Circuit cutting decomposes a large quantum circuit into smaller subcircuits that are executed independently; the original circuit’s expectation values are then recovered by classically combining the measured subcircuit outcomes. While prior work characterises cutting overhead in terms of subcircuit counts and sampling complexity, its end-to-end impact on iterative, estimator-driven training pipelines remains insufficiently measured from a systems perspective. We propose DistributedEstimator , a cut-aware estimator execution pipeline that treats circuit cutting as a staged distributed workload. Each estimator query is instrumented across four phases: partitioning, subexperiment generation, parallel execution, and classical reconstruction. Using logged runtime traces and learning outcomes on two binary classification workloads (Iris and MNIST), we quantify cutting overheads, scaling limits, and sensitivity to injected stragglers, and evaluate whether accuracy and robustness are preserved under matched training budgets. Our measurements reveal that reconstruction constitutes a dominant fraction of per-query time—reaching a median of 53% and a 95th percentile of 58% at three cuts—thereby bounding achievable speed-up under increased parallelism. Despite these overheads, test accuracy is fully preserved on Iris and maintained without systematic degradation on MNIST across all evaluated cut configurations. Robustness under Gaussian noise and FGSM perturbations is similarly preserved, with several cut configurations exhibiting comparable or improved robustness relative to the uncut baseline. The exponential growth of subexperiment counts with each additional cut ( O ( 9 c ) for CNOT-based decomposition) represents a fundamental computational barrier that limits practical experimentation to small qubit counts with current methods. These results establish that practical scaling of circuit cutting for learning workloads requires reducing and overlapping reconstruction, designing scheduling policies for barrier-dominated critical paths, and developing computationally efficient reconstruction strategies for larger qubit counts.

Abstract Localization in wireless sensor networks (WSNs) is used to determine the coordinates of the sensor nodes deployed in the sensing field. It is the process that determines the location of the target nodes relative to the location of deployed anchor nodes. These anchor nodes are deployed at known locations having GPS installed in them. However, mostly in all 3D applications, the area under observation may have a complexity in the sensing environment. In this work, a modified learning enthusiasm‐based teaching learning based optimization algorithm (LebTLBO) is proposed to deal with the 3D localization problem using single anchor and moving target nodes in anisotropic network with DOI 0.01. LebTLBO is a metaheuristic inspired by the classroom teaching and learning method of teaching learning based optimization algorithm. An improved LebTLBO algorithm aims to achieve enhanced performance by balancing the exploration and exploitation capabilities of conventional LebTLBO to improve its global performance. On the CEC2019 benchmark functions, the suggested technique is assessed, and computational findings show that it provides promising outcomes over other competitive algorithms. Also, mLebTLBO outperforms well in terms of localization error in 3D environment. The proposed technique is useful to cope up in case of rescue operations.

As stated by Spock, “change is the essential process of all existence,” which is reflected in everyday applications in our daily lives. We, as humans, just need to find a way to make the best use of the current technological advances. The pandemic has managed to exploit our deepest vulnerabilities and insecurities. We need to cope with a lot of things, just to be comfortable in the new normal. Hence, we can rely on technology, the greatest asset developed by humans. In this article, we discuss how we can enhance the work environment in offices post-pandemic. We combine federated learning with emotion analysis to create a state-of-the-art, simple, secure, and efficient emotion monitoring system. We combine facial expression and speech signals to find out macroexpressions and create an emotion index that is monitored to find the mental health of the user. Federated learning enables users to locally train the model without compromising his/her privacy. In place of sending data to the centralized server, the proposed scheme sends only model weights that are combined at the server to make a better global model, which is further pushed back to the users. This model is then trained interorganizational as it does not violate the privacy or data sharing to achieve optimal results. The data collected from users are monitored to analyze the mental health and presented with counseling solutions during low times. Technology is a panacea that has enabled us to survive in this pandemic, and by using our solution to improve work culture and the environment in post-pandemic times.

Localisation has become a major attraction of research in recent years in the field of wireless sensor networks (WSNs). It is required for various applications like monitoring of objects placed in indoors and outdoors environments. The main requirement in localisation is to assign a location to each node, since multiple sensor nodes in WSN are used to retrieve information. The aim of this research is to address a WSN localisation problem using various optimisation techniques. The concept of single anchor node placement at the centre of sensing field with its projection using hexagonal pattern is introduced. In this study, a novel hybrid optimisation technique named as dragonfly–firefly algorithm (DA–FA) is proposed. DA is an optimisation algorithm recently suggested based on the dragonfly's static and dynamic swarming behaviour. The suggested hybrid technique combines the exploration capability of explore DA and Firefly algorithm's to exploit to obtain ideal global solutions. To check the effectiveness of DA–FA CEC 2019 benchmark functions are used for comparison with competitive algorithms. DA–FA converges fast and provide optimum solution for most of the benchmark functions. In addition, DA–FA outperforms well in terms of localisation error in comparison to existing localisation solutions.

OBJECTIVE: Community health systems operating in remote areas require accurate information about where people live to efficiently provide services across large regions. We sought to determine whether a machine learning analyses of satellite imagery can be used to map remote communities to facilitate service delivery and planning. MATERIALS AND METHODS: We developed a method for mapping communities using a deep learning approach that excels at detecting objects within images. We trained an algorithm to detect individual buildings, then examined building clusters to identify groupings suggestive of communities. The approach was validated in southeastern Liberia, by comparing algorithmically generated results with community location data collected manually by enumerators and community health workers. RESULTS: The deep learning approach achieved 86.47% positive predictive value and 79.49% sensitivity with respect to individual building detection. The approach identified 75.67% (n = 451) of communities registered through the community enumeration process, and identified an additional 167 potential communities not previously registered. Several instances of false positives and false negatives were identified. DISCUSSION: Analysis of satellite images is a promising solution for mapping remote communities rapidly, and with relatively low costs. Further research is needed to determine whether the communities identified algorithmically, but not registered in the manual enumeration process, are currently inhabited. CONCLUSIONS: To our knowledge, this study represents the first effort to apply image recognition algorithms to rural healthcare delivery. Results suggest that these methods have the potential to enhance community health worker scale-up efforts in underserved remote communities.

In the recent years, vehicular adhoc networks (VANETs) can be an attractive choice for collecting and transferring the healthcare data of the passengers to the remote healthcare centers. In VANETs, some of the intermediate nodes may act as relay nodes in which case, these networks are called as vehicular relay networks (VRNs). However, the transmitted information in VRNs can be captured by intruders during transmission. Moreover, an attacker can launch selective forwarding, blackhole, and sinkhole attacks in the network, which may in turn degrade the network performance parameters like high end-to-end delay, low packet delivery ratio (PDR) and network throughput. Hence, to address these issues, a secure data dissemination scheme using VRNs is proposed. In the proposed scheme, first, a secure vehicular medical relay network system is designed for the users belonging to disconnected rural areas. The collected information is filtered at zonal levels before transmission to a nearby road side units, which further pass it to the incoming vehicles. Second, a secure passenger health monitoring network is designed which continuously monitors health services of the passengers traveling in different vehicles. The information collected through small body sensors installed in the vehicles act as data sets that is forwarded to the on-board monitoring unit within the vehicle. This collected data is then transmitted to centralized healthcare centers for processing by using VRNs. Lastly, a strong elliptic curve cryptography-based cryptographic solution is designed for secure communication among different vehicles. The performance of the proposed scheme is evaluated in various network scenarios with respect to different selected parameters, such as throughput, network delay, PDR, jitter, transmission and computation overheads, and key distribution overhead. The obtained results indicate that the proposed scheme provides improvement of 52% in average delay and 5% in PDR. This further indicates effective message delivery even with high mobility of the vehicles.