Miracle-dz/UniFAR-FTUs huggingface dataset:
Geographic and temporal variability in Pleistocene lion-like felids: Implications for their evolution and taxonomy

Several taxa of lions occurred in the Pleistocene of the Northern Hemisphere. Although crania of these large cats are relatively rare in the fossil record, they allow us to assess size and shape differences among Pleistocene lions from Europe, Asia, and North America (Panthera fossilis, P. spelaea, P. atrox) and to compare them with the extant P. leo . We use basic 14 morphometric data (cranial length/width dimensions, auditory bulla diameters, cranial profile) including data on sex and ontogenetic age in 44 fossil and eight recent specimens, along with their geological age and altitude. We show that: first, crania of the P. fossilis (including P. “intermedia”) differs from crania of the Last Glacial P. spelaea and the extant P. leo . Second, P. spelaea shows a high mor-phologic variation in cranial morphology across its geographic range, with partial morphological segregation between the Western European and Eastern European assemblages. However, the main axis of morphological variation between geographic forms of P. spelaea and P. fossilis–“intermedia” correlates with size (in contrast to major differences relative to P. atrox ), and cranial data thus do
Universities and smart specialisation in less developed European regions: an evidence-based overview

This paper aims to review the evidence demonstrating the role of universities in the knowledge diffusion function for Smart Specialisation strategies. It is not new if many experts question whether Smart Specialisation will apply equally in all regions, plus the reason that the study of the role of universities in Smart Specialisation still needs much attention. Through this evidence-based literature review, I have identified three main points that support the role of universities for Smart Specialisation in less developed regions of Europe, including resources in regional innovation systems, public sector investment support for RandD, and strong bonds of the Triple Helix actors.
Applications of the Discrete Adjoint Method in Computational Fluid Dynamics

The discrete adjoint method allows efficient evaluation of the derivative of a function I(s) with respect to parameters s in situations where I depends on s indirectly, via an intermediate variable w(s), which is computationally expensive to evaluate. In this thesis two applications of this method in the context of computational fluid dynamics are considered. The first is shape optimisation, where the discrete adjoint approach is employed to compute the derivatives with respect to shape parameters for a performance functional depending on the solution of a mathematical flow model which has the form of a discretised system of partial differential equations. In this context particular emphasis is given to efficient solution strategies for the linear systems arising in the discretisation of the flow models. Numerical results for two example problems are presented, demonstrating the utility of the approach. The second application, in adaptive mesh design, allows efficient evaluation of the derivatives of an a posteriori error estimate with respect to the positions of the nodes in a finite element mesh. This novel approach makes additional information available which may be utilised to guide the automatic design of adaptive meshes. Special emphasis is given to problems with anisotropic solution features, for which adaptive anisotropic mesh refinement can deliver significant performance improvements over existing adaptive hrefinement approaches. Two adaptive solution algorithms are presented and compared to existing approaches by applying them to a reaction-diffusion model problem.
Challenges and Opportunities of Arts-Based Science Learning Among Basic Level Students amid Covid 19 Pandemic

This paper provides an overview of the emergent practices of arts-based science teaching and learning in public schools in Nepal. It also shares why arts-based science learning has emerged as a shift in teaching and learning from a dogmatic approach to arts-based learning as well as the opportunities and challenges amid COVID 19 pandemic. QUAN qual research method → was applied in this research. The questionnaire survey was used to collect the quantitative data whereas an in-depth interview was taken to collect the qualitative information in this study. 300 students have been selected randomly out of 1300 study population by using the Raosoft sample size calculator with a 95% level of confidence and with a 5% margin error. It is found that the use of figures, posters, soil and sand art, songs and drama promote the understanding of students’ science learning and their achievement score. The findings from this study are expected to encourage the Nepal government, local governments, and public schools to bring local practices into central level policies to transform the dogmatic approach to arts-based teaching and learning.
Hybrid Method for Power Transformers Faults Diagnosis Based on Ensemble Bagged Tree Classification and Training Subsets Using Rogers and Gouda Ratios

: As a major piece of equipment in the electrical power transmission and distribution, the rapid and accurate assessment of emerging or existing internal faults in power transformers is a key factor in the safe and stable operation of the power grid. This paper proposes a hybrid approach to fault diagnosis for these transformers. This approach is based on ensemble bagged tree classification and training subsets obtain by a conventional pre-processing method. Two pre-processing approaches are performed, the first based on the maximum concentrations of the dissolved gas samples and the second based on the minimum concentrations of the dissolved gas samples. For each training subset, an ensemble classifier is constructed with as inputs the Rogers ratios, Gouda ratios, dissolved gas concentrations and their associations. The proposed hybrid methods are established with 475 samples of training dataset, tested on 117 samples dissolved gas analysis (DGA) data and validated on International Electrotechnical Commission (IEC) TC10 database. The performances of the proposed diagnostic models are evaluated and a comparison is done compared with the following diagnostic methods: IEC ratios method (IRM), Rogers ratios method (RRM), three ratios technique (TRT), Gouda’s triangle (GT), and self-organizing map (SOM) clusters. The results found by computer simulations carried out by the matrice laboratory (MATLAB) software show that, of the two pre-processing approaches used, the one based on the minimum sample concentration gives better results than the one based on the maximum concentration. In terms of fault type, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 94.02%, compared to 92.31% for the best diagnostic model using the maximum concentration-based pre-processing approach. This is lower than the 97.25% for SOM clusters and 96.58% for GT but higher than the 59.83% for RRM, 81.19% for IRM and 93.16% for TRT. In terms of fault severity, the best diagnostic model using the minimum concentration-based pre-processing approach has a diagnostic accuracy of 81.20%, compared to 74.36% for the best diagnostic model using the minimum concentration-based pre-processing approach. This result is lower than the 83.76% for TRT and 89.74% for GT but higher than the 49.57% for RRM, 66.67% for IRM and 78.90% for SOM clusters.
Design of decentralized proportional–integral proportional–retarded controllers in discrete-time domain for two-input two-output processes

In this paper, a decentralized proportional–integral proportional–retarded (PI-PR) controller design method is proposed for two-input two-output (TITO) systems in discrete-time domain. The well-known dominant pole assignment (DPA) approach is used as the basis of the proposed approach. The controller design starts with the decoupling of a given TITO system into two sub-systems and continues with the design of proportional–integral–retarded (PIR) controllers for each sub-system, respectively. The feasible discrete PIR controller parameter set is obtained through the Nyquist stability criterion by considering the desired closed-loop performance specifications. The obtained PIR controllers are then implemented using a PI-PR control structure to avoid poor performance of the closed-loop system (CLS) transient response, which can be caused by the controller zeros. Moreover, a case study is presented to show the performance of the PI-PR controller in a simulation environment. It is shown that the proposed control structure provides a satisfactory performance when compared with the other proportional–integral–derivative (PID) control methods from the literature.
Event-wise mean-$\bf p_t$ fluctuations vs minimum-bias jets (minijets) at the LHC

Fluctuation measurements of event-wise mean transverse momentum $\langle p_t \rangle$ for p-p and Pb-Pb collisions at the large hadron collider (LHC) have been reported recently. In that study it was concluded that the strength of "nonstatistical" $\langle p_t \rangle$ fluctuations decreases with increasing particle multiplicity $n_{ch}$ (or A-A centrality) and is nearly independent of collision energy over a large interval. Among several potential mechanisms for those trends onset of thermalization and collectivity are mentioned. The LHC analysis employed one fluctuation measure selected from several possibilities. An alternative fluctuation measure reveals strong increase of $p_t$ fluctuations with $n_{ch}$ (or A-A centrality) and collision energy, consistent with previous measurements at the relativistic heavy ion collider (RHIC). The $p_t$ fluctuation data for LHC p-p collisions can be described accurately by a two-component (soft+hard) model (TCM) in which the hard component represents dijet production. The data for Pb-Pb collisions are described accurately by a TCM reference for more-peripheral collisions (suggesting transparent collisions), but the data deviate quantitatively from the reference for more-central collisions suggesting modification of jet formation. Overall fluctuation data trends suggest that minimum-bias jets (minijets) dominate $p_t$ fluctuations at both the LHC and RHIC.
Leveraging Initial Cognitive Load to Predict User Response to Complex Visual Tasks

- In this study, we were able to show how cognitive load measurement during the initiation of a complex visual task can predict user response. We measured cognitive load using pupil size and microsaccade rate. The initial phase of task was defined as the first 25 percent of the trial Reaction Time (RT), which was variable up to 50 seconds. The complex visual task entailed a set of twelve words that could be grouped based into 1, 2, or 3 categories or sets, e.g., the words bed, pillow, headboard, etc. can be grouped into a single set that is bedroom. We found a significant correlation between initial cognitive load and final user response to the task. This study provides new insights into the initial cognitive processes that would have practical applications in adaptive user interface design, early warning controls, and detection in human performance.
Optronic convolutional neural network for SAR automatic target recognition

Abstract. Deep learning technology has been widely used in synthetic aperture radar automatic target recognition (SAR ATR) tasks due to its good performance and high efficiency. However, the development of semiconductor technology cannot completely satisfy the demand for electronic hardware with high computational capability to conduct deep neural networks. To solve this dilemma, we propose an optronic convolutional neural network (OPCNN) that can perform SAR ATR tasks directly in optical platform. In OPCNN, all computational operations are implemented in optics with high speed and low energy cost. Electronic platforms are used only to control devices and transmit data information without a massive computational burden. As in digital CNNs, the convolutional layer, downsampling layer, nonlinear activation layer, and fully connected layer are all contained in OPCNN. The simulations demonstrate the feasibility of our OPCNN in solving SAR ATR problems. The good performance in experiments, which achieves 87.4% and 93.8% recognition accuracy on original and denoised moving and stationary target acquisition and recognition dataset, validates the application ability of OPCNN in practical scenario. In one-time recognition tasks, the processing time of our OPCNN is only 0.26 s with the speed of light and the power consumption of our prototype is also far less than digital processer, which is 954 W. Through analysis, our OPCNN obtains the higher processing speed and lower energy cost than digital CNNs with the same structure due to the advantages of optical technology. Also, the scalability of optical structure contributes to build more complex networks to solve complicated dataset without the demand of advanced electronic hardware.
A statistical comparison of restaurant infractions between Toronto and Vancouver

Across Canada, restaurant inspections are conducted to ensure that the food served to the public in almost all public eating facilities, is safe to consume and sell. The ratings and infractions correspond with the standards a restaurant is operating in compliance with legislation. There have been indications in the past of restaurants in different regions or serving specific types of food, receiving lower ratings than others. Currently, there is a lack of knowledge and research on the difference between major cities in Canada, regarding restaurant infractions. By focussing on Vancouver and Toronto restaurants, this research may provide insight into the different legislation of the two regions, highlight different Environmental Health Officers (EHOs) practices and ultimately provide information for knowledge translation into policies that these regions follow.
Use of Electronic Seizure Diaries and Decision Trees to Predict Seizure Outcome for Patients with Epilepsy

- Epilepsy is a neurological disorder that causes unpredictable recurrent seizures. Most people with epilepsy dwell in fear of having unpredictable seizures. In attempts to predict future seizure occurrences, investigators have used data from electronic seizure diaries and machine-learning methods, like decision trees. Using individual patient e-diary data, the purpose of this study is to build patient specific decision trees to 1) determine decision trees overall accuracy in predicting seizures and depicting seizure predictors that influence seizure outcome, and 2) identify seizure predictors that have the most influence on seizure outcome. Patients (n=64) were examined, and their e-diary data was used to build patient specific decision trees. Using a 5-point Likert scale, patients e-diaries entailed information on how they rated the probability of experiencing subsequent seizures and rated their mood, predictive symptoms, stress, and seizure counts. Since e-diaries were recorded in the morning and in the evening, seizures for each patient were assessed by half days. R Programming software was used to generate the decision trees and depict seizure predictors that had the most influence on patient’s seizure outcome. A confusion matrix was performed to obtain the decision trees performance accuracy. Patients were categorized into groups based on certain seizure predictors that they shared. The results showed that for decision trees overall accuracy in predicting seizures and depicting seizure predictors that influenced seizure outcome, 49% of decision trees had an accuracy of 100%; 37% of decision trees had an accuracy ranging between 90-99%; and 13% of decision trees had an accuracy of <90%. Additionally, the results showed that there were more seizure predictors that had influence on patient’s seizure outcome in the morning than in the evening. This work introduces non-invasive precision medicine, with intentions to develop more personalized and reliable health care treatments for people with epilepsy.
Antibiotic De-Escalation in Emergency General Surgery

Background. Antibiotic treatment in emergency general surgery (EGS) is a major challenge for surgeons, and a multidisciplinary approach is necessary in order to improve outcomes. Intra-abdominal infections are at high risk of increased morbidity and mortality, and prolonged hospitalization. An increase in multi-drug resistance bacterial infections and a tendency to an antibiotic overuse has been described in surgical settings. In this clinical scenario, antibiotic de-escalation (ADE) is emerging as a strategy to improve the management of antibiotic therapy. The objective of this article is to summarize the available evidence, current strategies and unsolved problems for the optimization of ADE in EGS. Methods. A literature search was performed on PubMed and Cochrane using “de-escalation”, “antibiotic therapy” and “antibiotic treatment” as research terms. Results. There is no universally accepted definition for ADE. Current evidence shows that ADE is a feasible strategy in the EGS setting, with the ability to optimize antibiotic use, to reduce hospitalization and health care costs, without compromising clinical outcome. Many studies focus on Intensive Care Unit patients, and a call for further studies is required in the EGS community. Current guidelines already recommend ADE when surgery for uncomplicated appendicitis and cholecystitis reaches a complete source control. Conclusions. ADE in an effective and feasible strategy in EGS patients, in order to optimize antibiotic management without compromising clinical outcomes. A collaborative effort between surgeons, intensivists and infectious disease specialists is mandatory. There is a strong need for further studies selectively focusing in the EGS ward setting.
Intrahepatic Fat Content and COVID-19 Lockdown in Adults with NAFLD and Metabolic Syndrome

Background: COVID-19 lockdowns had a significant impact on people’s health, triggering levels of anxiety, perceived stress, and changes in food and nutritional status. Objectives: To assess the changes in dietary habits, metabolic syndrome (MetS) and liver parameters before and after the COVID-19 lockdown according to changes in intrahepatic fat content in adults with non-alcoholic fatty liver disease (NAFLD) and MetS. Design: Pre- and post-lockdown observation of the COVID-19 lockdown on fifty-nine 40–60-year-old participants with MetS and NAFLD, in a parallel group, randomised experiment intended to treat NAFLD. Methods: Anthropometrics, liver and MetS biochemical parameters, intrahepatic fat content by abdominal magnetic resonance imaging, and dietary assessment using a validated 148-item Food Frequency Questionnaire were collected pre-COVID-19 lockdown and post-lockdown. Results: COVID-19 lockdown led to negative changes in the liver of patients with NAFLD and MetS, with weight gain and increases in glycemia, ALT and intrahepatic fat content post lockdown. Participants with worsened liver status had low consumption of fibre, cheese, nuts and coffee, and high consumption of sweets and pastries. Participants who improved liver status ameliorated ALT values, waist circumference, and intrahepatic fat content, assessed by magnetic resonance imaging post-lockdown. Conclusions: The maintenance of healthy lifestyle habits is vital, especially for populations with NAFLD and MetS, to reduce unhealthy lifestyle patterns displayed during lockdown.
“A Difficult Balancing Act”: What Planning Involves

Abstract Drawing on verbatim transcriptions of over 200 interviews, the article systematically analyzes the use of the concept of “balance” in what planners and others say about the nature and role of planning and planners. Planning involves managing competing aims and economic interests in processes that are simultaneously political and technical: what many call “a balancing act. ” Discourse analysis of the content and form of utterances involving the root balanc* suggests that the words people choose in describing planning can simultaneously reflect and obscure power relations and decision processes.
A Comprehensive Review of Natural Compounds for Wound Healing: Targeting Bioactivity Perspective

Wound healing is a recovering process of damaged tissues by replacing dysfunctional injured cellular structures. Natural compounds for wound treatment have been widely used for centuries. Numerous published works provided reviews of natural compounds for wound healing applications, which separated the approaches based on different categories such as characteristics, bioactivities, and modes of action. However, current studies provide reviews of natural compounds that originated from only plants or animals. In this work, we provide a comprehensive review of natural compounds sourced from both plants and animals that target the different bioactivities of healing to promote wound resolution. The compounds were classified into four main groups (i.e., anti-inflammation, anti-oxidant, anti-bacterial, and collagen promotion), mostly studied in current literature from 1992 to 2022. Those compounds are listed in tables for readers to search for their origin, bioactivity, and targeting phases in wound healing. We also reviewed the trend in using natural compounds for wound healing.
Vision-Based Sensing for Electrically-Driven Soft Actuators

Developing reliable control strategies in soft robotics requires advances in soft robot perception. However, current soft robotic sensors pose many performance limitations, and available materials and manufacturing techniques complicate soft sensorized robot design. To address these long-standing needs, we introduce a method for using vision to sensorize robust, electrically-driven soft robotic actuators constructed from a new class of architected materials. Specifically, we use cameras positioned within the hollow interiors of handed shearing auxetic (HSA) actuators to record deformation during motion. We train a convolutional neural network (CNN) that maps the visual feedback to the actuator's tip pose. Our model provides predictions with sub-millimeter accuracy from only six minutes of training data, while remaining lightweight with an inference time of 18 milliseconds per frame. We also develop a model that additionally predicts the horizontal tip force acting on the actuator and generalizes to previously unseen forces. Finally, we demonstrate the viability of our sensorization strategy for contact-rich applications by training a CNN that predicts the tip pose accurately during tactile interactions. Overall, our methods present a reliable vision-based approach for designing sensorized soft robots built from electrically-actuated, architected materials.
Goal-Setting in Multiple Sclerosis-Related Spasticity Treated with Botulinum Toxin: The GASEPTOX Study

Spasticity is one of the most disabling symptoms in multiple sclerosis (MS). Botulinum toxin injection (BTI) is a first-line treatment for focal spasticity. There is a lack of evidence of a functional improvement following BTI in MS-related spasticity. To describe goal-setting for BTI in MS, and evaluate the degree of attainment, using goal attainment scaling (GAS) 4-to-6 weeks after injection session, a one-year multi-center retrospective observational study assessing goal-setting and achievement during BTI session in spastic patients with MS was set up. Following the GAS method, patients and their physicians set up to three goals and scored their achievement 4 to 6 weeks thereafter. Commonly used goals from three centers were combined into a standardized list and 125 single BTI sessions were analyzed. The most frequent goals regarded lower limb (LL) impairments (equinovarus foot, toe claw) or locomotion (stability, walking distance, clinging) and accounted for 89.1%, versus 10.9% for upper limb (UL), mostly for mild-to-moderate MS. Overall, goals were frequently achieved (85.77%) mainly when related to gait and mobility rather than hygiene and ease of care. This study gives an overview on the most frequent, relevant, and achievable goals to be set in real-life practice of BTI for spasticity management in MS.