Miracle-dz/UniFAR-FTUs huggingface dataset:
Exposure modeling and exposure measurement error correction in health outcome models with longitudinal data structure

The human organism is permanently exposed to various environmental factors, which influence its performance, e.g. climatic conditions, nutrition intake behavior or air quality. This thesis focuses on the human exposure to particulate matter. The “Augsburger Umweltstudie” (2007–2008) was conducted at the KORA study center in Augsburg and aimed at the investigation of the association between particulate matter and human health. An accompanying validation study was conducted in 2011 in order to collect information about the errors in the measurement of particulate matter. The complexity of these errors arises from different data sources: 1. ) The usage of population-specific exposure measurements, e.g. from one or several fixed site measurement stations, instead of personal measurements involves a Berkson type error. The deviations between personal and populations–specific measurements are driven by the microenvironment of the person and the climatic conditions. In the first part of the work, a two–level exposure model is developed for the association between the fixed–site and the personal exposure measurements of the validation study including the selection of relevant covariates and the appropriate consideration of categorical covariates in the analysis of longitudinal data. 2. ) The mobile devices used for personal exposure measurements exhibit classical measurement error, which is partially device–specific and autocorrelated. 3. ) In order to use as much information as possible missing personal exposure measurements are filled in with population–specific exposure measurements resulting in a mixture of Berkson and classical measurement error. The second part of the work aims at the development and application of methods to include knowledge about Berkson, classical and mixture error into regression models of the health outcome. Therefore, the method–of–moments is extended to longitudinal data and to the different types of errors with individual–specific and autocorrelated structures. Validation studies and expert knowledge provide information about the size of the measurement error, but prior knowledge is often afflicted with uncertainty. Approaches for the adequate inclusion of prior knowledge about the measurement errors in the Bayesian health outcome model are evaluated in the third part of the thesis. The role of prior knowledge in regression models with an error–prone covariate differs from conventional Bayesian regression models and is strongly affected by the interaction between the parameters in the model. The thesis is closed with the application of the developed method–of–moments and Bayesian approach to the Augsburger Umweltstudie by integrating information from the validation studies.
A Trust Aware Authentication Scheme for Wireless Sensor Networks Optimized by Salp Swarm Optimization and Deep Belief Networks

Presently, the integration of Internet of Things (IoT) and wireless sensor networks (WSN) offers a broad research field for enabling advanced networked services. It remains popular due to its applicability in various real time areas such as healthcare, environmental monitoring, factory configuration, and many more. While the benefits of WSNs are many, security is still a major concern due to the intrinsic prevalence of wireless links in the network. In order to achieve security and reliable communication, an optimized authentication scheme becomes necessary. Therefore, this research work introduces a novel salp swarm optimization with deep belief network based trust aware authentication (SSDBN-TAA) scheme for WSN. Primarily, the SSDBN-TAA technique undergoes a weighted clustering scheme to partition the network into a collection of clusters. Additionally, a trust factor is collectively derived between the nodes that exist in the network, and the nodes exceeding the threshold trust value are considered as valid. An SSDBN model is utilized for dynamically selecting the threshold trust value, and the hyperparameters of the DBN model are optimally adjusted using the salp swarm algorithm (SSA). The design of SSA is efficient and thereby enhances the authentication performance. To explore the enhanced outcomes of the SSDBN-TAA technique, we conduct extensive comparative experiments to ensure the enhanced outcomes of the SSDBN-TAA system dominate the present state of art approaches.
EqualNet: A Secure and Practical Defense for Long-term Network Topology Obfuscation

—Path tracing tools, such as traceroute, are simple yet fundamental network debugging tools for network operators to detect and fix network failures. Unfortunately, adversaries can also use such tools to retrieve previously unknown network topology information which is key to realizing sophisticated Denial-of-Service attacks, such as Link Flooding Attacks (LFAs), more efficiently. Over the last few years, several network obfuscation defenses have been proposed to proactively mitigate LFAs by exposing virtual (fake) topologies that conceal potential bottleneck network links from adversaries. However, to date there has been no comprehensive and systematic analysis of the level of security and utility their virtual topologies offer. A critical analysis is thus a necessary step towards better understanding their limitations and building stronger and more practical defenses against LFAs. In this paper, we first conduct a security analysis of the three state-of-the-art network obfuscation defenses. Our analysis reveals four important, common limitations that can significantly decrease the security and utility of their virtual topologies. Motivated by our findings, we present EqualNet, a secure and practical proactive defense for long-term network topology obfuscation that alleviates LFAs within a network domain. EqualNet aims to equalize tracing flow distributions over nodes and links so that adversaries are unable to distinguish which of them are the most important ones, thus significantly increasing the cost of performing LFAs. Meanwhile, EqualNet preserves subnet information, helping network operators who use path tracing tools to debug their networks. To demonstrate its feasibility, we implement a full prototype of it using Software-Defined Networking (SDN) and perform extensive evaluations both in software and hardware. Our results show that EqualNet is effective at equalizing the tracing flow distributions of small, medium and large networks even when only a small number of routers within the network support SDN. Finally, we analyze the security of EqualNet against a wide variety of attacks.
Role of Estrogens in Menstrual Migraine

Migraine is a major neurological disorder affecting one in nine adults worldwide with a significant impact on health care and socioeconomic systems. Migraine is more prevalent in women than in men, with 17% of all women meeting the diagnostic criteria for migraine. In women, the frequency of migraine attacks shows variations over the menstrual cycle and pregnancy, and the use of combined hormonal contraception (CHC) or hormone replacement therapy (HRT) can unveil or modify migraine disease. In the general population, 18–25% of female migraineurs display a menstrual association of their headache. Here we present an overview on the evidence supporting the role of reproductive hormones, in particular estrogens, in the pathophysiology of migraine. We also analyze the efficacy and safety of prescribing exogenous estrogens as a potential treatment for menstrual-related migraine. Finally, we point to controversial issues and future research areas in the field of reproductive hormones and migraine.
Hybrid Trust Multi-party Computation with Trusted Execution Environment

—Trusted execution environment (TEE) such as Intel SGX relies on hardware protection and can perform secure multi-party computation (MPC) much more efﬁciently than pure software solutions. However, multiple side-channel attacks have been discovered in current implementations, leading to various levels of trust among different parties. For instance, a party might assume that an adversary is unable to compromise TEE, while another might only have a partial trust in TEE or even does not trust it at all. In an MPC scenario consisting of parties with different levels of trust, one could fall back to pure software solutions. While satisfying the security concerns of all parties, those who accept TEE would not be able to enjoy the beneﬁt brought by it. In this paper, we study the above-mentioned scenario by proposing H YBR TC, a generic framework for evaluating a function in the hybrid trust manner. We give a security formalization in universal composability (UC) and introduce a new cryptographic model for the TEEs-like hardware, named multifaceted trusted hardware F TH , that captures various levels of trust perceived by different parties. To demonstrate the relevance of the hybrid setting, we give a distributed database scenario where a user completely or partially trusts different TEEs in protecting her distributed query, whereas multiple servers refuse to use TEE in protecting their sensitive databases. We propose a maliciously-secure protocol for a typical select-and-join query in the multi-party setting. Experimental result has shown that on two servers with 2 20 records in datasets, and with a quarter of records being selected, only 165.82s is incurred which achieves more than 18 , 752 . 58 × speedups compared to cryptographic solutions.
Evaluation of the effectiveness of infectious diseases immunoprophylaxis (review of our own research on the model of tick-borne encephalitis)

Introduction: The tick-borne encephalitis (TBE) virus causes a dangerous neuroinfection in humans can serve as a model for studying the mechanisms of interaction of pathogens with specific antibodies during active and passive immunization. Several commercial vaccines are currently used against TBE. This review analyzes long-term studies aimed at explaining the active and passive efficacy of specific immunoprophylaxis against TBE. Methods: The effectiveness of "Encepur® adult" TBE vaccine has been studied in terms of seroconversion and strength of the immune response by serological reactions, namely IFA, ELISA and neutralizing were reviewed. Results: Rapid elimination of the virus (after 1-2 days) can occur in vaccinated individuals with antibodies in titers of more than 1: 400. Persons with antibodies in titers of 1: 100 and 1: 200, most likely, should be offered mandatory revaccination. It should also take into account the duration of the retention of post-vaccination antibodies. Conclusion: In the year of TBE vaccination, the immune response was at a high level and practically did not differ. A particularly high level of immune protection was observed in persons who were vaccinated by a combination of TBE vaccines of various producers.
Improved Artificial Neural Network with State Order Dataset Estimation for Brain Cancer Cell Diagnosis

Brain cancer is one of the cell synthesis diseases. Brain cancer cells are analyzed for patient diagnosis. Due to this composite cell, the conceptual classifications differ from each and every brain cancer investigation. In the gene test, patient prognosis is identified based on individual biocell appearance. Classification of advanced artificial neural network subtypes attains improved performance compared to previous enhanced artificial neural network (EANN) biocell subtype investigation. In this research, the proposed features are selected based on improved gene expression programming (IGEP) with modified brute force algorithm. Then, the maximum and minimum term survivals are classified by using PCA with enhanced artificial neural network (EANN). In this, the improved gene expression programming (IGEP) effectual features are selected by using remainder performance to improve the prognosis efficiency. This system is estimated by using the Cancer Genome Atlas (CGA) dataset. Simulation outputs present improved gene expression programming (IGEP) with modified brute force algorithm which achieves accurate efficiency of 96.37%, specificity of 96.37%, sensitivity of 98.37%, precision of 78.78%, F-measure of 80.22%, and recall of 64.29% when compared to generalized regression neural network (GRNN), improved extreme learning machine (IELM) with minimum redundancy maximum relevance (MRMR) method, and support vector machine (SVM).
Functional, Physicochemical, Rheological, Microbiological, and Organoleptic Properties of Synbiotic Ice Cream Produced from Camel Milk Using Black Rice Powder and Lactobacillus acidophilus LA-5

Camel milk has become more popular among customers in recent years as a result of its therapeutic effects. In many parts of the world, it is considered one of the primary components of human nutrition. The present study aimed to develop a novel synbiotic ice cream from camel milk formulated with black rice powder (BRP) and investigate the viability of probiotic bacteria (Lactobacillus acidophilus LA-5) during the storage period (60 days). Skim milk powder was replaced by BRP at levels of 0, 25, 50, and 75%. The produced ice cream was examined for some physicochemical, rheological, microbiological, and sensorial properties. The obtained results indicated that the incorporation of BRP into ice cream blends resulted in significant increases in the overrun, viscosity, and melting resistance of ice cream samples (p < 0.05). However, the freezing point decreased with increasing the proportion of BRP in the blend. The sensory evaluation results showed that the most acceptable treatments were those formulated with 25% and could be increased to 50% of BRP with no significant differences. The incorporation of BRP improved the viability of Lactobacillus acidophilus LA-5 in ice cream samples over 60 days of storage. Collectively, a synbiotic camel milk ice cream formulated with black rice powder was produced that, in turn, enhanced the physicochemical and rheological properties of ice cream samples and produced a significant protective effect on the viability of probiotic bacteria.
Efficacy of Whey Protein Film Incorporated with Portuguese Green Tea (Camellia sinensis L.) Extract for the Preservation of Latin-Style Fresh Cheese

Fresh cheese composition favors the growth of microorganisms and lipid oxidation, leading to a short shelf life. Whey protein concentrates can be used to produce active films in which green tea (Camellia sinensis L.) extract, rich in bioactive compounds, namely catechins, can be incorporated. Thus, the main objective of this study was to evaluate the efficacy of an edible active film, incorporated with green tea extract, to preserve goat and mixture (goat and sheep) fresh cheeses. Our results demonstrated that Portuguese green teas (antioxidant activity coefficient—AAC = 746.7) had superior antioxidant capacity to that of the evaluated Asian green tea (AAC = 650). Furthermore, green tea produced from the leaves of the new Portuguese Chá Camélia tea plantation had the highest potential to retain the antioxidant capacity (97.3%). Additionally, solid–liquid extractions led to extracts with higher antioxidant activity (AAC = 1500), but Soxhlet extractions presented higher yield (43%). Furthermore, the active film incorporated with Portuguese green tea extract exhibited a high antioxidant capacity (AAC ≈ 595.4). In addition, the active film effectively delayed the lipid oxidation of the evaluated fresh cheeses (3.2 mg MDA Eq/kg) when compared with the control (4.2 mg MDA Eq/kg). Moreover, the active films effectively inhibited the growth of microorganisms, especially E. coli (1.5 × 10 CFU/g), when compared with the blank (2.2 × 102 CFU/g). This study suggests that the new whey protein film incorporated with Portuguese green tea extract has the potential to be used to extend fresh cheese shelf life.
Integrated Three-Level Dual-Phase Inverter

In view of reducing the number of inverter legs that provide dual-phase, three-level output voltages (as may be needed in an uninterruptible power supply), and that also provide a wide range of output frequencies (as needed in an advanced motor drive system with wide speed ranges), a three-level, dual-phase inverter topology is presented in this paper. Its three-level attribute was based on the F-type inverter topological concept, and its dual-output feature was based on the common representation of the inverter-leg concept. The proposed inverter could deliver single- and three-phase voltages to corresponding one- and three-phase loads, in common or different frequency modes of operation. A boundary between these modes of operation was established for the proposed inverter. An additional possibility of either operation in the one-phase or the three-phase system was offered by the inverter configuration. A modified carrier-based sinusoidal pulse-width modulation scheme is presented for the control of the inverter topology. The performances of the dual-phase inverter are given in the simulation results and demonstrated with a hardware prototype.
Chimney Identification Tool for Automated Detection of Hydrothermal Chimneys from High-Resolution Bathymetry Using Machine Learning

Identifying the locations of hydrothermal chimneys across mapped areas of seafloor spreading ridges unlocks the ability to research questions about their correlations to geology, the cooling of the lithosphere, and deep-sea biogeography. We developed a Chimney Identification Tool (CIT) that utilizes a Convolutional Neural Network (CNN) to classify 1 m gridded AUV bathymetry and identify the locations of hydrothermal vent chimneys. A CNN is a type of Machine-Learning model that is able to classify raster data based on the shapes and textures in the input, making it ideal for this task. The criteria that have been used in previous manual classifications of chimneys have focused on the round base and spire shape of the features, and are not easily quantifiable. Machine-Learning techniques have previously been implemented with sonar data to classify seafloor geology, but this is the first application of these methods to hydrothermal systems. In developing the CIT, we compiled the bathymetry data from two rasters from the Endeavor Ridge—each gridded at a 1 m resolution—containing 34 locations of known hydrothermal chimneys, and from the 92° W segment of the Galapagos Spreading Center (GSC) containing 14. The CIT produced a primary group of outputs with 96% agreement with the manual classification; moreover, it correctly caught 29 of the 34 known chimneys from Endeavor and 10 of the 14 from the GSC. The CIT is trained to identify features with the characteristic shape of a hydrothermal vent chimney; therefore, it is susceptible to the misclassification of unusually shaped cases, given the limited training data. As a result, to provide the option of having a more inclusive application, the CIT also produced a secondary group of output locations with 61% agreement with the manual classification; moreover, it caught three of the four additional known chimneys from the GSC and four of the five from Endeavor. The CIT will be used in future investigations where an inventory of individual chimneys is important, such as the cataloguing of off-axis hydrothermal venting and the investigation of chimney distribution in connection to seafloor eruptions.
A Locally Modal B-Spline Based Full-Vector Finite-Element Method with PML for Nonlinear and Lossy Plasmonic Waveguide

Abstract In this paper, we develop a B-spline finite-element method (FEM) based on a locally modal wave propagation with anisotropic perfectly matched layers (PMLs), for the first time, to simulate nonlinear and lossy plasmonic waveguides. Conventional approaches like beam propagation method, inherently omit the wave spectrum and do not provide physical insight into nonlinear modes especially in the plasmonic applications, where nonlinear modes are constructed by linear modes with very close propagation constant quantities. Our locally modal B-spline finite element method (LMBS-FEM) does not suffer from the weakness of the conventional approaches. To validate our method, first, propagation of wave for various kinds of linear, nonlinear, lossless and lossy materials of metal-insulator plasmonic structures are simulated using LMBS-FEM in MATLAB and the comparisons are made with FEM-BPM module of COMSOL Multiphysics simulator and B-spline finite-element finite-difference wide angle beam propagation method (BSFEFD-WABPM). The comparisons show that not only our developed numerical approach is computationally more accurate and efficient than conventional approaches but also it provides physical insight into the nonlinear nature of the propagation modes.
Flexible Piezoelectric Tactile Sensor With Cilia-Inspired Structures Based on Electrospun PVDF/Fe3O4 Nanofibers

Flexible tactile sensors have promising applications at the field of intelligent robotics. However, manufacturing flexible tactile sensors with excellent stability and high sensitivity remain a great challenge. In the present work, polyvinylidene fluoride (PVDF)-based nanofibers are prepared by electrospinning loaded with Fe3O4 nanoparticles (NPs). X-ray diffraction (XRD) and attenuated total reflection Fourier transform infrared spectroscope (ATR-FTIR) results indicate that the addition of Fe3O4 NPs promotes the generation of $\beta $ -phase in PVDF polymer, which greatly improves piezoelectric properties of composite nanofibers. Simultaneously, a flexible piezoelectric tactile sensor with cilia-inspired structures (PTSCS) based on PVDF/Fe3O4 nanofibers is proposed, which consists of the substrate with cilia structures, Ag electrodes, composite nanofiber membrane, and nanofiber protective layer. The PTSCS demonstrates a higher output voltage compared to sensors with bump and planar structures, which also exhibits high robustness, with a stable output after 30000 cycles of vibration testing. More importantly, the PTSCS demonstrates superior performance in recognizing surface texture and temperature of objects. The flexible PTSCS has potential use in areas such as intelligent soft robotics and material recognition.
FirmWire: Transparent Dynamic Analysis for Cellular Baseband Firmware

—Smartphones today leverage baseband processors to implement the multitude of cellular protocols. Basebands execute ﬁrmware, which is responsible for decoding hundreds of message types developed from three decades of cellular standards. Despite its large over-the-air attack surface, baseband ﬁrmware has received little security analysis. Previous work mostly analyzed only a handful of ﬁrmware images from a few device models, but often relied heavily on time-consuming manual static analysis or single-function fuzzing. To ﬁll this gap, we present F IRM W IRE , the ﬁrst full-system emulation platform for baseband processors that executes unmod-iﬁed baseband binary ﬁrmware. F IRM W IRE provides baseband-speciﬁc APIs to easily add support for new vendors, ﬁrmware images, and security analyses. To demonstrate F IRM W IRE ’s scalability, we support 213 ﬁrmware images across 2 vendors and 9 phone models, allowing them to be executed and tested. With these images, F IRM W IRE automatically discovers and bridges internal baseband APIs, allowing protocol messages to be injected with ease. Using these entry points, we selected the LTE and GSM protocols for fuzzing and discovered 7 pre-authentication memory corruptions that could lead to remote code execution – 4 of which were previously unknown. We reproduced these crashes over-the-air on real devices, proving F IRM W IRE ’s emulation accuracy. F IRM W IRE is a scalable platform for baseband