluctuations in standing and seated position showed adequate agreement (deviation 9.1%). Furthermore, good internal consistencies with a sufficient test re test reliability could be demonstrated for the measurements in seated position. Both CoP measurement methods showed a comparable Spearman correlation to obtained extension force measurements (standing: 0.24, seated: 0.23). Our results show that recording CoP flucutations in the seated position is a reliable and valid additional method to measurements in the single legged stance. It could serve as an additional alternative to quantify neuromuscular control in impaired patients in whom single-leg stance cannot be performed adequately. In addition, the measurement in sitting position enables the direct recording of neuromuscular control on the trunk.

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.