Miracle-dz/UniFAR-FTUs huggingface dataset:
Electrical impedance spectroscopy applied to quality control in the food industry

1 Associate Professor, Universidad de Caldas, Facultad de Ingeniería, Departamento de Sistemas e Informática. Manizales, Colombia. Email: julioc.caicedo@ucaldas.edu. co. Orcid: https://orcid.org/0000-0003-4073-9152 2 Associate Professor, Universidad de Caldas, Facultad de Ingeniería, Departamento de Ingeniería. Manizales, Colombia. Email: felix.diaz@ucaldas.edu. co. Orcid: https://orcid.org/0000-0002-1202-2376 3 Student, Facultad de Ingeniería, Master Degree Program in Food Engineering, Universidad de Caldas. Manizales, Colombia. Email: andrealturo@gmail.com. Orcid: https://orcid.org/0000-0002-3718-5374
Autonomous Planetary Landing via Deep Reinforcement Learning and Transfer Learning

The aim of this work is to develop an application for autonomous landing. We exploit the properties of Deep Reinforcement Learning and Transfer Learning, in order to tackle the problem of planetary landing on unknown or barely-known extra-terrestrial environments by learning good-performing policies, which are transferable from the training environment to other, new environments, without losing optimality. To this end, we model a real-physics simulator, by means of the Bullet/PyBullet library, composed by a lander, defined through the standard ROS/URDF framework and realistic 3D terrain models, for which we adapt official NASA 3D meshes, reconstructed from the data retrieved during missions. Where such model were not available, we reconstruct the terrain from mission imagery - generally SAR imagery. In this setup, we train a Deep Reinforcement Learning model - using DDPG - to autonomous land on the lunar environment. Moreover, we perform transfer learning on the Mars and Titan environment. While still preliminary, our results show that DDPG can learn a good landing policy, which can be transferred to other environments.
Supplemental Material: Face Forensics in the Wild

Detailed Network Architecture. We employ VGG16 [11] as the backbone network of Q-Net. Let q ∈ R denote the backbone feature. Two small multi-layer perceptrons are further added for score regression and domain adversarial learning, respectively. The score regression head has the following architecture: q→FC(1024)→FC(1024)→FC(2), and the domain adversarial head is implemented as: q→FC(1024)→FC(1024)→FC(3). Here, FC means a fullyconnected layer. Training Details. We train Q-Net using the SGD optimizer with a min-batch size of 128, learning rate of 1e-4 and momentum of 0.9. All the training images are resized to 224×224. During training, the domain-regularization parameter α is initialized as 0 and gradually increased to 1 following the schedule [5]:
Swimming exercise reduces native ⍺-synuclein protein species in a transgenic C. elegans model of Parkinson’s disease.

Exercise has been historically recommended to prevent many disease conditions. Intense exercise in particular, has been shown to be beneficial for Parkinson’s disease (PD) — stopping and even reversing symptoms in some patients. Recent research in mammalian animal models of Parkinson’s have shown that exercise affects ⍺-synuclein aggregate species, considered to be a hallmark of PD. However, the exact changes in native ⍺-synuclein protein species after exercise and the downstream effects of exercise upon the health of the animals remains unclear. Recently, it was shown that swimming constitutes a form of exercise in C. elegans worms that confers a protective effect in several worm models of tau and Huntington protein neurodegeneration. Here we show that a period of swimming exercise (Ex) — 15-20 mins — dramatically reduces several native human ⍺-synuclein protein species in the NL5901 C. elegans worm model of Parkinson’s. Exercise on Day 1 of adulthood was found to improve motor function measured by the thrashing rate of worms on Day 2 and Day 4 when compared to both control (untreated) and food restricted (FR) worms. Moreover, exercised worms show smaller ⍺-synuclein::YFP puncta than food restricted worms. Here we show that exercise reduces native human ⍺-synuclein levels independent of food restriction in C. elegans.
Trend Aware Investment Strategy for Stock Market based on a Classifier System and on Intelligent Optimization Techniques

—In this work, it is proposed a hybrid system that combines the use of Support Vector Machines with Genetic Algorithms to create trading strategies based on the current conditions of the market. This system uses a classifier based on the Support Vector Machine to analyze the current trend and select a specific trading strategy, previously optimized by a Genetic Algorithm for the corresponding trend. The system requires an initial training phase of the classifier, which will then be used to split the data into groups of different trends. Each of these groups is optimized separately based on technical analysis rules in order to achieve a tailored trading strategy for each type of trend. For this experience, it is used four important indexes from distinct countries, namely, the SP500, the DAX, the NIK225 and the IBEX35. The data used covers the market data since September 2001 up to September 2018 and the system is tested for each year since September 2008. This system is compared against a similar system without the trend classification component for evaluation purposes. The results for the three markets suggest a possible increase in performance of the strategy optimization method if it is implemented a classifier with the intent of strategy segregation. The data generated during the training and testing phase imply that the proposed system is capable of generating strategies better adapt to the present trend.
A Comparison of Traditional Aggregated Data to a Comprehensive Second-by-Second Data Depiction in Functional Analysis Graphs

By Erin MacKelvie University of the Pacific 2021 Functional analyses (FAs) are an important component of treatment and the data gathered from FAs are often graphed in an aggregate or summary format, such as mean rate per session. Given the prevalence of undifferentiated analyses, it may be that this common method of data depiction is incomplete. In this paper, we compare the traditional aggregate method to a comprehensive second-by-second demonstration of the data including all appropriate and inappropriate responses emitted, as well as programmed and accidental antecedent and consequent variables, which may help further clarify the results of a functional analysis. We compared the functional analysis results of two participants when the data were depicted using the traditional rate aggregate method and depicted using a comprehensive second-by-second method. Although both rate and comprehensive second-by-second data depiction resulted in similar conclusions regarding the maintaining variables for the participants, comprehensive second-by-second data depiction allowed us to draw the conclusions in less time. Additional advantages and disadvantages of each method as it relates to efficiency, therapeutic risk and safety, and practicality are also discussed.
Eliciting and Understanding Primary Care and Specialist Mental Models of Cirrhosis Care: A Cognitive Task Analysis Study

Background Gaps in coordination and transitions of care for liver cirrhosis contribute to high rates of hospital readmissions and inadequate quality of care. Understanding the differences in the mental models held by specialty and primary care physicians may help to identify the root causes of problems in the coordination of cirrhosis care. Aim To compare and identify differences in the mental models of cirrhosis care held by primary and specialty care physicians and nurse practitioners that may be addressed to improve coordination and transitions. Methods Cross-sectional formal elicitation of mental models using Cognitive Task Analysis. Purposive and chain-referral sampling to select family physicians (n = 8), specialists (n = 9), and cirrhosis-dedicated nurse practitioners (n = 2) across Alberta. Results Family physicians do not maintain rich mental models of cirrhosis care. They see cirrhosis patients relatively infrequently, rebuilding their mental models when required (knowledge on demand). They have reactive and patient-need-focused, rather than proactive and system-of-care, mental models. Specialists' mental models are rich but vary widely between patient-centered and task-centered and in the degree to which they incorporate responsibility for addressing system gaps. Nurse practitioners hold patient-centered mental models like specialists but take responsibility for addressing gaps in the system. Conclusions Improving the coordination of cirrhosis care will require infrastructure to design care pathways and work processes that will support family physicians' knowledge-on-demand needs, facilitate primary care-specialist relationships, and deliberately work toward building a shared mental model of responsibilities for addressing medical care and social determinants of health.
A Nonlinear Magnetic Stabilization Control Design for an Externally Manipulated DC Motor: An Academic Low-Cost Experimental Platform

The main objective of this paper is to present a position control design to a DC-motor, where the set-point is externally supplied. The controller is conceived by using vibrational control theory and implemented by just processing the time derivative of a Hall-effect sensor signal. Vibrational control is robust against model uncertainties. Hence, for control design, a simple mathematical model of a DC-Motor is invoked. Then, this controller is realized by utilizing analog electronics via operational amplifiers. In the experimental set-up, one extreme of a flexible beam attached to the motor shaft, and with a permanent magnet fixed on the other end, is constructed. Therefore, the control action consists of externally manipulating the flexible beam rotational position by driving a moveable Hall-effect sensor that is located facing the magnet. The experimental platform results in a low-priced device and is useful for teaching control and electronic topics. Experimental results are evidenced to support the main paper contribution.
The Effect of Psychospiritual Intervention on the Level of Spiritual Inpatients

The problem faced by sick human are not only physical problems but also psychological and spiritual patients will experience imbalances. The purpose of this study was to determine the effect of psychospiritual interventions on the spiritual level of patients. This was a one-group pre-test post-test study conducted in May 2018 at the Aisyiyah Islamic Hospital in Malang. Samples of 15 inpatients were taken by purposive sampling technique. A socio-demographic questionnaire, the DSES (Daily Spiritual Experiences Scale) was utilized for data collection. This intervention was carried out 4 days (4 meetings) for 30-60 minutes each. The data were analyzed using Wilcoxon test in SPSS. The majority of the spiritual level of patients at pre was 13 people (86.7%) and low 2 (13.3%), after intervention 14 people (93.3%) had a high level and 1 (6.67 %) medium. pvalue (0.001) <α (0.05), which means there is a spiritual level difference before and after intervention. The psychospiritual interventions is very effective in improving the spiritual quality of care patients. Thus, have a positive impact on patient health. Therefore, it is recommended that nurses not only focus on physical aspects but provide holistic care in all aspects, especially spiritual.
Long-Term Recovery of the Fecal Microbiome and Metabolome of Dogs with Steroid-Responsive Enteropathy

Simple Summary The impact of treatment of dogs with steroid-responsive enteropathy (a form of chronic diarrhea that responds to treatment with a corticosteroid) on the intestinal microbiome (the collection of all microorganisms in the GI tract) and metabolome (the collection of all small molecules produced by the microbiome or the host) was investigated in this study. Dogs receiving standard treatment were evaluated before (week 0), during (week 3), and at the end of treatment (week 8), as well as 1 year later, in comparison to healthy control dogs. None of the dogs had clinically relevant signs of disease at the end of treatment. While both the microbiome and the metabolome normalized to some degree after treatment, some key bacteria and many molecules remained different, suggesting that certain abnormalities persisted. However, 1 year after treatment, both the microbiome and the metabolome were no longer different from healthy dogs, suggesting that intestinal recovery from chronic steroid-responsive enteropathy takes longer than resolution of clinical signs would suggest. Abstract The long-term impact of treatment of dogs with steroid-responsive enteropathy (SRE) on the fecal microbiome and metabolome has not been investigated. Therefore, this study aimed to evaluate the fecal microbiome and metabolome of dogs with SRE before, during, and following treatment with standard immunosuppressive therapy and an elimination diet. We retrospectively selected samples from 9 dogs with SRE enrolled in a previous clinical trial, which received treatment for 8 weeks, and had achieved remission as indicated by the post-treatment clinical scores. Long-term (1 year) samples were obtained from a subset (5/9) of dogs. Samples from 13 healthy dogs were included as controls (HC). We evaluated the microbiome using 16S rRNA sequencing and qPCR. To evaluate the recovery of gut function, we measured fecal metabolites using an untargeted approach. While improvement was observed for some bacterial taxa after 8 weeks of treatment, several bacterial taxa remained significantly different from HC. Seventy-five metabolites were altered in dogs with SRE, including increased fecal amino acids and vitamins, suggesting malabsorption as a component of SRE. One year after treatment, however, all bacterial species were evaluated by qPCR and 16S rRNA gene sequencing, and all but thirteen metabolites were no longer different from healthy controls.
THE EFFECT OF FUEL PRICES ON FOOD PRICES IN KENYA

High food prices are one of the major risks facing households from developing countries. Food prices have attracted renewed interest among policy experts in identifying appropriate policy instruments to counter the effect of price vulnerability. This paper evaluates the effect of fuel prices on food prices by testing for Granger causality and cointegration applied to diesel, maize, beans, cabbage, and potatoes price data for the period 2010-2018. The results revealed a unidirectional Granger causality running from diesel prices to cabbage and potatoes prices but there was no causal relationship with maize and beans prices. The findings suggest that there is a long-run price relationship between perishable foods and fuel prices with an increase in the price of diesel resulting in a significant increase in the price of cabbages and potatoes. The study recommends a policy of cushioning an increase in food prices by introducing a tax relief once the fuel price hits a certain level.
International new ventures’ international performance: a matter of network entrepreneurial orientation and network management activities

Purpose – The purpose of this paper is to demostrate that commitment to developing knowledge sharing, coordination, adaptation and resolving potential conflict results in idiosyncratic relational assets for firms, which increases the benefits that international new ventures (INVs) can obtain from their networking activity. Design/methodology/approach – To test the theoretical model, a survey was conducted among a sample of INVs. The data obtained were examined with structural equation modelling using the maximum likelihood estimation procedure in linear structural relations software. Findings – The results showed positive effects of network entrepreneurial orientation (EO) on knowledge sharing, coordination, adaptation and resolving potential conflict, but only network coordination showed a positive effect on international performance. Research limitations/implications – The study introduces and extends EO to the network level and shows that it contributes to INVs’ international performance through its influence on the development of coordination activities among networked firms. Practical implications – The results provide guidance for building INVs’ networks. Entrepreneurs will find orientations about which partners could be more valuable to them. Originality/value – Little research has addressed the study of network management activities to create a network structure. This paper reveals howfirms’ volition and commitment to networking helps us to understand, in a fine-grained manner, how INVs gain benefits from their social networks. Additionally, EO at the network level is also studied, and arguments are proposed showing its relationships with the aforementioned relational activities based on the fact that entrepreneurial-oriented partners are supposed to bemore active in networking.
Finite element modelling of innovative interspinous process spacers for the lumbar spine

Low back pain is an emerging problem in society. This occurs as lumbar spine is a leaning site for spinal diseases to occur. Their diagnostic and treatment have a large impact in countries’ economics. The number of spinal fusion surgeries for treatment of spinal diseases as herniation, stenosis and spondylolisthesis has increased. Interspinous posterior fixation has been proposed as a less invasive alternative to older systems for these spinal procedures. The motivation for this thesis comes directly from that fact, the surgical procedure can be not efficient, thus new design proposals can be achieved with this type of studies, to facilitate whole process. This study had two main objectives that were achieved with the development of a new Finite Element model of L3-L5 spinal segment: i) study spinal degeneration process; ii) evaluation of a new design of innovative interspinous spacer to be inserted with lower risk for the patient, while having the same biomechanical performance as the existing device, Axle®. The FE analysis have shown that new devices designs can be considered without resulting in a lower biomechanical performance, confirming that the study and development of a new interspinous devices can be extremely important, as spinal surgery can become less invasive and facilitated procedure. This thesis has also achieved to add more knowledge to the spinal degeneration process studies. All these results can be an important basis for the future research on new models of degenerative spinal process, as in the study of new designs and/or improvements of Axle device.
Stable iPEPO Tensor-Network Algorithm for Dynamics of Two-Dimensional Open Quantum Lattice Models

Being able to describe accurately the dynamics and steady-states of driven and/or dissipative but quantum correlated lattice models is of fundamental importance in many areas of science: from quantum information to biology. An efficient numerical simulation of large open systems in two spatial dimensions is a challenge. In this work, we develop a tensor network method, based on an infinite Projected Entangled Pair Operator (iPEPO) ansatz, applicable directly in the thermodynamic limit. We incorporate techniques of finding optimal truncations of enlarged network bonds by optimising an objective function appropriate for open systems. Comparisons with numerically exact calculations, both for the dynamics and the steady-state, demonstrate the power of the method. In particular, we consider dissipative transverse quantum Ising and driven-dissipative hard core boson models in non-mean field limits, proving able to capture substantial entanglement in the presence of dissipation. Our method enables to study regimes which are accessible to current experiments but lie well beyond the applicability of existing techniques.
Real-Time Trajectory Planning for UAVs in Environments with Moving Obstacles

In the present work a real-time trajectory planning algorithm for multi-rotor is developed. The algorithm is capable of avoiding both static and moving unexpected obstacles, in real time. The trajectory-planner is capable of generating optimal trajectories regarding operational costs, which are given by a combination of the estimated energy consumption and total trajectory time. The algorithm was integrated with a simpliﬁed TCAS system, showing the capability of the autonomous path planner to follow protocols originally used in manned aircraft. The solution is based on a modiﬁed RRT algorithm and a trajectory optimization algorithm. Multiple basic simulations were performed to evaluate the real-time capabilities of the algorithms. A simulation in the physics engine Gazebo was also analysed to validate the capability of a realistic multi-rotor to follow aggressive trajectories generated by the proposed planner.
Indoor Panorama Planar 3D Reconstruction via Divide and Conquer

1 . Ground-truth H&V-plane annotation 1 1.1 . Local analysis . 1 1.2 . Horizontal planes extraction . 1 1.3 . Vertical planes extraction . 2 2 . Relation with panorama room layout 2 3 . Detailed network architecture 2 4 . Visualization of yaw-rotation ambiguity in 360° images 2 5 . V-planar orientation grouping 3 6 . More quantitative comparison of intermediate version 3 7 . More qualitative results reconstructed by our method. 3 8 . Qualitative comparisons with baselines 3
Fostering Sustainability and Critical Thinking through Debate—A Case Study

Transversal competences such as sustainability or critical thinking have become more important in the last decades in University teaching. The objective of this article is to assess the effectiveness of debate as a teaching method capable of fostering such competences in engineering students. To do this, a debate activity has been held facing two reasonable positions: Sustainable Development versus Degrowth. The research methodology consisted of performing this activity in the classroom (with 13 students in this particular case study), and conducting some tests before and after the debate that served as feedback to assess the effectiveness of the debate on the learning process. This feedback is composed of different documents, including a pre-post test, a specific survey about the debate, a pre-post knowledge survey and the Student Evaluation of Educational Quality (SEEQ) survey. A methodology for the organization of the debate is proposed and the answers of the students to these feedback documents are analyzed. As it can be inferred from the different surveys, the debate has been an efficient learning tool to acquire knowledge and to develop sustainability and critical thinking competences.
Radiological Monitor for Mobile Robots Operating in Scenarios with Nuclear Threats

Autonomous systems open up the possibility for automated field surveys and monitoring operations, which have important applications in scenarios involving chemical, biological, radiological, and nuclear threats, abbreviated as CBRN. The risk towards human lives in these situations can be mitigated or even neutralized by deploying robots carrying a suite of suitable sensors. This work proposes an informative path planner as an instrument to efficiently generate maps of scalar quantities (e.g., radiation intensity, chemical concentration), motivated by applications in radiological survey missions. The proposed path planner performs optimization in continuous space by combining concepts from information theory, Gaussian processes, and B-spline path parameterization. Results demonstrate that the informative path planner adapts to the environment and increases the number of observations made close to radioactive sources. Additionally, a maximum likelihood estimator for source position and activity was used to verify that the proposed solution increases the accuracy of the estimated source position.