Miracle-dz/UniFAR-FTUs huggingface dataset:
PRELIMINARY SKETCHING AND PROTOTYPING: COMPARISONS IN EXPLORATORY DESIGN-AND-BUILD ACTIVITIES

This study explores the role of sketching when designers are creating fast, preliminary prototypes during hands on design-and-build activities. Many studies have noted the value of both sketching and the building of preliminary prototypes in the early stages of the design process. In a typical design scenario, exploratory sketches are made before prototypes are fabricated. However, in certain cases, the differences in the design exploration value of a sketch and a simple, preliminary prototype may not always be clear. In this study, three conditions for a design-and-built activity were compared: a control group (allowed to freely sketch throughout), a limited sketch group (only allowed to sketch at the beginning) and a no sketch group. The study was conducted twice, using different prototyping materials each time. One focused on assembly only (an Erector set) and one that requires both part fabrication and assembly (foam core). The performance of the prototypes, the type and quality of the sketches, and the relationship between sketches and prototypes were evaluated. Results for this study suggest that fast, preliminary prototypes are equally as useful for design exploration as sketching in building simple mechanisms, though results would likely be different for more complex design tasks.
The validity of Pelophylax chosenicus (Okada, 1931) and P. hubeiensis (Fei & Ye, 1982) (Amphibia, Ranidae)

Pelophylax plancyi (Lataste, 1880), Pelophylax chosenicus (Okada, 1931) and Pelophylax hubeiensis (Fei & Ye, 1982) were described chronologically from East Asia. The three species have similar morphological and molecular characteristics, but the taxonomic relationships amongst them have long been ambiguous. To deal with this taxonomic issue, we examined newly-obtained topotypic specimens of P. plancyi , P. chosenicus and P. hubeiensis for morphological comparison. Furthermore, we investigated the phylogeny of pond frogs in Eurasia by Bayesian Inference and Maximum Likelihood analyses of a fragment consisting of mitochondrial DNA gene 16s and provided a molecular phylogeny of the genus Pelophylax . There were no morphological and molecular differences between P. plancyi and P. chosenicus , but both morphological and molecular differences between P. hubeiensis and P. plan-cyi . Hence, we conclude that P. chosenicus is a junior synonym of P. plancyi and P. hubeiensis is a separate species from P. plancyi .
Physical and Physiological Characteristics of Cameroon Professional Soccer Players According to Their Competitive Level and Playing Position

The present study aimed to determine the physical performances and physiological aptitudes of the soccer players of the first (L1) and second (L2) leagues of the Cameroon professional championship. One hundred and seventy-five male professional soccer players, including 91from L1and 84 from L2, participated in the study. Their age, anthropometric parameters (height and weight), physical performances (short-distances sprint times, squat jump) and physiological parameters such as maximum oxygen consumption (VO2max), blood lactate accumulation (BLA) and heart rate (HR) were assessed. No significant age difference was observed between of L1 and L2 (23.3±3.6 vs 23.64±1.00 years) soccer players. L1 soccer players were taller and heavier than those of L2. According to the playing position, a significant difference in weight (76.0±7.6 vs 72.2±8.2 kg) and height (179.1±7.2 vs 175.8±7.3 cm) was observed between L1 and L2 midfield players. VO2max of L2 players (56.4±6.9mlO2/L/kg) was better than that of L1 players (53.8±4.3 mlO2/L/kg), as well as the BLA (8.2±2.3vs 9.7±2.9 mmol/L). L2attackers showed significant higher VO2max (p<0.05) compared to L1 players at the same position. The BLA rate was higher (p<0.01) in midfielders compared to defenders or attackers in each league. Soccer players of L1 were heavier, taller and had better physical aptitude with lower physiological response compared to those of L2. Globally, male Cameroon professional soccer players have average age of 23.4±3,8 years; high of 177.4±7,2 cm and weight 74.1±7.9 kg. These findings suggest that male Cameroon professional soccer players have good physical characteristic but lower physiological aptitudes. This may explain the poor performance of local teams during international competitions.
Forecasting Multivariate Volatilities with Exogenous Predictors: An Application to Industry Diversification Strategies

We propose a procedure to forecast the realized covariance matrix for a given set of assets via spectral decomposition within a multivariate heterogeneous autoregressive (MHAR) framework. Utilizing high-frequency data for the U.S. aggregate and industry indexes and a large set of exogenous predictors that include financial, macroeconomic, sentiment, and climate-based factors, we evaluate the out-of-sample performance of industry portfolios constructed from forecasted realized covariance matrices across various univariate and multivariate forecasting models. While the climate and sentiment-based forecasting models generally yield more accurate forecasts of realized covariance compared to the macroeconomic and financial based models, particularly at the short forecast horizon, we find that the models that include industry-level information, generally yield better economic outcomes, in line with the established evidence of the predictive information captured at the industry level. Our results suggest that the MHAR framework coupled with DRD decomposition that splits the covariance matrix into a diagonal matrix of realized variances and realized correlations, can be utilized in a high-frequency setting to implement diversification and smart beta strategies for various investment horizons; however, the choice of the predictors should be aligned with the target investment horizon. JEL Codes: C32, C53, G10, G11
International Education International Education

This research study sought to understand the current state of Holocaust education in Romanian classrooms and how sociocultural and institutional forces influence its treatment. By identifying the obstacles, challenges, and successes of Holocaust education in Romania, this study can both disseminate the techniques and conditions that bring about meaningful Holocaust education and provide a generative knowledge base for curriculum proposals, symposia, and other initiatives that seek to disrupt reticence on this topic. Given their recent accession to the European Union, this is a timely study that also examines Romania’s educational efforts concerning the development of democratic skills and dispositions, many of which often result from addressing controversial topics and closed areas, including the Holocaust in Romania. Holocaust education is a relatively new phenomenon in Romania and studying its inception can offer insights for other societies and cultures that are working to introduce Holocaust or controversial issues into their middle and high school curricula.
Quality in Higher Education : Student dropout rates in Catalan universities : profile and motives for disengagement

Data from over 21,600 students who had left Catalan higher education institutions during the academic years 2000-2001 and 2001-2002 has been analysed in order to describe the academic and personal profiles of university dropouts. Additionally, a telephone survey and face-to-face interviews with a pilot group of leavers were undertaken to gather additional qualitative information on the reasons for their decision. The influences on non-completion can be reduced to three main factors, among which dissatisfaction with the quality of the students’ experience, family and work responsibilities, as well as economic difficulties, are prominent. Analysis conducted showed no significant differences among Catalan higher education institutions, the dropout percentages ranging from 28% to 33%. These rates should be understood within a context of broad access to higher education. Within the European Higher Education Area (EHEA) scenario, the findings have triggered institutional endeavours to improve the quality of the students’ and teachers’ experience.
Social media sentiment analysis: lexicon versus machine learning

Purpose With the soaring volumes of brand-related social media conversations, digital marketers have extensive opportunities to track and analyse consumers’ feelings and opinions about brands, products or services embedded within consumer-generated content (CGC). These “Big Data” opportunities render manual approaches to sentiment analysis impractical and raise the need to develop automated tools to analyse consumer sentiment expressed in text format. This paper aims to evaluate and compare the performance of two prominent approaches to automated sentiment analysis applied to CGC on social media and explores the benefits of combining them. Design/methodology/approach A sample of 850 consumer comments from 83 Facebook brand pages are used to test and compare lexicon-based and machine learning approaches to sentiment analysis, as well as their combination, using the LIWC2015 lexicon and RTextTools machine learning package. Findings Results show the two approaches are similar in accuracy, both achieving higher accuracy when classifying positive sentiment than negative sentiment. However, they differ substantially in their classification ensembles. The combined approach demonstrates significantly improved performance in classifying positive sentiment. Research limitations/implications Further research is required to improve the accuracy of negative sentiment classification. The combined approach needs to be applied to other kinds of CGCs on social media such as tweets. Practical implications The findings inform decision-making around which sentiment analysis approaches (or a combination thereof) is best to analyse CGC on social media. Originality/value This study combines two sentiment analysis approaches and demonstrates significantly improved performance.
Progressive Horizon Learning: Adaptive Long Term Optimization for Personalized Recommendation

As E-commerce and subscription services scale, personalized recommender systems are often needed to further drive long term business growth in acquisition, engagement, and retention of customers. However, long-term metrics associated with these goals can require several months to mature. Additionally, deep personalization also demands a large volume of training data that take a long time to collect. These factors incur substantial lead time for training a model to optimize a long-term metric. Before such model is deployed, a recommender system has to rely on a simple policy (e.g. random) to collect customer feedback data for training, inflicting high opportunity cost and delaying optimization of the target metric. Besides, as customer preferences can shift over time, a large temporal gap between inputs and outcome poses a high risk of data staleness and suboptimal learning. Existing approaches involve various compromises. For instance, contextual bandits often optimize short-term surrogate metrics with simple model structure, which can be suboptimal in the long run, while Reinforcement Learning approaches rely on an abundance of historical data for offline training, which essentially means long lead time before deployment. To address these problems, we propose Progressive Horizon Learning Recommender (PHLRec), a personalized model that can progressively learn metric patterns and adaptively evolve from short- to long-term optimization over time. Through simulations and real data experiments, we demonstrated that PHLRec outperforms competing methods, achieving optimality in both deployment speed and long-term metric performances.
Lumiere Research Paper – Neural Networks in Sports Predictions

The following research paper is a literature review that I have pursued based on my interest in machine learning and the football. The paper explores the different types and variations of neural network systems used in the prediction of football matches and analyses the data available. Considering different factors such as input encoding, data types, training time and overall accuracy of the system, the paper tries to answer the question of ‘What neural network design factors influence the successful prediction of football match outcomes? ’ The paper goes through and analyses specifically created data tables using a number of different papers in order to achieve this goal. The form of this paper is a literature review and provides an insight to what the future of predicting matches may be using machine learning and artificial neural networks
Rolling the DICE

Economists have almost uniformly treated anthropogenic greenhouse gas emissions as a market failure, requiring government measures to counterbalance the negative externality. William Nordhaus, a pioneer and leader in the field, uses the latest calibration of his DICE model to determine the “optimal” time-indexed carbon tax, starting at $42 per ton (of carbon, not ton of CO2) in 2015 and rising to $217 per ton in the year 2105. In the present paper, I document several weaknesses in Nordhaus’ standard case for a carbon tax, including his unduly pessimistic estimates and the dangers of a poorly implemented policy. I conclude that economists should reexamine several key issues before rushing to judgment on the need for government measures to combat global warming. * Robert P. Murphy is an economist with the Institute for Energy Research. Email: rpm213@nyu.edu. I would like to thank William Nordhaus, Robert Bradley, the members of the NYU Austrian Colloquium, Billy Pizer, and Chul Eddy Chung for their helpful comments.
Paranormal Belief Among College Students and Elderly People: A Comparative Study

This is a comparative study conducted for assessing the prevalence of paranormal belief among college students and elderly people, and for identifying if paranormal belief increases with increasing age. This study was conducted on two groups: college students and elderly people. The sample included 50 college students aged 20–25 years and 50 elderly persons aged 40–65 years. The study was conducted using the Revised Paranormal Belief Scale which was developed by Tobacyk in 2004. The study compared the paranormal beliefs of college students with those of elderly people to determine the relationship between paranormal beliefs and age. The study findings revealed that due to the negative correlation between both variables, elderly people and age, hypothesis 1 is rejected. Hypothesis 2 has a non-significant result in six sub-domains between two variables (elderly and college students), indicating that the elderly population will have higher levels of paranormal beliefs in the individual sub-domains of the scale than their younger counterparts. Hypothesis 2 was rejected in one sub-domain (extraordinary life forms) because it yielded a significant result in the study.
Software maintenance: An approach to impact analysis of objects change

The maintenance of a software system requires a tool for impact analysis and the propagation of change. This paper presents a knowledge‐based model for both. This model is generic because it is not based on any language or design method. Therefore, it can be applied on the basis of ‘problem’ to be solved. It also captures four life‐cycle phases: requirement, specification, design and programming. We also provide a domain‐specific view that allows the dependency analysis of fine‐grain objects. Two kinds of dependencies are identified: inter‐phase dependencies, these are dependency relations between the objects of the one phase and another; and intra‐phase dependencies, these are dependency relations between the objects of the same phase. In order to validate this model, we also present a prototype based on two life‐cycle phases: design and programming.
The Influence of Lower Limb Muscle Selection on Synergy Analysis during Running

: Muscle synergies have emerged as a novel framework that offers a deeper understanding of the physiological basis of motor control and have been extensively applied in various ﬁelds. This study aims to investigate muscle synergies, involving the selection of the muscles of the lower limb while running, including the deep muscles. The muscle synergies were identiﬁed from the 23 muscle activations generated by the OpenSim simulation and experimented EMG, respectively, and these outcomes were compared. Our results revealed an additional synergy required to achieve a comparable variability accounting for (VAF) levels when considering 23 muscles compared to 10 muscles. This study highlights that the potential contribution of deep muscles to explaining the muscle synergies involved in running. These ﬁndings provide valuable insights for a comprehensive understanding of muscle synergy during running and the neuromotor control of human locomotion. Further optimization of the experimental protocols and extraction methods is necessary to enhance the results of muscle synergy analyses and promote their broader application across various ﬁelds in the future.
Variation in the molecular weight of Photobacterium damselae subsp. piscicida antigens when cultured under different conditions in vitro

The antigenicity of Photobacterium damselae (Ph. d.) subsp. piscicida, cultured in four different growth media [tryptone soya broth (TSB), glucose-rich medium (GRM), iron-depleted TSB (TSB + IR-), and iron-depleted GRM (GRM + IR-)] was compared by enzyme-linked immunosorbent assay (ELISA) and Western blot analysis using sera obtained from sea bass (Dicentrarchus labrax) raised against live or heat-killed Ph. d. subsp. piscicida. The antigenic expression of Ph. d. subsp. piscicida was found to differ depending on the culture medium used. A significantly higher antibody response was obtained with iron-depleted bacteria by ELISA compared with non-iron depleted bacteria obtained from the sera of sea bass raised against live Ph. d. subsp. piscicida. The sera from sea bass raised against live bacteria showed a band at 22 kDa in bacteria cultured in TSB + IR- or GRM+ IR- when bacteria that had been freshly isolated from fish were used for the screening, while bands at 24 and 47 kDa were observed with bacteria cultured in TSB or GRM. When bacteria were passaged several times on tryptic soya agar prior to culturing in the four different media, only bands at 24 and 47 kDa were recognized, regardless of the medium used to culture the bacteria. It would appear that the molecular weight of Ph. d. subsp. piscicida antigens change in the presence of iron restriction, and sera from sea bass infected with live bacteria are able to detect epitopes on the antigens after this shift in molecular weight.
Measuring Atmospheric CO 2 from Space Using Full Spectral Initiation (FSI) WFM-DOAS: Initial Results and Validation

Atmospheric CO2 concentrations, retrieved from spectral measurements made in the near infrared (NIR) by the SCIAMACHY instrument, using a new retrieval algorithm called Full Spectral Initiation Weighting Function Modified Differential Optical Absorption Spectroscopy (FSI WFM-DOAS), are compared to ground based Fourier Transform Infrared (FTIR) data and to the output from a global chemistry-transport model. Comparisons to CO 2 measurements made by the ground based FTIR spectrometer at Egbert, Canada, reveal a negative bias in the FSI WFM-DOAS columns of approximately -4.0%, though this offset appears to be decreasing with time. Similar comparisons to the TM3 chemistry transport model show that that the temporal behaviour of the seasonal cycle is captured well but that its amplitude is over estimated. From these comparisons, the overall precision and bias of the CO2 columns retrieved by the FSI algorithm are estimated to be close to 1.0% and <4.0% respectively.
The E ﬀ ect of Di ﬀ erent Output Powers of Blue Diode Laser along with Curcumin and Ribo ﬂ avin against Streptococcus mutans around Orthodontic Brackets: An In Vitro Study

: Objectives: The aim of the present study was to determine the e ﬀ ects of antimicrobial photodynamic therapy (aPDT) using the blue diode laser (BDL) with di ﬀ erent output powers and the photosensitizers ribo ﬂ avin and curcumin on reducing the number of Streptococcus mutans around orthodontic brackets. Materials and methods: A total of 36 orthodontic brackets were contaminated with S. mutans and randomly assigned to 12 groups as follows: control, ribo ﬂ avin alone, ribo ﬂ avin + BDL with an output power of 200, 300, 400, or 500 mW, and curcumin alone, curcumin + BDL with an output power of 200, 300, 400, or 500 mW, and 0.2% chlorhexidine (CHX-positive control). Or-thodontic brackets were irradiated with a BDL (wavelength 445 nm) at a power density of 0.4–1.0 W/cm 2 for 30 s. All orthodontic brackets were examined under a stereomicroscope at 10× magni ﬁ - cation. Mean colony-forming units (CFUs)/mL were measured before and after treatment. A one-way analysis of variance with Tukey’s post hoc test was performed to compare CFU/mL between groups. Results: CHX and curcumin plus BDL with an output power of 500 mW had the highest reduction in S. mutans colony numbers ( p < 0.001). The curcumin groups were more e ﬀ ective than the ribo ﬂ avin groups. Ribo ﬂ avin alone and ribo ﬂ avin + BDL with an output power of 200 mW showed no signi ﬁ cant di ﬀ erence from the control group ( p = 0.99 and 0.74, respectively). Conclusion: Our results suggest that aPDT using curcumin as a photosensitizer plus BDL with an output power of 500 mW and a power density of 1.0 W/cm 2 at a wavelength of 445 nm can e ﬀ ectively reduce colonies of S. mutans around stainless steel brackets.
Validation of Artificial Intelligence Based Real Time Multi-Vitals Remote Monitoring Solution - A Clinical Study

Background: Recently the combination of telehealth with remote patient monitoring has paved the way for enhanced and augmented health-care services. Its benefits include obtaining efficient, cost and time saving data and minimizing error factors by remote monitoring methods. The validation of multi-vital artificial intelligence-based software (Vigo platform) was conducted to correlate reproducibility of real time vital recording solution. Methods: IEC approval and informed consents were obtained. Seventeen healthy participants were deployed on the multi-vital monitoring (MVM) solution in a controlled environment and continued for 24 hours. The vitals were measured by the standard methods periodically at intervals of 0, 2, 4, 6, 8, 10, 12 and 24 hours. ECG was measured at 0, 4, 8, 12 and 24 hours. Results: A total of 20 healthy volunteers were screened out of which seventeen volunteers participated as three volunteers withdrew consent. All the seventeen volunteers were males of mean age 21±3years and mean BMI 22.9±1.7Kg/m2. Vital parameters included Heart rate, Pulse rate, Respiratory rate, Temperature, Systolic blood pressure, Diastolic blood pressure and ECG Measures including P-wave, PR- interval, QRS-complex, RR-interval, QT-interval, ST-Segment, T-wave were strongly and significantly correlated internally (p>0.01) and 100% correlated externally (p>0.01). Conclusion: It was found that all the vital parameters and ECG measures were strongly matched and significantly correlated internally and externally. This work supports remote monitoring technology as part of remote patient care and decentralized research. Thus, software solution Vigo platform that was developed in this work may become a route to patients’ safety. Key words: Remote monitoring, multi-vitals, telehealth, decentralized trials, artificial intelligence, Wearable wireless biosensors.