Miracle-dz/UniFAR-FTUs huggingface dataset:
The Predictive Role of Perceived Support from Principals and Professional Identity on Teachers’ Motivation and Well-Being: A Longitudinal Study

The aim of this study was to estimate the influence of perceived support from principals and teacher professional identity (TPI) on teacher’s motivation, vigour and burnout using a longitudinal design during a school year. A sample of 544 secondary teachers reported their perceived support from principals and TPI at the beginning of the year (T1) and their self-determined motivation, vigour, and burnout both at the beginning (T1) and at the end of the year (T2). Structural equation modelling (SEM) revealed that the support from principals was associated with T1 TPI. T1 TPI only partially predicted T2 self-determined motivation (controlling T1 scores), and T2 self-determined motivation was associated with T2 burnout and vigour (controlling T1 scores). The SEM revealed a positive process involving perceived support from principals, pedagogical expertise, autonomous motivation, and well-being indicators. In summary, the present study extends the knowledge about the teacher well-being process and the role of contextual and individual antecedents. In an applied perspective, to prevent burnout, teachers need efficient initial and continuing pedagogical education to be armed in front of the students and need the support of their principals during the school year.
Osgood-Schlatter Disease: Appearance, Diagnosis and Treatment: A Narrative Review

Osgood-Schlatter disease is the most common osteochondritis of the lower limb in sport-practicing children and adolescents. Its manifestation usually coincides with the appearance of the secondary ossification center of the tibia and is linked to the practice of sports with an explosive component. In the present study, a review of the factors related to its appearance, diagnosis and treatment was carried out. Its appearance seems to be multifactorial and related to multiple morphological, functional, mechanical and environmental factors. Given all the above, risk factor reduction and prevention seem the most logical strategies to effectively prevent the appearance of the condition. In addition, it is essential to create prevention programs that can be objectively assessed and would allow to stop the progress of the pathology, particularly in those sports where high forces are generated on the insertion zone of the patellar tendon at sensitive ages. More studies are needed to clarify which type of treatment is the most appropriate—specific exercises or the usual care treatment.
AI-BASED 3D DETECTION OF PARKED VEHICLES ON A MOBILE MAPPING PLATFORM USING EDGE COMPUTING

Abstract. In this paper we present an edge-based hardware and software framework for the 3D detection and mapping of parked vehicles on a mobile mapping platform for the use case of on-street parking statistics. First, we investigate different point cloud-based 3D object detection methods on our extremely dense and noisy depth maps obtained from low-cost RGB-D sensors to find a suitable object detector and determine the optimal preparation of our data. We then retrain the chosen object detector to detect all types of vehicles, rather than standard cars only. Finally, we design and develop a software framework integrating the newly trained object detector. By repeating the parking statistics of our previous work (Nebiker et al., 2021), our software is tested regarding the detection accuracy. With our edge-based framework, we achieve a precision and recall of 100% and 98% respectively on any parking configuration and vehicle type, outperforming all other known work on on-street parking statistics. Furthermore, our software is evaluated in terms of processing speed and volume of generated data. While the processing speed reaches only 1.9 frames per second due to limited computing resources, the amount of data generated is just 0.25 KB per frame.
An Extra Set of Intelligent Eyes: Application of Artificial Intelligence in Imaging of Abdominopelvic Pathologies in Emergency Radiology

Imaging in the emergent setting carries high stakes. With increased demand for dedicated on-site service, emergency radiologists face increasingly large image volumes that require rapid turnaround times. However, novel artificial intelligence (AI) algorithms may assist trauma and emergency radiologists with efficient and accurate medical image analysis, providing an opportunity to augment human decision making, including outcome prediction and treatment planning. While traditional radiology practice involves visual assessment of medical images for detection and characterization of pathologies, AI algorithms can automatically identify subtle disease states and provide quantitative characterization of disease severity based on morphologic image details, such as geometry and fluid flow. Taken together, the benefits provided by implementing AI in radiology have the potential to improve workflow efficiency, engender faster turnaround results for complex cases, and reduce heavy workloads. Although analysis of AI applications within abdominopelvic imaging has primarily focused on oncologic detection, localization, and treatment response, several promising algorithms have been developed for use in the emergency setting. This article aims to establish a general understanding of the AI algorithms used in emergent image-based tasks and to discuss the challenges associated with the implementation of AI into the clinical workflow.
Digital Subthreshold CMOS : Sequencing and Logic Elements for Power Analysis Resistance

This thesis examines subthreshold operation for reducing power consumption and protection against power analysis attacks of digital CMOS circuits. Subthreshold operation is considered the most efficient way to reduce the power consumption of CMOS. There are few studies analyzing the performance of sequencing elements in subthreshold region. Sequencing elements play an important part of clocked sequential circuit systems.Therefore, it is necessary to have a good understanding of the different design types and their applicability in subthreshold circuits. In this thesis, different flip-flop designs commonly used in superthreshold systems are compared in subthreshold operation. According to process corner simulations, a PowerPC 603 type flip-flop operates successfully in all corners in a 65 nm process down to a power supply voltage of 125 mV. This flip-flop has a delay time of 28.7 ns and a power consumption of 2.4 nW in the typical corner. The power consumption decrease corresponds to a reduction factor of 20 000, compared to normal operation. As cryptographic algorithms have become more secure against cryptoanalysis attack, several types of attacks exploiting physical emitted informations have been reported. Power analysis attacks use the power consumption pattern to attack the chip. An increasing demand for secure data communication makes it even more important to design with resistance against side channel attacks in mind for certain applications. Operating in subthreshold region significantly reduces the signal amplitude and the dynamic power consumption component. The reduction of these elements is used to create a S-box for the AES encryption cipher with increased resistance against power analysis attacks. By running with subthreshold operation, the correlation between power consumption of different input values decreases with a factor of 2 500 at the cost of 350 times delay degradation. Simulations in 90 nm and 65 nm processes provided by STMicroelectronics are performed in Cadence Virtuoso Platform.
High Birefringence D-Shaped Germanium-Doped Photonic Crystal Fiber Sensor

In this work, a surface plasmon resonance (SPR) sensor based on a D-shaped germanium-doped photonic crystal fiber (PCF) is proposed. The finite element method (FEM) is introduced to analyze the structure parameters, such as germanium-doped concentration, lattice pitch, and air hole size. In addition, the coupling properties and birefringence properties of PCF are also studied. The computer simulation results indicate that two different surface plasmon polariton (SPP) coupling modes are produced on the polished surface, covered with metal film, when the analyte refractive index (RI) is 1.34. Then, with the increase of the RI, the incompleteness of one of the coupling modes will be transformed into the complete coupling. The effect of germanium concentration on the birefringence is also analyzed. It has an optimal wavelength sensitivity of 5600 nm/RIU when the RI is 1.37. This sensor exhibits a maximum birefringence of 1.06 × 10−2 and a resolution of 1.78 × 10−5 RIU with high linearity.
Supply of Antioxidants vs. Recruit Firefighters’ Cellular Immune Status: A Randomized Double-Blinded Placebo-Controlled Parallel-Group Trial

Background: Physical exercise can affect the immune system. We studied the effect of antioxidants on hematological and immune biomarkers after heavy training. Methods: 24 well-trained and well-fed male firefighters were randomly divided into supplemented and placebo groups, and tested for immunology-related variables using venous blood samples in the fasting state, pre- (M1) and post- (M2) five weeks of daily micronutrient supplementation (15 mg of beta-carotene, 200 mg of vitamin C, 136 mg of vitamin E, 200 μg of selenium, 15 mg of zinc, 100 mg of magnesium). Total leukocytes and a differential count for five populations were determined using standard procedures (MAXM—Beckman Coulter Diagnostics; Brea, CA, USA). Lymphocyte subsets were determined through immunophenotyping. Results: Although all values were within the normal range for healthy adults and athletes in the supplemented group (SG), mean CD3+CD8+, CD8+ and CD16+CD56+ decreased (p < 0.05; small to moderate effects), while mean CD4+, CD19+ and CD4+/CD8+ increased (p < 0.05; small effects) after five-weeks. Regarding the placebo group (PG), higher total leukocyte count (p < 0.05; trivial effect) and natural killer cells percentage (CD16+CD56+; p < 0.05; moderate effect) were observed when comparing M1 and M2. Conclusions: Antioxidants supplementation did not alter well-fed male firefighters recruit firefighters’ immune cell response during the five-week physical training program.
Assessment of coupled models of bridges considering time-dependent vehicular loading

Bridge vibration due to traffic loading has been a subject of extensive research in the last decades. The focus of such research has been to develop solution algorithms and investigate responses or behaviors of interest. However, proving the quality and reliability of the model output in structural engineering has become a topic of increasing importance. Therefore, this study is an attempt to extend concepts of uncertainty and sensitivity analyses to assess the dynamic response of a coupled model in bridge engineering considering time-dependent vehicular loading. A setting for the sensitivity analysis is proposed, which enables performing the sensitivity analysis considering random stochastic processes. The classical and proposed sensitivity settings are used to identify the relevant input parameters and models that have the most influence on the variance of the dynamic response. The sensitivity analysis exercises the model itself and extracts results without the need for measurements or reference solutions; however, it does not offer a means of ranking the coupled models studied. Therefore, concepts of total uncertainty are employed to rank the coupled models studied according to their fitness in describing the dynamic problem. The proposed procedures are applied in two examples to assess the output of coupled subsystems and coupled partial models in bridge engineering considering the passage of a heavy vehicle at various speeds.
Regional energy futures as decision support in the transition to net zero emissions: North of Tyne case study

ABSTRACT Futures studies can fulfil a variety of purposes; for informing policy, developing investment or business strategies, and changing perceptions of what the future may hold. Futures can therefore be described as having transformative potential. This paper uses a participatory scenario approach to develop regional energy futures for a U.K. case study in the North of Tyne region. Two sets of futures are developed; pre-pandemic, and during the pandemic. This was for insights into the challenges for developing subnational scale futures, and how futures thinking may have been impacted by the pandemic. The North of Tyne energy future scenarios are based on discussions in two facilitated stakeholder workshops, pre and during the pandemic. It was found that the pandemic had resulted in shifts in practices and behaviours such as increased home working, and an appreciation of local environments and communities. However, there were commonalities too. In both settings, decarbonisation was a strong driver for change in the energy system, as was the need for fairness in the transition. There were barriers in futures thinking that had to be overcome, most notably was the presumption that meeting the net zero target was a certainty. This presented difficulties in imagining futures that didn’t achieve this target or did so but more slowly. The importance of being able to imagine living in all kinds of future worlds will be critical in a system undergoing such profound change so that strategic foresight is developed to minimise risks of missing the net zero target.
Integration of distributed generations by coordinating electric vehicle charging station in medium voltage network by Swarm intelligence

Purpose Notable energy losses and voltage deviation issues in low-voltage radial distribution systems are a major concern for power planners and utility companies because of the integration of electric vehicles (EVs). Electric vehicle charging stations (EVCSs) are the key components in the network where the EVs are equipped to energize their battery. The purpose of this paper is coordinating the EVCS and distributed generation (DG) so as to place them optimally using swarm-based elephant herding optimization techniques by considering energy losses, voltage sensitivity and branch current as key indices. The placement and sizing of the EVCS and DG were found in steps. Design/methodology/approach The IEEE 33-bus test feeder and 52-bus Indian practical radial networks were used as the test system for the network characteristic analysis. To enhance the system performance, the radial network is divided into zones for the placement of charging stations and dispersed generation units. Balanced coordination is discussed with three defined situations for the EVCS and DG. Findings The proposed analysis shows that DG collaboration with EVCS with suitable size and location in the network improves the performance in terms of stability and losses. Research limitations/implications Stability and loss indices are handled with equal weight factor to find the best solution. Social implications The proposed method is coordinating EVCS and DG in the existing system; the EV integration in the low-voltage side can be incorporated suitably. So, it has societal impact. Originality/value In this study, the proposed method shows improved results in terms EVCS and DG integration in the system with minimum losses and voltage sensitivity. The results have been compared with another population-based particle swarm optimization method (PSO). There is an improvement of 18% in terms of total power losses and 9% better result in minimum node voltage as compared to the PSO technique. Also, there is an enhancement of 33% in the defined voltage stability index which shows the proficiency of the proposed analysis.
Emission Trading in India: A Study of Two Schemes

The paper reviews two schemes in India that have some degree of resemblance with the market based emission trading mechanism like EU-ETS. The first scheme is an innovative emission trading scheme on an air pollutant namely respiratory solid particulate matter (RSPM) with serious potential health implication. The scheme has been piloted in industrial clusters of three polluting states in India (Gujarat, Maharashtra and Tamilnadu). Although the scheme is not on CO2, this happens to be the first of its kind emission trading system in a developing country that mimics the EU-ETS system. The scheme shifts away from the traditional command and control regulation where the industrial point sources have to comply with the norms set by the Central Pollution Control Board (CPCB) or else pay a high penalty. It instead sets a pollution target for an area based on ambient air quality standard and allocates permits to industrial point sources that would then be traded based on gains or shortfalls from compliance after verification. For setting the baseline and verification the scheme relies on a continuous emission monitoring system (CEMS) that provides real-time information on emission and resolves much of the problems that are otherwise prevalent with spot checking and also minimises the problem that potentially arises with suspicious or wrong reporting by third-party auditors. The second scheme known as Perform Achieve and Trade (PAT) scheme is a flagship programme of the Bureau of Energy Efficiency, Ministry of Power, Government of India, under the National Mission on Enhanced Energy Efficiency (NMEEE). The programme also resembles a cap and trade mechanism and involves trading in energy saving certificates between energy intensive industrial production units identified as designated consumers (DCs). Although the scheme does not involve any direct trading based on absolute or relative CO2 emissions but the potential unit of energy saved (expressed in tonnes of oil equivalent) could easily be converted into CO2 emission equivalent. The scheme has the potential to pave the way for creating a more holistic market for emission trading in India. The scheme also holds lot of promises in linking with the international carbon offsets market through adjustments and harmonisation in monitoring, reporting and verification (MRV). In the light of this, the paper also provides a review of the operation and institutional mechanism of the scheme and explores the potential in its linking with other international carbon offsetting schemes.
Reading and Storying in Circles

This article discusses findings from the qualitative research study which was conducted at a K-12 parochial school in Midwestern city. The study was conducted to understand how first and second graders who were located in one particular educational setting, with reading levels that ranged from low to high in a split classroom, made meanings from stories they read while in leveled reading circles. Using the frameworks of how learners respond to literature and share lived experiences with peers, student engagement was closely analyzed to understand readers’ connections to stories. Observations with field notes, active participation, and casual talk were used for gathering data. Three students from different reading circles and their teacher were participants in the study.
Effect of Blunging/Dispersion Parameters on Separation of Halloysite Nanotubes from Gangue Minerals

Clay minerals need to be dispersed with blungers before their utilization in the related industries due to their plastic properties, and size reduction is carried out in a wet medium. Clay minerals also contain impurities such as nonplastic materials in their structure. Mechanical dispersion parameters are important in the separation of clay group minerals (halloysite and kaolinite) from their typical non-clayey gangue minerals (quartz and goethite). In this study, the removal of impurities from halloysite ore obtained from Kızıldam, Turkey, was examined in terms of mechanical dispersion parameters, namely, feed size, blunging time and speed, pulp concentration, pulp temperature, and the aging process. The effect of these parameters on halloysite dispersion was determined by particle size, chemical, and mineralogical analysis, and optical and scanning electron microscope images. The results obtained from the studies of the mechanical dispersing and particle size distribution of the products indicated that the optimum dispersion parameters were determined as −10 mm feed size, 8 h, and 1000 rpm blunging time, and speed, 35% pulp concentration at 25 °C pulp temperature. Under these optimum conditions, a 72.3% amount of −38 μm clay product containing 35.6% of halloysite, 46.5% of kaolinite, 12.0% quartz, 1.9% goethite, 0.9% gibbsite, and 3.2% other minerals were obtained from the halloysite ore, having 30.5% of halloysite, 43.4% of kaolinite, 19.1% quartz, 2.9% goethite, 1.4% gibbsite, and 2.7% other minerals. In this study, it was understood that feed size, pulp concentration, blunging time, and speed were important parameters, while pulp temperature and the aging process had no significant effect on the mechanical dispersion of Kızıldam halloysite. In addition, impurities such as quartz and iron-bearing minerals were separated from the ore by blunging and sieving.
Impact analysis of keyword extraction using contextual word embedding

A document’s keywords provide high-level descriptions of the content that summarize the document’s central themes, concepts, ideas, or arguments. These descriptive phrases make it easier for algorithms to find relevant information quickly and efficiently. It plays a vital role in document processing, such as indexing, classification, clustering, and summarization. Traditional keyword extraction approaches rely on statistical distributions of key terms in a document for the most part. According to contemporary technological breakthroughs, contextual information is critical in deciding the semantics of the work at hand. Similarly, context-based features may be beneficial in the job of keyword extraction. For example, simply indicating the previous or next word of the phrase of interest might be used to describe the context of a phrase. This research presents several experiments to validate that context-based key extraction is significant compared to traditional methods. Additionally, the KeyBERT proposed methodology also results in improved results. The proposed work relies on identifying a group of important words or phrases from the document’s content that can reflect the authors’ main ideas, concepts, or arguments. It also uses contextual word embedding to extract keywords. Finally, the findings are compared to those obtained using older approaches such as Text Rank, Rake, Gensim, Yake, and TF-IDF. The Journals of Universal Computer (JUCS) dataset was employed in our research. Only data from abstracts were used to produce keywords for the research article, and the KeyBERT model outperformed traditional approaches in producing similar keywords to the authors’ provided keywords. The average similarity of our approach with author-assigned keywords is 51%.