In the era of an accelerated process of digitalization, electronic communications have become the backbone of modern society, supporting economic and social interaction on a global scale. In this context, cryptography emerges not only as a technological tool but as a fundamental element of security architecture. This article aims at exploring relevant cryptographic strategies that can be applied in the field of modern communications, analyzing both their role in preventing security breaches and their contribution to recovery and trust restoration processes following cyber incidents.

Reducing losses in the plastic injection molding process is an essential objective for maximizing efficiency of entire process. This can be achieved either through experimental methods (e.g., Design of Experiments – DOE) or through simulation methods and direct iterations in production. In the absence of access to advanced simulation software, optimization can be carried out through careful planning and rigorous execution of injection trials, analysis of the obtained results, and implementation of adjustments based on the process engineer’s technological knowledge. Thus, optimization becomes not only a technical activity but also a strategic component of the industrialization and production launch process.

Effective inventory management in multi-echelon supply chains is challenged by stochastic demand and uncertain lead times, which amplify variability and increase operational costs. This study presents a Markov chain framework for modeling, analyzing, and optimizing multi-echelon inventory systems under stochastic lead-time conditions. The framework represents inventory levels and lead-time states as a probabilistic transition system, enabling computation of steady-state distributions, stockout probabilities, and transient recovery times. Analytical results are validated through Monte Carlo simulations, demonstrating high fidelity between theoretical and empirical distributions. Numerical experiments quantify the impact of lead-time variability on inventory performance, revealing nonlinear increases in stockout probability and total system cost as lead-time variance grows. Multi-echelon analyses demonstrate the emergence of the bullwhip effect and highlight the effectiveness of information sharing in mitigating variability propagation across echelons. Comparative benchmarking against deep reinforcement learning (DRL) policies shows that while DRL achieves marginally lower total costs, the Markov-based approach provides superior interpretability, robustness, and computational efficiency. The study offers theoretical contributions by unifying stochastic multi-echelon dynamics and transient analysis within a tractable Markov framework. Managerially, it provides actionable insights on lead-time variance reduction, cross-echelon visibility, and hybrid analytical–learning policy design. The framework establishes a foundation for resilient and cost-effective inventory control in complex, uncertain supply-chain networks.

Historic buildings represent an ensemble of related to architecture, materials and technology of the era in which they were built and contribute to the knowledge of contemporary ones. Materials resulting from the demolition of old buildings, such as: bricks, wood and vegetal materials, concrete, gravel, stone and sand, masonry and rubble, metals, plastic, glass, drywall, cardboard and paper, sanitary fixtures, fibers (asbestos, glass, steel, natural), can get a new life, becoming part of a new building, streets, facades or even a city, in the context of circular architecture and the Smart City concept. The case of the specimens based on polyurethane waste will be discussed in this paper, as partial substitutes in the composition of new building structures. Also, methods for characterization and testing of these new compositions will be discussed and evaluated.

The expansion of the manufacturing processes network requires algorithms that can enable better planning and optimization of the manufacturing processes. Therefore, in the recent years the developments within the machine-learning (ML) and artificial intelligence (AI) have led to a new terminology, the so-called Industry 4.0. The fastest growth of Industry 4.0 has been encountered in the manufacturing, supply chain, services and products. The machine learning is prone to enable the development of smart supply-chains and manufacturing processes. The present research concerns the suitability and efficiency of the machine learning algorithm for the enhanced supply chain in manufacturing processes. The results show that the machine learning algorithm enables and enhances the efficiency of the manufacturing processes by clustering the machine-tools and increasing the number of manufactured components at the same tool location.

Supply chain risk management plays a critical role in the any business or industry environments, and it enables a good coordination of the input and outputs parameters that may affect the smooth processes development such as a manufacturing process for example. However, the supply chain risk management is often prone to the impact of various uncertainties associated with supply chain disruptions caused by meteorological, pandemic, resources shortage, etc. Therefore, one way to quantify these uncertainties are the stochastic modeling approaches of supply chain management. The stochastic modeling is a powerful tool that can predict with certain probability the events that may occur within the supply chain such as that associated with manufacturing processes. In the present research a stochastic model, based on probability theory, is developed and proposed for the analysis of supply chain risk management, for manufacturing processes. Therefore, the studies are performed to investigate the impact of the number of manufacturing processes on the supply chain proper evolution. The current study shows that the increase of the number of the manufacturing processes results in an increase of uncertainty in the supply chain management and thus, it increases the probability of supply chain disruption occurrences, within the supply chain. Therefore, it is recommended that a supply chain should contain a minimum number of manufacturing process, if the delivery time and final product allows.

The paper presents the composition of an asphalt mixture (asphalt concrete) for the base layer composed of a bituminous mixture containing aggregates derived from recycled and sorted recycled glass and recycled shredded plastic to be used in the road infrastructure. The use of this recipe has several important advantages for environmental protection, thus reducing the existing recyclable glass and plastic landfills in our country and saving local resources. The results indicate that the mixture of asphalt with recycled glass and plastic aggregates can be a valuable resource for using in asphalt mixtures and satisfactory performance has been achieved that meet the requirements within national regulations.

Today’s business environment demands production systems that are easy to use, flexible, yield great output, require little to no maintenance and provide great return of investments in short periods of time. This work focuses on providing a solution for a linear guide that is economical, light weight, precise and maintenance free. The guide comes within the scope of developing an automated linear axis that may be used in a heavily air borne contaminated environment.