Structure of lithium catena-poly[3,4-dihydroxopentaborate-1:5-[mu]-oxo]

LiH2B5O9, M(r) = 207.0, monoclinic, P2(1)/a, a = 13.576 (4), b = 9.077 (4), c = 5.543 (3) angstrom, beta = 91.47 (1)degrees, V = 682.8 (4)) angstrom3, Z = 4, D(x) = 2.013 g cm-3, lambda(Mo Kalpha) = 0.7107 angstrom, mu = 2.06 cm F(000) = 408, T = 293 K, R = 0.049 for 1689 independent observed reflections. The structure contains chains of B5O9H2]- anions linked through shared O atoms. In each anionic unit two B3O3 rings, each incorporating two triangular BO3 units, are connected by a shared tetrahedral BO4 unit. The Li atom has four O-atom neighbours arranged in an approximately tetrahedral configuration. The Li polyhedra connect B-O polyanions to form a two-dimensional network. Further connections are provided by hydrogen bonds

RECENT TRENDS IN MANAGEMENT OF KERATOCONJUNCTIVITIS SICCA (DRY EYE DISEASE)

At the air-water interface, the tear film lipid layer (TFLL), a combination of lipids and proteins plays an important role in surface tension of the tear and is necessary for the physiological hydration of the ocular surface and maintenance of ocular homeostasis. Alteration in lacrimal fluid rheology, differences in lipid constitution or down regulation of particular tear proteins are found in maximum types of ocular surface disease including dry eye disease (DED). Dry eye is a disorder of the tear film due to tear deficiency or excessive tear evaporation, which causes damage to the interpalpebral ocular surface and is associated with symptoms of discomfort. It results in changes on the ocular surface epithelia causing reduced tear quantity and surface sensitivity which leads to inflammation reactions. Managing this inflammation is very helpful in dry eye disease patients. In this article we revise the current understanding of tear film properties, ocular surface and review the effectiveness of topically applied tear supplements, thermo sensitive atelocollagen punctal plug, subtrasal ultrasonic transducers, novel liposome based gelling tear formation and insulin based ophthalmic delivery systems which help in restoring the healthy tear film

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

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

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

Recognition and Classification of Leaf Disease in Potato Plants

Farming is one of the most important lifelines of the country. A nation’s growth majorly depends on how advanced and effective their agricultural practices are in improving the crop yield. When a crop is grown many at times, farmers are unable to identify the health and wellbeing of the plant; they only recognize the problems when it becomes too late hence losing out on that year's expected yield. In this study, we have introduced a recognition and classification technique which is able to detect any ailments that the plant is suffering from at an early stage itself thus enabling the farmers to do the needful at a recoverable stage itself. To make the system as user-friendly as possible, we have provided a feature where the farmers are able to assess the health of the plant by providing a picture of the potato plants’ leaf

False positives in PIRADS (V2) 3, 4, and 5 lesions:relationship with reader experience and zonal location

PURPOSE: To investigate the effect of reader experience and zonal location on the occurrence of false positives (FPs) in PIRADS (V2) 3, 4, and 5 lesions on multiparametric (MP)-MRI of the prostate. MATERIALS AND METHODS: This retrospective study included 139 patients who had consecutively undergone an MP-MRI of the prostate in combination with a transrectal ultrasound MRI fusion-guided biopsy between 2014 and 2017. MRI exams were prospectively read by a group of inexperienced radiologists (cohort 1; 54 patients) and an experienced radiologist (cohort 2; 85 patients). Multivariable logistic regression analysis was performed to determine the association of experience of the radiologist and zonal location with a FP reading. FP rates were compared between readings by inexperienced and experienced radiologists according to zonal location, using Chi-square (χ2) tests. RESULTS: A total of 168 lesions in 139 patients were detected. Median patient age was 68 years (Interquartile range (IQR) 62.5-73), and median PSA was 10.9 ng/mL (IQR 7.6-15.9) for the entire patient cohort. According to multivariable logistic regression, inexperience of the radiologist was significantly (P = 0.044, odds ratio 1.927, 95% confidence interval [CI] 1.017-3.651) and independently associated with a FP reading, while zonal location was not (P = 0.202, odds ratio 1.444, 95% CI 0.820-2.539). In the transition zone (TZ), the FP rate of the inexperienced radiologists 59% (17/29) was significantly higher (χ2P = 0.033) than that of the experienced radiologist 33% (13/40). CONCLUSION: Inexperience of the radiologist is significantly and independently associated with a FP reading, while zonal location is not. Inexperienced radiologists have a significantly higher FP rate in the TZ

T1 vs. T2 weighted magnetic resonance imaging to assess total kidney volume in patients with autosomal dominant polycystic kidney disease

Purpose: In ADPKD patients total kidney volume (TKV) measurement using MRI is performed to predict rate of disease progression. Historically T1 weighted images (T1) were used, but the methodology of T2 weighted imaging (T2) has evolved. We compared the performance of both sequences. Methods: 40 ADPKD patients underwent an abdominal MRI at baseline and follow-up. TKV was measured by manual tracing with Analyze Direct 11.0 software. Three readers established intra- and interreader coefficients of variation (CV). T1 and T2 measured kidney volumes and growth rates were compared with ICC and Bland-Altman analyses. Results: Participants were 49.7 +/- 7.0 years of age, 55.0% female, with estimated GFR of 50.1 +/- 11.5 mL/min/1.73 m(2). CVs were low and comparable for T2 and T1 (intrareader: 0.83% [0.48-1.79] vs. 1.15% [0.34-1.77], P = 0.9, interreader: 2.18% [1.59-2.61] vs. 1.69% [1.07-3.87], P = 0.9). TKV was clinically similar, but statistically significantly different between T2 and T1: 1867 [1172-2721] vs. 1932 [1180-2551] mL, respectively (P = 0.006), with a bias of only 0.8% and high agreement (ICC 0.997). Percentage kidney growth during 2.2 +/- 0.3 years was similar for T2 and T1 (9.3 +/- 10.6% vs. 7.8 +/- 9.9%, P = 0.1, respectively), with a bias of 1.5% and high agreement (ICC 0.843). T2 was more often of sufficient quality for volume measurement (86.7% vs. 71.1%, P <0.001). Conclusions: In patients with ADPKD, measurement of kidney volume and growth rate performs similarly when using T2 compared to T1 weighted images, although T2 performs better on secondary outcome parameters; they are more often of sufficient quality for volume measurement and result in slightly lower intra- and interreader variability