Design data for solar heating of air Using a Heat Exchange and Storage System

An arrangement for heating with solar energy by means of a heat exchanger and storage coupled to the two water heaters has been described^*. In the present paper, the amount of the heat transferred from water to air in the shell-and-tube heat exchanger and the temperature of the outgoing air have been estimated by considering various parameters, viz. flow rate and temperature of incoming air, temperature of incoming and outgoing water, length and diameter of tubes, etc. Both natural and forced convection mode of heat transfer have been considered.

Under boundary conditions of free convection, heat transfer would be rapid and efficient with a possible reduction in the pipe length. The maximum permissible limit of pressure drop per foot of each tube for pipes of different diameters at different Reynolds numbers has been given.

As conditions of drying vary from material to material, the data presented will help in arriving at the final design of the shell-and-tube heat exchanger to be used in drying a particular material.     

Adiponectin Deficiency Alters Placenta Function but Does Not Affect Fetal Growth in Mice

Adiponectin administration to pregnant mice decreases nutrient transport and fetal growth. An adiponectin deficiency, on the other hand, as seen in obese women during pregnancy, alters fetal growth; however, the mechanism is unclear. To determine the role of adiponectin on placenta function and fetal growth, we used adiponectin knockout, adiponectin heterozygote that displays reduced adiponectin levels, and wild-type mice on a control diet or high fat/high sucrose (HF/HS) diet. Triglycerides (TGs) in the serum, liver, and placenta were measured using colorimetric assays. Gene expression was measured using quantitative RT-PCR. Adiponectin levels did not affect fetal weight, but it reduced adiponectin levels, increased fetal serum and placenta TG content. Wildtype dams on a HF/HS diet protected the fetuses from fatty acid overload as judged by increased liver TGs in dams and normal serum and liver TG levels in fetuses, while low adiponectin was associated with increased fetal liver TGs. Low maternal adiponectin increased the expression of genes involved in fatty acid transport; Lpl and Cd36 in the placenta. Adiponectin deficiency does not affect fetal growth but induces placental dysfunction and increases fetal TG load, which is enhanced with obesity. This could lead to imprinting effects on the fetus and the development of metabolic dysfunction in the offspring.     

Adaptive Group Teaching Based Clustering and Data Aggregation with Routing in Wireless Sensor Network

Wireless Personal Communications - Nowadays, Wireless Sensor Networks (WSNs) is enhancing for different applications. Simultaneously, energy consumption for processing the tasks in most of the...     

A simulation as a service cloud middleware

Many seemingly simple questions that individual users face in their daily lives may actually require substantial number of computing resources to identify the right answers. For example, a user may want to determine the right thermostat settings for different rooms of a house based on a tolerance range such that the energy consumption and costs can be maximally reduced while still offering comfortable temperatures in the house. Such answers can be determined through simulations. However, some simulation models as in this example are stochastic, which require the execution of a large number of simulation tasks and aggregation of results to ascertain if the outcomes lie within specified confidence intervals. Some other simulation models, such as the study of traffic conditions using simulations may need multiple instances to be executed for a number of different parameters. Cloud computing has opened up new avenues for individuals and organizations with limited resources to obtain answers to problems that hitherto required expensive and computationally-intensive resources. This paper presents SIMaaS, which is a cloud-based Simulation-as-a-Service to address these challenges. We demonstrate how lightweight solutions using Linux containers (e.g., Docker) are better suited to support such services instead of heavyweight hypervisor-based solutions, which are shown to incur substantial overhead in provisioning virtual machines on-demand. Empirical results validating our claims are presented in the context of two case studies.     

Intercycle switching (ICS)-based dynamic reconfiguration of p-cycle for dual-failure survivability of WDM networks

Today, the most promising technique used for the survivability of optical transport networks is p-cycle. However, it provides longer restoration path at failure state of the network. The intercycle switching (ICS) is one of the recent approaches that is based on idle p-cycles and is used for shortening the length of restoration path in single-fault model. Utilization of idle p-cycles degrades the inherent dual-failure restorability of single-failure design model of p-cycle, whereas ICS releases the maximum portion of conventional restoration path by utilizing a small segment of the idle p-cycle. Here, the authors proposed a new approach to reconfiguring the released portion of restoration path and unused segment of corresponding idle p-cycle as new cycle(s). In respect of idle p-cycles, the new reconfigured cycle(s) provides more dual-failure restorability in single-failure design of p-cycle. Therefore, the proposed approach mitigates the above-said drawback of ICS and minimizes additional spare capacity requirement for dual-failure survivability.     

联邦学习在移动通信网络智能化的应用

为解决移动通信网络智能化中因用户隐私保护、数据孤岛等因素导致数据无法集中训练等问题,网络可融入联邦学习技术,构建高效的分布式可信网络智能化架构。概述了联邦学习研究现状以及标准现状,介绍了基于联邦学习的网络智能化架构,并基于该架构验证了客户体验管理应用案例,为移动通信网络与联邦学习的融合提供了参考和借鉴,进一步推动了联邦学习技术在网络智能化的应用。     

Smart Skin-Adhesive Patches: From Design to Biomedical Applications

With the advancement of medical and digital technologies, smart skin adhesive patches have emerged as a key player for complex medical purposes. In particular, skin adhesive patches with integrated electronics have created an excellent platform for monitoring health conditions and intelligent medication. However, the efficient design of the adhesive patches is still challenging as it requires a strong combination of network structure, adhesion, physical properties, and biocompatibility. To design an assimilated device, one must have a deep knowledge of various skin adhesive patches. This article provides a comprehensive review of the recent advances in skin-adhesive patches, including hydrogel-based adhesive patches, transdermal patches, and electronic skin (E-skin) patches, for various biomedical applications such as wound healing, drug delivery, biosensing, and health monitoring. Furthermore, the key challenges, implementable strategies, and future designs that can potentially provide researchers in designing innovative multipurpose smart skin patches are discussed. These advanced approaches are promising for managing the health and fitness of patients who require regular medical care.     

A time optimal wavelength rerouting algorithm for dynamic trafficin WDM networks

In this paper, we consider wavelength rerouting in wavelength routed wavelength division multiplexed (WDM) networks with circuit switching, wherein lightpaths between source-destination pairs are dynamically established and released in response to a random pattern of arriving connection requests and connection holding times. The wavelength continuity constraint imposed by WDM networks leads to poor blocking performance. Wavelength rerouting is a viable and cost effective mechanism that ran improve the blocking performance by rearranging certain existing lightpaths to accommodate a new request. Recently, a rerouting scheme called “parallel move-to-vacant wavelength retuning (MTV-WR)” with many attractive features such as shorter disruption period and simple switching control, and a polynomial time rerouting algorithm, for this scheme, to minimize the weighted number of rerouted lightpaths have been proposed. This paper presents a time optimal rerouting algorithm for wavelength-routed WDM networks with parallel MTV-WR rerouting scheme. The algorithm requires only O(N²W) time units to minimize the weighted number of existing lightpaths to be rerouted, where N is the number of nodes in the network and W is the number of wavelength channels available on a fiber link. Our algorithm is an improvement over the earlier algorithm proposed in that it requires O(N³W+N²W²) time units, which is not time optimal. The simulation results show that our algorithm improves the blocking performance considerably and only very few lightpaths are required to be rerouted per rerouting. It is also established through simulation that our algorithm is faster than the earlier rerouting algorithm by measuring the time required for processing connection requests for different networks     

A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

Real-time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO-SVM) as a binary classifier with optimally pruned extreme learning machine (OP-ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance-based lightweight K-Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.