Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Approach for Probabilistic Dual Source Location Privacy Protection Scheme in Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have been widely deployed to monitor valuable objects. In these applications, the sensor node senses the existence of objects and transmitting data packets to the sink node (SN) in a multi hop fashion. The SN is a powerful node with high performance and is used to collect all the information sensed by the sensor nodes. Due to the open nature of the wireless medium, it is easy for an adversary to trace back along the routing path of the packets and get the location of the source node. Once adversaries have got the source node location, they can capture the monitored targets. In this project, we focus on the source location privacy problem in WSNs, a hot research topic in security, and propose a novel approach of two
Electromagnetic and Calorimetric Validation of a Direct Oil Cooled Tooth Coil Winding PM Machine for Traction Application

Tooth coil winding machines offer a low cost manufacturing process, high efficiency and high power density, making these attractive for traction applications. Using direct oil cooling in combination with tooth coil windings is an effective way of reaching higher power densities compared to an external cooling jacket. In this paper, the validation of the electromagnetic design for an automotive 600 V, 50 kW tooth coil winding traction machine is presented. The design process is a combination of an analytical sizing process and FEA optimization. It is shown that removing iron in the stator yoke for cooling channels does not affect electromagnetic performance significantly. In a previous publication, the machine is shown to be thermally capable of 25 A/mm 2 (105 Nm) continuously, and 35 A/mm 2 (140 Nm) during a 10 s peak with 6 l/min oil cooling. In this paper, inductance, torque and back EMF are measured and compared with FEA results showing very good agreement with the numerical design. Furthermore, the efficiency of the machine is validated by direct loss measurements, using a custom built calorimetric set-up in six operating points with an agreement within 0.9 units of percent between FEA and measured results.
QSAR modeling of antiradical properties of phenolic compounds using DFT calculations

This paper describes a quantitative structure – activity relationship study of the antiradical properties of 31 flavonoids belonging to different groups such as: flavonols, flavonones, dihydroflavonols and biflavonones. Using density functional theory (DFT) calculations, some structural characteristics such as frontier molecular orbitals, molecular descriptors, have been studied. To gain insights into the chemical structure and property of the studied compounds, many types of descriptors are generated by using DFT/B3LYP 6-31G(d,p) and other software. Also, The Principle Component Analysis (PCA), Multiple Linear and Nonlinear Regression (MLR and MNLR), and Artificial Neural Network (ANN) have been investigated to select the descriptors, and to generate the correlation models that relate the structural feature to the biological activity. The statistical results of the MLR, MNLR, and ANN indicate that the determination coefficient R 2 were 0.811, 0.646, 0.982, respectively. A good correlation coefficient is obtained, and the antiradical activities of these compounds are well predicted. These models are expected to be useful for screening of polyphenolic antioxidants.
Adversarial Classification with Model Interpretation

Machine learning (ML) systems have been increasingly applied in web security applications such as spammer detection, malware detection and fraud detection. These applications have an intrinsic adversarial nature where intelligent attackers can adaptively change their behaviors to avoid being detected by the deployed detectors. Existing efforts against adversaries are usually limited by the type of applied ML models or the specific applications such as image classification. Additionally, the working mechanisms of ML models usually cannot be well understood by users, which in turn impede them from understanding the vulnerabilities of models nor improving their robustness. To bridge the gap, in this paper, we propose to investigate whether model interpretation could potentially help adversarial detection. Specifically, we develop a novel adversary-resistant detection framework by utilizing the interpretation of ML models. The interpretation process explains the mechanism of how the target ML model makes prediction for a given instance, thus providing more insights for crafting adversarial samples. The robustness of detectors is then improved through adversarial training with the adversarial samples. A data-driven method is also developed to empirically estimate costs of adversaries in feature manipulation. Our approach is model-agnostic and can be applied to various types of classification models. Our experimental results on two real-world datasets demonstrate the effectiveness of interpretation-based attacks and how estimated feature manipulation cost would affect the behavior of adversaries.
Applying stable isotopes of carbon (δ13C) and nitrogen (δ15N) to examine the role of pikeperch (Sander lucioperca Linnaeus, 1758) in structuring the food web of Lake Eğirdir (Central Anatolia), Turkey

Meral APAYDIN YAĞCI*, Şenol AKIN, Ahmet ALP, Abdulkadir YAĞCI, Mehmet CESUR, Rahmi UYSAL, Fuat BİLGİN, Vedat YEĞEN Fisheries Research Institute, Republic of Turkey Ministry of Agriculture and Forestry, Isparta, Turkey Department of Fisheries, Faculty of Agriculture, Yozgat Bozok University, Yozgat, Turkey Department of Fisheries, Faculty of Agriculture, Kahramanmaraş Sütçü İmam University, Kahramanmaraş, Turkey
Status versus nature of work: pre-service language teachers envisioning their future profession

ABSTRACT Considering the central role of identity in understanding teacher development, this paper addresses the ways in which pre-service language teachers envision their identities as future professionals. The paper is based on a qualitative study of 61 students’ visualisations of their future work during their first semester in language teacher education. The visualisations and accompanying descriptive texts were analysed using the principles of qualitative content analysis. In the analysis, two different ways of perceiving future professions, and thereby identities as professionals, were identified. The first was a nature-oriented perspective that focused on desired characteristics of the profession, its activities, environment and social relationships, and the other a status-oriented perspective that focused on the societal status of the profession. The nature-oriented perspective was further divided into three subcategories that illustrated different career options. The implications of the different ideal professional selves for teacher education are also highlighted.
Chip-scale Optical Resonator Enabled Synthesizer (CORES)

We describe research on an optical-frequency synthesizer in a low-power chip-scale package that provides a laser output with a programmable optical frequency across 50 nm of bandwidth centered at 1550 nm, with a resolution smaller than one part in 1014. Keywords— heterogeneous integration; nonlinear optics; optical frequency combs; tunable lasers
Neuroanatomical Alterations in Patients With Tinnitus Before and After Sound Therapy: A Combined VBM and SCN Study

Many neuroanatomical alterations have been detected in patients with tinnitus in previous studies. However, little is known about the morphological and structural covariance network (SCN) changes before and after long-term sound therapy. This study aimed to explore alterations in brain anatomical and SCN changes in patients with idiopathic tinnitus using voxel-based morphometry (VBM) analysis 24 weeks before and after sound therapy. Thirty-three tinnitus patients underwent magnetic resonance imaging scans at baseline and after 24 weeks of sound therapy. Twenty-six age- and sex-matched healthy control (HC) individuals also underwent two scans over a 24-week interval; 3.0T MRI and high-resolution 3D structural images were acquired with a 3D-BRAVO pulse sequence. Structural image data preprocessing was performed using the VBM8 toolbox. The Tinnitus Handicap Inventory (THI) score was assessed for the severity of tinnitus before and after treatment. Two-way mixed model analysis of variance (ANOVA) and post hoc analyses were performed to determine differences between the two groups (patients and HCs) and between the two scans (at baseline and on the 24th week). Student-Newman-Keuls (SNK) tests were used in the post hoc analysis. Interaction effects between the two groups and the two scans demonstrated significantly different gray matter (GM) volume in the right parahippocampus gyrus, right caudate, left superior temporal gyrus, left cuneus gyrus, and right calcarine gyrus; we found significantly decreased GM volume in the above five brain regions among the tinnitus patients before sound therapy (baseline) compared to that in the HC group. The 24-week sound therapy group demonstrated significantly greater brain volume compared with the baseline group among these brain regions. We did not find significant differences in brain regions between the 24-week sound therapy and HC groups. The SCN results showed that the left superior temporal gyrus and left rolandic operculum were significantly different in nodal efficiency, nodal degree centrality, and nodal betweenness centrality after FDR correction. This study characterized the effect of sound therapy on brain GM volume, especially in the left superior temporal lobe. Notably, sound therapy had a normalizing effect on tinnitus patients.
Adaptive Genetic Algorithm for Feature Weighting in Multi-Criteria Recommender Systems

Recommender Systems (RS) have proven to be a successful personalization technique in this era of ever increasing information overload. Among many available recommendation techniques, Collaborative Filtering (CF) is the most popularly used. However, most of the CF applications use single ratings for recommending items and the use of multi-criteria ratings in the recommendation process is still under-explored. This paper proposes multi-criteria RS based on Adaptive Genetic Algorithm (AGA). The AGA design, which updates the crossover and mutation rates dynamically, is employed to model the users’ preferences for multi-criteria ratings on different attributes of items. The AGA optimizes a user’s preferences for different attributes in the form of a weight vector. Thus, the AGA finds an individual optimal weight vector in relation to each user. The weight vector is used to recommend items to the respective user. The experiments are conducted on Yahoo movies, a well known multi-criteria rating dataset. The experimental results confirm that the AGA based multi-criteria RS outperforms the traditional single criteria based Collaborative Filtering RS and the simple GA based multi-criteria RS.
Effects of environmental estrogens on the growth hormone-insulin-like growth factor system in rainbow trout (Oncorhynchus mykiss)

iii ACKNOWLEDGEMENTS.v LIST OF TABLES.xi LIST OF FIGURES.xii ABBREVIATIONS.xv GENERAL INTRODUCTION.1 Endocrine Disruption . 1 Endocrine Disrupting Compounds.1 Growth Effects of EDCs . 5 Organismal Growth . 6 Other Vertebrates . 19 Additional Endocrine Effects . 20 Signaling Mechanisms . 23 Environmental Factors.29 Conclusion.30 Objectives.31 References.32 CHAPTER 1: EFFECTS OF 17β-ESTRADIOL, 4-NONYLPHENOL, AND β-SITOSTEROL ON THE GROWTH HORMONE-INSULIN-LIKE GROWTH FACTOR SYSTEM AND SEAWATER ADAPTATION OF RAINBOW TROUT (ONCORHYNCHUS MYKISS) . 62 Abstract.62 Introduction . 63 Materials and Methods.65
Depression and anxiety subtypes in Parkinson’s disease

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Depression and anxiety subtypes in Parkinson’s disease Richard G Brown, Sabine Landau, John V Hindle, Jeremy R Playfer, Michael Samuel, Kenneth C Wilson, Catherine S Hurt, Rachel Anderson, Joanna Carnell, Lucy Dickinson, et al.
Paediatric tympanoplasty: surgical outcomes and successful prognostc factors

Introducton: Paediatric tympanoplasty (PT) is a common performed procedure with variable described success rates. There’re many factors that may in? uence the anatomic and functonal surgical outcomes and this study’s aim is to present our PT experience and to assess the factors that may in? uence the PT success. Methods: This retrospectve study is about the children who underwent tympanoplasty in our insttuton (2011-2016). The factors tested as outcome predictors were: patent age, gender, previous adenoidectomy, operated ear side, perforaton cause, site and size, infecton at the tme of surgery, middle ear mucosa state, graf material used, contralateral ear status, pre and postoperatve hearing levels and postoperatve complicatons. We defned anatomic success as an intact graf and functonal success as a minimum of 10-dB gain in the auditory threshold afer a 6 months postoperatve period. Results: Of the 60 patents and 78 procedures included, 29 were female and the patent’s mean age was 12(5) years. When only perforaton closure was considered, the success rate was 71%; however, when we measured the anatomic success plus the auditory threshold gain, the surgical success rate fell to 61%. There is a statstcally signifcant associaton between the middle ear mucosa and anatomic success rate (p=0,02) and between type of tympanoplasty performed and functonal success results (p=0,03). Conclusions: Tympanoplasty’s the frst-line chronic otts media treatment and there’re di? erent factors that in? uence the surgical outcomes. This study should and will be complement with a bigger sample to defne the best prognosis factors. Keywords: Tympanoplasty, Paediatric tympanoplasty, Chronic otts media.
Insider sales and future stock price crash risk: Firm-level analysis

This study investigates whether insiders trade on future stock price crash risk. We find that insider sales are positively associated with future stock price crash risk. This evidence is consistent with the view that insiders are able to assess and anticipate future crash risk and to exploit this information advantage to fulfil personal trading incentives. We also find that the positive association between insider sales and future crash risk is stronger for firms that have high information opacity but weaker for firms that are in financial constraints or in the post SOX period. Additional analysis reveals that insider sales can predict future crash risk as far as 39 months ahead. Our findings have important practical implications. Market participants can use insider sales in ex ante assessing future stock price crash risk, and in appraising the likelihood and extent of insiders’ bad news hoarding which results in the crash risk. Our study should be of particular interest to (i) investors that make portfolio investment decisions, (ii) suppliers and creditors who are concerned about their clients’ creditworthiness, (iii) boards of directors contemplating the design of optimal equity incentive compensation schemes for insiders, and (iv) policymakers regulating insider trading and corporate disclosure transparency.
Learning Fuzzy Taxonomy for Ontology Construction from Semi-Structured Data

Ontology have shown to be a good solution for the problems by providing a formal conceptualization of a special domain that is shared by a group of users. However, the construction of a complete ontology is a tedious and hard task. In this paper, we propose an automatic clustering approach to learn fuzzy taxonomy and build ontology from semi-structured datasets. A general fuzzy concept and hierarchical relationships between the concepts are given. Based on the definition of fuzzy taxonomy, the framework of constructing ontology and the algorithm of generating fuzzy hierarchy are proposed. The model proposed here can describe practical knowledge as taxonomy generally.
SYNTHECEL Dura Repair — Biosynthesized Cellulose for Dura Repair

SYNTHECEL Dura Repair is the fi rst-in-its-class product manufactured from a novel building block called bionanocellulose (BNC, Figure 1). BNC is entirely non–animal-derived and is associated with an inherently low risk of prion transmission. Produced through a biofabrication process, using a proprietary strain of vinegar bacteria, SYNTHECEL Dura Repair belongs to a category of organic materials that closely resembles human dura in terms of appearance, feel, and function. These characteristics were evaluated in a prospective, randomized, multi-center clinical trial comparing SYNTHECEL Dura Repair to other dura repair products. This study demonstrated that SYNTHECEL Dura Repair has superior handling characteristics (including strength and seal quality) and non-inferior safety and effi cacy compared to the other dura repair products tested.
Quality in Use of an Android-based Mobile Application for Calculation of Bone Mineral Density with the Standard ISO/IEC 25022

One of the most critical bone diseases is osteoporosis, which can be evaluated through measurements of bone mineral density. Though there is a lot of commercialized portable about health in general, few are oriented towards bone health and with a lack of user-friendly interface and data management system. This paper presents a mobile application development for the calculation of the Bone Mineral density, which integrates with Google technology. The Mobile-D methodology for the development of mobile applications, due to the sequentially in the processes or stages is used. Bone mineral density (BMD) was calculated using anthropometric regression equations, and an Android-based Mobile Application with Google technology was developed. By using Firebase Authentication and Firebase Storage provided by Google technology, it allows admin to have full control over database management. In short, this mobile application allows the calculation of the BMD of the students and data storage and data uploading to cloud storage for post-processing, online data management system with user authentication. In addition, the Internacional Organization for Standardization / International Electrotechnical Commission (ISO/IEC) 25022 standard was used to evaluate the quality in use of the mobile app, resulting in 93% of quality in use, this app being able to be used by health professionals for better decision-making.
A novel system for continuous temperature swing adsorption: parameter study at bench scale

Capture and subsequent sequestration of CO2 emitted from large point sources such as power plants or industrial facilities is considered as one of the key mitigation measures against climate change for the next decades to come. While the most critical part of CO2 sequestration lies in public acceptance, the capture of CO2 also induces a parasitic energy demand and thus a ects the operating costs of the actual CO2 emitting process. For this reason, a great e ort has been put world-wide into the development of cost e ective CO2 capture technologies and the research group Zero Emission Technology (ZET) at the Vienna University of Technology is among the leaders in this eld. Recently, a novel reactor design for post-combustion CO2 capture based on temperature swing adsorption (TSA) has been developed by the ZET group together with an international oil and gas company. A bench scale unit (BSU) has been built basing on the developed reactor design in order to conduct experimental investigations, concerning the performance of the TSA process. This work presents results obtained from an extensive experimental campaign that has been conducted within the BSU using a PEI impregnated porous silica as adsorbent material. A variety of di erent operating conditions were tested to get an in-depth understanding of the correlations between operating conditions and performance of the unit. When a ue-gas with a CO2 concentration of 7, 7vol%CO2 and a ow rate of 16, 8Sm/h was fed to the cooled adsorber and 8Sm/h steam was used to regenerate sorbents at a circulating rate of around 30kg/h, a capture e ciency of over 80% was achieved at various occasions. Furthermore, it was shown that interaction between steam, used for regeneration, and the sorbent material, entailed improved process performance through an internal heat displacement e ect. Reduction of the stripping steam feeding rate had a comparatively small in uence on the unit's performance; such, that a reduction of the steam ow rate for regeneration can be considered in future experiments. Increased feed rates of CO2 to the adsorber, showed an improved capture rate (in kgCO2capt./day), however, at signi cantly lower CO2 capture e ciencies. The uidization velocity as well as the bed height of the uidized bed stages had a strong impact on the CO2 capture e ciency, most likely through a ecting the actual heat transfer rate with the immersed stage heat exchangers. Finally, it was observed that introduction of small amounts of purge gas into the moving bed section after the desorber solids outlet, reduces the steam slip from the desorber to the adsorber signi cantly, which proved the feasibility of this
AUTOMATING POWERLINE INSPECTION: A NOVEL MULTISENSOR SYSTEM FOR DATA ANALYSIS USING DEEP LEARNING

Abstract. Powerline infrastructure provides the backbone for the electricity supply of industrial, administrative and private sectors. Its maintenance requires regular inspections, that are still largely carried out manually. In this work, we propose an automated inspection system instead. We review current inspection processes as a baseline, give an overview of relevant inspection criteria, propose a suitable multi-modal sensor system, and discuss methods to automate the inspection tasks. In our system, we particularly focus on the high-level organization of the sensor data and inspection results to form a Digital Twin of the power line, that allows operators to browse through the recorded data in a meaningful way and review the status of their powerline from the desk.