Are metabolic syndrome, obstructive sleep apnoea & syndrome Z sequential?-a hypothesis

Background & Objectives: The metabolic syndrome (MS) is a risk factor for development of cardiovascular disease and is closely associated with obstructive sleep apnoea (OSA). Co-occurrence of both OSA and MS is called syndrome Z. It has been hypothesized that the OSA may be a manifestation of MS. We collected data on polysomnography (PSG) and biochemical investigations on middle aged urban Indians during a community based study in South Delhi while studying prevalence of obstructive sleep apnoea and analysed to find out the ages at which the OSA, MS and syndrome Z exist in these subjects. Methods: A 2-stage, cross-sectional, population-based study in subjects of either gender between 30-65 yr of age in 4 different socio-economic zones of the South Delhi, India, was performed earlier (from April 2005 through June 2007). In-hospital, supervised PSG studies were performed and biochemical investigations for the MS using National Cholesterol Education Programmme Adult Treatment Panel (NCEP ATP) III criteria were carried out. In this communication, the data were further analysed to estimate the prevalences of MS alone, OSA alone and syndrome Z and average ages of 3 conditions. Results: Three hundred and fifty one subjects had satisfactory PSG studies. The MS alone was present in 105 [29.9%; (95% CI 25.1-34.7)] while OSA alone was present in 24 [6.8%; (95% CI 4.2-9.5)] subjects and the syndrome Z was present in 70 [19.9%; (95% CI 15.8-24.1)] subjects. Median ages of normal subjects, and subjects with MS, OSA and syndrome Z were 40, 43, 43 and 47 yr respectively. Minimum ages of normal subjects, and subjects with MS, OSA and syndrome Z were 30, 30, 32 and 32 yr respectively. Interpretation & Conclusions: When body mass index (BMI) was normal, the increasing median ages of these conditions indicated that the MS may be the first event followed by OSA and eventually syndrome Z develops. With BMI >25 or >30 no clear-cut difference was noted, indicating that the BMI itself could have an independent role in MS, OSA and syndrome Z

Stipe anatomical studies on selected pteridophytes of South India

Present study is based on the stipe anatomy of 13 selected species of pteridophytes of South India. Detailed description, key to the taxa based on stipe anatomy, photographs and descriptions are provided

Geometry Meets Vectors: Approximation Algorithms for Multidimensional Packing

We study the generalized multidimensional bin packing problem (GVBP) that generalizes both geometric packing and vector packing. Here, we are given $n$ rectangular items where the $i^{\textrm{th}}$ item has width $w(i)$, height $h(i)$, and $d$ nonnegative weights $v_1(i), v_2(i), \ldots, v_{d}(i)$. Our goal is to get an axis-parallel non-overlapping packing of the items into square bins so that for all $j \in [d]$, the sum of the $j^{\textrm{th}}$ weight of items in each bin is at most 1. This is a natural problem arising in logistics, resource allocation, and scheduling. Despite being well studied in practice, surprisingly, approximation algorithms for this problem have rarely been explored. We first obtain two simple algorithms for GVBP having asymptotic approximation ratios $6(d+1)$ and $3(1 + \ln(d+1) + \varepsilon)$. We then extend the Round-and-Approx (R&A) framework [Bansal-Khan, SODA'14] to wider classes of algorithms, and show how it can be adapted to GVBP. Using more sophisticated techniques, we obtain better approximation algorithms for GVBP, and we get further improvement by combining them with the R&A framework. This gives us an asymptotic approximation ratio of $2(1+\ln((d+4)/2))+\varepsilon$ for GVBP, which improves to $2.919+\varepsilon$ for the special case of $d=1$. We obtain further improvement when the items are allowed to be rotated. We also present algorithms for a generalization of GVBP where the items are high dimensional cuboids

Guaranteeing Envy-Freeness under Generalized Assignment Constraints

We study fair division of goods under the broad class of generalized assignment constraints. In this constraint framework, the sizes and values of the goods are agent-specific, and one needs to allocate the goods among the agents fairly while further ensuring that each agent receives a bundle of total size at most the corresponding budget of the agent. Since, in such a constraint setting, it may not always be feasible to partition all the goods among the agents, we conform -- as in recent works -- to the construct of charity to designate the set of unassigned goods. For this allocation framework, we obtain existential and computational guarantees for envy-free (appropriately defined) allocation of divisible and indivisible goods, respectively, among agents with individual, additive valuations for the goods. We deem allocations to be fair by evaluating envy only with respect to feasible subsets. In particular, an allocation is said to be feasibly envy-free (FEF) iff each agent prefers its bundle over every (budget) feasible subset within any other agent's bundle (and within the charity). The current work establishes that, for divisible goods, FEF allocations are guaranteed to exist and can be computed efficiently under generalized assignment constraints. In the context of indivisible goods, FEF allocations do not necessarily exist, and hence, we consider the fairness notion of feasible envy-freeness up to any good (FEFx). We show that, under generalized assignment constraints, an FEFx allocation of indivisible goods always exists. In fact, our FEFx result resolves open problems posed in prior works. Further, for indivisible goods and under generalized assignment constraints, we provide a pseudo-polynomial time algorithm for computing FEFx allocations, and a fully polynomial-time approximation scheme (FPTAS) for computing approximate FEFx allocations.Comment: 29 page

Pattern & correlates of neurocognitive dysfunction in Asian Indian adults with severe obstructive sleep apnoea

Background & Objectives: No published data are available on neurocognitive dysfunction in Asian Indians with obstructive sleep apnoea (OSA). We therefore, studied the pattern and correlates of neurocognitive dysfunction in Indian adults with severe OSA. Methods: Fifty patients aged 25-65 yr with severe OSA (apnoea-hypopnoea index>30) and 25 age, sex, and education level-matched normal controls were studied. Both groups were administered a standardized battery of neurocognitive tests. Results: Patients with severe OSA had significantly impaired performance on tests of alertness, working memory, response inhibition, problem solving, and executive function. However, the difference in executive function between the groups disappeared after adjusting for delayed information processing. The test scores did not correlate with apnoea-hypopnoea index, arousal index, or Epworth sleepiness score. However, the percentage of time spent at <90 per cent oxygen saturation had a weak correlation with the number of stroop errors (Spearman's rho=0.64; P=0.033), number of trials required (rho=0.05; P=0.02), and perseverative errors on Wisconsin card sorting test (rho=0.36; P=0.02). Interpretation & Conclusions: Our results suggested that delayed information processing rather than impaired abstract thinking was probably the cause of impaired performance on composite tests of neurocognitive function in patients with severe OSA

Design and Development IoT based Smart Energy Management Systems in Buildings through LoRa Communication Protocol

Energy management is a vital tool for reducing significant supply-side deficits and increasing the efficiency of power generation. The present energy system standard emphasizes lowering the total cost of power without limiting consumption by opting to lower electricity use during peak hours. The previous problem necessitates the development and growth of a flexible and mobile technology that meets the needs of a wide variety of customers while preserving the general energy balance. In order to replace a partial load decrease in a controlled manner, smart energy management systems are designed, according to the preferences of the user, for the situation of a full power loss in a particular region. Smart Energy Management Systems incorporate cost-optimization methods based on human satisfaction with sense input features and time of utilization. In addition to developing an Internet of Things (IoT) for data storage and analytics, reliable LoRa connectivity for residential area networks is also developed. The proposed method is named as LoRa_bidirectional gated recurrent neural network (LoRa_ BiGNN) model which achieves 0.11 and 0.13 of MAE, 0.21 and 0.23 of RMSE, 0.34 and 0.23 of MAPE for heating and cooling loads

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

Proton magnetic resonance spectroscopy of brain in obstructive sleep apnoea in North Indian Asian subjects

Background & Objectives: Repeated apnoeic/hypoapnoeic episodes during sleep may produce cerebral damage in patients with obstructive sleep apnoea (OSA). The aim of this study was to determine the absolute concentration of cerebral metabolites in apnoeic and non-apnoeic subjects from different regions of the brain to monitor the regional variation of cerebral metabolites. Methods: Absolute concentration of cerebral metabolites was determined by using early morning proton magnetic resonance spectroscopy (1H MRS) in 18 apnoeic patients with OSA (apnoeics) having apnoea/hypopnoea index (AHI) > 5/h, while 32 were non-apnoeic subjects with AHI < 5/h. Results: The absolute concentration of tNAA [(N-acetylaspartate (NAA)+N-acetylaspartylglutamate (NAAG)] was observed to be statistically significantly lower (P<0.05) in apnoeics in the left temporal and left frontal gray regions compared to non-apnoeics. The Glx (glutamine, Gln + glutamate, Glu) resonance showed higher concentration (but not statistically significant) in the left temporal and left frontal regions of the brain in apnoeics compared to non-apnoeics. The absolute concentration of myo-inositol (mI) was significantly high (P<0.03) in apnoeics in the occipital region compared to non-apnoeics. Interpretation & Conclusions: Reduction in the absolute concentration of tNAA in apnoeics is suggestive of neuronal damage, probably caused by repeated apnoeic episodes in these patients. NAA showed negative correlation with AHI in the left frontal region, while Cho and mI were positively correlated in the occipital region and Glx showed positive correlation in the left temporal region of the brain. Overall, our results demonstrate that the variation in metabolites concentrations is not uniform across various regions of the brain studied in patients with OSA. Further studies with a large cohort of patients to substantiate these observations are required