Using Low-cost IoT-based inclinometers for damage detection of a Bridge model

Nowadays, researchers are paying close attention to using inclinometers for Structural Health Monitoring (SHM) applications. Moreover, the applications based on using inclinometers can detect the magnitude and location of bridge pathologies. However, as these applications are based on expensive commercial inclinometers, their use is typically exclusive to the SHM of structures with a high monitoring budget. There is a gap in the literature with the development and validation of low-cost accurate angular-meters for decreasing the monitoring cost of inclinometer-based damage detection applications. This work aims to develop low-cost IoT-based inclinometers for detecting damage in bridge structures. The Low-cost Adaptable Reliable Angle-meter (LARA) is a novel inclinometer that accurately measures an induced inclination by combining the measurements of five gyroscopes and five accelerometers. The accuracy, resolution, Allan variance, and standard deviation of LARA are examined through laboratory experiments and are compared with those obtained by numerical slope calculations and a commercial inclinometer (HI-INC). For further experimental validation, a robotic vehicle model is designed and developed to simulate a moving load over a bridge model. The vehicle model integrates IoT technology and can be utilized in different damage detection experiments. The outcomes of a load test experiment using a simple beam model demonstrate the high accuracy (0.003 degrees) of LARA measurements. LARA may be used for structural damage identification and location in bridges utilizing inclinometers because of its low cost and high accuracy

Protection of the texts using Base64 and MD5

The encryption process combines mathematics and computer science. Cryptography consists of a set of algorithms and techniques to convert the data into another form so that the contents are unreadable and unexplainable to anyone who does not have the authority to read or write on these data. The main objective of the use of encryption algorithms is to protect data and information in order to achieve privacy. This paper discusses an encryption method using base64, which is a set of encoding schemes that convert the same binary data to the form of a series of ASCII code. Also, The MD5 hash function is used to hash the encrypted file performed by Base64. As an example for the two protection mechanisms, Arabic letters are used to represent the texts. So using the two protection methods together will increase the security level for protecting the data

Clinical presentation and genetic characterisation of mitochondrial disease in Kuwait

Ph. D. ThesisMitochondrial disorders are a group of clinically heterogenous conditions affecting multiple systems with a prevalence that is estimated to affect 1 in 4,300 individuals. Mitochondrial function is under the control of both the mitochondrial and nuclear genomes which encode >1200 mitochondrial proteins. Manifold biochemical pathways and possible gene targets contribute to the highly variable genotype-phenotype correlations observed in mitochondrial patients, posing distinct challenges in reaching a genetic diagnosis. Whole Exome Sequencing (WES) is a gene agnostic approach that has been hugely powerful in diagnosing mitochondrial disease patients and broadening the genotypic spectrum of disease. Mitochondrial genetic disease is largely understudied in Kuwait where levels of consanguinity reach 50% in the community. Studying the genetics and aetiology of mitochondrial disorders in Kuwait presents huge potential in identifying novel Mendelian causes of disease. I custom-designed mitochondrial disease criteria to evaluate and recruit patients suspected of mitochondrial disease in Kuwait. WES led to the diagnosis of 14 out of 22 recruited families: 8 families harboured variants in known mitochondrial disease genes (SLC19A3, PDHX, SURF1, MPC1, TTC19, NDUFA13, NDUFB9 and RRM2B), 2 harboured variants in a novel mitochondrial disease gene (LETM1), and 4 were diagnosed with phenocopies of mitochondrial disease (RNASEH2C, TREX1, VPS13B and ATP8A2). Functional validation of novel variant pathogenicity was performed in patient fibroblasts from 4 families. Functional validation was also carried out on additional mitochondrial patients from Newcastle and external collaborators (COX15, TTC19, NDUFAF3 and NDUFC2). Complexome profiling helped characterise the effect of NDUFC2 variants (a novel candidate gene) on Complex I assembly while a controlled lentiviral rescue experiment partially recovered protein expression and validated variant pathogenicity. My work highlights the potential of employing WES to identify novel causes of disease in understudied consanguineous populations and emphasises the importance of establishing functional pipelines alongside the genetic studies in the Kuwait Medical Genetics CentreGovernment of the State of Kuwai

Modified differential transformation method for solving classes of non-linear differential equations

In this research article, a numerical scheme namely modified differential transformation method (MDTM) is employed successfully to obtain accurate approximate solutions for classes of nonlinear differential equations. This scheme based on differential transform method (DTM), Laplace transform and Pad´e approximants. Validity and efficiency of MDTM are tested upon several examples and comparisons.are made to demonstrate that. The results lead to conclude that the MDTM is effective, explicit and easy to use.Publisher's Versio

Data-Driven Charging Demand Prediction at Public Charging Stations Using Supervised Machine Learning Regression Methods

Plug-in Electric Vehicle (PEV) user charging behavior has a significant influence on a distribution network and its reliability. Generally, monitoring energy consumption has become one of the most important factors in green and micro grids; therefore, predicting the charging demand of PEVs (the energy consumed during the charging session) could help to efficiently manage the electric grid. Consequently, three machine learning methods are applied in this research to predict the charging demand for the PEV user after a charging session starts. This approach is validated using a dataset consisting of seven years of charging events collected from public charging stations in the state of Nebraska, USA. The results show that the regression method, XGBoost, slightly outperforms the other methods in predicting the charging demand, with an RMSE equal to 6.68 kWh and R2 equal to 51.9%. The relative importance of input variables is also discussed, showing that the user’s historical average demand has the most predictive value. Accurate prediction of session charging demand, as opposed to the daily or hourly demand of multiple users, has many possible applications for utility companies and charging networks, including scheduling, grid stability, and smart grid integration

Framework to Develop Time- and Voltage-Dependent Building Load Profiles Using Polynomial Load Models

The power consumption of buildings over the course of each minute, hour, day and season plays a major role in how this load influences the Electric Power System voltage and frequency, and vice versa. This consumption is based on the building\u27s load component types, efficiencies, and how they consume power and react to changes in real time. Due to this complexity, standard full-building load models are typically voltage-invariant. This paper proposes a novel framework to transform these voltage-invariant building load models into fully time- and voltage-dependent load profiles using available data on the voltage sensitivity of individual load components. While a voltage-dependent building model could theoretically be generated from static load models of every component in a building, this approach faces two challenges: first, load models representing all load components are impractical to develop for all possible load component types; second, building energy consumption is never measured or modeled at the individual component level. The proposed framework compiles available component data in the form of static ZIP load model parameters, and maps them into the end use categories utilized by standard building modeling programs. The voltage sensitivity of each end use category is then bounded by the extrema of the component models within it. This framework is applied to a load profile case study representing the aggregate U.S. residential building stock. In addition to the minimum/ maximum conditions, a load profile based on typical load composition and weighted ZIP parameters is generated for the same building stock. The results show that for a 10% drop in voltage, using the least sensitive ZIP parameters, active power is expected to be 3% to 14% lower than nominal, depending on the season and time of day. Using the most sensitive ZIP parameters, the active power is expected to be 9% to 20% lower than nominal, also depending on the season and time of day

Protection of text using SHA1 and Base64

Protection of information is a prerequisite demand in the world of computers today. Protection of information can be accomplished in different methods. The main objective of the use of the protection of information is to protect data and information in order to achieve privacy. This paper discusses two methods of protection of information, an encryption method called Base64, which is a set of encoding schemes that convert the same binary data to the form of a series of ASCII code. Also, The SHA1 hash function is used to hash the encrypted file performed by Base64. As an example of an ASCII code, Arabic letters are used to represent the texts. So using the two protection methods together will increase the security level for protecting the data

Observability analysis for structural system identification based on state estimation

The concept of observability analysis (OA) has garnered substantial attention in the field of Structural System Identification. Its primary aim is to identify a specific set of structural characteristics, such as Young's modulus, area, inertia, and possibly their combinations (e.g., flexural or axial stiffness). These characteristics can be uniquely determined when provided with a suitable subset of deflections, forces, and/or moments at the nodes of the structure. This problem is particularly intricate within the realm of Structural System Identification, mainly due to the presence of nonlinear unknown variables, such as the product of vertical deflection and flexural stiffness, in accordance with modern methodologies. Consequently, the mechanical and geometrical properties of the structure are intricately linked with node deflections and/or rotations. The paper at hand serves a dual purpose: firstly, it introduces the concept of State Estimation (SE), specially tailored for the identification of structural systems; and secondly, it presents a novel OA method grounded in SE principles, designed to overcome the aforementioned challenges. Computational experiments shed light on the algorithm's potential for practical Structural System Identification applications, demonstrating significant advantages over the existing state-of-the-art methods found in the literature. It is noteworthy that these advantages could potentially be further amplified by addressing the SE problem, which constitutes a subject for future research. Solving this problem would help address the additional challenge of developing efficient techniques that can accommodate redundancy and uncertainty when estimating the current state of the structure

A novel low-cost inclinometer sensor based on fusion technology for structural health monitoring applications

The fundamental purpose of structural health monitoring (SHM) is to examine the accuracy of the structural health state and predict its future strength. Lately, researchers have been paying close attention to the structural damage detection process employing inclinometers. However, this technique can only be used with unique structures with a sizable Structural Health Monitoring (SHM) budget due to the high cost of inclinometers. Therefore, the use of low-cost sensors by implementing various techniques to improve their accuracy compared to high-cost precision sensors has attracted much attention for structural assessment. This paper introduces a novel, low-cost inclinometer that measures inclination by fusion technology combining gyroscopes and accelerometers. The microcontroller technology used in this gadget is an open-source Internet of Things (IoT) based platform, allowing for wireless data streaming and free commercial software for data collecting. Not only are the coding and placement issues of these sensors thoroughly explained, but detailed answers to the problems mentioned above are also provided, as well as an efficient way to assemble and prepare the sensors.Postprint (published version

Accurate approximate solution of classes of boundary value problems using modified differential transform method

In this paper, a numerical scheme so-called modified differential transformation method (MDTM) based on differential transformation method (DTM), Laplace transform and Pad´e approximation will be used to obtain accurate approximate solution for a class of boundary value problems (BVP’s). The MDTM is employed as an alternative technique to overcome some difficulties in the behavior of the solution and to be valid for a large region. The numerical results obtained demonstrate the applicability and validity of this technique. Numerical comparison is made with existing exact solution