Medium chain triglyceride in early life: Effects on growth of adipose tissue

Effects of feeding early in life a diet high in either long chain (LCT) or medium chain triglyceride (MCT) were studied on the development of adipose tissue in post-weanling rats. The diets were similar in calorie distribution and identical in nutrients except for type of fat. The caloric distribution of the two diets by percent was LCT (corn oil)/protein/carbohydrate, 70/18/12 and MCT/corn oil/protein/carbohydrate, 66/4/18/12. Male littermates with less than 5% weight difference were pair-fed the two diets randomly at age 18–20 days. One-fourth of the rats were killed at 10, 16, 22 and 28 weeks of age and analyzed for adipose depots and adipose tissue cellularity. Results showed that the LCT-fed rats were significantly heavier, with larger epididymal, retroperitoneal, omental and subcutaneous fat pads than the respective pair-fed MCT rats. Also, LCT-fed rats had larger size and number of adipocytes than MCT-fed littermates. It is concluded that the type of fat in the diet, namely LCT or MCT, when fed early in life can influence the development of adipose tissue. MCT appears less lipogenic than LCT. The mechanism for the diminished adiposity of MCT-fed rats is related to extensive oxidation of MCT and its enhancement of thermogenesis leading to lessened energy efficiency. Presented at the symposium on “Specialty Lipids and Their Biofunctionality” at the annual meeting of the American Oil Chemists' Society, Philadelphia, May 1985.     

A reverse osmosis desalting plant operated by gas turbines

Power plants in Kuwait use gas turbines (GT) only for a few hours to produce power at peak load times. Peak loadoccurs in the summer due to the air-conditioning load. As an example, the average number of operating hours for six gas turbines in the Doha East power plant was 16 in the summer of 2001. There is little concern about efficiency of these GT since they work for a very short time during the year. However, a recent increase in desalted seawater demand suggests the use of these GT to operate reverse osmosis (RO) desalting systems all year around. The summer outside design temperature in Kuwait for air-conditioning calculations is 48°C dry bulb temperature (DBT), and 28°C wet bulb temperature (WBT); but the ambient temperature can easily reach 60°C. Gas turbine power output and efficiency are drastically reduced by the increase in temperature of intake air to the gas turbine's compressor, especially during harsh Kuwaiti summer conditions. Thus, it is essential to investigate cooling of air intake to the GT compressor. The performance of a typical GT unit and its ability to produce desalted waterby a RO desalting system at different ambient temperatures are presented. Calculation of needed capacities for the cooling of intake air to the GT compressor was performed for evaporative cooling, single and multiple mechanical vapor compression cycles, and combined indirect evaporation cooling with the refrigeration system. The improvements of power output and efficiency due to the cooling of air intake of the GT and the resulting increase in desalted water are also presented.     

Color object segmentation and tracking using flexible statistical model and level-set

Multimedia Tools and Applications - This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with...     

A novel secure solution of using mixed reality in data transmission for bowel and jaw surgical telepresence: enhanced rivest cipher RC6 block cipher

Security threats are crucial challenges that deter Mixed reality (MR) communication in medical telepresence. This research aims to improve the security by reducing the chances of types of various attacks occurring during the real-time data transmission in surgical telepresence as well as reduce the time of the cryptographic algorithm and keep the quality of the media used. The proposed model consists of an enhanced RC6 algorithm in combination. Dynamic keys are generated from the RC6 algorithm mixed with RC4 to create dynamic S-box and permutation table, preventing various known attacks during the real-time data transmission. For every next session, a new key is created, avoiding possible reuse of the same key from the attacker. The results obtained from our proposed system are showing better performance compared to the state of art. The resistance to the tested attacks is measured throughout the entropy, Pick to Signal Noise Ratio (PSNR) is decreased for the encrypted image than the state of art, structural similarity index (SSIM) closer to zero. The execution time of the algorithm is decreased for an average of 20%. The proposed system is focusing on preventing the brute force attack occurred during the surgical telepresence data transmission. The paper proposes a framework that enhances the security related to data transmission during surgeries with acceptable performance.

     

Laser melting of Ti-15Al-20Nb alloy

Titanium aluminides have become promising materials for high-temperature applications, but the relatively poor oxidation resistance and elevated- temperature strength of these alloys limit their application to temperatures lower than 1000 °C. Niobium addition improves the properties of titanium aluminide. However, the mechanical, metallurgical, and corrosion properties of Ti- Al- Nb may be improved by treatment with a laser beam. Consequently, the present study examines the properties of Ti- 15Al- 20Nb alloy subjected to the Nd:YAG laser melting process. Hardness in the surface region increases to twice the base material hardness, and corrosion resistance improves considerably after laser treatment.     

Studying the needed effort for identifying duplicate issues

Many recent software engineering papers have examined duplicate issue reports. Thus far, duplicate reports have been considered a hindrance to developers and a drain on their resources. As a result, prior research in this area focuses on proposing automated approaches to accurately identify duplicate reports. However, there exists no studies that attempt to quantify the actual effort that is spent on identifying duplicate issue reports. In this paper, we empirically examine the effort that is needed for manually identifying duplicate reports in four open source projects, i.e., Firefox, SeaMonkey, Bugzilla and Eclipse-Platform. Our results show that: (i) More than 50 % of the duplicate reports are identified within half a day. Most of the duplicate reports are identified without any discussion and with the involvement of very few people; (ii) A classification model built using a set of factors that are extracted from duplicate issue reports classifies duplicates according to the effort that is needed to identify them with a precision of 0.60 to 0.77, a recall of 0.23 to 0.96, and an ROC area of 0.68 to 0.80; and (iii) Factors that capture the developer awareness of the duplicate issue’s peers (i.e., other duplicates of that issue) and textual similarity of a new report to prior reports are the most influential factors in our models. Our findings highlight the need for effort-aware evaluation of approaches that identify duplicate issue reports, since the identification of a considerable amount of duplicate reports (over 50 %) appear to be a relatively trivial task for developers. To better assist developers, research on identifying duplicate issue reports should put greater emphasis on assisting developers in identifying effort-consuming duplicate issues.     

Active fault tolerant control for nonlinear systems with simultaneous actuator and sensor faults

The goal of this paper is to describe a novel fault tolerant tracking control (FTTC) strategy based on robust fault estimation and compensation of simultaneous actuator and sensor faults. Within the framework of fault tolerant control (FTC) the challenge is to develop an FTTC design strategy for nonlinear systems to tolerate simultaneous actuator and sensor faults that have bounded first time derivatives. The main contribution of this paper is the proposal of a new architecture based on a combination of actuator and sensor Takagi-Sugeno (T-S) proportional state estimators augmented with proportional and integral feedback (PPI) fault estimators together with a T-S dynamic output feedback control (TSDOFC) capable of time-varying reference tracking. Within this architecture the design freedom for each of the T-S estimators and the control system are available separately with an important consequence on robust L ₂ norm fault estimation and robust L ₂ norm closed-loop tracking performance. The FTTC strategy is illustrated using a nonlinear inverted pendulum example with time-varying tracking of a moving linear position reference.     

Neural networks in process life cycle profit modelling

Changes in operational environment of the process industry such as decreasing selling prices, increased competition between companies and new legislation, set requirements for performance and effectiveness of the industrial production lines and processes. For the basis of this study, a life cycle profit (LCP) model of a pulp process was constructed using different kind of process information including chemical consumptions and production levels of material and energy flows in unit processes. However, all the information needed in the creation of relevant LCP model was not directly provided by information systems of the plant. In this study, neural networks was used to model pulp bleaching process and fill out missing information and furthermore to create estimators for the alkaline chemical consumption. A data-based modelling approach was applied using an example, where factors affecting the sodium hydroxide consumption in the bleaching stage were solved. The results showed that raw process data can be refined into new valuable information using computational methods and moreover to improve the accuracy of life cycle profit models.     

Detection of Abnormal Activities from Various Signals Based on Statistical Analysis

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

     

An automatic query expansion based on hybrid CMO-COOT algorithm for optimized information retrieval

The World Wide Web(WWW) comprises a wide range of information, and it is mainly operated on the principles of keyword matching which often reduces accurate information retrieval. Automatic query expansion is one of the primary methods for information retrieval, and it handles the vocabulary mismatch problem often faced by the information retrieval systems to retrieve an appropriate document using the keywords. This paper proposed a novel approach of hybrid COOT-based Cat and Mouse Optimization (CMO) algorithm named as hybrid COOT-CMO for the appropriate selection of optimal candidate terms in the automatic query expansion process. To improve the accuracy of the Cat and Mouse Optimization (CMO) algorithm, the parameters are tuned with the help of the Coot algorithm. The best suitable expanded query is identified from the available expanded query sets also known as candidate query pools. All feasible combinations in this candidate query pool should be obtained from the top retrieved documents. Benchmark datasets such as the GOV2 Test Collection, the Cranfield Collections, and the NTCIR Test Collection are utilized to assess the performance of the proposed hybrid COOT-CMO method for automatic query expansion. This proposed method surpasses the existing state-of-the-art techniques using many performance measures such as F-score, precision, and mean average precision (MAP).