Energy Optimization in Multi-UAV-Assisted Edge Data Collection System

In the IoT (Internet of Things) system, the introduction of UAV (Unmanned Aerial Vehicle) as a new data collection platform can solve the problem that IoT devices are unable to transmit data over long distances due to the limitation of their battery energy. However, the unreasonable distribution of UAVs will still lead to the problem of the high total energy consumption of the system. In this work, to deal with the problem, a deployment model of a mobile edge computing (MEC) system based on multi-UAV is proposed. The goal of the model is to minimize the energy consumption of the system in the process of data transmission by optimizing the deployment of UAVs. The DEVIPSK (differential evolution algorithm with variable population size based on a mutation strategy pool initialized by K-Means) is proposed to solve the model. In DEVIPSK, the population is initialized by K-Means to obtain better initial positions of UAVs. Besides, considering the limitation of the fixed mutation strategy in the traditional evolutionary algorithm, a mutation strategy pool is used to update the positions of UAVs. The experimental results show the superiority of the DEVIPSK and provide guidance for the deployment of UAVs in the field of edge data collection in the IoT system

A RFID-Based Monitoring System for Characterization of Perching Behaviors of Individual Poultry

Perching is a natural behavior of poultry. However, it is difficult to distinguish individual birds in a large group in order to relate perching behavior to health condition or productivity. To enable such research, this study developed and validated a radio frequency identification (RFID)-based automated perching monitoring system (APMS) for characterizing individual perching behaviors of group-housed poultry. The APMS consisted of a RFID module, a load cell module, and a round wooden perch. The RFID module was comprised of a high-frequency RFID reader, three customized rectangular antennas, and multiple RFID transponders. The load cell module was comprised of a data acquisition system and two load cells supporting the two ends of the perch. Daily number of perch visits (PV) and perching duration (PD) of individual birds were used to delineate perching behavior. Three identical experimental pens, five hens per pen, were equipped with the monitoring system. Two RFID transponders were attached to each hen (one per leg) and a distinct color was marked on the bird‘s head for video or visual identification. Performance of the APMS was validated by comparing the system outputs with manual observation/labeling over an entire day. Sensitivity and specificity of the system were shown to improve from 97.77% and 99.88%, respectively, when using only the RFID module, to 99.83% and 99.93%, respectively, when incorporating weight information from the load cell module. This study revealed that the APMS has an excellent performance in measuring perching behaviors of individual birds in a group. The APMS offers great potentials for delineating differences in perching behavior among hens with different social status or health conditions in a group setting

Applications of Aptasensors in Clinical Diagnostics

Aptamers are artificial oligonucleotides (DNA or RNA) selected in vitro that bind a broad range of targets with high affinity and specificity; a sensitive yet simple method to utilize aptamers as recognition elements for the development of biosensors (aptasensors) is to transduce the signal electrochemically. So far, aptasensors have been applied to clinical diagnostics and several technologies are in development. Aptasensors will extend the limits of current clinical diagnostics. Although the potential diagnostic applications are unlimited, the most current applications are foreseen in the areas of biomarker detection, cancer clinical testing, detection of infectious microorganisms and viruses. This review attempts to list examples of the research progresses of aptamers in biosensor platforms that have been published in recent years; in particular, we display cases of aptasensors that are already incorporated in clinical diagnostics or have potential applications in clinical diagnostics

Apoptosis Governs the Elimination of Schistosoma japonicum from the Non-Permissive Host Microtus fortis

The reed vole, Microtus fortis, is the only known mammalian host in which schistosomes of Schistosoma japonicum are unable to mature and cause significant pathogenesis. However, little is known about how Schistosoma japonicum maturation (and, therefore, the development of schistosomiasis) is prevented in M. fortis. In the present study, the ultrastructure of 10 days post infection schistosomula from BALB/c mice and M. fortis were first compared using scanning electron microscopy and transmission electron microscopy. Electron microscopic investigations showed growth retardation and ultrastructural differences in the tegument and sub-tegumental tissues as well as in the parenchymal cells of schistosomula from M. fortis compared with those in BALB/c mice. Then, microarray analysis revealed significant differential expression between the schistosomula from the two rodents, with 3,293 down-regulated (by ≥2-fold) and 71 up-regulated (≥2 fold) genes in schistosomula from the former. The up-regulated genes included a proliferation-related gene encoding granulin (Grn) and tropomyosin. Genes that were down-regulated in schistosomula from M. fortis included apoptosis-inhibited genes encoding a baculoviral IAP repeat-containing protein (SjIAP) and cytokine-induced apoptosis inhibitor (SjCIAP), genes encoding molecules involved in insulin metabolism, long-chain fatty acid metabolism, signal transduction, the transforming growth factor (TGF) pathway, the Wnt pathway and in development. TUNEL (terminal deoxynucleotidyl transferase dUTP nick end labeling) and PI/Annexin V-FITC assays, caspase 3/7 activity analysis, and flow cytometry revealed that the percentages of early apoptotic and late apoptotic and/or necrotic cells, as well as the level of caspase activity, in schistosomula from M. fortis were all significantly higher than in those from BALB/c mice

Robust estimation of bacterial cell count from optical density

Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data

Convergence of asynchronous distributed gradient methods over stochastic networks

We consider distributed optimization problems in which a number of agents are to seek the global optimum of a sum of cost functions through only local information sharing. In this paper, we are particularly interested in scenarios, where agents are operating asynchronously over stochastic networks subject to random failures. Most existing algorithms require coordinated and decaying stepsizes to ensure zero gap between the estimated value of each agent and the exact optimum, restricting it from asynchronous implementation and resulting in slower convergence results. To deal with this issue, we develop a new asynchronous distributed gradient method (AsynDGM) based on consensus theory. The proposed algorithm not only allows for asynchronous implementation in a completely distributed manner but also, most importantly, is able to seek the exact optimum even with constant stepsizes. We will show that the assumption of boundedness of gradients, which is widely used in the literature, can be dropped by instead imposing the standard Lipschitz continuity condition on gradients. Moreover, we derive an upper bound of stepsize within which the proposed AsynDGM can achieve a linear convergence rate for strongly convex functions with Lipschitz gradients. A canonical example of sensor fusion problems is provided to illustrate the effectiveness of the proposed algorithm

A dual splitting approach for distributed resource allocation with regularization

We deal with a class of distributed resource allocation problems where each agent attempts to minimize its own cost while respecting network-wide resource constraints as well as local capacity limits. This problem arises from many areas, such as economic dispatch, network utility maximization, and demand response. Most existing methods are centralized while few works are devoted to solving the problem in a distributed manner. The problem becomes even more challenging when there is a (nonsmooth) regularization term in the cost function. In this paper, we propose a novel distributed algorithm (termed DuSPA) to solve the above problem based on duality analysis and splitting methods. For privacy concerns, this algorithm is not required to communicate sensitive gradient information while still achieving the optimum without sacrificing the performance. We will show that the proposed algorithm converges at a nonergodic convergence rate of O(1/k) for general convex cost functions and a linear convergence rate for smooth and strongly convex cost functions, respectively. Furthermore, we apply the proposed algorithm to an economic dispatch problem to show its effectiveness.NRF (Natl Research Foundation, S’pore)Accepted versio

A Bregman splitting scheme for distributed optimization over networks

We consider distributed optimization problems, in which a group of agents are to collaboratively seek the global optimum through peer-to-peer communication networks. The problem arises in various application areas, such as resource allocation, sensor fusion, and distributed learning. We present a general algorithmic framework based on the Bregman method and operator splitting, which allows us to easily recover most of the existing distributed algorithms. Under this framework, we propose a general efficient distributed algorithm-distributed forward-backward Bregman splitting (D-FBBS)-to simultaneously solve the above primal problem as well as its dual. The proposed algorithm allows agents to communicate asynchronously and, thus, lends itself to stochastic networks. This algorithm is shown to have close connections with some existing well-known algorithms when dealing with fixed networks. However, we will show that it is generally different from the existing ones due to its effectiveness in handling stochastic networks. With proper assumptions, we establish a nonergodic convergence rate of O(1/k) in terms of fixed-point residuals over fixed networks both for D-FBBS and its inexact version (ID-FBBS) that is more computationally efficient and an ergodic convergence rate of O(1/k) for D-FBBS over stochastic networks. We also apply the proposed algorithm to sensor fusion problems to show its superior performance compared to existing methods

High relative humidity improves leaf burn resistance in flowering Chinese cabbage seedlings cultured in a closed plant factory

Plant factories that ensure the annual production of vegetable crops have sparked much attention. In the present study, thirty types of common vegetable crops from 25 species and eight families, were grown in a multi-layer hydroponic system in a closed-type plant factory to evaluate the adaptive performance. A total of 20 vegetable crops, belonging to 14 species and 4 families, unexpectedly exhibited different degrees of leaf margin necrosis in lower leaves firstly, then the upper leaves gradually. We defined this new physiological disorder as “leaf burn”. It occurred more commonly and severely in cruciferous leafy vegetables. Two different light intensities (150 and 105 µmol m−2 s−1 photosynthetic photon flux density (PPFD)), three photoperiod conditions (12, 10 and 8 h d−1) and two canopy relative air humidity (RH) (70% and 90% RH) were set to evaluate the suppression effects on leaf burn occurrence in two commercial flowering Chinese cabbage cultivars (‘Sijiu’ and ‘Chixin’), the special cruciferous vegetable in South China. We discovered that changing light conditions did not fully suppress leaf burn occurrence in the cultivar ‘Sijiu’, though lower light intensity and shorter photoperiod partly did. Interestingly, the occurrence of leaf burn was completely restrained by an increased canopy RH from 70% to 90%. Specifically, the low RH-treated seedlings occurred varying degree of leaf burn symptoms, along with rapidly decreased water potential in leaves, while the high RH treatment significantly lessened the drop in leaf water potential, together with increased photosynthetic pigment contents, net photosynthetic rate, stomatal conductance and transpiration rate, decreased leaf stomatal aperture and density, and thus reduced the incidence of leaf burn in ‘Sijiu’ and ‘Chixin’, from 28.89% and 18.52% to zero, respectively. Taken together, high canopy RH may favor maintaining leaf water potential and improving photosynthesis performance, jointly regulating leaf burn incidence and plant growth

DEVELOPMENT OF AN AUTOMATIC WEIGHING PLATFORM FOR MONITORING BODYWEIGHT OF BROILER CHICKENS IN COMMERCIAL PRODUCTION

● An automatic weighing system for monitoring bodyweight of broilers was developed.● The new system was compared to the established live-bird sales weighing system data and tested in various conditions.● The system demonstrated superior accuracy and stability for commercial houses. Bodyweight is a key indicator of broiler production as it measures the production efficiency and indicates the health of a flock. Currently, broiler weight (i.e., bodyweight) is primarily weighed manually, which is time-consuming and labor-intensive, and tends to create stress in birds. This study aimed to develop an automatic and stress-free weighing platform for monitoring the weight of floor-reared broiler chickens in commercial production. The developed system consists of a weighing platform, a real-time communication terminal, computer software and a smart phone applet user-interface. The system collected weight data of chickens on the weighing platform at intervals of 6 s, followed by filtering of outliers and repeating readings. The performance and stability of this system was systematically evaluated under commercial production conditions. With the adoption of data preprocessing protocol, the average error of the new automatic weighing system was only 10.3 g, with an average accuracy 99.5% with the standard deviation of 2.3%. Further regression analysis showed a strong agreement between estimated weight and the standard weight obtained by the established live-bird sales system. The variance (an indicator of flock uniformity) of broiler weight estimated using automatic weighing platforms was in accordance with the standard weight. The weighing system demonstrated superior stability for different growth stages, rearing seasons, growth rate types (medium- and slow-growing chickens) and sexes. The system is applicable for daily weight monitoring in floor-reared broiler houses to improve feeding management, growth monitoring and finishing day prediction. Its application in commercial farms would improve the sustainability of poultry industry