ABSTRACT Drones have become pivotal in the Russo‑Ukrainian war, yet the mechanisms behind their transformation to a key military capability remain poorly understood. This article examines how Ukrainian state, military, and commercial actors applied an open innovation model to scale unmanned systems during Russia’s full‑scale invasion. Using a case study approach and data from 36 stakeholder interviews, it identifies the key actors and organisational structures – like the Unmanned Systems Force and the Brave1 platform – that shaped battlefield innovation across three stages of ecosystem development. The findings highlight the critical role of engineer‒soldier collaboration and inclusion of non-traditional defence technology producers. Sustaining innovation required significant bureaucratic adaptation, including new organisational actors and institutional mechanisms that facilitated decentralised development and cross‑governmental coordination. The study underscores the value of structures that support rapid iterative development and simplified procurement, and contributes theoretical and practical insights into the procedural aspects of military innovation in software-defined systems.

Abstract This paper studies a computationally efficient and robust method for path planning and path following tasks for unmanned systems. The method is based on relaxed differential-algebraic equations (DAEs), which allow to handle deviations from a prescribed path. The relaxation is designed by a suitably formulated optimal control problem with the aim to guide the system back to the desired path in a smooth way. The approach merely requires to solve a higher index DAE numerically. In addition, a parametric sensitivity analysis can be used to obtain Taylor approximations of perturbed solutions at very low computational cost. We demonstrate the method for a path following task with an unmanned ground vehicle (UGV) and a transition to hover maneuver of an unmanned aerial vehicle (UAV).

ABSTRACT The recent Armenia-Azerbaijani conflict, particularly the 2020 Nagorno-Karabakh war, highlighted the transformative role of advanced military technologies, notably unmanned systems with varying degrees of automation and autonomy. This conflict represents a pivotal case study in the evolution of military doctrine, as Azerbaijan’s effective use of drones, loitering munitions, and other autonomous systems significantly shaped its military strategy and outcomes. The war showcased the effectiveness of these technologies in gaining tactical and operational superiority, prompting a re-evaluation of traditional military doctrines that rely heavily on manpower and conventional hardware. This paper explores how Azerbaijan integrated these technologies into its military operations and examines the resulting shifts in military doctrine. The focus is on the interplay between technology and strategy, investigating whether AI/autonomy-based systems fostered new forms of military doctrine and, crucially, whether these doctrines proved effective in the context of modern warfare. The analysis also delves into Armenia’s contrasting reliance on more traditional military systems, revealing a stark doctrinal mismatch that contributed to its operational vulnerabilities. Furthermore, the paper addresses broader implications for future conflicts, asking whether this localized war signals a broader trend in military strategy, where autonomous technologies will continue to redefine the modern system of warfare.

Fruit harvesting systems are undergoing a paradigm shift toward sustainable and energy-efficient mechanized platforms driven by robotics, artificial intelligence, and advanced sensing technologies. This review synthesizes recent engineering developments in fruit harvesting, focusing on system architecture, fruit detachment mechanics, and mechanized harvesting strategies. It examines harvesting classifications, mechanical principles governing detachment, and pre-harvest factors affecting performance, along with principal mechanisms including shaking, cutting, and alternative detachment techniques. Post-detachment handling and fruit recovery processes are also analyzed, together with economic and sustainability-related trade-offs between manual and mechanized harvesting systems. Recent progress in robotic harvesting systems, machine vision, and multi-sensor fusion is evaluated within the framework of smart orchard engineering, with increasing emphasis on energy-efficient design, resource optimization, reduced postharvest losses, and environmental sustainability as key performance drivers. Despite these advancements, current technologies remain constrained by fruit damage susceptibility, biological variability, limited cross-crop adaptability, and high implementation costs, limiting large-scale adoption in commercial orchards. The novelty of this review lies in establishing a unified engineering framework that links mechanical detachment principles with robotic systems and intelligent sensing technologies under an energy-efficient sustainability perspective, enabling a system-level understanding of harvesting performance and supporting the development of next-generation adaptive and sustainable fruit harvesting systems.

Precise, highly spatial resolution surface pressure measurement is critical for reliable state assessment of unmanned systems, such as the flight safety of unmanned aerial vehicles. Conventionally, the acquisition of such high-resolution data requires dense, expensive sensing arrays or relies on external computational resources. Here, we introduce a flexible electronic skin featuring a sparse sensing network integrated with in-sensor numerical hyperdimensional computing, thereby facilitating super-resolution sensing and in-sensor learning. This innovative approach processes sparse inputs to produce high-resolution pressure field maps on-device, enabling rapid real-time analysis and precise flight state identification. The numerical hyperdimensional computing enhances the efficiency of in-sensor learning, overcoming the limitations of conventional vector symbolic architectures previously confined to classification tasks. Furthermore, the integration of hyperdimensional computing substantially boosts the performance of the flexible sensing skin. Compared to the computer-aided super-resolution method, our approach reduces power consumption from ~40 watts to ~0.09 milliwatts, memory usage from ~2274 to ~32 kilobytes, and latency from ~115 to ~10 milliseconds, respectively. The achieved super-resolution capability reaches an enhancement factor of 5.517. Compared with conventional high-density sensing arrays, our approach reduces the number of interconnects by 91.5% (using a 4 by 4 array). Last, the developed super-resolution skin is applied in unmanned aerial vehicle flight control.

Since the 21st century, with the continuous advancement of intelligent technology and the rapid development of aviation technology, the low-altitude economy has gradually attracted global attention and importance, becoming a new growth point for high-quality economic development in various countries. As a new economic form with an activity range of below 1,000 meters, the unmanned delivery system, with its unique advantages of breaking through ground traffic restrictions and improving logistics efficiency, has become one of the most commercially promising application scenarios in the low-altitude economy. The unmanned delivery system is reconfiguring the traditional logistics and delivery system. Technological iterations and policy breakthroughs are driving the unmanned delivery system into a period of rapid development. The integrated application of 5G networks, artificial intelligence, and high-precision navigation has significantly enhanced the autonomous decision-making capabilities of unmanned systems. However, it is worth noting that the current unmanned delivery still faces technical bottlenecks such as short battery life and poor adaptability to adverse weather, as well as systemic challenges like incomplete airspace management rules and high operating costs, which restrict the industry from expanding on a larger scale.

Background:Posterior cruciate ligament (PCL) injuries may lead to significant anteroposterior and rotational knee instability. Traditional reconstruction techniques, including single-bundle (SB) and double-bundle (DB) approaches, have limitations in terms of restoring full biomechanical function. A posterolateral tenodesis (PLT) augmentation associated with PCL reconstruction has been described, consisting of a nonanatomic bundle that shares the PCL tibial tunnel, courses along the posterior aspect of the lateral femoral condyle from an intra-articular to an extra-articular position, and is fixed at the lateral femoral epicondyle to provide additional control of tibial external rotation.Purpose:To determine the impact of adding a posterolateral tenodesis (PLT) to a PCL reconstruction to restore knee rotational and posterior stability.Study Design:Controlled laboratory study.Methods:In total, 24 embalmed cadaveric human knees were randomized into 3 main groups: intact (control), isolated PCL-deficient, and combined PCL + posterolateral corner (PLC)–deficient (Fanelli type A). Each group underwent sequential reconstructions using SB and DB PCL techniques, with and without the addition of PLT. Biomechanical testing included measurements of posterior tibial translation (PTT) and external tibial rotation (ER) at 30° and 90° of flexion using a custom-built robotic testing machine and inertial sensors. Porcine flexor tendons were used as grafts.Results:Sectioning the PCL significantly increased both PTT and ER, with values peaking in the combined PCL + PLC-deficient group (PTT: 10.3 ± 0.8 mm; ER: –30.4°± 0.8° at 90° of flexion). SB and DB reconstructions alone partially restored stability, but residual laxity persisted, especially in the rotational parameters. The addition of PLT significantly reduced both PTT and ER. In the DB + PLT group with combined PCL + PLC injury, the PTT and ER values (3.2 ± 0.3 mm and –12.5°± 0.3°, respectively) were statistically indistinguishable from the intact group (P > .05).Conclusion:Combined PCL and PLC injuries result in marked rotational and posterior instability. When added to SB or DB PCL reconstruction, PLT significantly improves biomechanical function, particularly regarding rotational control. These findings support the inclusion of PLT in surgical protocols addressing complex PCL-related instability.Clinical Relevance:Adding a posterolateral tenodesis to PCL reconstruction may reduce residual instability and improve outcomes in complex ligament injuries.

Visual SLAM (VSLAM) is a key technology for intelligent unmanned systems to achieve environmental perception and autonomous localization. In response to the issues of unstable feature extraction and the decreased adaptability and accuracy of SLAM systems caused by dynamic illumination changes in practical application scenarios, this paper proposes a robust VSLAM system based on heterogeneous feature data association, which does not require pre-trained models and can operate stably on resource-constrained platforms. The system designs a boundary-aware adaptive feature detection framework that maintains a stable number of features under complex illumination conditions through mirror padding and dynamic threshold adjustment. It also incorporates an edge-feature-guided quadtree optimization mechanism, constructing a region of interest (ROI) spatial mask to guide feature distribution and improve feature repeatability. By combining the GMS algorithm to enhance matching robustness, high-quality feature points are provided for back-end pose estimation, comprehensively improving the accuracy and adaptability of the SLAM system. Experimental results show that the proposed method achieves more stable and abundant feature extraction under complex illumination conditions. Compared with ORB-SLAM2, the feature repeatability rate is improved by approximately 39.8%, and the RMSE in multiple indoor and outdoor scenarios is reduced by 21.9%, providing an effective solution for the stable deployment and reliable operation of SLAM in practical applications.

Accurate perception of spatial position is essential for both biological vision and intelligent unmanned systems. Existing radio-based positioning approaches are susceptible to interference and require bulky infrastructures, while optical systems often trade accuracy for compactness. Here, we present a compound meta-eye system (CMES) that integrates an array of metalens sub-eyes to capture angular parallax from multiple targets simultaneously. Each sub-eye focuses light onto a detector to form arrayed images, from which a global-ratio algorithm reconstructs spatial coordinates with high precision. The CMES enables multi-target positioning and motion tracking within a meter-scale range, achieving a relative depth error below 2% and trajectory-fitting deviations under 0.5 mm. The metalens design provides diffraction-limited focusing and wide angular tolerance, combining biological compound eye compactness with flat meta-optics. This compact optical-perception platform offers an efficient solution for real-time multi-target spatial perception, with potential applications in formation control, visual navigation, and environmental perception for unmanned aerial vehicles and embodied intelligent agents.

YouTube has become a widely used tool for surgical education, offering open access to procedural videos for trainees and professionals alike. However, the reliability and pedagogical quality of these publicly available resources remain uncertain. In the context of minimally invasive inguinal hernia repair, we hypothesized that robotic (RT) surgery videos provide superior educational value compared with laparoscopic (LAP) ones. This study aimed to systematically evaluate and compare the quality of RT and LAP transabdominal preperitoneal (TAPP) inguinal hernia repair videos available on YouTube. Based on a priori sample size calculation for moderate effect size (Cohen's d = 0.5), we determined that 63 videos per group would be required for adequate statistical power. On March 19, 2025, a structured search was performed on YouTube using the term "Transabdominal preperitoneal repair for inguinal hernia." This strategy generated an initial pool of 300 potentially eligible videos, which were screened sequentially until the predetermined sample size of 63 videos per group was achieved. Eligible content featured TAPP repairs via RT or LAP approach. Duplicates, non-inguinal TAPP procedures, videos consisting exclusively of animations, conference lectures, or irrelevant videos were excluded. The primary objective was to evaluate videos containing operative demonstrations of surgical procedures. After this selection, two blinded hernia surgeons independently assessed all videos using a newly developed 21-item qualitative evaluation tool and the validated LAParoscopic surgery Video Educational GuidelineS (LAP-VEGaS) score, a tool for evaluating surgery videos submitted to presentations and publications. Group comparisons were conducted using Welch's t-test and Mann-Whitney U test. Effect size was reported using Cohen's d. Both assessment tools demonstrated adequate inter-rater agreement and internal consistency, supporting their reliability for evaluating educational video content. From 300 videos screened, 126 met inclusion criteria (63 RT, 63 LAP). RT videos scored significantly higher than LAP videos on the newly developed qualitative evaluation tool (mean score 0.54 vs. 0.44; P < .001; Cohen's d = -0.60), indicating a moderate effect size. Similarly, RT videos demonstrated higher LAP-VEGaS scores (7.46 vs. 6.34), although this difference did not reach statistical significance (P = .091). These findings suggest that RT videos present superior adherence to technical and educational standards, respectively. Both assessment tools demonstrated adequate inter-rater agreement and internal consistency, supporting their reliability for evaluating educational video content. YouTube contains a large repository of TAPP repair videos, but quality is inconsistent. The new qualitative tool demonstrated strong reliability and internal consistency, supporting its use for educational video assessment. RT videos showed greater adherence to technical and educational standards compared with LAP. RT videos may therefore offer more structured learning content, but general quality improvements remain necessary across both approaches.

Fixed-time control strategy for high-speed unmanned system under switching topology

Adaptive risk-aware reinforcement learning for safe navigation of unmanned systems

Robotic machining quality enhancement via physics-informed error prediction and collaborative compensation

Kinematic equivalence-based functional mapping for pose error modeling and online compensation of mobile machining robots

Background: In the modern era new technology has overflowed in each and every field. Many things are getting mechanized overnight. Manual labour is getting reduced or replaced by newly developed instruments and robotic machines. Even then many machines are operated by human beings only. Electricians use hand held drilling machines routinely and because of that are exposed to hand vibration. So they are at high risk of developing either HAVS, carpel tunnel syndrome (CTS) or both. Material &amp; methods: In the present study 40 electricians using drill machines &amp; 40 Controls (not exposed to hand vibration) were selected and Nerve conduction study (NCS) of both upper limbs was carried out. Motor &amp; sensory nerve conduction of Ulnar, Median &amp; Radial nerves was studied. Results of both the groups were compared. Results:The Distal Motor &amp; sensory Latency (Min.) of both Median, both Radial &amp; both Ulnar was significantly prolonged in study group compared to control Group (p &lt; 0.05).The amplitude of CMAP of both Median, both Ulnar &amp; Left Radial was significantly reduced in study group compared to control Group (p &lt; 0.05). The amplitudes of SNAPs &amp; sensory conduction velocities were significantly reduced in study group compared to control group. Motor conduction velocities of Ulnar, Median &amp; Radial on both sides were significantly reduced in subjects compared to control Group (p &lt; 0.05). Conclusion:From our study we conclude that the long term repetitive exposure to hand vibration is associated with distal neuropathy more of sensory than motor. The median nerve is mostly affected but in few cases there is also involvement of ulnar &amp; radial nerve. Dominant hand is affected most of the time. However in few cases there is bilateral involvement.

Chatter severely limits the stability and efficiency of robotic milling, making accurate chatter detection essential for effective suppression. While data-driven machine learning methods outperform physics-based models by learning from diverse milling conditions, they typically require fully labeled datasets tailored to various robots, tools, and materials—resources often unavailable in real-world applications. To address the label scarcity issue, this paper proposes TFMSP (Time-Frequency Multi-Scale Prediction), a self-supervised learning model designed for robotic chatter detection. TFMSP consists of two structurally identical but parameter-independent AIZ-FUSE1D encoders that separately process time-and frequency-domain signals. Each encoder integrates a spatial attention-based 1D CNN with a channel attention-based Transformer to extract rich nonlinear features across multiple time-frequency scales with fast convergence. A time-frequency prediction task enables the model to learn discriminative chatter features from unlabeled data, followed by fine-tuning with limited labeled samples. Experimental results on robotic milling datasets under various conditions show that TFMSP achieves 98.13% accuracy with only 0.5% labeled data, outperforming TFPred (94.67%), SimCLR (89.34%), and TFAI (88.79%). Moreover, it requires only 30 pretraining and 30 fine-tuning epochs—fewer than the baseline models. This approach provides an effective solution for chatter recognition under diverse robotic machining modes and dynamic poses with minimal labeled data.

ABSTRACT To promote the efficient, comprehensive, reliable, and low‐cost testing and application of intelligent algorithms for autonomous underwater vehicles (AUVs), this paper proposes an innovative six‐dimensional digital twin (6D DT) conceptual model and provides detailed engineering implementation strategies of this twin system. This model integrates six core dimensions, including physical entity, virtual entity, virtual native entity (VNE), twin data, services, and communication connection. The concept of VNE is introduced to significantly enhance the practicability, security, and reliability of AUV testing by constructing diversified test scenarios. To implement the proposed model, a high‐fidelity underwater Cyberspace visualization is developed using Unreal Engine 5, which improves the granularity of virtual–real mapping and enhances human–computer interaction. An efficient data bridge plugin is implemented to ensure real‐time, stable bidirectional communication. The DT system (DTS) supports both offline simulation and online DT modes, enabling flexible testing from pure software simulation to real‐time virtual–physical interaction, thereby enhancing the credibility of algorithm validation. Two experimental cases conducted on this DTS demonstrate the technical feasibility and reliability of the proposed conceptual model. The approach provides a valuable reference for applying digital twin technology in underwater unmanned systems and accelerates the development of autonomous intelligent AUVs.

This study develops a new concept of computer-assisted exercises (CAX) on constructive simulation systems and how the proposed concept affects the strategy and teaching methods. The current state of affairs in the field of defense and security, both in Europe and in the world, requires the acquisition of competencies (European Qualifications Framework—EQF: knowledge, skills, independence, and responsibility), i.e., the education and training of a significantly larger number of personnel in the field of defense and security than has been the case in the last 70 years. In addition, an important specificity of today is that students need to acquire some competencies that were almost unknown until recently. Most of these competencies are the result of the rapid development of technology, which has significantly changed human life in all areas. In order to respond to the modern requirements of conducting operations, where the transfer of information both horizontally and vertically is exponentially accelerated, current concepts of preparation and implementation of education and training, of which exercises are often the most important part, need to be replaced with new concepts, and one such concept is developed in this paper. New information introduced is mostly related to the new weapons that are being introduced (unmanned systems, hypersonic missiles, weapons based on microwaves and lasers, etc.), which all result in necessary changes to the traditional approach to conducting war, i.e., tactics, techniques, and procedures (TTP). This novel exercise concept allows for the simultaneous implementation of training for up to three or four hierarchical levels (e.g., TF Div, brigade, battalion, and company) in one exercise, while in most countries, including the NATO alliance, it is still common for such exercises to be conducted according to a concept that is over 20 years old and, as a rule, is focused on the implementation of exercises for one or two hierarchical levels. This approach allows key personnel from the headquarters of units from four hierarchical levels to be simulated in real time, which is not provided by current concepts for preparing and conducting exercises. The new concept was applied as a multi-level, computer-assisted exercise (CAX) on constructive simulation systems. In addition, significant advantages of the new concept relate to the flexibility and adaptability of the proposed concept to be applied in addition to operational units and in training institutions such as academies and higher education institutions. In addition to the above, the new concept requires a shorter planning period as well as fewer total resources needed for the preparation and implementation of the exercise. The management, organizational, and technological components of the proposed exercise concept are implemented in the CAX model. The hypotheses in this paper will be tested in an applied study, which was evaluated through an external evaluation body. The implemented CAX model was tested in Croatia on the example of using exercises at the Croatian Defense Academy.

To meet the application requirements for detecting UXO using magnetic detection, the micro fluxgate tensor technology has shown significant value in target recognition, localization, and interference resistance. A board-level micro fluxgate tensor is developed using heterogeneous multi-dimensional integrated triaxial fluxgate technology, achieving synchronous detection and identification of dual targets. The micro fluxgate tensor consists of a typical cross-array formed by four MEMS integrated triaxial fluxgate sensors bonded onto a PCB board, with the size of each triaxial sensor being 17.4 mm × 13.3 mm × 13.8 mm, and the overall size of the micro fluxgate tensor being 86 mm × 80 mm × 16 mm. The micro fluxgate tensor uses a total of 12 uniaxial MEMS fluxgate chips, the size of each chip being 10.8 mm × 6mm × 0.5mm, with an average sensitivity of approximately 1930 V/T and a noise power spectral density below 0.05 nT/√Hz @1Hz. Within a test area of 1.2m × 1.2 m, two differently shaped magnetic targets, an olive-shaped magnet and a spherical magnet, are detected by the micro fluxgate tensor successfully. By comparing the magnetic tensor figure aspect ratios of the olive-shaped magnet (175%) and the spherical magnet (122%), the two targets are distinguished. Furthermore, the magnetic field tensor detection of coexisting cylindrical and spherical magnets is performed, and the results of the magnetic tensor figure indicate the presence of both targets and achieve identification differentiation based on shape aspect ratios, with aspect ratios of 241% and 132% respectively. The micro fluxgate tensor will have advantages such as integration, miniaturization, lightweight design, and low power consumption. It will be more suitable for deployment on portable platforms and unmanned systems, thereby enhancing the efficiency of UXO detection.

The paper aims to examine the digitalisation of tax administrations in economically developed countries and taxpayers’ perceptions of digital tax services. The study compares the adoption of digital technology in tax administrations and analyses tax players' attitudes toward these tools. Our findings confirm that a large share of countries is using a set of emerging digital technologies, including robotic process automation, machine learning, network analytics, artificial intelligence, and blockchain. Socio-economic factors appear to play an important role in supporting digital tax administration. Younger respondents, respondents with higher income, and those with better tax knowledge show greater support for fully digital tax filing systems and mobile tax apps. A successful digital transformation of tax administration requires policies that support digital literacy and trust in digital public services.