Miracle-dz/UniFAR-FTUs huggingface dataset:
Joint Operation between a PSO-Based Global MPP Tracker and a PV Module Array Configuration Strategy under Shaded or Malfunctioning Conditions

This paper aims to present a smart, particle swarm optimization (PSO)-based, real time configuration strategy for a photovoltaic (PV) module array in the event of shadow cast on a PV module(s) and/or module failure as an effective approach to power generation efficiency elevation. At the first step, the respective maximum output power levels provided by a normal operating array at various levels of irradiation and module surface temperatures are measured and entered as references into a database. Subsequently, the maximum output power (MPP) level, tracked by a MPP tracker, is feedbacked for a comparison with an aforementioned reference as a way to tell whether there is either a shadow or a malfunction event on a PV module(s). Once an abnormal operation is detected, the presented smart configuration algorithm is performed to reconfigure the PV module array such that the array is operated at the global MPP as intended. Furthermore, by use of a PIC microcontroller that is a family of microcontrollers made by Microchip Technology for compact implementation, this study is experimentally validated as an effective approach to locating the global MPP at all events.
A review of routing protocols in internet of vehicles and their challenges

Purpose This paper is a review of a number routing protocols in the internet of vehicles (IoV). IoV emphasizes information interaction among humans, vehicles and a roadside unit (RSU), within which routing is one of the most important steps in IoV network. Design/methodology/approach In this paper, the authors have summarized different research data on routing protocols in the IoV. Several routing protocols for IoV have been proposed in the literature. Their classification is made according to some criteria such as topology-based, position-based, transmission strategy and network structure. This paper focuses on the transmission strategy criteria. There exist three types of protocols that are based on this strategy: unicast protocol, broadcast protocols and multicast protocols. This later type is classified into two subclasses: geocast and cluster-based protocols. The taxonomy of the transmission strategy is presented in this study. We discuss the advantages and disadvantages of each type with a general comparison between the five types. Findings The authors can deduce that many challenges are encountered when designing routing protocols for IoV. Originality/value A simple and well-explained presentation of the functioning of the IoV is provided with a comparison among each categories of protocols is well presented along with the advantages and disadvantages of each type. The authors examined the main problems encountered during the design of IoV routing protocol, such as the quick change of topology, the frequent disconnection, the big volume of data to be processed and stored in the IoV, and the problem of network fragmentation. This work explores, compares existing routing protocols in IoV and provides a critical analysis. For that, the authors extract the challenges and propose future perspectives for each categories of protocols.
Desigualdad de oportunidades en el ingreso a la licenciatura de la UNAM por concurso de selección: reconfiguraciones y persistencias

El objetivo del artículo es analizar los efectos de las características sociodemográficas, los antecedentes escolares y los orígenes sociales de los aspirantes sobre el ingreso a la licenciatura de la Universidad Nacional Autónoma de México (UNAM) por concurso de selección. El artículo se inscribe en la perspectiva sociológica de la estratificación social y la desigualdad de oportunidades. La información proviene de la Hoja de Datos Estadísticos de la UNAM y la estrategia de análisis descansa en un modelo de regresión logística. Los resultados muestran que el ingreso por concurso de selección continúa siendo inequitativo y resaltan la necesidad de pensar en mecanismos alternativos orientados a reducir la desigualdad.
A Physiological-Signal-Based Thermal Sensation Model for Indoor Environment Thermal Comfort Evaluation

Traditional heating, ventilation, and air conditioning (HVAC) control systems rely mostly on static models, such as Fanger’s predicted mean vote (PMV) to predict human thermal comfort in indoor environments. Such models consider environmental parameters, such as room temperature, humidity, etc., and indirect human factors, such as metabolic rate, clothing, etc., which do not necessarily reflect the actual human thermal comfort. Therefore, as electronic sensor devices have become widely used, we propose to develop a thermal sensation (TS) model that takes in humans’ physiological signals for consideration in addition to the environment parameters. We conduct climate chamber experiments to collect physiological signals and personal TS under different environments. The collected physiological signals are ECG, EEG, EMG, GSR, and body temperatures. As a preliminary study, we conducted experiments on young subjects under static behaviors by controlling the room temperature, fan speed, and humidity. The results show that our physiological-signal-based TS model performs much better than the PMV model, with average RMSEs 0.75 vs. 1.07 (lower is better) and R2 0.77 vs. 0.43 (higher is better), respectively, meaning that our model prediction has higher accuracy and better explainability. The experiments also ranked the importance of physiological signals (as EMG, body temperature, ECG, and EEG, in descending order) so they can be selectively adopted according to the feasibility of signal collection in different application scenarios. This study demonstrates the usefulness of physiological signals in TS prediction and motivates further thorough research on wider scenarios, such as ages, health condition, static/motion/sports behaviors, etc.
Monitoring Network Telescopes and Inferring Anomalous Traffic Through the Prediction of Probing Rates

Network reconnaissance is the first step preceding a cyber-attack. Hence, monitoring the probing activities is imperative to help security practitioners enhancing their awareness about Internet’s large-scale events or peculiar events targeting their network. In this paper, we present a framework for an improved and efficient monitoring of the probing activities targeting network telescopes. Particularly, we model the probing rates which are a good indicator for measuring the cyber-security risk targeting network services. The approach consists of first inferring groups of network ports sharing similar probing characteristics through a new affinity metric capturing both temporal and semantic similarities between ports. Then, sequences of probing rates targeting similar ports are used as inputs to stacked Long Short-Term Memory (LSTM) neural networks to predict probing rates 1 hour and 1 day in advance. Finally, we describe two monitoring indicators that use the prediction models to infer anomalous probing traffic and to raise early threat warnings. We show that LSTM networks can accurately predict probing rates, outperforming the non-stationary autoregressive model, and we demonstrate that the monitoring indicators are efficient in assessing the cyber-security risk related to vulnerability disclosure.
Statistical Properties and Applications of Empirical Mode Decomposition

Signal analysis is key to extracting information buried in noise. The decomposition of signal is a data analysis tool for determining the underlying physical components of a processed data set. However, conventional signal decomposition approaches such as wavelet analysis, Wagner-Ville, and various short-time Fourier spectrograms are inadequate to process real world signals. Moreover, most of the given techniques require \emph{a prior} knowledge of the processed signal, to select the proper decomposition basis, which makes them improper for a wide range of practical applications. Empirical Mode Decomposition (EMD) is a nonparametric and adaptive basis driver that is capable of breaking-down non-linear, non-stationary signals into an intrinsic and finite components called Intrinsic Mode Functions (IMF). In addition, EMD approximates a dyadic filter that isolates high frequency components, e.g. noise, in higher index IMFs. Despite of being widely used in different applications, EMD is an ad hoc solution. The adaptive performance of EMD comes at the expense of formulating a theoretical base. Therefore, numerical analysis is usually adopted in literature to interpret the behavior. This dissertation involves investigating statistical properties of EMD and utilizing the outcome to enhance the performance of signal de-noising and spectrum sensing systems. The novel contributions can be broadly summarized in three categories: a statistical analysis of the probability distributions of the IMFs and a suggestion of Generalized Gaussian distribution (GGD) as a best fit distribution; a de-noising scheme based on a null-hypothesis of IMFs utilizing the unique filter behavior of EMD; and a novel noise estimation approach that is used to shift semi-blind spectrum sensing techniques into fully-blind ones based on the first IMF. These contributions are justified statistically and analytically and include comparison with other state of art techniques.
REFRIGERACIÓN DE FRUTOS DE ZAPOTE MAMEY [Pouteria sapota (Jacq.) H. E. Moore & Stearn] COSECHADOS EN DIFERENTES FECHAS EN TABASCO, MÉXICO

En Jalpa de Méndez, Tabasco, se cosecharon frutos de zapote mamey [Pouteria sapota (Jacq.) H. E. Moore & Stearn] en madurez fisiológica, en cuatro fechas (15 y 22 de abril, 5 y 12 de mayo de 2004), para determinar su comportamiento poscosecha después de almacenarlos a 12 °C y 95 % HR, por 14, 21, 28 y 35 d; adicionalmente, en cada fecha se mantuvo un grupo de frutos a temperatura ambiente (30 ± 2 °C), el cual se utilizó como testigo. En condiciones de laboratorio se midió: días a madurez comercial, velocidad de respiración, producción de etileno, concentración de azúcares totales y color de la pulpa (ángulo matiz). Los frutos testigo maduraron a los 4 ó 5.6 d después de cosechados; los frutos almacenados a 12 °C por 14 y hasta 35 d maduraron 4 ó 1 d después de salir del almacenamiento. La maduración de frutos cosechados después del 15 de abril fue 1 ó 2 d más rápida. En las primeras dos cosechas se registró un patrón climatérico, con máximos de producción de CO2 entre 219 y 365 mL kg-1 h-1 y de etileno entre 161 y 503 µL kg-1 h-1; en las dos últimas fechas de cosecha sólo se detectó la fase postclimatérica. Los azúcares totales incrementaron su concentración 3 ó 5 veces durante la maduración, y en esta variable no hubo efecto de la fecha de cosecha. El ángulo matiz del color fue superior en los frutos cosechados en abril, con valores entre 73.3 y 80.6, que los cosechados en mayo cuyos valores fueron de 61.2 a 71.6. El almacenamiento a 12 °C no causó daños por frío y permitió incrementar la vida poscosecha por más de 28 d. El comportamiento poscosecha difirió entre fechas de cosecha.
A Comprehensive Review of Recent Progress on GaN High Electron Mobility Transistors: Devices, Fabrication and Reliability

GaN based high electron mobility transistors (HEMTs) have demonstrated extraordinary features in the applications of high power and high frequency devices. In this paper, we review recent progress in AlGaN/GaN HEMTs, including the following sections. First, challenges in device fabrication and optimizations will be discussed. Then, the latest progress in device fabrication technologies will be presented. Finally, some promising device structures from simulation studies will be discussed.
Périodicité, cadences et fiction en régime sériel (1900-1970)

Article abstract Through the study of the situation of popular publisher series that were born in France at the beginning of the 20th century (and whose mutations we will follow until the 1960s), we wish to highlight the relationship that exists between the interval (as a temporal and spatial characteristic of serialized goods), its media formulation in popular publisher series (through the question of periodicity and series), and a certain number of thematic, narrative and diegetic phenomena which result from it. Narrative and diegetic properties can only be understood in the broader framework of the media and market serialities at their origin since they are their poetic and aesthetic reformulation. Indeed, media practices have undergone significant transformations. These transformations corresponded to the new logic of standardization, serialization and brand enhancement induced by the new Abstract Through the study of the situation of popular publisher series that were born in France at the beginning of the 20th century (and whose mutations we will follow until the 1960s), we wish to highlight the relationship that exists between the interval (as a temporal and spatial characteristic of serialized goods), its media formulation in popular publisher series (through the question of periodicity and series), and a certain number of thematic, narrative and diegetic the texts (their poetics) and that of apprehending them (the aesthetic relationship that the works engage).
Proximate Chemical Composition, Amino Acids Profile and Minerals Content of Meat Depending on Carcass Part, Sire Genotype and Sex of Meat Rabbits

Simple Summary Rabbit meat is popular with consumers mainly for its high-quality protein. Among the main factors influencing the characteristics of rabbit meat proteins to appertain the breed, genotype, carcass part and age. Conventional production of rabbit meat in many European countries is mainly ensured by intensive production systems, when commercial meat-type albinotic rabbit crossbreds are used. However, spotted and solidly coloured lines of meat rabbit breeds have begun to be used in rabbit breeding schemes as some consumers have begun to negatively perceive meat from albinotic coloured rabbits. The aim of the present study was to assess the effects of the sire genotype, sex and carcass part on the composition of meats of rabbits fattened under conditions where no synthetic drugs were used. Crossbreeding of Mecklenburger Schecke sires with a commercial dam line of HYLA rabbits resulted in a worse nutritional quality of meat proteins in progeny. These findings point to a possible risk of alterations in the nutritional quality of meat proteins when using different rabbit sire genotypes than those originally intended for the specific commercial crossbreeding scheme. Abstract The aim of the study was to assess the effects of the sire genotype, sex and carcass part on the composition of the meat of rabbits, which were fattened under conditions where no synthetic drugs were used. As for carcass parts, the higher content of both total amino acids (AA) and all essential AA (EAA) monitored was found in the Longissimus thoracis et lumborum (LTL) muscle as compared to hind leg meat (p ˂ 0.001). Significant effects of the rabbit sire genotype and the genotype x sex interaction on proportions of some AA in meat were found (p ˂ 0.001). Crossbreeding of the Mecklenburger Schecke (MS) sires with a commercial dam line of HYLA rabbits resulted in a lower proportion of the total AA and all EAA monitored in meats of MS sired males as compared to MS sired females and HYLA rabbits (p ˂ 0.05). The sex-related effect on AA profile was not so noticeable in final commercial crossbreds of HYLA rabbits when compared to MS sired progeny. These findings point to a possible risk of alterations in the nutritional quality of meat proteins when using different rabbit sire genotypes than those originally intended for the specific commercial crossbreeding scheme. However, on the contrary, higher contents of magnesium (p ˂ 0.05), manganese (p ˂ 0.001) and zinc (p ˂ 0.05) were found in meats of MS sired progeny as compared to HYLA rabbits.
Gradient estimation for smooth stopping criteria

Abstract We establish sufficient conditions for differentiability of the expected cost collected over a discrete-time Markov chain until it enters a given set. The parameter with respect to which differentiability is analysed may simultaneously affect the Markov chain and the set defining the stopping criterion. The general statements on differentiability lead to unbiased gradient estimators.
Interlingua-Based Numeral Translation In Web-Application With Knowledge-Testing

The purpose of this research is developing an Interlingua-based technique of the natural language numeral processing and translation. We propose a three-level generalized numeral model as Interlinguarepresentation. The model formal grammar describes the natural language numeral structure. The first level grammar rules define that a numeral consists of sign, integer part, separation symbol, and fractional part. The second level decribes numeral integer part as a triad sequence. The third level defines the triad structure. We developed number-into-numeral, numeral-into-number, and translating algorithms based on the model. The algorithms are implemented in the Markov normal algorithms. We realized the model and the algorithms in web-application in the Internet. The web-application has a knowledge-testing function. The function allows to users test numeral convering and translating knowledge. Users from more than 100 countries visit web-application and convert numerals. The largest number of users resides in the US and the Russian Federation. The web-application log contains more than 200,000 records. The largest number of user requests related to the conversion of cardinal numerals of Spanish. The web-application is integrated in toolbox of a complex linguistic web-portal for translators as well. We conclude the Interlgua-based technique is effective for numeral processing and translation and realization in webapplications. © 2017 Published by Future Academy www.FutureAcademy. org.UK
Experimental measurements for evaluation of the network throughput of the RC4 channel in the in-home PLC network

Abstract For the expansion of in-home PLC networks, it is necessary to have a detailed knowledge of the PLC transmission channel properties. This contribution shortly discusses theoretical characteristics of the RC4 reference channel. A main part is focused on the network throughput and on the interphase PLC signal liaising presented in the RC4 reference channel for higher frequencies than 10 MHz. For evaluation of considered transmission parameters, experimental measurements related to various noise sources and to interactions between different phase conductors are realized in the real in-home PLC network.
Resveratrol-Mediated Reversal of Doxorubicin-Induced Osteoclast Differentiation

Secondary osteoporosis has been associated with cancer patients undertaking Doxorubicin (DOX) chemotherapy. However, the molecular mechanisms behind DOX-induced bone loss have not been elucidated. Molecules that can protect against the adverse effects of DOX are still a challenge in chemotherapeutic treatments. We investigated the effect and mechanism of DOX in osteoclast differentiation and used the Sirt 1 activator resveratrol (RES) to counteract DOX-induced effects. RAW 264.7 cells were differentiated into osteoclasts under cotreatment with DOX and RES, alone or combined. RES treatment inhibited DOX-induced osteoclast differentiation, reduced the expression of osteoclast fusion marker Oc-stamp and osteoclast differentiation markers Rank, Trap, Ctsk and Nfatc1. Conversely, RES induced the upregulation of antioxidant genes Sod 1 and Nrf 2 while DOX significantly reduced the FoxM1 expression, resulting in oxidative stress. Treatment with the antioxidant MitoTEMPO did not influence DOX-induced osteoclast differentiation. DOX-induced osteoclastogenesis was studied using the cathepsin-K zebrafish reporter line (Tg[ctsk:DsRed]). DOX significantly increased ctsk signal, while RES cotreatment resulted in a significant reduction in ctsk positive cells. RES significantly rescued DOX-induced mucositis in this model. Additionally, DOX-exposed zebrafish displayed altered locomotor behavior and locomotory patterns, while RES significantly reversed these effects. Our research shows that RES prevents DOX-induced osteoclast fusion and activation in vitro and in vivo and reduces DOX-induced mucositis, while improving locomotion parameters.
Decreased Expression of CIRP Induced by Therapeutic Hypothermia Correlates with Reduced Early Brain Injury after Subarachnoid Hemorrhage

Early brain injury is considered to be a primary reason for the poor prognosis of patients suffering from subarachnoid hemorrhage (SAH). Due to its pro-inflammatory activity, cold-inducible RNA-binding protein (CIRP) has been implicated in the ischemic brain insult, but its possible interplay with hypothermia in SAH treatment remains to be evaluated. One-hundred and thirty-eight Sprague-Dawley rats (300–350 g males) were randomly allocated into the following groups: sham-operated (Sham); SAH; and SAH + hypothermia (SAH + H), each comprised of 46 animals. After treatments, the brain tissues of the three groups were randomly collected after 12 h, 1 d, 3 d, and 7 d, and the expression levels of the CIRP and mitochondrial apoptosis pathway-related proteins Bax, Bcl-2, caspase-9, caspase-3, and cytochrome c measured using Western blotting and real-time PCR. Brain damage was assessed by TUNEL and Nissl staining, the electron microscopy of brain tissue slices as well as functional rotarod tests. Expression of CIRP, Bax, caspase-9, caspase-3, and cytochrome c as well as reduced motor function incidence were higher in the SAH group, particularly during the first 3 d after SAH induction. Hypothermia blunted these SAH responses and apoptosis, thereby indicating reduced inflammatory signaling and less brain cell injury in the early period after SAH. Hypothermia treatment was found to effectively protect the brain tissue from early SAH injury in a rat model and its further evaluation as a therapeutic modality in SAH patients requires further study.
Short-term temperature prediction based on hybrid CEEMDAN, neural networks, and linear regression methods

Weather prediction is one problem of great importance in social life. To predict the future temperature with past data and without computationally intense physical modeling, a hybrid machine learning-based prediction model that combines the empirical mode decomposition (EMD), linear regression, and two different neural networks is proposed in this project. Since the temperature is a timeseries data, the periodic patterns in the data are extracted into intrinsic mode functions (IMFs) with the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and the future values of individual IMFs are forecasted by learning past patterns with the long short-term memory (LSTM) and multilayer perceptron (MLP) models for each IMFs. Linear regression is also used to predict the change in the nonperiodic trend. The predictions are added together to construct the result. By comparing with the actual test set results and the errors of other models, experiments show that this proposed model displays good performance in temperature prediction.