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Brain imaging studies have revealed that functional and structural brain connectivity in the so-called triple network (i.e., default mode network (DMN), salience network (SN) and central executive network (CEN)) are consistently altered in schizophrenia. However, similar changes have also been found in patients with major depressive disorder, prompting the question of specific triple network signatures for the two disorders. In this study, we proposed Supervised Convex Nonnegative Matrix Factorization (SCNMF) to extract distributed multi-modal brain patterns. These patterns distinguish schizophrenia and major depressive disorder in a latent low-dimensional space of the triple brain network. Specifically, 21 patients of schizophrenia and 25 patients of major depressive disorder were assessed by T1-weighted, diffusion-weighted, and resting-state functional MRIs. Individual structural and functional connectivity networks, based on pre-defined regions of the triple network were constructed, respectively. Afterwards, SCNMF was employed to extract the discriminative patterns. Experiments indicate that SCNMF allows extracting the low-rank discriminative patterns between the two disorders, achieving a classification accuracy of 82.6% based on the extracted functional and structural abnormalities with support vector machine. Experimental results show the specific brain patterns for schizophrenia and major depressive disorder that are multi-modal, complex, and distributed in the triple network. Parts of the prefrontal cortex including superior frontal gyri showed variation between patients with schizophrenia and major depression due to structural properties. In terms of functional properties, the middle cingulate cortex, inferior parietal lobule, and cingulate cortex were the most discriminative regions. Keywords: Schizophrenia, Major depressive disorder, Triple network, Brain connectivity, Nonnegative matrix factorization

Voice over Long-Term Evolution enables reliable transmission among enormous Internet of Things devices, by providing end-to-end quality of service for Internet protocol–based services such as audio, video, and multimedia messaging. The research of covert timing channels aims at transmitting covert message stealthily to the receiver using variations of timing behavior. Existing approaches mainly modulate the covert message into inter-packet delays of overt traffic, which are not suitable for Voice over Long-Term Evolution, since most of the inter-packet delays of Voice over Long-Term Evolution traffic are of regular distribution, and any modification on inter-packet delays is easy to be detected. To address the issue, in this work, we propose a novel covert timing channel for the video stream in Voice over Long-Term Evolution, which modulates the covert message by deliberately dropping out video packets. Based on the two-dimensional mapping matrix, the blocks of covert message are mapped into dropout-packet sequence numbers. To recover the covert message, the receiver retrieves the sequence numbers of lost packets and identifies them to be translated into blocks of the covert message. To evaluate our scheme, the simulations with different packet loss rates are conducted to validate the undetectability, throughput, and robustness, finally, the results show that this scheme is effective and reliable.

Application of solar collectors for hot water supply, space heating, and cooling plays a significant role in reducing building energy consumption. For conventional solar collectors, solar radiation is absorbed by spectral selective coating on the collectors’ tube/plate wall. The poor durability of the coating can lead to an increased manufacturing cost and unreliability for a solar collector operated at a higher temperature. Therefore, a novel nanofluid-based direct absorption solar collector (NDASC) employing uncoated collector tubes has been proposed, and its operating characteristics for medium-temperature solar collection were theoretically and experimentally studied in this paper. CuO/oil nanofluid was prepared and used as working fluid of the NDASC. The heat-transfer mechanism of the NDASC with parabolic trough concentrator was theoretically evaluated and compared with a conventional indirect absorption solar collector (IASC). The theoretical analysis results suggested that the fluid’s temperature distribution in the NDASC was much more uniform than that in the IASC, and an enhanced collection efficiency could be achieved for the NDASC operated within a preferred working temperature range. To demonstrate the feasibility of the proposed NDASC, experimental performances of an NDASC and an IASC with the same parabolic trough concentrator were furthermore evaluated and comparatively discussed.

The hybrid-power gas engine-driven heat pump (HPGHP) combines hybrid power technology with a gas engine heat pump. The engine in the power system is capable of operating constantly with high thermal efficiency and low emissions during different operating modes. In this paper, the mathematical models of various components is established, including the engine thermal efficiency map and the motor efficiency map. The comprehensive charging/discharging efficiency model and energy management optimization strategy model which is proposed to maximize the efficiency of instantaneous HPGHP system are established. Then, different charging/discharging torque limits are obtained. Finally, a novel gas engine economical zone control strategy which combined with the SOC of battery in real time is put forward. The main operating parameters of HPGHP system under energy management are simulated by Matlab/Simulink and validated by experimental data, such as engine and motor operating torque, fuel consumption rate and comprehensive efficiency, etc. The results show that during 3600 s’ run-time, the SOC value of battery packs varies between 0.58 and 0.705, the fuel consumption rate reaches minimum values of approximately 291.3 g/(kW h) when the compressor speed is nearly 1550 rpm in mode D, the engine thermal efficiency and comprehensive efficiency reach maximum values of approximately 0.2727 and 0.2648 when the compressor speed is 1575 rpm and 1475 rpm, respectively, in mode D. In general, the motor efficiency can be maintained above 0.85 in either mode.

Cryogenic CO2 capture is considered as a promising CO2 capture method due to its energy saving and environmental friendliness. The phase equilibrium analysis of CO2-mixtures at low temperature is crucial for the design and operation of a cryogenic system because it plays an important role in analysis of recovery and purity of the captured CO2. After removal of water and toxic gas, the main components in typical boiler gases are N2/CO2. Therefore, this paper evaluates the reliabilities of different cubic equations of state (EOS) and mixing rules for N2/CO2. The results show that Peng-Robinson (PR) and Soave-Redlich-Kwong (SRK) fit the experimental data well, PR combined with the van der Waals (vdW) mixing rule is more accurate than the other models. With temperature decrease, the accuracy of the model improves and the deviation of the N2 vapor fraction is 0.43% at 220 K. Based on the selected calculation model, the thermodynamic properties of N2/CO2 at low temperature are analyzed. According to the results, a new liquefaction combined with a desublimation system is proposed. The total recovery and purity of CO2 production of the new system are satisfactory enough for engineering applications. Additionally, the total energy required by the new system to capture the CO2 is about 3.108 MJ·kg−1 CO2, which appears to be at least 9% lower than desublimation separation when the initial concentration of CO2 is 40%.

This paper proposes a polygeneration system based on a multi-input chemical looping combustion system, which generates methanol and electricity, through the use of natural gas and coal. In this system, the chemical looping hydrogen (CLH) production system and the coal-based methanol production system are integrated. A high quality fuel, natural gas, is used to improve the conversion ratio of coal. The Gibbs energy of the two kinds of fuels is fully used. Benefitting from the chemical looping process, 27% CO2 can be captured without energy penalty. With the same outputs of methanol and electricity, the energy savings ratio of the new system is about 12%. Based on the exergy analyses, it is disclosed that the integration of synthetic utilization of natural gas and coal plays a significant role in reducing the exergy destruction of the new system. The promising results obtained in this paper may lead to a clean coal technology that will utilize natural gas and coal more efficiently and economically.

In recent years, the botnets have been the most common threats to network security since it exploits multiple malicious codes like a worm, Trojans, Rootkit, etc. The botnets have been used to carry phishing links, to perform attacks and provide malicious services on the internet. It is challenging to identify Peer-to-peer (P2P) botnets as compared to Internet Relay Chat (IRC), Hypertext Transfer Protocol (HTTP) and other types of botnets because P2P traffic has typical features of the centralization and distribution. To resolve the issues of P2P botnet identification, we propose an effective multi-layer traffic classification method by applying machine learning classifiers on features of network traffic. Our work presents a framework based on decision trees which effectively detects P2P botnets. A decision tree algorithm is applied for feature selection to extract the most relevant features and ignore the irrelevant features. At the first layer, we filter non-P2P packets to reduce the amount of network traffic through well-known ports, Domain Name System (DNS). query, and flow counting. The second layer further characterized the captured network traffic into non-P2P and P2P. At the third layer of our model, we reduced the features which may marginally affect the classification. At the final layer, we successfully detected P2P botnets using decision tree Classifier by extracting network communication features. Furthermore, our experimental evaluations show the significance of the proposed method in P2P botnets detection and demonstrate an average accuracy of 98.7%.

BACKGROUND:The incidence of lower genital tract infections in China has been increasing in recent years. The link between high-risk human papillomavirus (HR-HPV) and other sexually transmitted diseases (STDs) remains unclear. METHODS:From March to October 2014, gynecological examinations and questionnaires were conducted on 1218 married women. Cervical secretions and vaginal swab specimens were tested for Chlamydia trachomatis (CT), Neisseria gonorrhoeae (NG), Ureaplasma urealyticum (UU), yeast, clue cells and HR-HPV. RESULTS:Laboratory results were available for 1195 of 1218 married women. HR-HPV was detected in 7.0% of participants. Forty-seven percent of women had lower genital tract infections (LGTIs). UU was the most common infection (35.5%), followed by bacterial vaginosis (BV) (10.5%), yeast infection (3.7%), CT (2.2%), and Trichomonas vaginalis (1.7%). BV was associated with an increased risk of HR- HPV (P < 0.0001; odds ratio, 3.0 [95% CI, 1.7-5.4]). There was a strong correlation between abnormal cervical cytology and HR-HPV infection (P < 0.0001). CONCLUSIONS:The prevalence of LGTIs in Beijing is at a high level. It is clinically important to screen for the simultaneous presence of pathogens that cause co-infections with HR-HPV.

Many IoT (Internet of Things) systems run Android systems or Android-like systems. With the continuous development of machine learning algorithms, the learning-based Android malware detection system for IoT devices has gradually increased. However, these learning-based detection models are often vulnerable to adversarial samples. An automated testing framework is needed to help these learning-based malware detection systems for IoT devices perform security analysis. The current methods of generating adversarial samples mostly require training parameters of models and most of the methods are aimed at image data. To solve this problem, we propose a testing framework for learning-based Android malware detection systems (TLAMD) for IoT Devices. The key challenge is how to construct a suitable fitness function to generate an effective adversarial sample without affecting the features of the application. By introducing genetic algorithms and some technical improvements, our test framework can generate adversarial samples for the IoT Android application with a success rate of nearly 100% and can perform black-box testing on the system.

With the growing era of the Internet of Things (IoT), more and more devices are connecting with the Internet using android applications to provide various services. The IoT devices are used for sensing, controlling and monitoring of different processes. Most of IoT devices use Android applications for communication and data exchange. Therefore, a secure Android permission privileged mechanism is required to increase the security of apps. According to a recent study, a malicious Android application is developed almost every 10 s. To resist this serious malware campaign, we need effective malware detection approaches to identify malware applications effectively and efficiently. Most of the studies focused on detecting malware based on static and dynamic analysis of the applications. However, to analyse the risky permission at runtime is a challenging task. In this study, first, we proposed a novel approach to distinguish between malware and benign applications based on permission ranking, similarity-based permission feature selection, and association rule for permission mining. Secondly, the proposed methodology also includes the enhancement of the random forest algorithm to improve the accuracy for malware detection. The experimental outcomes demonstrate high proficiency of the accuracy for malware detection, which is pivotal for android apps aiming for secure data exchange between IoT devices.

With the popularization of IoT (Internet of Things) devices and the continuous development of machine learning algorithms, learning-based IoT malicious traffic detection technologies have gradually matured. However, learning-based IoT traffic detection models are usually very vulnerable to adversarial samples. There is a great need for an automated testing framework to help security analysts to detect errors in learning-based IoT traffic detection systems. At present, most methods for generating adversarial samples require training parameters of known models and are only applicable to image data. To address the challenge, we propose a testing framework for learning-based IoT traffic detection systems, TLTD. By introducing genetic algorithms and some technical improvements, TLTD can generate adversarial samples for IoT traffic detection systems and can perform a black-box test on the systems.

The solar gravity heat pipe has been widely used for solar thermal water heating because of its high efficient heat transfer and thermal diode characteristics. Operated on fluctuant and low intensity solar radiation conditions, a solar gravity heat pipe may frequently start up. This severely affects its solar collection performance. To enhance the thermal performance of the solar gravity heat pipe, this study proposes using graphene/water nanofluid as the working fluid instead of deionized water. The stability of the prepared graphene/water nanofluid added with PVP was firstly investigated to obtain the optimum mass ratios of the added dispersant. Thermophysical properties—including the thermal conductivity and viscosity—of nanofluid with various graphene nanoplatelets (GNPs) concentrations were measured at different temperatures for further analysis. Furthermore, based on the operational evaluation on a single heat pipe’s start-up process, the performance of nanofluid-enhanced solar gravity heat pipes using different concentrations of GNPs were compared by using water heating experiments. Results indicated that the use of 0.05 wt % graphene/water nanofluid instead of water could achieve a 15.1% and 10.7% reduction in start-up time under 30 and 60 W input heating conditions, respectively. Consequently, a higher thermal efficiency for solar collection could be expected.

With the help of botnets, intruders can implement a remote control on infected machines and perform various malicious actions. Domain Name System (DNS) is very famous for botnets to locate command and control (C and C) servers, which enormously strengthens a botnet’s survivability to evade detection. This paper focuses on evasion and detection techniques of DNS-based botnets and gives a review of this field for a general summary of all these contributions. Some important topics, including technological background, evasion and detection, and alleviation of botnets, are discussed. We also point out the future research direction of detecting and mitigating DNS-based botnets. To the best of our knowledge, this topic gives a specialized and systematic study of the DNS-based botnet evading and detecting techniques in a new era and is useful for researchers in related fields.

In this paper we proposed the novel air-conditioning system which combined induction ventilation and radiant air-conditioning. The indoor terminal device is the radiant induction-unit (RIDU). The RIDU is the induction unit combined with the pore radiant panel on which the copper pipes with rigid aluminum diffusion fins are installed. The two-stage evaporator chiller with the non-azeotropic mixture refrigerant is utilized in the system to reduce the initial investment in equipment. With the performance test and the steady state heat transfer model based on the theory of radiative heat transfer, the relationship between the induction ratio of the RIDU and the characteristic of the air supply was studied. Based on this, it is verified that the RIDU has a lower dew-point temperature and better anti-condensation performance than a traditional plate-type radiant panel. The characteristics of the radiation and convection heat transfer of the RIDU were studied. The total heat exchange of the RIDU can be 16.5% greater than that of the traditional plate-type radiant terminal.

Traditional imaging techniques for the localization and monitoring of bacterial infections, although reasonably sensitive, suffer from a lack of specificity. This is particularly true for musculoskeletal infections. Bacteria possess a thymidine kinase (TK) whose substrate specificity is distinct from that of the major human TK. The substrate specificity difference has been exploited to develop a new imaging technique that can detect the presence of viable bacteria.Eight subjects with suspected musculoskeletal infections and one healthy control were studied by a combination of [(124)I]FIAU-positron emission tomography and CT ([(124)I]FIAU-PET/CT). All patients with proven musculoskeletal infections demonstrated positive [(124)I]FIAU-PET/CT signals in the sites of concern at two hours after radiopharmaceutical administration. No adverse reactions with FIAU were observed.[(124)I]FIAU-PET/CT is a promising new method for imaging bacterial infections.