Implementation Of Protection Measures Research Articles

Effects of embedded walls on tunnelling-induced sandy ground displacements: a numerical investigation

Urban tunnelling projects pose significant risks to the integrity of nearby structures due to ground movements induced by the excavation process. Embedded walls are commonly employed as a protective measure to mitigate these adverse effects. This paper presents a comprehensive numerical investigation into the effects of embedded walls on tunnelling-induced ground displacements, aiming to provide insights and recommendations for optimal embedded wall design. The study assesses the impact of varying embedded wall length and horizontal distance from the tunnel on soil settlement and horizontal displacements. Results demonstrate the complex interplay between embedded wall length, horizontal distance and ground movement patterns, and the highly non-linear influence of key parameters on embedded wall efficiency (i.e. its ability to reduce settlements). A preliminary design chart is proposed to guide engineers in determining the appropriate horizontal location and depth of embedded walls to effectively reduce tunnelling-induced ground displacements. The findings contribute to a better understanding of embedded wall performance in the context of tunnelling and provide valuable guidance for the practical design and implementation of protective measures in urban areas.

Hospital managers' perspectives on pregnancy policy and work adjustments: A cross-sectional study.

Risk assessment and work adjustment according to EU legislation may safeguard pregnant employees and their offspring. Knowledge on management perspectives in relation to implementation of protective measures is limited. The primary aim was to describe Danish hospital managers' engagement in pregnancy policy and work adjustment for pregnant employees. The secondary aim was to investigate how managers' characteristics and the setting affect engagement and behaviour. This was a cross-sectional study of survey data from 212 managers. Outcomes were within dimensions of health promotion, pregnancy policy, work adjustment, collaboration, manager support, and sick leave. Logistic and ordinal logistic regression models were applied to identify associations between background information and outcomes. Of the managers included, 84% arranged meetings and 76% conducted occupational risk assessment. Most managers (96%) engaged in dialogue with the employees before sick leave. Most managers felt competent in providing guidance for pregnant employees and 99% considered work adjustment important, mainly to safeguard mothers and children. The self-reported data showed positive associations between female managers and feeling competent to guide the employee. Further, management training was associated with meetings with pregnant employees. Seniority was associated with feeling competent to guide and dialogue. Midwifery support was associated with competence in guiding employees about risk factors. Work adjustment and risk assessment for pregnant employees are considered a priority by Danish hospital managers. Overall, managers feel competent guiding pregnant employees. However, managers experience midwifery support beneficial for the guidance of pregnant employees.

Assessing environmental exposure to viruses in wastewater treatment plant and swine farm scenarios with next-generation sequencing and occupational risk approaches

Occupational exposure to pathogens can pose health risks. This study investigates the viral exposure of workers in a wastewater treatment plant (WWTP) and a swine farm by analyzing aerosol and surfaces samples. Viral contamination was evaluated using quantitative polymerase chain reaction (qPCR) assays, and target enrichment sequencing (TES) was performed to identify the vertebrate viruses to which workers might be exposed. Additionally, Quantitative Microbial Risk Assessment (QMRA) was conducted to estimate the occupational risk associated with viral exposure for WWTP workers, choosing Human Adenovirus (HAdV) as the reference pathogen. In the swine farm, QMRA was performed as an extrapolation, considering a hypothetical zoonotic virus with characteristics similar to Porcine Adenovirus (PAdV). The modelled exposure routes included aerosol inhalation and oral ingestion through contaminated surfaces and hand-to-mouth contact.HAdV and PAdV were widespread viruses in the WWTP and the swine farm, respectively, by qPCR assays. TES identified human and other vertebrate viruses WWTP samples, including viruses from families such as Adenoviridae, Circoviridae, Orthoherpesviridae, Papillomaviridae, and Parvoviridae. In the swine farm, most of the identified vertebrate viruses were porcine viruses belonging to Adenoviridae, Astroviridae, Circoviridae, Herpesviridae, Papillomaviridae, Parvoviridae, Picornaviridae, and Retroviridae.QMRA analysis revealed noteworthy risks of viral infections for WWTP workers if safety measures are not taken. The probability of illness due to HAdV inhalation was higher in summer compared to winter, while the greatest risk from oral ingestion was observed in workspaces during winter. Swine farm QMRA simulation suggested a potential occupational risk in the case of exposure to a hypothetical zoonotic virus.This study provides valuable insights into WWTP and swine farm worker's occupational exposure to human and other vertebrate viruses. QMRA and NGS analyses conducted in this study will assist managers in making evidence-based decisions, facilitating the implementation of protection measures, and risk mitigation practices for workers.

Monitoring of phytophages in spring wheat agrocenoses in view of applying different agrotechnological methods in the conditions of the North-East of Kazakhstan

The article discusses issues related to the impact of agrotechnical practices (sowing time and preceding crop) on the number and species diversity of pests in spring wheat varieties of foreign and Kazakhstan breeding in the conditions of the North-East of Kazakhstan. In order to increase plant resistance to pests and identify the impact of various preceding crops the research in 2022 was conducted in agricultural organizations located in the regions of North-Eastern Kazakhstan. The spring and summer of the vegetation period of 2022 were characterized by high temperatures and a lack of moisture in the soil, impact of which is seen in the form of turgor decrease in plant organisms and their resistance to various damages. In the course of early sowing of Triso wheat, the plants were more affected at the initial stage of development by the lack of moisture in the soil and high temperatures, which identified in the form growth retardation and increased plant susceptibility to pest damage. The sowing of other varieties was carried out at the optimal time recommended by the scientific institutions of the region ― in the period of May 15‒24. This allowed the culture to develop normally and avoid the most vulnerable phases during the maximum level of summer precipitation, which is typical for North-Eastern Kazakhstan in the third decade of June. The practical significance of the work is in the possibility of effective implementation of protective measures and optimization of the agricultural technology used in accordance with the soil and climatic conditions of crop cultivation and species diversity of phytophages

Hedging effectiveness in EU energy market: A case study

The current conditions in the European Union energy market are closely linked to significant fluctuations in both demand (due to the marked reduction in economic activity across the EU region due to the implementation of protection measures against COVID-19) and supply (characterized by high volatility in electricity pricing due to irregular fluctuations in fossil fuel prices). Given these challenging conditions, energy companies are forced to adopt hedging strategies to navigate the complexities associated with the extensive market risks. This study empirically examines the impact of the implementation of hedging strategies (measured by the value of financial derivatives relative to the total assets of company values) on financial value (assessed using the Tobin Q ratio) of power generation companies operating in the EU region for 2016 - 2021. The results, derived from an econometric study using panel data analysis, revealed that a potential increase in the value of financial derivatives as a percentage of total assets held by power generation firms in the EU region positively affects the Tobin Q index and, consequently, enhance their financial value. There is no statistically significant evidence to support the relationship between the growth of the domestic forward electricity market and the Tobin's Q index.

Forecasting the phytosanitary state of agrocenoses: innovation developments

Goal. Development and implementation of new theories, methods, technologies and models that would allow more reliable forecasting of the development of pests and pathogens of agricultural crops.
 Methods. Analysis of innovative development of the Institute of Plant Protection of the National Academy of Agrarian Sciences of Ukraine and other institutions of the Scientific and Methodological Center «Protection of Plants» for 2001—2020. Isolation of those relating to the problem forecasting the development of pests and pathogens of plant diseases in agrocenoses of Ukraine.
 Results. The concept of formation of the phytosanitary state of crops and plantations under the conditions of climate change has been developed. The theory of the dynamics of the number of locusts and the prediction of emergency situations in the agricultural sphere of Ukraine has been developed. The widespread use of pheromone traps for diagnosing the phytosanitary state of agrocenoses of Ukraine is recommended. The cyclicity of outbreaks of mass reproduction of the Anisoplia austriaca beetle and the European corn borer was established, depending on the amount of solar activity. The possibility of applying GIS-technologies for the analysis of long-term data was studied. Predictive computer programs (models) of harvest shortages of rapeseed, sunflower, corn, sugar beet, and winter wheat from a complex of pests have been developed. An algorithm for the application of information technology programs for the analysis of multi-year dynamics of the phytosanitary state of agrocenoses has been developed. The role of the developed computer model of the development of leaf spots of grain crops in determining the moment for the most effective application of fungicides has been established. Methods of forecasting the development of pathogens of grape diseases in the South of Ukraine have been developed. A system of forecasting the phytosanitary state of agrocenoses of Ukraine in the conditions of the year and planning the implementation of protective measures has been developed.
 Conclusions. Innovative developments of the Scientific and Methodological Center «Plant Protection» make it possible to solve the following issues at a modern level: forecasting changes in the agrosphere based on the analysis of a long-term database of hydrothermal conditions and indicators of the phytosanitary state of agrocenoses; prevention of emergency situations in the agricultural sector on the basis of a modern monitoring system using GPS-navigation and the development of regulations for carrying out protective measures; forecasting the development of dangerous pests and diseases of agricultural crops; forecasting crop failures and determining the economic feasibility of chemical plant protection; forecasting the appearance and spread of quarantine pests.

The role of children’s emotion regulation and maternal emotion socialization in preschoolers’ behavioral difficulties during the COVID-19 lockdown

AbstractTurkey, like many countries, implemented protective measures to combat the COVID-19 pandemic. One such measure involved isolating young children to reduce virus transmission. However, this regulation presented parents with the challenging task of juggling work responsibilities while caring for their young children without the usual support from external resources. Given these difficult circumstances, young children may struggle with emotion regulation, while isolation can impede parents from effectively addressing their children’s negative emotions. This online study aims to examine the dynamic relationship between children’s emotion regulation ability and maternal emotion socialization capacity in predicting children’s behavioral difficulties. The study revealed a positive correlation between maternal punitive and minimizing emotion socialization reactions and children’s behavioral difficulties. However, this association was primarily observed in children with high negative emotionality and low effortful control. Notably, when children had low negative emotionality scores, maternal minimizing emotion socialization behaviors were negatively associated with their behavior problems. These findings support the idea that during stressful periods, such as a pandemic, the diathesis-stress model may become more applicable. It emphasizes the interaction between individuals’ vulnerabilities and environmental triggers leading to unfavorable outcomes, such as higher levels of children’s externalizing problems. These results underscore the importance of tailored intervention programs for parent-child dyads experiencing limited psychological resources during stressful events like a pandemic.

Machine learning in cybersecurity: A review of threat detection and defense mechanisms

The cybersecurity concerns get increasingly intricate as the digital world progresses. In light of the increasing complexity of cyber threats, it is imperative to develop and implement advanced and flexible security strategies. Machine Learning (ML) has become a potent tool in strengthening cybersecurity, providing the capacity to scrutinise extensive information, recognise trends, and improve threat detection and defence methods. This paper examines the significance of ML in the field of cybersecurity, with a special emphasis on the identification of threats and the implementation of protective measures. By incorporating ML algorithms into cybersecurity frameworks, organisations may automate decision-making processes, facilitating prompt responses to ever-changing threats. The initial segment explores the terrain of cyber threats, highlighting the necessity for dynamic and aggressive security methods. Conventional solutions that rely on signatures are frequently inadequate when it comes to handling sophisticated, shape-shifting attacks. ML algorithms, in contrast, have exceptional proficiency in identifying nuanced patterns and irregularities within extensive datasets, therefore offering a more efficient method of detecting potential threats. The second section delves into several ML methodologies utilised in cybersecurity, including supervised and unsupervised learning, deep learning, and reinforcement learning. Every approach is assessed based on its suitability for threat detection, demonstrating its advantages and constraints. Furthermore, the relevance of feature engineering and data pretreatment in improving machine learning models for cybersecurity applications. The versatility of ML algorithms allows them to grow with emerging threats, making them a useful tool in the ever-changing arena of cyber warfare. The final segment focuses on real-world applications of machine learning in cybersecurity, presenting successful use cases across sectors. From anomaly detection to behavior analysis, ML algorithms contribute to the discovery of dangerous activity, lowering false positives and strengthening the overall security posture. Lastly, the paper covers the obstacles and ethical issues related to the adoption of ML in cybersecurity. Issues like as adversarial assaults, skewed datasets, and the interpretability of ML models are examined, highlighting the necessity for a holistic strategy that integrates modern technology with ethical considerations. The fusion of human expertise and machine intelligence offers a formidable defense against evolving cyber threats, paving the way for a more resilient and secure digital future.

Firefly algorithm based WSN-IoT security enhancement with machine learning for intrusion detection

A Wireless Sensor Network (WSN) aided by the Internet of Things (IoT) is a collaborative system of WSN systems and IoT networks are work to exchange, gather, and handle data. The primary objective of this collaboration is to enhance data analysis and automation to facilitate improved decision-making. Securing IoT with the assistance of WSN necessitates the implementation of protective measures to confirm the safety and reliability of the interconnected WSN and IoT components. This research significantly advances the current state of the art in IoT and WSN security by synergistically harnessing the potential of machine learning and the Firefly Algorithm. The contributions of this work are twofold: firstly, the proposed FA-ML technique exhibits an exceptional capability to enhance intrusion detection accuracy within the WSN-IoT landscape. Secondly, the amalgamation of the Firefly Algorithm and machine learning introduces a novel dimension to the domain of security-oriented optimization techniques. The implications of this research resonate across various sectors, ranging from critical infrastructure protection to industrial automation and beyond, where safeguarding the integrity of interconnected systems are of paramount importance. The amalgamation of cutting-edge machine learning and bio-inspired algorithms marks a pivotal step forward in crafting robust and intelligent security measures for the evolving landscape of IoT-driven technologies. For intrusion detection in the WSN-IoT, the FA-ML method employs a support vector machine (SVM) machine model for classification with parameter tuning accomplished using a Grey Wolf Optimizer (GWO) algorithm. The experimental evaluation is simulated using NSL-KDD Dataset, revealing the remarkable enhancement of the FA-ML technique, achieving a maximum accuracy of 99.34%. In comparison, the KNN-PSO and XGBoost models achieved lower accuracies of 96.42% and 95.36%, respectively. The findings validate the potential of the FA-ML technique as an active security solution for WSN-IoT systems, harnessing the power of machine learning and the Firefly Algorithm to bolster intrusion detection capabilities.

Elaboration and Analysis of SARS-CoV-2 Data in the Frame of Occupational Safety and Health Assessment in Sustainable Engineering Systems

Facing the threat of SARS-CoV-2, several countries implemented protective measures to annihilate the waves of the pandemic. Apart from quarantine, social distancing, frequent disinfection, and the use of a face mask, vaccination against COVID-19 soon became available. The measures taken in the workplace to inhibit the spread of the virus were important, as some controversial policies emerged regarding the vaccination status of employees. The “health pass” changed the workplace environment immensely, as in many job sectors vaccination became mandatory. Unvaccinated employees were required to undergo specific COVID-19 tests to access their workplace, while other specialized workers such as health workers were removed from their work altogether. Such measures would be justified if it was certain that vaccinated people cannot transmit the virus, but over the course of years this hypothesis seems to have faded. The main aim of this study is the confirmation (or the non-validation) of this hypothesis and of the specific applied measures through the elaboration and statistical analysis of vaccination data from 35 countries in relation to their daily announced infections over the time frame from the forty-fourth week of 2021 to the fourth week of 2022. This is examined from an occupational safety and health (OSH) perspective (taking into account the three pillars of sustainability) concerning risk management and safety assessment at the workplaces of sustainable engineering systems (SES). The findings imply that this hypothesis is contestable. Therefore, it is doubtful whether workplace segregation measures were socially and economically sustainable. It is deduced that (i) the complete freedom of vaccinated employees was a situation which intensified occupational risk, degraded the safety level at the workplaces of sustainable engineering systems, and increased the OSH risk management difficulties, and, on the other hand, (ii) the financial burden of mandatory unemployment and frequent testing was not justified and economically sustainable for the unvaccinated individuals in the middle of a worldwide economic crisis.

Fire and Explosion Hazard Aspects for Bucket Elevators

Technological installations for the vertical transport of cereal products, are affected by the presence of combustible dusts that can be mixed with air, in suspension, or in the form of dust layers of different thicknesses. The presence of these combustible dusts is a constant danger of fires or even explosions. In combustible dusts are swirled in the air in concentrations in their explosive range and there is also an ignition source, the dusts can burn quickly and even give rise to explosions, in which case the force and pressure developed is considerable. Since, in most cases, following a fire and especially a combustible dust explosion, the damage caused is quantified by immeasurable material damage and even by recording human losses, special attention must be paid to how they are identified and implemented protection and prevention measures to prevent fires and combustible dust explosions. In this paper, the principles and main factors that must be considered when quantifying the risk of fire and/or explosion for a bucket elevator used for the vertical transport of cereal products are highlighted. A number of recommendations for the safe use of these lifts are also presented.

Assessment of Hepato-Renal Biomarkers in Petrol Pump Attendants: Impact of Protective Gear Usage

Background: Petrol Pump attendants (PPA) in India wear a variety of safety gear, including as overalls, masks, and gloves. It is still entirely unknown if they can effectively protect vulnerable organs like the liver and kidney. The study's objective is to examine the hepato-renal axis' biochemical characteristics in PPA employees who wore protective gear while on duty and those who did not.
 Methods: The research population was divided into three Groups. Group A consisted of 15 adults male PPAs who had consistently implemented protective measures when administering petroleum products. 32 PPA who did not wear protective gear made up Group B. A total of twenty-eight male adults who had not been exposed to petroleum products made up Group C, the control Group. For PPA selected for the research, a minimum of five years of exposure was required. Through the administration of a questionnaire, data on worker safety was gathered about the usage of self-protective gear as a standard safety procedure for personal protection. To evaluate biochemical indicators of hepato-renal functioning, serum was used. Both the Student's t test and the analysis of variance were used to find statistical differences. It was deemed significant at p 0.05.
 Results: ALP, AST, ALT, creatinine, urea, albumin, and total protein activities or levels in both Group A and Group B were substantially different from control (Group C), suggesting liver impairment.
 Conclusion: Data from this study indicate that none of the three protective gear options employed by PPA in Group B significantly decreased exposure.

Multi-faceted analysis of dust storm from satellite imagery, ground station, and model simulations, a study in China

An intense dust storm from 8 April to 11 April 2023 originated from the Gobi deserts bordering Mongolia and Inner Mongolia of China and affected most parts of northern, central, and eastern China, the Korean Peninsula, and Japan. It has a significant impact on air quality in these regions and alters the optical and microphysical properties of the aerosols. In this work, a comprehensive analysis of the storm was carried out by using reanalysis data, model simulation, satellite, and ground-based observations. The geopotential height along with the wind flow reveals that the development, transportation, and dissipation of the dust storms were closely related to the movement of the Mongolia cyclones caused by Mongolia low and its surrounding highs. The Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) data showed a two-layer dust vertical distribution, with the bottom one below 5 km and the upper one stretching from 5 to 10 km over the Japan Sea. The Hybrid Single-Particle Lagrangian Integrated Trajectory model (HYSPLIT) backward trajectory uncovered Central Mongolia being the origin of the dust storm that arrived over the Japan Sea on April 9, 2022, and the forward trajectory revealed that the dust aerosols would be further transported to the Pacific Ocean. Air quality analysis demonstrated that the regions of inner Mongolia (IMG) and Jing-Jin-Ji (JJJ) suffered the most from the dust storm events, followed by the Yangtze River Delta (YRD), while the Pearl River Delta (PRD) was hardly affected, as indicated by the regional-averaged PM10 and PM2.5. It was also found that the dust storm event has almost no significant influence on the other air quality indicators including CO, NO2, O3, and SO2. Historical record tracking revealed that the springtime averaged PM10 and PM2.5 of the year 2023 are comparable to those in 2021 which has the record-breaking dust storm case. AERONET data indicated dramatic increments of aerosol optical depth at 550 nm (AOD550) and effective radius of total size (ReffT) in regions of Gobi and JJJ, accompanied by low Angstrom Exponent (AE), and high SSA870 values, while in the regions of Korea and Japan, minor increments of AOD550 and ReffT were found. This study provides a holistic scientific view of the dust storm which could aid in the implementation of protective measures against dust storms.

Occupational safety and infection control for risk groups in the Covid-19 pandemic and the participation of employee representative bodies

Abstract The Covid-19 pandemic has posed new challenges for occupational safety and health. The protection of disabled and other vulnerable employees (risk groups) is no exception. For these groups of employees, the special hazards and risks associated with the workplace must be given special consideration. This article presents our study of company practices for dealing with risk groups, with particular reference to remote working constellations, and shows that the involvement of employee representative bodies has a positive impact on the scope and quality of protective measures. However, the low level of involvement of employee representatives in the planning and implementation of protective measures is problematic. The results are from an online survey of 1543 representatives of works and staff councils and representatives of severely disabled people in Germany.

Status, distribution, and threats of the last surviving fan mussel populations in Greece

Since the first confirmed records of mass mortality events (MME) in the Aegean Sea in 2018, Pinna nobilis populations in Greek seas have been decimated. To bolster recovery efforts, this study aimed to assess the status of fan mussel populations in the Aegean and Ionian seas and investigate potential recolonization through natural recruitment. From May 2022 to May 2023, 163 independent underwater visual surveys were conducted across various locations and depths along the Greek coastline. A total of 4348 P. nobilis individuals was recorded, of which 87.3% were found dead and 12.7% were alive. The sole surviving fan mussel populations were located in the semi-enclosed gulfs of Amvrakikos in the Ionian Sea and of Kalloni in the Aegean Sea, with estimated recent mortality rates (excluding potential poaching) of 7.7% and 6.3%, respectively. To track potential new recruitment, a network of larvae collectors was deployed in multiple locations. Additionally, an ocean circulation model (OCM) was developed to predict the export and fate of larvae from the surviving populations in the Marmara Sea towards the Aegean Sea. Beyond the MME, this study identified several other threats, which significantly endanger fan mussel survival. The findings of this study underscore the urgent need to implement protection measures and restoration actions to enhance the chances of P. nobilis survival and recovery in the Greek seas.

High Impedance Fault Detection and Location in the Nigerian 330 kV Transmission Network Using Adaptive Neuro-Fuzzy Inference System

Fundamentally, the occurrence of high impedance fault (HIF) can be established when an energy filled conductor contacts a semiconductor. The occurrence of HIF can be described to be subtle and disastrous as it results to low current signal and a signal voltage arc if undetected and without implementation of protective measures, leads to the damage of the power system equipment and in many cases fire outbreak. In this study, the occurrence of HIF on the south-south region of Nigerian 330kV transmission line was studied with the data utilized obtained from the Nigerian control center (NCC) Osogbo. The data obtained was modeled in simulink and the outcome at normal condition was obtained. The transmission line distance was split into 4 points with the current signal at each point generated in simulink and exported to the Matlab file. The data in Matlab file was split to train, test and validate at 70%, 15% and 15% respectively. The data analysis performed was sent to Adaptive Neuro-fuzzy inference system (ANFIS) with the current signal at normal condition and at 3-phase HIF was used as input data while the split distance was implemented as the target data to the model. The effectiveness of the model in detecting and locating HIF was obtained and analyzed in a comparative plot of the location distance. From the results presented, the highest error deviation for fault location with ANFIS was 10%. Hence, it was concluded that ANFIS model had a satisfactory outcome in HIF detection and as such should be utilized for the HIF detection and location.

Self-rated health impact of COVID 19 confinement on inmates in Southeastern of Europe: a qualitative study

IntroductionThe COVID-19 pandemic necessitated the implementation of various measures within closed institutions like prisons to control the spread of the virus. Analyzing the impact of these measures on the health of inmates is crucial from a public health perspective. This study aimed to explore inmates’ subjective perception of the COVID-19 lockdown, the implemented measures, their physical self-perception, and their views on the vaccination process.MethodBetween April 2021 and January 2022, 27 semi-structured individual interviews and 1 focus group were conducted with inmates in a prison located in northwest Spain. The interviews were conducted in person and audio-recorded. Thematic content analysis was employed, utilizing methodological triangulation to enhance the coherence and rigor of the results.ResultsThe analysis revealed two main themes and nine subthemes. The first theme focused on inmates’ perception of the implementation of protective measures against COVID-19 within the prison and its impact on their well-being. The second theme explored the pandemic’s emotional impact on inmates. All participants reported negative consequences on their health resulting from the measures implemented by the institution to contain the pandemic. However, they acknowledged that measures like lockdowns and mass vaccination helped mitigate the spread of the virus within the prison, contrary to initial expectations.ConclusionCOVID-19 and related measures have directly affected the health of inmates. To improve their health and minimize the impact of pandemic-induced changes, community participation and empowerment of individuals are essential tools, particularly within closed institutions such as prisons.

Vulnerable Integrity

This paper presents a social-theologically informed interpretation of the term integrity, as it occurs in fundamental law. It explores the manifestations of integrity violations and proceeds to draw an inference: an integrity violation can directly emanate from a misconception regarding integrity itself, as well as the implementation of protective measures that follow it. Integrity in its wholeness dimension is understood as open-endedness and non-seclusion rather than as a substantial, clearly definable characteristic of a person. This open-endedness and non-seclusion results from the relational constitution of an individual. Consequently, it follows that a violation of integrity occurs when the open-endedness and non-seclusion of a person and their relational Becoming is hindered. The new definition of integrity is particularly important when it comes to the governance of new health technologies, especially Digital Twins that can become representatives of a person. Human integrity is non-violated only when it is understood as open to relational Becoming and this Becoming shows its expression in the mutual enabling and support of self-articulation.

The Importance of Enterococci in the Monitoring of Fecal Pollution in River Water in Forests and Urban Areas

The aim of this study was to determine the spatial distribution of enterococci as indicators of fecal pollution in river water in forest reserves and urban areas. The biodiversity, multidrug resistance, and virulence of enterococci were monitored using conventional and molecular methods, including fluorescence in situ hybridization (FISH). Enterococcal (ENT) counts determined via the FISH method were several thousand times higher than those determined by the culture-based method. This observation points to the abundance of viable but non-culturable (VBNC) bacteria that are often more toxic. Water from the river source was characterized by the lowest number of multidrug-resistant and virulent enterococcal strains. The abundance of the analyzed bacteria was highest (more than 50% of the strains) in the area where treated wastewater was evacuated to the river. Statistical analysis confirmed the presence of relationships between these environments. Pathogenic enterococci, which are not effectively eliminated during wastewater treatment, spread in the river continuum, thus posing a health threat to humans and animals. A combination of conventional and molecular techniques for the identification of bacteria supports a rapid and reliable assessment of pollution sources in the examined environment and the implementation of protective measures.

Effects of Wind Erosion Control Measures on Vegetation Dynamics and Soil-Surface Materials through Field Observations and Vegetation Indices in Arid Areas, Southeastern Tunisia

Effective land management in the Djeffara plain, southeastern Tunisia, is being constrained by increasing land degradation issues due to arid climate conditions and soil erosion. Thus, this study aims to assess the impact of the integrated control measures, namely windbreaks and controlled grazing, on the restoration of land cover dynamics in six managed rangeland areas. Land cover changes were monitored using satellite data and the derived vegetation indices (the normalized difference vegetation index (NDVI) and the soil-adjusted vegetation index (SAVI)) from Landsat 8 (OLI), both within and outside the protected areas. The findings reveal that the implemented protection measures lead to an increase in vegetation cover, diversity, and plant density. They play an important role in stabilizing the upper soil layer. The oldest protected areas, particularly those that are well-maintained with controlled seasonal grazing, experienced a reduction in sand movement. The reintroduction of grazing should, however, be controlled to prevent degradation risks. The results show strong correlations between vegetation cover and both calculated vegetation indices, (0.73 < R2 < 0.91), with more accurate estimating for the SAVI. The findings of this research can guide decision-makers for restoring degraded rangelands and planning effective control measures for wind erosion.