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Assuring the required quality of service, despite the growing volume and variety of Internet of Things (IoT) traffic, has remained an immense challenge in the popular WiFi networks. The IoT devices using short TCP flows often attain very different levels of service due to the complicated interactions between the transport layer protocol and the shared dynamic wireless medium. We develop in this paper a novel queue management policy by using a transient model to capture the interactions of IoT (short TCP) flows over traditional traffic in WiFi networks. Based on the Markov regenerative processes coupled with fluid model, we discover that the adaptive admission control (AAC) mechanism at the WiFi access point (AP) improves the response time and fairness of IoT traffic over the lossy WiFi links. To this end, under the proposed AAC, packets are admitted into the AP in such a way that the fractions of packets in the AP buffer belonging to the different IoT devices are balanced resulting in a comparable level of service. We furthermore prove the stability of the system dynamics under AAC which provides important practical insights in designing home WiFi IoT system.

The power load demand of a data centre is in many ways different from that of a residential area. There are differences in the load profiles, the impact of the environmental parameters, the number of peak load demand occurrences in a day as well as peak and off-peak demand. The residential load demand is a complex function of human behaviour which varies based on the geographical, economical, and social characteristics of the consumers. In contrast, estimating the power load of a data centre is rather straightforward. Due to the specific nature of the power load demand of a data centre, finding the optimal configuration of the supplying microgrids can be achieved using off-the-shelf optimisers once an optimisation model has been established. This paper presents a model for finding high-quality microgrid configurations for an enterprise-size green data centres. We present an optimisation model that considers the costs and greenhouse gas emissions associated with all components of the microgrid system, as well as their interactions. The capital, operational, and degradation costs are calculated based on a lifetime of 20 years for the system. Our application of this model to a real scenario of a data centre with a given load demand in a specified environment demonstrates that the model can compose good-quality microgrid configurations for different tradeoffs of cost and sustainability.

Task offloading plays a critical role in cloud networked multi-robot systems for leveraging computation support from cloud infrastructure and benefiting greatly from the well-developed cloud network facilities. However, considering the delay constraint, the extra costs of data transmission and remote computation, it is not trivial to make optimized offloading decisions. In particular, task offloading for robots is more complex due to their on-demand mobility and network connectivity that significantly influence the robot–cloud communication links. Moreover, for multi-robot systems, a suitable balance of workload between local network (robot–robot) and global cloud (robot–cloud) is also required, so as to attain proper utilization of resources. Therefore, it is essential to establish more comprehensive offloading schemes for modeling systems that can handle these higher level of complications. With that view, this paper aims to develop a novel multi-layer decision-making scheme for task offloading which jointly considers the following four aspects: (i) selection of task for offloading, (ii) selection of robot to offload a task, (iii) selection of location to offload/perform task, (iv) selection of access point for offloaded task. An integrated framework for cloud networked multi-robot systems is presented to enable our task offloading scheme where the primary robot can aid from additional local robots to improve the offloading process. In particular, we consider a warehouse scenario with 36 cell workspace where a 40 node taskflow is motivated from a “parcel sorting and distribution” application, to be completed by the primary robot. The offloading decision for each task is formulated as part of a joint optimization problem and it is solved by developing a multi-layer genetic algorithm scheme that takes into account motion, network connectivity and local sharing for its offloading decisions. We evaluate the results of the scheme via comparison with two validated benchmarks. The outcome highlights a significant improvement in overall system performance due to joint involvement of motion (path planning), connectivity (bandwidth estimation) and robot–robot communication (local offloading) that facilitates energy-efficient offloading to cloud, faster completion of tasks and better utilization of available resources.

Vision-based measurement (VBM) systems are powerful tool to extract quantitative information by acquiring video sequence or static images. When a VBM is applied to the evaluation of nominal properties, such as image characteristics, the term VBM is substituted with vision-based evaluation (VBE) by extending the framework of operation unit to a new concept of evaluation unit (EU) for the image analysis and machine learning phases. To this regard, deep learning (DL) approaches have gained an exponential interest in the research and industrial community, thanks to incredible flexibility toward visual words and the possibility to abandon subjective feature extraction procedures. From such an explosiveness of applications, it emerges the need to conduct studies on the capability of DL strategies to deal with uncertainty contributions, i.e., definitional uncertainty related to the measurand and reference uncertainty that may occur during the calibration process. In order to present a benchmark platform to analyze the effect of the major sources of uncertainties estimated, we use here an atomic force microscopy (AFM) imaging scenario for the evaluation of the effect of nanoparticles exposure on human cells in the laboratory. These studies are nowadays fundamental in toxicity analysis for monitoring the health conditions of workers and for protecting people from atherosclerosis disease. The performance of the proposed VBE-DL system to recognize cell alterations from the AFM images is related to three different sources of uncertainty and a critical analysis of the results achieved is provided.

The fourth industrial revolution introduces an ideal opportunity for inclusion of cloud-enabled robots in a factory environment to improve productivity and reduce human intervention. For this novel paradigm, task offloading plays a critical role in leveraging computation support from resourceful cloud infrastructure. In particular, network connectivity and on-demand mobility of robot significantly influence the task offloading decision-making and vice-versa. While current studies in the literature separately consider mobility or communication aspects to accommodate offloading, ours is the first approach to integrate these three interdependent factors together in order to formulate a joint optimization problem for the proposed oil factory maintenance application. A modified genetic algorithm scheme is then developed to solve the problem with a novel 3-layer decision: task offloading, path planning, and access point selection. Simulation results and comparison with existing techniques suggest that communication-aware and mobility-driven offloading in industrial scenario leads to superior system performance and minimum consumption of resources.

Social isolation is considered one of the main risk factors leading to intimate partner violence episodes; this evidence also emerged during the application of stay-at-home policies to contain the COVID-19 pandemic. For this reason, we aimed to collect data on intimate partner violence over the last year, comparing data reported by victims with data collected by help professionals. In accordance with PRISMA guidelines, through keywords related to abuse, pandemic and containment measures, 3174 articles were identified for screening. After full-text reading and risk of bias analysis, 19 studies were included, and a thematic synthesis was conducted according to two categories: “studies with victims” and “studies with help professionals”. The results of the present review showed that there were significant differences between the data provided by victims and the data collected by health care facilities and police departments; additionally, differences among different forms and severity of victimization emerged. The results have been discussed according to the literature; in particular, we reflected on how containment measures have apparently made it more difficult for victims to report, thus making the existence of the dark figure of crime even more salient.

In this paper, we propose a novel mathematical framework to formulate a unified Bus Rapid Transit (BRT) with Transit Signal Priority (TSP) system for single destination networks, namely, Bus Priority System Optimal Dynamic Traffic Assignment with Signal Control (BP SODTA-SC). This framework considers dedicated bus lanes, bus routes, and priority for public bus transport in mixed bus-car scenarios. Furthermore, this approach assures fairness to all road users. It is linear and can be applied to analyze city-size BRT-TSP systems. Our numerical results show that bus priority significantly reduces Total System-wide Passenger Travel Time (TSPT).

In this paper we present our work on the use of LoRa as a network technology for detonation of low explosives in underground mining. Using explosives underground is a potentially hazardous activity that mining companies are keen to make safer by removing personnel from near the site of the detonation. Currently detonation is commonly carried out using lengths of copper cable or infrared transmission, both of which limit the distance between the initiator and the explosives. The wireless technology LoRa, is an attractive alternative. LoRa has a much longer transmission range than infrared transmission and other wireless technologies such as WiFi and ZigBee. We have developed and trialed in a working underground mine a prototype system for carrying out such detonations. The system makes use of LoRa as a multi-hop message passing system from an Initiator to a Detonator via a number of Relays. We describe the messages that are passed through the network. We also describe how we deal with contention, broadcast storms and duplicate messages. Our approach is robust, easy to deploy and gives deterministic delay. We also present measurements of signal strength taken underground. Our results indicate that underground LoRa wireless transmission suffers severe fading without line of sight but where this is a line of sight underground, we show that LoRa propagates considerable distances.

This paper describes a novel approach to the design of low complexity, robust, wireless relay networks based on the LoRa physical layer for transmission of short and infrequent messages such as sensor and actuator data. Relay networks comprise a message source, one or more relays that forward the message and a destination. These networks are sometimes described as 'long and thin'. Relay networks are a natural fit for pipes, underground mines, tunnels, communications along international borders and on transport links such as rail and road. Of particular interest to us are dynamic environments, in particular underground mining, where networks need to be deployed quickly with minimal time spent on site surveys and configuration. A key design feature of a relay network is relay forwarding. Relays must forward messages they receive but manage collisions caused by other relays. Collision management in wireless relay networks is challenging because there may be coverage overlap between multiple relays. In underground mines and tunnels, wireless coverage may be affected by the movement of machinery, dust or other environmental causes. Since relay coverage may overlap, multiple relays may be visible to and receive messages from each other, potentially leading to collisions that may corrupt the message. In this paper we propose a forwarding scheme based on broadcast scheduling where the schedule is such that regardless of overlapping transmissions, a collision will not occur. We have simulated and implemented the approach in an experimental network for detonation of explosives. The approach appears to be effective.

In today’s highly interlinked international economy, accurate and timely real-time predictions of foreign exchange (fx) market offer tremendous business, social and political values to our community. In this work, an effective and accurate neural predictive model based on recurrent neural network and Regularised Extreme Learning Machine is developed. Hidden layer of the developed model is designed to map complex financial input patterns into a higher dimensional hidden feature space where input patterns are represented is a clear and delicate way. Output feedback loops and regularisation are implemented in the model to facilitate strong robustness and capability of capturing complex dynamical characteristics of the fx system. Experimental results show superior performance of the developed model comparing to benchmark models under varying market conditions.

With the dramatic worldwide growth of information technology sectors, the traffic of data centres is projected to experience an increase of about 80% by 2020. Green data centres are proposed as a solution towards sustainable platform providing IT services. This paper presents an optimal microgrid configuration for a green data centre. The mathematical models for the costs and the green gas emissions associated with all components of the microgrid system as well as their interactions are taken into consideration in the optimisation model. The capital, operational, and degradation costs during a life-time of 20 years of the system are also considered. The optimum microgrid configuration is composed of 38% renewable energy generation that reduces the total cost by 37% and greenhouse gas emissions by 16%.

A proposal for building a Free Electron Laser, EuPRAXIA@SPARC_LAB, at the Laboratori Nazionali di Frascati, is at present under consideration. This FEL facility will provide a unique combination of a high brightness GeV-range electron beam generated in a X-band RF linac, a 0.5 PW-class laser system and the first FEL source driven by a plasma accelerator. The FEL will produce ultra-bright pulses, with up to 10 12 photons/pulse, femtosecond timescale and wavelength down to 3 nm, which lies in the so called &ldquo, water window&rdquo, The experimental activity will be focused on the realization of a plasma driven short wavelength FEL able to provide high-quality photons for a user beamline. In this paper, we describe the main classes of experiments that will be performed at the facility, including coherent diffraction imaging, soft X-ray absorption spectroscopy, Raman spectroscopy, Resonant Inelastic X-ray Scattering and photofragmentation measurements. These techniques will allow studying a variety of samples, both biological and inorganic, providing information about their structure and dynamical behavior. In this context, the possibility of inducing changes in samples via pump pulses leading to the stimulation of chemical reactions or the generation of coherent excitations would tremendously benefit from pulses in the soft X-ray region. High power synchronized optical lasers and a TeraHertz radiation source will indeed be made available for THz and pump&ndash, probe experiments and a split-and-delay station will allow performing XUV-XUV pump&ndash, probe experiments.

In this work, a robust and accurate neural predictive model based on a randomized neural learning scheme is developed for foreign exchange market modelling and forecasting purpose. In our predictive model, a dynamic single-hidden layer feedforward neural network (SLFN) is constructed with tapped-delay-memories applied at its input layer. A modified sigmoid function is designed and input weights and hidden biases are randomly assigned in such a way that highly coupled financial input patterns can be represented in the hidden feature space in a clearer way and sensitivities of the network’s hidden outputs to the changes in the financial input signals are enhanced. Also, a large number of hidden nodes in the hidden layer is used to improve the clarity of input patterns’ representation in the hidden feature space. Output weights of the network are optimized using regularised batch-learning type of least square method to improve robustness of the predictive model against external and internal disturbances. Simulation results show excellent performance of the developed model in both target deviation and directional performance measurements.

Smart grids are predicted to play an important role in the future power system that will be deployed in smart cities. The integration of renewable power generation, energy storage, and demand response algorithms has led to the development of complex structures for managing the increasing energy demands. This paper proposes a decentralised multi-agent based demand response algorithm designed for a smart grid that relies on photovoltaic generation while feeding both a green data centre and supplying the residential load demand. The main objective of the proposed methodology is to minimise the total daily costs incurred due to shortfalls in the solar energy generation. The cost optimisation is performed by taking the user inconvenience of the residential load and the energy cost into account. The simulation results show that participation of green data centres in demand response can significantly reduce the costs incurred across the system due to shortages in renewable power as well as reducing CO2 emissions.

Introduction: Because of their accessibility and biologic features, dental pulp stem cells (DPSCs) hold great promise for regeneration in clinical applications. To the best of the authors’ knowledge, no data are available concerning their regenerative potential in human periodontal intrabony defects. The present case series explores the clinical and radiographic effectiveness of autologous DPSCs/collagen sponge biocomplex in the treatment of non-contained intrabony defects in patients with chronic periodontitis.Case Series: Four healthy patients displaying at least one prevalently 1- or 2-wall intrabony defect and one vital third molar requiring extraction were consecutively included in the study. Before surgery, patients were enrolled in a strict periodontal program including oral hygiene instructions and scaling and root planing. The third molar was extracted and used as the autologous DPSC source. The dental pulp was mechanically dissociated and filtered through 50-μm pores to obtain a cellular suspens...

A single human oesophageal adenocarcinoma cell (OE33) has been imaged using aperture infrared scanning near-field optical microscopy (IR-SNOM) in transmission and reflection and also by Fourier-transform infrared (FTIR) microspectroscopy in transmission only. This work presents the first images obtained in both transmission and reflection of the same specimen using the aperture IR SNOM technique. The results have been used to compare the two SNOM modes and also the two techniques, which have complementary capabilities. The SNOM technique necessitates a very stable source and a careful choice of wavelengths, since it is too slow to yield images at the thousands ofwavelengths obtained with FTIR. However the SNOM technique is not diffraction limited and with careful fabrication of tips can yield images with high spatial resolution. There is no significant correlation between the SNOM images obtained in transmission and reflection and the correlations between images obtained at different wavelengths vary with the different imaging modes. These results are attributed to the strong dependence of the evanescent wave on both the wavelength and the distance between the tip and the source of the signal within the sample. While both transmission and reflection SNOM images show some correlation with topography this is not a dominant effect. These results indicate that with suitable calibration a combination of reflection and transmission aperture IR-SNOM measurements has the potential to reveal information on the depth distribution of the chemical structure of a specimen.

Engineered nanomaterials play an even more relevant role in nanotechnology advances. However, care must be taken due to their suspected detrimental effects on human cells. Such alterations can be monitored through Atomic Force Microscopy (AFM) equipment and image digitalization. With the purpose to depict a metrological compliant scenario, a novel vision-based evaluation system is proposed with an evaluation unit based on a deep learning architecture. Inspired by the recent trends in trying to extend the standard concept of quantities to nominal properties and measurement to evaluation, we proposed here a platform for the evaluation of morphological alterations in AFM images of human cells exposed to different concentrations of carbon nanotubes. Results reveal the feasibility to automatically investigate such alterations with the aim to improve occupational medicine protocols and cells cataloguing procedures.

Task offloading opens a gateway for robotic applications to leverage computation support from the cloud infrastructure. It exploits a trade-off between the robot's and cloud's processing capabilities, and the communication between the two entities plays a critical role in making these decisions. Two major factors that significantly influence communication are network connectivity (bandwidth) and mobility of the robot. We integrate these two factors with the offloading decisions to formulate an optimization problem. Our objective in this paper is to improve the quality of service (QoS) for a 25 node application taskflow, known as direct acyclic graph. We propose a genetic algorithm based approach to solve the optimization problem which performs a novel three-layer decision making: (i) whether to offload a task or not, (ii) path planning to reach a desired location for offloading/local execution, (iii) select access point to associate with for offloading. We simulate for a smart city scenario consisting of 36-cell workspace with obstacles and compare the offloading results with a well- established fixed movement offloading method. The outcomes of our study suggest that motion and connectivity aware offloading leads to more efficient performance in terms of improved QoS and minimum consumption of resources, i.e., energy, time or distance.

Anatomical characteristics, such as shallow vestibular depth, high frenum attachments, and thin labial bone tend to make treatment of mandibular gingival recession defects particularly challenging. Even though a number of recent systematic reviews have identified the connective tissue graft (CTG) in combination with coronally advanced flaps (CAF) as the most consistently effective treatment procedure for Miller Class I and II defects, they did not separate maxillary vs mandibular outcomes, reporting instead aggregate results. In this prospective consecutive case series, recombinant human platelet-derived growth factor-BB (rhPDGF-BB) was combined with CTGs for the treatment of Miller Class I or II mandibular gingival recession defects in 11 healthy patients. Changes for both mean recession depth and width from baseline to week 24 were statistically significant, with mean recession depth decreasing from 3.4 ± 0.5 mm to 0.8 ± 0.8 mm and mean recession width decreasing from 3.1 ± 0.7 mm to 1.7 ± 1.3 mm. As a result of the improvement in recession depth, the mean percent root coverage seen in this mandibular prospective consecutive case series at 6 months was 79.6%. Despite treatment results that were comparable to historical norms, the outcomes, especially percent root coverage at 24 weeks, were somewhat less favorable, emphasizing the anatomical challenges of mandibular recession defects.

The advent of scanning near-field optical microscopy (SNOM) has augmented at a microscopic level the usefulness of optical spectroscopy in the region between 300 nm and 10 mum. Two-dimensional imaging of chemical constituents makes this a very attractive and powerful new approach. In this paper we show SNOM results obtained in several geometrical configurations on boron clusters in silicon, Li clusters embedded in a LiF sample and BN growth on silicon. We also show some results on the wavelength dependence of the reflectivity (R) in the near infrared (IR) of biological cells in liquid environment with the observation of the local fluorescence. The SNOM images revealed features that were not present in the corresponding shear-force (SF) images and which were due to localized changes in the bulk properties of the sample. The size of the smallest detected features clearly demonstrated that near-field conditions were reached both in the visible and infrared region. (C) 2004 Elsevier B.V. All rights reserved.

The subepithelial connective tissue graft (SCTG) is the most widely used procedure for the treatment of gingival recession defects. Little is known, however, as to whether the apposed gingival flaps are more predisposed to develop plaque-related inflammation compared to healthy sites. This has salient clinical implications, as the long-term results of root coverage will depend largely on the level of inflammation of the grafted tissue.In the present split-mouth case-control study, clinical and biomolecular parameters were used to assess the level of inflammation of periodontal sites 12 mo after treatment with SCTG (test) and healthy non-treated gingivae (control) following the induction of plaque-related gingivitis in 19 patients.The data showed that test sites had a significantly (P0.05) lower gingival index and angulated bleeding score compared to control sites (gingival index = 1.05 ± 0.23 vs. 1.34 ± 0.47; angulated bleeding score = 0.34 ± 0.37 vs. 0.61 ± 0.39) after induction of experimental gingivitis, whereas the plaque index did not differ in the two groups (P0.05). With regard to the biomolecular parameters, baseline levels of the proinflammatory cytokine interleukin-1β were higher in the gingival crevicular fluid of test sites. However, control sites exhibited more pronounced increase in the levels of interleukin-1β compared to test sites, upon plaque accumulation, so that the final concentration was similar in both groups. No changes were recorded in the gingival crevicular fluid volume.Analysis of the data demonstrates that the sites of gingival recession treated with SCTG develop a lower degree of plaque-induced inflammation compared to healthy gingivae. This strongly suggests that SCTG does not predispose to inflammation and to further gingival recession and makes it a safe technique in the treatment of gingival defects.

Presence of epithelial desquamation, erythema, and erosions on gingival tissue is usually described in the literature as desquamative gingivitis (DG). A wide range of autoimmune/dermatologic disorders can manifest as DG, although the two more common are oral lichen planus and mucous membrane pemphigoid. The aim of this study is to investigate prevalence of 11 periodontopathogenic microorganisms in patients with DG and to compare it with the microbiologic status of individuals affected by plaque-induced gingivitis (pGI).Cross-sectional clinical and microbiologic data were obtained from 66 patients (33 in each group). Subgingival plaque samples were analyzed using semiquantitative polymerase chain reaction analysis.Statistically significant difference, but with little clinical significance, was observed in gingival conditions between the two groups, probably due to the worse home control hygiene of patients with DG. Prevalence and levels of Aggregatibacter actinomycetemcomitans, Eikenella corrodens, and Fusobacterium nucleatum/periodonticum were statistically higher in samples from patients with DG than in those with pGI. In multivariate regression models, subgingival colonization of A. actinomycetemcomitans and F. nucleatum/periodonticum was not statistically associated with DG, whereas, high levels of E. corrodens were associated with 13-fold increased odds for DG.Microbiologic differences were found in subgingival plaque for patients with DG and pGI. This may suggest possible association between periodontal pathogens and DG.

The goal of this study was to histologically evaluate the healing process of subepithelial connective tissue graft in humans. A sample of 16 patients with Miller Class I or II localized gingival recession defects were consecutively treated with a bilaminar technique. At baseline and after 2 months, tissue samples were taken from the donor and recipient sites. The revascularization and reinnervation of the graft were examined by means of hematoxylin-eosin and immunohistochemical techniques. After 2 months of healing, the integration of the grafted tissue was evident with the connective tissue expressing features belonging to both the recipient (cellular component) and the donor site (extracellular matrix) in the inner and superficial layers, respectively. This was accompanied by increased neovascularization and reduction in intraepithelial free nerve endings.

Background. Several clinical techniques and a variety of biomaterials have been introduced over the years in an effort to overcome bone remodeling and resorption after tooth extraction. However, the predictability of these procedures in sockets with severely resorbed buccal/lingual plate due to periodontal disease is still unknown. Case Description. A patient with advanced periodontitis underwent extraction of upper right lateral and central incisors. The central incisor exhibited complete buccal bone plate loss and a 9 mm vertical bone deficiency on its palatal side. The alveolar sockets were filled with collagen sponge and covered with a nonresorbable high-density PTFE membrane. Primary closure was not attained and any rigid scaffold material was not used. Histologic analysis provided evidence of new bone formation. At 12 months a cone-beam computed tomographic scan revealed enough bone volume to insert two conventional dental implants in conjunction with minor horizontal bone augmentation procedures. Clinical Implications. This case report would seem to support the potential of the proposed reconstructive approach in changing the morphology of severely resorbed alveolar sockets, minimizing the need for advanced bone regeneration procedures during implant placement.

The aim of this study was to histologically examine any epithelial cell inclusions in submerged subepithelial connective tissue graft (SCTG) after clinical healing was achieved. A total of 16 patients with Miller Class I or II gingival recessions were consecutively treated with a bilaminar procedure. At 2 months after surgery, a gingival tissue specimen was harvested from all SCTG-treated sites and stained with hematoxylin-eosin. The histologic evaluation revealed connective tissue in active reorganization without epithelial inclusions in 14 of the 16 tissue specimens. In the remaining 2 specimens, epithelial islands were observed deep in the connective tissue. In one case they developed in a solid cystic space, while in the second case they were strictly integrated in the lamina propria. Complete recession coverage was obtained in 14 of the 16 treated defects, with a mean root coverage of 95.1% ± 14.2% at 12 months.

Cloud robotics in smart cities is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with a cloud computing infrastructure. It complements the Internet of Things (IoT) by creating an expanded network where robots offload data-intensive computation to the ubiquitous cloud to ensure quality of service (QoS). However, offloading for robots is significantly complex due to their unique characteristics of mobility, skill-learning, data collection, and decision-making capabilities. In this paper, a generic cloud robotics framework is proposed to realize smart city vision while taking into consideration its various complexities. Specifically, we present an integrated framework for a crowd control system where cloud-enhanced robots are deployed to perform necessary tasks. The task offloading is formulated as a constrained optimization problem capable of handling any task flow that can be characterized by a Direct Acyclic Graph (DAG). We consider two scenarios of minimizing energy and time, respectively, and develop a genetic algorithm (GA)-based approach to identify the optimal task offloading decisions. The performance comparison with two benchmarks shows that our GA scheme achieves desired energy and time performance. We also show the adaptability of our algorithm by varying the values for bandwidth and movement. The results suggest their impact on offloading. Finally, we present a multi-task flow optimal path sequence problem that highlights how the robot can plan its task completion via movements that expend the minimum energy. This integrates path planning with offloading for robotics. To the best of our knowledge, this is the first attempt to evaluate cloud-based task offloading for a smart city crowd control system.

Cloud robotics is an emerging paradigm that enables autonomous robotic agents to communicate and collaborate with cloud computing infrastructures. It further complements Internet of Things (IoT) to improve the performance of smart city applications. By offloading heavy data- intensive computation to the ubiquitous cloud, quality of service (QoS) guarantee can be ensured. Unlike their mobile counterpart, the robots have unique characteristics of mobility, skill- learning, data collection and decision-making capabilities, which makes offloading decisions significantly complex. This paper proposes a generic cloud robotics framework to realize smart city vision while taking into consideration its various complexities. Specifically, task offloading is formulated as a constrained optimization problem capable of handling Direct Acyclic Graph (DAG) known as task flow. Given the constraints, a genetic algorithm (GA) based scheme is further developed to solve the problem. The performance of the algorithm is verified by evaluating the results via three benchmarks. To the best of our knowledge, this is one of the first attempts of task offloading approach for smart city applications of cloud robotics.

In this paper, a novel sliding mode (SM) control is developed for vehicle lane keeping. The proposed SM controller can enable the vehicle to track the reference road center-line and yaw direction with zero errors. The idea behind the SM controller are as follows: first, lateral vehicle dynamics system is divided into two subsystems. Lateral position error dynamics system is recognized as a predominant subsystem, while yaw angle error dynamics system is an affiliate subsystem. Then, an SM controller is designed for the predominant subsystem to achieve asymptotical convergence of lateral position error. Since the components related to yaw angle dynamics in the equivalent control input signal of front wheel steering angle have the pole-placement function to yaw angle dynamics, the stability of the affiliate subsystem and asymptotical convergence of yaw angle error finally can be ensured. Thus, the SM controller designed for predominant subsystem can achieve decoupled control for both lateral position error dynamics and yaw angle error dynamics. The excellent performance of the proposed SM control is demonstrated by simulation results.

This paper proposes a new dynamic neural modelling method for mechatronic system prognostics. A simulated turbofan engine system is firstly introduced. Then, so-called health index (HI) for the system is evaluated by an artificial neural network model called extreme learning machine (ELM) to recognize the health conditions. In order to track and predict the propagation process of the system based on the health index, a new dynamic neural network (DNN) with feedback from outputs to both output layer and hidden layer is developed. With the batch learning type of least square, both the feedback weights and output weights are optimized. With the turbofan engine datasets from NASA, simulations are carried out, using both the ELM model and the proposed DNN. The excellent performance of the proposed DNN shows that the DNN with hidden layer feedback has a superior capability of capturing the dynamic features of the mechatronic system.

Extremely brilliant infrared (IR) beams provided by synchrotron radiation sources are now routinely used in many facilities with available commercial spectrometers coupled to IR microscopes. Using these intense non-thermal sources, a brilliance two or three order of magnitude higher than a conventional source is achievable through small pinholes (

In much of the literature the extreme distribution has been used to model first-person shooter (FPS) game packet length distributions. In this paper we show that a skewed mixture distribution, the Ex-Gaussian, is also suitable for modelling the packet payload lengths for two-player games of seven popular FPS games in the server-to-client direction. Also there is a plausible physical justification for the choice of a mixture distribution as a suitable model. The Ex-Gaussian distribution has properties that can be exploited to synthesise the server-to-client packet payload length distributions for larger numbers of players using measurements taken from game trials with a small number of players. We have also outlined a computationally simple technique that can be used to synthesise FPS game server-to-client packet payload length distributions for N-players from measurements from two-player games. This technique is useful for building realistic traffic models for FPS game traffic that can be used in simulation studies.

The need to image objects on increasingly finer scales and spatially localized specific molecules can be met by the combination of infrared, visible and Raman spectroscopy with scanning near-field microscopy, giving rise to a powerful nanospectroscopic tool used to perform simultaneous topographical measurements and optical/chemical characterization with subwavelength resolution, overcoming the diffraction limit of light. Conventional spectroscopy is often not enough sensitive and spatially resolved to detect specific elements or domains in a sample. Scanning near-field optical microscopy is a sensitive and flexible technique that achieves an enhanced optical resolution through the very close placement of the sensing element to the object. We present several results of near field spectroscopy with infrared radiation emitted by a free electron laser to investigate material science and biological samples. The local reflectivity revealed features, as a function of photon energy, that were not present in the corresponding shear-force (topology) images and were due to localized changes in the bulk properties of the sample. The size of the smallest detected features clearly demonstrated that near-field conditions were reached, with an optical spatial resolution well below the diffraction limit.

Fast pump-probe magneto-optic techniques have been used to investigate the hot carrier and magnetization dynamics in InAs nanostructures deposited on GaAs(001). From these time resolved reflectivity and Faraday rotation measurements, different dynamics responses were found for samples containing different InAs thicknesses. We also present some results of near field spectroscopy with infrared radiation emitted by a free electron laser to investigate biological samples. The local reflectivity revealed features as a function of photon energy that were not present in the corresponding shear-force (topology) images and were due to localized changes in the bulk properties of the sample.

This work reports the use of an infrared spectroscopic version of scanning near-field optical microscopy (IR-SNOM) and shows that it is possible to reveal subcellular entities via their chemical constituents in a label-free human oesophageal adenocarcinoma cell (OE33). This work presents the first high spatial resolution (similar to 0.15 mu m) study of features imaged at various wavelengths within a cancer cell. The clear illumination of sub-micron sized moieties within a cell is a major step forward and is a key requirement for understanding cancer and for the study of other diseases and healthy tissue. The stable and tuneable light source was provided by the infrared free electron laser on the ALICE accelerator at Daresbury. The images reveal a structure with a size and wavelength absorption that are consistent with a chromosome and open the possibility of observing other localized structures, such as microvesicles, that play an important role in the development and spread of cancers.

A hybrid conventional inverted/scanning near-field optical microscope (Inverted/SNOM) has been obtained, by coupling an Olympus IX-70 optical microscope with a SNOM head to combine the versatility and ease of use of the conventional microscope with the high-resolution and three-dimensional reconstruction obtainable with the SNOM technique. The head can be run in shear or tapping mode and is optimized to characterize soft, biological samples including living cells in physiological environment by including the SNOM in a cylindrical chamber that insulates it from the external noise, while maintaining a controlled temperature and atmosphere. Applications in the fields of biology and material science are presented.

Scanning near field optical microscopy (SNOM) is a well-established technique to obtain a sub-wavelength resolution optical characterization, together with nanometer-scale topography images, on any kind of biological or non-biological sample. Recently we have modified a classical SNOM unit to work in the tapping modality, ensuring stability, versatility and good optical resolution and signal to noise ratio. Exploiting the vertical tip movement, in particular, we were able to access two different optical detection modes: light modulation, which can be obtained by a mechanical chopper or by electronically switching the laser on and off; gap modulation in which the tip's vertical oscillation is used to produce, itself, a modulation of the collected light. Several biological and non-biological samples have been investigated and the data reveal that, despite the signal collected in gap-modulation is at least one order of magnitude smaller than in laser-modulation, resolution, and signal-to-noise ratio in the gap-modulated images is preferable. On the other hand, the higher intensity of the laser-modulation signal allows to deconvolve the optical information at higher harmonics of the tip oscillation frequency. This is a well-known procedure used in the apertureless-SNOM setup to enhance the near-field contribution of the scattered light and reduce the noise content. The interesting results obtained in this Aperture setup are described and commented.

The advent of Scanning Near-field Optical Microscopy (SNOM) has augmented at a microscopic level the usefulness of optical spectroscopy and two-dimensional imaging of chemical constituents makes this a very attractive and powerful new approach. In this paper we will review various configurations of the SNOM technique and some representative results obtained in material science and on biological samples by means of shear-force, reflectivity and fluorescence, with a lateral resolution well below the diffraction limit. (© 2008 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Infrared scanning near-field optical microscopy is used to detect AMPA-type glutamate receptor clusters in hippocampal neuron cells. AMPA's GluR2-subunits were marked with the fluorophore Alexa488 and high-resolution micrographs were taken at 6.25 µm wavelength, corresponding to the maker's strong infrared absorption. The use of infrared light avoids photobleaching and makes this approach suitable for long-term observations; this technique can be used in general to localize different IR-absorbing molecules over the complex background of other cell components.

Summary We present the implementation of a tapping-mode aperture scanning near-field optical microscope (Tapping-SNOM) to a Binder CB incubator (Istituto di Struttura della Materia, Rome, Italy). The microscope operates in the intermittent contact mode using a nonbent optical fibre allowing to reduce the perturbation exerted on the sample, while the incubator maintains a constant temperature, humidity and CO2 level. This instrument can maintain and analyse in a controlled environment different samples, both organic and nonorganic. In particular, the Tapping-SNOM can study different cell lines at nanometric resolution and in physiological buffer, following the evolution of the living cells almost indefinitely. We will present several examples of the capabilities of the tapping scanning near-field optical microscope in the study of different lines of living cells, showing corresponding topographical, optical or phase-lag images of the live samples, evidencing the excellent stability, versatility and resolution of the system.

The optical properties of metal nanoparticles play a fundamental role for their use in a wide range of applications. In hyperthermia treatment, for example, gold nanoshells (NSs, dielectric core+gold shell) pre-embedded in a cancer cell absorb energy when exposed to appropriate wavelengths of a laser beam and heat up, thereby destroying the cancer cell. In this process, nevertheless, healthy tissues (not targeted by the NSs) along the laser path are not affected; this is because most biological soft tissues have a relatively low light absorption coefficient in the near-infrared (NIR) regions—a characteristic known as the tissue optical window. Over such a window, NIR light transmits through the tissues with scattering-limited attenuation and minimal heating, thereby avoiding damage to healthy tissues. As a consequence, the identification of NSs assumed a fundamental role for the further development of such cancer treatment. Recently, we have demonstrated the possibility to identify 100–150 nm diameter gold NSs inside mouse cells using a scanning near-optical microscope (SNOM). In this paper, we provide a numerical demonstration that the SNOM is able to locate NSs inside the cell with a particle–aperture distance of about 100 nm. This result was obtained by developing an analytical approach based on the calculation of the dyadic Green function in the near-field approximation. The implications of our findings will remarkably affect further investigations on the interaction between NSs and biological systems.

This article illustrates a novel approach for planning user defined Earth's areas coverage by satellites with Synthetic Aperture Radar (SAR) payloads, optimizing the strategy related to acquisition scheduling. A dedicated coverage planning algorithm is implemented in a user-friendly proprietary software called Tool For Coverage (TFC). The TFC user has the freedom to configure any Earth Observation (EO) space mission with SAR satellites, defining the characteristics of the satellite such as the orbit, the parameters of the SAR beams, the operational modes and the Area of Interest to be covered. In a short time, TFC allows to obtain optimal acquisitions lists that satisfies the total coverage of that area.

The COSMO-SkyMed Seconda Generazione System simulation study cannot be performed by means of conventional SW COTS because of its complexity. Thus a specific Mission Simulator has been designed with the main objective of supporting a range of activities starting from the design phase, through the verification and validation phase up to the end-to-end tests of the commissioning phase. The COSMO-SkyMed Seconda Generazione Mission Simulator (CSG-MS) is a run-time software application which is able to configure, plan and simulate the Mission with the capability to replicate the end-to-end mission “behaviour” in response to a given “scenario” defined by a specific mission configuration together with the “user requests handbook”.

A new scanning near field optical microscope operating in the intermittent contact mode (Tapping SNOM) using a non-bent optical fiber is presented. The main advantages in terms of versatility and quality of the collected signal are described together with some results obtained studying fluorescence on material science samples and spectroscopy images of living cells.

Nowadays the possibility to obtain micro-radiographs is limited in resolution by the detection properties of the used technique. Here we show a new method to create and observe micro-radiographs. A thin film of Lithium Fluoride works as image detector, storing the micro-radiograph obtained exposing biological material to Extreme Ultra-Violet and soft X-ray radiations. To read the stored image, collecting the optically stimulated luminescence of the active color centers produced by the X-rays, a Scanning Near-Field Optical Microscope (SNOM) is used, for its peculiarity to overcome the diffraction limit, showing details smaller than the wavelength of the collected light. The samples were illuminated with 458 nm laser light and an uncoated tapered optical fiber was collecting the fluorescent signal emitted by the Lithium Fluoride color centers. The SNOM images of these micro-radiographs stored in Lithium Fluoride films showed an optical resolution of 50 nm, corresponding to ∼λ /12, where λ is the wavelength of the collected light. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

With confocal Raman microscopy, detailed images of the three-dimensional structure of thick samples (such as porous materials, aggregated nanoparticles etc.) could be readily acquired and visualized with sub-micron resolution. However, confocal Raman microscopy is not a panacea, especially for the studies of nanostructures, mainly because of diffraction limits for the optical resolution. The spatial resolution of Raman systems employing traditional optical microscopes is limited to approximately the wavelength of the light (about 0.5 µm), because both the illuminating laser light and the Raman scattered light are collected in the optical far-field (i.e. many wavelengths of light away from the scattering material). We will describe a new setup for nano-Raman experiments by using the fiber-optic scanning probe of a Scanning Near Field Optical Microscope (SNOM). The collected Raman signal in this near-field geometry reaches spatial resolutions at the level of tens of nanometers. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

A new implementation of a SNOM is described, aiming at the topographical study of large areas and exploiting the advantages of the shear-force detection system. This technique finds very interesting application in the study of archeological or artistic samples, where it often occurs that an area to be examined at high resolution (optically, topographically or chemically) is enclosed in a wider one. The implemented system allows to obtain scans of wide areas by using dc-motors to move the sample under the probe tip, and thus it can face rough samples with height differences of several tens of micrometers. It allows the user to choose whether to use the SNOM to study the optical and topographical properties of a small part of the sample (up to tens of square micrometers), or to use the motor-driven scan technique, to study the topographical characteristic of a large area of the sample (up to some square millimeters) and even over rough surfaces. We show results detecting worn relieves over a coin. (© 2005 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim)

Scanning near-field optical microscopy (SNOM or NSOM) is the technique with the highest lateral optical resolution available today, while infrared (IR) spectroscopy has a high chemical specificity. Combining SNOM with a tunable IR source produces a unique tool, IR-SNOM, capable of imaging distributions of chemical species with a 100 nm spatial resolution. We present in this paper boron nitride (BN) thin film images, where IR-SNOM shows the distribution of hexagonal and cubic phases within the sample. Exciting potential applications in biophysics and medical sciences are illustrated with SNOM images of the distribution of different chemical species within cells. We present in this article images with resolutions of the order of λ/60 with SNOM working with infrared light. With our SNOM setup, we routinely get optical resolutions between 50 and 150 nm, regardless of the wavelength of the light used to illuminate the sample.

Upon deposition of silicon onto the (1 1 0) surface of a silver crystal we have grown massively parallel one-dimensional Si nanowires. They are imaged in scanning tunnelling microscopy as straight, high aspect ratio, nanostructures, all with the same characteristic width of 16 A, perfectly aligned along the atomic troughs of the bare surface. Low energy electron diffraction confirms the massively parallel assembly of these self-organized nanowires. Photoemission reveals striking quantized states dispersing only along the length of the nanowires, and extremely sharp, two-components, Si 2p core levels. This demonstrates that in the large ensemble each individual nanowire is a well-defined quantum object comprising only two distinct silicon atomic environments. We suggest that this self-assembled array of highly perfect Si nanowires provides a simple, atomically precise, novel template that may impact a wide range of applications.

We present ellipsometry data of the dielectric function of wurtzite ZnO in a wide energy range (2.5-32 eV). The ordinary and extraordinary components show a strong anisotropy above 10 eV, a feature for which ZnO deviates from the other II-VI wurtzite compounds. With the aid of ab initio calculations, performed within many-body perturbation theory (MBPT) and within time-dependent density-functional theory (TDDFT), we analyze the origin of the measured optical structures. TDDFT, with the use of a static long-range exchange-correlation kernel, proves to be a cheaper computational tool than MBPT to yield a good description of the whole spectrum. Theoretical results for the zinc-blende phase are also presented.