Tool For Protection Research Articles

LEVERAGING ENVIRONMENTAL PERFORMANCE IN ALGERIAN ECONOMIC INSTITUTIONS FOR SUSTAINABLE DEVELOPMENT

Purpose: The study explored how environmental performance contributes to sustainable development, focusing on its role in achieving key dimensions of sustainability. Theoretical Framework: It emphasized that essential administrative tools for environmental protection are primarily found within the legal and institutional framework, which includes various national bodies established to support sustainable development. These organizations play a significant role in raising awareness, providing training, and emphasizing the importance of adhering to environmental standards through preventive, conservation, and regulatory measures, all backed by legal authority. Additionally, civil society has a crucial role in fostering environmental awareness, particularly within economic institutions. Design/Methodology/Approach: The designof the study is descriptive and analytical. Data for this study were collected from official Algerian sources. Findings: Cleaner Production this approach is a practical implementation of sustainable development, enabling greater efficiency in production while minimizing the use of raw materials and resources, all while protecting the environment.

O REGISTRO DE MARCA COMO VANTAGEM COMPETITIVA: UM PANORAMA DO SERTÃO SERGIPANO NO ANO DE 2021

The study explores the importance of registering trademarks as a tool for protection and competitive advantage for companies in the Sergipano Hinterland. It highlights how registered trademarks differentiate themselves in the market, creating identity and avoiding legal conflicts. The research identifies the risks that small and medium-sized companies face when operating without registration, such as losing their name and rebranding costs. Despite the increase in trademark registration in Brazil, many businesses in the region have yet to register their trademarks, which makes them vulnerable. The objectives include identifying the benefits of registration, assessing the risks of not registering and understanding the reasons for the low number of registrations. The methodology involves a bibliographic and documentary survey. The results show that the lack of registration compromises competitiveness and the need to raise awareness of its importance.

Assessment of sweet whey fortified with Bifidobacteria and selenium on reduction of pesticide liver toxicity in albino rats

Deltamethrin (DLM) represents one of the most commonly used pesticides. It passes through milk, vegetables, and fruits to humans or through animals (veterinary drugs and feeding on contaminated forage) to milk; it can escape from skin to blood and be secreted in breast milk in lactating women. It is believed to have neurotoxic, nephrotoxic, and hepatotoxic properties. To investigate deltamethrin-induced hepatotoxicity, 64 rats were divided into eight groups. The control group did not receive any treatment. The groups were as follows: D 30 mg DLM/kg body weight (BW) dissolved in corn oil; B 1 mL whey (1010 cfu/mL of Bifidobacterium longum ATCC 15707); S 1 mL whey (0.5 ppm selenium); BS 1 mL whey (1010 cfu/mL of B. longum ATCC 15707 + 0.5 ppm selenium); BD 1 mL whey (1010 cfu/mL of B. longum ATCC 15707 + DLM); SD 1 mL whey (0.5 ppm selenium) + DLM; and BSD 1 mL whey (1010 cfu/mL of B. longum ATCC 15707) + 0.5 ppm selenium + DLM. Results revealed that the manipulation of Bifidobacteria with selenium triggered a significant improvement in AST (U/mL), ALT (U/mL), GSH (mg/g), TNF-α (pg/mL), NF-κB (ng/mL), and BCL2 (ng/mL) from 166.7 ± 6.42, 30.67 ± 0.55, 0.252 ± 0.005, 17.18 ± 0.42, 1.14 ± 0.10, and 1.77 ± 0.06 versus 334.9 ± 4.7, 72.83 ± 2.49, 0.108 ± 0.005, 33.57 ± 0.59, 2.58 ± 0.05, and 1.04 ± 0.04, respectively, compared to DLM group. As well as reduction in histopathological necrosis, congestion, and degradation. Whey beverages fortified with B. longum and selenium implicated a reduction in oxidative stress and histopathological degradation that accomplished DLM toxicity. The utilization of whey (a byproduct of cheese making) is considered a recycling process that supports eco-friendly practices and sustainability, thus encouraging its use as a protective tool in animal feed or manipulation by humans, especially workers in pesticide plants.

Windthrow in riparian buffers affects the water quality of freshwater ecosystems in the eastern Canadian boreal forest

Despite the wide application of riparian buffers in the managed boreal forest, their long-term effectiveness as freshwater protection tools remains unknown. Here, we evaluate windthrow incidence in riparian buffers in the eastern Canadian boreal forest and determine the effect of windthrow on the water quality index of the adjacent freshwater ecosystems. We studied 40 sites—20 riparian buffers, aged 10 to 20 years after harvesting and 20 control sites within intact riparian environments—distributed among clay and sandy (esker) soils and black spruce (Picea mariana) and jack pine (Pinus banksiana) stands. We observed more windthrow in the harvested stands (36%) relative to the control sites (16%), regardless of substrate and species. We determined that the most important factors explaining windthrow were exposition, harvesting, aquatic environment size, and stand characteristics. These factors drive wind exposure, speed, and force, which determine post-harvest windthrow risk. Furthermore, windthrow negatively affected the water quality index of the adjacent aquatic systems, i.e., greater windthrow decreased the protective effect of the riparian buffer. We recommend increasing the use of partial harvest near riparian environments and adapting riparian buffers to site conditions to ensure the long-term protection of adjacent freshwater ecosystems.

Research by the method of an extended systematical literature review E-SLR the problem of ensuring the security of personal data when using OSINT

Objective. The amount of personal data in open sources increases, which makes it possible for third parties to access it using open source intelligence (OSINT) methods, which can be used for malicious purposes. The aim of the work is to identify threats and existing methods and means of ensuring the security of a user's personal data and his reputation when using OSINT by intruders, as well as to identify the main problems in protecting user PD taking into account OSINT. Method. The study uses an extended method of systematic literature review (e-SLR), which is a systematic literature review (SLR) supplemented by responses from ChatGPT, GigaCHAT, YndexGPT neural networks. Result. 41 sources were received for the analysis of the problem, on the basis of which threats to personal data were identified: violation of the confidentiality of personal data and the operation of information systems, targeted attacks using social engineering, password disclosure, espionage; protection tools: data processing before publication, anonymization and depersonalization, limitation of personal data, selection of sites, protection using OSINT, creation of complex passwords, use of protection tools, organizational measures; problems in the development of protection tools: working with big data, unreliability of information and sources, labor-intensiveness of data analysis, technical limitations, bias, ethical and legal aspects. Conclusion. The results were used to develop models for protecting personal data in open sources, methods and means for detecting and preventing violations of their security.

Methods used in sea turtle genetic and molecular studies

Sea turtles have a tropical and subtropical distribution and can be found in nearly all seas and oceans. They have been the subject of considerable genetic research. However, it does not yet appear that the molecular techniques used for these genetic studies follow a consensus or universal set of tools to be followed for subsequent studies. This is not desirable since it may preclude data exchange and use among studies. Thus, the aim of this review was to survey the main genetic and molecular methods used for sea turtle research worldwide. To achieve this goal, a total of 95 scientific papers were compiled from online databases. We considered articles that used molecular tools for genetic analysis and provided detailed locality data. The following aspects were assessed: species studied, local of sample collection, type of tissue used for molecular studies and type of genetic material used. The seven known sea turtle species have different distribution patterns, with some overlapping of occurrence. Chelonia mydas has been studied genetically along the coasts of all continents. Skin is the most common type of tissue used for molecular analyses. From genetic studies on sea turtles, it is possible to verify the occurrence of hybridism. This phenomenon is relevant to the conservation of the species and was reported in six articles on this topic. Therefore, it is considered that genetic and molecular assays in sea turtles are important tools for biological evaluation and protection. The aim was to survey the main methods used in genetic and molecular research on marine turtles. Keywords: DNA, Chelonian, Testudines, marine turtles

Cross-Kingdom RNA Transport Based on Extracellular Vesicles Provides Innovative Tools for Plant Protection.

RNA interference (RNAi) shows great potential in plant defense against pathogens through RNA-mediated sequence-specific gene silencing. Among RNAi-based plant protection strategies, spray-induced gene silencing (SIGS) is considered a more promising approach because it utilizes the transfer of exogenous RNA between plants and microbes to silence target pathogen genes. The application of nanovesicles significantly enhances RNA stability and delivery efficiency, thereby improving the effectiveness of SIGS and further enhancing plant resistance to diseases and pathogens. This review explores the role of RNAi in plant protection, focusing on the cross-kingdom transport of small RNAs (sRNAs) via extracellular vesicles. It also explores the potential of nanotechnology to further optimize RNA-based plant protection, offering innovative tools and methods in modern plant biotechnology.

Exploring user privacy awareness on GitHub: an empirical study

GitHub provides developers with a practical way to distribute source code and collaboratively work on common projects. To enhance account security and privacy, GitHub allows its users to manage access permissions, review audit logs, and enable two-factor authentication. However, despite the endless effort, the platform still faces various issues related to the privacy of its users. This paper presents an empirical study delving into the GitHub ecosystem. Our focus is on investigating the utilization of privacy settings on the platform and identifying various types of sensitive information disclosed by users. Leveraging a dataset comprising 6,132 developers, we report and analyze their activities by means of comments on pull requests. Our findings indicate an active engagement by users with the available privacy settings on GitHub. Notably, we observe the disclosure of different forms of private information within pull request comments. This observation has prompted our exploration into sensitivity detection using a large language model and BERT, to pave the way for a personalized privacy assistant. Our work provides insights into the utilization of existing privacy protection tools, such as privacy settings, along with their inherent limitations. Essentially, we aim to advance research in this field by providing both the motivation for creating such privacy protection tools and a proposed methodology for personalizing them.

Introduction of a guaranteed basic income as a probable consequence of intellctual automation of labor

Objective: to form a holistic view of the unconditional basic income as a tool of social protection, taking into account the changes in the labor sphere and allowing to solve a number of growing problems associated with the consequences of the economy digitalization, as well as to assess the prospects of creating legal regulation on this issue. Methods: the study was conducted using both general and specific scientific methods, including analysis and synthesis, formal-legal method, systemic and comparative approaches, methods of legal forecasting and legal modeling. Results: unconditional basic income can become one of the elements of a new model of social security based on the unstable nature of labor relations, which begins to prevail over the model of sustainable employment traditional for industrial society. If established, this institution will be cross-sectoral within the labor law and social security law, providing social protection simultaneously to all members of society, most of whom in the near future will face an increased risk of being absorbed by technological unemployment. Unconditional basic income as an element of the social protection system will have to comply with the principle of the universal nature of protection based on social solidarity, on the one hand, and to take into account the need to balance the interests of the parties, on the other hand. As experimental models of basic income, which can be put into practice in the coming years, we can name the model of basic income as a measure of social support for persons involved in atypical forms of employment. This model will affect a number of existing provisions of labor legislation and will allow over time, in case of technological unemployment, to expand the circle of unconditional basic income recipients to all people. Scientific novelty: it consists in the fact that the unconditional basic income is analyzed from the viewpoint of its compliance with the fundamental principles of labor, employment, and social security. Practical significance: it consists in revealing the significance of basic income for the implementation of social policy and in forming recommendations for modeling experiments to introduce the unconditional basic income in the Russian Federation.

Monitoring and Predicting Air Quality with IoT Devices

The growing concern about air quality and its influence on human health has prompted the development of sophisticated monitoring and forecast systems. This article gives a thorough investigation into forecasting the air quality index (AQI) with an Internet of Things (IoT) device that analyzes temperature, humidity, PM10, and PM2.5 levels. The dataset used for this analysis comprises 5869 data points across six critical parameters essential for accurate air quality prediction. The data from these sensors is sent to the ThingSpeak cloud platform for storage and preliminary analysis. The system forecasts AQI using a TensorFlow-based regression model, delivering real-time insights. The combination of IoT technology and machine learning improves the accuracy and responsiveness of air quality monitoring systems, making it a useful tool for environmental management and public health protection. This work presents comparatively the effectiveness of feedforward neural network models trained with the ‘adam’ and ‘RMSprop’ optimizers over different epochs, as well as the machine learning algorithm random forest with varying numbers of estimators to forecast AQI. The models were trained using both types of regression analysis: linear regression and random forest regression. The findings show that the model achieves a high degree of accuracy, with the predictions closely aligning with the actual AQI values, thus having the potential to significantly reduce the negative health impact associated with poor air quality, protecting public health and alerting users when pollution levels are higher than allowed. Specifically, the random forest model with 100 estimators delivers the best overall performance for both AQI 10 and AQI 2.5, achieving the lowest Mean Absolute Error (MAE) of 0.2785 for AQI 10 and 0.2483 for AQI 2.5. This integration of IoT technology and advanced predictive analysis addresses the significant worldwide issue of air pollution by identifying the pollution hotspots and allowing decision-makers for quick reactions, and the development of effective strategies to reduce pollution sources.

Integrating Fuzzy C-Means Clustering and Explainable AI for Robust Galaxy Classification

The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing a degree of uncertainty. The objective of this study is to integrate Fuzzy C-Means (FCM) clustering with explainability methods to achieve a precise and interpretable model for galaxy classification. We applied FCM to manage this uncertainty and group galaxies based on their morphological characteristics. Additionally, we used explainability methods, specifically SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-Agnostic Explanations), to interpret and explain the key factors influencing the classification. The results show that using FCM allows for accurate classification while managing data uncertainty, with high precision values that meet the expectations of the study. Additionally, SHAP values and LIME provide a clear understanding of the most influential features in each cluster. This method enhances our classification and understanding of galaxies and is extendable to environmental studies on Earth, offering tools for environmental management and protection. The presented methodology highlights the importance of integrating FCM and XAI techniques to address complex problems with uncertain data.

Association between vitamin A supplementation and stunting and anemia in socially vulnerable Brazilian children.

Vitamin A is related to concentrations of insulin-like growth factor type 1, a protein produced in response to growth hormone, and to increased mobilization of body iron stores. Thus, vitamin A aids in increased hematopoiesis and may be useful in preventing stunting and anemia. This study aimed to identify the association between vitamin A supplementation from the National Vitamin A Supplementation Program instituted in Brazil and stunting and anemia in socially vulnerable Brazilian children. This is a Cross-sectional population-based study. Children aged 6-59months old, living in favelas of a capital city in the Northeast of Brazil, were included. Sociodemographic variables were collected. Vitamin A supplementation was also evaluated using the child's vaccination card information. Anthropometric and capillary hemoglobin evaluations were performed to identify the presence of stunting and anemia, respectively. The association analysis was performed using Poisson regression with robust variance estimation. 598 children participated in this study; 11.3% and 55.6% had stunting and anemia, respectively. As for vitamin A supplementation, 59.5% had taken at least one dose of the supplement,and 3.5% were on the complete supplementation scheme. In the adjusted association analysis, vitamin A supplementation decreased the likelihood of children having stunting and anemia by 8% (RP:0.86; 95% IC 0.86-0.98; p = 0.014) and 31% (RP:0.69; 95% IC 0.53-0.89; p = 0.004), respectively. Children who were fully supplemented were 58% (RP:0.42; 95% IC 0.24-0.77; p = 0.008) less likely to have anemia. Thus, vitamin A supplementation is a protective tool against stunting and anemia in children living in a situation of social vulnerability.

Acceptability of the National Health Insurance scheme and preferred benefit package among civil servants in Rivers State, Nigeria

Health-care insurance is one of the financial risk protection tools available to clients in the health-care industry. In most developing countries, citizens are in favor of paying for their health-care expenses out of pocket, a payment mode that potentially tips these people into poverty. This study aims to determine the level of acceptance of and the preferred benefit package available in the National Health Insurance Scheme (NHIS) among civil servants in Rivers State, Nigeria. In this descriptive cross-sectional study, respondents were selected through simple random sampling. The collected data were cleaned and analyzed with Statistical Product and Service Solution version 23. Out of the 351 respondents, male subjects accounted for the big majority of the sample (n = 208; 59.3%); only 30 (8.5%) had any form of health insurance; and about two-thirds of the respondents, at around 223 (63.5%), expressed acceptance of using NHIS as a strategy for payment. The most preferred benefits package was the one that covers all services 178 (50.7%). The poor and the poorest showed a preference for the health-care benefits package that covers all expenses, whereas the rich showed a preference for services covering basic diseases. The overall acceptability of the NHIS was statistically associated with socio-economic status (p ≤ 0.05). Thus, the opinions of the consumers and their health needs must be taken into consideration when planning a health insurance benefits package.

Evaluation of radiation protection effectivity in a cardiac angiography room using visualized scattered radiation distribution

In this study, we devised a radiation protection tool specifically designed for healthcare professionals and students engaged in cardiac catheterization to easily monitor and evaluate scattered radiation distribution across diverse C-arm angles and arbitrary physician associated staff positions—scrub nurse and technologist positions. In this study, scattered radiation distributions in an angiography room were calculated using the Monte Carlo simulation of particle and heavy ion transport code system (PHITS) code. Four visualizations were performed under different C-arm angles with and without radiation protection: (1) a dose profile, (2) a 2D cross-section, (3) a 3D scattered radiation distribution, and (4) a 4D scattered radiation distribution. The simulation results detailing the scattered radiation distribution in PHITS were exported in Visualization Toolkit format and visualized through the open-source visualization application ParaView for analysis. Visualization of the scattered dose showed that dose distribution depends on the C-arm angle and the x-ray machine output parameters (kV, mAs s−1, beam filtration) which depend upon beam angulation to the patient body. When irradiating in the posterior–anterior direction, the protective curtain decreased the dose by 62% at a point 80 cm from the floor, where the physician’s gonads are positioned. Placing the protection board close to the x-ray tube reduced the dose by 24% at a location 160 cm from the floor, where the lens of the eye is situated. Notably, positioning the protection board adjacent to the physician resulted in a 95.4% reduction in incident air kerma. These visualization displays can be combined to understand the spread and direction of the scattered radiation distribution and to determine where and how to operate and place radiation protection devices, accounting for the different beam angulations encountered in interventional cases. This study showed that scatter visualization could be a radiation protection teaching aid for students and medical staff in angiography rooms.

European Union’s Withdrawal from Energy Charter Treaty: A Result of Climate Policy?

This article analyzes the political and economic motivations behind the coordinated decision of the European Union and its member states to withdraw from the Energy Charter Treaty (ECT) in March 2024. Special emphasis is placed on the role of the EU’s climate policy in this process. A comprehensive review of the thirty years since the treaty’s inception is presented, aiming to uncover its influence on the domestic political dynamics within EU member states. Through an examination of investment arbitration practices and official EU documents, it is concluded that, despite declarative statements, the climate agenda was merely an external and secondary factor, while the primary driver was a shift in the EU’s investment policy. The article illustrates that the treaty did not obstruct the achievement of the EU’s climate objectives; however, the investment protection mechanism in international arbitration proved incompatible with the principles of autonomy and primacy of EU law. Furthermore, the treaty indeed provided investors with avenues to seek compensation from EU member state governments for assets “blocked” as a result of climate policies. The abandonment of a proven international investments protection tool may negatively impact the foreign investment attractiveness of the EU’s green energy sector.

Discriminating the origin of fish from closely related water bodies by combining NMR spectroscopy with statistical analysis and machine learning

Pikeperch, perch and bream are among the most traded and valued fish species in North-Eastern Europe. Therefore, it is necessary to be able to distinguish fish from different lakes and coastal sea regions to ensure a good traceability of products in the fish market and to protect both consumers and fish stocks. Untargeted metabolomics using nuclear magnetic resonance (NMR) spectroscopy is a suitable tool for this purpose. It is an established method for determining various properties of biological and living systems, such as health, origin, type, etc. Statistical methods including principal component analysis (PCA) and linear discriminant analysis (LDA) are typically applied to NMR data to correlate spectra with a particular research question.Herein we examine fish from three closely related water bodies and demonstrate that reliable determination of the water body that a particular fish originates from by traditional statistical analysis (PCA and LDA) of fish NMR spectra is not possible. In contrast, determining the fish species is possible. We proceed to show that machine learning methods perform better and that a combination of statistical analysis (LDA) and random forest (RF), a supervised machine learning technique, allows reliable determination of the originating water body, while being also tolerant to seasonal variations. This is an improvement over prior work, which has dealt with more clearly distinguished origins of fish. Exceptional accuracy was achieved in correctly assigning fish to their origin even in a scenario where two of the water bodies are connected by a river through which the fish are known to migrate. Since determining the origin of fish is important in environmental protection, we recommend following up this approach and using it as the basis of a robust tool for environmental protection and other monitoring purposes.

Toxicity Equivalency Factors for Tetrodotoxin Analogues Determined with Automated Patch Clamp on Voltage-Gated Sodium Channels in Neuro-2a Cells.

Tetrodotoxin (TTX) is a potent marine neurotoxin, responsible for numerous poisoning incidents and some human fatalities. To date, more than 30 TTX analogues have been identified, but their individual toxicities and roles in poisoning remain largely unknown. In this work, the toxicity equivalency factors (TEFs) of five TTX analogues were determined by assessing the blockade of voltage-gated sodium channels in Neuro-2a cells using automated patch clamp (APC). All TTX analogues were less toxic than TTX. The derived TEFs were applied to the individual TTX analogues concentrations measured in pufferfish samples, using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). A comparison of these results with those obtained from APC analysis demonstrated that TEFs can be effectively used to translate LC-MS/MS analytical data into meaningful toxicological information. This is the first study to utilize APC device for the toxicological assessment of TTX analogues, highlighting its potential as a bioanalytical tool for seafood safety management and human health protection.

Two-Way Perspectives and Attitudes about Environmentally Friendly Agro-Food Production and Consumption: An Empirical Survey for Greece

Relying on pesticides to safeguard crops has led to pest resistance and detrimental environmental and health consequences. Many integrated pest management programs focus on producers’ decision-making processes when selecting and implementing pest management interventions. Alternative approaches such as integrated pest management and biological control have been implemented to address these challenges, with Greece as the research area. A quantitative study was oriented to consumers and producers to investigate their views on the effectiveness of pesticides, aiming to assess whether these perspectives influence their attitudes toward adopting pest management practices. Primary data were gathered through structured questionnaires to consumers and producer associations (focusing on tomatoes, olives, and citrus fruits) and analyzed through multivariate methods. The findings unveiled varying preferences and concerns regarding plant protection tools, with considerations such as product effectiveness, method awareness, and sustainability. Specifically, the study revealed aspects like the popularity and demand for specific plant protection tools, the knowledge of these experts about products and pest control methods, and their stance on sustainable agriculture and environmental aspects of pest control.

Rapid discriminant analysis for the origin of specialty yam based on multispectral data fusion strategies

In order to achieve rapid and effective identification of Hebei yam, a qualitative discrimination model was constructed based on near infrared (NIR), mid infrared (MIR), and microscopic Raman spectra in combination with individual spectra and multispectral data fusion strategies. The results showed that the gray wolf optimizer-support vector machine (GWO-SVM) model constructed by mid-level fusion using the three feature spectra performed the best in distinguishing the geographic origin of the yam, with a prediction accuracy of 100.00% in both the training set and the test set, and an F1 score of 1.00. The results indicated that due to spectral complementarity, NIR, MIR and Raman combined with feature-level fusion can be used as a powerful, non-destructive, fast and feasible tool for geographic origin classification and brand protection of Hebei yam. This work is expected to be a potential method for origin identification analysis and quality monitoring in the food and pharmaceutical industries.

A new attention-based CNN_GRU model for spatial-temporal PM2.5 prediction.

Accurately predicting thespatial-temporal distribution of PM2.5 is challenging due to missing data and selecting an appropriate modeling method. Effective imputation of missing data must consider the relationships between variables while preserving their inherent variability and uncertainty. In this study, we employed machine learning techniques to impute missing data by analyzing the relationships between meteorological variables and other pollutants. Subsequently, we introduced an innovative spatiotemporal hybrid model, AC_GRU, which integrates a one-dimensional convolutional neural network (CNN), GRU, and an attention-based network to predict PM2.5 concentrations in urban areas. The AC_GRU model utilizes meteorological variables, PM2.5 concentrations from nearby air quality monitoring stations, and concentrations of other pollutants as inputs. This approach allows the model to learn spatiotemporal correlations within the time-series data, enhancing the accuracy of PM2.5 predictions. Additionally, the attention mechanism improves prediction accuracy by automatically weighting the past input variables based on their importance for future PM2.5 predictions. The experimental results demonstrate that our AC_GRU model outperforms state-of-the-art methods, making it a valuable tool for urban air quality management and public health protection.