Miracle-dz/UniFAR-FTUs huggingface dataset:
Correlation between Ocular and Rectal Temperature with Intra Ocular Pressure in Horse during Exercise

Simple Summary The athletic horse has been found to be sensitive to numerous metabolic and physiological changes during physical exercise, making it a stressor which may go on to affect thermal and homeostatic well-being. In the present study we aimed to evaluate some ocular physiological variables (ocular temperature and intraocular pressure) and how they may be correlated to the rectal temperature, which better describes the thermoregulatory system. This study was conducted on 14 healthy horses from the same horse training center. Ocular temperature, intraocular pressure and rectal temperature were evaluated under different experimental conditions (am and pm hours) and before and after a show jumping exercise protocol. Ocular and rectal temperature increased after the exercise as a result of muscle metabolism activation, increasing blood flow in several regions of the body to improve oxygen supply and heat dissipation. On the contrary, intraocular pressure significantly decreased after exercise, probably due to peripheral vasodilatation. Our results showed a correlation between increased body temperatures and IOP variations during exercise. It would be interesting to consider these variables as indicators to determine physiological status of horses during physical exercise. Abstract The aim of the present study was to investigate the response to physical exercise of some ocular physiological variables (ocular temperature and intraocular pressure) in horses performing a jumping course in the morning (a.m.) or in the afternoon hours (p.m.), before and after the exercise, in correlation with the rectal temperature. Data collection was carried out on 14 clinical healthy Italian saddle horses. All horses were trained from 9:00 to 10:00 a.m. and from 19:00 to 20:00 p.m. according to a specific training program. Ocular temperature (OT), rectal temperature (RT) and intraocular pressure (IOP) were determined. Statistical analysis showed no differences between the results for the left and the right eyes. The application of two-way repeated-measures analysis of variance (ANOVA) showed a statistically significant effect of time (before vs. after) on IOP (p = 0.0001). RT and OT were statistically influenced by time of day and by experimental conditions (a.m. vs. p.m.) (p = 0.0001). Bland–Altman (B-A) testing showed an agreement between the values of RT and OT. Our results showed a correlation between increased body temperatures and IOP variations during exercise, so they can probably be considered indicators of athletic horses’ physical fitness during exercise.
Numerical simulation of thermal loads of the truck’s power train mounting system due to vibrations

Dynamic simulation based on modelling has a significant role during a vehicle development process. It is especially important in the first design stages, when relevant parameters are to be defined. Power train mounting system is exposed to thermal loads which can lead to damage and degradation of its characteristics. Therefore, this paper aims to analyse conversion of mechanical work into heat energy in power train mounting system using a method of dynamic simulation. Considering the presence of classic–mechanical and hydraulic power train mounting systems in modern trucks, analysis of power train mounting thermal loads of FAP 1213 vehicle was conducted. Thermal loads of vehicle power train mounts were calculated by dynamic simulation, while their cooling process was not analysed.
Application and Evaluation of the Reinforcement Learning Approach to Eco-Driving at Intersections under Infrastructure-to-Vehicle Communications

Eco-driving behavior is able to improve vehicles’ fuel consumption efficiency and minimize exhaust emissions, especially with the presence of infrastructure-to-vehicle (I2V) communications for connected vehicles. Several techniques such as dynamic programming and neural networks have been proposed to study eco-driving behavior. However, most techniques need a complicated problem-solving process and cannot be applied to dynamic traffic conditions. Comparatively, reinforcement learning (RL) presents great potential for self-learning to take actions in a complicated environment to achieve the optimal mapping between traffic conditions and the corresponding optimal control action of a vehicle. In this paper, a vehicle was treated as an agent to select its maneuver, that is, acceleration, cruise speed, and deceleration, according to dynamic conditions while approaching a signalized intersection equipped with I2V communication. An improved cellular automation model was utilized as the simulation platform. Three parameters, including the distance between the vehicle and the intersection, signal status, and instant vehicle speeds, were selected to characterize real-time traffic state. The total CO2 emitted by the vehicle on the approach to the intersection serves as a measure of reward policy that informs the vehicle how good its operation was. The Q-learning algorithm was utilized to optimize vehicle driving behaviors for eco-driving. Vehicle exhaust emissions and traffic performance (travel time, stop duration, and stop rate) were evaluated in two cases: (1) an isolated intersection, and (2) a medium-scale realistic network. Simulation results showed that the eco-driving behavior obtained by RL can not only reduce emissions but also optimize traffic performance.
Genotoxic and histopathological effects of the Ili River (Kazakhstan) water pollution on the grass carp Ctenopharyngodon idella

ABSTRACT In the present study, genotoxic, histological and hematological effects of water pollution were evaluated in Ctenopharyngodon idella from the Ili river. Water and fish were sampled at three sites. The concentrations of the following heavy metals were measured in the water: Pb, Co, Mg, Cd, Cu, Zn, Fe. Water pollution with metals gradually increased from P1 to P3: in P1, Cu and Fe levels exceeded the maximum permissible concentrations for fish culture, in P2 – Pb, Cu, Zn, Fe, and in P3 – Pb, Cd, Cu, Zn, Fe. In fish from the Ili river, the highest frequency and severity of DNA damage and liver damage were noted in P3, the lowest in P1. Gill lesions were more pronounced and frequent in fish from P3 compared to grass carp from P2 and P1. Fish from P1 showed a higher frequency of neutrophils and a lower percentage of lymphocytes compared to the control. The results also revealed: genotoxicity measured by comet analysis and liver histology were the most sensitive and showed the magnitude of lesions directly related to the level of water contamination. Gill histology also clearly showed pathological changes caused by pollution, while differential leukocyte count was the least useful indicator, as it showed only minor differences between fish from unpolluted and polluted water.
Retinoblastoma: Assessing the Level of Knowledge of Tumour By Midwives in Brazzaville

Objective : To assess the level of knowledge of this tumour by midwives in two hospitals (Talangai and Makelekele) in Brazzaville. Design : An analytical cross-sectional study. Setting : Two maternity units (government hospitals) in the outskirts of Brazzaville Subjects : One hundred midwives who responded to a questionnaire in the form of multiple-choice question (MCQ). These midwives were randomly selected based on their availability to respond to questions. Each midwife was seen once and had 30 minutes to answer three questions, define the retinoblastoma and recognise its two main early clinical signs (leucocoria, strabismus). Results: Forty percent were able to define the retinoblastoma. For 60% leucocoria was the only sign suspect of retinoblastoma, for 80% strabismus was the only sign of retinoblastoma, only 10% had associated leucocoria and strabismus as two early signs of this cancer. Twelve percent had a score equal to 3/3, eight (8%) had a score equal to 2/3, and 80% had a score of less than or equal to 1/3. Conclusion : midwives in Brazzaville poorly understand the retinoblastoma. Training programmes should be tailored to effectively fight against mortality of this pathology.
Prognostic Nutritional Index as a Predictor of Prognosis in Patients with Chronic Limb Threatening Ischemia Who Underwent Endovascular Revascularization Therapy

Background : Patients with chronic limb-threatening ischemia (CLTI) have poor prognosis. Prognostic nutritional index (PNI) is an established independent predictor of adverse outcome in various chronic illnesses. The use of PNI in patients with CLTI has not been studied. To evaluate the impact of PNI on 12-month adverse cardiovascular and limb outcomes in CLTI patients after endovascular revascularization therapy (ERT). Methods : 270 consecutive patients with CLTI (mean age 73.7 ± 11.9 years; 53% male) who underwent ERT at a single tertiary referral hospital between January 2009 and October 2016 were prospectively enrolled and analyzed. Patients were grouped by tertiles of PNI (lowest ≤ 37.5; middle 37.6 e 45.5; and highest > 45.5) at baseline de ﬁ ned as 10 £ serum albumin (g/dL) þ 0.005 £ total lymphocyte count (per mm 3 ). Composite endpoint including all-cause mortality and amputation was considered as primary endpoint. All-cause mortality and amputation were also analyzed individually as secondary endpoints at 12-months. Multivariate Cox proportional hazards regression analyses were performed. Results : Mean PNI of the 3 groups were 32.8, 42.2 and 50.4 respectively. Co-morbidities such as end-stage renal failure, heart failure and Rutherford classi ﬁ cations 6 were signi ﬁ cantly more prevalent in the lowest PNI tertile (all P < 0.05). Patients in the highest PNI tertile was associated with lowest incidence of 12-month composite endpoint (9.9%), all-cause mortality (7.7%) and amputation (3.3%) compared to those in the middle (27.0%, 22.5%, 9.0%) and lowest (52.2%, 47.8%, 16.7%) respectively, and reached statistical signi ﬁ cance (p < 0.05). Multivariate analysis demonstrated high PNI was an independent protective predictor of composite endpoint (adjusted Hazards Ratio (HR) 0.26, 95% con ﬁ dence interval (CI): 0.12 e 0.57) and all-cause mortality (adjusted HR 0.20; 95% CI: 0.09 e 0.49). Kaplan-Meier analysis revealed that higher PNI was signi ﬁ cantly associated with better prognosis with regard to amputation, all-cause mortality and primary composite endpoint (log rank < 0.05). Conclusions : Our results demonstrated that higher nutritional status de ﬁ ned by prognostic nutritional index (PNI) predicted lower all-cause mortality and amputation rates in chronic limb-threatening ischemia patients after endovascular revascularization therapy.
ON-LINE HANDWRITTEN ARABIC CHARACTER RECOGNITION BASED ON GENETIC ALGORITHM

On-line Arabic handwritten character recognition is one of the most challenging problems in pattern recognition field. By now, printed Arabic character recognition and on-line Arabic handwritten recognition has been gradually practical, while offline Arabic handwritten character recognition is still considered as "The hardest problem to conquer" in this field due to its own complexity. Recently, it becomes a hot topic with the release of database, which is the first text-level database and is concerned about the area of realistic Arabic handwritten character recognition. At the realistic Arabic handwritten text recognition and explore two aspects of the problem. Firstly, a system based on segmentation-recognition integrated framework was developed for Arabic handwriting recognition. Secondly, the parameters of embedded classifier initialed at character-level training were discriminatively re-trained at string level. The segmentation-recognition integrated framework runs as follows: the written character is first over-segmented into primitive segments, and then the consecutive segments are combined into candidate patterns. The embedded classifier is used to classify all the candidate patterns in segmentation lattice. According to Genetic Algorithm (Crossover, mutation, and population), the system outputs the optimal path in segmentation-recognition lattice, which is the final recognition result. The embedded classifier is first trained at character level on isolated character and then the parameters are updated at string level on string samples.
OPÇÕES ESTRATÉGICAS EM UMA EMPRESA DA INDÚSTRIA CALÇADISTA: QUADRO DE TRABALHO STRATEGIC CHOICE IN A COMPANY OF THE FOOTWEAR INDUSTRY: FRAMEWORK

1 Programa de Pos-Graduacao em Engenharia de Producao e Sistemas PPGEPS Universidade do vale do Rio dos Sinos – UNISINOS – Sao Leopoldo/RS – Brasil timstuker84@hotmail.com 2 Programa de Pos-Graduacao em Engenharia de Producao e Sistemas PPGEPS Universidade do vale do Rio dos Sinos – UNISINOS – Sao Leopoldo/RS – Brasil fabricioeidelwein@gmail.com 3 Programa de Pos-Graduacao em Engenharia de Producao e Sistemas PPGEPS Universidade do vale do Rio dos Sinos – UNISINOS – Sao Leopoldo/RS – Brasil junico@produttare.com. br 4 Programa de Pos-Graduacao em Engenharia de Producao e Sistemas PPGEPS Universidade do vale do Rio dos Sinos – UNISINOS – Sao Leopoldo/RS – Brasil sellitto@unisinos.br
Broadband dual‐polarized MICROSTRIP antenna fed with horizontal coaxial baluns

A novel feed mechanism for dual‐polarized microstrip patch antennas with broadband operation and high isolation is presented. The feed structure is a coaxial balun composed of coaxial cables and copper tubes, and it is placed parallel to the ground plane. With the horizontal coaxial baluns, the radiating patch can be symmetrically excited through proximity coupling. For the proposed feed mechanism, the parametric analyses are performed, and the critical dimension affecting the impedance bandwidth of the microstrip antenna is also addressed. An antenna prototype is constructed, and its measured results indicate that the microstrip antenna can give an impedance bandwidth of more than 20%, and within the bandwidth, the isolation between the two orthogonal ports is higher than 40 dB; moreover, symmetrical broadside radiation with low cross polarization is obtained. Simulated results for the cases of microstrip antennas with various substrate thicknesses are also provided.
Associations between university students’ online learning preferences, readiness, and satisfaction

The aim of this study was to examine the associations between the preferences, readiness, and satisfaction of freshman learners after taking online courses. The study group was comprised of Turkish students (892 females, 396 males) who attended their first-year classes at a public university. Participating students attended courses in different departments, but all took these courses for one year online. Data were analysed using optimal scaling analysis. Nonlinear canonical correlation analysis (OVERALS) was used as the variables examined were of different scale levels. According to our findings, learners who preferred face-to-face format were individuals with low levels of learner control, motivation, and satisfaction, and individuals who preferred the online format had high satisfaction levels. Individuals who preferred to take courses in a blended format had low or medium levels of self-directed learning, no previous online course experience, and a medium level of control.
A Rotation Independent In-Place Inclinometer/Tilt Sensor Based on Fiber Bragg Grating

The inclinometer is one of the most important instruments used in geotechnical engineering. The inclinometer is installed into a borehole to monitor the angle of inclination at regular intervals along the borehole to profile the land/soil displacement. In this paper, a novel design of an in-place inclinometer utilizing fiber Bragg grating (FBG) sensor technology is presented. The presented inclinometer is highly sensitive and has an angle measurement resolution of 0.006°. The conventional inclinometers may be twisted involuntarily due to the force that they experience during installation. This twisting may hamper the measurement accuracy of inclinometers. The performance of the proposed FBG-based in-place inclinometer is independent of twisting; hence, its accuracy remains intact even if it is twisted.
Poly(N-isopropylacrylamide)-Based Hydrogels for Biomedical Applications: A Review of the State-of-the-Art

A prominent research topic in contemporary advanced functional materials science is the production of smart materials based on polymers that may independently adjust their physical and/or chemical characteristics when subjected to external stimuli. Smart hydrogels based on poly(N-isopropylacrylamide) (PNIPAM) demonstrate distinct thermoresponsive features close to a lower critical solution temperature (LCST) that enhance their capability in various biomedical applications such as drug delivery, tissue engineering, and wound dressings. Nevertheless, they have intrinsic shortcomings such as poor mechanical properties, limited loading capacity of actives, and poor biodegradability. Formulation of PNIPAM with diverse functional constituents to develop hydrogel composites is an efficient scheme to overcome these defects, which can significantly help for practicable application. This review reports on the latest developments in functional PNIPAM-based smart hydrogels for various biomedical applications. The first section describes the properties of PNIPAM-based hydrogels, followed by potential applications in diverse fields. Ultimately, this review summarizes the challenges and opportunities in this emerging area of research and development concerning this fascinating polymer-based system deep-rooted in chemistry and material science.
The Japan-United States Infrared Interferometry Experiment (JUStIInE): balloon-borne pathfinder for a space-based far-IR interferometer

The balloon-borne Japan-United States Infrared Interferometry Experiment (JUStIInE) is a pathfinder for the first space-based far-IR interferometer. JUStIInE will mature the system-level technology readiness of spatio-spectral far-IR interferometry and demonstrate this technique with scientific observations. Operating at wavelengths from 30 to 90 µm, JUStIInE will provide unprecedented sub-arcsecond angular resolution and spectroscopic data. Our plan is to develop a cryogenic Michelson beam combiner and integrate it with an existing and tested telescope optical system and gondola from the Japanese Far-infrared Interferometric Telescope Experiment (FITE). With two JUStIInE balloon flights we plan to collect, calibrate, analyze, and publish scientific results based on the first far-IR spatio-spectral observations of young stellar objects, evolved stars, and the active galactic nucleus of NGC 1068. The NASA Astrophysics Roadmap envisages a future in which interferometry is applied across the electromagnetic spectrum, starting in the far-infrared. The Far-IR Probe recommended in the 2021 Decadal Survey presents an opportunity to take that important step. A Far-IR Probe mission based on this concept will enable us to understand terrestrial planet formation and spectroscopically study individual distant galaxies to understand the astrophysical processes that govern their evolution.
A Cost, Time, and Demographic Analysis of Participant Recruitment and Urine Sample Collection Through Social Media Optimization

Abstract Introduction: Clinical research can be expensive and time consuming due to high associated costs and/or duration of the study. We hypothesized that urine sample collection using online recruitment and engagement of research participants via social medial has the potential to reach a large population in a small timeframe, at a reasonable cost. Methods: We performed a retrospective cost analysis of a cohort study comparing cost per sample and time per sample for both online and clinically recruited participants for urine sample collection. During this time, cost data were collected based on study associated costs from invoices and budget spreadsheets. The data were subsequently analyzed using descriptive statistics. Results: Each sample collection kit contained 3 urine cups, 1 for the disease sample and 2 for control samples. Out of the 3,576 (1,192 disease + 2,384 control) total sample cups mailed, 1,254 (695 control) samples were returned. Comparatively, the 2 clinical sites collected 305 samples. Although the initial startup cost of online recruitment was higher, cost per sample for online recruited was found to be $81.45 compared to $398.14 for clinic sample. Conclusions: We conducted a nationwide, contactless, urine sample collection through online recruitment in the midst of the COVID-19 pandemic. Results were compared with the samples collected in the clinical setting. Online recruitment can be utilized to collect urine samples rapidly, efficiently, and at a cost per sample that was 20% of an in-person clinic, and without risk of COVID-19 exposure.
Prediction analysis on the pre and post COVID outbreak assessment using machine learning and deep learning

In this time of a global urgency where people are losing lives each day in a large number, people are trying to develop ways/technology to solve the challenges of COVID-19. Machine learning (ML) and artificial intelligence (AI) tools have been employed previously as well to the times of pandemic where they have proven their worth by providing reliable results in varied fields this is why ML tools are being used extensively to fight this pandemic as well. This review describes the applications of ML in the post and pre COVID-19 conditions for contact tracing, vaccine development, prediction and diagnosis, risk management, and outbreak predictions to help the healthcare system to work efficiently. This review discusses the ongoing research on the pandemic virus where various ML models have been employed to a certain data set to produce outputs that can be used for risk or outbreak prediction of virus in the population, vaccine development, and contact tracing. Thus, the significance and the contribution of ML against COVID-19 are self-explanatory but what should not be compromised is the quality and accuracy based on which solutions/methods/policies adopted or produced from this analysis which will be implied in the real world to real people.
Controlling Collision-Induced Aggregations in a Swarm of Micro Bristle Robots

Systematically designing local interaction rules to achieve collective behaviors in robot swarms is a challenging endeavor, especially in micro robots, where size restrictions imply severe sensing, communication, and computation limitations. In such robot swarms, performing useful functions is often preconditioned on the formation of high-density aggregations which can facilitate collective signaling and information sharing. In this article, we present a systematic approach to control aggregation behaviors by leveraging the physical interactions in a swarm of 300 3-mm vibration-driven micro bristle robots that we designed and fabricated. We demonstrate the ability to control the degree of aggregation by varying the motility characteristics of the robots through global vibration frequency and amplitude inputs, after comprehensive characterization, modeling, and simulation of the locomotion dynamics and robot interactions. To quantify the degree of aggregation, we also introduce a new metric, the motility-induced phase separation index index, which unlike many existing methods does not require a scenario-specific tuning of parameters. Our investigations reveal how physics-driven interaction mechanisms can be exploited to achieve desired behaviors in minimally equipped robot swarms and highlight the specific ways in which hardware and software developments aid in the achievement of collision-induced aggregations.