Every year, a large amount of population reconciles gun-related violence all over the world. In this work, we develop a computer-based fully automated system to identify basic armaments, particularly handguns and rifles. Recent work in the field of deep learning and transfer learning has demonstrated significant progress in the areas of object detection and recognition. We have implemented YOLO V3 “You Only Look Once” object detection model by training it on our customized dataset. The training results confirm that YOLO V3 outperforms YOLO V2 and traditional convolutional neural network (CNN). Additionally, intensive GPUs or high computation resources were not required in our approach as we used transfer learning for training our model. Applying this model in our surveillance system, we can attempt to save human life and accomplish reduction in the rate of manslaughter or mass killing. Additionally, our proposed system can also be implemented in high-end surveillance and security robots to detect a weapon or unsafe assets to avoid any kind of assault or risk to human life.

As the world keeps advancing, the need for automated interconnected devices has started to gain significance; to cater to the condition, a new concept Internet of Things (IoT) has been introduced that revolves around smart devicesʼ conception. These smart devices using IoT can communicate with each other through a network to attain particular objectives, i.e., automation and intelligent decision making. IoT has enabled the users to divide their household burden with machines as these complex machines look after the environment variables and control their behavior accordingly. As evident, these machines use sensors to collect vital information, which is then the complexity analyzed at a computational node that then smartly controls these devicesʼ operational behaviors. Deep learning‐based guessing attack protection algorithms have been enhancing IoT security; however, it still has a critical challenge for the complex industries’ IoT networks. One of the crucial aspects of such systems is the need to have a significant training time for processing a large dataset from the networkʼs previous flow of data. Traditional deep learning approaches include decision trees, logistic regression, and support vector machines. However, it is essential to note that this convenience comes with a price that involves security vulnerabilities as IoT networks are prone to be interfered with by hackers who can access the sensor/communication data and later utilize it for malicious purposes. This paper presents the experimental study of cryptographic algorithms to classify the types of encryption algorithms into the asymmetric and asymmetric encryption algorithm. It presents a deep analysis of AES, DES, 3DES, RSA, and Blowfish based on timing complexity, size, encryption, and decryption performances. It has been assessed in terms of the guessing attack in real‐time deep learning complex IoT applications. The assessment has been done using the simulation approach and it has been tested the speed of encryption and decryption of the selected encryption algorithms. For each encryption and decryption, the tests executed the same encryption using the same plaintext for five separate times, and the average time is compared. The key size used for each encryption algorithm is the maximum bytes the cipher can allow. To the comparison, the average time required to compute the algorithm by the three devices is used. For the experimental test, a set of plaintexts is used in the simulation—password‐sized text and paragraph‐sized text—that achieves target fair results compared to the existing algorithms in real‐time deep learning networks for IoT applications.

This research aims to implement green walls as thermal insulators on the east and west facades of the adjacent areas of the School of Biological Sciences at Ricardo Palma University. The growth of urban cities causes an increase in CO₂ emissions and the loss of agricultural land. The methodology applied involves evaluating the thermal behavior, analyzing the local climate, and assessing plant species for implementation. As a result, the successful implementation of green walls on the east and west facades was achieved, which has proven to effectively reduce wall heating, decrease the temperature rise in nearby spaces, and minimize reliance on artificial ventilation systems. This has led to energy and cost savings, decreased CO₂ emissions, and increased oxygen production. By implementing green walls on the east facade, there has been an increase of 60.73% in comfort hours, while on the west facade, there has been an increase of 64.28% in comfort hours. In addition, a total of 977.50 kg of CO₂ has been absorbed, resulting in 722.50 kg of purified air. In conclusion, green walls have proven to be a viable solution to the problems faced in urban cities, helping to mitigate the internal temperature of indoor spaces.

The research aims to evaluate various management techniques of Ancient Hydraulic Systems (AHS) in different civilizations in arid climates, in cities located in Nasca in Perú (Puquio), Marrakech in Marruecos (Khettara) andTabriz in Irán (Qanat). The scarcity of water resources in these areas compelled the inhabitants to seek water management solutions to meet the necessary water supply for the population at the time. The methodology employed was a case study in which climatic data, supply, and operation of AHS were analyzed. The different indicators studied resulted in findings that, in the case of Nasca, the system relied on lintels, utilizing robust materials such as stone. They employed geometry to control water flow velocity, inclined walls to prevent collapses, and terraces to facilitate access to underground galleries. In the cases of Tabriz and Marrakech, their systems were based on excavations and reinforcements primarily using clay and earth as materials. In conclusion, the techniques employed in different civilizations are responses to contextual realities, offering an adaptive solution to environmental and physical challenges with a sustainable focus within their immediate surroundings.

Climate change is an imminent threat to humanity that brings significant environmental, social, and economic consequences worldwide, with population growth and deforestation among these effects. The research aims to analyze the causes and effects of climate change over the last 30 years. Various sources of information were analyzed to interpret the consequences; therefore, it is important to understand and analyze the causes and effects of climate change, generating information on temperature trends, precipitation, and glacier loss from 1990 to 2020. The evidence of the increase in the average temperature of the planet is becoming increasingly solid. The average annual temperature in the Coast region ranges from 21.1 to 22.6 °C, in the Sierra region from 12.6 to 14.4 °C, and in the Jungle region from 22.7 to 25.7 °C. Annual average precipitation in the Coast region varies from 22.3 to 174.1 mm, in the Highlands region from 570 to 834.3 mm, and in the Jungle region from 1156 to 2093 mm. The White Mountain Range has lost approximately 40.5% of its total glacier coverage on average, and between 1996 and 2019 the amount of tropical glaciers has decreased by 28.17%. It was concluded that the threats of climate change are increasingly evident, due not only to environmental pollution but also to the various human activities that generate changes in the environment.

The objective of this study is to evaluate the contamination of water resources in the Parac subbasin. The existing contamination by heavy metals due to the presence of mining companies is of great concern to the population that inhabits the area since health is being affected. A methodology has been used as an integrated approach based on pollution assessment indices. Five surface water samples and seven groundwater samples were collected in the Parac subbasin, in the vicinity of the Tamboraque tailings. Physicochemical parameters and heavy metals (Al, As, Br, Pb, Fe, Cd, Mn, Hg, B, Ni, Cu, and Se) were analyzed. The mean concentrations of Al, As, Ba, Cu, Fe, Pb, and Zn were 0.3 mg/L, 0.02 mg/L, 0.02 mg/L, 0.05 mg/L, 0.8 mg/L, 0.04 mg/L, and 0.12 mg/L respectively. HPI, HEI, and Cd indices were averaged around 1378.5, 134.5, and 126.5 respectively, HPI and Cd values were above 100 as a critical contamination index value, which indicates that the water samples are critically contaminated with respect to the heavy metals studied. The total variance was 88.11% of the water quality with two components according to the ACP analysis. Al, As, Cu, Fe, Pb, Zn, and Mn showed a high positive charge on PC1 (69.68%) while Ba and Mo had a high positive charge on PC2 (18.12%). The intensity of distribution of metals As, Fe, Mn, and Pb in surface and groundwater is an environmental concern and a more complete monitoring network is needed for the sustainable management of water resources.