Representation of Frequency and Time Information by Using Wavelets Transform; The Method and Applications

The Fourier Transform (FT) is the well-known classical representation of signals components by providing the frequency analysis representations of the signals. The Fourier transformation is found with some determinant such as signal dependent transforming, in another word, [15] the FT is helpful with only particular types of signals such as the pseudo-stationary signals and stationary signals, whereas the FT is not fulfilling the expectations while i

Probabilistic analysis of supply chains resilience based on their characteristics using dynamic Bayesian networks

Previously held under moratorium from 14 December 2016 until 19 January 2022There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions.There is an increasing interest in the resilience of supply chains given the growing awareness of their vulnerabilities to natural and man-made hazards. Contemporary academic literature considers, for example, so-called resilience enablers and strategies, such as improving the nature of collaboration and flexibility within the supply chain. Efforts to analyse resilience tend to view the supply chain as a complex system. The present research adopts a distinctive approach to the analysis of supply resilience by building formal models from the perspective of the responsible manager. Dynamic Bayesian Networks (DBNs) are selected as the modelling method since they are capable of representing the temporal evolution of uncertainties affecting supply. They also support probabilistic analysis to estimate the impact of potentially hazardous events through time. In this way, the recovery rate of the supply chain under mitigation action scenarios and an understanding of resilience can be obtained. The research is grounded in multiple case studies of manufacturing and retail supply chains, involving focal companies in the UK, Canada and Malaysia, respectively. Each case involves building models to estimate the resilience of the supply chain given uncertainties about, for example, business continuity, lumpy spare parts demand and operations of critical infrastructure. DBNs have been developed by using relevant data from historical empirical records and subjective judgement. Through the modelling practice, It has been found that some SC characteristics (i.e. level of integration, structure, SC operating system) play a vital role in shaping and quantifying DBNs and reduce their elicitation burden. Similarly, It has been found that the static and dynamic discretization methods of continuous variables affect the DBNs building process. I also studied the effect of level of integration, visibility, structure and SC operating system on the resilience level of SCs through the analysis of DBNs outputs. I found that the influence of the integration intensity on supply chain resilience can be revealed through understanding the dependency level of the focal firm on SC members resources. I have also noticed the relationship between the span of integration and the level of visibility to SC members. This visibility affects the capability of SC managers in the focal firm to identify the SC hazards and their consequences and, therefore, improve the planning for adverse events. I also explained how some decision rules related to SC operating system such as the inventory strategy could influence the intermediate ability of SC to react to adverse events. By interpreting my case data in the light of the existing academic literature, I can formulate some specific propositions

SNOMED CT standard ontology based on the ontology for general medical science

Background: Systematized Nomenclature of Medicine—Clinical Terms (SNOMED CT, hereafter abbreviated SCT) is acomprehensive medical terminology used for standardizing the storage, retrieval, and exchange of electronic healthdata. Some efforts have been made to capture the contents of SCT as Web Ontology Language (OWL), but theseefforts have been hampered by the size and complexity of SCT. Method: Our proposal here is to develop an upper-level ontology and to use it as the basis for defining the termsin SCT in a way that will support quality assurance of SCT, for example, by allowing consistency checks ofdefinitions and the identification and elimination of redundancies in the SCT vocabulary. Our proposed upper-levelSCT ontology (SCTO) is based on the Ontology for General Medical Science (OGMS). Results: The SCTO is implemented in OWL 2, to support automatic inference and consistency checking. Theapproach will allow integration of SCT data with data annotated using Open Biomedical Ontologies (OBO) Foundryontologies, since the use of OGMS will ensure consistency with the Basic Formal Ontology, which is the top-levelontology of the OBO Foundry. Currently, the SCTO contains 304 classes, 28 properties, 2400 axioms, and 1555annotations. It is publicly available through the bioportal athttp://bioportal.bioontology.org/ontologies/SCTO/. Conclusion: The resulting ontology can enhance the semantics of clinical decision support systems and semanticinteroperability among distributed electronic health records. In addition, the populated ontology can be used forthe automation of mobile health applications

Jurisprudence Rulings Related to Osteogenesis Syndrome (Lobstein) in Islamic Jurisprudence

``Osteogenesis imperfecta'' and ``vitreous osteoporosis'' are genetic diseases in most of their cases, that is, it is sufficient for one of the parents to be a disease carrier in order to have one of their children suffering from it. It is the main protein source in the bone structure leading to this disorder; however, the genetic factor is the most common and accounts for about 80 to 85% of the causes of osteogenesis imperfecta. The congenital bone disorder is associated with a defect in the connective tissue, which leads to the inability to build or form bones. It leads to easy bone fractures, and the cause of these fractures is often unclear. Keywords: rulings, jurisprudence, syndrome, epigenetics, bone, Lobstein, jurisprudence, Islamic

Implementation and evaluation of semantic clustering techniques for Fog nodes

Growing at an extremely rapid rate, the Internet of Things (IoT) devices are becoming a crucial part of our everyday lives. They are embedded in almost everything we do on a daily basis. From simple sensors, cell phones, wearable devices to smart city technologies, we are becoming heavily dependent on such devices. At this current state, the Cloud paradigm is being ooded by massive amounts of data continuously. The current amounts of data is minimal compared to the amounts that we are about to witness in the near future, mainly because of the 5G deployment expediting and the increase in network intelligence. This increased data could lead to more network congestion and higher latency, due to the physical distance between the devices and the Cloud data centers. Therefore, a need for a new model is paramount, and will be essential in realizing the Internet of Everything (IoE) and the next stage in the digital evolution. Fog computing is one of the promising paradigms, since it extends the Cloud with intelligent computing units, placed closer to where the data is being generated to o oad the Cloud. This tackles the issues of latency, mobility and network congestion. In this work we present a conceptual Fog computing ecosystem, where we model the Cloud to Fog (C2F) environment. Then we implement two dynamic clustering techniques of Fog nodes to utilize combined resources, using a semantic description of the Fog nodes' resources and properties of the edge devices. Finally, we optimize the assignment of applications over Fog cluster resources, using Linear programming and a First Fit Heuristic Algorithm. We evaluate our implementation by analyzing the di erences between the two clustering techniques. We perform several experiments to evaluate our implementation, and the results prove that the heuristic optimization of task allocation is much faster and more consistent than the Linear programming solver, as expected. Moreover, the results show that clustering Fog nodes is bene cial in o oading the Cloud and reducing response times

Emotions Recognition in people with Autism using Facial Expressions and Machine Learning Techniques: Survey

في الآونة الأخيرة ، اهتمت الكثير من الدراسات بالتعرف على المشاعر واكتشافها لدى الأشخاص المصابين بالتوحد. الهدف الرئيسي من هذه الورقة هو مسح الدراسات المختلفة التي تتعلق بالحالة العاطفية للأشخاص المصابين بالتوحد. يتضمن الاستطلاع جزأين ، يركز الجزء الأول على الدراسات التي استخدمت تعابير الوجه للتعرف على المشاعر واكتشافها. حيث تعتبر تعبيرات الوجه من التقنيات العاطفية المهمة التي تستخدم للتعبير عن أنماط مختلفة من المشاعر. ركزت الأجزاء الثانية من هذه الدراسة على الأساليب التقنية المختلفة مثل التعلم الآلي والتعلم العميق والخوارزميات الأخرى التي تستخدم لتحليل وتحديد سلوكيات الوجه للأشخاص المصابين بالتوحد. للعثور على الحل الأمثل ، يتم من خلال التحقيق في مقارنة أنظمة الكشف عن المشاعر الحالية في هذه الورقة.Recently, a lot of studies have been interested in recognizing and detection of emotions in people with autism.  The main goal of this paper is to survey different studies which have been concerned emotional state of people with autism.  The survey includes two parts, first one focused on studies which use facial expressions to recognize and detect emotions. As facial expressions are considered the affective and important techniques which is used to express different patterns of emotions.  Second parts of this study, focuses on different technical methods like machine learning, deep learning and other algorithms that are employed to analyze and determine the facial behaviors of people with autism. To find the optimal solution, a comparison of current emotion-detecting systems is investigated in this paper

A Comparative Performance Evaluation of Hive and Map Reduce for Big-Data

Advances in information stockpiling and mining advances make it conceivable to safeguard expanding measures of information created specifically or in a roundabout way by clients and break down it to yield important new bits of knowledge. Huge information can uncover individuals' shrouded behavioral examples and even revealed insight into their expectations. All the more absolutely, it can overcome any and all hardships between what individuals need to do and what they really do and how they connect with others and their surroundings. This data is valuable to government offices and in addition privately owned businesses to bolster choice making in zones going from law requirement to social administrations to country security. One of the proficient advancements that arrangement with the Big Data is Hadoop, which will be talked about in this paper. Hadoop, for preparing extensive information volume employments utilizes MapReduce programming model. Hadoop makes utilization of diverse schedulers for executing the occupations in parallel. The default scheduler is FIFO (First In First Out) Scheduler. Different schedulers with need, pre-emption and non-pre-emption alternatives have likewise been produced. As the time has passed the MapReduce has come to few of its restrictions. So keeping in mind the end goal to beat the constraints of MapReduce, the up and coming era of MapReduce has been produced called as YARN (Yet Another Resource Negotiator). Along these lines, this paper gives a review on Hadoop, few booking strategies it uses and a brief prologue to YARN. Keywords: Big-Data, Hive, Map Reduc