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Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.

Background Changing clinical practice is a difficult process, best illustrated by the time lag between evidence and use in practice and the extensive use of low-value care. Existing models mostly focus on the barriers to learning and implementing new knowledge. Changing clinical practice, however, includes not only the learning of new practices but also unlearning old and outmoded knowledge. There exists sparse literature regarding the unlearning that takes place at a physician level. Our research objective was to elucidate the experience of trying to abandon an outmoded clinical practice and its relation to learning a new one. Methods We used a grounded theory-based qualitative approach to conduct our study. We conducted 30-min in-person interviews with 15 primary care physicians at the Cleveland VA Medical Center and its clinics. We used a semi-structured interview guide to standardize the interviews. Results Our two findings include (1) practice change disturbs the status quo equilibrium. Establishing a new equilibrium that incorporates the change may be a struggle; and (2) part of the struggle to establish a new equilibrium incorporating a practice change involves both the “evidence” itself and tensions between evidence and context. Conclusions Our findings provide evidence-based support for many of the empirical unlearning models that have been adapted to healthcare. Our findings differ from these empirical models in that they refute the static and unidirectional nature of change that previous models imply. Rather, our findings suggest that clinical practice is in a constant flux of change; each instance of unlearning and learning is merely a punctuation mark in this spectrum of change. We suggest that physician unlearning models be modified to reflect the constantly changing nature of clinical practice and demonstrate that change is a multi-directional process.

Energy power flows are an important factor to be calculated and, thus, are needed to be enhanced in an electrical generation system. It is very necessary to optimally locate the Flexible Alternating Current Transmission Systems (FACTS) devices and improve the Available Transfer Capability (ATC) of the power transmission lines. It relieves the congestion of the system and increases the flow of power. This research study has been accomplished in two stages: optimization of location of FACTS device by the novel Sensitivity and Power loss-based Congestion Reduction (SPCR) method and the calculation of ATC using the proposed Metaheuristic Evolutionary Particle Swarm Optimization (MEEPSO) technique. The Thyristor Controlled Series Capacitor (TCSC) is used as a FACTS device to control the reactance of power transmission line. The effectiveness of the proposed methods is validated, utilizing the six bus as well as 30 bus system. The acquired outcomes are contrasted with conventional ACPTDF and DCPTDF procedures. These values are determined with the assistance of MATLAB version 2017 on the Intel Core i5 framework by taking two-sided exchanges and they are contrasted and values determined with the assistance of Power World Simulator (PWS) programming.

The universal problem of bacterial resistance to antibiotic reflects a serious threat for physicians to control infections. Evolution in bacteria results in the development of various complex resistance mechanisms to neutralize the bactericidal effect of antibiotics, like drug amelioration, target modification, membrane permeability reduction, and drug extrusion through efflux pumps. Efflux pumps acquire a wide range of substrate specificity and also the tremendous efficacy for drug molecule extrusion outside bacterial cells. Hindrance in the functioning of efflux pumps may rejuvenate the bactericidal effect of conventional antibiotics. Efflux pumps also play an important role in the exclusion or inclusion of quorum-sensing biomolecules responsible for biofilm formation in bacterial cells. This transit movement of quorum-sensing biomolecules inside or outside the bacterial cells may get interrupted by impeding the functioning of efflux pumps. Metallic nanoparticles represent a potential candidate to block efflux pumps of bacterial cells. The application of nanoparticles as efflux pump inhibitors will not only help to revive the bactericidal effect of conventional antibiotics but will also assist to reduce biofilm-forming capacity of microbes. This review focuses on a novel and fascinating application of metallic nanoparticles in synergy with conventional antibiotics for efflux pump inhibition.

The substantial advancements offered by the edge computing has indicated serious evolutionary improvements for the internet of things (IoT) technology. The rigid design philosophy of the traditional network architecture limits its scope to meet future demands. However, information centric networking (ICN) is envisioned as a promising architecture to bridge the huge gaps and maintain IoT networks, mostly referred as ICN-IoT. The edge-enabled ICN-IoT architecture always demands efficient in-network caching techniques for supporting better user’s quality of experience (QoE). In this paper, we propose an enhanced ICN-IoT content caching strategy by enabling artificial intelligence (AI)-based collaborative filtering within the edge cloud to support heterogeneous IoT architecture. This collaborative filtering-based content caching strategy would intelligently cache content on edge nodes for traffic management at cloud databases. The evaluations has been conducted to check the performance of the proposed strategy over various benchmark strategies, such as LCE, LCD, CL4M, and ProbCache. The analytical results demonstrate the better performance of our proposed strategy with average gain of 15% for cache hit ratio, 12% reduction in content retrieval delay, and 28% reduced average hop count in comparison to best considered LCD. We believe that the proposed strategy will contribute an effective solution to the related studies in this domain.

The performance of drones, especially for time-sensitive tasks, is critical in various applications. Fog nodes strategically placed near IoT devices serve as computational resources for drones, ensuring quick service responses for deadline-driven tasks. However, the limited battery capacity of drones poses a challenge, necessitating energy-efficient Internet of Drones (IoD) systems. Despite the increasing demand for drone flying automation, there is a significant absence of a comprehensive drone network service architecture tailored for secure and efficient operations of drones. This research paper addresses this gap by proposing a safe, reliable, and real-time drone network service architecture, emphasizing collaboration with fog computing. The contribution includes a systematic architecture design and integration of blockchain technology for secure data storage. Fog computing was introduced for the Drone with Blockchain Technology (FCDBT) model, where drones collaborate to process IoT data efficiently. The proposed algorithm dynamically plans drone trajectories and optimizes computation offloading. Results from simulations demonstrate the effectiveness of the proposed architecture, showcasing reduced average response latency and improved throughput, particularly when accessing resources from fog nodes. Furthermore, the model evaluates blockchain consensus algorithms (PoW, PoS, DAG) and recommends DAG for superior performance in handling IoT data. Fog; Drones; Blockchain; PSO; IoT; Vehicular.

Vehicular Content Networks (VCNs) represent key empowering solution for content distribution in fully distributed manner for vehicular infotainment applications. In VCN, both on board unit (OBU) of each vehicle and road side units (RSUs) facilitate content caching to support timely content delivery for moving vehicles when requested. However, due to limited caching capacity available at both RSUs and OBUs, only selected content can be cached. Moreover, the contents being demanded in vehicular infotainment applications are transient in nature. The transient content caching in vehicular content networks with the use of edge communication for delay free services is fundamental issue and need to get addressed (Yang et al. in ICC 2022-IEEE international conference on communications. IEEE, pp 1–6, 2022). Therefore, this study focuses on edge communication in VCNs by firstly organizing a region based classification for vehicular network components including RSUs and OBUs. Secondly, a theoretical model is designed for each vehicle to decide its content fetching location (i.e. either RSU or OBU) in current region or neighboring region. Further, the caching of transient contents inside vehicular network components (such as RSU, OBU) is based on content caching probability. Finally, the proposed scheme is evaluated under different network condition in Icarus simulator for various performance parameters. The simulation results proved outstanding performance of the proposed approach over various state of art caching strategies.

Radio frequency-sputtered zinc oxide films are implanted with 30 keV Ar + ions at various fluences ranging from 1 × 10 15 to 2 × 10 16 ions·cm −2 . Raman spectra reveal the presence of the E 2 (low), E 2 (high), and A 1 (LO) Raman modes in pristine and implanted ZnO films. A gradual fall and rise in peak intensity of, respectively, the E 2 (high) and A 1 (LO) Raman modes is observed with increases in ion fluence. However, the E 2 (low) mode broadens and merges completely with disorder-induced broad band at higher fluences. Moreover, the deconvolution of the A 1 (LO) Raman peak affirms the presence of defect-related Raman modes in the implanted samples. A gradual reduction in crystallinity of the implanted ZnO films with increasing ion fluence is observed in grazing incidence angle X-ray diffraction patterns. Atomic force microscopy images show grain size reduction and a fall in the surface roughness value of films after implantation. The implantation-induced structural modifications are further correlated with the variation in diffuse reflectance, Urbach energy, and optical bandgap. The low reflectance values of implanted films assure their suitability as transparent windows and anti-reflective coating in various optoelectronic devices.

Mechano-acoustic signals emanating from the heart and lungs contain valuable information about the cardiopulmonary system. Unobtrusive wearable sensors capable of monitoring these signals longitudinally can detect early pathological signatures and titrate care accordingly. Here, we present a wearable, hermetically-sealed high-precision vibration sensor that combines the characteristics of an accelerometer and a contact microphone to acquire wideband mechano-acoustic physiological signals, and enable simultaneous monitoring of multiple health factors associated with the cardiopulmonary system including heart and respiratory rate, heart sounds, lung sounds, and body motion and position of an individual. The encapsulated accelerometer contact microphone (ACM) utilizes nano-gap transducers to achieve extraordinary sensitivity in a wide bandwidth (DC-12 kHz) with high dynamic range. The sensors were used to obtain health factors of six control subjects with varying body mass index, and their feasibility in detection of weak mechano-acoustic signals such as pathological heart sounds and shallow breathing patterns is evaluated on patients with preexisting conditions.

Climate action plans are essential for climate mitigation and adaptation as well as to achieve climate and development goals like the Paris Agreement and the Sustainable Development Goals (SDGs). However, the development and implementation of climate action plans at multiple levels involve decision-making processes. In this article, we examine the barriers and opportunities to decision-making in climate action plans for adaptation at three different governance levels in India: national, sub-national and local. Through a literature review and analysis of case studies, we find that lack of usable climate information, institutional weaknesses and capacity of actors are critical barriers to decision-making at all three levels in India. We recommend that providing usable and accessible climate information, creating evidence and knowledge on adaptation, strengthening the science-policy interface and institutional mechanisms, as well as building the capacities of actors can contribute to better decision-making and achieving targeted climate action plans and SDGs.