Manual Work Research Articles

Integrating Multimodal Generative AI Technologies in Postgraduate Marketing Education

While industry practices evolve rapidly, marketing education in Australia and New Zealand faces challenges in keeping pace, particularly regarding the adoption of current marketing technologies (Harrigan et al., 2022). Generative AI, exemplified by systems like ChatGPT and DALL·E, has demonstrated benefits for learning (Baidoo-Anu & Ansah, 2023). However, despite its potential, there remains a dearth of practical guidance on effectively incorporating these technologies into marketing courses. This gap persists even as general frameworks for responsible and ethical AI use, such as the Australian Framework for Generative AI in Schools (2023), emerge. As the demand for graduates with generative AI skills grows in the job market, educators must explore innovative pedagogical approaches to bridge this gap. This academic poster presents an innovative application of generative artificial intelligence (GenAI) in the context of teaching digital marketing at the postgraduate level. Its purpose is to bridge the gap between academic theory and industry practice by encouraging educators to integrate AI tools into their curriculum through experiential learning pedagogy (Kolb, 2014), characterized by a learning process whereby knowledge is created through hands-on experiences. The poster exemplifies how various types of GenAI technologies — specifically text-based, image-based, and video-based — can enhance teaching content, tutorial exercises, and assessments within the digital marketing course. The poster showcases examples of how these GenAI tools are integrated in the course content, to guide students in generating innovative ideas for using AI in marketing to gain a competitive edge: Text-based GenAI: Tools like ChatGPT and Gemini can automatically generate search keywords for search engine marketing. By integrating text-based GenAI tools with established marketing technology (MarTech) tools such as Google Ads and Google Ads Keyword Planner, students engage in practical exercises that combine AI-generated initial ideas (e.g., search keywords) with further analysis (e.g., search volume, click-through rates, and bidding costs) using established MarTech tools. This hands-on approach enhances their learning experience and prepares them for real-world applications. Image-based GenAI: Platforms such as DALL·E, Midjourney, and Stable Diffusion enable the creation of custom images for display advertising, enhancing visual communication in marketing materials. Through experiential learning activities, students can explore ideas, seek unusual combinations, and inspire creativity faster with image-based GenAI tools, resulting in a greater variety of display ad materials. Video-based GenAI: Applications like Sora and Synthesia facilitate the production of short video clips suitable for social media marketing (e.g., YouTube Shorts, TikTok). By engaging in dynamic content creation exercises, students learn to streamline content creation, reduce manual work, and save both time and budget, thereby gaining practical skills in social media marketing. By incorporating these GenAI technologies through experiential learning pedagogy, educators can enrich the learning experience, foster critical thinking, and prepare students for the evolving landscape of digital marketing. Future research can study the use of GenAI in marketing education using theoretical frameworks such as the Unified Theory of Acceptance and Use of Technology (Venkatesh et al., 2016).

Automated mapping of electronic data capture fields to SDTM

Objective The goal of this work is to reduce the amount of manual work required to go from data capture to regulatory submission. It will be shown that the use of Siamese networks will allow for the generation of embeddings that can be used by traditional machine learning classifiers to perform the classification at much higher levels of accuracy than standard approaches. Methods Siamese networks are a method for training data embeddings such that data within the same class are closer with respect to a given distance metric than they are to data points in another class. Because they are designed to learn similarity within pairs of data points, they work well in situations where the number of classes is relatively large compared to the number of training samples. In this work, we will show that embeddings generated via a Siamese network from metadata associated with electronic data capture forms can be used to predict the associated SDTM field. Results With a relatively simple network coupled with a basic classification algorithm, the proposed method can achieve accuracies greater than 90%, which is significantly higher than what has been achieved with traditional methods, with many of the inaccurate mappings due to a lack of training data. In many cases, there is a 15% increase in accuracy vs. more traditional methods. Conclusion Leveraging Siamese networks, it is possible to generate embeddings that efficiently represent data fields in a lower dimensional space. This allows the creation of a system that can automatically map between data schemas at high levels of accuracy. Such systems represent the first step in automating one of the many labor-intensive data management tasks associated with clinical trials.

Digital Manufacturing System of Custom Wigs with Measurable 3D Images

Wigs are major important means for improving life quality of the increasing people suffered from hair loss through their image enhancements. For the production of custom made wigs, there requires a mold that reproduces the wearer’s head dimension and the shape of hair loss. The conventional method for this is to make a resin film that adheres to the wig wearer’s head using vinyl tape, and to make a wig pattern with drawing the shape of hair loss onto the film, and to produce a head mold with this pattern, and then to plant hair in a wig cap made based on this mold and wig pattern. This method requires highly skilled manual work, and generates a large amount of chemical waste during the manufacturing process, and takes a long manufacturing period. However, the digital automation of measuring head dimension and the shape of hair loss can replace or omit the production of wig patterns. In this study, a 3D head image capable of actual measurement was implemented by using a 3D polygon head shape measurement device and a camera, which makes a digital production system possible, which can replace the manual wig pattern production process for custom wigs.

Two-level integrated verification evaluation method based on comprehensive weighted assessment in multiple scenarios

The complexity and diversity brought by the distributed architecture of the new generation electric information collection system have deepened the difficulty of constructing evaluation verification index systems and quality evaluation models. Moreover, the presence of differentiated components has made fair and scientific verification challenging. Therefore, leveraging graph neural networks and siamese networks, a integrated construction quality evaluation system based on comprehensive weighted assessment in multiple scenarios was developed. Firstly, a graph neural network was constructed based on terminal data of the branches electricity usage information collection system and the link topology structure. Subsequently, this network was deployed on the headquarters side to directly acquire terminal data and generate mirror network input from the branches data, enabling real-time acquisition of various system operational indicators. Finally, the similarity between the headquarters and branches data was calculated using siamese networks to compute accuracy compensation weights for checking and evaluating various indicators, thereby obtaining comprehensive weighted quality evaluation indicators of the branches new generation electric information collection system. We use three types of services including electricity data collection, load forecasting, and task scheduling as experimental scenarios. The results showed that the multidimensional comprehensive weighted quality assessment combined with accuracy compensation obtained from the siamese network resulted in business construction quality assessment values of 97.92%, 95.95%, and 99.96% in branch. This value is approximately equal to the quality evaluation value of manual work, so the method can effectively verify the construction quality of new systems in the branch.

ONTO-TDM domain ontology population for a specific discipline

Ontologies play a vital role in organizing and constructing knowledge across various domains, enabling effective knowledge management and sharing. The development of domain-specific ontologies, such as the ONTO-TDM ontology for teaching domain modeling, is essential for providing a comprehensive and standardized representation of knowledge within a given discipline. However, to maximize the usefulness and relevance of such ontologies, it is crucial to automate their population with domain-specific information, reducing manual work and ensuring scalability. This paper presents a novel method for ontology population by extracting and integrating relevant information from diverse sources. The method combines the TextRank algorithm with Word2Vec to enhance keyword extraction, capturing both semantic meaning and textual importance. Keywords are then annotated and used to train a machine learning classifier, which aids in integrating new instances into the ontology. Experiments show that the proposed method achieves a precision of 63.33%, a recall of 61.29% and an F1-score of 62.28%, significantly improving keyword extraction and ontology population accuracy compared to existing methods. This validates the method’s effectiveness in semi-automatically extracting relevant instances from diverse data sources, enhancing the efficiency and accuracy of ontology population, and advancing automated knowledge management in domain-specific contexts.

Analysis of Mental Workload in Appropriate Technology (AT) for Agricultural Machinery Using Heart Rate Variability (HRV) Parameters

Appropriate Technology (AT) is designed to substitute manual work and to increase productivity for small-medium businesses. Due to its low-cost technology that affects its design quality, the acceptance of AT appears to be low. A guideline has yet to be available to determine AT's design quality. The purpose of this study was to explore the efficacy of physiological measures collected from a Heart Rate Monitor (HRM) in differentiating mental workload in using AT. If applicable, these measures can provide objective data for AT evaluation. Several parameters were collected from HRM, including frequency domain and time domain. Two ATs were used for evaluation that represented “good” and “poor” agricultural processing machines. Eleven participants were involved in this study in which they used both ATs. The result of this studyis the ratio of Low Frequency and High Frequency (LF/HF) parameter detects differences perceived by the participants. The increase in the LF/HF value from the Commercial Extruder to the Customized Extruder is directly proportional to the increase in mental workload, where the Commercial Extruder is defined as a “good” AT design, and the Customized Extruder as a “poor” AT design. Therefore, it can be concluded that designs with specific guidelines can reduce the mental workload perceived by operators.

A Database-based Automatic Cartography Method

Abstract. At present, GIS data, which contains a large amount of semantic information, has become the main data source for map cartography. In this paper, based on the existing multi-scale database, we study a database-based automatic cartography method to meet the needs of multi-scale map cartography, and replace the traditional process by the intelligent processing of software to shorten the cycle of cartography, improve the productivity of map cartography, and at the same time, reduce the error rate caused by manual work, and improve the overall quality of map production.

Use Of Digital Technologies In Prosthodontic Clinical Practice Among Prosthodontists In Tamilnadu And Pondicherry

Background: Digital dentistry includes treatments performed by means of digital or computer-controlled components rather than using mechanical or electrical equipment. The advancement of digital technology and the commencement of Computer-Aided Design (CAD) and Computer-Aided Manufacturing (CAM) and 3D printing has brought large changes to the traditional manufacturing method, where manual work is carried out after oral impression taking. Aim: To conduct a questionnaire survey on use of digital technology in prosthodontic clinical practice among Prosthodontists in Tamilnadu and Pondicherry. Materials and methods: A cross-sectional questionnaire based online survey was undertaken amongst 183 Prosthodontists in Tamilnadu and Pondicherry (Group 1 – Both private practitioners and teaching faculty, Group 2 – Private practitioners, Group 3 – Teaching faculty). The questionnaire consisted of 21 questions which evaluated their awareness, knowledge and practices towards digital dentistry. Statistical analysis was done using Chi-square test in each group, using Statistical Package for the Social Sciences (SPSS) version 23.0. The p-value 0.05 was considered significant. Results: Majority of the study participants were private practitioners in the age group of 25 – 45 years(93%).Among the three groups private practitioners had good knowledge, practice and awareness about the application of digital technologies. Conclusion: Most of the participants were aware about digital technology in dentistry. The private practitioners showed better understanding about the digital technology. However, to make them familiar with CAD/CAM, dental education programs, workshops and hands on program should be conducted which will enable an emerging dentists who will be well versed with digital dentistry.

Factors influencing the choice of specialization - a cross-sectional study with civilian medical students and prospective medical officers in Germany

BackgroundThe recruitment of physicians is an increasing challenge. The aim of the study was to explore factors that might influence future civilian and military doctors’ choice of specialization.MethodsA 28 item author developed questionnaire was used to survey civilian medical students and prospective medical officers. The questionnaire consisted of 20 items addressing choice of specialization, and eight socio-demographic factors. Response options were available in the form of open and closed questions as well as in the form of a Likert scale (1 ‘not at all’ to 6 ‘very much’). The questionnaire was completed online by prospective medical officer from June to September 2021 and by civilian students from November 2020 to March 2021.ResultsIn total, 2,030 students (56.1% female, mean age = 24, mean semester 6.5) participated in the survey, 1,553 civilian students and 477 prospective medical officers. Regular feedback and clear structures (mean = 5.35) followed by good teamwork (mean = 5.33) during postgraduate training was rated particularly important. Moreover, a secure job after graduation (mean = 5.23) and a compatibility of family and job (mean = 5.14) were important parameters for career choice. The specializations most frequently chosen were surgery (32.0%), internal medicine (27.1%) and anesthesiology (22.1%). The regression models showed that the choice depends on own attitude concerning the specialty would be a patient-orientation job, or a job with more manual work or more career option within in the job preferred.ConclusionsConsidering the value students place on receiving feedback and the structure of their specialist training, this seems to be a promising strategy for future recruitment. Good team dynamics and job security are key concerns for prospective physicians.

Object Detection in Historical Images: Transfer Learning and Pseudo Labelling

The automatic analysis of images in the historical sciences often requires the identification of objects. Object identification is a well researched problem for modern photographs, however, for historical material annotations are often necessary. We present a solution for finding objects without manual work. The method consists of a style transfer of images from the COCO dataset into the domain using CycleGAN and training with items obtained through pseudo labelling on the original and the additional transferred COCO images. Different strategies to assemble the dataset are compared. The best method obtains a F1 score of 0.58 for 15 object types without any labelling.

Hazard Analysis for Massive Civil Aviation Safety Oversight Reports Using Text Classification and Topic Modeling

There are massive amounts of civil aviation safety oversight reports collected each year in the civil aviation of China. The narrative texts of these reports are typically short texts, recording the abnormal events detected during the safety oversight process. In the construction of an intelligent civil aviation safety oversight system, the automatic classification of safety oversight texts is a key and fundamental task. However, all safety oversight reports are currently analyzed and classified into categories by manual work, which is time consuming and labor intensive. In recent years, pre-trained language models have been applied to various text mining tasks and have proven to be effective. The aim of this paper is to apply text classification to the mining of these narrative texts and to show that text classification technology can be a critical element of the aviation safety oversight report analysis. In this paper, we propose a novel method for the classification of narrative texts in safety oversight reports. Through extensive experiments, we validated the effectiveness of all the proposed components. The experimental results demonstrate that our method outperforms existing methods on the self-built civil aviation safety oversight dataset. This study undertakes a thorough examination of the precision and associated outcomes of the dataset, thereby establishing a solid basis for furnishing valuable insights to enhance data quality and optimize information.

Rebar-tying Robot based on machine vision and coverage path planning

Automation technology can replace manual work in the traditional construction industry, improve quality and efficiency, and reduce costs. This paper developed a rebar-tying robot for tying the intersection of rebars. It proposed a Hough transform multi-segment fitting method to detect the intersections of rebars in real-time acquired RGB-D images. To cover the surface of the rebar net as much as possible under the condition of limited camera FOV, this paper designed a coverage path planning method to plan the path of the photo positions and the detected intersections of rebars efficiently and orderly. The experimental results show that the robot achieved an accuracy rate of 99.4 % in intersection detection, the detection error is within 2.8 mm, the single tying time is 1.85 s, and the average tying time is 5.5 s, which is faster than most robots. The robot realizes the task of automatically tying the intersection of rebars, which is robust and efficient, without duplication or omission.

Accelerated Data Engine: A faster dataset construction workflow for computer vision applications in commercial livestock farms

Large-scale, high-quality dataset was the foundation of developing advanced artificial intelligence applications. However, creating such a benchmark dataset in a professional field, such as precision management of animals, was always a challenge because of the costly and labor-intensive process of annotation and review. This study introduced a novel workflow named Accelerated Data Engine (ADE), designed to efficiently produce representative and high-quality computer vision datasets from raw animal surveillance footage. By incorporating referring and grounding models (R&G models) as auto-annotators, along with a distillation mechanism for dataset-auditors, ADE significantly speeded up the dataset construction process. The new workflow received natural language inputs as referrals to identify animal instances, delineated their body shapes, and then refined the auto-annotated data through a selection process. To demonstrate the efficacy of ADE, three 30-minute surveillance video samples featuring pigs, sheep, and cattle were discussed in this study. The results indicated the R&G models effectively annotated animals across various farms, while distillation mechanisms could identify various detection errors, balance the data representations, refine annotations, and verify the data quality. Two high-quality cattle datasets (6.5 k and 486 frames), including 26 k and 2.5 k cattle instances, were generated through the ADE workflow from 24-hour surveillance videos on a commercial cattle farm and made publicly available. The proposed dataset has achievable performance between 74.6 %∼84.1 %. The ADE workflow saved 78.4 % of manual work compared to the traditional dataset construction workflow (approximately 141 h). This pioneering approach empowered the fast creation of benchmark animal datasets and would enhance computer vision applications in the livestock production industry in the future.

Linking the ECRIN Metadata Repository with the BBMRI-ERIC Directory to connect clinical studies with related biobanks and collections.

There is much value to be gained by linking clinical studies and (biosample-) collections that have been generated in the context of a clinical study. However, the linking problem is hard because usually no direct references between a clinical study and an associated collection are available. The BBMRI-ERIC Directory and the ECRIN Metadata Repository (MDR), already include much of the information required to link clinical studies and related sample collections. In this study, we present the work performed to find and implement those links across existing corresponding records in the two systems. The linking process between MDR studies and related collections in the BBMRI-ERIC Directory started with exploring linkage in both directions - searching the BBMRI-ERIC Directory for candidate hits to try to link with MDR records, and searching the ECRIN MDR for candidate hits to try to link with Directory collections. Thereafter, a systematic search through the BBMRI-ERIC Directory was performed. The investigation of linkages in both directions resulted in a limited but promising number of linkages. The results of the systematic search of the Directory identified linkage of 202 studies, spanning 284 collections. The analysis with existing data sources indicated that links between the BBMRI-ERIC and ECRIN collections exist, but also that they would be difficult to continuously identify and maintain without a great deal of manual work which neither organisation could support. The question arises whether, in the future, systems could be put into place to make the exchange of information and the linkage of identifiers almost automatic.

Trajectories of work disability and unemployment before and after a common mental disorder diagnosis among young private sector employees in Sweden-a register-based longitudinal study.

To identify trajectory groups of work disability (WD), including sick leave and disability pension, and unemployment three years before and sixyears (from Y-3 to Y + 6) after a common mental disorder (CMD) diagnosis and to investigate associations of socio-demographic, work-related and clinical factors with trajectory membership. A longitudinal nationwide register-based study was conducted including individuals aged 22-29 years, gainfully employed in the private sector, with a CMD diagnosis in specialised healthcare or prescribed antidepressant (N = 12,121) in 2014 (Year 0/Y0), with follow-up from Y-3 to Y + 6. Group-based trajectory analyses identified groups of individuals who followed similar trajectories of months of WD and unemployment, respectively. Multinomial logistic regression determined associations between socio-demographic, work-related and clinical factors and trajectory membership. In the CMD group, we identified three trajectory groups, each for WD and unemployment. Only 7% individuals belonged to a 'Fluctuant high' trajectory group with four months of WD in Y0, which peaked at 7months in Y + 3 and reduced to 5months in Y + 6. For unemployment, 15% belonged to an 'Increasing medium' trajectory group that steadily increased from 1.3months in Y0 to 2.6months in Y + 6. Sex, educational level and musculoskeletal disorders for WD, and educational level, living area and occupational class for unemployment, influentially determined the variance across the CMD trajectory groups. Specific vulnerable groups regarding unfavourable WD (women, low education and musculoskeletal disorders) and unemployment (manual work, low education and rural residence) trajectories require special attention regarding their return-to-work process following a CMD diagnosis.

Paradigm shift in mechanical system design: toward automated and collaborative design with digital twin web

AbstractAnalyzing multi-vendor mechanical system designs requires a significant amount of manual work, resulting in a design paradigm where analysis is conducted only after the design is locked and components are selected. This leads to a suboptimal design with compatibility issues, over-dimensioned components, inferior performance, poor energy efficiency, and a lack of collaboration between OEMs (original equipment manufacturers) and system integrators. To overcome these issues, this paper proposes Co-Des (collaborative design) framework for automated and collaborative multi-vendor system design. The framework relies on standardized digital twin documents (DTD) of system designs, components, and analyses. The discoverability and distribution of these DTDs are enabled with digital twin web (DTW). Co-Des framework allows for finding suitable components for the design task by automatically running selected analyses employing component digital twins. In addition, OEMs can provide customized components for system integrators using the initial system design defined in the system design DTD. The use of the Co-Des framework was demonstrated with a windmill powertrain design use case, and the applicability of the automated assembly analysis for component selection was verified with performance measurements. The adoption of the proposed framework will lead to a paradigm shift from manual and siloed work relying on the exchange of PDFs to a more automated and collaborative design of mechanical systems. The adoption rate is defined by the willingness of system integrators to publish their initial system designs and OEMs their components as public digital twins.

Distinct Element macro-crack networks for expedited discontinuum seismic analysis of large-scale URM structures

Discontinuum analysis is a powerful tool for the detailed seismic assessment of unreinforced masonry (URM) structures, whose widespread use is however still mostly limited to small-scale assemblies or isolated components. Despite their unique capabilities, including the explicit simulation of separation phenomena, collapses and out-of-plane (OOP) failures that are hardly replicable using traditional continuum solutions, the high computational cost entailed by micro-to-meso-scale discontinuum modelling strategies, which are the standard approaches in applied research, presently prevent their employment for building-scale problems. The few available macro-scale discontinuum models specifically conceived for URM, on the other hand, rely on complex geometrical discretization processes that require costly manual work, as well as on less efficient deformable block formulations. In this paper, a new simplified discontinuum macro-model is presented and validated against full-scale laboratory test outcomes on walls, pier-spandrel systems and building specimens, in addition to various meso-scale numerical results, under either in-plane (IP) or OOP actions, and considering quasi-static (monotonic, cyclic) or dynamic loadings. The proposed simulation technique, implemented in a robust Distinct Element Method (DEM) framework, leverages a semi-automated Equivalent Frame discretization algorithm that idealizes masonry piers, spandrels and nodes as an assembly of interlocked rigid macro-blocks connected by a network of zero-thickness interface springs. The latter are herein demonstrated to effectively replicate URM damage at the component-level through fracture energy contact laws, providing macro-scale yet representative failure patterns, as well as adequate predictions of overall strength and deformation capacities. Results obtained show a good agreement between macro- and more sophisticated meso-scale predictions, as well as with experimental outcomes, albeit dissimilarities among measured and computed force-displacement responses – similarly to other simplified strategies – were detected as vertical overburden increases. Notably, for analogous levels of accuracy, the analysis time required by our new macro-models is up to 150 times lower than its meso-counterparts. The use of the proposed simplified strategy also enabled the satisfactory simulation of the quasi-static cyclic and seismic responses of full-scale building-scale specimens, prohibitive tasks using traditional detailed DEM models, while also being up to 10 times faster than previous deformable macro-models.

Modernizing agricultural workflows using artificial intelligence and deep learning: Enhancing plant species identification

Modernizing agricultural processes is imperative due to the labor-intensive nature that impedes crop production. Despite the steps of farm technologies, the manual tasks of identifying plants and weeds remain very time-consuming. The current paper asserts that Convolutional Neural Networks (CNNs), a type of Artificial Intelligence, can enhance plant species recognition efficiency. The paper focuses explicitly on how CNNs can be leveraged to improve the process of identifying different plant species, thereby promoting efficiency in plant species identification. With a dataset of images of 12 species of plants, we have come up with an entirely accurate CNN model for the classification of plant types. Implementing this AI-powered approach can reduce manual work, enhance decision-making processes, and enrich environmental sustainability in agriculture by rendering the workflow smoother. The present study has broad ramifications for agriculture, technology, and environmental science professionals seeking to embed AI solutions into conventional workflows. The proposed system is meant to show how AI can improve productivity, reduce labor costs, and allow environmentally sustainable agriculture.

Automatic sleep stage classification using deep learning: signals, data representation, and neural networks

In clinical practice, sleep stage classification (SSC) is a crucial step for physicians in sleep assessment and sleep disorder diagnosis. However, traditional sleep stage classification relies on manual work by sleep experts, which is time-consuming and labor-intensive. Faced with this obstacle, computer-aided diagnosis (CAD) has the potential to become an intelligent assistant tool for sleep experts, aiding doctors in the assessment and decision-making process. In fact, in recent years, CAD supported by artificial intelligence, especially deep learning (DL) techniques, has been widely applied in SSC. DL offers higher accuracy and lower costs, making a significant impact. In this paper, we will systematically review SSC research based on DL methods (DL-SSC). We explores DL-SSC from several important perspectives, including signal and data representation, data preprocessing, deep learning models, and performance evaluation. Specifically, this paper addresses three main questions: (1) What signals can DL-SSC use? (2) What are the various methods to represent these signals? (3) What are the effective DL models? Through addressing on these questions, this paper will provide a comprehensive overview of DL-SSC.

The Nazi regime in Lithuania: recovery of agricultural obligations imposed

The purpose of this study is to investigate methods and means of recovery of imposed agricultural obligations. The aim of this paper is to analyse the methods and means for recovery of imposed agricultural obligations in Lithuania, occupied by military forces of Nazi Germany. To achieve the aim, the following objectives have been set: 1) to discuss adaptation of production of agricultural products to meet needs of the war in Lithuania under the Nazi regime; 2) to analyse the methods and means used by occupational Nazi authorities for recovery of agricultural obligations imposed on farmers of Lithuania; 3) to discuss other obligations imposed (other than delivery of agricultural products) on Lithuanian farmers, analyse methods and means for their recovery; 4) submit the exhaustive and reliable statistics on recovery of imposed obligations related to delivery of agricultural products. The topic chosen is a relevant and new one, as this problem has not been presented exhaustively in Lithuanian historiography. First of all, we lack a generalized analysis on the entire Lithuania scale, analysis which must be more detailed, based on archive or reliable statistical data related to practical application of the mechanism of recovery of agricultural obligations (primarily aiming at agricultural products) imposed. This article aims at filling these and other gaps in relevant historiography. During the research the following conclusions have been drawn: 1. In the case of Germany victory in the war, the territories of all Baltic states, including Lithuania, were to be included into the Nazi Reich. During the wartime, the economy of these countries had to serve demands of the Vermacht and meet other Reich needs, irrespective of the interests of the local people and local economies. It had not been planned to create a considerable consumer goods industry. These countries had to be cheap labour sources and base for provision of raw materials, as well as a market for realization of German industry products.2. The essence of the Nazi agricultural policy in the occupied Lithuania meant a unilateral demand to increase production of basic food products and raw materials in order to provide the German troops as well as local residents and Germans in Germany with food products. The Nazi imposed on farmers various obligations related to agricultural products and various equipment (mobilization of horses with carts and harnesses, obligations of manual work and transport, etc.)3. Civil occupational Nazi authorities adapted Lithuanian municipal system to meet their agricultural needs during the wartime. The occupational authorities through threatening the officers of Lithuanian municipal system with penalties and negative consequences related to agriculture and farmers, using the help of Lithuanian officers, managed to recover a huge amount of agriculture products from this country agriculture sector. Whereas Lithuanian municipal officers of higher rank were forced to apply constantly the psychological pressure or even coercion on their own subordinates and colleagues of lower rank.4. In order to recover agricultural obligations fulfilment, the occupants applied both repressive and promoting measures. Among repressive ones, farmer removal from his/her farm should be mentioned as well as requisition of agricultural products, cash penalties, farmers suing with the German Special Court, forcible confining in detention facilities, forced-labour camps or even killing by firing squad. Especially famous for farmers killing by firing squad was H. Wulff, the commissar of Vilnius county district. By killing the farmers, the occupants sought to intimidate the villagers so that they would better fulfil the obligations imposed.5. To perform recovery of fulfilment of imposed agricultural obligations, the following promoting measures used to be applied: issue of ownership documents, granting the possibility of acquisition of industrial goods, etc.6. The Nazi failed to convert Lithuanian farmers into obedient executors of alien will. Due to harsh conditions of the wartime and sabotage of Lithuanian farmers in order to protect their farms from the complete deterioration, occupational government of the Nazi each economic year failed to collect from Lithuanian villages planned agricultural products contingencies irrespective of requisition of the large part of agricultural production of this country.