A Novel Cryptography-Based Multipath Routing Protocol for Wireless Communications

Communication in a heterogeneous, dynamic, low-power, and lossy network is dependable and seamless thanks to Mobile Ad-hoc Networks (MANETs). Low power and Lossy Networks (LLN) Routing Protocol (RPL) has been designed to make MANET routing more efficient. For different types of traffic, RPL routing can experience problems with packet transmission rates and latency. RPL is an optimal routing protocol for low power lossy networks (LLN) having the capacity to establish a path between resource constraints nodes by using standard objective functions: OF0 and MRHOF. The standard objective functions lead to a decrease in the network lifetime due to increasing the computations for establishing routing between nodes in the heterogeneous network (LLN) due to poor decision problems. Currently, conventional Mobile Ad-hoc Network (MANET) is subjected to different security issues. Weathering those storms would help if you struck a good speed-memory-storage equilibrium. This article presents a security algorithm for MANET networks that employ the Rapid Packet Loss (RPL) routing protocol. The constructed network uses optimization-based deep learning reinforcement learning for MANET route creation. An improved network security algorithm is applied after a route has been set up using (ClonQlearn). The suggested method relies on a lightweight encryption scheme that can be used for both encryption and decryption. The suggested security method uses Elliptic-curve cryptography (ClonQlearn+ECC) for a random key generation based on reinforcement learning (ClonQlearn). The simulation study showed that the proposed ClonQlearn+ECC method improved network performance over the status quo. Secure data transmission is demonstrated by the proposed ClonQlearn + ECC, which also improves network speed. The proposed ClonQlearn + ECC increased network efficiency by 8-10% in terms of packet delivery ratio, 7-13% in terms of throughput, 5-10% in terms of end-to-end delay, and 3-7% in terms of power usage variation

Decentralized Machine Learning based Energy Efficient Routing and Intrusion Detection in Unmanned Aerial Network (UAV)

Decentralized machine learning (FL) is a system that uses federated learning (FL). Without disclosing locally stored sensitive information, FL enables multiple clients to work together to solve conventional distributed ML problems coordinated by a central server. In order to classify FLs, this research relies heavily on machine learning and deep learning techniques. The next generation of wireless networks is anticipated to incorporate unmanned aerial vehicles (UAVs) like drones into both civilian and military applications. The use of artificial intelligence (AI), and more specifically machine learning (ML) methods, to enhance the intelligence of UAV networks is desirable and necessary for the aforementioned uses. Unfortunately, most existing FL paradigms are still centralized, with a singular entity accountable for network-wide ML model aggregation and fusion. This is inappropriate for UAV networks, which frequently feature unreliable nodes and connections, and provides a possible single point of failure. There are many challenges by using high mobility of UAVs, of loss of packet frequent and difficulties in the UAV between the weak links, which affect the reliability while delivering data. An earlier UAV failure is happened by the unbalanced conception of energy and lifetime of the network is decreased; this will accelerate consequently in the overall network. In this paper, we focused mainly on the technique of security while maintaining UAV network in surveillance context, all information collected from different kinds of sources. The trust policies are based on peer-to-peer information which is confirmed by UAV network. A pre-shared UAV list or used by asymmetric encryption security in the proposal system. The wrong information can be identified when the UAV the network is hijacked physically by using this proposed technique. To provide secure routing path by using Secure Location with Intrusion Detection System (SLIDS) and conservation of energy-based prediction of link breakage done by location-based energy efficient routing (LEER) for discovering path of degree connectivity.  Thus, the proposed novel architecture is named as Decentralized Federate Learning- Secure Location with Intrusion Detection System (DFL-SLIDS), which achieves 98% of routing overhead, 93% of end-to-end delay, 92% of energy efficiency, 86.4% of PDR and 97% of throughput

Secure Energy Aware Optimal Routing using Reinforcement Learning-based Decision-Making with a Hybrid Optimization Algorithm in MANET

Mobile ad hoc networks (MANETs) are wireless networks that are perfect for applications such as special outdoor events, communications in areas without wireless infrastructure, crises and natural disasters, and military activities because they do not require any preexisting network infrastructure and can be deployed quickly. Mobile ad hoc networks can be made to last longer through the use of clustering, which is one of the most effective uses of energy. Security is a key issue in the development of ad hoc networks. Many studies have been conducted on how to reduce the energy expenditure of the nodes in this network. The majority of these approaches might conserve energy and extend the life of the nodes. The major goal of this research is to develop an energy-aware, secure mechanism for MANETs. Secure Energy Aware Reinforcement Learning based Decision Making with Hybrid Optimization Algorithm (RL-DMHOA) is proposed for detecting the malicious node in the network. With the assistance of the optimization algorithm, data can be transferred more efficiently by choosing aggregation points that allow individual nodes to conserve power The optimum path is chosen by combining the Particle Swarm Optimization (PSO) and the Bat Algorithm (BA) to create a fitness function that maximizes across-cluster distance, delay, and node energy. Three state-of-the-art methods are compared to the suggested method on a variety of metrics. Throughput of 94.8 percent, average latency of 28.1 percent, malicious detection rate of 91.4 percent, packet delivery ratio of 92.4 percent, and network lifetime of 85.2 percent are all attained with the suggested RL-DMHOA approach

Global patient outcomes after elective surgery: prospective cohort study in 27 low-, middle- and high-income countries.

BACKGROUND: As global initiatives increase patient access to surgical treatments, there remains a need to understand the adverse effects of surgery and define appropriate levels of perioperative care. METHODS: We designed a prospective international 7-day cohort study of outcomes following elective adult inpatient surgery in 27 countries. The primary outcome was in-hospital complications. Secondary outcomes were death following a complication (failure to rescue) and death in hospital. Process measures were admission to critical care immediately after surgery or to treat a complication and duration of hospital stay. A single definition of critical care was used for all countries. RESULTS: A total of 474 hospitals in 19 high-, 7 middle- and 1 low-income country were included in the primary analysis. Data included 44 814 patients with a median hospital stay of 4 (range 2-7) days. A total of 7508 patients (16.8%) developed one or more postoperative complication and 207 died (0.5%). The overall mortality among patients who developed complications was 2.8%. Mortality following complications ranged from 2.4% for pulmonary embolism to 43.9% for cardiac arrest. A total of 4360 (9.7%) patients were admitted to a critical care unit as routine immediately after surgery, of whom 2198 (50.4%) developed a complication, with 105 (2.4%) deaths. A total of 1233 patients (16.4%) were admitted to a critical care unit to treat complications, with 119 (9.7%) deaths. Despite lower baseline risk, outcomes were similar in low- and middle-income compared with high-income countries. CONCLUSIONS: Poor patient outcomes are common after inpatient surgery. Global initiatives to increase access to surgical treatments should also address the need for safe perioperative care. STUDY REGISTRATION: ISRCTN5181700

Efficacy of garlic extract and sodium hypochlorite on dental pulp dissolution: An in vitro study

Aim: The present study was aimed to assess and compare the human pulp dissolution capacity of sodium hypochlorite (NaOCl) (2.5%) and garlic extract. Materials and Methods: Several pulp tissues are obtained from both endodontically treated teeth and also by orthodontically extracted premolar teeth and were sectioned with a surgical blade into seventy-five pieces of same size and weight. Pulp tissues were then divided into Allium sativum extract (ASE) group, NaOCl group, and NaCl group and tissues were immersed in the test solutions for 30, 60, and 90 min, respectively. Further tissues were weighed using a precision balance at tested intervals, and the percentage weight loss was calculated and statistically analyzed. Results: In this study, pulp tissue treated with 2.5% NaOCl solution at all tested time intervals showed significant (P < 0.001) pulp tissue dissolution capacity and a maximum of dissolution was observed at 90 min. Pulp tissue treated with ASE showed little dissolution ability at all tested time periods. Whereas, 0.9% NaCl showed no ability to dissolve human pulp tissue. Conclusion: It can be concluded that 2.5% NaOCl had the maximum tissue dissolving capacity when compared to different concentrations of ASE. Saline had no effect on the human pulp dissolution

Effect of tungsten addition to Pd/ZrO₂ system in the hydrodechlorination activity of CCl₂F₂

The catalytic activity of Pd on W-ZrO2 in the hydrodechlorination of CCl2F2 was studied by varying WO3 content. The TPR results of Pd/W-ZrO2 were related to effects caused by contact with hydrogen. XRD data of spent catalysts shows that there is no change in the structure of zirconia indicating that these catalysts are highly resistant to the corrosive reaction atmosphere. Addition of tungsten to Pd/ZrO2 system led to the formation of CHClF2 (HCFC-22) in the hydrodechlorination of CCl2F2. Thus, the role of WO3 istocatalyze the partial dechlorination of CCl2F2

Pressure-induced structural changes and insulator-metal transition in layered bismuth triiodide, BiI₃: a combined experimental and theoretical study

Noting that BiI3 and the well-known Topological Insulator (TI) Bi2Se3 have the same high symmetry parent structures and that it is desirable to find a wide-band gap TI, we determine here the effects of pressure on the structure, phonons and electronic properties of rhombohedral BiI3. We report a pressure-induced insulator-metal transition near 1.5 GPa, using high pressure electrical resistivity and Raman measurements. X-ray diffraction studies, as a function of pressure, reveal a structural peculiarity of the BiI3 crystal, with a drastic drop in c/a ratio at 1.5 GPa and a structural phase transition from rhombohedral to monoclinic structure at 8.8 GPa. Interestingly, the metallic phase, at relatively low pressures, exhibits minimal resistivity at low temperatures, similar to that in Bi2Se3. We corroborate these findings with first-principles calculations and suggest that the drop in the resistivity of BiI3 in the 1–3 GPa range of pressure arises possibly from the appearance of an intermediate crystal phase with a lower band-gap and hexagonal crystal structure. Calculated Born effective charges reveal the presence of metallic states in the structural vicinity of rhombohedral BiI3. Changes in the topology of the electronic bands of BiI3 with pressure, and a sharp decrease in the c/a ratio below 2 GPa, are shown to give rise to changes in the slope of phonon frequencies near that pressure