Miracle-dz/UniFAR-FTUs huggingface dataset:
ENERGY 2017 Proceedings

In order to introduce load management in the manufacturing industry, some obstacles need to be pointed out. This paper presents a feasible approach on how to implement load management measures in companies. To do so, load management and energy management are explained and distinguished in a first step. Subsequently, the implementation method is introduced. Therefore, by using this paper, companies will be enabled to use load management measure and reduce their energy costs significantly.
Shellfish : Risk for Norovirus Outbreaks and Sporadic Infections

NoVs, previously known as Norwalk-like viruses from the Caliciviridae family, are non-enveloped, positive-sense, single-stranded RNA viruses with icosahedral capsid symmetry. NoV infection causes both secretory and osmotic diarrhea. A few NoV particles are sufficient to produce infection. NoV is mainly spread by contaminated food and water. Direct person-to-person contact or contact with contaminated surfaces and fomites are also responsible for spreading the virus. In general, epidemic and pandemic outbreaks occur due to NoVs. Sporadic cases have also been reported. Outbreaks and sporadic infections have been reported to be resulting from consumption of NoV contaminated shellfish (e.g. mussel, oyster, and crab), soft red fruits (strawberry and raspberry), vegetables (lettuce, tomatoes and parsley), bakery products, delicatessen meats, salads, sandwiches, water and ice. NoV is responsible for more than half of nonbacterial diarrheal diseases around the world. Outbreaks of NoV originating from shellfish such as mussels and oysters are common in the world. It has been reported that shellfish grown in virus contaminated waters and seafood products prepared for consumption by infected individuals may play a role for outbreaks. Bivalve molluscan shellfish, such as mussels and oysters, can filter out large quantities of water as part of their feeding activities and, accumulate and concentrate viruses from shallow, coastal and river waters frequently contaminated with sewage. Depth, water temperature, precipitation, wind and salinity of the water in the mussel and oyster growing area are important factors for the prediction of outbreak risks. Shellfish has been reported to be important in the spread of foodborne diseases in developed and developing countries. In Turkey, 78 tons of black mussels per year are collected from sea for consumption. The presence of NoV in mussels indicates that this shellfish could also be an important contaminant for foodborne viral infections and outbreaks in Turkey. Monitoring the prevalence of NoV in shellfish collected from Turkey can be used to prevent outbreaks in touristic locations and to monitor the potential of Turkish seas for viral contaminations. In this study, the role of shellfish in viral gastroenteritis outbreaks associated with NoV and strategies for preventing virus transmission via shellfish are reviewed.
Bladder Cancer Chemotherapy Resistant Cell Harbors Stem-Like Characteristics Construction and Characterization of T24 Resistance Strain of Bladder Cancer

Objective: Establish a human drug-resistant bladder cancer cell line (T24/MMC), which are integral to the initiation, high recurrence and chemo-resistance of bladder cancer. BCSCs are heterogeneous and originate from multiple cell types, including urothelial stem cells and differentiated cell types, including basal, intermediate stratum and umbrella cells, then investigate its characteristics. Methods: Human bladder cancer cell line T24 was established using gradually increased concentration of mitomycin (MMC) in culture. Basic characteristics of both the parental cell line T24 and the drug-resistant cell line (T24/MMC) were analyzed and compared, including the ability of proliferation, colony formation, mobility and invasion, sphere forming ability. Results: Compared with T24, the (T24/MMC) cell grows quickly, and the ability of proliferation, colony formation, mobility and invasion, stemness gene, sphere forming ability increased. Conclusion: Gradually increased concentration of MMC can induce drug-resistant bladder cancer cell line, and it was enriched of cancer stem cells.
Quantifying the Flexibility by Energy Storage Systems in Distribution Networks with Large-Scale Variable Renewable Energy Sources

To counter the intermittent nature of variable Renewable Energy Sources (vRESs), it is necessary to deploy new technologies that increase the flexibility dimension in distribution systems. In this framework, the current work presents an extensive analysis on the level of energy storage systems (ESSs) in order to add flexibility, and handle the intermittent nature of vRS. Moreover, this work provides an operational model to optimally manage a distribution system that encompasses large quantities of vRESs by means of ESSs. The model is of a stochastic mixed integer linear programming (MILP) nature, which uses a linearized AC optimal power flow network model. The standard IEEE 119-bus test system is used as a case study. Generally, numerical results show that ESSs enable a much bigger portion of the final energy consumption to be met by vRES power, generated locally.
Privacy in Location-Based Services , Concern vs . Coolness

New location-based technology attempts to facilitate people’s privacy but developers have little knowledge of how users are in fact concerned about their location privacy. I present two case studies indicating that users are initially concerned with their privacy, but when actually using location-based services, users discontinue to have the same level of concern. The paper concludes by addressing three issues of design that should be of concern to designers of location-based services.
Analysis of Long Molecular Dynamics Simulations Using Interactive Focus+Context Visualization

Analyzing molecular dynamics (MD) simulations is a key aspect to understand protein dynamics and function. With increasing computational power, it is now possible to generate very long and complex simulations, which are cumbersome to explore using traditional 3D animations of protein movements. Guided by requirements derived from multiple focus groups with protein engineering experts, we designed and developed a novel interactive visual analysis approach for long and crowded MD simulations. In this approach, we link a dynamic 3D focus+context visualization with a 2D chart of time series data to guide the detection and navigation towards important spatio‐temporal events. The 3D visualization renders elements of interest in more detail and increases the temporal resolution dependent on the time series data or the spatial region of interest. In case studies with different MD simulation data sets and research questions, we found that the proposed visual analysis approach facilitates exploratory analysis to generate, confirm, or reject hypotheses about causalities. Finally, we derived design guidelines for interactive visual analysis of complex MD simulation data.
CHARACTERIZATION OF OLIVE OIL OBTAINED FROM WHOLE FRUIT AND FRUIT FLESH OF CULTIVAR : KAISSY GROWN IN SYRIA

The quality of extra virgin olive oils (EVOO) from whole fruits and fruit flesh of Kaissy olive (Olea europaea) cultivar was investigated in this study. Acid value (AV), peroxide value (PV), iodine value (IV), specification number (SV), Thiobarbituric acid (TBA) value, phenol content, refractive index (RI) and viscosity were measured after 0, 6 and 12 months of storage. The physicochemical properties of oil extracted from whole fruit and fruit flesh samples of olive were: AV (0.32 and 0.40%), PV (4.79 and 6.13%), TBA (0.056 and 0.052 mg MDA kg oil), IV (84.41 and 83.87 g oil), SV (195.48 and 187.56 mg KOH g oil), total phenolic (339.52 and 226.68 mg gallic acid kg oil), RI (1.4669 and 1.4668) and viscosity (129.33 and 130.00 mPa s) respectively. The results demonstrated that the AV, PV, RI and viscosity values significantly (p<0.05) increased, while TBA value and total phenolic content significantly (p<0.05) decreased during storage.
AIG Investments.AI at the FinSBD Task: Sentence Boundary Detection through Sequence Labelling and BERT Fine-tuning

This paper describes the method that Investments AI at AIG (American International Group, Inc.) submitted to the FinSBD-2019 shared task ( “Sentence Boundary Detection (SBD) in PDF Noisy Text of the Financial Domain” ) to extract meaningful, well-formed sentences from noisy unstructured ﬁnancial text. We approach sentence boundary detection as a sequence labelling task to recognise the start and end token boundaries of sentence(-like) constructs. We evaluated two neural architectures, namely 1) Bidirectional Long Short-Term Memory (BiLSTM) and 2) Bidirectional Encoder Representations from Trans-formers (BERT). Our extensive experiments on the ofﬁcial FinSBD-2019 datasets demonstrate that a ﬁne-tuned BERT model with customised hyper-parameters (BERT-SBD) outperforms BiL-STM models in several evaluation metrics. Our BERT-SBD submission ranked ﬁrst on the English test set in terms of MEAN F1 score in the joint sentence-begin-and-end test condition.
Multiple Hedging on Energy Market Multiple Hedging on Energy Market

Energy market can significantly influence economy on different levels. Therefore, hedging of energy price risk has become an important issue for households, firms and policy makers. Due to recent increase of energy price volatility a wide range of papers devoted to volatility estimation and hedging strategy building have been published (see in [1, 2]). The original hedging strategies assumed that conditional volatility is unchanged in time [3], i. e. they were static. Later new approaches have been proposed including dynamic hedging strategies, as well as cross-hedging. Futures contracts are one of the most widely used financial assets for hedging due to such features as fixed term of expiration, low transaction cost, high liquidity and low margin requirement. Dynamic hedging strategies, based on a time-dependent optimal hedge ratio, allow managing a portfolio of different assets considering cross-correlation of spot and futures contracts. To calculate the optimal hedge ratio, the first step is to investigate the timevarying volatility transmission among the assets using one of the parametric models, for example, VAR-GARCH [4], BEKK-GARCH [5], Markov switching GARCH [6]. In [4] VAR-GARCH and CCC-GARCH (constant conditional correlations) models are used to define volatility spillovers between oil and stock prices and the authors come to the conclusion that a better understanding of such links is crucial for portfolio management. The results also show that for all the pairs, consisted of oil and stock from some economic sector, short position of financial sector stock is the most effective strategy to hedge oil price risks. Time-varying conditional correlations and hedge ratios of Goldman Sach’s Energy index pared with S&P 500 are examined in [7]. The authors implement a BEKK-GARCH specification for conditional variances and include US dollar index as an independent variable in mean equation. According to their empirical results, energy index show better performance as a hedge instrument for equities during extreme downturns.
1 A Comparative Study of Rocchio Classifier Applied to supervised WSD Using Arabic Lexical Samples

This work studies the possibilities of using the Rocchio classifier to solve the Word Sense Disambiguation problem in a supervised manner through the usage of the lexical samples of five Arabic words. The performance of the Rocchio classifier is compared to three supervised machine learning algorithms, namely the Most Frequent Sense (MFS), Naïve Bayesian Classifier (NBC) and the Support Vector Machine (SVM) representing the baseline and stateof-the-art algorithms for WSD. Results indicate that the Rocchio classifier outperforms the other classification approaches by reducing the error by over 14% compared to the best performing NBC due to its superior ability in feature selection.
Improvement of Napier Grass Silage Nutritive Value by Using Inoculant and Crude Enzymes from Trichoderma reesei and Its Effect on in Vitro Rumen Fermentation

This research was aimed to evaluate the effect of inoculant and crude enzyme of Trichoderma reesei on fermentation quality and rumen digestibility of napier grass silage. This research consisted of two stages. The first stage was fermentation quality using a completely randomized design with three treatments and six replications. The second stage was rumen fermentability using a randomized block design with three treatments and six replications. The treatments of both stages were: T0= control of napier grass silage (Hi-fer+), T1= control + T. reesei inoculant at the level of 2.13 x 10 7 cfu/ kg, and T2= control + crude enzyme from T. reesei at the level of 11.4 unit/kg. Napier grass silage were kept at room temperature and opened 21 d after fermentation. The result of the first stage experiment showed that T2 decreased (P<0.01) dry matter, organic matter, crude fiber, and neutral detergent fiber (NDF) and also increased (P<0.05) crude protein, total digestible nutrient (TDN), pH, acetic acid, and propionic acid of napier grass silage while T1 decreased (P<0.01) NDF and increased (P<0.05) propionic acid. The result of the second stage experiment showed that there were no significant differences in rumen pH, in vitro dry matter and organic matter digestibility, total VFA, proportion of VFA, ratio of acetic to propionic acids, and also NH 3 . It is concluded that the addition of crude enzyme from T. reesei could improve fermentation quality of napier grass silage with decreasing NDF and increasing TDN but did not affect rumen fermentability.
EFFICIENCY OF MESOPOROUS SILICA NANOPARTICLES ON AFLATOXIN B 1 ADSORPTION

Food contaminants such as mycotoxins represent a considerable risk to human and animal health. The development of methodologies that allow controlling the presence of these hazardous contaminants is of special relevance. In this sense, mesoporous silica nanoparticles (MSN) are important because of their interesting structural features and the potential applications, including adsorption process. Furthermore, the silica matrices are nontoxic and biocompatible for biomedical or food research. Considering that the most applied method for protecting animals against mycotoxicosis is the utilization of adsorbents, this study aims to prepare and characterize new MSN and evaluate their effectiveness on the adsorption of aflatoxin B1 (AFB1). The MSN was duly characterized by analytical techniques and they were significantly able to AFB1 adsorption (89%) in the higher concentration (2 mg/mL). Due to strong effects on mycotoxin adsorption, could be studied as an effective agents for agriculture and food safety applications.
The Saccharomyces cerevisiae NPS 1 gene , a novel CDC gene which encodes a 160 kDa nuclear protein involved in G 2 phase control

We have cloned the gene NPSJ (nuclear protein of Saccharomyces) which encodes a nuclear protein of mol. wt 156 735 Daltons (1359 amino acids) essential for cell growth. NPS1 contains a 2 kb sequence that is highly homologous to the S.cerevisiae SNF2/GAMI gene known as a transcriptional regulator for multiple genes. However, the NPS1 gene was found to have a distinct function from SNF2/GAMI. The growth of the cells carrying a npslA:: URA3 deletion allele and galactose-inducible NPS1 on a plasmid was arrested under NPSI-repressed conditions with a cell cycle arrest phenotype, being arrested at the large-bud stage with a single nucleus that had a DNA content of G2/M phase. When the arrested cells were further incubated under NPSI-repressed conditions, re-replication of DNA occurred in some of the arrested cells without passage through mitosis. In the predicted amino acid sequence of NPS1, sequences homologous to the catalytic domain of protein kinases were found. We constructed a mutation which results in the substitution of a highly conserved lysine residue (Lys792) in the presumed ATP-binding site of this kinaselike domain with a glutaminc acid codon. The mutant gene failed to rescue the growth defect caused by NPS1 disruption, suggesting that Lys792 is essential for the function of NPSJ.
QC-GO Submission for MADAR Shared Task: Arabic Fine-Grained Dialect Identification

This paper describes the QC-GO team submission to the MADAR Shared Task Subtask 1 (travel domain dialect identification) and Subtask 2 (Twitter user location identification). In our participation in both subtasks, we explored a number of approaches and system combinations to obtain the best performance for both tasks. These include deep neural nets and heuristics. Since individual approaches suffer from various shortcomings, the combination of different approaches was able to fill some of these gaps. Our system achieves F1-Scores of 66.1% and 67.0% on the development sets for Subtasks 1 and 2 respectively.
Control in the Internet of Things

The new generation of Wireless Sensor Networks, that is known as the Internet of Things enables the direct connection of physical objects to the Internet using microcontrollers. In most cases these microcontrollers have very limited computational resources. The global connectivity provides great opportunities for data collection and analysis as well as for interaction of objects that cannot be connected to the same local area network. Many of application scenarios have high requirements to security and privacy of transmitted data. At the same time security solutions that are utilized for general purpose computers are not always applicable for constrained devices. That leaves a room for new solutions that takes into account the technological aspects of the Internet of Things. In this thesis we investigate the access control solution for the IETF standard draft Constrained Application Protocol, using the Datagram Transport Layer Security protocol for transport security. We use the centralized approach to save access control information in the framework. Since the public key cryptography operations might be computationally too expensive for constrained devices we build our solution based on symmetric cryptography. Evaluation results show that the access control framework increases computational effort of the handshake by 6.0%, increases the code footprint of the Datagram Transport Layer Security implementation by 7.9% and has no effect on the overall handshake time. Our novel protocol is not vulnerable to Denial of Service or Drain Battery Attack.
Analysis of subsurface stormflow from 147 storms at the 20 m long trench in the Panola Mountain Research Watershed

[1] Analysis of subsurface stormflow from 147 storms at the 20 m long trench in the Panola Mountain Research Watershed by Tromp-van Meerveld and McDonnell (2006a) showed that there was a distinct 55 mm precipitation threshold for significant subsurface stormflow production. This second paper in the series investigates the processes responsible for this threshold response. We installed a dense spatial array of maximum rise crest stage gauges and recording wells on the hillslope and studied the temporal and spatial patterns of transient saturation at the soil-bedrock interface and its relation to subsurface stormflow measured at the trench face. Results show that while transient groundwater developed on parts of the hillslope during events smaller than 55 mm, it was not until more than 55 mm of rain fell before bedrock depressions on the hillslope were filled, water spilled over microtopographic relief in the bedrock surface, and the subsurface saturated areas became connected to the trench. When connectivity was achieved, the instantaneous subsurface stormflow rate increased more than fivefold compared to before the subsurface saturated areas were connected to the trench face. Total subsurface stormflow was more than 75 times larger when connectivity was achieved compared to when connectivity was not achieved. The fill and spill hypothesis presented in this paper is a process explanation for the observed threshold behavior of Tromp-van Meerveld and McDonnell (2006a), thereby linking patterns and processes.
Social Authentication Vulnerabilities , Mitigations , and Redesign

As social networks have become an integral part of online user activity, a massive amount of personal information is readily available to such services. In an effort to hinder malicious individuals from compromising user accounts, high-value services have introduced two-factor authentication to prevent adversaries from compromising accounts using stolen credentials. Facebook has recently released a two-factor authentication mechanism, referred to as Social Authentication (SA), which requires users to identify some of their friends in randomly selected photos to be allowed access to their accounts. In this work, we first study the attack surface of social authentication, showing how any attacker can obtain the information needed to solve the challenges presented by Facebook. We implement a proof-ofconcept system that utilizes widely available face recognition software and cloud services, and evaluated it using real public data collected from Facebook. We have empirically calculated the probability of an attacker obtaining the information necessary to solve SA tests when relying on publicly accessible data as well as following a more active approach to gather restricted information, and we have then designed an automated attack able to break the SA, to demonstrate the feasibility of carrying out large-scale attacks against social authentication with minimal effort on behalf of an attacker. We then revisited the Social Authentication concept and propose reSA, a two-factor authentication scheme that can be easily solved by humans but is robust against face-recognition software. Our core concept is to select photos in which state-of-the-art facerecognition software detects human faces, but cannot identify them due to certain characteristics. We implemented a web application that recreates the SA mechanism and conducted a user study that sheds light on user behavior regarding photo tagging, and demonstrated the strength of our approach against automated attacks.