Broadband mirrors for thermophotovoltaics.

We present an innovative solution to improve the efficiency of thermophotovoltaic (TPV) devices by tackling the problem of sub-bandgap photon losses. We propose an optimized design for thin-film mirrors using inverse electromagnetic design principles, thereby enhancing the average reflectivity and photon re-use. Our method surpasses the traditional Bragg mirror by employing a gradient-descent based optimization over Bragg mirror geometrical parameters, leveraging the transfer matrix method for derivative calculations. The optimized structure, based on continuously chirped distributed Bragg reflectors proposed herein demonstrates a remarkable increase in reflectivity beyond 98%, over an almost three-octaves bandwidth (0.1eV-0.74eV). We show that the incident power loss in InGaAs TPV cells at an emitter temperature of 1200°C is significantly reduced. While our work shows considerable promise, further exploration is needed to ascertain the practicability and robustness of these designs under various operational conditions. This study thus provides a major step forward in TPV technology, highlighting a new route towards more effective energy conversion systems.

Performance evaluation of deep neural network on malware detection: visual feature approach.

Nowadays, several malicious applications target computers and mobile users. So, malware detection plays a vital role on the internet so that the device is secure without any malicious activity affecting or gathering the useful content of the user. Researches indicate that the vulnerability of adversarial attacks is more in deep neural networks. When there is a malicious sample in a family, there will not be many changes in the variants, but there will be more signatures. So, a deep learning model, DenseNet was used for detection. The adversarial samples are created by other types of noise, including the Gaussian noise. We added this noise to a subset of malware samples and observed that for Malimg, the modified samples were precisely identified by the DenseNet, and the attack cannot be done. But for BIG2015, we found that there was some marginal decrease in the performance of the classifier, which shows that the model performs well. Further, experiments on the Fast Gradient Sign Method (FGSM) were conducted, and it was observed that a significant decrease in classification accuracy was detected for both datasets. We understand that deep learning models should be robust to adversarial attacks.

Towards Energy-Efficient and Delay-Optimized Opportunistic Routing in Underwater Acoustic Sensor Networks for IoUT Platforms: An Overview and New Suggestions.

In underwater acoustic sensor networks (UASNs), the reliable transfer of data from the source nodes located underwater to the destination nodes at the surface through the network of intermediate nodes is a significant challenge due to various unique characteristics of UASN such as continuous mobility of sensor nodes, increased propagation delay, restriction in energy, and heightened interference. Recently, the location-based opportunistic routing protocols seem to show potential by providing commendable quality of service (QoS) in the underwater environment. This study initially reviews all the latest location-based opportunistic routing protocols proposed for UASNs and discusses its possible limitations and challenges. Most of the existing works focus either on improving the QoS or on energy efficiency, and the few hybrid protocols that focus on both parameters are too complex with increased overhead and lack techniques to overcome communication voids. Further, this study proposes and discusses an easy-to-implement energy-efficient location-based opportunistic routing protocol (EELORP) that can work efficiently for various applications of UASN-assisted Internet of Underwater Things (IoUTs) platforms with reduced delay. We simulate the protocol in Aqua-Sim, and the results obtained show better performance than existing protocols in terms of QoS and energy efficiency.

An Intrusion Detection Mechanism for Secured IoMT Framework Based on Swarm-Neural Network.

The seamless integration of medical sensors and the Internet of Things (IoT) in smart healthcare has leveraged an intelligent Internet of Medical Things (IoMT) framework to detect the criticality of the patients. However, due to the limited storage capacity and computation power of the local IoT devices, patient's health data needs to transfer to remote computing devices for analysis, which can easily result in privacy leakage due to lack of control over the patient's health data and the vulnerability of the network for various types of attacks. Motivated by this, in this paper, an Empirical Intelligent Agent (EIA) based on a unique Swarm-Neural Network (Swarm-NN) method is proposed to identify attackers in the edge-centric IoMT framework. The major outcome of the proposed strategy is to identify the attacks during data transmission through a network and analyze the health data efficiently at the edge of the network with higher accuracy. The proposed Swarm-NN strategy is evaluated with a real-time secured dataset, namely the ToN-IoT dataset that collected Telemetry, Operating systems, and Network data for IoT applications and compares the performance over the standard classification models using various performance metrics. The test results demonstrate that the proposed Swarm-NN strategy achieves 99.5% accuracy over the ToN-IoT dataset.

The Utility of Preoperative Phone Calls for Endoscopic Sinus Surgery Procedures.

OBJECTIVE: Perioperative patient education improves patient satisfaction, surgical outcomes, and can reduce postoperative call volume. Here, we investigate whether the use of standardized preoperative phone calls elicits similar results in patients undergoing endoscopic sinus surgery (ESS). METHODS: Patients undergoing ESS at a tertiary rhinology center were identified prospectively through the electronic medical record (EMR). In the intervention cohort, a standardized preoperative educational phone call was performed. A postoperative survey was utilized to collect self-assessment of satisfaction and understanding in all patients. Postoperative call rates were obtained from the EMR. Wilcoxon rank sum and chi-squared analyses were conducted to compare results. Demographics of the otology and rhinology cohorts were compared with a Mann Whitney U-test. RESULTS: Data from 43 cases and 58 controls were collected. Patients receiving the intervention were similar to controls with regard to patient-reported understanding (case:9.1 ± 1.1 vs control:9.0 ± 1.4, p = 0.801) and satisfaction (case:9.4 ± 1.1 vs 8.9 ± 1.4, p = 0.155). Both cases and controls called the clinic regarding surgical outcomes more often than for postoperative medications or administrative concerns. Independent of receiving the intervention, patients that did not call clinic postoperatively had significantly better understanding of their procedures (call:8.6 ± 1.6 vs no-call:9.5 ± 1.0, p < 0.015) and satisfaction with their experience (call:8.8 ± 1.4 vs no-call:9.5 ± 1.1, p < 0.028). Patient age may contribute to lack of impact in the rhinology cohort, as compared to the otology group, but socioeconomic status does not seem to differentiate the two samples. CONCLUSION: Though shown in other settings, a significant impact of educational phone calls prior to surgery was not observed in this sample. Patient education calls prior to endoscopic sinus surgery were not associated with changes in postoperative call volume to the clinic. Patient understanding and satisfaction may be related to other factors, such as patient selection or demographics. Future studies may target such patients prior to ESS.

The development of a current events and dialogue forum at a large U.S. academic medical center.

BACKGROUND: The Vanderbilt Community Circle (VC2) was designed to provide all faculty, staff, and students within the entire Vanderbilt University Medical Center community a dedicated venue to discuss current events and ongoing societal issues. APPROACH: During the 2017-18 academic year, four VC2 events were held on: "Race, identity, and conflict in America," "Gun violence in America," "Gender in the workplace," and "Immigration in America." Facilitators guided participants to share their views and perspectives on these matters with pre-developed open-ended questions. Attendees started discussions in small groups and then eventually combined into a large one. Pre- and post-event surveys were administered to measure the program's effectiveness. EVALUATION: One-hundred and twenty-four participants were included, 75 of whom completed both the pre- and post-event surveys. Sixty-four of the 75 (85%) agreed or strongly agreed that "multiple perspectives and opinions were represented" and 73% felt that their "own perspective was broadened on the issue." Most (89%) believed that the format and setting of the event was conducive to dialogue and discussion, and almost all (91%) reported that they would attend a similar event in the future. Groningen Reflection Ability Scale scores were high before (94 [25th-75th: 88-99]) and remained high after the events (93 [25th-75th: 88-93.3], p > 0.05). REFLECTION: We successfully implemented a medical center-wide, recurring current events and dialogue forum in hopes of increasing reflection, unity, and understanding across our own community.

An AI-enabled lightweight data fusion and load optimization approach for Internet of Things.

In the densely populated Internet of Things (IoT) applications, sensing range of the nodes might overlap frequently. In these applications, the nodes gather highly correlated and redundant data in their vicinity. Processing these data depletes the energy of nodes and their upstream transmission towards remote datacentres, in the fog infrastructure, may result in an unbalanced load at the network gateways and edge servers. Due to heterogeneity of edge servers, few of them might be overwhelmed while others may remain less-utilized. As a result, time-critical and delay-sensitive applications may experience excessive delays, packet loss, and degradation in their Quality of Service (QoS). To ensure QoS of IoT applications, in this paper, we eliminate correlation in the gathered data via a lightweight data fusion approach. The buffer of each node is partitioned into strata that broadcast only non-correlated data to edge servers via the network gateways. Furthermore, we propose a dynamic service migration technique to reconfigure the load across various edge servers. We assume this as an optimization problem and use two meta-heuristic algorithms, along with a migration approach, to maintain an optimal Gateway-Edge configuration in the network. These algorithms monitor the load at each server, and once it surpasses a threshold value (which is dynamically computed with a simple machine learning method), an exhaustive search is performed for an optimal and balanced periodic reconfiguration. The experimental results of our approach justify its efficiency for large-scale and densely populated IoT applications.

Internet of Things-Based Optimized Routing and Big Data Gathering System for Landslide Detection.

An exponential progression in the miniaturization of communicating devices has proliferated the generation of a large volume of data termed as "big data." The technological advancements in the micro-electro/mechanical system has made it possible to design the low-cost, low-power consuming artificial intelligence (AI)-based wireless sensor nodes to gather the big data belonging to various attributes from their surroundings. These nodes help in the early detection and prediction for the occurrence of landslides, which are among the catastrophic hazards. A profusion of research has focused on exploiting the potential of sensors for continuous monitoring and detecting the landslides at the earliest. However, the limited energy resources of sensor nodes give rise to the huge challenge for the network longevity pertaining to landslide detection. To address this concern, in this article, we propose an optimized routing and big data gathering system for landslide detection using (AI)-based wireless sensor network (WSN) (ORLAW). Since we propose a distributed routing mechanism, AI has a major role to play in the intelligent detection of landslides that too without the intervention of an external entity. We use the Dynamic Salp Swarm Algorithm for the cluster head selection in ORLAW. Two data collecting sinks are deployed on the opposite sides of the network, which is assumed to be a mountainous area. It is discerned from the simulation examination that ORLAW elongates the reliability period by 23.9% compared with the recently proposed cluster-based intelligent routing protocol, and also outperforms many others in the perspective of energy efficient management of big data.

A Lightweight Mutual Authentication and Privacy-preservation Scheme for Intelligent Wearable Devices in Industrial-CPS.

Industry 5.0 is the digitalization, automation and data exchange of industrial processes that involve artificial intelligence, Industrial Internet of Things (IIoT), and Industrial Cyber-Physical Systems (I-CPS). In healthcare, I-CPS enables the intelligent wearable devices to gather data from the real-world and transmit to the virtual world for decision-making. I-CPS makes our lives comfortable with the emergence of innovative healthcare applications. Similar to any other IIoT paradigm, I-CPS capable healthcare applications face numerous challenging issues. The resource-constrained nature of wearable devices and their inability to support complex security mechanisms provide an ideal platform to malevolent entities for launching attacks. To preserve the privacy of wearable devices and their data in an I-CPS environment, we propose a lightweight mutual authentication scheme. Our scheme is based on client-server interaction model that uses symmetric encryption for establishing secured sessions among the communicating entities. After mutual authentication, the privacy risk associated with a patient data is predicted using an AI-enabled Hidden Markov Model (HMM). We analyzed the robustness and security of our scheme using BurrowsAbadiNeedham (BAN) logic. This analysis shows that the use of lightweight security primitives for the exchange of session keys makes the proposed scheme highly resilient in terms of security, efficiency, and robustness. Finally, the proposed scheme incurs nominal overhead in terms of processing, communication and storage and is capable to combat a wide range of adversarial threats.

An interesting radiological picture of post traumatic TMJ ankylosis due to sagittal condylar fracture.

INTRODUCTION: In a condylar fracture whether to intervene or to go for conservative management still remains a dilemma. Studies and hypothesis suggests that it's medially dislocated condylar fracture segment that is more likely to ankylose, moreover no consensus have been put forth as to whether to remove the medially displaced fracture segment. PRESENTATION OF CASE: The current article describes a case of unilateral temporomandibular joint (TMJ) ankylosis, which resulted as a sequlae from conservative management of a bilateral condylar fracture of which, the ankylosed side had a sagittal fracture of condyle. In our case the post trauma CT shows the lateral segment abutting with the arch and that the area has become ankylotic in a span of 2 years. Here we report a case of posttraumatic unilateral TMJ ankylosis resulting from closed reduction of a bilateral condylar fracture with interesting radiological findings. DISCUSSION: We have tried to discuss a rather interesting radiological picture of posttraumatic TMJ ankylosis which resulted as a sequlae from conservative management of a bilateral condylar fracture. CONCLUSION: The dilemma for a clinician as to whether to intervene in a condylar fracture or to go for conservative management still remains at large. As in this case the medial fracture segment was intact and the lateral segment was resulting in ankylosis.

Pentafid tongue: A new entity.

Tongue plays a pivotal role in both physiological and functional life of human beings. Structural and developmental abnormalities of the tongue in various forms have been reported in isolation or in combination with various syndromes. Though cases of bifid tongues have been mentioned in literature, no reports of pentafid tongue have been reported till date. Here we describe a unique case of congenital pentafid tongue along with bilateral polydactyly and its surgical management.

Hand hygiene compliance among healthcare workers in an accredited tertiary care hospital.

AIM: We are using multimodal technique to improve hand hygiene (HH) compliance among all health care staff for the past 1-year. This cross-sectional observational study was conducted in the surgical ICU to assess adherence to HH among nurses and allied healthcare workers, at the end of the training year. MATERIALS AND METHODS: This was a cross-sectional observational study using direct observation technique. A single observer collected all HH data. During this analysis, 1500 HH opportunities were observed. HH compliance was tested for all 5 moments as per WHO guidelines. RESULTS: Overall compliance as per WHO Guidelines was 78%. Nurses had an adherence rate of 63%; allied staff adherence was 86.5%. Compliance was 93% after patient contact versus 63% before patient contact. Nurses'compliance before aseptic procedures was lowest at 39%. 92% staff was aware of the facts viz. Diseases prevented by hand washing, ideal duration of HH, reduction of health care associated infections, etc. CONCLUSION: After 1-year of aggressive multimodal intervention in improving HH compliance, we have an overall compliance of 78%. It implies that sustained performance and compliance to HH can be ensured by ongoing training. Direct observation remains a widely used, easily reproducible method for monitoring compliance.