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This work presents a point cloud dataset of vehicles passing through a toll station in Colombia to be used to train artificial vision and computational intelligence algorithms. This article details the process of creating the dataset, covering initial data acquisition, range information preprocessing, point cloud validation, and vehicle labeling. Additionally, a detailed description of the structure and content of the dataset is provided, along with some potential applications of its use. The dataset consists of 36,026 total objects divided into 6 classes: 31,432 cars, campers, vans and 2-axle trucks with a single tire on the rear axle, 452 minibuses with a single tire on the rear axle, 1158 buses, 1179 2-axle small trucks, 797 2-axle large trucks, and 1008 trucks with 3 or more axles. The point clouds were captured using a LiDAR sensor and Doppler effect speed sensors. The dataset can be used to train and evaluate algorithms for range data processing, vehicle classification, vehicle counting, and traffic flow analysis. The dataset can also be used to develop new applications for intelligent transportation systems. Dataset: The data presented in this study are openly available at: https://doi.org/10.5281/zenodo.10974361 Dataset License: Creative Commons Attribution 4.0 International [ABSTRACT FROM AUTHOR]

Type Ia supernovae are used as distance indicators to measure the expansion rate of the Universe and to constrain the nature of dark energy. Current and upcoming surveys will allow to extend supernova Hubble diagrams to higher redshifts and to improve further their statistics. It is accepted that Type Ia supernovae are thermonuclear explosions of carbon-oxygen white dwarfs in binary systems. However, the identification of their progenitors, the evolutionary path leading to the explosion and the explosion mechanism itself have not been identified yet. This is critical, as we need to understand the potential evolution of their luminosity with cosmic time and, thus, with their stellar progenitors. We will review the current situation, considering observational hints. We will focus on our recent models, that follow the evolution of carbon-oxygen white dwarfs accreting mass up to thermonuclear runaway, and on their dependence with the initial metallicity of the white dwarf progenitors.

The challenges of today's energy landscape, marked by the search for sustainable development, the expansion of coverage, and the diversification of the energy matrix, allow for electricity systems focusing on renewable energy resources. Microgrids are considered an efficient paradigm for managing distributed renewable energy generation and providing reliable access to electricity in remote areas where the grid has not been extended. However, their planning is a complex task that requires a thorough understanding of various multi-dimensional aspects and decision-making scenarios to define feasible and sustainable alternatives. In this context, this study presents a new planning framework based on a two-stage strategy. The strategy seeks to optimize the capacity of generation resources, considering the microgrid's operational knowledge in various scenarios and aspects related to its sustainability. The framework was evaluated through a case of planning a microgrid for a remote community in Vaupés, Colombia, considering the local energy potential and demand requirements. Twenty optimized alternatives were identified based on the best compromise levels achieved for a set of performance criteria in the technical, economic, environmental, and social dimensions. [ABSTRACT FROM AUTHOR]

Dravet syndrome (DS) is a severe childhood-onset epilepsy commonly due to mutations of the sodium channel gene SCN1A. Patients with DS have a high risk of sudden unexplained death in epilepsy (SUDEP), widely believed to be due to cardiac mechanisms. Here we show that patients with DS commonly have peri-ictal respiratory dysfunction. One patient had severe and prolonged postictal hypoventilation during video EEG monitoring and died later of SUDEP. Mice with an [Scn1a.sup.R1407X/+] loss-of-function mutation were monitored and died after spontaneous and heat-induced seizures due to central apnea followed by progressive bradycardia. Death could be prevented with mechanical ventilation after seizures were induced by hyperthermia or maximal electroshock. Muscarinic receptor antagonists did not prevent bradycardia or death when given at doses selective for peripheral parasympathetic blockade, whereas apnea, bradycardia, and death were prevented by the same drugs given at doses high enough to cross the blood-brain barrier. When given via intracerebroventricular infusion at a very low dose, a muscarinic receptor antagonist prevented apnea, bradycardia, and death. We conclude that SUDEP in patients with DS can result from primary central apnea, which can cause bradycardia, presumably via a direct effect of hypoxemia on cardiac muscle., Introduction Patients with epilepsy have a high risk of sudden death (1). The underlying mechanisms of this sudden unexplained death in epilepsy (SUDEP) are unknown, but it is believed that [...]

The mission of INISAV is to contribute to the reduction of risks and losses by pests without affecting the environment on a sustainable basis. Its management is based on a science model linked with environment and characterized by 4 phases: the planning of research’s, their implementation, validation and adoption in the agriculture practice. Three main results of research are described: Biological control Program (PBC), Pest Management Programs (PMP) and the Elimination of the use of methyl bromide. A national network of 251 Laboratories for reproduction of entomophagues and entomopathogens (CREE) was designed and implemented, together with 4 biopesticide production plants. Thirteen (13) biological products and technologies were created. Furthermore, pest management programs (PMP) were extended in more than 25 crops for conventional and agro-ecological systems, as well as adoption of PMP to replace methyl bromide. The impacts of the results to the environment, agricultural production, the country's economy, and rural communities, were confirmed by the significant reduction of imports of chemical pesticides, from 40 000 t in 1974 to about 3000 t in 2012. In 1988 the arable area benefited by bioproducts was 300 000 ha while in 2012 amounted to 1 354 000 ha. The elimination of 80 t of methyl bromide in tobacco, 35 t in the other sectors, the reduction of other agrochemicals, the incorporation of biological control applications and some management measures, are considered the main basis for sustainability in crops. At present, 72% of the total area planted is under applications of pesticides in PMP. Of these, 38% with only biological products, 34% the combination of biological and chemical pesticides and in the remaining 28% apply phytosanitary alternatives included in the pest management programs.

At present, the energy landscape of many countries faces transformational challenges driven by sustainable development objectives, supported by the implementation of clean technologies, such as renewable energy sources, to meet the flexibility and diversification needs of the traditional energy mix. However, integrating these technologies requires a thorough study of the context in which they are developed. Furthermore, it is necessary to carry out an analysis from a sustainable approach that quantifies the impact of proposals on multiple objectives established by stakeholders. This article presents a framework for analysis that integrates a method for evaluating the technical feasibility of resources for photovoltaic solar, wind, small hydroelectric power, and biomass generation. These resources are used to construct a set of alternatives and are evaluated using a hybrid FAHP-TOPSIS approach. FAHP-TOPSIS is used as a comparison technique among a collection of technical, economic, and environmental criteria, ranking the alternatives considering their level of trade-off between criteria. The results of a case study in Valle del Cauca (Colombia) offer a wide range of alternatives and indicate a combination of 50% biomass, and 50% solar as the best, assisting in decision-making for the correct use of available resources and maximizing the benefits for stakeholders. [ABSTRACT FROM AUTHOR]

The growing interest in deploying photovoltaic systems and achieving their benefits as sustainable energy supplier raises the need to seek reliable medium-term and long-term operations with optimal performance and efficient use of economic resources. Cleaning scheduling is one of the activities that can positively impact performance. This work proposes a methodological framework to define the optimal scheduling of the cleaning activities of photovoltaic systems. The framework integrates a forecast model of the performance ratio, including the environmental variables' effect. In addition, an economic analysis involving the economic losses and maintenance costs of cleaning is used. This framework is applied to a case study of a photovoltaic system located in Yumbo, Colombia. Based on the historical data on irradiance, active energy, temperature, rainfall, and wind speed, the obtained forecast model of the photovoltaic system's performance ratio in a 60-day horizon has a mean absolute percentage error lesser of than 11%. The next cleaning date is forecasted to be beyond the horizon in a 19-day range, which will decrease as time goes by. This framework was applied to historical data and compared to actual cleaning dates performed by the utility company. The results show a loss of USD 33.616 due to unnecessary, early, or late cleaning activities. [ABSTRACT FROM AUTHOR]

This research introduces an algorithm that automatically detects five primary emotions in individuals with Down syndrome: happiness, anger, sadness, surprise, and neutrality. The study was conducted in a specialized institution dedicated to caring for individuals with Down syndrome, which allowed for collecting samples in uncontrolled environments and capturing spontaneous emotions. Collecting samples through facial images strictly followed a protocol approved by certified Ethics Committees in Ecuador and Colombia. The proposed system consists of three convolutional neural networks (CNNs). The first network analyzes facial microexpressions by assessing the intensity of action units associated with each emotion. The second network utilizes transfer learning based on the mini-Xception architecture, using the Dataset-DS, comprising images collected from individuals with Down syndrome as the validation dataset. Finally, these two networks are combined in a CNN network to enhance accuracy. The final CNN processes the information, resulting in an accuracy of 85.30% in emotion recognition. In addition, the algorithm was optimized by tuning specific hyperparameters of the network, leading to a 91.48% accuracy in emotion recognition accuracy, specifically for people with Down syndrome. [ABSTRACT FROM AUTHOR]

Sudden unexpected death in epilepsy (SUDEP) has been linked to respiratory dysfunction, but the mechanisms underlying this association remain unclear. Here we found that both focal and generalized convulsive seizures (GCSs) in epilepsy patients caused a prolonged decrease in the hypercapnic ventilatory response (HCVR; a measure of respiratory CO2 chemoreception). We then studied Scn1aR1407X/1 (Dravet syndrome; DS) and Scn8aN1768D/1 (D/1) mice of both sexes, two models of SUDEP, and found that convulsive seizures caused a postictal decrease in ventilation and severely depressed the HCVR in a subset of animals. Those mice with severe postictal depression of the HCVR also exhibited transient postictal hypothermia. A combination of blunted HCVR and abnormal thermoregulation is known to occur with dysfunction of the serotonin (5-hydroxytryptamine; 5-HT) system in mice. Depleting 5-HT with para-chlorophenylalanine (PCPA) mimicked seizure-induced hypoventilation, partially occluded the postictal decrease in the HCVR, exacerbated hypothermia, and increased postictal mortality in DS mice. Conversely, pretreatment with the 5-HT agonist fenfluramine reduced postictal inhibition of the HCVR and hypothermia. These results are consistent with the previous observation that seizures cause transient impairment of serotonergic neuron function, which would be expected to inhibit the many aspects of respiratory control dependent on 5-HT, including baseline ventilation and the HCVR. These results provide a scientific rationale to investigate the interictal and/or postictal HCVR as noninvasive biomarkers for those at high risk of seizure-induced death, and to prevent SUDEP by enhancing postictal 5-HT tone. [ABSTRACT FROM AUTHOR]

This article presents a study based on evaluating different techniques to automatically recognize the basic emotions of people with Down syndrome, such as anger, happiness, sadness, surprise, and neutrality, as well as the statistical analysis of the Facial Action Coding System, determine the symmetry of the Action Units present in each emotion, identify the facial features that represent this group of people. First, a dataset of images of faces of people with Down syndrome classified according to their emotions is built. Then, the characteristics of facial micro-expressions (Action Units) present in the feelings of the target group through statistical analysis are evaluated. This analysis uses the intensity values of the most representative exclusive action units to classify people's emotions. Subsequently, the collected dataset was evaluated using machine learning and deep learning techniques to recognize emotions. In the beginning, different supervised learning techniques were used, with the Support Vector Machine technique obtaining the best precision with a value of 66.20%. In the case of deep learning methods, the mini-Xception convolutional neural network was used to recognize people's emotions with typical development, obtaining an accuracy of 74.8%. [ABSTRACT FROM AUTHOR]

The precise progenitor system of type Ia supernovae (SNe Ia), whether it is a white dwarf (WD) close to the Chandrasekhar limit or substantially less massive, has been a matter of debate for decades. Recent research by our group on the accretion and simmering phases preceding the explosion of a massive WD has shown that the central density at thermal runaway lies in the range 3.6 − 6.3 × 109 g cm−3 for reasonable choices of accretion rate on to the WD and progenitor metallicity. In this work, we have computed one-dimensional simulations of the explosion of such WDs, with special emphasis on the chemical composition of the ejecta, which in all cases is extremely rich in neutronized isotopes of chromium (54Cr) and titanium (50Ti). We show that, in order to reconcile such a nucleosynthesis with the isotopic abundances of the Solar system, Chandrasekhar-mass WDs can account for at most 26 per cent of normal-luminosity SNe Ia, or at most 20 per cent of all SNe Ia. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal sizing of islanded microgrids (MGs), which use diesel generators to supply energy in off-grid areas. The MG under study integrates photovoltaic (PV) and diesel generation, a battery energy storage System (BESS), and an inverter for the connection between AC and DC voltage buses. Levelised cost of energy (LCOE) and annual system cost (ASC) are considered economic indicators, while the loss of power supply probability (LPSP) is used as a reliability indicator. Fiscal incentives such as the tax benefits and accelerated depreciation applied in Colombia are considered for the optimally sizing of each MG element. Solar measurements were taken at a weather station located in the main campus of Universidad de Antioquia in Medellin, Colombia at a latitude of 6.10 and longitude of −75.38. The objective function is the minimization of the total energy delivered from the power sources that successfully meets the load. The model was implemented in Python programming language considering several scenarios. Two cases were evaluated: the first one considered PV panels, a BESS and a diesel generator, while the second one only considered PV panels and a BESS. The option that does not include the diesel generator turned out to be the most expensive, since additional PV and BESS resources are required to meet the load profile. Furthermore, it was found that the LCOE was lower when tax benefits were taken into account. [ABSTRACT FROM AUTHOR]

Silicone oil (SO) still represents the main choice for long‐term intraocular tamponade in complicated vitreoretinal surgery. This review compared the complications associated with the use of SO and other vitreous substitutes after pars plana vitrectomy in patients with different underlying diseases. Meta‐analysis was conducted in accordance with PRISMA guidelines. We retrieved randomized clinical trials (RCTs), retrospective case–control and cohort studies evaluating the risk of using SO, published between 1994 and 2020, conducting a computer‐based search of the following databases: PubMed, Web of Science, Scopus and Embase. Primary outcome was the rate of complications such as intraocular hypertension, retinal re‐detachment, unexpected vision loss or hypotony. Secondary outcome was to compare the rate of adverse events of different SO viscosities, especially emulsification. Forty‐three articles were included. There were significant differences in intraocular hypertension (p = 0.0002, OR = 1.66; 95% CI = 1.27–2.18) and the rate of retinal re‐detachment (p < 0.0009, OR = 0.65; 95% CI = 0.50–0.64) between SO and other agents, including placebo. However, there were no differences in other complication rates. Silicone oil (SO)‐emulsification rate was non‐significantly higher in low than high SO viscosity, and results from other complications were comparable in both groups. The high quality of most of the studies included in this study is noteworthy, which provides some certainty to the conclusions. Among them is the high variability of the SO residence time. The fact that ocular hypertension and not hypotension is related to SO use. A clear relationship is not found for the so‐called unexplained vision loss, which affects a significant percentage of eyes. Re‐detachment cases are less if SO is used and that surprisingly there does not seem to be a relationship in the percentage of emulsification between the low‐ and high‐viscosity silicones. All these data warrant more standardized prospective studies. [ABSTRACT FROM AUTHOR]

Summary: The Energy Management System (EMS) is a strong need for achieving cost‐effective, reliable, and pollution‐free operation of microgrids operating in isolated areas. Commonly, the EMS approaches use optimization and rolling‐horizon predictions. However, the inclusion of very short‐term predictions increases the complexity of the optimization, compromising its ability and reliability to operate online. In response to this problem, this paper proposes a rule‐based EMS (RB‐EMS) which uses neither optimization nor prediction horizons. Instead, the RB‐EMS evaluates historical data, and very short‐term predictions to calculate the setpoints of distributed energy resources, and schedulable loads in a cost‐effective, reliable, and sustainable manner. As a form of self‐regulation, the RB‐EMS introduces the Reliability Index to calculate the maximum state of charge of the energy storage system. Given the speed imposed by the very short‐term predictions, the RB‐EMS also introduces a start‐up manager for diesel units that present a dead time in their enlistment. The isolated ESUSCON‐HUATACONDO microgrid was used as a testbed for performance validation. The work shows a first scenario comparing the RB‐EMS against a rolling horizon EMS based on MILP optimization. A second scenario shows the energy management performance with 1 minute predictions. The better results obtained, and the computational simplicity of the RB‐EMS allows propose it to achieve faster and more reliable energy management in other isolated microgrids. [ABSTRACT FROM AUTHOR]

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Vision is the principal source of information of the surrounding world. It facilitates our movement and development of everyday activities. In this sense, blind people have great difficulty for moving, especially in unknown environments, which reduces their autonomy and puts them at risk of suffering an accident. Electronic Travel Aids (ETAs) have emerged and provided outstanding navigation assistance for blind people. In this work, we present the methodology followed for implementing a stereo vision-based system that assists blind people to wander unknown environments in a safe way, by sensing the world, segmenting the floor in 3D, fusing local 2D grids considering the camera tracking, creating a global occupancy 2D grid, reacting to close obstacles, and generating vibration patterns with an haptic belt. For segmenting the floor in 3D, we evaluate normal vectors and orientation of the camera obtained from depth and inertial data, respectively. Next, we apply RANSAC for computing efficiently the equation of the supporting plane (floor). The local grids are fused, obtaining a global map with data of free and occupied areas along the whole trajectory. For parallel processing of dense data, we leverage the capacity of the Jetson TX2, achieving high performance, low power consumption, and portability. Finally, we present experimental results obtained with ten (10) participants, in different conditions, with obstacles of different height, hanging obstacles, and dynamic obstacles. These results show high performance and acceptance by the participants, highlighting the easiness to follow instructions and the short period of training. [ABSTRACT FROM AUTHOR]

Planning and operation of smart energetic projects have increased in complexity and requires a deeper analysis. The inclusion of renewable energy and distributed generation sources lead to an inherent difficulty to define appropriate models for decision-making. Nowadays, decisions in planning and operation are made level by level due to a disarticulation of information within the electrical sector and the stakeholders. Therefore, here is proposed an architectural decision model of multilevel-multiobjective to solve this problem. In this scheme, the problem of energetic assets sizing is on the upper level, while the problem of operation and dispatch of unit power is on the lower level. Both levels are related through quantitative tools (key performance indicators (KPI)) and qualitative tools (quality function deployment (QFD) and hierarchical analytical process (AHP)). This model was applied in the Yucatán peninsula. As a result, an integral solution with a lower cost and better environmental factor than the baseline for the region was obtained. [ABSTRACT FROM AUTHOR]

Recent advances in remote sensing have shown great potential for different kinds of applications such as vegetation and crop supervision. Using hyperspectral image processing, this process can be used to assess large areas of land, allowing for a fast and non-invasive analysis of variables such as water stress, diseases, and type of crops. In this context, representative spatial-spectral features are crucial for developing the data classification process of hyperspectral remote sensing images. In this way, extended attribute profiles (EAPs) are a powerful tool for efficiently capturing spatial and spectral properties. In addition, deep learning networks have shown a remarkable feature extraction performance in many applications, such as in the processing of hyperspectral images. In this paper, a data classification model is proposed that uses EAPs and an inception network to generate deep spatial-spectral features, which are labelled using a Riemannian classifier as a fine-tuning stage. This model was validated using three different datasets. Results obtained shows that the classification strategy proposed in this paper has better performance regarding the results generated by a traditional model of stacked convolutional neural networks (CNNs). Quantitatively speaking, the overall accuracy for the experiments with the proposed model and the tested data sets is greater than 99%. [ABSTRACT FROM AUTHOR]

Planning and operation of Smart energetic have become more complex to analyse due to structural changes in the energy sector. The inclusion of distributed generation sources, generation with renewable sources, storage systems, and the dislocation of information between the different organization levels and actors lead to the inherent difficulty of defining appropriate models that help decision making. Nowadays, decisions in planning and operation are made level by level rather than integrated manner. To address this problem, this work proposes a multi-level methodological framework based on Key Performance Indicators and System of Systems concepts. Involving methods, both quantitative and qualitative, this work serves as guidance to managers, planners, or political decision-makers from any electrical enterprise to help them find suitable solutions for planning and operation of smart grid projects. [ABSTRACT FROM AUTHOR]

Background: Children with cerebral palsy have difficulty to sit, stand, walk, run and jump independently. Therapy is an important factor in improving these aspects, and if applied in early intervention treatments, when the child is growing, it could have many benefits. These therapies require intensive and extended sessions, which in turn demand dedication and effort. New strategies that provide interesting and motivating interventions are often incorporated to improve the participation and performance of the children in the therapies. Therapies using social assistive robots can be alternative and complementary methods to promote the participation and motivation of children with cerebral palsy. Methods: The objective of this work is to validate the effectiveness of a 16-session physical therapy program to improve the participation and fulfillment of therapeutic objectives on an 8 year-old boy with dyskinetic cerebral palsy for motor learning to walk using a social assistive robot. The therapy program was carried out through a methodological proposal that uses SMART objectives (Specific, Measurable, Achievable, Realistic and Timed), Goal-Directed Therapy (GDT) and its evaluation through Goal Attainment Scaling (GAS). Results: A NAO robot was used as a social assistive robot to support a physical therapy for a child with cerebral palsy. In this work, it was observed that the motivation generated by the interaction with the social assistive robot facilitated the persistence in the walking and the fulfillment of the objectives. Conclusion: Using humanoid robots as social assistive robots may benefit therapeutic processes on children with motor disabilities. The methodology developed provides a formal way to achieve objectives in therapeutic processes for children with cerebral palsy. It requires researchers to conduct more studies to validate the potential of the use of social robots in therapeutic interventions that promote development in children with motor disabilities, such as cerebral palsy. Promoting the use of new technologies in therapeutic processes such as humanoid robots allows us to create new strategies to know the impact of this technology in the area of rehabilitation. The use of formal methodologies focused on the patient, along with multidisciplinary teams, could increase the possibilities of using social robots to improve cognitive and motor outcomes in children with cerebral palsy. The formulation of SMART objectives and their quantification through the GAS scale can be used as recommendations to improve the formulation of goals in therapeutic interventions for children with cerebral palsy. [ABSTRACT FROM AUTHOR]