Scaling of critical connectivity of mobile ad hoc communication networks

In this paper, critical global connectivity of mobile ad hoc communication networks (MAHCN) is investigated. We model the two-dimensional plane on which nodes move randomly with a triangular lattice. Demanding the best communication of the network, we account the global connectivity $\eta$ as a function of occupancy $\sigma$ of sites in the lattice by mobile nodes. Critical phenomena of the connectivity for different transmission ranges $r$ are revealed by numerical simulations, and these results fit well to the analysis based on the assumption of homogeneous mixing . Scaling behavior of the connectivity is found as $\eta \sim f(R^{\beta}\sigma)$, where $R=(r-r_{0})/r_{0}$, $r_{0}$ is the length unit of the triangular lattice and $\beta$ is the scaling index in the universal function $f(x)$. The model serves as a sort of site percolation on dynamic complex networks relative to geometric distance. Moreover, near each critical $\sigma_c(r)$ corresponding to certain transmission range $r$, there exists a cut-off degree $k_c$ below which the clustering coefficient of such self-organized networks keeps a constant while the averaged nearest neighbor degree exhibits a unique linear variation with the degree k, which may be useful to the designation of real MAHCN.Comment: 6 pages, 6 figure

Energy Efficient Mobile Routing in Actuator and Sensor Networks with Connectivity Preservation

International audienceIn mobile wireless sensor networks, flows sent from data col- lecting sensors to a sink could traverse inefficient resource expensive paths. Such paths may have several negative effects such as devices bat- tery depletion that may cause the network to be disconnected and packets to experience arbitrary delays. This is particularly problematic in event- based sensor networks (deployed in disaster recovery missions) where flows are of great importance. In this paper, we use node mobility to im- prove energy consumption of computed paths. Mobility is a two-sword edge, however. Moving a node may render the network disconnected and useless. We propose CoMNet (Connectivity preservation Mobile routing protocol for actuator and sensor NETworks), a localized mechanism that modifies the network topology to support resource efficient transmissions. To the best of our knowledge, CoMNet is the first georouting algorithm which considers controlled mobility to improve routing energy consump- tion while ensuring network connectivity. CoMNet is based on (i) a cost to progress metric which optimizes both sending and moving costs, (ii) the use of a connected dominating set to maintain network connectivity. CoMNet is general enough to be applied to various networks (actuator, sensor). Our simulations show that CoMNet guarantees network connec- tivity and is effective in achieving high delivery rates and substantial energy savings compared to traditional approaches

GreeDi: Energy Efficient Routing Algorithm for Big Data on Cloud

The ever-increasing density in cloud computing parties, i.e. users, services, providers and data centres, has led to a significant exponential growth in: data produced and transferred among the cloud computing parties; network traffic; and the energy consumed by the cloud computing massive infrastructure, which is required to respond quickly and effectively to users requests. Transferring big data volume among the aforementioned parties requires a high bandwidth connection, which consumes larger amounts of energy than just processing and storing big data on cloud data centres, and hence producing high carbon dioxide emissions. This power consumption is highly significant when transferring big data into a data centre located relatively far from the users geographical location. Thus, it became high-necessity to locate the lowest energy consumption route between the user and the designated data centre, while making sure the users requirements, e.g. response time, are met. The main contribution of this paper is GreeDi, a network-based routing algorithm to find the most energy efficient path to the cloud data centre for processing and storing big data. The algorithm is, first, formalised by the situation calculus. The linear, goal and dynamic programming approaches used to model the algorithm. The algorithm is then evaluated against the baseline shortest path algorithm with minimum number of nodes traversed, using a real Italian ISP physical network topology

A micro-pupil device for point-of-care testing of viable Escherichia coli in tap water

[[abstract]]Herein, we present a microfluidic-based point-of-care testing approach for viable Escherichia coli detection in tap water. In this approach, antibody-modified magnetic particles were used for capturing E. coli from water, followed by concentrating the bacteria-bound particles in the device using an external magnet. Then, redox indicator reagent mixed culture medium was added into the device, which allows colorimetric signal change as a result of reduction reaction linked to bacteria proliferation. Corresponding color change was monitored from microfluidic access holes, which are named μ-pupil, in which the colorimetric signal can be recorded from the perspective view angles of the transparent device, and analyzed using a software. Concentrating sample in the small volume of the microfluidic device shortened the incubation period for the detection of viable E. coli. The correlation of the colorimetric signal and E. coli concentration showed linearity within the range of 8 × 103–8 × 105 CFU/ml E. coli and 8 × 100–8 × 103 CFU/ml E. coli after 6 h and 12 h of incubation at 37 °C, respectively. The filtration integrated μ-pupil method was found advantageous by processing large sample volumes that allows the detection of 2 CFU/100 ml E. coli after 12 h of incubation. The developed approached which enables wide-range concentrations of viable E. coli testing using portable components, including a temperature controlled mini-incubator and a mobile-phone based optical setup, can be useful for microbial testing of water in resource limited remote areas

A microfluidic hanging drop-based spheroid co-culture platform for probing tumor angiogenesis

[[abstract]]Co-culturing of embryoid bodies (EBs) and tumor spheroids (TSs) allows mimicking tumor angiogenesis in vitro. Here, we report a microfluidic hanging drop-based spheroid co-culture device (μ-CCD) that permits the generation and co-culturing of EBs and TSs using a simple manual operation procedure and setup. In brief, uniform-sized EBs and TSs can be generated on the device in eight pairs of hanging droplets from adjacent microfluidic channels, followed by the confrontation of EB and TS pairs by merging the droplet pairs to culture the EB-TS spheroids to investigate tumor-induced angiogenic sprouting. The physical parameters of the device were optimized to maintain the long-term stability of hanging droplets for up to ten days. The mouse embryonic stem cell line ES-D3 and breast cancer cell lines MDA-MB-231 and MCF-7 were used to generate EBs, invasive TSs, and non-invasive TSs respectively. Confocal imaging results showed that the vessel percentage area and total vessel length which are linked to tumor angiogenesis increased after 6 days of co-culturing. An anti-angiogenesis drug testing on the co-cultured EB-TS spheroids was also demonstrated in the device. The μ-CCD provides a simple yet high-efficiency method to generate and co-culture cell spheroids and may also be useful for other applications involving spheroid co-culturing

A simple magnetic-assisted microfluidic method for rapid detection and phenotypic characterization of ultralow concentrations of bacteria

[[abstract]]Isolation and enumeration of bacteria at ultralow concentrations and antibiotic resistance profiling are of great importance for early diagnosis and treatment of bacteremia. In this work, we describe a simple, rapid, and versatile magnetic-assisted microfluidic method for rapid bacterial detection. The developed method enables magnetophoretic loading of bead-captured bacteria into the microfluidic chamber under external static and dynamic magnetic fields in 4 min. A shallow microfluidic chamber design that enables the monolayer orientation and transportation of the beads and a glass substrate with a thickness of 0.17 mm was utilized to allow high-resolution fluorescence imaging for quantitative detection. Escherichia coli (E. coli) with green fluorescent protein (GFP)-expressing gene and streptavidin-modified superparamagnetic microbeads were used as model bacteria and capturing beads, respectively. The specificity of the method was validated using Lactobacillus gasseri as a negative control group. The limit of detection and limit of quantification values were determined as 2 CFU/ml and 10 CFU/ml of E. coli, respectively. The magnetic-assisted microfluidic method is a versatile tool for the detection of ultralow concentrations of viable bacteria with the linear range of 5–5000 CFU/ml E. coli in 1 h, and providing growth curves and phenotypic characterization bead-captured E. coli in the following 5 h of incubation. Our results are promising for future rapid and sensitive antibiotic susceptibility testing of ultralow numbers of viable cells

Advances in AI-assisted biochip technology for biomedicine

[[abstract]]The integration of biochips with AI opened up new possibilities and is expected to revolutionize smart healthcare tools within the next five years. The combination of miniaturized, multi-functional, rapid, high-throughput sample processing and sensing capabilities of biochips, with the computational data processing and predictive power of AI, allows medical professionals to collect and analyze vast amounts of data quickly and efficiently, leading to more accurate and timely diagnoses and prognostic evaluations. Biochips, as smart healthcare devices, offer continuous monitoring of patient symptoms. Integrated virtual assistants have the potential to send predictive feedback to users and healthcare practitioners, paving the way for personalized and predictive medicine. This review explores the current state-of-the-art biochip technologies including gene-chips, organ-on-a-chips, and neural implants, and the diagnostic and therapeutic utility of AI-assisted biochips in medical practices such as cancer, diabetes, infectious diseases, and neurological disorders. Choosing the appropriate AI model for a specific biomedical application, and possible solutions to the current challenges are explored. Surveying advances in machine learning models for biochip functionality, this paper offers a review of biochips for the future of biomedicine, an essential guide for keeping up with trends in healthcare, while inspiring cross-disciplinary collaboration among biomedical engineering, medicine, and machine learning fields

A simple gradient centrifugation method for bacteria detection in skim milk

[[abstract]]Development of simple methods for bacteria detection is of great importance, especially in the undeveloped countries where there are limited resources for microbial testing. Herein, we present a simple gradient centrifugation-based optical detection (GCB-OD) method for bacteria testing in skim milk. Our method incorporates capturing bacteria using nanoparticles in Eppendorf tubes, followed by separation of bacteria-captured particles in the sucrose gradient within 6 min of centrifugation. Using a simple mobile phone-setup, images of sucrose-gradients are captured, and optical density of the particle-bound bacteria zones are measured with a MATLAB software, without requirement of a sophisticated optical instrument. In this approach, different nanoparticles are compared according to delta-E (ΔE) color difference value and sedimentation rate to obtain detectable narrow particle-bound bacteria zones. The spherical shaped carbon nanoparticles (CNP) with mean diameters of 119 nm, are found suitable for low concentrations of bacteria detection for having high sedimentation rates comparing to smaller sized particles and having higher ΔE than other particles as well. For CNP-captured bacteria band detection, sucrose-gradient consist of 1 ml of 60–65–70 % (w/v) and 2 ml of 75 % (w/v) sucrose was found suitable. Escherichia coli (E. coli) was used as model fecal indicator bacteria, and the correlation of optical density and E. coli concentration showed linearity in the range of 1.5x100–1.5x104 CFU/ml E. coli in skim milk. The culture plating and transmission electron micrographs were utilized to evaluate bacterial capture performance. The developed simple centrifugation-based method that allows detection of 2 CFU/ml and 3 CFU/ml E. coli in 1XPBS and skim milk, can be useful for microbial testing in resource limited areas