Data augmentation is standard practice in remote sensing (RS) detection pipelines. However, the performance degradation it induces through sensor physics violations, semantic corruption, and domain shift remains largely uncharacterised across application domains. No prior survey has addressed these mechanisms across the full breadth of RS detection tasks. This paper presents the first PRISMA-guided systematic review of the negative impacts of data augmentation in RS detection, synthesising 113 peer-reviewed publications (2014–2026, inter-rater agreement κ = 0.81 ) spanning nine application domains: aerial object detection, flood inundation mapping, infrastructure crack detection, building damage assessment, wildfire detection, landslide and change detection, crop and vegetation monitoring, synthetic aperture radar-based detection, and oil spill detection. We identify and mathematically characterise six principal failure classes: oriented bounding box annotation corruption (up to 6.2 - 3.3 + 8.2–14.7 - 4.7 - 2.8 - 11.3 + 7.3 × computational overhead). The review further delivers a 24-subtype cross-domain failure taxonomy, a severity heatmap across nine tasks and six sensor modalities, 24 physics-grounded mathematical models, and eight evidence-based open research directions. Together, these contributions establish a principled, sensor-aware framework for safe augmentation design in operational Earth observation systems.
The non-biodegradability and inherent toxicity of petroleum-based lubricants have created a strong need for environment friendly bio-based alternatives. Biolubricants enriched with nano-additives serve as a highly effective and sustainable alternative to conventional mineral oils. In this work, a set of bio-oil based mono and hybrid nanolubricants were prepared. Nanoparticles of CuO were used to develop mono nanolubricant and CuO along with MWCNT was utilized for synthesizing hybrid nanolubricant. Nanoparticles were added in a range of 0.02
Internet of Things (IoT) is a rapidly growing domain encompassing various industries, applications, and technologies. Although data remains secure in encrypted form during transmission, managing individual keys for pairwise communication in large IoT networks is a challenging and complex task. A group key can enable effective message delivery by reducing key management overhead. Several proposed frameworks for secure group key management exist in literature but scalability challenges still remain. In particular, they become more significant during the join or leave operation phase which results in group-wide rekeying causing unnecessary computational overhead (proportional to number of users involved) and reduced network scalability. Additionally in few existing protocols, there is a threat to backward secrecy as the re-keying process is not secure; the already left devices can quickly recalculate the new group key. This work proposes a blockchain-based scalable framework that addresses these problems and maintains a consistent group key in dynamic IoT environments. The proposed framework utilises a highly efficient and secure Group Key Management protocol, ensuring both forward and backward secrecy. The scalability is achieved by limiting the initial group key setup phase to 2N-2 steps and only 1 step for adding or removing a user. The robustness to various kinds of attacks like sybil attack, replay attack, collusion attack and insider attack is demonstrated. Additionally, the security of the proposed framework is formally analysed using Burrows-Abadi-Needham logic. The performance of the proposed framework is evaluated through Network Simulator ‘NS2’ to show that the time cost is feasible and effective for IoT networks.
Vehicle Routing Problem with Time Windows (VRPTW) is a common problem in logistics whose goal is to deliver goods to all customers while keeping the total travel distance as short as possible and minimizing the number of vehicles used. This paper proposes a new hybrid algorithm that is K-Means-NN-ALNS-SA, which combines four techniques. K-Means Clustering groups customers on the basis of their location so that the customers which are nearby are in the same cluster. This helps to make the problem shorter, simpler and faster to solve. Greedy Nearest Neighbour (NN) creates a delivery route by visiting the closest customer within time limit, while keeping in mind the capacity of the vehicle inside each cluster. Adaptive Large Neighbourhood Search (ALNS) provides improvement in routes by removing and rebuilding parts. Over time it understands which operator works best and selects the operator accordingly. Simulated Annealing (SA) prevents the algorithm from being stuck in the local best solution by sometimes accepting slightly worse solutions, in order to find the global best solution. This algorithm was tested using the Solomon benchmark dataset which consists of 56 instances – each instance having 100 customers and is commonly used for VRPTW research. The results show that K-Means-NN-ALNS-SA provides shorter distances of time travel and uses fewer vehicles. It performs better than traditional or single-method algorithms.
The benefits of the Internet of Medical Things (IoMT) in providing seamless healthcare to the world are at the forefront of technological advancement. However, security concerns of any IoMT systems are high since they threaten to compromise personal information of patients and can even cause health hazards. Researchers are exploring the use of various techniques to ensure a high level of security of IoMT systems. One key concern is that the computing power of any Internet of Things (IoT) device is relatively low, hence mechanisms that require low computational power are appropriate for designing Intrusion Detection Systems (IDS). In this research work, a blockchain IDS coalition is proposed for securing IoMT networks and devices. The blockchain ledger is compact and uses less processing resources. Additionally, the ledger requires less communication overhead. The cryptographic hashes in the suggested architecture ensure complete data secrecy and integrity between parties who are trusted and those who are untrustworthy. Peer-to-peer networks in both central and cluster networks are also included in this work for complete decentralization. The proposed model can counter various attacks, including Denial of Service (DoS), anonymity attacks, impersonation attacks, Man-In-The-Middle (MITM), and Cross-Site Scripting (XSS). The proposed method achieved an F1- score as high as 100% and reported an AUC value of over 99%.
