Industrial Objects Research Articles

6D Pose Estimation of Industrial Parts Based on Point Cloud Geometric Information Prediction for Robotic Grasping

In industrial robotic arm gripping operations within disordered environments, the loss of physical information on the object’s surface is often caused by changes such as varying lighting conditions, weak surface textures, and sensor noise. This leads to inaccurate object detection and pose estimation information. A method for industrial object pose estimation using point cloud data is proposed to improve pose estimation accuracy. During the feature extraction process, both global and local information are captured by integrating the appearance features of RGB images with the geometric features of point clouds. Integrating semantic information with instance features effectively distinguishes instances of similar objects. The fusion of depth information and RGB color channels enriches spatial context and structure. A cross-entropy loss function is employed for multi-class target classification, and a discriminative loss function enables instance segmentation. A novel point cloud registration method is also introduced to address re-projection errors when mapping 3D keypoints to 2D planes. This method utilizes 3D geometric information, extracting edge features using point cloud curvature and normal vectors, and registers them with models to obtain accurate pose information. Experimental results demonstrate that the proposed method is effective and superior on the LineMod and YCB-Video datasets. Finally, objects are grasped by deploying a robotic arm on the grasping platform.

Improved growth and immunity in Nile tilapia Oreochromis niloticus fed a fermented rice bran supplement

Improvement of growth performance and disease resistance of cultured species is an important objective of the aquaculture industry. In this study, solid-state fermentation (SSF) was applied for increasing the nutritional value of rice bran with baker's yeast (Saccharomyces cerevisiae). Four diets containing different levels of fermented rice bran (FRB) at 0, 100, 200 and 300 g/kg (FRB0, FRB10, FRB20 and FRB30, respectively) were tested using juvenile Nile tilapia Oreochromis niloticus (average body weight = 5.22±0.02 g) for 56 days. Compared to FRB0, all diets improved growth performance of the experimental fish (P<0.05). Intestinal amylase and protease amounts were significantly increased (P<0.05). The experimental fish were intraperitoneally injected with Streptococcus agalactiae and the cumulative mortality rate was monitored for 10 days. All FRB-supplemented diets resulted in greater survival rates in challenge fish. The FRB20 and FRB30 diets promoted expression of insulin-like growth factor I (IGF-I) transcripts and enhanced non-specific immunity; lysozyme and antioxidant enzyme activities; myeloperoxidase (MPO), catalase (CAT), and superoxide dismutase (SOD) (P<0.05). The expression level of interleukin 8 (IL-8) was down-regulated in fish fed FRB20 and FRB30 (P<0.05) but IL-10 was up-regulated in fish fed FRB10 and FRB30 (P<0.05) while IL-1β was up-regulated in fish fed FRB20 (P<0.05). The expression of complementary 3 (C3) transcripts was significantly increased while nuclear factor-kappa B (NF-κB) was decreased in fish fed all FRB-supplemented diets (P<0.05). Conventional histology revealed increased villus height following FRB30 treatment (P<0.05). These results suggest the beneficial use of FRB supplementation on growth, immune defense and stress tolerance for juvenile O. niloticus.

FORMATION OF INVESTMENT IDEAS IN THE CONDITIONS OF CHANGE IN TECHNOLOGICAL STRUCTURES OF THE ECONOMY

In the interests of developing theoretical and methodological foundations for the reproduction of fixed capital in industrial economic systems, the methodology for forming an investment idea as the first and most important stage of the investment and construction and life cycle of industrial real estate objects is studied. The existing methodological approach is based on marketing research of the goods and services market, the search for unsatisfied demand, excess material, labor and financial resources and does not take into account the patterns of development and change in technological structures in the world economy, which determine the terms of effective use of fixed capital in industry. Reproduction of fixed capital in industry, as is known, is carried out in the form of capital investments - investments in fixed capital. These are the costs of expanded reproduction of fixed capital in the form of construction of new industrial facilities, their expansion, reconstruction and technical re-equipment and the costs of simple reproduction of fixed capital in the form of major and current repairs of industrial facilities, including the costs of design and survey work, production of construction and installation works, acquisition of machines, equipment and other costs. Considering that fixed capital in the economic systems of industry dominates over circulating capital in terms of investment volume, and the terms of its effective use determine the return and profitability of costs for expanded and simple reproduction of fixed capital in industry, this study proposes to base the methodological approach to the formation of an investment plan on the patterns of evolution and change of technological structures of the economy that determine the development of industry.

Design of China's Modern Parks in Post-industrial Territories: Project Approaches in the Processing of Industrial Heritage

The transformation of industrial heritage is an essential issue in the urban development process in China. Currently, most of China’s industrial heritage has undergone numerous changes. Such territories, where industrial buildings are stored, have undergone adaptive changes for recreational use. Therefore, the purpose of the research was to establish project approaches to the processing of industrial heritage and the formation of modern Chinese parks in post-industrial territories. Research methods. Achieving the goal of this work involves studying the object and identifying various types of connections that exist in it, which determines the use of a systemic approach. In the process of conducting the research, both the comparative-historical method and the morphological and compositional methods were used. Their inclusion in the research procedure made it possible to consider the park environment as a complex compositional structure characterised by the interrelationship between functional and morphological solutions and the artistic characteristics of park environment design. Scientific novelty. For the first time, the work systematises material on the usage of industrial heritage objects in the design of modern parks created on post-industrial territories. Project approaches to its development have been established. Conclusions. The contem porary approach in the processes of renewal of industrial heritage is characterised by the transition from only adaptive reuse to the creation of urban cultural landscapes. This is due to combining the original industrial texture with the new design, which forms a landscape with unique characteristics. Scale is the most characteristic feature of industrial heritage. This scale marks industrial buildings and individual equipment that characterises a particular territory. The following project approaches have been established in the processing of buildings: exposing the frameworks of industrial buildings while preserving their authentic appearance, when the brutal textures of building materials of industrial buildings act as carriers of memory; another approach – frame structures in the environment of the park landscape are solved as expressive accents thanks to bright painting, lighting, wrapping with a glass shell. Also, an expressive object of industrial heritage is the equipment that ensured the production process in each production. Depending on the type of this equipment, several approaches are distinguished in its processing: large-sized objects retain their original appearance, are brightly painted, and play the role of visual anchors in the park environment, marking the identity of the area; as for small individual elements of production equipment, they become art objects, organising specific areas of the park.

Dietary Oregano Oil Supplementation Improved Egg Quality by Altering Cecal Microbiota Function in Laying Hens.

Improving egg quality is one of the main objectives of the poultry industry. The objective of this study was to investigate the effects of dietary oregano oil supplementation with different concentrations on the egg quality, intestinal morphology and cecal microbiome of late-phase laying hens. A total of 300 55-week-old Snowy white laying hens were randomly divided into five groups and fed a basal diet (control) or basal diets supplemented with oregano oil at 25, 50, 75 and 100 mg/kg (O25, O50, O75 and O100) over a 100-day period. Compared with the control group, eggshell thickness and eggshell weight were increased significantly in eggs when oregano oil was added (p < 0.05). The highest content of polyunsaturated fatty acids (PUFAs) and C18:2n6c was observed in yolks with 25 mg/kg supplement among 5 groups (p < 0.05). The highest average content of riboflavin in egg yolk and thiamine in egg white was observed in the O25 group (p < 0.05). When the supplemental levels of oregano oil were 25 mg/kg and 50 mg/kg, the selenium and phosphorus content in the egg was significantly increased (p < 0.05). The ratio of villus height to crypt depth (V/C) and villus height of the small intestine was significantly increased compared with the chickens fed the basal diet (p < 0.05). Compared with the control group, the abundance of Megamonas was increased in the O50 group (p < 0.05). The unclassified_o__Bacteroidales in the O25 group were significantly higher than those in the other four groups (p < 0.05). These differential microorganisms were significantly correlated with egg quality characteristics. Dietary supplementation of oregano oil can significantly improve egg quality via gut microbiota alteration in laying hens.

Targeting Full-Hydrogen Operation on Industrial-Scale Gas Turbines: Impact of Unconventional Fuels on Turbine Module Performance and Aeromechanics

Abstract With the trend to full decarbonization, a full hydrogen economy development is a key industrial objective. Gas turbines, currently one of the cleanest fossil fuel-based power generation solutions, provide reliable and on-demand power. The introduction of hydrogen into the fuel mix of existing gas turbines represents a solution with great potential to provide low-carbon or even carbon-free energy. High-hydrogen content fuels, however, challenge the efficient operation of the gas turbine expander, as crucial aspects such as increased flow rate, different gas properties and temperature operating range may affect performance and structural integrity. To evaluate the impact of this conversion, a numerical investigation of five cases with increasing percentages of hydrogen in the fuel was carried out for an industrial four stages gas turbine module. The variation of turbine inlet temperature distribution from combustion chamber was considered, to assess the impact of the unconventional fuel on the well-known aeromechanical behavior of the last stage blades. Variations in capacity, efficiency, power and potential limits due to aeroelasticity were evaluated, identifying the most relevant differences with respect to the full methane case. All these analyses confirm the possibility to employ high-hydrogen fuel operation in a current gas turbine without the need of a further redesign, while maintaining acceptable performance levels.

Digital Twin Used in Real-Time Monitoring of Operations Performed on CNC Technological Equipment

This article presents the development and implementation of a real-time monitoring solution designed for CNC machines, specifically applied to 150 industrial printing machines, leveraging Digital Twin (DT) technology. The system integrates an SQL database with Android and .NET interfaces, ensuring seamless data synchronization across all machines and optimizing production processes. The real-time monitoring enables immediate reflection of operational changes, enhancing predictive maintenance and reducing machine downtime. A notable feature of the system is its 1 s average data synchronization rate per machine, managing 150 resources distributed over a 10,000 mp area. This fast synchronization improves workflow coordination, reducing production time by approximately 10%, and minimizing operator delays caused by material issues, machine malfunctions, or product defects. The integration of advanced analytics further supports real-time decision-making, predictive maintenance, and performance optimization, aligning the solution with the objectives of Industry 4.0 and Industry 5.0 initiatives. This version reflects the specific results of the research, including the 1 s synchronization rate, the 10% reduction in production time, and the scalability of the system for 150 resources.

Protection of Nuclear Power Plants from Fraudulent Items

At present a lot of problems with fraudulent items occurred. They disrupt the safety of industry, power engineering and other objects that are important for the life of human society. The article gives infor-mation on causes of occurrence of fraudulent items in nuclear power plants and shows that protection against fraudulent items needs to perform during the whole supply chain of critical items. In detail, it concentrates to the quality of awarding terms of references, which nuclear power plant operator prepares when it gives order to commercial supplier and of process of take-over control of the deliver commodity. Due to national security, the safety of nuclear facilities is also supervised by national nuclear regulatory body. During our research we processed checklists by which national nuclear regulatory body can reveal risks in awarding the contracts to commercial suppliers and in take-over control, and ensures improve-ment of these processes.

Adaptive practices of working with industrial heritage in a historical settlement

Examples of industrial heritage located in a historical urban environment that have partially or completely lost their original function are considered. An analysis of world and domestic practices in working with similar objects is carried out, the possibility of their adaptation to use in the conditions of the historical settlement of the city of Samara is assessed. Based on the conducted research, a number of techniques have been identified that make it possible to competently adapt industrial facilities and industrial territories to modern use, while revealing their architectural, construction, historical and cultural potential. The above practices of working with industrial heritage objects can be applied when developing a strategy for the renovation of partially used or lost their original function historical objects of industrial architecture in the historical settlement of Samara and in a number of historical settlements with similar characteristics.

Industrial Metaverse-Based Intelligent PID Optimal Tuning System for Complex Industrial Processes.

In this article, the method of dynamic performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes is proposed. This method combines the digital twin model of the PID control process based on system identification and adaptive deep learning and the PID tuning intelligent algorithm based on reinforcement learning with virtual reality and immersive interaction of industrial metaverse. An industrial metaverse-based intelligent PID tuning system is proposed by combining the above method with the end-edge-cloud collaboration technology of Industrial Internet. The challenging problem that the actual operating PID control system in complex industrial processes cannot be optimized online is solved. Using the energy-intensive equipment, the fused magnesium furnace, as an industrial object, we conducted comparative simulation experiments between the proposed control method and several advanced control methods, as well as industrial experiments for the proposed intelligent system. Simulation experiments demonstrate the effectiveness of the proposed control method. The industrial experimental results indicate that the performance monitoring and adaptive self-tuning of parameters for actual PID control systems of industrial processes in virtual reality scenes can be realized, which achieves excellent control effects.

Design and Optimization of an Origami Gripper for Versatile Grasping and Manipulation

Robotic grasping and manipulation demand the ability to handle a multitude of objects with different shapes, sizes, quantities, surface smoothness, vulnerability, and stiffness, which is challenging without prior knowledge about object properties. Herein, a novel origami‐inspired gripper for universal grasping is presented. The innovative structure seamlessly transforms a simple uniaxial pulling motion into a flexible and robust envelope or pinch grasp, enabling it to tackle various scenarios. The origami gripper offers distinctive advantages, including scalable and optimizable design, grasping compliance and robustness, providing material flexibility, and providing solutions to challenging manipulation tasks. The working principles of the origami gripper are characterized and analyzed. An optimization‐based inverse design method is presented to adjust gripper properties for various scenarios. Through comprehensive experimentation and evaluation, the gripper's capabilities to grasp various objects with a wide range of distinctive properties, including ultrasoft, slippery, granular, and multiple objects, which is a challenge for the existing robotic grippers, are demonstrated. The research holds promise for transformative applications in areas such as the food industry, waste handling, fine and fragile objects grasping, and environmental sampling.

Integration of geoelectric methods and remote sensing in assessing the environment state in the area of impact of ecologically hazardous objects

Relevance. The need for comprehensive assessment of environmental components in the zones of ecologically hazardous industrial facilities impact, which enables to operatively identify the spatial structure of technogenic pollution. Aim. Chemical pollution assessment of the geological environment in the zones of industrial objects impact, based on the combined use of remote sensing and ground-based geoelectric methods. Objects. Sources of pollution during the production of mineral fertilizers (phosphogypsum dumps, sulfur storage). Methods. The author has developed a complex of methods, consisting of multispectral space imaging (Sentinel-2 satellite images) and ground-based geoelectric researches: water surface resistivity measurements, natural electric field potential imaging, vertical electrical sounding. According to satellite image data (vegetation indices NDVI, NBR, SWVI), technogenic geosystems modifications are distinguished, characterizing different levels of chemical pollution (TM-1, TM-2 and natural geosystem). Within the modification zones, geoelectrical studies are carried out to assess the depth of contamination. Results. In the zone of phosphogypsum dumps impact, two technogenic modifications were distinguished, with a total area of 83.3 hectares, characterized by a statistically significant decrease in the values of vegetation indices compared to the average level. Based on the results of vertical electrical sounding, it was established that the depth of contamination in the TM-1 zone reaches up to 20 m, in the TM-2 zone – up to 2.5 m. The migration of contaminated substances from the dumps occurs with surface drain. In the zone of impact of the sulfur storage, the area of pollution, specified with vegetation indices, covers two hectares. According to the results of the natural electric field method and vertical electrical sounding, the flow of pollution from the sulfur storage site penetrates into groundwater, which is discharged in the TM-1 zone, causing the death of vegetation. In the TM-2 zone, contamination is registered only in the ground aquifer. The developed set of methods enables to quickly and relatively comprehensively diagnose the state of the undergoing technogenic impacts geological environment.

Leveraging genetics to support forensic toxicology analysis: Demonstrating concordance among marijuana samples

Cannabis sativa is an important plant for industrial purposes. Indeed, it is legal to cultivate and supply authorized low level Δ9-tetrahydrocannabinol (THC) cannabis plants for fiber and seeds (i.e., 0.2 % according to the European Union regulation on drugs). Today, chromatography/mass spectrometry-based procedures are successfully applied to determine THC concentration. Unfortunately, when cannabis samples have been degraded because wrongfully stored, toxicological results were affected, altering the final status of legal sample or illegal sample. Rather, genetic identification could give more information about the identity of these plants. For this reason, a degradation study was run to support how genetics can help to detect concordance in cannabis highly-degraded samples. Forty cannabis sub-samples were stored (for one week, two weeks, one month and two months) in auto-sealing bag to assess the effects of a wrong cannabis storage on weight reduction, genomic DNA changes, and corresponding STR profiles. Once respective time-period elapsed, each sub-sample’s weight was measured, the percentage weight loss calculated, and the genomic DNA was extracted and quantified, obtaining a complete STR profile for all samples. Furthermore, the aim of this study was to assess the same 13-loci short tandem repeat (STR) multiplex system used for the degradation experiment and evaluate the power of such markers in the identification and discrimination of highly degraded cannabis samples coming from real cases. The results of this study demonstrated that the 13-loci STR multiplex system successfully achieved the objective both for industry and forensic purposes. For each sample, all 13 loci were amplified, and degraded samples were correctly identified, suggesting that genetic typification could be a useful tool. The proposed procedure could be parallelly applied to toxicology analysis to detect if vegetable sample become from authorized plant, to help courts track back illegal samples or to achieve illegal cannabis genetic profiles for further comparison.

Surface insight: Leveraging high-density dataset fusion for enhanced roughness classification

The ability to assess the surface quality quickly and accurately is of immense importance in manufacturing system. Modern metrology system along with machine learning is great at classification but requires more time. Traditionally accessing surface roughness is a time-consuming process. The progress in manufacturing technology necessitates improved approaches for quality control, specifically in the categorization of surface roughness, which has a substantial impact on the performance of materials. This research study introduces a novel method for classifying surface roughness by combining image data and point cloud data to create a comprehensive model. It then compares the performance of this model with a model that just relies on image data. A comprehensive analysis is conducted in this study, where image and point cloud data is collected and analysed. Multilinear principal component analysis (MPCA) along with random forest classifier is employed to create a model that classifies the surface texture. The primary goal is to showcase the enhanced precision and comprehensive understanding offered by the fused data model compared to the model that solely relies on images.Furthermore, the work presents a pragmatic approach for developing this enhanced model offline and applying it online in real-time production environments, with a particular focus on using only image data. This strategy is in line with the objectives of Industry 4.0, which seeks to achieve more intelligent and data-driven manufacturing processes. Subsequent investigations will prioritize expanding the model’s suitability to various manufacturing settings, particularly highlighting its capacity to ensure quality in manufacturing lines through the utilization of images.

SIRT: A distinctive and smart invasion recognition tool (SIRT) for defending IoT integrated ICS from cyber-attacks

With the rise of smart industries, Industrial Control Systems (ICS) has to move from isolated settings to networked environments to meet the objectives of Industry 4.0. Because of the inherent interconnection of these services, systems of this type are more vulnerable to cybersecurity breaches. To protect ICSs from cyberattacks, intrusion detection systems equipped with Artificial Intelligence characteristics have been used to spot unusual system behavior. The main research problem focused on this work is to guarantee ICS security, a variety of security strategies and automated technologies have been established in past literary works. However, the main problems they face include a high proportion of incorrect predictions, longer execution times, more complex system designs, and decreased efficiency. Thus, developing and putting in place a Smart Invasion Recognition Tool (SIRT) to defend critical infrastructure systems against new cyberattacks is the main goal of this project. This system cleans and normalizes the supplied ICS data using a unique preprocessing technique called Variational Data Normalization (VDN). Furthermore, a novel hybrid technique called Frog Leap-based Ant Movement Optimization (FLAMO) is applied to choose the most important and necessary features from normalized industrial data. Furthermore, the methodology of Weighted Bi-directional Gated Recurrent Network (WeBi-GRN) is utilized to precisely distinguish between genuine and malicious samples from information collected by ICS. This work validates and evaluates the performance findings using many assessment indicators and a range of open-source ICS data. According to the study's findings, the proposed SIRT model accurately classifies the different types of assaults from the industrial data with 99 % accuracy.

Prediction of typical gas components in cigarette smoke based on transformer

Abstract Harm reduction of cigarette products has become one of the primary objectives of the entire tobacco industry. The prediction of typical gas components (TGC) and dynamic adjustment of process parameters are crucial for cigarette coke reduction and harm reduction. In this study, a deep learning (DL)-assisted TGC prediction framework is proposed to predict CO and NI simultaneously. Firstly, a large number of experimental and simulation parameters of cigarette process are collected as the data base for subsequent model construction. Then, the feature importance analysis of the process parameters was carried out by combining the mechanism of the combustion process through the mutual information method. Finally, DL models based on Multilayer Perceptron, One-Dimension Convolutional Neural Network and Transformer (TF) was developed as data-driven surrogate models to establish the mapping relationship between process parameters and TGC. The results show that TF model generalizes best and can predict TGC quickly and accurately with R2 over 0.99. This work will provide a valuable predictive and decision-making tool for cigarette harm reduction.

The 3D tree dataset: an artistic experiment using a voxel-based GAN

AbstractIn visual culture, Generative Adversarial Networks (GANs) have been used to generate two-dimensional images, video, and three-dimensional forms. Whilst there are a relatively large number of conditional datasets of two-dimensional images, there are fewer datasets of three-dimensional objects available for training, evaluation, and practical use. In this paper, we introduce a synthetic dataset of 3D trees that provide novel geometric challenges for machine learning and describe our use of this dataset in training and qualitative evaluation via a public exhibition and survey. The 3D Tree Dataset was made using random variations of 76 bespoke tree templates based on art historical references to favor interesting, beautiful, and varied trunk and branch shapes. In generating and experimenting with this dataset, we wanted to know how a geometrically complex series of organic forms would challenge voxel-GANs compared to existing datasets comprising industrial objects such as chairs and cars. As an interdisciplinary project between visual arts and computer science, we used 3D printing and animation to visualize and communicate our outputs to non-specialist audiences. In this paper, we describe and share the 3D Tree Dataset, outline the process of generating the dataset, describe the GAN architecture and application used to test the dataset and discuss the results relative to our artistic and cultural goals.

Optimal mixture design for organic Rankine cycle using machine learning algorithm

This study presents a new design tool for working fluid mixtures in organic Rankine cycles. The proposed tool comprises a blend model for the thermophysical properties of the formulated mixtures, an ORC modelto predict the performance of the mixtures in a specific application, and an optimizer based on the Bayesian inference method to identify the optimal mixtures compositions to be assessed. The tool is programmed to optimize an objective function based on predefined optimization targets. Importantly, the targets and their respective weights within the objective function can be adjusted to meet the specific requirements of the application under analysis, making this approach adaptable to diverse research and industrial objectives. The algorithm is applied to a case study to demonstrate its ability to define a low-GWP blend that can replace HFC-134a in a micro-scale ORC with recuperator, while maintaining and potentially enhancing performance. The optimization targets specified for the case study are the net power output, the net efficiency, the GWP and the blend size. Power and efficiency are computed through a validated model of the low-temperature ORC system used as benchmark case. The results showed that the procedure was able to formulate several blends that comply with the targets of the assigned task. Amongst the high-scoring mixtures, the most used pure fluids are R32, R152a, R1234yf, and R1234ze(E). The presence of HCs is limited to fewer mixtures, playing the main role of GWP-limiter. A method to estimate the flammability classification of the blends has been also applied, obtaining that most of them belong to the ASHRAE class 2l, except when an HC is present, in which case the fluid is may result in class 3.

On the Heterogeneity of Iron‐Oxo Species in Zeolites for the Oxidation of Methane to Methanol by Nitrous Oxide: A Theoretical Perspective

AbstractThe conversion of methane to methanol (MTM) represents a pivotal objective in the C1 chemical industry. Transition metals, such as iron, exchanged zeolites are one category of the most active catalysts for direct conversion of MTM. One important topic in understanding the mechanism of Fe‐zeolite catalyzed MTM is how the heterogeneity of catalytic (Fe) sites influences the system stability and reactivity. Employing DFT calculations and machine learning method, we herein studied the stability–reactivity relationship of a MTM catalytic cycle with N2O as the oxidant over Fe‐exchanged zeolites. The Fe heterogeneity was introduced by using CHA and FER zeolites and looking at a number of related Fe species (FeII, FeO, and FeOH). A strong correlation was observed between the stability of such Fe species, which is primarily determined by the formation energy of FeII, and such a stability trend remains consistent throughout the MTM catalytic cycle. The reactivity analysis then demonstrated that less stable Fe species may exhibit higher reactivities when situated in specific sites. Further machine learning analysis validated the significant relevance of activation barriers with reaction energies in the N2O decomposition step that is not sufficiently captured by the traditional one‐dimensional Brønsted–Evans–Polanyi (BEP) relationship.

Organized labour migration of residents of Lithuania to the USSR in the sixth–ninth decades of the 20th century

Referring to archive and historiographic materials, in this paper problematics of labour migration to the USSR from Lithuania in 1953-1984 is analysed. Attention is drawn to migration directions, motivation of residents, quantitative indices, territorial aspects related to persons participating in labour migration process and social picture. Residents of Lithuania became participants of the campaign for organized all Soviet Union-wide labour force migration to participate in implementation of large and important land, forestry and industry projects. By the way, the residents of our Republic joined this campaign later than residents from other Republics – in 1953 only. Organized labour force migration from Lithuania to the USSR was of lower level that it had been planned in Moscow. The main directions of labour force migration were: 1) agriculture intensification program (virgin lands campaigns in Kazakhstan and Russia); 2) Russian forestry development program; 3) program of construction of gigantic transport and industrial objects in Russia (Siberia, etc.); 4) the “Third Semester” program – season-based works of students in industry and agriculture sectors of Russia, Kyrgyzstan, Uzbekistan and other Soviet Republics. In 6th-9th decades of the 20th century, about 75 thousand residents of Lithuanian Soviet Socialist Republic were sent to work in other regions of the Soviet Union; labour migrants worked on industrial objects, farms and forestry units. Mostly, they were men from urban areas, by ethnic composition – Russians and Lithuanians. The most intensive time period of sending to work as labour migrants was the 1953-1965 period, as at that time in Kazakhstan and Russia huge programs of economic development had been implemented (virgin lands campaign, forestry development). Namely during this period, over 80 per cent of residents of Lithuania who participated in this campaign were sent to labour destinations. Although in later years the migration campaign covered entire Lithuania, its scale diminished markedly. Due to certain national traits of Lithuanians (sedentary life, attachment to the land) and social-economic situation in the Lithuanian Soviet Socialist Republic (fast and intensive industry development ensuring quite good wages, well-developed social infrastructure, etc.), the residents of Lithuania liked to conclude short-term employment agreements, and after termination of these agreements made efforts to return to Lithuania and stay there not attracted by labour migration campaign anymore.