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...     

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 hybrid feature extraction and machine learning approaches for epileptic seizure detection

Multidimensional Systems and Signal Processing - Epileptic seizure detection from the brain EEG signals is an essential step for speeding up the diagnosis that assists researchers and medical...     

A cascade energy band structure enhances the carrier energy in organic vertical-type triodes

Organic vertical-type triodes (OVTs) based on the cascade energy band structure as emitter layer are studied. The electric characteristics were dramatically enhanced while incorporating the cascade energy under current driving and voltage driving modes. The improvement is attributed to that injection carriers can obtain higher energy through a stepwise energy level. When the device has a layered structure of F₁₆CuPC (10 nm)/PTCDI (10 nm)/pentacene (100 nm) in emitter, it exhibits a common-base transport factor of 0.99 and a common-emitter current gain of 225 under current driving mode and exhibits a high current modulation-exceeding ?520 μA for a low collector voltage of ?5 V and a base voltage of ?5 V and the current on/off ratio of 10³ under voltage driving mode. Furthermore, we realized first organic current mirror that exhibited out/in current ratio of 0.75 and output resistance of 10⁵ Ω by using the OVTs.     

De-Duplication Complexity of Fingerprint Data in Large-Scale Applications

De-duplication using biometrics has gained much attention from research communities as it provides a unique identity for each and every individual among the large population.De-duplication is the process of removing the instances of multiple enrollments by the same person using the person’s biometric data.An important issue in the large-scale de-duplication applications is the speed of matching and the accuracy of the matching because the number of persons to be enrolled runs into millions.This paper presents an efficient method to improve the accuracy of fingerprint de-duplication in de-centralized manner.De-duplication accuracy decreases because of the noise present in the data,which would cause improper slap fingerprint segmentation.In this paper,an attempt is made to remove the noise present in the data by using binarization of slap fingerprint images and region labeling of desired regions with 8-adjacency neighborhood.The distinct feature of this technique is to remove the noise present in the data for an accurate slap fingerprint segmentation and improve the de-duplication accuracy.Experimental results demonstrate that the fingerprint segmentation rate and de-duplication accuracy are improved significantly.     

Convergence Properties of Higher Order Modeling of the Cylindrical Wire Kernel

Extensive analysis of wire antennas using method of moments (MoM) has been performed over the past decades. Despite the thorough understanding and widespread use of the wire antennas, higher order modeling of wire structures has received little to no attention over the years. This may be because the inaccuracies associated with the classic reduced kernel approach used to analyze wire antennas have precluded the use of higher order basis functions. This paper combines a new method for computing integrating the cylindrical wire kernel with higher order basis functions for the accurate modeling of wire antennas. Numerical results demonstrate the higher order convergence of the approach