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Carbon dioxide (CO2) is the main greenhouse gas responsible for global warming. Early prediction of CO2 is critical for developing strategies to mitigate the effects of climate change. A sophisticated version of the extreme learning machine (ELM), the wavelet enhanced extreme learning machine (W-EELM), is used to predict CO2 on different time scales (weekly, monthly, and yearly). Data were collected from the Mauna Loa Observatory station in Hawaii, which is ideal for global air sampling. Instead of the traditional method (singular value decomposition), a complete orthogonal decomposition (COD) was used to accurately calculate the weights of the ELM output layers. Another contribution of this study is the removal of noise from the input signal using the wavelet transform technique. The results of the W-EELM model are compared with the results of the classical ELM. Various statistical metrics are used to evaluate the models, and the comparative figures confirm the superiority of the applied models over the ELM model. The proposed W-EELM model proves to be a robust and applicable computer-based technology for modeling CO2concentrations, which contributes to the fundamental knowledge of the environmental engineering perspective. Validerad;2023;Nivå 2;2023-06-01 (joosat);Funder: Al-Maarif University CollegeLicens fulltext: CC BY License

The manufacturing sector has undergone significant growth due to the integration of technologies from the Fourth Industrial Revolution. Industry 4.0 has revolutionized industrial operations, leading to increased utilization of smart and automated systems in manufacturing. Among these technologies is additive manufacturing (AM), which has been widely adopted in various industries to enhance new product development with minimal time constraints. This research aimed to identify and analyze the potential enablers of AM that support its adoption in the manufacturing sector. This study identified 15 enablers through a literature review, and they were analyzed using a grey decision-making trial and evaluation laboratory (DEMATEL)-based multicriteria decision-making technique. The results were used to develop a causal diagram that depicts the enablers in cause and effect groups. This study provides insights that will help manufacturing firms adopt AM by identifying its enablers and benefits. Overall, this study is significant as it contributes to a deeper understanding of AM technology and its potential enablers, thus facilitating its adoption in the manufacturing sector.

The study aimed at preparing a list of reading comprehension skills that are suitable for fifth-grade students. Moreover, it aimed at revealing the extent of availability of reading comprehension levels in the Arabic language textbook (my beautiful language reading skills) for the fifth grade in the Sultanate of Oman. The researchers used the descriptive approach based on content analysis. The first aim was reached by studying previous studies and preparing a list of reading comprehension skills in six levels: literal comprehension, appreciative comprehension, inferential comprehension, applied comprehension, critical comprehension and creative comprehension students of fifth grade students. While the second objective was achieved by analyzing the sample of the study (372) activities in the first part and the second part. The study introduced several results, including a list of 43 skills distributed at the six reading comprehension levels suitable for fifth-grade students. The book included 38 skills out of a total of 43 reading comprehension skills included in the study instrument. The availability of the six levels of reading comprehension skills rates is varied. The highest is the inferential comprehension 36.74%, followed by a literal comprehension level of 25.98%, followed by a practical comprehension level of 16.01%, followed by a creative comprehension level of 11.54% followed by a critical comprehension level of 5.24%, then a level of comprehension 4.46%.The study had several recommendations, including determining the actual rates of reading comprehension skills that should be available in the studying activities while developing the book and enriching the book with assessment in the skills that the study showed in the six levels.

Atmospheric air temperature is the most crucial metrological parameter. Despite its influence on multiple fields such as hydrology, the environment, irrigation, and agriculture, this parameter describes climate change and global warming quite well. Thus, accurate and timely air temperature forecasting is essential because it provides more important information that can be relied on for future planning. In this study, four Data-Driven Approaches, Support Vector Regression (SVR), Regression Tree (RT), Quantile Regression Tree (QRT), ARIMA, Random Forest (RF), and Gradient Boosting Regression (GBR), have been applied to forecast short-, and mid-term air temperature (daily, and weekly) over North America under continental climatic conditions. The time-series data is relatively long (2000 to 2021), 70% of the data are used for model calibration (2000 to 2015), and the rest are used for validation. The autocorrelation and partial autocorrelation functions have been used to select the best input combination for the forecasting models. The quality of predicting models is evaluated using several statistical measures and graphical comparisons. For daily scale, the SVR has generated more accurate estimates than other models, Root Mean Square Error (RMSE = 3.592°C), Correlation Coefficient (R = 0.964), Mean Absolute Error (MAE = 2.745°C), and Thiels’ U-statistics (U = 0.127). Besides, the study found that both RT and SVR performed very well in predicting weekly temperature. This study discovered that the duration of the employed data and its dispersion and volatility from month to month substantially influence the predictive models’ efficacy. Furthermore, the second scenario is conducted using the randomization method to divide the data into training and testing phases. The study found the performance of the models in the second scenario to be much better than the first one, indicating that climate change affects the temperature pattern of the studied station. The findings offered technical support for generating high-resolution daily and weekly temperature forecasts using Data-Driven Methodologies. Validerad;2022;Nivå 2;2022-11-07 (joosat)

Air pollution is one of humanity's most critical environmental issues and is considered contentious in several countries worldwide. As a result, accurate prediction is critical in human health management and government decision-making for environmental management. In this study, three artificial intelligence (AI) approaches, namely group method of data handling neural network (GMDHNN), extreme learning machine (ELM), and gradient boosting regression (GBR) tree, are used to predict the hourly concentration of PM2.5 over a Dorset station located in Canada. The investigation has been performed to quantify the effect of data length on the AI modeling performance. Accordingly, nine different ratios (50/50, 55/45, 60/40, 65/35, 70/30, 75/25, 80/20, 85/15, and 90/10) are employed to split the data into training and testing datasets for assessing the performance of applied models. The results showed that the data division significantly impacted the model's capacity, and the 60/40 ratio was found more suitable for developing predictive models. Furthermore, the results showed that the ELM model provides more precise predictions of PM2.5 concentrations than the other models. Also, a vital feature of the ELM model is its ability to adapt to the potential changes in training and testing data ratio. To summarize, the results reported in this study demonstrated an efficient method for selecting the optimal dataset ratios and the best AI model to predict properly which would be helpful in the design of an accurate model for solving different environmental issues. Validerad;2022;Nivå 2;2022-10-03 (hanlid);Funder: Al-Maarif University College, Ramadi, Iraq

Saturated total dissolved gas (TDG) is recently considered as a serious issue in the environmental engineering field since it standsbehind the reasons for increasing the mortality rates of fish and aquatic organisms. +e accurate and more reliable prediction ofTDG has a very significant role in preserving the diversity of aquatic organisms and reducing the phenomenon of fish deaths.Herein, two machine learning approaches called support vector regression (SVR) and extreme learning machine (ELM) have beenapplied to predict the saturated TDG% at USGS 14150000 and USGS 14181500 stations which are located in the USA. For theUSGS 14150000 station, the recorded samples from 13 October 2016 to 14 March 2019 (75%) were used for training set, and therest from 15 March 2019 to 13 October 2019 (25%) were used for testing requirements. Similarly, for USGS 14181500 station, thehourly data samples which covered the period from 9 June 2017 till 11 March 2019 were used for calibrating the models and from12 March 2019 until 9 October 2019 were used for testing the predictive models. Eight input combinations based on differentparameters have been established as well as nine statistical performance measures have been used for evaluating the accuracy ofadopted models, for instance, not limited, correlation of determination (R2), mean absolute relative error (MAE), and uncertaintyat 95% (U95). +e obtained results of the study for both stations revealed that the ELM managed efficiently to estimate the TDG incomparison to SVR technique. For USGS 14181500 station, the statistical measures for ELM (SVR) were, respectively, reported asR2 of 0.986 (0.986), MAE of 0.316 (0.441), and U95 of 3.592 (3.869). Lastly, for USGS 14181500 station, the statistical measures forELM (SVR) were, respectively, reported as R2 of 0.991 (0.991), MAE of 0.338 (0.396), and U95 of 0.832 (0.837). In addition, ELM’straining process computational time is stated to be much shorter than that of SVM. +e results also showed that the temperatureparameter was the most significant variable that influenced TDG relative to the other parameters. Overall, the proposed model(ELM) proved to be an appropriate and efficient computer-assisted technology for saturated TDG modeling that will contribute tothe basic knowledge of environmental considerations. Validerad;2021;Nivå 2;2021-04-19 (alebob);Finansiär: AlMaarif University College

Introduction: It is estimated that cardiovascular diseases (CVDs) are responsible for over 17.3 million deaths per year and are the leading causes of death and disability in the world. In one hand, atherosclerotic disease of the carotid arteries has been associated with the development of cerebrovascular events due to rupture of carotid plaques. Determining plaque vulnerability is critical when selecting the most suitable treatment for patients with carotid stenosis. On the other hand, venous thrombus diagnosis and ageing is critically important in allowing physicians to introduce an appropriate treatment plan. The decision whether or not to perform a thrombectomy or thrombolysis is dependent upon the age of the blood clot. Currently, clinical non-invasive ultrasound-based methods for plaque characterization are limited to visual assessment of plaque morphology and thrombus ageing and new quantitative methods are needed. The aim of this research is to evaluate the utility of Shear Wave Elastography Ultrasound (SWEU) in characterising and ageing the status of atherothromboembolism disease. The current gold standard method to provide more detail about atherothromboembolism status is histopathology. Other modalities such as Magnetic Resonance Imaging (MRI), Computed Tomography Angiography (CTA) are available for diagnosis. MRI is considered the superior imaging modality regarding the atherothromboembolism status, but it has some limitations. SWEU is a novel imaging modality. SWEU is currently used to characterize and stage liver fibrosis and provide more details about the type of breast tumours; it is a new technology that may offer a better method to characterise plaques morphology in patient with cardiovascular disease and to determine the age of venous thrombus.SWEU is a non-invasive imaging technique that works with the B-mode ultrasound to provide more detail about mechanical properties and tissue stiffness of the internal structure. This technique uses high-intensity pulses that are transmitted throughout the tissue to produce shear waves. These waves can be tracked to calculate the shear velocity and tissue stiffness, and the resulting data provides plaque characterization information and thrombus ageing. Our hypothesis is that SWEU can characterize age of the thrombus, provide information about the plaque composition and arterial stiffness, which will be similar to histological finding. Our goal was to (1) estimate the carotid plaque stiffness and characterization in comparison with histopathology. (2) To assess the arterial stiffness in the arterial wall of patient with coronary artery disease and haemodialysis patients using SWE. (3) To determine whether Shear Wave Elastography (SWE) can differentiate and age the thrombus in an experimental model of vascular tissue and thrombosed arteriovenous fistula (AVF) patients. Material and Methods: three different prospective analytical studies were conducted to achieve the goals of this project. Patient with cardiovascular disease, DM, and those undergoing carotid endarterectomy had carotid scan. This study involved three experimental and one control group. Arterial stiffness was calculated using SWE proximal to carotid bifurcation or proximal to plaque when it is present at bifurcation. Another prospective in- vivo study was performed in human vascular tissue. Long saphenous veins and mammary arteries leftover after CABG surgery were used for this study. These saphenous veins segment were infused with human blood from a volunteer who was not on any anticoagulation. These segments were allowed to thrombus and sequentially scanned using SWE to assess the age of thrombus for a period of 14 days. In order to further validate the findings of this in-vivo experiment, arteriovenous patients who present with acute thrombus or DVT in the upper limb veins were recruited. The data that acquired from various parts of the thrombus using SWE was compared with histology. The third study was carried out in patients with existing AVF and a second set who were booked for AVF creation. The patients were divided into three groups Group-A: ESRD patient with AVF and diabetes, Group-B: ESRD with AVF but no diabetes, and Group C: ESRD with no AVF and no diabetes. These groups were divided into further subgroups according to maturation period of AVF. Wall stiffness were measured where the intimal media thicken. Result: The first study findings revealed that stiffness in all the segments in subject having diabetes with carotid plaques was significantly higher as compared to controls (P=0.04, P=0.01 and P =0.02 in segment 1, 2and 3 respectively). The carotid wall stiffness was almost similar in all segments whether adjacent or away from plaque. Moreover, the SWE investigation demonstrated increased stiffness of normal IMT in patients with CAD, the mean stiffness was 80+/- 20KPa compared with 85+/-25KPa for thicken IMT with CAD, P 0.05). However, venous stiffness showed a significant increase with the maturation of AVF (p < 0.05). The mean values of elasticity showed that there was a large increase in the stiffness of venous wall in patients with AVF (47.44 kPa) compared to patients without AVF (21.29 kPa) and healthy subjects (17.11 kPa). Conclusion: Arterial stiffness values quantified by shear wave elastography are reproducible. Higher arterial stiffness is associated with plaque formation. SWE can predict and detect subclinical changes in the arterial and venous wall. SWE was able to age thrombus in experimental and clinical cases. SWE can be used to predict the maturation rate of AVF and can also help to detect venous thinning especially in venous aneurysm, associated with AVF, so the potential rupture and leakage can be minimised. SWE can incorporated as an integral modality into routine scanning of vascular patients. This will provide additional pathological information similar to histology. Thereby, help with better management of patients with cardiovascular diseases. Open Access

The exchange of power among the microgrid (MG), electric vehicles (EVs), energy storages (batteries), and the utility grid is a great challenge in the formulation of the optimal scheduling of the MGs. Furthermore, considering the unit commitment (UC) with the uncertainties that derive from the fluctuations of the renewable generation, open market pricing (OMPs), demand side, and the EVs, result in a significantly complex optimisation problem. Optimised operation of the MGs can result in enormous economic benefits to both the users and the environment. Therefore, there are considerable interests to develop algorithms and approaches to formulate and solve the optimisation problems of the MGs efficiently. In this research, a novel multi-period security-constrained unit commitment unified active and reactive dynamic economic and emission dispatch (SCUC-UARDEED) of the connected and isolated MG is presented. The formulation of the UC is developed and extended to accommodate both the active and reactive power of the distributed generators (DGs). The emission costs of the greenhouse gases in keeping with the emission level constraints are considered in the proposed optimisation problem to reduce the emission of the pollutant gases and achieve a low emission energy system. The overall formulation of the proposed SCUC-UARDEED of the MG takes into consideration the models of the reactive power production cost of the DGs, fuel cost, environmental costs, battery degradation cost, start-up and shutdown costs of the DGs, maintenance cost of the DGs, and the cost of the renewable power generation. The proposed optimisation of the MG is subjected to a comprehensive set of constraints, including active and reactive power security of supply for the connected and isolated MG and emission limit constraints. The impacts of the battery on the scheduling problem of the MG are determined by comparing scenarios with and without battery. Similarly, the impacts of the security of supply constraints are analysed. Uncertainties resulting from the fluctuations of the renewable generation and OMPs are modelled and incorporated into the scheduling problem of the MG as a two-stage stochastic optimisation with taking into account the aforementioned models of the cost functions and constraints. Integration of the active and reactive demand side management (DSM) with the SCUC-UARDEED of the connected and isolated MG is addressed. The DSM is considered as a separate appliance with an operation cycle, and it is considered as a decision variable within optimisation problems. Different types of the DSM techniques are applied to the various types of loads simultaneously under deterministic and stochastic environments. Accordingly, novel approaches and techniques are proposed in the thesis to allow the analysis and detailed investigation of the impacts of the DSM on the optimal scheduling of the MGs, the UC results, the exchanging active and reactive power with the utility grid, the system loads, the spinning reserve, and the secure supply of the MG. The uncertainties arising from both the generation and demand side are systematically modelled and incorporated into the optimisation problem in a two-stage stochastic approach with consideration given to the above cost functions and constraints, where the DSM is considered as a source of uncertainty. A novel scheduling strategy is proposed to integrate the EVs with the SCUC-UARDEED of the connected and isolated MG. The EVs charging and discharging operations are considered as decision variables in the optimisation approaches. The EVs are incorporated with the grid as bidirectional, grid to vehicle (G2V) as energy storage and vehicle to grid (V2G) as energy source. This integration of the EVs with the scheduling problem of the MG that includes all aforementioned cost functions and constraints increases the complexity of the optimisation problem. Accordingly, the economic models involved in the integration of the EVs with MG are developed, and a variety of charging and discharging scenarios are conducted to analyse the impacts of the EVs on the optimal scheduling of the MGs. The uncertainties resulting from the availability of the EVs and fluctuation of the generation of the renewable energy resources are modelled and incorporated with SCUC-UARDEED into a two-stage stochastic optimisation approach.

FTIR spectra of DESs and their individual components as well as DES-modified graphene oxide, TGA/DTG curves for DES-functionalized graphene oxide (Gr 1–18), Raman Spectra (for Gr 6–18), XRD patterns (Gr 1–18) and particle size distribution (Gr 5 and Gr 18).

Detected FTIR peaks from DES spectra with the matching functional groups and origin.

Description of data: Extracted information from Raman spectra of pristine, oxidized and DES-functionalized graphene oxide.

Detected FTIR peaks from DES spectra with the matching functional groups and origin.