Supply Chain Risk Assessment Using Fuzzy Logic
Business's strength arises from the strength of its supply chain. Therefore, a proper supply chain management is vital for business continuity. One of the most challenging parts of SCM is the contract negotiation, and one main aspect of the negotiation is to know the risk associated with each range of quantity agreed on. Currently Managers assess the quantity to be supplied based on a binary way of either full or 0 supply, This paper aims to assess the corresponding quantities risks of the suppliers on a multilayer basis. The proposed approach uses fuzzy logic as an artificial intelligence
Evaluation of Different Sudan Dyes in Egyptian Food Samples Utilizing Liquid Chromatography/Tandem Mass Spectrometry
A sensitive and a precise method was developed for the quantification of different Sudan dyes in some Egyptian food samples. They were analyzed utilizing two-fragment ion transition under multiple reaction monitoring (MRM) mode. Separation was carried out on Kinetex 2.6u C18 100 A (75 mm × 4.6 mm) phenomenex using isocratic elution with 10:90% water and acetonitrile containing 2.0 mmol/L ammonium formate and 0.2% formic acid. The validation parameters were obtained and verified. The linearity was 0.2–10.0 ng/mL with r2 > 0.9975. LOD and LOQ were 0.06 and 0.19 ng/mL, respectively, for Sudan (I
Optimizing inspection policies for buried municipal pipe infrastructure
Condition assessment is an integral component in any infrastructure asset management system. Without condition information, asset managers lack the ability to make appropriate decisions regarding needed maintenance, rehabilitation, and replacement of infrastructure. Existing and emerging technologies for assessing the condition of water and sewer pipes provide a better picture of the state of these buried assets. Unfortunately, many of these technologies are costly and provide results that are not always highly reliable. This paper presents a methodology to assist asset managers in balancing
Leveraging primary feedback and spectrum sensing for cognitive access
We consider a time-slotted primary system where both the primary channel and primary activity are modeled as two independent two-state Markov chains. The primary transmitter can be idle or busy, whereas the channel can be in erasure or not. Moreover, the sensing channel between the primary transmitter and secondary transmitter is modeled as a two-state Markov chain to represent two levels of sensing reliability. At the beginning of each time slot, the secondary transmitter may remain idle, transmit directly, or probe the channel and access the channel only if it is sensed to be free. At the
Improved Semantic Segmentation of Low-Resolution 3D Point Clouds Using Supervised Domain Adaptation
One of the key challenges in applying deep learning to solve real-life problems is the lack of large annotated datasets. Furthermore, for a deep learning model to perform well on the test set, all samples in the training and test sets should be independent and identically distributed (i.i.d.), which means that test samples should be similar to the samples that were used to train the model. In many cases, however, the underlying training and test set distributions are different. In such cases, it is common to adapt the test samples by transforming them to their equivalent counterparts in the
Myocardial segmentation using constrained multi-seeded region growing
Multi-slice short-axis acquisitions of the left ventricle are fundamental for estimating the volume and mass of the left ventricle in cardiac MRI scans. Manual segmentation of the myocardium in all time frames per each cross-section is a cumbersome task. Therefore, automatic myocardium segmentation methods are essential for cardiac functional analysis. Region growing has been proposed to segment the myocardium. Although the technique is simple and fast, non uniform intensity and low-contrast interfaces of the myocardium are major challenges of the technique that limit its use in myocardial
Artificial intelligence for retail industry in Egypt: Challenges and opportunities
In the era of digital transformation, a mass disruption in the global industries have been detected. Big data, the Internet of Things (IoT) and Artificial Intelligence (AI) are just examples of technologies that are holding such digital disruptive power. On the other hand, retailing is a high-intensity competition and disruptive industry driving the global economy and the second largest globally in employment after the agriculture. AI has large potential to contribute to global economic activity and the biggest sector gains would be in retail. AI is the engine that is poised to drive the
Labour productivity in building construction: A field study
This paper describes a field study conducted over a period of 11-months on labour productivity observed during the construction of a new university campus in Cairo, Egypt. The campus is being built on 127 acres and the field study was conducted during the construction of two main buildings; each of 20,000 m 2 built up area. The study utilized work sampling (WS), craftsman questionnaire (CQ), and foreman delay survey (FDS) methods to analyze labour productivity of three indicative and labour-intensive trades, namely formwork, masonry work, and HVAC duct installation. The results were also
A critical review on green corrosion inhibitors based on plant extracts: Advances and potential presence in the market
Corrosion occurs in all sectors including oil pipelines, drinking water and sewerage in the majority of cases linked to corrosion of steel. Good corrosion management includes optimising corrosion control actions and minimising lifecycle corrosion costs whilst meeting environmental goals. The toxicity of commonly used synthetic inhibitors are the subject of recent legislations (REACH and PARCOM) have led to search on more eco-friendly corrosion inhibitors. Extensive research is conducted to assess the corrosion inhibition rate of diverse green inhibitors. However, it was not adequately
Determining the effect of changing channel geometry of irrigation canals on dissolved oxygen concentration
Dissolved oxygen (DO) is an important water quality parameter. It is considered the most important parameter. DO concentration in water is affected by different parameters such as volume flow rate, water velocity, and re-aeration rate. Those parameters are directly affected by the geometry of the waterway. Thus, studying the impact of changing channel geometry on DO is very important. Many researchers studied the effect of influential parameters on water quality variables but the influence of channel geometric parameters on DO was not studied thoroughly before. This research aims to study the
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