KTU Open Journal Systems (Kaunas University of technology)
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Labor Remuneration in the Agriculture Sector of Ukraine from the Decent Work Perspective
No full textAgriculture has become one of the key sectors of Ukraine’s economy. It plays a vital role in ensuring the security of the Ukrainian and global food markets. To have qualified and competent employees, an effective remuneration policy must be designed to provide decent wages. The purpose of the research is to substantiate the theoretical and methodological foundations for the study of the remuneration policy from the point of view of the implementation of the concept of decent work and to analyze the remuneration policy in the agriculture sector of Ukraine to determine the directions for its improvement. The analysis of scientific literature, the ILO’s target priorities of decent work, and the parameters of decent pay led to the creation of a system of 31 indicators with defined standards and methodological principles for determining each of them and a methodology for calculating a composite indicator. The analysis of wages in the agriculture sector in Ukraine was carried out according to the developed indicators, and a composite indicator was calculated. The study showed that the agriculture sector belongs to the industries with the composite indicator of decent wages below the average level. It has been proven that the remuneration policy in the agriculture sector of Ukraine is currently not favorable and negatively affects the human and innovative potential of the sector. To overcome the identified problems, recommendations were made regarding the development of a remuneration policy in the agriculture sector based on the principles of decent work, namely, ensuring a decent level of wages, eliminating arrears in wages, developing a transparent remuneration policy, increasing the effectiveness of social dialogue in regulating wages, strengthening the responsibility of social partners, implementation of measures to create equal opportunities in practice
Prospects of Application of the European Charter of Local Self-Governance for Kazakhstan: Experience of Germany and Poland
No full textThe article aims to identify the shortcomings of local self-government in Kazakhstan and to create ways to overcome them based on the principles of the European Charter of Local Self-Government and the experience of individual states. The article highlights the main challenges faced by the state, namely financial constraints, overlapping powers, insufficient centralisation and low level of democratic participation. The author also examines the example of Poland and Germany in the context of the local self-government system and analyses the possibility of its application to the realities of Kazakhstan. The author\u27s main focus in this study is on the steps that the government of Kazakhstan can take to bring its legislation in line with the standards and principles of the European Charter of Local Self-Government. Among them, the author identifies the following: legislative and institutional reforms; financial independence; changes in control and coordination mechanisms; decentralisation; and increased public participation
Technological Transfer in EU Civilian Missions: Bureaucratic Heterarchy and Agent-driven Opportunities
No full textThis study examines the integration of emerging technologies into EU civilian Common Security and Defense Policy (CSDP) missions. Using Multi-Level Governance and Principal-Agent theories as initial conceptual frames, the research investigates the complexities of EU governance like bureaucratic heterarchy, and the dynamics of agent-driven opportunities. Based on interviews with European External Action Service (EEAS) staff, EU mission personnel, and EU Ministry of Foreign Affairs representatives, the findings reveal systemic obstacles such as bureaucratic irregularity, information asymmetries and expertise gaps, which hinder effective technological transfer to host countries. Despite strategic frameworks like the Civilian CSDP Compact, technological transfer remains highly uneven and fractured, relying on proactive agents operating under informal mechanisms. The study concludes that empowering proactive agents can help to advance technological integration and enhance the operational impact of civilian CSDP missions, making them more capable to support host countries
Research and Innovation Funding Policy in Indonesia in the Post-2019 National Science and Technology System Era
No full textThis paper aims to examine the regulatory framework for funding innovation and science research in Indonesia, with a specific focus on the 2019 National Science and Technology System Law implementation. Through a directed qualitative content analysis of sixteen national laws, the present study identifies various shortcomings and fragmentation of governance frameworks, instruments, public-private partnerships (PPPs), and outcome-based performance assessment mechanisms. The paper highlights important shortcomings like the lack of thorough integration of innovative funding mechanisms and homogeneous performance assessment frameworks with an orientation towards outputs. Relying on international policy reports—including the OECD\u27s and the United Nations Economic Commission for Africa\u27s—as well as supported by corresponding academic scholarship, the study identifies the necessity for adopting performance assessments with an orientation towards outputs, regulatory harmonization, as well as enhanced policy coherence between the federal government and regional administrations. This study concludes with strategic recommendations with a view towards enhancing the effectiveness, transparency, and inclusiveness of Indonesia\u27s innovation and science funding framework. These recommendations include enhancing coordination across multiple ministries, expanding the reach of output-based funding schemes, adopting a national PPP framework, and introducing a matching fund scheme for linking efforts between federal and local governments
An Efficient Point Cloud Correlation Enhancement RCNN for 3D Object Detection
No full textTo meet the requirement of 3D object detection task , an efficient point clouds correlation enhancement RCNN(EPCE-RCNN) is proposed. The proposed method reduces the computational complexity and time consumption of the network through a lightweight proposal generation module, and accelerates the generation of the 3D proposal box. Meanwhile, during region of interest feature coding, the relevance among different grid points is enhanced through an efficient self-attention pooling module, so that the limitation that the pooling operation is influenced by the radius of a neighborhood query sphere is addressed. In addition, the combination of an attention mechanism and a feedforward network ensures the nonlinearity of the model, so that the model can perform feature expression better. Thus, the synchronous improvement of the network detection efficiency and the detection precision is realized. On the KITTI dataset, the detection accuracy of three difficulty levels reaches 89.99%, 81.69% and 77.17% respectively. Compared with the baseline Voxel-RCNN, the detection efficiency of EPCE-RCNN is improved by 12%. To verify the generalization and application value of the proposed method, a power equipment dataset with 3D label information is constructed, the 3D label frame information of the YCB dataset is also supplemented. Experiments are carried out on these datasets. In the experimental results of the verification set, the mAP of a mug, gelatin box, single clip, wedge clip and C clip can reach 37.67%, 40.06%, 35.63%, 30.01% and 37.31% respectively. Compared with the baseline, the proposed algorithm has a significant improvement and its generalization has been fully verified
A Two-stage Cattle Face Recognition Method Based on Target Detection and Recognition Network
No full textTraditional methods of cattle management have problems such as high error rates, easy failure of tags, and the need to consume a lot of time and manpower costs. However, as one of the biological characteristics, the recognition of cattle face is one of the important technical means to achieve intelligent farming, accurate feeding, and health management of cattle. Thus, the article proposed improved algorithms based on YOLOv7 and VoVNet for cattle face detection and recognition using a contactless approach. For the improved YOLOv7 cattle face detection model, the efficient layer aggregation networks (ELAN) structures in the backbone and neck networks were replaced with the ConvNeXt network and CoTNet Transformer module, respectively, aiming to improve the detection speed and robustness while reducing computation. The SimAM (A Simple, Parameter-Free Attention Module) attention mechanism, considering both spatial and channel dimensions, was introduced in the neck network to enhance feature representation without adding extra parameters to the original network. Experimental results on the constructed facial detection dataset of Holstein and Simmental beef cattle showed that the improved CCS-YOLOv7 cattle face detection model achieved a precision of 99.43% and a recall rate of 99.10%, with significantly improved detection speed and reduced model size. As for the improved VoVNet cattle face recognition model, residual connections (RC) were added from the input to the output of the One-Shot Aggregation (OSA) modules of VoVNet to enhance the representation of deep features. The Efficient Channel Attention (ECA) was added to the final feature extraction layer of the OSA modules to improve the feature extraction capability for cattle face image classification. Experimental results on the facial recognition dataset of Holstein dairy cows and Simmental beef cattle, built upon the improved CCS-YOLOv7 cattle face detection model, demonstrated that the VoVNet-ECA-RC model achieved a precision of 99.37% for cattle face recognition with a final model size of 41.4MB. Therefore, the proposed research structures can provide a reference for non-contact individual recognition in the process of intelligent farming.
Application of Intelligent Obstacle Avoidance Algorithm Combined with Internet of Things Technology in Navigation
No full textWith the prosperity and development of the Maritime Silk Road, China\u27s maritime industry has reached a new height. While the maritime transport industry has been vigorously developed, it has also brought great challenges to safe navigation. To realize intelligent navigation, effectively prevent maritime collision accidents, and improve navigation safety, a structural model of intelligent navigation obstacle avoidance platform based on Internet of Things technology is first proposed. Then the research combines the analytic hierarchy process, artificial neural network and BP neural network algorithm, and introduces environmental factors to design an optimized intelligent navigation obstacle avoidance algorithm, so that the algorithm can make real-time intelligent adjustment strategies according to the changes of the actual environment. Finally, the collision risk at the location of the research ship is judged, and the priority list of obstacle avoidance is constructed by the risk value between different ships and the research ship, providing reference for the pilot. The research results show that the prediction accuracy of I-INOA algorithm is 97.83%. In the two obstacle avoidance experiments, the decision-making efficiency of the four ships based on I-IONA algorithm is the highest, which is 1. In practical application, the priority list of obstacle avoidance is P, O and S2. In conclusion, I-INOA algorithm has better performance and practicability, enabling the research ship to respond more intelligently and quickly
SAEDF: A Synthetic Anomaly-Enhanced Detection Framework for Detection of Unknown Network Attacks
No full textDetecting unknown cyber-attacks (i.e., zero-day) is difficult because network environments change frequently and there are few labeled examples of anomalies. Traditional methods for detecting anomalies often struggle to handle unknown attack types and work effectively with complex, high-dimensional data. To overcome these problems, we propose a new approach called the synthetic attack-enhanced detection framework (SAEDF). SAEDF combines synthetic anomaly generation, flexible feature extraction, and unsupervised anomaly detection. The framework employs a model known as the adaptive and dynamic generative variational autoencoder (ADGVAE). This model generates realistic synthetic attacks and adapts its structure to work effectively with datasets of varying complexity. This helps the model work well with a wide range of attack patterns while still being efficient. Tests on benchmark datasets show that SAEDF performs better than other methods. It achieves higher scores for F1, Recall, and has a much lower rate of false positives. These results show that SAEDF is effective in finding unknown attacks, improving detection accuracy, and handling complex and changing network traffic.
Efficient Screening-Based Optimization: A Greedy Approach for Large-Scale Sparse Learning
No full textThis paper proposes Efficient Screening-Based Optimization (ESO), a dual-threshold greedy screening framework for large-scale sparse learning. ESO integrates adaptive feature evaluation with dynamic parameter updates to address computational inefficiency in ultra-sparse scenarios. By employing a probabilistic screening mechanism and prox-based test functions, it achieves 50-70% faster computation than state-of-the-art methods when regularization parameters approach 105. Experiments on synthetic and real-world datasets demonstrate robustness across penalty functions (L1, SCAD, MCP) and data types (image, genomic). Theoretical analysis confirms solution consistency, while parameter sensitivity studies guide practical implementation. The method significantly enhances scalability for high-dimensional problems
Unpacking Competitive Performance in 5* Hotels: The Joint Effect of Dynamic Managerial Capabilities and Digital Transformation
No full textDigital transformation in the hospitality sector was accelerated by the COVID-19 pandemic. Strategy scholars discussed dynamic managerial capabilities as capabilities needed to adapt to rapid change and navigate digital transformation to gain a competitive advantage in the hospitality sector. It suggests that by leveraging digital technologies, fostering a culture of innovation, and building agile business ecosystems, hospitality organizations can enhance their competitive position and ensure long-term sustainability in an increasingly digitalized marketplace. The research question of our study is how dynamic managerial capabilities and the adoption of digital technologies affect the competitive advantage of the hospitality sector. We address the research question by deploying a multiple case study research design and collecting data from 5* Hotels in Lithuania. The study finds that the synergy between dynamic managerial capabilities such as data-driven decision-making, connectivity to internal and external networks, also digitally driven operational efficiency, and sustainability-oriented attitudes significantly enhances the competitive performance of 5-star hotels by fostering innovation, strategic alignment, and superior guest value. We also provide insights for 5* hotel owners and managers