Miracle-dz/UniFAR-FTUs huggingface dataset:
A Digital Twin Architecture Model Applied with MLOps Techniques to Improve Short-Term Energy Consumption Prediction

Using extensive databases and known algorithms to predict short-term energy consumption comprises most computational solutions based on artificial intelligence today. State-of-the-art approaches validate their prediction models in offline environments that disregard automation, quality monitoring, and retraining challenges present in online scenarios. The existing demand response initiatives lack personalization, thus not engaging consumers. Obtaining specific and valuable recommendations is difficult for most digital platforms due to their solution pattern: extensive database, specialized algorithms, and using profiles with similar aspects. The challenges and present personalization tactics have been researched by adopting a digital twin model. This study creates a different approach by adding structural topology to build a new category of recommendation platform using the digital twin model with real-time data collected by IoT sensors to improve machine learning methods. A residential study case with 31 IoT smart meter and smart plug devices with 19-month data (measurements performed each second) validated Digital Twin MLOps architecture for personalized demand response suggestions based on online short-term energy consumption prediction.
Damage Detection and System Identification using a Wavelet Energy Based Approach

Damage Detection and System Identification using a Wavelet Energy Based Approach Duk Jin Joo Structural Health Monitoring (SHM) is central to the goal of maintaining civil engineering structures saftely and efficiently, and thereby securing people’s lives and property. Furthermore, it plays an essential role in infrastructure design at a fraction of the capital cost of construction. In this thesis, we seek to address two of the central concerns of the SHM: parametric system identification and damage detection. The first proposed method seeks to identify the physical parameters of an analytical model. First, the connection coefficients for the scaling function were developed for deriving the responses of the velocity and displacement from the acceleration responses. Next, defining the dominant sets based on the relative energies of the Wavelet Components of the acceleration responses, the equations of motion of the system in the time domian were converted to a reduced representation of the equations of motion in terms of the DWT and of the DWPT. Finally, the least square error minimization was conducted over the dominant sets to estimate the best estimation of the physical parameters. The second proposed method seeks to detect damage in a structure. Motivated by the fact that the Empirical Mode Decomposition is seen to have different features from the Discrete Wavelet Transform when one investigates nonstationary signals, we have combined the Empirical Mode Decomposition with the Discrete Wavelet Transform to enhance the performance of the proposed method for identifying damage in the structure. Numerical verification showed that the performance of our two proposed methods, tested across various simulation cases, was quite satisfactory.
How Reading Motivation and the Expectancy-Value Beliefs of Ninth Graders Influence Language Arts Course Enrollment Decisions and Why This Matters

Abstract The purpose of this explanatory mixed methods study was to understand how 9th grade honors and general education language arts students made course enrollment decisions through the lens of the Expectancy-Value (E-V) theory. A survey was administered to 9th grade students (N = 118) attending a public high school with the overall mean score indicating fairly high reading motivation. A point biserial correlation analysis was conducted next to determine any significant correlations between course selection and the E-V expectancies for success and task values. Attainment was the only task value reporting a positive and statistically significant correlation. Six students with either high, medium, or low reading motivation were interviewed from which four themes emerged: expectancy for success and reader identity, utility and usefulness of reading in the future, cost and enjoyment associated with reading, and attainment and course enrollment decisions. Implications and recommendations for educators and researchers are provided.
A Study on Knowledge, Attitude and Practices of Obtaining Informed Consent for Medical and Surgical Procedures Among Medical Officers in Teaching Hospital Kalubowila and Karapitiya, Sri Lanka

Introduction: At present most of the patients are educated, knowledgeable and actively participating in medical decision making. However, the literature on medical officers' knowledge, attitudes, and practices, as well as informed consent, in Sri Lanka is quite limited and our study is carried out to fulfil this gap. Methodology: A cross-sectional descriptive study was conducted among randomly selected 404 medical officers at two Teaching Hospitals in Sri Lanka. Results: Among the study group of total 404 medical officers, 57% (n=232) were males and 82% (n=331) were Grade Medical Officers. Ninety five percent (n=384) of them knew that informed consent should be obtained in vaginal examination and rectal examination and 98% (n=396) of them knew and apply the knowledge into practice in the case of children and other persons who are incapable of giving consent and 97% (n=392) of them knew that they have to consider the mental state of the patient before requesting consent. Almost all medical officers 96% (n=348) believe that the doctor who carries out the treatment or medical procedures is the best person to obtain informed consent from the patient. The majority of the medical officers 70% (n=283) only explain common risks. In comparison, 29% (n=117) explain all risks and only 01% (n=04) explain all risks including possible remote risks as well. Conclusion: A reasonable number of doctors have adequate knowledge regarding informed consent but are very poor in some areas. It is highly recommended continuous professional development program and knowledge updating program covering medical ethics at each hospital level.
NARX Neural Network Modelling of Hydraulic Suspension Dampers for Steady-state and Variable Temperature Operation

Summary Two NARX-type neural networks are developed for modelling nonlinear dynamic characteristics of passive twin-tube hydraulic dampers used in vehicle suspension systems. Quasi-isothermal and variable temperature NARX models are rigorously tested and compared with a state-of-the-art physical model proposed by Duym and Reybrouck (1998) and Duym (2000). Measured damper data, generated under isothermal and temperature varying conditions, is used for NARX training, physical model calibration, and predictive comparisons. Test kinematics include high amplitude sinusoidal displacements up to 14 Hz, and realistic random road profiles. The NARX models are trained via ‘teacher forcing’ and the feedforward backpropagation algorithm using both ‘Early Stopping’ and Bayesian Regularisation. Stable network design is also examined using the minimum posterior prediction error as the criterion for selecting a good network from a small number of tests. Calibration of the physical model proves highly complicated owing to considerable nonlinearity-in-the-parameters, requiring use of Sequential Quadratic Programming with an implicitly nonlinear constraint. The paper shows that NARX neural network modelling is vastly superior in terms of calibration efficiency, and prediction times, whilst offering roughly similar, if not better, model accuracy.
The Role of Factor Xa-Independent Pathway and Anticoagulant Therapies in Cancer-Related Stroke

Background: The optimal strategy for stroke prevention in cancer patients is unknown. We compared the underlying mechanisms of coagulopathy and the effects of anticoagulants in patients with active cancer and atrial fibrillation (AF). Methods: We retrospectively enrolled 46 consecutive patients with embolic stroke of unknown source and active cancer (cancer stroke). We consecutively screened patients with cancer patients without stroke (n = 29), AF stroke (n = 52), and healthy subjects (n = 28), which served as controls. Patients with cancer stroke were treated with either enoxaparin (a low-molecular-weight heparin) or a factor Xa inhibitor, and those with AF stroke were treated with factor Xa inhibitors. D-dimer, factor Xa, and circulating cell-free DNA (cfDNA), a marker of neutrophil extracellular traposis, were measured at both before and after anticoagulation. Results: In AF stroke, factor Xa activity and cfDNA and D-dimer levels were decreased by treatment with factor Xa inhibitors. In contrast, in cancer stroke, factor Xa activity was decreased, D-dimer levels were unchanged, and cfDNA levels were increased by treatment with factor Xa inhibitors. In cancer stroke patients treated with enoxaparin, D-dimer levels were decreased (p = 0.011) and cfDNA levels were unchanged. Conclusion: The anticoagulation effects of factor Xa inhibitors differed between cancer stroke and AF stroke.
Influence of hydrodynamic models on the prediction of submicrometer device characteristics

The influence of different hydrodynamic models on the prediction of submicron device characteristics is studied using the General Hydrodynamic Equation solver. We analyze the simulation results of various structures of Silicon-on-insulator (SOI) Metal-Oxide-Semiconductor Field-Effect-Transistors (MOSFETs) and silicon germanium (SiGe) Heterostructure Bipolar Transistors (HBTs). When the size of devices is reduced to the submicron regime, the current drive of a SOI-MOSFET is enhanced mainly by the nonlocal effect. However, the current drive of a HBT is reduced by the dominant thermal back-diffusion effect. The sensitivity of predicted device characteristics on various transport parameters is also discussed.
The Growth and Morphological Characterization Of Tropical Thermophilic Bacterium Parageobacillus caldoxylosilyticus ER4B

Parageobacillus caldoxylosilyticus is a rod-shaped thermophilic bacterium that can grow optimally at high temperatures. The thermophilicity of the bacterium is expected to be largely accounted for by the production of thermostable enzymes which has valuable applications in many fields. However, the species is poorly studied, hence, the growth conditions at high temperatures remained unclear until today. Therefore, this study aimed to determine the growth characterization of P. caldoxylosilyticus, including growth media preferences, optimal growth temperature, as well as minimum and maximum growth temperature. P. caldoxylosilyticus strain ER4B isolated from oil palm empty fruit bunch compost was used in this study. The bacterial strain was first identified using 16S rRNA sequencing, and the subsequent BLAST result showed that it is closest to P. caldoxylosilyticus strain UTM6. It is found that ER4B grew best in LB as compared to R2A, TSB, and NB medium. Further temperature tests determined the optimum growth temperature of the strain to be at 64°C Besides, the bacterium forms mucoid circular punctiform colonies that are yellowish in color on an agar plate, and the colony is usually 2 mm to 4 mm in diameter. The microscopic analysis also revealed that strain ER4B is a Gram-positive rod-shaped bacterium that has a length ranging from 3 µm to 6 µm, with a diameter of around 0.5 µm.
Knowledge-Driven Multi-Objective Evolutionary Scheduling Algorithm for Cloud Workflows

Cloud workflow scheduling often encounters two conflicting optimization objectives of makespan and monetary cost, and is a representative multi-objective optimization problem (MOP). Its challenges mainly come from three aspects: 1) a large number of tasks in a workflow cause large-scale decision variables; 2) the two optimization objectives are of quite different scales; 3) and cloud resources are heterogeneous and elastic. So far, many studies focus on adopting multi-objective evolutionary algorithms (MOEAs) to solve the cloud workflow scheduling problem without mining the domain knowledge. To make a good trade-off between the makespan and monetary cost, this paper puts forward a knowledge-driven multi-objective evolutionary workflow scheduling algorithm, abbreviated as KMEWSA, including two novel features. On the one hand, the structural knowledge of workflow is mined to simplify the large-scale decision variables into a series of small-scale components, such accelerating the convergence speed of MOEAs. On the other hand, the knowledge on the Pareto front range is mined to estimate the ideal and nadir points for the objective space normalization during the search process, which helps maintain population diversity for MOEAs. At last, based on twenty real-world workflows and parameters of Amazon EC2, extensive experiments are performed to compare the KMEWSA with three baseline algorithms. The results demonstrate the effectiveness of the KMEWSA in balancing makespan and monetary cost for deploying workflows into cloud computing.
Estimating passenger numbers in trains using existing weighing capabilities

Knowing passenger numbers is important for the planning and operation of the urban rail systems. Manual and electronic counting systems (typically infrared or video) are expensive and therefore entail small sample sizes. They usually count boarding and alighting passengers, which means that errors in estimates of total numbers of passengers propagate along train runs. Counting errors in manual and electronic counting systems are typically flow-dependent, making uncertainty a function of volume. This paper presents a new counting technique that exploits the weighing systems installed in most modern trains to control braking. This technique makes passenger counting cheaper and ensures a complete sample. The paper compares numbers estimated by this technique with manual counts and counts from an infrared system in trains in urban Copenhagen. It shows that the weighing system provides more accurate passenger counts than the infrared equipment. The method has been validated on a large data set and is now in full operation in the urban Copenhagen rail system.
High performance sensorless control of induction motor drives for industry applications

This paper deals with some aspects concerning the implementation of a sensorless induction motor control for industry applications. The proposed control method uses an adaptive rotor flux observer and a suitable adaptation law for the speed estimation. It has been found that this method is superior to many other methods when the performance versus complexity criterion is considered. The adaptation law is obtained by a general one, valid for any induction motor parameter estimation. A comparison between the floating and the fixed point DSP implementation of the proposed method is presented. In fact, the appearance of new and powerful fixed point DSP microcontrollers makes the fixed point solution very attractive for many industrial applications, such as retrofits, conveyors control, and a lot of manufacturing process control. Different algorithm implementation issues, such as the online computation of the observer gain matrix and of the discrete motor model are discussed in this paper. Simulation and experimental results for the two cases (fixed and floating point DSP implementation) are shown.
Protein Oxidation in Muscle Foods: A Comprehensive Review

Muscle foods and their products are a fundamental part of the human diet. The high protein content found in muscle foods, as well as the high content of essential amino acids, provides an appropriate composition to complete the nutritional requirements of humans. However, due to their special composition, they are susceptible to oxidative degradation. In this sense, proteins are highly susceptible to oxidative reactions. However, in contrast to lipid oxidation, which has been studied in depth for decades, protein oxidation of muscle foods has been investigated much less. Moreover, these reactions have an important influence on the quality of muscle foods, from physico-chemical, techno-functional, and nutritional perspectives. In this regard, the loss of essential nutrients, the impairment of texture, water-holding capacity, color and flavor, and the formation of toxic substances are some of the direct consequences of protein oxidation. The loss of quality for muscle foods results in consumer rejection and substantial levels of economic losses, and thus the control of oxidative processes is of vital importance for the food industry. Nonetheless, the complexity of the reactions involved in protein oxidation and the many different factors that influence these reactions make the mechanisms of protein oxidation difficult to fully understand. Therefore, the present manuscript reviews the fundamental mechanisms of protein oxidation, the most important oxidative reactions, the main factors that influence protein oxidation, and the currently available analytical methods to quantify compounds derived from protein oxidation reactions. Finally, the main effects of protein oxidation on the quality of muscle foods, both from physico-chemical and nutritional points of view, are also discussed.
Seismic Performance Assessment of Unreinforced Masonry Buildings with a Hybrid Modeling Approach

This study proposes a hybrid modeling approach for the seismic performance assessment of unreinforced masonry buildings. The method combines finite-element and equivalent-frame approaches such that more powerful features of each approach are utilized. The finite-element approach is used to model the masonry components of different geometrical and material characteristics with a high level of accuracy. Then this numerically simulated database is used in the analytical modeling of masonry buildings with equivalent beams and columns instead of spandrels and piers. Thus it becomes possible to model a masonry building as a frame structure that can simply be analyzed in order to capture the global behavior. The method has been verified by comparing the analytical results with the previous experimental findings. The last part of the study is devoted to the implementation of the method to an existing masonry building that was damaged during a severe earthquake.
Influence of Perceived Stress and Stress Coping Adequacy on Multiple Health-Related Lifestyle Behaviors

Stress is a primary target of national health promotion efforts such as Healthy Japan in the 21st century (HJ21). However, little is known about how the combination of perceived stress and coping adequacy influence health-related lifestyle behaviors in line with national health promotion. This study assessed the association between combined perceived stress and coping adequacy and multiple health-related lifestyle behaviors in HJ21 practices. This cross-sectional survey that included specialists in health management comprehensively assessed multiple health-related lifestyle behaviors in accordance with HJ21. Total health-related lifestyle behavior scores were calculated and perceived stress and coping adequacy were recorded and categorized into four groups with group 1 to 4 being high to none, and highly adequate to not at all, respectively. The average total lifestyle behavior scores (standard deviation [SD]) were 35.1 (3.5), 33.7 (3.6), 31.8 (3.8), and 30.5 (4.9) for groups 1 to 4 of coping adequacy (p < 0.001). Further, individuals who had higher stress coping adequacy had better multiple health-related lifestyle behaviors after adjusting for demographic factors and perceived stress in the linear trend among the groups. Stress coping skills might be an essential target for stress reduction, ultimately leading to health promotion for disease prevention and longevity.
Underfill viscous flow between parallel plates and solder bumps

Underfill is used to improve the reliability of the flip-chip interconnect systems. A conventional underfill is filled to the gap between the chip and substrate around the solder joints by capillary flow. A modified Hele-Shaw flow, that considered the flow resistance in both the thickness direction and the restrictions between solder bumps, was used. This model estimates the flow resistance induced by the chip and substrate as well as the solder bumps, and provides a reasonable flow front prediction as compared to the experimental observations. A method to simulate the edge effects during the underfill process was also proposed, which gave a close flow front prediction in a chip assembly with full array solders.
Targeting Aggressive Pituitary Adenomas at the Molecular Level—A Review

Pituitary adenomas (PAs) are mostly benign endocrine tumors that can be treated by resection or medication. However, up to 10% of PAs show an aggressive behavior with invasion of adjacent tissue, rapid proliferation, or recurrence. Here, we provide an overview of target structures in aggressive PAs and summarize current clinical trials including, but not limited to, PAs. Mainly, drug targets in PAs are based on general features of tumor cells such as immune checkpoints, so that programmed cell death 1 (ligand 1) (PD-1/PD-L1) targeting may bear potential to cure aggressive PAs. In addition, epidermal growth factor receptor (EGFR), mammalian target of rapamycin (mTOR), vascular endothelial growth factor (VEGF), fibroblast growth factor (FGF) and their downstream pathways are triggered in PAs, thereby modulating tumor cell proliferation, migration and/or tumor angiogenesis. Temozolomide (TMZ) can be an effective treatment of aggressive PAs. Combination of TMZ with 5-Fluorouracil (5-FU) or with radiotherapy could strengthen the therapeutic effects as compared to TMZ alone. Dopamine agonists (DAs) are the first line treatment for prolactinomas. Dopamine receptors are also expressed in other subtypes of PAs which renders DAs potentially suitable to treat other subtypes of PAs. Furthermore, targeting the invasive behavior of PAs could improve therapy. In this regard, human matrix metalloproteinase (MMP) family members and estrogens receptors (ERs) are highly expressed in aggressive PAs, and numerous studies demonstrated the role of these proteins to modulate invasiveness of PAs. This leaves a number of treatment options for aggressive PAs as reviewed here.
Protection of MV and LV networks against lightning. I. Basic information

The characteristics of lightning overvoltages initiated on overhead distribution lines vary greatly depending on the lightning stroke characteristics, the method of overvoltage generation (direct or induced), the orographic conditions (shielding effects) and the configuration of the line (medium or low voltage line, insulation level, presence of neutral conductors, earth resistance of the neutral etc.). In this paper the main characteristics of lightning overvoltages were clarified, as well as tools provided for the prediction of the number of lightning strokes to the lines. Waveshapes and peak-values of lightning overvoltages due to direct or indirect strokes have been described as well as their dependance on the line configuration, both for MV and LV networks. Since it is necessary for the Power utilities to protect their distribution networks in order to minimize the customers outages and the distribution apparatus damage caused by lightning, the information given in this paper consists of the basic knowledge which is helpful for the distribution engineers in order to determine the proper specific measures they have to take for this purpose in their LV and MV networks.