Markov fluid queue model of an energy harvesting IoT device with adaptive sensing

Energy management is key in prolonging the lifetime of an energy harvesting Internet of Things (IoT) device with rechargeable batteries. Such an IoT device is required to fulfill its main functionalities, i.e., information sensing and dissemination at an acceptable rate, while keeping the probability that the node first becomes non-operational, i.e., the battery level hits zero the first time within a given finite time horizon, below a desired level. Assuming a finite-state Continuous-Time Markov Chain (CTMC) model for the Energy Harvesting Process (EHP), we propose a risk-theoretic Markov fluid queue model for the computation of first battery outage probabilities in a given finite time horizon. The proposed model enables the performance evaluation of a wide spectrum of energy management policies including those with sensing rates depending on the instantaneous battery level and/or the state of the energy harvesting process. Moreover, an engineering methodology is proposed by which optimal threshold-based adaptive sensing policies are obtained that maximize the information sensing rate of the IoT device while meeting a Quality of Service (QoS) constraint given in terms of first battery outage probabilities. Numerical results are presented for the validation of the analytical model and also the proposed engineering methodology, using a two-state CTMC-based EHP. © 2017 Elsevier B.V

Fixed-point analysis of a network of routers with persistent TCP/UDP flows and class-based weighted fair queuing

Fixed-point models have already been successfully used to analytically study networks consisting of persistent TCP flows only, or mixed TCP/UDP flows with a single queue per link and differentiated buffer management for these two types of flows. In the current study, we propose a nested fixed-point analytical method to obtain the throughput of persistent TCP and UDP flows in a network of routers supporting class-based weighted fair queuing allowing the use of separate queues for each class. In particular, we study the case of two classes where one of the classes uses drop-tail queue management and is intended for only UDP traffic. The other class targeting TCP, but also allowing UDP traffic for the purpose of generality, is assumed to employ active queue management. The effectiveness of the proposed analytical method is validated in terms of accuracy using ns-3 simulations and the required computational effort. © 2016, Springer Science+Business Media New York

Mapping time-varying IP traffic to flexible optical paths in flexgrid optical networks

A spectrum slot is the frequency range allocated to a single channel within a flexible grid, and its width needs to be an integer multiple of the so-called slot width granularity. The slot width of the spectrum slots to be used for an optical path in flexgrid optical networks can be adjusted in time to align with time-varying client traffic demand for both bandwidth and energy efficiency purposes. However, frequent adjustment of the slot width of optical paths places substantial signaling load on the control plane. In this paper, an online slot width adjustment mechanism is proposed for flexgrid optical networks under slot width update rate constraints in order to maintain the associated signaling load at acceptable levels. Real traffic traces are used to validate the effectiveness of the proposed mechanism. © 2014, Springer Science+Business Media New York

Density-Guided Label Smoothing for Temporal Localization of Driving Actions

Temporal localization of driving actions plays a crucial role in advanced driver-assistance systems and naturalistic driving studies. However, this is a challenging task due to strict requirements for robustness, reliability and accurate localization. In this work, we focus on improving the overall performance by efficiently utilizing video action recognition networks and adapting these to the problem of action localization. To this end, we first develop a density-guided label smoothing technique based on label probability distributions to facilitate better learning from boundary video-segments that typically include multiple labels. Second, we design a post-processing step to efficiently fuse information from video-segments and multiple camera views into scene-level predictions, which facilitates elimination of false positives. Our methodology yields a competitive performance on the A2 test set of the naturalistic driving action recognition track of the 2022 NVIDIA AI City Challenge with an F1 score of 0.271

Polypeptide-grafted macroporous polyHIPE by surface-initiated N-Carboxyanhydride (NCA) polymerization as a platform for bioconjugation

A new class of functional macroporous monoliths from polymerized high internal phase emulsion (polyHIPE) with tunable surface functional groups was developed by direct polypeptide surface grafting. In the first step, amino-functional polyHIPEs were obtained by the addition of 4-vinylbenzyl or 4-vinylbenzylphthalimide to the styrenic emulsion and thermal radical polymerization. The obtained monoliths present the expected open-cell morphology and a high surface area. The incorporated amino group was successfully utilized to initiate the ring-opening polymer- ization of benzyl-L-glutamate N-carboxyanhydride (BLG NCA) and benzyloxycarbonyl-L-lysine (Lys(Z)) NCA, which resulted in a dense homogeneous coating of polypeptides throughout the internal polyHIPE surfaces as confirmed by SEM and FTIR analysis. The amount of polypeptide grafted to the polyHIPE surfaces could be modulated by varying the initial ratio of amino acid NCA to amino-functional polyHIPE. Subsequent removal of the polypeptide protecting groups yielded highly functional polyHIPE-g-poly(glutamic acid) and polyHIPE-g- poly(lysine). Both types of polypeptide-grafted monoliths responded to pH by changes in their hydrohilicity. The possibility to use the high density of function (−COOH or −NH2) for secondary reaction was demonstrated by the successful bioconjugation of enhanced green fluorescent protein (eGFP) and fluorescein isocyanate (FITC) on the polymer 3D-scaffold surface. The amount of eGFP and FITC conjugated to the polypeptide-grafted polyHIPE was significantly higher than to the amino- functional polyHIPE, signifying the advantage of polypeptide grafting to achieve highly functional polyHIPEs

Composite Films of Arabinoxylan and Fibrous Sepiolite: Morphological, Mechanical, and Barrier Properties

Hemicelluloses represent a largely unutilized resource for future bioderived films in packaging and other applications. However, improvement of film properties is needed in order to transfer this potential into reality. In this context, sepiolite, a fibrous clay, was investigated as an additive to enhance the properties of rye flour arabinoxylan. Composite films cast from arabinoxylan solutions and sepiolite suspensions in water were transparent or semitransparent at additive loadings in the 2.5-10 wt % range. Scanning electron microscopy showed that the sepiolite was well dispersed in the arabinoxylan films and sepiolite fiber aggregation was not found. FT-IR spectroscopy provided some evidence for hydrogen bonding between sepiolite and arabinoxylan. Consistent with these findings, mechanical testing showed increases in film stiffness and strength with sepiolite addition and the effect of poly(ethylene glycol) methyl ether (mPEG) plasticizer addition. Incorporation of sepiolite did not significantly influence the thermal degradation or the gas barrier properties of arabinoxylan films, which is likely a consequence of sepiolite fiber morphology. In summary, sepiolite was shown to have potential as an additive to obtain stronger hemicellulose films although other approaches, possibly in combination with the use of sepiolite, would be needed if enhanced film barrier properties are required for specific applications.</p

Brain metastases from hepatocellular carcinoma: clinical features and prognostic factors

<p>Abstract</p> <p>Background</p> <p>Brain metastases (BM) from hepatocellular carcinoma (HCC) are extremely rare and are associated with a poor prognosis. The aim of this study was to define clinical outcome and prognostic determinants in patients with BM from HCC.</p> <p>Methods</p> <p>Between January 1994 and December 2009, all patients with HCC and BM treated in Sun Yat-sen University Cancer Center were retrospectively reviewed. Univariate and multivariate survival analyses were performed to identify possible prognostic factors.</p> <p>Results</p> <p>Forty-one patients were diagnosed with BM from HCC, an incidence of 0.47%. The median age at diagnosis of BM was 48.5 years. Thirty-three patients (80.5%) developed extracranial metastases at diagnosis of BM, and 30 patients (73.2%) had hepatitis B. Intracranial hemorrhage occurred in 19 patients (46.3%). BM were treated primarily either with whole brain radiation therapy (WBRT; 5 patients), stereotactic radiosurgery (SRS; 7 patients), or surgical resection (6 patients). The cause of death was systemic disease in 17 patients and neurological disease in 23. Patients in a high RPA (recursive partitioning analysis) class, treated with conservatively and without lung metastases, tended to die from neurological disease. Median survival after the diagnosis of BM was 3 months (95% confidence interval: 2.2-3.8 months). In multivariate analysis, the presence of extracranial metastases, a low RPA class and aggressive treatment, were positively associated with improved survival.</p> <p>Conclusions</p> <p>BM from HCC is rare and associated with an extremely poor prognosis. However, patients with a low RPA class may benefit from aggressive treatment. The clinical implication of extracranial metastases in HCC patients with BM needs further assessment.</p