Analysis of a Mathematical Model in a Food Web System Containing Scavenger Species

في هذا البحث قد تم رياضيا صياغة ديناميكية أنواع صائد الفرائس في نموذج شبكة الغذاء بدمج التأخير الزمني و حصاد الفريسة. ان حدود جميع حلول النموذج قد تم تنفيذها. تمت مناقشة الوجود وكذلك تحليل الأستقرار لجميع احتمالات نقاط الأتزان الموجبة. كذلك في ظل تأخير زمني معين أثبتنا أن نموذجنا. قد أظهر تشعب هوبف دون الحرج. وعلاوة على ذلك فاننا قد درسنا المحاكاة العددية للنموذج لتأكيد اكتشافنا التحليلي.In this paper, the dynamics of scavenger species  in a web food model  incorporating time delay and  prey harvesting  is formulated mathematically. Boundednes of all  solutions of the model carried out. The existence as well as stability analysis of all possible positive equilibrium points are discussed. Also, we proved that under certain time delay, our model exhibits a subcritical Hopfbifurcation. Furthermore, to confirm our analytical finding, we studied numerical simulation for the model

RANCANG BANGUN PEMANAS INDUKSI DENGAN MENGGUNAKAN NE555 SEBAGAI PEMBANGKIT FREKUENSI

Sebuah mesin pemanas memiliki peran yang sangat penting bagi masyarakat pada saat ini. Pada umunya mesin pemanas yang digunakan menggunakan energi yang dikonversi dari gas, minyak fisol, kayu, atau benda-benda lain yang dapat menghasilkan api. Akan tetapi ketersediaan bahan bakar itu cukup minim di era saat ini dan penggunaanya terlalu beresiko. Masyarakat pada saat ini mencari sebuah mesin pemanas yang memiki efisiensi yang tinggi dan yang paling utama adalah memiliki resiko yang kecil. Ada berbagai jenis pemanas yang telah ditemukan hingga saat ini yang memiliki resiko kecelakaan yang kecil, yaitu salah satunya pemanas induksi. Pemanas induksi berkerja dengan mengubah daya listrik menjadi energi panas dengan dibantu oleh frekuensi yang tinggi. Tujuan dari penelitian ini adalah untuk mengetahui panas yang dihasilkan dari frekuensi yang berbeda. Pengujian dilakukan dengan menggunakan frekuensi 20, 25, 30, 35, 40, 45, 50, 55, 60, 70, 80, 90, dan 100 KHz. Tidak ada patokan daya dalam penelitian ini, penelitian ini hanya berfokus pada suhu yang dihasilkan dengan frekuensi yang berbeda

Hybrid Multi-Level Detection and Mitigation of Clone Attacks in Mobile Wireless Sensor Network (MWSN).

Wireless sensor networks (WSNs) are often deployed in hostile environments, where an adversary can physically capture some of the sensor nodes. The adversary collects all the nodes' important credentials and subsequently replicate the nodes, which may expose the network to a number of other security attacks, and eventually compromise the entire network. This harmful attack where a single or more nodes illegitimately claims an identity as replicas is known as the node replication attack. The problem of node replication attack can be further aggravated due to the mobile nature in WSN. In this paper, we propose an extended version of multi-level replica detection technique built on Danger Theory (DT), which utilizes a hybrid approach (centralized and distributed) to shield the mobile wireless sensor networks (MWSNs) from clone attacks. The danger theory concept depends on a multi-level of detections; first stage (highlights the danger zone (DZ) by checking the abnormal behavior of mobile nodes), second stage (battery check and random number) and third stage (inform about replica to other networks). The DT method performance is highlighted through security parameters such as false negative, energy, detection time, communication overhead and delay in detection. The proposed approach also demonstrates that the hybrid DT method is capable and successful in detecting and mitigating any malicious activities initiated by the replica. Nowadays, crimes are vastly increasing and it is crucial to modify the systems accordingly. Indeed, it is understood that the communication needs to be secured by keen observation at each level of detection. The simulation results show that the proposed approach overcomes the weaknesses of the previous and existing centralized and distributed approaches and enhances the performance of MWSN in terms of communication and memory overhead

Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study

$\textbf{Background}$: Immune infiltration of breast tumours is associated with clinical outcome. However, past work has not accounted for the diversity of functionally distinct cell types that make up the immune response. The aim of this study was to determine whether differences in the cellular composition of the immune infiltrate in breast tumours influence survival and treatment response, and whether these effects differ by molecular subtype. $\textbf{Methods and Findings}$: We applied an established computational approach (CIBERSORT) to bulk gene expression profiles of almost 11,000 tumours to infer the proportions of 22 subsets of immune cells. We investigated associations between each cell type and survival and response to chemotherapy, modelling cellular proportions as quartiles. We found that tumours with little or no immune infiltration were associated with different survival patterns according to oestrogen receptor (ER) status. In ER-negative disease, tumours lacking immune infiltration were associated with the poorest prognosis, whereas in ER-positive disease, they were associated with intermediate prognosis. Of the cell subsets investigated, T regulatory cells and M0 and M2 macrophages emerged as the most strongly associated with poor outcome, regardless of ER status. Among ER-negative tumours, CD8+ T cells (hazard ratio [HR] = 0.89, 95% CI 0.80-0.98; $p$ = 0.02) and activated memory T cells (HR 0.88, 95% CI 0.80-0.97; $p$ = 0.01) were associated with favourable outcome. T follicular helper cells (odds ratio [OR] = 1.34, 95% CI 1.14-1.57; $p$ < 0.001) and memory B cells (OR = 1.18, 95% CI 1.0-1.39; $p$ = 0.04) were associated with pathological complete response to neoadjuvant chemotherapy in ER-negative disease, suggesting a role for humoral immunity in mediating response to cytotoxic therapy. Unsupervised clustering analysis using immune cell proportions revealed eight subgroups of tumours, largely defined by the balance between M0, M1, and M2 macrophages, with distinct survival patterns by ER status and associations with patient age at diagnosis. The main limitations of this study are the use of diverse platforms for measuring gene expression, including some not previously used with CIBERSORT, and the combined analysis of different forms of follow-up across studies. $\textbf{Conclusions}$: Large differences in the cellular composition of the immune infiltrate in breast tumours appear to exist, and these differences are likely to be important determinants of both prognosis and response to treatment. In particular, macrophages emerge as a possible target for novel therapies. Detailed analysis of the cellular immune response in tumours has the potential to enhance clinical prediction and to identify candidates for immunotherapy.HRA is an NIHR Academic Clinical Lecturer and was a recipient of a Career Development Fellowship from The Pathological Society of GB and N Ireland, and a Starter Grant for Clinical Lecturers from the Academy of Medical Sciences. LC, CC, and FM received funding from the CRUK & EPSRC Cancer Imaging Centre in Cambridge & Manchester (grant C197/A16465)

Developing a decomposable measure of profit efficiency using DEA

In for-profit organizations efficiency measurement with reference to the potential for profit augmentation is particularly important as is its decomposition into technical, and allocative components. Different profit efficiency approaches can be found in the literature to measure and decompose overall profit efficiency. In this paper, we highlight some problems within existing approaches and propose a new measure of profit efficiency based on a geometric mean of input/output adjustments needed for maximizing profits. Overall profit efficiency is calculated through this efficiency measure and is decomposed into its technical and allocative components. Technical efficiency is calculated based on a non-oriented geometric distance function (GDF) that is able to incorporate all the sources of inefficiency, while allocative efficiency is retrieved residually. We also define a measure of profitability efficiency which complements profit efficiency in that it makes it possible to retrieve the scale efficiency of a unit as a component of its profitability efficiency. In addition, the measure of profitability efficiency allows for a dual profitability interpretation of the GDF measure of technical efficiency. The concepts introduced in the paper are illustrated using a numerical example

Osmoregulators proline and glycine betaine counteract salinity stress in canola

Salt inundation leads to increased salinization of arable land in many arid and semi-arid regions. Until genetic solutions are found farmers and growers must either abandon salt-affected fields or use agronomic treatments that alleviate salt stress symptoms. Here, field experiments were carried out to study the effect of the osmoregulators proline at 200 mg L-1 and glycine betaine at 400 mg L-1 in counteracting the harmful effect of soil salinity stress on canola plants grown in Egypt. We assessed growth characteristics, yield and biochemical constituents. Results show first that all growth characters decreased with increasing salinity stress but applied osmoregulators alleviated these negative effects. Second, salinity stress decreased photosynthetic pigments, K and P contents, whilst increasing proline, soluble sugars, ascorbic acid, Na and Cl contents. Third, application of osmoregulators without salt stress increased photosynthetic pigments, proline, soluble sugars, N, K and P contents whilst decreasing Na and Cl contents. It is concluded that the exogenously applied osmoregulators glycine betaine and proline can fully or partially counteract the harmful effect of salinity stress on growth and yield of canola.© INRA and Springer-Verlag, France 2012

Building robust prediction models for defective sensor data using Artificial Neural Networks

Predicting the health of components in complex dynamic systems such as an automobile poses numerous challenges. The primary aim of such predictive systems is to use the high-dimensional data acquired from different sensors and predict the state-of-health of a particular component, e.g., brake pad. The classical approach involves selecting a smaller set of relevant sensor signals using feature selection and using them to train a machine learning algorithm. However, this fails to address two prominent problems: (1) sensors are susceptible to failure when exposed to extreme conditions over a long periods of time; (2) sensors are electrical devices that can be affected by noise or electrical interference. Using the failed and noisy sensor signals as inputs largely reduce the prediction accuracy. To tackle this problem, it is advantageous to use the information from all sensor signals, so that the failure of one sensor can be compensated by another. In this work, we propose an Artificial Neural Network (ANN) based framework to exploit the information from a large number of signals. Secondly, our framework introduces a data augmentation approach to perform accurate predictions in spite of noisy signals. The plausibility of our framework is validated on real life industrial application from Robert Bosch GmbH.Comment: 16 pages, 7 figures. Currently under review. This research has obtained funding from the Electronic Components and Systems for European Leadership (ECSEL) Joint Undertaking, the framework programme for research and innovation Horizon 2020 (2014-2020) under grant agreement number 662189-MANTIS-2014-

State estimation for discrete-time neural networks with Markov-mode-dependent lower and upper bounds on the distributed delays

Copyright @ 2012 Springer VerlagThis paper is concerned with the state estimation problem for a new class of discrete-time neural networks with Markovian jumping parameters and mixed time-delays. The parameters of the neural networks under consideration switch over time subject to a Markov chain. The networks involve both the discrete-time-varying delay and the mode-dependent distributed time-delay characterized by the upper and lower boundaries dependent on the Markov chain. By constructing novel Lyapunov-Krasovskii functionals, sufficient conditions are firstly established to guarantee the exponential stability in mean square for the addressed discrete-time neural networks with Markovian jumping parameters and mixed time-delays. Then, the state estimation problem is coped with for the same neural network where the goal is to design a desired state estimator such that the estimation error approaches zero exponentially in mean square. The derived conditions for both the stability and the existence of desired estimators are expressed in the form of matrix inequalities that can be solved by the semi-definite programme method. A numerical simulation example is exploited to demonstrate the usefulness of the main results obtained.This work was supported in part by the Royal Society of the U.K., the National Natural Science Foundation of China under Grants 60774073 and 61074129, and the Natural Science Foundation of Jiangsu Province of China under Grant BK2010313

Natural killer cells attenuate cytomegalovirus-induced hearing loss in mice

<div><p>Congenital cytomegalovirus (CMV) infection is the most common non-hereditary cause of sensorineural hearing loss (SNHL) yet the mechanisms of hearing loss remain obscure. Natural Killer (NK) cells play a critical role in regulating murine CMV infection via NK cell recognition of the Ly49H cell surface receptor of the viral-encoded m157 ligand expressed at the infected cell surface. This Ly49H NK receptor/m157 ligand interaction has been found to mediate host resistance to CMV in the spleen, and lung, but is much less effective in the liver, so it is not known if this interaction is important in the context of SNHL. Using a murine model for CMV-induced labyrinthitis, we have demonstrated that the Ly49H/m157 interaction mediates host resistance in the temporal bone. BALB/c mice, which lack functional Ly49H, inoculated with mCMV at post-natal day 3 developed profound hearing loss and significant outer hair cell loss by 28 days of life. In contrast, C57BL/6 mice, competent for the Ly49H/m157 interaction, had minimal hearing loss and attenuated outer hair cell loss with the same mCMV dose. Administration of Ly49H blocking antibody or inoculation with a mCMV viral strain deleted for the m157 gene rendered the previously resistant C57BL/6 mouse strain susceptible to hearing loss to a similar extent as the BALB/c mouse strain indicating a direct role of the Ly49H/m157 interaction in mCMV-dependent hearing loss. Additionally, NK cell recruitment to sites of infection was evident in the temporal bone of inoculated susceptible mouse strains. These results demonstrate participation of NK cells in protection from CMV-induced labyrinthitis and SNHL in mice.</p></div

Studying Fake News via Network Analysis: Detection and Mitigation

Social media for news consumption is becoming increasingly popular due to its easy access, fast dissemination, and low cost. However, social media also enable the wide propagation of "fake news", i.e., news with intentionally false information. Fake news on social media poses significant negative societal effects, and also presents unique challenges. To tackle the challenges, many existing works exploit various features, from a network perspective, to detect and mitigate fake news. In essence, news dissemination ecosystem involves three dimensions on social media, i.e., a content dimension, a social dimension, and a temporal dimension. In this chapter, we will review network properties for studying fake news, introduce popular network types and how these networks can be used to detect and mitigation fake news on social media.Comment: Submitted as a invited book chapter in Lecture Notes in Social Networks, Springer Pres