KSUTraffic: A Microscopic Traffic Simulator for Traffic Planning in Smart Cities

Simulation is a powerful tool for improving, evaluating and analyzing the performance of new and existing systems. Traffic simulators provide tools for studying transportation systems in smart cities as they describe the evolution of traffic to the highest level of detail. There are many types of traffic simulators that allow simulating traffic in modern cities. The most popular traffic simulation approach is the microscopic traffic simulation because of its ability to model traffic in a realistic manner. In many cities of Saudi Arabia, traffic management represents a major challenge as a result of expansion in traffic demands and increasing number of incidents. Unfortunately, employing simulation to provide effective traffic management for local scenarios in Saudi Arabia is limited to a number of commercial products in both public and private sectors. Commercial simulators are usually expensive, closed source and inflexible as they allow limited functionalities. In this project, we developed a local traffic simulator “KSUtraffic” for traffic modeling, planning and analysis with respect to different traffic control strategies and considerations. We modeled information specified by GIS and real traffic data. Furthermore, we designed experiments that manipulate simulation parameters and the underlying area. KSUTraffic visualizes traffic and provides statistical results on the simulated traffic which would help to improve traffic management and efficiency.     

Reliability simulation of solar concentrator receiver

The reliability of solar concentrator is investigated using finite element (FE) modelling. An FE model of the receiver absorber is built and simulated using latin hypercube sampling. A transient thermal structural simulation is conducted, and the maximum thermal stress affecting the absorber is determined. Based on the failure criterion, the most effective parameters are determined and assigned as random variables. A stochastic simulation is performed resulting in a probability density function (PDF) of the thermal stress-life. The PDF is used to estimate the reliability of the absorber. Different designs and materials of the absorber tubes are investigated. Consequently, methods to improve the reliability of the absorber are identified.     

家庭住宅 法国拉马蒂埃尔

正几个世纪以来,不同的地理环境一直都采用这类封闭的庭院形式——每一次都根据当地气候、风俗习惯和建筑实践进行改造。作为一种在建筑物内捕捉户外景观的手段,庭院由建筑内部结构决定。流畅的庭院不但提供内外部隐私保护,而且也不断地向外延伸,积极融入周边景观。该住宅遵守严格的分区指导方针,同时通过在屋顶上扩建自然植物区系与景观融合。坐落在橄榄树林、松树和葡萄园内,该住宅充分利用坡地,从而获得可持续的拓展和种植     

Time-variant balanced routing strategy for underwater wireless sensor networks

In the past decade, researchers’ interest in Underwater Wireless Sensors Networks has rapidly increased. There are several challenges facing the lifetime of UWSNs due to the harsh characteristics of the underwater environment. Energy efficiency is one of the major challenges in UWSNs due to the limited battery budget of the sensor nodes. In this paper, we aim at tackling the energy sink-hole problem that especially hits nodes close to the sink when they run out of battery power before other sensors in the network. We prove that we can evenly distribute the transmission load among mobile sensor nodes by letting sensor nodes adjust their transmission ranges. In this paper, we suppose that sensor nodes may adjust their transmission power up to three levels. Consequently, we strive for deriving the optimal load weight for each possible transmission power level that leads to fair energy consumption among all underwater sensors while taking into account the underwater sensors mobility. Performance results show that energy sink-hole problem is overcame and hence the network lifetime is maximized.

     

Joint block and stream cipher based on a modified skew tent map

Image encryption is very different from that of texts due to the bulk data capacity and the high redundancy of images. Thus, traditional methods are difficult to use for image encryption as their pseudo-random sequences have small space. Chaotic cryptography use chaos theory in specific systems working such as computing algorithms to accomplish dissimilar cryptographic tasks in a cryptosystem with a fast throughput. For higher security, encryption is the approach to guard information and prevent its leakage. In this paper, a hybrid encryption scheme that combines both stream and block ciphering algorithms is proposed in order to achieve the required level of security with the minimum encryption time. This scheme is based on an improved mathematical model to cover the defects in the previous discredited model proposed by Masuda. The proposed chaos-based cryptosystem uses the improved Skew Tent Map (STM) RQ-FSTM as a substitution layer. This map is based on a lookup table to overcome various problems, such as the fixed point, the key space restrictions, and the limitation of mapping between plain text and cipher text. It uses the same map as a generator to change the byte position to achieve the required confusion and diffusion effects. This modification improves the security level of the original STM. The robustness of the proposed cryptosystem is proven by the performance and the security analysis, as well as the high encryption speed. Depending on the results of the security analysis the proposed system has a better dynamic key space than previous ones using STM, a double encryption quality and a better security analysis than others in the literature with speed convenience to real-time applications.

     

Adipose‐Derived Biogenic Nanoparticles for Suppression of Inflammation

Extracellular vesicles secreted from adipose‐derived mesenchymal stem cells (ADSCs) have therapeutic effects in inflammatory diseases. However, production of extracellular vesicles (EVs) from ADSCs is costly, inefficient, and time consuming. The anti‐inflammatory properties of adipose tissue‐derived EVs and other biogenic nanoparticles have not been explored. In this study, biogenic nanoparticles are obtained directly from lipoaspirate, an easily accessible and abundant source of biological material. Compared to ADSC‐EVs, lipoaspirate nanoparticles (Lipo‐NPs) take less time to process (hours compared to months) and cost less to produce (clinical‐grade cell culture facilities are not required). The physicochemical characteristics and anti‐inflammatory properties of Lipo‐NPs are evaluated and compared to those of patient‐matched ADSC‐EVs. Moreover, guanabenz loading in Lipo‐NPs is evaluated for enhanced anti‐inflammatory effects. Apolipoprotein E and glycerolipids are enriched in Lipo‐NPs compared to ADSC‐EVs. Additionally, the uptake of Lipo‐NPs in hepatocytes and macrophages is higher. Lipo‐NPs and ADSC‐EVs have comparable protective and anti‐inflammatory effects. Specifically, Lipo‐NPs reduce toll‐like receptor 4‐induced secretion of inflammatory cytokines in macrophages. Guanabenz‐loaded Lipo‐NPs further suppress inflammatory pathways, suggesting that this combination therapy can have promising applications for inflammatory diseases.     

A multifractal edge detector

Multimedia Tools and Applications - Significant research has been conducted into image edge detection, usually focusing on gradient and higher order derivative approaches. Recent development in the...     

A pair-mode model for underwater single image enhancement

Enhancing underwater images is a challenging problem owing to light scattering and absorption in underwater environments. Such environments provoke several combined degradations in images including color attenuation, blurring and low contrast. Using image processing techniques to enhance this kind of image remains very attractive because of its low-cost of implementation and typically its small number of parameters when compared to more complex learning techniques. This paper proposes an image processing model which first, analyses the color characteristics of the degraded image. Second, decides about the suitable enhancement steps (i.e., mode of operation) to be performed. It operates in two modes (mode-1 and mode-2), both of which investigate a combination of contrast and chromaticity enhancement techniques. The proposed model was tested on 5141 images collected from various, well-known datasets. It was evaluated using eight different measures, some of which are reference-based, and the rest are blind-based. A set of qualitative and quantitative comparisons was conducted, applying more than 20 methods varying between image processing and deep learning. Besides its efficiency and simplicity, the proposed model demonstrates an ability to achieve good contrast ranges, natural-looking colors, and superior or equivalent quality enhancements when compared to other methods.

     

PAI-1 in Diabetes: Pathophysiology and Role as a Therapeutic Target

Hypofibrinolysis is a key abnormality in diabetes and contributes to the adverse vascular outcome in this population. Plasminogen activator inhibitor (PAI)-1 is an important regulator of the fibrinolytic process and levels of this antifibrinolytic protein are elevated in diabetes and insulin resistant states. This review describes both the physiological and pathological role of PAI-1 in health and disease, focusing on the mechanism of action as well as protein abnormalities in vascular disease with special focus on diabetes. Attempts at inhibiting protein function, using different techniques, are also discussed including direct and indirect interference with production as well as inhibition of protein function. Developing PAI-1 inhibitors represents an alternative approach to managing hypofibrinolysis by targeting the pathological abnormality rather than current practice that relies on profound inhibition of the cellular and/or acellular arms of coagulation, and which can be associated with increased bleeding events. The review offers up-to-date knowledge on the mechanisms of action of PAI-1 together with the role of altering protein function to improve hypofirbinolysis. Developing PAI-1 inhibitors may form for the basis of future new class of antithrombotic agents that reduce vascular complications in diabetes.     

Smart Materials Enabled with Artificial Intelligence for Healthcare Wearables

Contemporary medicine suffers from many shortcomings in terms of successful disease diagnosis and treatment, both of which rely on detection capacity and timing. The lack of effective, reliable, and affordable detection and real-time monitoring limits the affordability of timely diagnosis and treatment. A new frontier that overcomes these challenges relies on smart health monitoring systems that combine wearable sensors and an analytical modulus. This review presents the latest advances in smart materials for the development of multifunctional wearable sensors while providing a bird's eye-view of their characteristics, functions, and applications. The review also presents the state-of-the-art on wearables fitted with artificial intelligence (AI) and support systems for clinical decision in early detection and accurate diagnosis of disorders. The ongoing challenges and future prospects for providing personal healthcare with AI-assisted support systems relating to clinical decisions are presented and discussed.