Miracle-dz/UniFAR-FTUs huggingface dataset:
Automatic street light control system using wireless sensor networks

Energy consumption due to the street lights needs proper monitoring and control to reduce wastage of power. Conventional street light systems suffer from certain drawbacks as they are manually controlled and are powered through the electrical board power station. This may lead to more power consumption if not monitored properly. In this paper, an automatic street light control system is implemented based on wireless sensor networks (WSN). The street lamp post forms the WSN node. It uses an Arduino Uno board and various sensors to detect traffic on the road. A Raspberry Pi3 computer is used as a web server at the central base station to monitor the status of the street lights. The lights are turned on based on the sensor signals only during nighttime and when traffic is present. A webpage is developed using HTML and PHP language for uploading the sensor data and web control of the street lights. Solar cells are used at each node to power on the street lights.
A modified hybrid prediction method for spinning reserve requirement

Ancillary services is used to refer to a variety of operations beyond generation and transmission which are requested to maintain grid stability, security and reliability of power system. These services generally consist, frequency control, Spinning Reserves (SR) and operating reserves. Accordingly, an accurate day ahead forecast of SR requirement helps the Independent System Operator (ISO) to manage a reliable and economic operation of the power system. This prediction model needs a strong and accurate method to tackle the complexity, non-stationarity and volatility due to the load profile or equipment fault. Hence, a new hybrid forecasting model is proposed in this paper, to solve the SR requirement. The proposed structure consists of three stage Neural Network (NN) based forecast engine with different learning algorithms. Also, the input signal of this forecast engine is filtered by a new Improved Fuzzy Clustering Mechanism (IFCM) to find the high relevancy and low redundancy of features. The proposed strategy is implemented and tested on real data from Pennsylvania-New Jersey-Maryland (PJM) and is compared with other techniques. The resulting numerical results demonstrate the validity of proposed method.
No genetic adaptation of the Mediterranean keystone shrub Cistus ladanifer in response to experimental fire and extreme drought

In Mediterranean ecosystems, climate change is projected to increase fire danger and summer drought, thus reducing post-fire recruitment of obligate seeder species, and possibly affecting the population genetic structure. We performed a genome-wide genetic marker study, using AFLP markers, on individuals from one Central Spain population of the obligate post-fire seeder Cistus ladanifer L. that established after experimental fire and survived during four subsequent years under simulated drought implemented with a rainout shelter system. We explored the effects of the treatments on marker diversity, spatial genetic structure and presence of outlier loci suggestive of selection. We found no effect of fire or drought on any of the genetic diversity metrics. Analysis of Molecular Variance showed very low genetic differentiation among treatments. Neither fire nor drought altered the small-scale spatial genetic structure of the population. Only one locus was significantly associated with the fire treatment, but inconsistently across outlier detection methods. Neither fire nor drought are likely to affect the genetic makeup of emerging C. ladanifer, despite reduced recruitment caused by drought. The lack of genetic change suggests that reduced recruitment is a random, non-selective process with no genome-wide consequences on this keystone, drought- and fire tolerant Mediterranean species.
Performance enhancement of DBSCAN density based clustering algorithm in data mining

Clustering is a form of learning by observations. It is an unsupervised learning method and does not require training data set to generate a model. Clustering can lead to the discovery of previously unknown groups within the data. It is a common method of data mining in which similar and dissimilar type of data would be clustered into different clusters for better analysis of the data. In this paper the DBSCAN algorithm has been applied to compute the EPS value and Euclidian distance on the basis of similarity or dissimilarity of the input data. Also back propagation algorithm is applied to calculate Euclidian distance dynamically and simulation study is conducted that shows improvement to increase accuracy and reduce execution time.
p38δ MAPK regulates aggresome biogenesis by phosphorylating SQSTM1 in response to proteasomal stress

ABSTRACT Aggresome formation is a major strategy to enable cells to cope with proteasomal stress. Misfolded proteins are assembled into micro-aggregates and transported to the microtubule organizing center (MTOC) to form perinuclear aggresomes before their degradation through autophagy. So far, multiple factors have been identified as the activators of micro-aggregate formation, but much less is known about the regulatory mechanisms of their transport. Here, we report that proteasomal stress leads to the activation of p38 MAPK family members. Two of them, p38γ (MAPK12) and p38δ (MAPK13), are dispensable for micro-aggregate formation but are required for their targeting to the MTOC. Interestingly, p38δ promotes micro-aggregate transport by phosphorylating SQSTM1, a major scaffold protein that assembles soluble ubiquitylated proteins into micro-aggregates. Expression of the phospho-mimetic mutant of SQSTM1 in p38δ-knockout cells completely rescued their aggresome formation defects and enhanced their resistance to proteasomal stress to wild-type levels. This study reveals p38δ-mediated SQSTM1 phosphorylation as a critical signal for the targeting of micro-aggregates to the MTOC and provides direct evidence for the survival advantages associated with aggresome formation in cells under proteasomal stress. Highlighted Article: Proteasomal stress promotes the recruitment of small protein aggregates to perinuclear aggresomes through the activation of p38γ and p38δ.
The optical conductivity and electron energy loss of graphene under different hydrostatic pressures using first-principles

The optical conductivity, refractive index and electron energy loss function og graphene have been studied under different pressures using first-principles. In this study the above mentioned optical parameters have been studied for the first time under 0 GPa, 20 GPa and 40 GPa external pressures. The calculated values agrees well with the published results earlier at zero pressure.
RP-HPLC Method Development and Validation for the Simultaneous Determination of Clindamycin and Miconazole in Pharmaceutical Dosage Forms

Objective: A simple, precise, reliable, rapid and reproducible reversed phase–high-performance liquid chromatography method was developed and validated for the simultaneous estimation of Clindamycin (CDM) and Miconazole (MCZ) present in tablet dosage forms. Method: Chromatographic separation achieved isocratically on Inertsil ODS C 18 (250x4.6 mm, 5 mm) column and buffer (pH 3.5) and acetonitrile (65:35 v/v) as mobile phase, at a flow rate of 1 ml/min. Detection was carried out at 220 nm. Parameters such as linearity, precision, accuracy, recovery, specificity and ruggedness are studied as reported in the ICH guidelines. Results: The retention times for CDM and MCZ was found to be 2.2 and 3.2 min, respectively. Linearity for CDM and MCZ was in the range of 5-30 μg/ml and 10-60 μg/ml, respectively. The mean recoveries obtained for CDM and MCZ were 99.73 ± 0.8 and 100.2 ± 0.58%, respectively, and Relative standard deviation (RSD) was less than 2. The correlation coefficients for all components are close to 1. The RSDs for three replicate measurements in three concentrations of samples in tablets are always less than 2%. Conclusion: Developed method was found to be accurate, precise, selective and rapid for simultaneous estimation of CDM and MCZ in tablets. Key words: Clindamycin, Miconazole, RP-HPLC, Simultaneous estimation, Tablets.
Learning nuisances to track pedestrians in autonomous vehicles

Autonomous vehicles rely on an accurate perception module. One of the fundamental challenges is to efficiently track pedestrians surrounding a vehicle to anticipate risky situations. Over the past decades, researchers have formulated the tracking problem as a data association one where they proposed various representations aiming for invariance to nuisances such as viewpoint changes, body deformation, object occlusion, and illumination changes. However, these methods still suffer to address abrupt changes since they do not explicitly model the nature of the nuisances. In this work, we propose to train a classifier that recognizes these nuisances, more specifically rotational body deformation of pedestrians. We aim to detect this deformation and use it to improve the tracking process.
Design and implementation of smart garage- An IoT perspective

Internet of Things is a burgeoning field in the world of technology. It helps ease life for a person by means of internetworking physical devices, automotive, home appliances and other items embedded with electronics, software, sensors and actuators. Smart garage is one such device that helps ease the access of the garage doors in domestic use. Unlike most solutions, smart garage help you to access your doors with the help of a computable device such as smart phones or tablets connected to a network, and access by means of secured pins to authorize the access. We designed one such garage using Arduino web server, and a smartphone. Which we compared with existing methods. We have illustrated the building process of the setup for the prototype of our garage, and various means to improve power consumptions and alternate methods for a secured pin.
Sentimental analysis of tweets using ant colony optimizations

Sentiment analysis is a form of information extraction from text of growing research and commercial interest. In this paper, we present our machine learning experiments in regarding to sentiment analysis on public tweets, which is collect by tweet programmer account then extracting the features after the preprocessing of text then given the relative weight to every features. These features are learned by two types of classifiers: one is generative classifier (Naive Bayes) which understands why some text is positive sentiment and others are negative. Second classifier is discriminative classifier (Support vector machine (SVM)). In proposed methodology, features scarcity reduced by optimization which reduces the complexity of processing and learns features according to its relative weight leads to the reduction of overlapping in features. In this paper, our experiment clearly shows the difference between optimized and without optimized features in case of SVM but to some extent in Naïve Bayes.
Market orchestrators: The effect of platform certification on complementor performance and behavior in the context of kiva (2010-2013)

Platforms need to carefully balance the needs of their customers and complementors. Certification of complementors is often used as part of this complex “market orchestration” process. We study how a platform’s use of certification affects complementors’ behavior, as pertains to the bundle of products offered. We use a unique dataset of Kiva’s microfinance platform to take advantage of a quasi-exogenous shock: Kiva’s unexpected introduction of a certification program in late 2011. We show that Kiva’s certification leads microfinance institutions to experience positive performance effects and reorient their loan portfolio composition. We further show that these effects vary across complementors depending on demand-side and supply-side factors. We interpret our results to suggest that there are limits to the extent platforms can influence the behavior of complementors.
ENCRYPTED DATA MANAGEMENT WITH DEDUPLICATION IN CLOUD COMPUTING

Deduplication of Data is a technique for reducing the amount of storage space an organization needs to save its data. It is a specialized data compression technique for removing duplicate copies of repeating data. To protect the discretion of sensitive data while supporting deduplication, the CE convergent encryption technique has been proposed to encrypt the data before outsourcing. The differential privileges of users are considering edit duplicate check besides the data itself which has not done in traditional deduplication systems. The proposed system supports authorized duplicate check in hybrid cloud architecture. Proposed authorized duplicate check scheme acquires minimal overhead compared to normal operations. To reduce the weaknesses of convergent encryption, we are proposing LFSR (Linear Feedback Shift Register) encryption technique. Security analysis determines that this system is secure in terms of the definitions specified in the proposed security model.
Optimization methods for multi-level lot-sizing problems. (Méthodes d'optimisation pour la gestion de stocks multi-échelon)

In this thesis we are interested in several multi-level lot-sizing problems taking into account production capacities. We first study a 2-level in series lot-sizing problem with identical and stationary capacities at both levels, for which we propose an exact dynamic algorithm running in polynomial time under some hypothesis. Next chapter extends this result on two main lines: we consider the multi-level in series lot-sizing problem with batch deliveries and with a number of level which is part of the input. We provide a very efficient exact algorithm for this problem, which is polynomial in the number of levels and in the number of periods, based on an original decomposition into induced connected components. Then, we consider more general versions of this problem, for which we provide NP-hardness results when batch sizes or capacities are level-dependent. We propose 2-approximation algorithms for these problems, based on the sandwiching of the objective function by two affine functions. Finally, we study a single-level lot-sizing problem in a system composed of identical machines working in parallel. The originality of this study is to consider a periodic energy limitation. At each period it must be decided how many machines to switch on or off and the volume to be produced and stored. Complexity results are provided, showing that this problem is NP-hard, even under some restrictive assumptions, and an exact dynamic algorithm running in polynomial time is proposed for the case of stationary energy parameters
Clinical depression detection for adolescent by speech features

Depression is a common mental illness with a prevalence of 10–15% in general population. It is one of the most prevalent of all psychopathology disorders and if left untreated may lead to many negative consequences (i.e., suicidality, poor physical health, economic cost). Depression affects a person's psychosocial function and can eventually lead to family problems, problems in social life and loss of job. The increase in the prevalence of clinical depression in adolescents (i.e., those aged 13–20 years) has been connected to a scope of genuine results, especially an expansion in the quantity of suicide endeavors and passing's, making it a general wellbeing concern. Indications of depression often first appear during adolescence at a time when the voice is changing, in both males and females, recommending that particular investigations of these marvels in pre-adult populaces are justified. This study reports on an investigation of 14 acoustic features used as potential indicators of clinical depression that were grouped into five main feature categories(i.e Teager energy operator (TEO)-based, Cepstral (C), Prosodic (P), Spectral (S), Glottal (G) ) extracted from adolescents' speech using Distress Analysis Interview Corpus (DAIC) database. Most interestingly, the TEO-based features, when used on their own, clearly outperformed all other features and feature combinations. TEO together with glottal feature using GMM classifier outperformed all other feature combination with accuracy 90.58%. Also it is seen that TEO together with MFCC provide accuracy (85.88%) GMM, (76.4%) SVM and (76.5%) SVM+GMM classifier.
Biorefinery supply chain design optimization under sustainability dimensions. (Optimisation de la conception de la chaîne d'approvisionnement pour une bioraffinerie durable)

La croissance de la population mondiale et son effet sur la securite alimentaire et l'urgence du changement climatique, sont des facteurs qui favorisent des diverses innovations pour accroitre l'efficacite de l'utilisation des ressources naturelles. Parmi lesquelles la biomasse est une ressource renouvelable d’une grande disponibilite. Une bioraffinerie peut transformer la biomasse en energie durable, materiaux et des produits chimiques. Par contre, au debut d'un projet de bioraffinerie, des decisions strategiques doivent etre prises. Et ainsi, le processus de decision doit tenir en compte diverses aspects, comme des conditions specifiques du territoire ou le projet est destine a etre deploye. Une etude recente montre que, bien que ce probleme ait ete traite par la communaute scientifique, l'accent est mis sur les facteurs de rentabilite economique. Cependant, considerer toutes les dimensions de la durabilite, «Economique», «Social», «Environnemental», «Technologique» et «Politique» est essentielle dans ce type de projets. Dans ces conditions, tous les outils d'optimisation disponibles ne conviennent pas. Par consequent, une etude preliminaire sur les outils d'optimisation multi-objectifs est realisee. Par la suite, une strategie d'optimisation integrant les dimensions de durabilite dans la phase amont du projet a ete developpee. En fin, le modele developpe a ete applique a l'etude du deploiement de bioraffineries en Colombie. Ce modele et son optimisation permettent une meilleure visibilite pour les decideurs, grâce a sa capacite de proposer des scenarios et d’evaluer les compromis de la durabilite en integrant les preferences des parties prenantes
Investigating sentiment analysis using machine learning approach

Sentiment Analysis is an approach of studying people's emotions and sentiments based on a particular topic, service, event and it attributes. Sentiment analysis appears as a part of various business analysis systems to find opinions about their services or products. Both the availability of CPU resources and enormous amount of data generated by the users makes sentiment analysis an active research field in upcoming years. Most of the existing approaches focus on efficient feature extraction, at the same time some approaches focus on extracting semantic features, which makes mush contribution to sentiment analysis. This review paper gives a comprehensive overview on Sentiment analysis using NLP (Natural Language Processing) techniques. First, we start with some NLP techniques which are generally needed for preprocessing the input data. Second, we introduce various approaches and some problems related to sentiment analysis and discuss some challenges and problem in sentiment analysis. Finally, we illustrate recent trend in sentiment analysis and its related works.
Theoretical Analysis of Local Search and Simple Evolutionary Algorithms for the Generalized Travelling Salesperson Problem

The generalized travelling salesperson problem is an important NP-hard combinatorial optimization problem for which metaheuristics, such as local search and evolutionary algorithms, have been used very successfully. Two hierarchical approaches with different neighbourhood structures, namely a cluster-based approach and a node-based approach, have been proposed by Hu and Raidl (2008) for solving this problem. In this article, local search algorithms and simple evolutionary algorithms based on these approaches are investigated from a theoretical perspective. For local search algorithms, we point out the complementary abilities of the two approaches by presenting instances where they mutually outperform each other. Afterwards, we introduce an instance which is hard for both approaches when initialized on a particular point of the search space, but where a variable neighbourhood search combining them finds the optimal solution in polynomial time. Then we turn our attention to analysing the behaviour of simple evolutionary algorithms that use these approaches. We show that the node-based approach solves the hard instance of the cluster-based approach presented in Corus et al. (2016) in polynomial time. Furthermore, we prove an exponential lower bound on the optimization time of the node-based approach for a class of Euclidean instances.
Pre-diagnosis plasma immune markers and risk of non-Hodgkin lymphoma in two prospective cohort studies

Inflammation and B-cell hyperactivation have been associated with non-Hodgkin lymphoma development. This prospective analysis aimed to further elucidate pre-diagnosis plasma immune marker profiles associated with non-Hodgkin lymphoma risk. We identified 598 incident lymphoma cases and 601 matched controls in Nurses’ Health Study and Health Professionals Follow-up Study participants with archived pre-diagnosis plasma samples and measured 13 immune marker levels with multiplexed immunoassays. Using multivariable logistic regression we calculated Odds Ratios (OR) and 95% Confidence Intervals (CI) per standard deviation unit increase in biomarker concentration for risk of non-Hodgkin lymphoma and major histological subtype, stratifying additional models by years (<5, 5 to <10, ≥10) after blood draw. Soluble interleukin-2 receptor-α, CXC chemokine ligand 13, soluble CD30, and soluble tumor necrosis factor receptor-2 were individually positively associated, and B-cell activating factor of the tumor necrosis factor family inversely associated, with all non-Hodgkin lymphoma and one or more subtypes. The biomarker combinations associated independently with lymphoma varied somewhat by subtype and years after blood draw. Of note, the unexpected inverse association between B-cell activating factor and chronic lymphocytic leukemia/small lymphocytic lymphoma risk (OR: 95%CI: 0.51, 0.43-0.62) persisted more than ten years after blood draw (OR: 0.70; 95%CI: 0.52-0.93). In conclusion, immune activation precedes non-Hodgkin lymphoma diagnosis by several years. Decreased B-cell activating factor levels may denote nascent chronic lymphocytic leukemia many years pre-diagnosis.