This study provides a comprehensive view of the research field of construction and demolition waste (CDW) management in the circular economy based on a literature review. The increased intensity of interest is due to the need to create frameworks, mechanisms, and tools for the process of mind-shifting towards circularity. Research topics, researched life cycle stages, strategies for CDW management, sustainability assessment, building stock quantification, assessment tools and forecast methods, materials with CDW content, waste treatment solutions, and the barriers and drivers for efficient waste management in the construction industry are identified as the main concerns in the analyzed research field. The results show that a major concern in the academic field directs research to the path of innovative strategy elaboration, identifying the enablers and barriers in CDW management, computational tool creation for design and assessment, building stock modeling, and circular building material development. The environmental approach prevails, leaving economic and social assessments in CDW management uncovered. Although stakeholders’ involvement is stressed in most cases, strategies for awareness-raising and education for a sustainable circular activity in the field are lacking. The circularity of CDW management being a multifaceted and multi-disciplinary complex challenge, it is approached on different levels. This study introduces the novelty of structuring the trends of existing knowledge in a holistic view, identifying the research directions, dimensions, specific aspects, and instruments.

Wood is used as a raw material in various industries, including the production of furniture, which puts pressure on the exploitation of the forests and the continuous reduction of their surfaces, with undesirable effects on the environment. The paper provides a way of sustainably manufacturing furniture by replacing wood with composite materials based on natural fibers obtained from fast-growing renewable crops (hemp, willow, flax, etc.) and at the same time a method of assessing the forest areas which can be saved from cutting. The method’s algorithm is based on the estimation of forest area that ensures the annual consumption of wood for the production of furniture, both in the conventional production of furniture and in the unconventional one, where part of the products is made of composites. The agricultural areas required to be cultivated with technical plants to provide the natural fibers necessary for the wood replacement composite were also determined. The case study, based on the data of an upholstered furniture company, shows that replacing only part of the wood for the production of furniture can save about 3000 hectares of beech forests per year and the necessary plant fibers can be obtained from a surface area about 10 to 100 times smaller.

This paper investigates the strategic and institutional dynamics of centralized public procurement in Romania, with a specific focus on the National Office for Centralized Procurement (ONAC) and its role in enabling digital transformation across technical universities. Using a qualitative case study methodology, the research evaluates ONAC’s capacity to implement procurement policy aligned with sustainability and digital governance objectives. Drawing on official reports, legislative documents, and secondary data sources, the paper applies an analytical framework covering institutional performance, technological adaptation, and policy coherence. The findings highlight both the opportunities and challenges of centralized procurement, emphasizing systemic rigidities, underutilization of digital tools, and weak feedback mechanisms. The study contributes to the literature on strategic procurement and digital public management by offering a grounded assessment of governance capacity and sustainability alignment in a transitional EU context.

The avoidance of obstacles placed in the workspace of the robot is a problem which makes controlling them more difficult. The known avoidance methods used for the robots control are based on bypass trajectory programming or on using the sensors that detect the position of the obstacle. This paper describes a method of training industrial robots in order for them to avoid certain obstacles in the workspace. The method is based on the modelling of the robot’s kinematics by means of an artificial neural network and by including the neural model in the robot’s controller. The neural model simulates the robot’s inverse kinematics, and provides the joint coordinates, as referential values for the controller. The novelty of the method consists in the deliberately erroneous training of the network, so that, when programming a direct trajectory in the workspace, the robot avoids a known obstacle.

Change management is an important aspect of organizational development, especially in the automotive industry, where products and processes are in continuous development. This paper aims to describe the general change management process, highlighting its relevance and application in the automotive sector. It provides an analysis of how change management is implemented in this industry, addressing the challenges and dynamics specific to automotive sector. The study further proposes an improved change management process designed to improve change implementation in automotive organizations. This improved approach focuses on strategic planning, stakeholder engagement and resource optimization to facilitate smoother transitions. A good change management strategy can significantly increase the effectiveness and success rate of change initiatives in the automotive industry.

This paper presents a method for obtaining a neural model used in industrial robots control. The method refers to the forming of a small number of examples used in the training of a neural network that lead to the creation of a suitable model. This paper constitutes a development of the work [2] in order to increase the opportunities for its application in various fields. The description of the method is generally done, without relying on a specific application in the domain of industrial robots. The testing and the validation of the shown method were completed using the example of a system in which the relationship between inputs and outputs is described by means of mathematical functions. The set of learning examples, generated through the proposed method, served to the ANN training by a cross-validation technique, in case of these functions. The evaluation of the proposed method has been done by analysing the results obtained by applying it compared to those obtained with a known method, namely the uniform generation of training examples. The use of the method in the field of industrial robots’ control was illustrated by a concrete application in the case of a robot with 6 degrees of freedom.