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Aerial drone imaging is an efficient tool for mapping and monitoring of coastal habitats at high spatial and temporal resolution. Specifically, drone imaging allows for time- and cost-efficient mapping covering larger areas than traditional mapping and monitoring techniques, while also providing more detailed information than those from airplanes and satellites, enabling for example to differentiate various types of coastal vegetation. Here, we present a systematic method for shallow water habitat classification based on drone imagery. The method includes:

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  Collection of drone images and creation of orthomosaics.

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  Gathering ground-truth data in the field to guide the image annotation and to validate the final map product.

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  Annotation of drone images into – potentially hierarchical – habitat classes and training of machine learning algorithms for habitat classification.

Randomized clinical trials are considered the gold standard for studies with dietary interventions, which is mainly due to the fact that they can establish causal relationships between food exposure and body composition measures or biomarkers. The aim of this study was to describe the details of a double-blind, randomized, clinical trial protocol to identify, characterize and evaluate the effects of human dietary exposure to pesticide residues in food. Specific aspects of planning (development of a research question, determination of objectives, selection of participants, randomization and blinding) and performance (recruitment of participants, measures to improve adherence, data collection, follow-up and evaluation of results) are addressed in this study. The study design proved effective in characterizing dietary patterns with foods originating from both conventional and organic agriculture. A total of 148 individuals were recruited for the study. The conventional group was represented by 47 % of the sample and the organic group was represented by 53 %. The practice of evidence-based nutrition has demanded that trials be well designed and systematically performed in the field of clinical nutrition. Therefore, this clinical trial emphasizes the importance of improving studies with toxicological nutrition that assess sources of exposure through food.

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  This double-blind, randomized clinical trial details the protocol for identifying, characterizing, and evaluating the effects of dietary exposure to pesticide residues.

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  The protocol demonstrates that well-designed and systematically conducted trials emphasize the importance of robust methodologies in evidence-based nutrition.

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  In the face of the global climate crisis, this clinical trial underscores the importance of enhancing studies in toxicological nutrition, particularly those evaluating sources of exposure through food, to better understand the dietary impacts on health.

This study introduces an innovative methodology employing Cellular Automata (CA) models to simulate seagrass dynamics in response to global environmental changes. The primary objective is to outline a procedural framework for constructing and deploying CA models applied to seagrass ecosystems, and potentially to other marine or terrestrial environments. The methodology encompasses various components, including conceptualization, workflow delineation, model parameterization, and execution steps. By utilizing Mediterranean and Zanzibari (East Africa) seagrass ecosystems as case studies, we demonstrate the versatility and applicability of the proposed approach across diverse geographical regions, species composition and model components. Through these case studies, we demonstrated how CA models can effectively capture the dynamics of seagrass communities subjected to climate change, invasive species, and nutrient regimes. Despite its strengths, the proposed CA model has limitations, including parameterization complexity and uncertainties related to species-specific environmental thresholds, growth rates and species interactions, alongside the difficulty of validating our models with real-world scenarios. Addressing these limitations in future studies will enhance the model's accuracy and applicability. This study serves as a foundation for future research in other regions and ecosystems, facilitating a better understanding of the complex interactions driving ecosystem dynamics.

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  This study introduces a methodology using Cellular Automata (CA) models to simulate seagrass dynamics detailing conceptualization, workflow, parameterization, and execution.

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  Case studies in Mediterranean and East Africa ecosystems demonstrate the versatility of CA models in capturing the impacts of climate change, invasive species, and nutrient regimes.

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  Despite strengths, the CA model has limitations and uncertainties like parameterization complexity and model validations suggesting future research to enhance accuracy and applicability.

Studies of free-ranging wildlife often involve individual tracking by sequentially recording animals’ positions over a continuous and extended period. Automatic, programmable, operational continuously, and user-friendly thanks to the development of intuitive software, GPS (Global Positioning System) enable the acquisition of large quantities of data, day and night, regardless of field and weather conditions, while allowing for levels of spatial and temporal resolution in the location data never before achieved in wildlife tracking studies. However, GPS collars deployment on wild fauna does not directly translate into scientific outcomes. This article delves into the hidden aspects of telemetry programs, offering a reflective account of our transdisciplinary experience (between researchers and wildlife managers) in GPS tracking of urban wild boar. The described protocol and its discussion aim to outline the necessary conditions to benefit from GPS programs. The program first requires a common construction of the protocol, which meets the objectives of each partner. Second, raw data collection and transformation into information involve four steps. Finally, both technical and human-related dimensions are to be anticipated and considered for further analyses.

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  Transdisciplinary research requires a common construction of the protocol in line with the research question.

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  Technical constraints and negotiations between partners need to be considered.

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  Multiple steps are required to leverage the scientific advantages of the monitoring.

A comprehensive risk assessment method was applied to examine the risks associated with airborne formaldehyde occupational exposure among hospital laboratory staff. The method assessed exposure levels and health impacts by integrating area and personal air sampling, biological monitoring, and self-reported health data. Samples were collected from 74 workplaces across various departments using NIOSH method 3500 and were analyzed via UV-vis spectrophotometry. The data showed significant differences in exposure levels between departments (p≤0.05) and confirmed the efficacy of the method in identifying risk differences. Despite average personal exposure levels being measured lower than occupational limits, individual assessments indicated that some participants surpassed these limits, emphasizing the necessity of personal monitoring for workers with higher risks. The high prevalence of respiratory symptoms, such as cough and wheezing among staff, indicated the need for further investigation and targeted interventions. Although estimated cancer and non-cancer risks were within safe thresholds, the study emphasized the importance of continuous exposure monitoring and the implementation of effective control measures in hospital laboratory departments with formaldehyde emission. This integrated method improved the reliability and generalizability of formaldehyde exposure risk assessments and aided in the development of safe occupational health practices.

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  The method integrated personal and area sampling with advanced calibration for precise occupational exposure evaluation in laboratories.

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  The method used of biomarkers to assess formaldehyde absorption in the body estimating both cancerous and non-cancerous health risks associated with occupational exposure.

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  Addressed traditional method limitations and integrated risk components to improve data reliability for workplace safety and health risk management.

A long-standing challenge in the study of RNA structure-function dynamics using fluorescence-based methods has been the precise attachment of fluorophores to structured RNA molecules. Despite significant advancements in the field, existing techniques have limitations, especially for 3ʹ end labeling of long, structured RNAs. In response to this challenge, we developed a chemo-enzymatic method that uses Klenow DNA polymerase to label RNAs. In this method:

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  Klenow DNA polymerase adds an amino-modified nucleotide to the 3ʹ end of the RNA, guided by the DNA oligonucleotide template.

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  An NHS-ester dye is then conjugated to the amino-modified RNA, forming a covalent amide bond.

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  For highly structured RNAs, DNA oligonucleotides complementary to the RNA disrupt pre-existing intramolecular RNA structures.

This methodological advancement enables site-specific incorporation of a single modified nucleotide at the 3′ terminus of various RNA substrates, irrespective of their length or secondary structure. The user-friendly nature of the technique, with minimal modifications required for different RNA targets, makes it readily adaptable by a broad range of researchers. This approach has the potential to significantly improve the development of functionalized RNA for various applications.

In this research is presented a new method for determining the weights of criteria called simple weight calculation (SIWEC) method. The steps of this method are presented in the practical example of determining the importance of criteria for the needs of sales of agricultural products in the Semberija region. During the presentation of this method two methods are elaborated the simple SIWEC method which includes numerical ratings and the fuzzy SIWEC method which includes ratings in the form of linguistic value. In the selected example is presented how to use this method in order to determine the importance of criteria and in both cases the criterion of sales reliability is given the greatest weight. The contribution SIWEC method is reflected in its simplicity, which facilitates decision-making.

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  The method presented in this research apart from others is that it uses the evaluation of the criteria by decision makers, so the criteria should not be ranked and compared, but simply evaluated.

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  Unlike similar methods, the presented method uses the adjusted steps of the method for ranking the alternatives, and decision makers are given a different importance in the decision-making.

Cadmium is a common environmental heavy metal that is very toxic and carcinogenic for human and other flora and fauna. Therefore, this study is aimed to evaluate the fisibility of vermicompost fertilizer for cadmium uptake from soil by the root of radish (Raphanus sativus). For the purpose of the study, four different ratios of one case control, 1 per 1, 1 per 4, 2 per 4, 3 per 4 vermicompost fertilizer per soil with 0, 50000 and 100000 µg/L cadmium concentrations was evaluated. Cadmium in the samples was measured using an Atomic Absorption Spectroscopy (AAS). The results showed that the minimum uptake of cadmium by the plant was observed for 3 per 4 ratio of fertilizer per soil. In addition, results revealed that highest growth rate of Raphanus sativus roots occurred in maximum ratio of fertilizer per soil usage (3 per 4). This study showed that vermicompost as a organic fertilizer has a good ability to adsorb cadmium metal from soil. Therfore, vermicompost application can be considered as an inexpensive natural adsorbent in arable land contaminated with cadmium.

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  Heavy metals are very toxic and carcinogenic to human and animals.

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  Adding organic fertilizer to the soil increases the absorption of heavy metal (cadmium) in the soil and prevents it from entering the food chain.

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  The relationship between the concentration of cadmium absorbed by the tuber of radish plant and the percentage of vermicompost added to the soil is presented.

The deregulation of electricity market has led to the development of the short-term electricity market. The power generators and consumers can sell and purchase the electricity in the day ahead terms. The market clearing electricity price varies throughout the day due to the increase in the consumers bidding for electricity. Forecasting of the electricity in the day ahead market is of significance for appropriate bidding. To predict the electricity price the modified method of Exponential Smoothing-CNN-LSTM is proposed based on the time series method of Exponential Smoothing and Deep Learning methods of Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). The dataset used for assessment of the forecasting algorithms is collected from the day ahead electricity market at the Indian Energy Exchange (IEX). The forecasting results of the Exponential Smoothing-CNN-LSTM method evaluated in terms of Mean Absolute Error (MAE) as 0.11, Root Mean Squared Error (RMSE) as 0.17 and Mean Absolute Percentage Error (MAPE) as 1.53 % indicates improved performance. The proposed algorithm can be used to forecast the time series in other domains as finance, retail, healthcare, manufacturing.

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  The method of Exponential Smoothing-CNN-LSTM is proposed for forecasting the electricity price a day ahead for accurate bidding for the short-term electricity market participants.

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  The forecasting results indicate the better performance of the proposed method than the existing techniques of Exponential Smoothing, LSTM and CNN-LSTM due to the advantages of the Exponential Smoothing to extract the levels and seasonality and with the CNN-LSTM methods ability to model the complex spatial and temporal dependencies in the time series.

Nowadays, layered double hydroxides (LDH), sometimes referred as hydrotalcite-like compounds, have gained great attention since their composition and structure can be easily modified, so that they can be implemented in multiple fields. LDH-based composite materials based on LDH exhibit tremendously improved properties such as high specific surface area, which promotes the accessibility to a greater number of LDH active sites, considerably improving their catalytic, adsorbent and biological activities. Therefore, this review summarizes and discusses the synthesis methods of composites constituted by LDH with other inorganic compounds such as zeolites, cationic clays, hydroxyapatites, among many others, and describe the resulting characteristics of the resulting composites, emphasizing the morphology. Brief descriptions of their properties and applications are also included.