Increasing the Quality of Life of the Prison Officers and their Families by Recovering their Work Capacity

The prison officers belong to a slightly known category, despite the transparency ensured after 1989 both by strengthening the relationship with the media institutions and by organizing the "open days" activities. They work in a closed environment between 8 and 12 hours a day, inconstant interaction with a relatively stable population the size of the administrative-territorial unit of a village type. The working schedule is on the one hand routine, on the other hand impacted by unforeseen situations, from those of major risk to those related to the management of the relationship and communication issues within the groups formed in the detention rooms. The mission is twofold, one side consisting in actions taken to maintain the security and safety of the community and the place of detention, and the other side represent the contribution to the education / re-education of convicts by influencing them, both directly and indirectly. The prison staff is seen as a teaching tool itself, their example being the trigger factor of the prisoners’ desire to change. Taking in consideration the numerical ratio between staff and prisoners, the situations in which one can ‘abdicate’ from the mission are excluded, so that the efficiency of the activity is conditioned by the maintenance of a continuous state of alert. After the working schedule, the prison officers continue their personal life, ensuring adequate emotional support for family members, participating in activities of the group of friends, etc., without projecting the accumulated stress from the workplace. But, intentionally or not, the transfer occurs, so that the effects of working in a closed environment marked by tensions and negative emotional charge are also felt by those close to them. Through their work, the prison officers contribute to the increasing of the quality of life to the community they belong to and the quality of life in detention (ensuring community security, individual and group safety, supporting the educational/re-educational process, ensuring somato-psycho-emotional health, etc.).Starting from this reality, through an opinion poll, we identified some of the needs to recover the work capacity, a necessary step to ensure a quality life for prison staff.

A Review on Self-Cleaning Coatings for Tunnels

Self-cleaning coatings for tunnels can effectively remove dust and stains accumulated over the surface of tunnel linings and their appurtenances due to the closed environment and poor ventilation. This paper systematically introduces the current research status of self-cleaning coatings for tunnels, focusing on the development of super-hydrophobic self-cleaning coatings, superamphiphobic self-cleaning coatings, exhaust gas degradation coatings, fire retardant coatings, and tunnel de-icing coatings. The advantages and disadvantages of the five functional coatings are then briefly described, and the problems of self-cleaning coatings for tunnels at the present stage are pointed out. Finally, the development direction of self-cleaning coatings for tunnels is proposed to provide a reference for the research and application of self-cleaning coatings for tunnels.

Facemask Global Challenges: The Case of Effective Synthesis, Utilization, and Environmental Sustainability

Coronavirus disease (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has caused a rapidly spreading pandemic and is severely threatening public health globally. The human-to-human transmission route of SARS-CoV-2 is now well established. The reported clinical observations and symptoms of this infection in humans appear in the range between being asymptomatic and severe pneumonia. The virus can be transmitted through aerosols and droplets that are released into the air by a carrier, especially when the person coughs, sneezes, or talks forcefully in a closed environment. As the disease progresses, the use and handling of contaminated personal protective equipment and facemasks have become major issues with significant environmental risks. Therefore, providing an effective method for treating used/contaminated facemasks is crucial. In this paper, we review the environmental challenges and risks associated with the surge in facemask production. We also discuss facemasks and their materials as sources of microplastics and how disposal procedures can potentially lead to the contamination of water resources. We herein review the potential of developing nanomaterial-based antiviral and self-cleaning facemasks. This review discusses these challenges and concludes that the use of sustainable and alternative facemask materials is a promising and viable solution. In this context, it has become essential to address the emerging challenges by developing a new class of facemasks that are effective against the virus, while being biodegradable and sustainable. This paper represents the potentials of natural and/or biodegradable polymers for manufacturing facemasks, such as wood-based polymers, chitosan, and other biodegradable synthetic polymers for achieving sustainability goals during and after pandemics.

CBD: A Deep-Learning-Based Scheme for Encrypted Traffic Classification with a General Pre-Training Method

With the rapid increase in encrypted traffic in the network environment and the increasing proportion of encrypted traffic, the study of encrypted traffic classification has become increasingly important as a part of traffic analysis. At present, in a closed environment, the classification of encrypted traffic has been fully studied, but these classification models are often only for labeled data and difficult to apply in real environments. To solve these problems, we propose a transferable model called CBD with generalization abilities for encrypted traffic classification in real environments. The overall structure of CBD can be generally described as a of one-dimension CNN and the encoder of Transformer. The model can be pre-trained with unlabeled data to understand the basic characteristics of encrypted traffic data, and be transferred to other datasets to complete the classification of encrypted traffic from the packet level and the flow level. The performance of the proposed model was evaluated on a public dataset. The results showed that the performance of the CBD model was better than the baseline methods, and the pre-training method can improve the classification ability of the model.

Detection of Username Enumeration Attack on SSH Protocol: Machine Learning Approach

Over the last two decades (2000–2020), the Internet has rapidly evolved, resulting in symmetrical and asymmetrical Internet consumption patterns and billions of users worldwide. With the immense rise of the Internet, attacks and malicious behaviors pose a huge threat to our computing environment. Brute-force attack is among the most prominent and commonly used attacks, achieved out using password-attack tools, a wordlist dictionary, and a usernames list—obtained through a so-called an enumeration attack. In this paper, we investigate username enumeration attack detection on SSH protocol by using machine-learning classifiers. We apply four asymmetrical classifiers on our generated dataset collected from a closed-environment network to build machine-learning-based models for attack detection. The use of several machine-learners offers a wider investigation spectrum of the classifiers’ ability in attack detection. Additionally, we investigate how beneficial it is to include or exclude network ports information as features-set in the process of learning. We evaluated and compared the performances of machine-learning models for both cases. The models used are k-nearest neighbor (K-NN), naïve Bayes (NB), random forest (RF) and decision tree (DT) with and without ports information. Our results show that machine-learning approaches to detect SSH username enumeration attacks were quite successful, with KNN having an accuracy of 99.93%, NB 95.70%, RF 99.92%, and DT 99.88%. Furthermore, the results improve when using ports information.

The effect of saturation Methods on natural gypsum rocks

When the gypsum rock is exposed to the relative humidity in a closed environment for a continuous period, the result is an increase in the weight of these rocks after the passage of the first 24 hours, and there is no effect on the percentage of gypsum that the rocks contain, as the weight increase occurred at the same time for the different gypsum ratios. For rocks submerged in potable water and located within a humid environment, most of these rocks witnessed weight gain, with some exhibiting dissolving behavior. As for the gypsum rocks submerged with water saturated with aqueous calcium sulfate salt, they witnessed weight gain without any solubility. It is worth noting the important effect that relative humidity plays in the atmosphere at high levels, as it stimulated the saturation property to cover and overcome the famous property of gypsum rocks, which is the melting at high humidity.

CHARACTERIZATION OF AIRBORNE PARTICULATE MATTER TO ASSESS THE IMPACT ON DEGRADATION OF CULTURAL HERITAGE: THE TROPAEUM TRAIANI MONUMENT

The present paper is focused on the microclimatic investigation and weather-climatic phenomena matrix assessment, which can be generated for heritage objectives at different spatial and temporal resolutions, correlated with physicochemical analysis of the particulate matter (PM2.5-10). In the literature the importance of atmospheric PM monitoring in the proximity of monuments is not yet sufficiently highlighted. In this respect, the microclimatic investigation of the Tropaeum Traiani Monument (Adamclisi, Romania) was performed to assess the suitability of a closed environment, located outdoors, according to the conservation requirements of heritage materials. The monitoring campaigns (four seasons, e.g., from summer of the year 2018 to spring of the year 2019) were carried out by non-invasive measuring equipment. The collected data were used to investigate the hygrothermal and chemical behavior inside and outside of Tropaeum Traiani Monument, built in 109, to assess the risks on the oldest structural material. Principal component analysis (PCA) was performed by IBM SPSS Statistics software to assess the similarities between the microclimatic parameters.