Miracle-dz/UniFAR-FTUs huggingface dataset:
Energy hub modeling for the design of solar thermal energy systems with short-term and long-term storage

A mixed integer linear programming (MILP) energy hub model is developed to design a solar thermal district-heating network for a cluster of residential buildings in Rheinfelden, Switzerland. The model is employed to determine the area of roof-mounted solar thermal collectors that are required to meet the space heating and domestic hot water loads of the 11 buildings in the neighborhood. The installation and operation of electric heaters is permitted in order to supply back up heat during periods when the solar energy is not sufficient to meet thermal loads. The results of the energy hub model show that thermal energy storage (TES) is required in order to achieve solar fractions (i.e. the percentage of total energy demand that is met by solar energy) that are over 10%. Furthermore, the volume of required TES increases exponentially with the solar fraction. Due to the seasonality of space heating demand, the installation of short-term storage tanks alone is not sufficient for achieving solar fractions that are above 60%, and long-term TES units are also required. However, even when both shortterm and long-term TES units are installed, 100% solar fraction is still not possible due to the loss of heat in the TES units over time. Finally, for the size and characteristics of the neighborhood analysed in this study, the decentralised storage configuration with both shortterm and long-term storage units results in a cheaper energy system with a higher solar fraction, when compared with the centralised storage configurations.
Analysis on the Readiness of Vocational College Lecturers to Implement OBE Approach

This study was conducted to analyse the readiness of Vocational College (KV) lecturers in teaching and learning. The outcome-based education (OBE) approach in vocational colleges brings about changes to the vocational education system in line with the transformation of TVET. OBE is a method of teaching and curriculum design that focuses on learning outcomes. This method has completely replaced the conventional method, which is more centred on examination alone. However, the extent to which KV lecturers understand and are willing to practice OBE in curriculum delivery is yet to be discovered. This study was a quantitative study that used a set of questionnaires as a research instrument. The respondents of this study consisted of 368 lecturers of KV throughout Malaysia. Data collected using questionnaire instrument were analysed using Statistical Package for The Social Science (SPSS) software. The results from the analysis of the study data demonstrated that the level of understanding of KV lecturers on the implementation of OBE approach was at a high level with a mean score of 3.07. Meanwhile, the level of readiness of KV lecturers on the implementation of OBE approach was at a moderate level, which has a mean score of 2.82. This study is an analysis of the understanding and readiness of vocational college lecturers on OBE approach in teaching and learning dimension. This study also examines the constraints faced by KV lecturers in implementing the OBE approach. The mean for the eight items scoring in comprehension portion was above 3.0, while the other two items were in the intermediate level mean of 2.97. This indicates that KV lecturers' understanding of the OBE's approach to teaching and learning is at a high level. From the analysis conducted, KV lecturers have knowledge of PEO, PLO, CLO, LO and outcome-based assessments. They also have the knowledge of the concepts of Face to face (F2F), None face to face (NF2F) and Formal Assessment in the context of Self Learning Time (SLT). Based on this study, it was also clear that KV lecturers have adopted the OBE approach in the teaching methodology. This situation indicates that the lecturers have understood the purpose and importance of the OBE approach to be adopted in KV. However, there were also lecturers who were not motivated to implement teaching and learning using the OBE approach, which showed a moderate level mean (mean = 2.97). In terms of demographics for this item, male lecturers showed a low level of agreement to implement OBE in teaching. This finding is consistent with the study of Dahdi and Iksan who stated that efforts to enhance vocational education under the KPM be successful if KV lecturers are able to improve their level of competence in knowledge, skills and attitudes as well as understanding. This study also discovered that KV lecturers agreed that the OBE approach can foster a culture of innovation among students and it can produce students who are successful and balanced in terms of cognitive psychomotor as well as affective. The analysis of this study by Yusof et al application by Raof
Exploring the Adaptive Responses of Plants to Abiotic Stresses Using Transcriptome Data

In recent decades, global climate change and heavy metal stress have severely affected plant growth and biomass, which has led to a serious threat to food safety and human health. Anthropogenic activities, the rapid pace of urbanization, and the use of modern agricultural technologies have further aggravated environmental conditions, resulting in limited crop growth and productivity. This review highlights the various adaptive transcriptomic responses of plants to tolerate detrimental environmental conditions, such as drought, salinity, and heavy metal contamination. These stresses hinder plant growth and development by disrupting their physiological and biochemical processes by inducing oxidative stress, nutritional imbalance, and osmotic disturbance, and by deteriorating their photosynthetic machinery. Plants have developed different strategies to safeguard themselves against the toxic effects of these environmental stresses. They stimulate their secondary messenger to activate cell signaling, and they trigger other numerous transcriptomic responses associated with plant defense mechanisms. Therefore, the recent advances in biological sciences, such as transcriptomics, metabolomics, and proteomics, have assisted our understanding of the stress-tolerant strategies adopted by plants, which could be further utilized to breed tolerant species. This review summarizes the stress-tolerant strategies of crops by covering the role of transcriptional factors in plants.
A Review on the Estimation of Power Loss Due to Icing in Wind Turbines

The objective of this article is to review the methodologies used in the last 15 years to estimate the power loss in wind turbines due to their exposure to adverse meteorological conditions. Among the methods, the use of computational fluid dynamics (CFD) for the three-dimensional numerical simulation of wind turbines is highlighted, as well as the use of two-dimensional CFD simulation in conjunction with the blade element momentum theory (BEM). In addition, a brief review of other methodologies such as image analysis, deep learning, and forecasting models is also presented. This review constitutes a baseline for new investigations of the icing effects on wind turbines’ power outputs. Furthermore, it contributes to a continuous improvement in power-loss prediction and the better response of icing protection systems.
COVID-19 Vaccine Perceived Risks and Benefits: Scale Development and Initial Tests of the Psychometric Properties

ABSTRACT Background COVID-19 pandemic has been a global health threat since December 2019. It has brought the world to a standstill. The development of a vaccine has sparked great hope among all the individuals across the globe. Purpose This study aimed to measure the perceived risks and benefits associated with the COVID-19 vaccines and to develop a scale to measure the perceived risks and benefits associated with the COVID-19 vaccines. Method This research closely follows the procedure for developing better measures given by Churchill. Psychometrics of the scale is tested with the help of data collected over two rounds of data collection. The analytical tools used are Cronbach's alpha, item to total correlation, and factor analysis. Results Drawing on the previous literature and focus group, a five-dimensional scale of perceived risks and a four-dimensional scale of perceived benefits was developed finally. Discussion This scale has desirable reliability and validity properties that could be used by aspiring researchers and health professionals for gauging the perception of an individual related to COVID-19 vaccines. Translation to Health Education Practice: This research provides a useful insight for health educators for generating and highlighting the various benefits associated with the vaccines.
On the Long Tail Products Recommendation using Tripartite Graph

The growth of the number of e-commerce users and the items being sold become both opportunities and challenges for e-commerce marketplaces. As the existence of the long-tail phenomenon, the marketplaces need to pay attention to the high number of rarely sold items. The failure to sell these products would be a threat for some B2C e-commerce companies that apply a non-consignment sale system because the products cannot be returned to the manufacturer. Thus, it is important for the marketplace to boost the promotion of long-tail products. The objective of this study is to adapt the graph-based technique to build the recommendation system for long-tail products. The set of products, customers, and categories are represented as nodes in the tripartite graph. The Absorbing Time and Hitting Time algorithms are employed together with the Markov Random Walker to traverse the nodes in the graph. We find that using Absorbing Time achieves better accuracy than the Hitting Time for recommending long-tail products. Keywords—Long tail; recommender system; tripartite graph; random walker; hitting time; absorbing time
Exploring the effectiveness of word embedding based deep learning model for improving email classification

PurposeClassifying emails as ham or spam based on their content is essential. Determining the semantic and syntactic meaning of words and putting them in a high-dimensional feature vector form for processing is the most difficult challenge in email categorization. The purpose of this paper is to examine the effectiveness of the pre-trained embedding model for the classification of emails using deep learning classifiers such as the long short-term memory (LSTM) model and convolutional neural network (CNN) model.Design/methodology/approachIn this paper, global vectors (GloVe) and Bidirectional Encoder Representations Transformers (BERT) pre-trained word embedding are used to identify relationships between words, which helps to classify emails into their relevant categories using machine learning and deep learning models. Two benchmark datasets, SpamAssassin and Enron, are used in the experimentation.FindingsIn the first set of experiments, machine learning classifiers, the support vector machine (SVM) model, perform better than other machine learning methodologies. The second set of experiments compares the deep learning model performance without embedding, GloVe and BERT embedding. The experiments show that GloVe embedding can be helpful for faster execution with better performance on large-sized datasets.Originality/valueThe experiment reveals that the CNN model with GloVe embedding gives slightly better accuracy than the model with BERT embedding and traditional machine learning algorithms to classify an email as ham or spam. It is concluded that the word embedding models improve email classifiers accuracy.
Reference Equations for The Six-Minute Walk Distance in Obese Chinese Subjects Older Than 40 Years

Background Some studies have showed that the reference equations for the walking distance derived mainly from healthy, normal-weight people are not suitable for obese subjects. The main purpose of this study is to establish reference equations for the 6MWD in Chinese obese subjects. Methods In the study, a total of 272 obese subjects underwent American Thoracic Society (ATS) protocolized six-minute walk testing (6MWT), and the longer walking distance was used for further analysis. The reference equations of the 6MWD were developed by stepwise multiple regression analysis. The established equations for the 6MWD were compared to existing prediction equations. Results The mean 6MWD for the cohort was 523±56 m. We found that the reliability of two 6MWTs was good. Age and BMI were identified as independent factors and explained 31% and 27% of the variance in the 6MWD for the male and female groups, respectively. These reference equations reported in previous studies cannot accurately predict the 6MWD in our subjects. Conclusion The study was the first to describe the 6MWD in Chinese obese subjects and to propose predictive equations. These established equations can contribute to improving the assessment of Chinese obese patients with diseases that affect their exercise capacity.
Algorithms for Queueing Systems with Reneging and Priorities Modeled as Quasi-Birth-Death Processes

We consider a Markovian multiserver queueing system with two customer classes, preemptive priorities, and reneging. We formulate this system as an infinite level-dependent quasi-birth-death process (LDQBD). We introduce an algorithm that endogenously truncates the level and calculates lower and upper bounds on stationary probabilities of this LDQBD such that the gap between the bounds can be any desired amount. Our algorithm can be applied to any LDQBD for which the rate matrices become elementwise nonincreasing above some level. This appears to be the first algorithm that provides bounds on stationary probabilities for an infinite-level LDQBD. To obtain these bounds, the algorithm first obtains lower and upper bounds on the rate matrices of the LDQBD using a novel method, which can be applied to any LDQBD. We then extend this algorithm to approximate performance measures of the system of interest and calculate exact lower and upper bounds for those that can be expressed as probabilities, such as the probability that an incoming low-priority customer will wait. We generate a wide range of instances with up to 100 servers and compare the solution times and accuracy of our algorithm with two existing algorithms. These numerical experiments indicate that our algorithm is faster than the other two algorithms for a given accuracy requirement. We investigate the impact of changing service rates on the proportion of low-priority customers served and their wait time, and we demonstrate how ignoring one of these measures can possibly mislead decision makers. Summary of Contribution: We contribute to operations research by modeling a practically important queueing system and developing an algorithm to accurately compute performance measures for that system. We also contribute to computer science by providing error and complexity analysis for the algorithm to solve a broad class of two-dimensional Markov chains with infinite state space.
Operational Risk Assessment of Power Systems with Distributed Energy Resources Using Minimal Cut Sets

Electric power system networks are facing major challenges because of the rapid increase in penetration of distributed energy resources (DERs). Reliability evaluation plays an important role in system analysis, design, upgrades, and operations, especially in bulk power systems. The research presented in this thesis focuses on the evaluation of the composite system reliability under steady state conditions, and also goes a step further towards assessing operational risks in real-time system operations using direct probabilistic analysis techniques. The thesis also examines the reliability and risk improvements that are accrued from penetration of DERs into the power system. The challenge of using analytical methods in reliability evaluation of composite power systems is the large computational burden involved, to examine all the possible outage events. This thesis presents the mathematical foundations, evaluation procedures, and reliability indices associated with composite power system reliability evaluation using the minimal cut set calculations. The objective of this approach is to evaluate reliability and risk indices for the system and for every load bus in the system. The performance of the system under outage condition of generators, transmission lines, or both, is examined by conducting an appropriate power flow study. An optimal power flow (OPF) is solved to find the system minimal cut sets which are then used to evaluate reliability and operational risk. DER units are incorporated to investigate the enhancement in system reliability and operational risk. The concepts and developed model are illustrated by application to the 24-bus IEEE Reliability Test System.
Mineral Physics

Mineral physics emerged as an independent discipline of Earth sciences in the middle of the 20th century, drawing together geophysics and mineralogy. Using the principles of condensed matter physics and solid-state chemistry, it focuses on exploring how physical properties of minerals depend on atomic structure. With the advent of new experimental tools (e.g., automated X-ray diffractometers, electron microscopes, various spectrometric techniques, digital computers and synchrotron X-radiation sources) in the past 70 years, geophysicists and mineralogists began to talk with one another.
Physiological and Molecular Response of Wheat Cultivars to Titanium Dioxide or Zinc Oxide Nanoparticles under Water Stress Conditions

A field trial was conducted through two successive winter seasons 2017/2018 and 2018/2019 to evaluate the influence of TiO2 or ZnO nanoparticles on two wheat cultivars (Gimeza 12 and Sids 13) under different water irrigation requirements of 100% (WW) and 75% (WS). The results showed that drought stress decreases morphological parameters, photosynthetic pigments, and wheat yield (ha−1). However, the total soluble sugars, total free amino acids, proline content, and water productivity were increased. Application of TiO2 or ZnO nanoparticles declines the negative influence of water deficit. Furthermore, SDS-PAGE revealed in Gimeza 12 treated with TiO2 or ZnO nanoparticles, it stimulates the appearance of some proteins at the MW of 28 kDa in WW and WS, while in WS, it records the new polypeptide at 18 kDa. Moreover, Gimeza 12 treated with nano-TiO2 led to the disappearance of two bands at 163 and 51 kDa in WS. However, for Sids13, there was no difference between the treatments in WW and WS except in nano-ZnO at WS that disappeared the polypeptides at MWs of 163, 51, and 18 kDa. Primer SRAP results showed that the plants treated with TiO2 or ZnO nanoparticles had a minor effect at the genomic DNA level, which was illustrated by the appearance or absence of some bands. Besides, the low concentrations of nanoparticles did not damage DNA. On the other hand, one negative marker of −233 bp disappeared in the Gimeza 12 cultivar treated with WS + nano-TiO2 and was revealed in the other treatments using primer SRAP-2. The results showed that the Gimeza 12 cultivar which had the highest grain yield was more tolerant to drought than the Sids 13 cultivar.
Unexpected occurrence of Aeromonas species in the cutaneous mucus of Barbour's seahorses (Hippocampus barbouri)

Because the skin microbiota is valuable for elucidating health status and common disorders in the host, this study aimed to describe the bacterial community composition of the skin of Barbour’s seahorse ( Hippocampus barbouri ) using high-throughput sequencing of 16S ribosomal RNA genes. Water and sediment samples from the surrounding environment were also analyzed for comparative purposes. The results revealed the presence of operational taxonomic units (OTUs) affiliated to the Aeromonas genus in the skin, whose abundance was slightly similar to the sediment samples. Given that some Aeromonas species are known to be important pathogens in humans and fish, these results may provide information to show the dependency of the skin microbial composition on the environment, as well as determine whether the presence of Aeromonas species has implications on seahorse health.
Investigating the Role of Childhood Trauma, Emotion Dysregulation, and Self-criticism in Predicting Self-harming Behaviors

Objective: Self-harming behavior is a major clinical issue in adolescenc. Childhood trauma, emotion dysregulation, and Self-criticism are the main concerns associated with self-harming behavior. In this study, we investigated the relationship between childhood trauma, emotion dysregulation, and Self-criticism with self-harming behaviors among adolescents in Iran. Methods: A sample of 558 (263 girls and 295 boys) middle school-aged adolescents (aged 13–17) was recruited. Participants had at least one self-harming behavior in their clinical records. Self-harm Inventory (SHI), Childhood Trauma Questionnaire (CTQ), Difficulties in Emotion Regulation Scale (DERS), and Levels of Self-criticism questionnaire (LOSC) were completed online. Results: Findings of step by step regression revealed a significant relationship between childhood trauma, emotion dysregulation, and Self-criticism with self-harming behaviors. More specifically, childhood trauma (β=0.253, t=6.42), emotion dysregulation (β=0.135, t=2.77), and Self-criticism (β=0.345, t=8.67) predicted self-harming behaviors. Conclusion: Our findings provided a therapeutic insight to prevent suicide and other related destructive behaviors among adolescents.
In-situ Experiment Study of Crushed Red-stone Embankment Intensified by Dynamic Compaction

It is difficult to control the construction quality of crushed red-stone embankment dynamic compaction was adopted.Through compaction test and observational research of ground settlements and deep deformation,the method that using the degree of compaction to determine the design parameters was proposed.Meanwhile,the characteristic or the relationships for the strengthening effect,deformation orderliness,tamping-energy,tamp frequency,tamp influence depth and the horizontal influence distance via a novelty in-situ experiment was studied,and the fitting formula was obtained.The paper also put forward some suggestions for the design and construction of crushed red-stone embankment.
Collision-free path planning for lane keeping system of intelligent vehicle

Considering the possibility of obstacles existing on the road when the lane keeping system is utilized for the driver assistance,the multisensory fusion based local collision-free path planning of the intelligent vehicle is studied.Firstly,the traversable road boundaries are detected by using a monocular vision sensor and a target path and virtual obstacles are generated by combining the detected boundaries and the road information consequently.Secondly,the possible real obstacles existing on the road are detected by using the fusion information from laser ranging radar and vision sensors.Finally,the real obstacles detected by sensors are combined with virtual obstacles,and a local collisionfree path is planned by using improved vector field histogram(VFH)method.The results of simulation experiment indicate the validity of the proposed collision-free path planning method.