Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic early detection of decay in citrus fruit using optical technologies and machine learning techniques

Los citricos representan el cultivo frutal de mayor valor en terminos de comercio internacional, siendo Espana el primer exportador mundial de citricos para consumo en fresco. Sin embargo, la presencia de podredumbres causadas por hongos del genero Penicillium se encuentra entre los principales problemas que afectan la postcosecha y comercializacion de citricos. Un numero reducido de frutas infectadas puede contaminar una partida completa de citricos durante el almacenamiento de la fruta por largos periodos de tiempo o en el transporte al extranjero, lo que conlleva grandes perdidas economicas y el desprestigio de los productores de citricos. Por lo tanto, la deteccion temprana de infecciones por hongos de forma efectiva y la eliminacion de la fruta infectada son asuntos de especial interes en los almacenes de confeccion de fruta para impedir la propagacion de las infecciones fungicas, asegurando de esta forma una excelente calidad de la fruta y la ausencia total de fruta infectada. En este sentido, la presente tesis doctoral se centra en abordar un reto tan importante para la industria citricola como es la automatizacion del proceso de deteccion de podredumbres incipientes, con el fin de proporcionar alternativas a la inspeccion manual con peligrosa luz ultravioleta que permitan realizar esta deteccion de forma mas eficiente y, en consecuencia, reducir potencialmente el uso de fungicidas. En concreto, esta tesis doctoral avanza en el campo de la deteccion automatica de podredumbres en citricos mediante sistemas opticos y tecnicas de aprendizaje automatico. Especificamente, se investigan tres tecnicas opticas diferentes que operan en las regiones del visible e infrarrojo cercano del espectro electromagnetico, incluyendo la tecnica de imagen basada en backscattering, vision hiperespectral y espectroscopia. Los sistemas opticos usados en esta tesis no estan limitados a la parte visible del espectro, por lo que sus capacidades superan a las del ojo humano y a las de los sistemas de vision convencionales basados en camaras de color, lo cual resulta de especial interes para detectar danos en citricos que son dificilmente visibles a simple vista, como las podredumbres en estadios tempranos de infeccion. Ademas, se exploran numerosas tecnicas de aprendizaje automatico de reduccion de la dimensionalidad de los datos y clasificacion, con la finalidad de usar las medidas opticas de los citricos para discriminar la fruta afectada por podredumbre de la fruta sana. Las tres tecnicas opticas, junto con metodos de aprendizaje automatico adecuados, proporcionan buenos resultados en la clasificacion de la piel de los frutos citricos en sana o podrida, consiguiendo un porcentaje de muestras bien clasificadas superior al 90% para ambas clases, a pesar de la gran similitud entre ellas. En vista de los resultados obtenidos, esta tesis doctoral sienta las bases para la futura implementacion de las tecnicas opticas estudiadas en un sistema comercial de clasificacion automatica de fruta destinado a la deteccion de podredumbres en citricos.
Mycelial Biomass Production and Antioxidant Activity of Lentinus tigrinus and Lentinus sajor-caju in Indigenous Liquid Culture

With the increasing demand of functional foods with antioxidant properties, it is necessary to establish new sources such as wild edible mushrooms which could provide beneficial effects to human health. Herein, we evaluated the different indigenous liquid culture media for biomass production and elucidated the free radical scavenging activity and total phenolics of Lentinus tigrinus and Lentinus sajor-caju. Results revealed that L. tigrinus efficiently grew on rice bran decoction which significantly recorded the highest yield of mycelia (11.53 g), volume loss of the medium (24.33 ml), radical scavenging activity (18.94%) and total phenolics (26.59 mg AAE/g sample). Similarly, rice bran decoction significantly registered the highest yield of mycelia (9.75 g), volume loss of the medium (20.95 ml), scavenging activity (16.94%) and total phenolics (25.60 mg AAE/g sample) for L. sajor-caju. Both species also showed considerable antioxidant properties when cultured in coconut water, corn grit decoction and potato broth. Hence, it is noteworthy that both studied Lentinus species hold promising antioxidants which are influenced by different liquid media.
Face recognition method based on multi-scale adaptive LDA

The facial feature extraction based on linear discriminant analysis(LDA) method is deeply influenced by high frequency information,such as lighting condition,gesture.So that some low frequency characters of an image are lost,but these characters may contain important discriminant information.Also,human face recognition is small sample size problem,it will degrade within-class scatter matrix greatly.To solve the two problems,a method based on multi-scale adaptive linear discriminant analysis(MA-LDA) is proposed,and some experiments are done based on ORL and Yale human face image database.The experimental results performed on Matlab show this method achieves better performance than traditional methods.
Cerebral Autoregulation is Impaired During Deep Hypothermia-A Porcine Multimodal Neuromonitoring Study.

Effects of brain temperature modulation on cerebral hemodynamics are unclear. We aimed at investigating changes of dynamic cerebral autoregulation (AR) indices during induction of deep hypothermia (HT) in a porcine model mimicking the clinical scenario of accidental HT. Thirteen pigs were surface-cooled to a core temperature of 28°C. High-frequency monitoring included brain temperature, mean arterial blood pressure (MAP), intracranial pressure (ICP), brain tissue oxygen tension (PbtO2), and regional oxygen saturation (rSO2) assessed by near-infrared spectroscopy to calculate AR-indices (pressure reactivity index [PRx], oxygen reactivity index [ORx], and cerebral oximetry index [COx]). Brain temperature decreased from 39.3°C ± 0.8°C to 28.8°C ± 1.0°C within a median 160 minutes (interquartile range 146-191 minutes), reflecting a rapid induction of deep HT (-4°C/h). MAP and cerebral perfusion pressure (CPP) remained stable until a brain temperature of 35°C (69 ± 8 mmHg, 53 ± 7 mmHg) and decreased to 58 ± 17 mmHg and 40 ± 17 mmHg at 28°C (p = 0.031 and p = 0.015). Despite the decrease in MAP and CPP, brain oxygenation increased (PbtO2: +5 mmHg, p = 0.037; rSO2: +7.3%, p = 0.029). There was no change in ICP during HT induction. Baseline AR-indices reflected normal cerebral AR and did not change until a brain temperature of 34°C (ORx), 33°C (PRx), and 30°C (COx). At lower temperature, AR-indices increased (PRx: p < 0.001, ORx: p = 0.02, COx: p = 0.03), reflecting impaired cerebral AR. Cerebrovascular reactivity is impaired at lower brain temperature levels. Although these temperatures are usually not targeted in clinical routine, this should be kept in mind when treating patients with accidental deep HT.
Spectral Methods in Spatial Statistics

When the spatial location area increases becoming extremely large, it is very difficult, if not possible, to evaluate the covariance matrix determined by the set of location distance even for gridded stationary Gaussian process. To alleviate the numerical challenges, we construct a nonparametric estimator called periodogram of spatial version to represent the sample property in frequency domain, because periodogram requires less computational operation by fast Fourier transform algorithm. Under some regularity conditions on the process, we investigate the asymptotic unbiasedness property of periodogram as estimator of the spectral density function and achieve the convergence rate.
Lowering the bleeding risk in intra-arterial thrombolysis without losing efficiency by excluding adjunctive heparin?

Patients with cryptogenic polyneuropathies should be screened for OSA Chronic sensory or sensorimotor polyneuropathy is a common clinical problem referred to neurologists. Even with modern diagnostic approaches, up to one third of them remain unclassified.1,2 Recent studies have provided insight into their clinical spectrum and course. The age of onset tends to be in the sixth …
TUMOUR SUPPRESSOR GENE (P53) MODULATES LIF ACTIVITY - A CAUSATIVE FACTOR FOR THE DEVELOPMENT OF NEURAL TUBE DEFECTS IN INDIA

Neural tube defect (NTDs) is the most severe from of congenital malformation of central nervous system leading to infant mortality and morbidity. The most common form of NTDs is anencephaly (spina bifida) and myelomeningocele. The incidence in the population varies depending on racial/ethical variation including genetic and unknown environmental factors. The etiopathology of NTDs is still unknown be- tween two different gene, hence present study has been designed with the aims to evaluate gene mutation between tumour suppressor gene and leukaemia inhibitory factor during organogenesis. We have collected the blood samples (n= 43) of clinically diagnosed NTDs with respec- tive controls (n= 46) for PCR based DNA analysis to evaluate the frequency of p53 and LIF gene mutation using specific primers. Present findings reveals that highly significant differences (P<0.05) were observed in LIF gene using two different exons confirming p53 gene inactiva- tion influences neural ectoderm proliferation result increased "risk factor" for the development of NTDs. Keywords- Neural tube defect, tumour suppressor gene, leukaemia inhibitory factor. Short Title- LIF and p53 gene interaction in NTDs
Hierarchical multiscale modeling of polymer-solid interfaces: atomistic to coarse-grained description, and structural and conformational properties of polystyrene-gold systems

A hierarchical simulation approach was developed in order to study polystyrene films sandwiched between two parallel Au(111) surfaces. The coarse-grained potentials describing the interaction of polystyrene with the gold surface were developed systematically using constrained all-atom molecular simulations of a styrene trimer on the Au(111) surface. The model was validated by studying a 5 nm film of short (10-mer) atactic polystyrene chains using all-atom and coarse-grained molecular dynamics simulations. The density, structure, and conformational properties of coarse-grained films were found to be in excellent agreement with all-atom ones. The coarse-grained model was then used to study the structural and conformational properties of roughly 10 and 20 nm thick films with 10-, 50-, 100-, and 200-mer chains. The width of the interphase region of the polymer films is property specific. The density profiles reached the bulk value around 1.5 nm from the interface, for all chain lengths. An estimate of the width of the interphase region based on the conformation tensor profile indicates that the interphase width is around 2–3 times the radius of gyration, which is proportional to the square root of the chain length (number of monomers), and for 200-mer chains is approximately 6–10 nm.
Clostridium difficile Returns with a Vengeance

Unlike old soldiers, old infectious diseases don’t die. They bide their time and return, often in a more dangerous guise. So it is with Clostridium difficile. This bacterium has long been known to cause antibiotic-associated diarrhea, mostly in hospitalized elderly. Recently an obscure strain has re-emerged and is causing outbreaks of high morbidity and mortality in populations not previously considered at high risk, including children. An estimated 3 million cases of symptomatic gastrointestinal disease in the US per year are now ascribed to C. difficile, making it the most common bacterial cause of diarrhea in the country.
Adverse Cardiovascular Events with Nonsteroidal Anti-inflammatory Agents

The extensive use of nonsteroidal anti-inflammatory drugs (NSAIDs), both nonselective and cyclooxygenase- 2–specific inhibitors, for the relief of acute and chronic musculoskeletal pain, in addition to gastrointestinal toxicity, confers serious cardiovascular toxicity that affects the overall risk/benefit ratio of this commonly employed therapy. A plethora of studies have provided convincing evidence for a high risk of adverse cardiovascular events associated with NSAIDs related to a number of risk factors, which are herein briefly reviewed and an algorithm is suggested for their safer use.
Three DIMENSIONAL-CHIPS

This paper illustrates the performance advantages of 3D integrated circuits with two specific examples, namely 3D-FPGA and 3D-SRAM. Three-dimensional Chip (3D IC, 3D-IC, or 3-D IC) is a chip in which two or more layers of active electronic components are integrated both vertically and horizontally into a single circuit. The semiconductor industry is pursuing this promising technology in many different forms, but it is not yet widely used. Through strategic modification of the architectures to take advantage of 3D, significant improvement in speed and reduction in power consumption can be achieved. The unprecedented growth of the computer and the Information technology industry is demanding Very Large Scale Integrated (VLSI) circuits with increasing functionality and performance at minimum cost and power dissipation. VLSI circuits are being aggressively scaled to meet this Demand, which in turn has some serious problems for the semiconductor industry. Additionally heterogeneous integration of different technologies in one single chip (SoC) is becoming increasingly desirable, for which planar (2-D) ICs may not be suitable.3-D ICs are an attractive chip architecture that can alleviate the interconnect related problems such as delay and power dissipation and can also facilitate integration of heterogeneous technologies in one chip (SoC). The multi-layer chip industry opens up a whole new world of design. With the Introduction of 3-D ICs, the world of chips may never look the same again
Geo-Tagged Social Media Data-Based Analytical Approach for Perceiving Impacts of Social Events

Studying the impact of social events is important for the sustainable development of society. Given the growing popularity of social media applications, social sensing networks with users acting as smart social sensors provide a unique channel for understanding social events. Current research on social events through geo-tagged social media is mainly focused on the extraction of information about when, where, and what happened, i.e., event detection. There is a trend towards the machine learning of more complex events from even larger input data. This research work will undoubtedly lead to a better understanding of big geo-data. In this study, however, we start from known or detected events, raising further questions on how they happened, how they affect people’s lives, and for how long. By combining machine learning, natural language processing, and visualization methods in a generic analytical framework, we attempt to interpret the impact of known social events from the dimensions of time, space, and semantics based on geo-tagged social media data. The whole analysis process consists of four parts: (1) preprocessing; (2) extraction of event-related information; (3) analysis of event impact; and (4) visualization. We conducted a case study on the “2014 Shanghai Stampede” event on the basis of Chinese Sina Weibo data. The results are visualized in various ways, thus ensuring the feasibility and effectiveness of our proposed framework. Both the methods and the case study can serve as decision references for situational awareness and city management.
Diseño de procesadores con VHDL

El diseno electronico ha sufrido importantes modificaciones desde su aparicion, en la decada de los ochenta. En la actualidad, los lenguajes de descripcion hardware son una herramienta indispensable para todos aquellos ingenieros que deseen adentrarse en el apasionante mundo del diseno. El presente libro plantea un analisis de estos lenguajes que resulta de gran interes para los estudiantes de Informatica. Ademas, a lo largo de sus paginas se han recopilado y desarrollado diversos ejercicios que facilitan la comprension de los conceptos teoricos. La finalidad pedagogica de esta publicacion hace que tambien sea de utilidad para cualquier persona interesada en conocer VHDL como lenguaje de descripcion hardware. ANGEL GREDIAGA OLIVO es Dr. Ingeniero, con una experiencia docente de mas de veinticinco anos en estructuras de computadores y en hardware. Sus principales lineas de investigacion son el hardware reconfigurable aplicado y la seguridad informatica
Effect of Intervening Bandwidth Feedback on Motor Skill Learning in Self-Controlled Feedback Context

The feedback mechanism of self control and bandwidth feedback is inconsistent. This study integrates two feedback paradigms, taking learners' performance as the basis for feedback, and combines bandwidth feedback advantage with self-controlled feedback, to explore the additional motor learning effect of self-control feedback and to test the difference in motor performance between feedback provision and non-feedback provision. The result is as follow: Under the condition of self-controlled feedback exercise, learners require feedback to perform more accurately than those who do not require feedback. However, under the restriction of practice conditions, learners may seek feedback information through learning strategies, and produce different feedback requests in motor performance.
Getting simultaneous red and near infrared bands from a single digital camera for plant monitoring applications

Les images multispectrales incluant une bande rouge et une bande infrarouge ont prouve leur efficacite pour la discrimination entre sol et vegetation et le suivi cultural en teledetection. Mais elles restent rarement utilisees pour l'imagerie au sol ou par drone, du fait de la non disponibilite de capteurs adaptes. Nous proposons ici une solution originale pour obtenir simultanement les bandes rouge et infrarouge a partir d'un appareil photographique couleur ordinaire, apres avoir retire le filtre interne bloquant l'infrarouge. Nous decrivons d'abord l'approche theorique, ainsi que des resultats simules sur un jeu de donnees spectrales, pour deux types d'appareils. Des exemples d'acquisition sur le terrain en conditions reelles sont ensuite presentes, et compares a une acquisition couleur standard pour la discrimination sol/plantes. Dans la plupart des cas notre approche apporte une amelioration significative, ouvrant de nouvelles opportunites pour les applications de suivi de culture. / Multispectral images including red and near-infrared bands have proved their efficiency for vegetation-soil discrimination and agricultural monitoring in remote sensing applications. But they remain rarely used in ground and UAV imagery, due to a limited availability of adequate 2D imaging devices. In this paper, we propose and evaluate an original solution to obtain simultaneously the near-infrared and red bands from a standard RGB camera, after having removed the near-infrared blocking filter inside. First, the theoretical approach is described, as well as simulated results on a set of soil and vegetation luminance spectra with two different still cameras (Canon 500D and Sigma SD14). Then examples of images obtained in real field conditions are given, and compared with standard colour image acquisition for pixel-based plant/soil discrimination, using an automatic thresholding method. It appears that in most cases our new acquisition procedure brings a significative improvement, opening new opportunities for crop monitoring applications.
A novel systematic algorithm paradigm for the electric vehicle data anomaly detection based on association data mining

The electric automobile electric regenerative braking control strategy request achieves two goals, namely, enhances the entire vehicle the energy returns‐ratio as well as optimizes the pilot to the feel. When the former request brake the electric regenerative braking first, it can satisfy the brake request under the premise first to then use the electric regenerative braking recycling brake energy, and before, the latter request rational distribution the trailing wheel braking force uses in tracking pilot's deceleration intention. Under this basis, this paper proposes the novel systematic algorithm paradigm for the electric car data anomaly detection based on association data mining. With the continuous improvement of the intelligent vehicle and the continuous improvement of the function of the system, the system can be used as a hybrid vehicle detection and vehicle maintenance standard equipment and has broad application prospects. The effectiveness of the proposed system is verified through the experiment.
Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

The rapid development of deep learning, a family of machine learning techniques, has spurred much interest in its application to medical imaging problems. Here, we develop a deep learning algorithm that can accurately detect breast cancer on screening mammograms using an "end-to-end" training approach that efﬁciently leverages training datasets with either complete clinical annotation or only the cancer status (label) of the whole image. In this approach, lesion annotations are required only in the initial training stage, and subsequent stages require only image-level labels, eliminating the reliance on rarely available lesion annotations. Our all convolutional network method for classifying screening mammograms attained excellent performance in comparison with previous methods. On an independent test set of digitized ﬁlm mammograms from Digital Database for Screening Mammography (DDSM), the best single model achieved a per-image AUC of 0.88, and four-model averaging improved the AUC to 0.91 (sensitivity: 86.1%, speciﬁcity: 80.1%). On a validation set of full-ﬁeld digital mammography (FFDM) images from the INbreast database, the best single model achieved a per-image AUC of 0.95, and four-model averaging improved the AUC to 0.98 (sensitivity: 86.7%, speciﬁcity: 96.1%). We also demonstrate that a whole image classiﬁer trained using our end-to-end approach on the DDSM digitized ﬁlm mammograms can be transferred to INbreast FFDM images using only a subset of the INbreast data for ﬁne-tuning and without further reliance on the availability of lesion annotations. These ﬁndings show that automatic deep learning methods can be readily trained to attain high accuracy on heterogeneous mammography platforms, and hold tremendous promise for improving clinical tools to reduce