Smart farming technology is becoming of the actual topics in the modern world of technology. Contemporary farming technology expands robot applications by using AI for the recognition of variable patterns. Moreover, the agriculture field demands a safety robot, due to the fragile surrounded confined space and it must be adaptable to extremely constrained working environments. Therefore, this research paper presents a novel tomato harvesting robot arm based on a continuum robot structure. The proposed continuum robot arm flexible backbone structure provides safety and efficient work in a confined workspace. This research paper consists of three parts: the first part of the paper contains the robot design and the newly designed tomato harvesting gripper tool. The second part of the paper describes the machine learning part for detecting matured tomatoes and the distance measuring technique with a single camera. The third part of the research paper explains robot kinematics and control algorithms. The final part of the research paper explains the experimental results. As a result of the conducted experiment, the tomato harvesting speed of the proposed robot was 56 s for a single tomato. Meanwhile, the tomato recognition accuracy was 96 percent.

The detection of weeds at the stages of cultivation is very important for detecting and preventing plant diseases and eliminating significant crop losses, and traditional methods of performing this process require large costs and human resources, in addition to exposing workers to the risk of contamination with harmful chemicals. To solve the above tasks, also in order to save herbicides and pesticides, to obtain environmentally friendly products, a program for detecting agricultural pests using the classical K-Nearest Neighbors, Random Forest and Decision Tree algorithms, as well as YOLOv5 neural network, is proposed. After analyzing the geographical areas of the country, from the images of the collected weeds, a proprietary database with more than 1000 images for each class was formed. A brief review of the researchers' scientific papers describing the methods they developed for identifying, classifying and discriminating weeds based on machine learning algorithms, convolutional neural networks and deep learning algorithms is given. As a result of the research, a weed detection system based on the YOLOv5 architecture was developed and quality estimates of the above algorithms were obtained. According to the results of the assessment, the accuracy of weed detection by the K-Nearest Neighbors, Random Forest and Decision Tree classifiers was 83.3 %, 87.5 %, and 80 %. Due to the fact that the images of weeds of each species differ in resolution and level of illumination, the results of the neural network have corresponding indicators in the intervals of 0.82–0.92 for each class. Quantitative results obtained on real data demonstrate that the proposed approach can provide good results in classifying low-resolution images of weeds.

Genomic and epigenomic instability are defining features of cancer, driving tumor progression, heterogeneity, and therapeutic resistance. Central to this process are epigenetic echoes, persistent and dynamic modifications in DNA methylation, histone modifications, non-coding RNA regulation, and chromatin remodeling that mirror underlying genomic chaos and actively influence cancer cell behavior. This review delves into the complex relationship between genomic instability and these epigenetic echoes, illustrating how they collectively shape the cancer genome, affect DNA repair mechanisms, and contribute to tumor evolution. However, the dynamic, context-dependent nature of epigenetic changes presents scientific and ethical challenges, particularly concerning privacy and clinical applicability. Focusing on lung cancer, we examine how specific epigenetic patterns function as biomarkers for distinguishing cancer subtypes and monitoring disease progression and relapse.

Purpose: The purpose of this paper is to present a novel hybrid pre-tension mechanism for continuum manipulators to prevent wire slack and improve continuum robot payload capacity, as well as to present a new method to control continuum manipulators’ shape. Design/methodology/approach: This research explains the hardware design of a hybrid pre-tension mechanism device and proposes a mathematic formulation wire-tension based on robot design. Also, the wire-tension control method and payload estimation model would be discussed. Findings: Wire-tension is directly related to the continuum manipulators’ rigidity and accuracy. However, in the case of robot motion, wires lose their tension and such an issue leads to the inaccuracy and twist deformation. Therefore, the proposed design assists in preventing any wire slack and derailing the problem of the wires. Originality/value: The novelty of this research is proposed pre-tension mechanism device design and control schematics. Proposed pre-tension mechanism designed to maintain up to eight wires simultaneously.

Attentions to the bio-inspired robots are increasing dramatically in last two decades. Flexible structure, excellent flexibility and possibility of working in constrained environment made continuum robots attractive in robotic community. The purpose of this paper is to introduce, describe and test a novel design of continuum robot backbone design with variable stiffness which using of coil springs. A kinematic model is introduced and explained which could be applied for to a wide range of construction with two pairs of cables per section design. Furthermore, based on the geometry and material property of compliant joint, the robot is verified for justification of the robot construction. Finally, prototype motion analysis tested and repeatability experiments are carried out.

In the course of our research work, the American, Russian and Turkish sign languages were analyzed. The program of recognition of the Kazakh dactylic sign language with the use of machine learning methods is implemented. A dataset of 5000 images was formed for each gesture, gesture recognition algorithms were applied, such as Random Forest, Support Vector Machine, Extreme Gradient Boosting, while two data types were combined into one database, which caused a change in the architecture of the system as a whole. The quality of the algorithms was also evaluated. The research work was carried out due to the fact that scientific work in the field of developing a system for recognizing the Kazakh language of sign dactyls is currently insufficient for a complete representation of the language. There are specific letters in the Kazakh language, because of the peculiarities of the spelling of the language, problems arise when developing recognition systems for the Kazakh sign language. The results of the work showed that the Support Vector Machine and Extreme Gradient Boosting algorithms are superior in real-time performance, but the Random Forest algorithm has high recognition accuracy. As a result, the accuracy of the classification algorithms was 98.86 % for Random Forest, 98.68 % for Support Vector Machine and 98.54 % for Extreme Gradient Boosting. Also, the evaluation of the quality of the work of classical algorithms has high indicators. The practical significance of this work lies in the fact that scientific research in the field of gesture recognition with the updated alphabet of the Kazakh language has not yet been conducted and the results of this work can be used by other researchers to conduct further research related to the recognition of the Kazakh dactyl sign language, as well as by researchers, engaged in the development of the international sign language