Miracle-dz/UniFAR-FTUs huggingface dataset:
SealFS: A Stackable File System for Tamper-evident Logging

—Log analysis is essential for a forensic investigation. Upon intrusion, log ﬁles are usually forged in order to hide or fake evidence. If the system is completely compromised, malicious code can be executed in kernel or hypervisor mode making even signed log ﬁles vulnerable. As a countermeasure, some systems send the log ﬁles to another system through the network. This solution is not always suitable or desirable and it just shifts the problem elsewhere. The log ﬁles need to be preserved on another networked machine which may itself be attacked. In this paper, we present a very simple scheme to authenticate local log ﬁles based on a forward integrity model. We can authenticate the logged data generated, starting from boot time to the instant that the malicious code elevates privileges. This tamper-evident scheme does not depend on special security hardware or securing a distributed system. We also present a prototype implementation of this scheme, SealFS. Our implementation, which showcases this approach, is a stackable ﬁle system for Linux. It enables tamper-evident logging to all existing applications, provides backwards compatibility and instant deployability. Last, we present a performance evaluation of this prototype that shows the viability of this approach.
A Case Study on the Belief and Practice of Curriculum Ideological and Political Education of Foreign Language Teachers in Colleges and Universities

: This study takes the curriculum ideological and political education carried out by two teachers who teach English as the second language in a municipal university as the research object. Through the method of case study, this paper analyzes the belief and teaching practice of curriculum ideological and political education of two teachers. The study finds that the two teachers’ beliefs of curriculum ideological and political education have a direct impact on their teaching practice. In addition, it is found that students' attitude towards curriculum ideological and political education in foreign language courses is positive. The findings show that college foreign language teachers can effectively guide students' value while improving students' language ability. This study aims to provide practical experience and reference for the curriculum ideological and political education of foreign language courses in colleges and universities.
Kidney Stone Detection Using Image Processing and Convolutional Neural Networks

:- Renal calculi, often known as kidney stones, are solid masses made mostly of crystals. Detecting the perfect and correct site of urinary calculus is essential for surgical procedures. Because CT pictures include greater speckle noise, manually detecting urinary calculi is difficult, hence automated systems for detecting kidney stones in CT images are necessary. CT imaging is one of the imaging modalities available for diagnosing kidney abnormalities, which can include changes in shape and position, as well as swelling of the limb; other kidney abnormalities include the creation of stones, cysts, urine obstruction, congenital defects, and malignant cells. This research presents a new method for detecting kidney stones. This project is divided into two parts: kidney CT image classification and kidney stone detection. For renal CT image classification, VGG16 convolutional neural networks are employed, and for kidney stone identification, Fuzzy c means clustering is used. Filtering and processing of kidney CT scans removes undesired noise and enhances the image. Using complicated techniques such as the Convolutional Neural Network (CNN) model, classify the image using the SoftMax classifier algorithm, and ultimately detect the kidney stone using FCM. This technology will produce more accurate findings and will do it faster than the previous way.
TimeEval: A Benchmarking Toolkit for Time Series Anomaly Detection Algorithms

Detecting anomalous subsequences in time series is an important task in time series analytics because it serves the identification of special events, such as production faults, delivery bottlenecks, system defects, or heart flicker. Consequently, many algorithms have been developed for the automatic detection of such anomalous patterns. The enormous number of approaches (i. e., more than 158 as of today), the lack of properly labeled test data, and the complexity of time series anomaly benchmarking have, though, led to a situation where choosing the best detection technique for a given anomaly detection task is a difficult challenge. In this demonstration, we present TimeEval, an extensible, scalable and automatic benchmarking toolkit for time series anomaly detection algorithms. TimeEval includes an extensive data generator and supports both interactive and batch evaluation scenarios. With our novel toolkit, we aim to ease the evaluation effort and help the community to provide more meaningful evaluations.
Modellbasierte Entwicklung eines mobilen Scouts für extreme Gelände in der planetaren Exploration

Robotic exploration of planetary bodies has historically been dominated by wheeled rovers. These rovers are successful, but due to the danger of getting stuck they have to be operated very carefully. In this work an alternative robotic locomotion concept is examined that has been proven to be successful in traversing rough terrain on earth: the rimless wheel, also known as “whegs”. This approach to robotic locomotion represents a compromise between wheels and legs by using discrete footholds to push the robot forward but still performing full wheel revolutions. The former eliminates the need for a continuous track that conventional wheels have and the latter considerably simplifies the mechanism and its control. These aspects as well as its inherent redundancy (multiple spokes per wheel and multiple wheels per robot) make the rimless wheel particularly suited for planetary exploration. Analytical examinations of the kinematics of rimless wheels and different mechanical models of compliant spokes are conducted. Multi-body simulations are then used to verify the conclusions drawn from analytical considerations about running on a plane, step climbing and movement on general unstructured terrain. Suitable parameters of a wheel for a future scout rover are derived by means of a parameter variation. A single rimless wheel is designed and tested to verify the calculations and simulations. The experiments are conducted on a novel testbed at DLR-SR, the “Terramechanics Robotics Locomotion Laboratory”, and support the assertion that the developed compliant locomotion system has the potential to facilitate movement on rough terrain as well as to increase the efficiency of locomotion on hard and flat surfaces.
PAMELLA CRISTINE ANUNCIAÇÃO EFFECT OF SORGHUM (SORGHUM BICOLOR L.) ON METABOLIC VARIABLES IN ADULTS

Purpose: Glycaemic control is essential to prevent the manifestation of diabetes in predisposed individuals and the development of associated comorbidities. It is believed that sorghum may modulate the glucose response. In this study, we investigated the effect of extruded sorghum consumption, and the profile of bioactive compounds, on postprandial glycaemia of a subsequent meal in normal weight and normoglycaemic subjects. Methods: This was a randomized, single-blind, crossover designed study. After a 12 h overnight fasting, ten subjects reported to the laboratory to participate in four experimental sessions, and consumed one of three sorghum test drinks: sorghum P 3DXAs (with proanthocyanidins—P and rich in 3-deoxyanthocyanidins—3-DXAs); 3DXAs (without proanthocyanidins and rich in 3-DXAs); and control (low in 3-DXAs and without proanthocyanidins); or a non-sorghum drink. 30 min later, the subjects consumed a glucose solution (25 g glucose). Glycaemic response was monitored at times 0 (before glucose solution), 15, 30, 45, 60, 90, 120 min (after glucose solution consumption). The incremental areas under the glycaemic curve (iAUC) were calculated by the trapezoidal method. Results: Intake of P 3-DXAs drink before the glucose solution resulted in a postprandial iAUC lower than the other sorghum test drinks. Sorghum drinks minimized the postprandial glycaemia peak.
Decoding Working Memory Load from High-Density EEG

This study investigates the mental representations that underlie working memory (WM) processing. High-density EEG was recorded from 20 subjects performing a verbal WM task with three memory load conditions. A classifier was trained to decode pairs of load conditions from EEG potential values and a time course of classifier decoding accuracy was obtained. Notably, the decoding accuracy curve exhibits three episodic increases in performance that coincide with each stage of WM processing. Furthermore, decoding accuracy during memory delay drops to chance level when potential values are used, but there is a re-emergence in decoding accuracy when functional connectivity values are used as feature inputs. Likewise, the same classifier exhibits much more robust generalization ability when connectivity values are used as features in place of EEG potential values. Lastly, the correlation between decoding accuracy during different WM stages was analyzed. The correlation between retention 2 and retrieval is not significant when using potential decoding values, but connectivity decoding exhibits a significant positive relationship. Taken together, these findings support the notion that WM processing may not be accompanied by persistent activity and instead depends on changes in short-term synaptic plasticity (STSP) that are governed by functional connectivity.
Working at the limit: a review of thermodynamics and optimality of the Earth system

Abstract. Optimality concepts related to energy and entropy have long been proposed to govern Earth system processes, for instance in the form of propositions that certain processes maximize or minimize entropy production. These concepts, however, remain quite obscure, seem contradictory to each other, and have so far been mostly disregarded. This review aims to clarify the role of thermodynamics and optimality in Earth system science by showing that they play a central role in how, and how much, work can be derived from solar forcing and that this imposes a major constraint on the dynamics of dissipative structures of the Earth system. This is, however, not as simple as it may sound. It requires a consistent formulation of Earth system processes in thermodynamic terms, including their linkages and interactions. Thermodynamics then constrains the ability of the Earth system to derive work and generate free energy from solar radiative forcing, which limits the ability to maintain motion, mass transport, geochemical cycling, and biotic activity. It thus limits directly the generation of atmospheric motion and other processes indirectly through their need for transport. I demonstrate the application of this thermodynamic Earth system view by deriving first-order estimates associated with atmospheric motion, hydrologic cycling, and terrestrial productivity that agree very well with observations. This supports the notion that the emergent simplicity and predictability inherent in observed climatological variations can be attributed to these processes working as hard as they can, reflecting thermodynamic limits directly or indirectly. I discuss how this thermodynamic interpretation is consistent with established theoretical concepts in the respective disciplines, interpret other optimality concepts in light of this thermodynamic Earth system view, and describe its utility for Earth system science.
Spin wave excitations in the antiferromagnetic Heisenberg-Kondo model for heavy fermions

Recent inelastic neutron scattering experiments in CeIn$_{3}$ and CePd$_{2}$Si$_{2}$ single crystals measured spin wave excitations at low temperatures. These two heavy fermion compounds exhibit antiferromagnetic long-range order, but a strong competition between the Ruderman-Kittel-Kasuya-Yosida(RKKY) interaction and Kondo effect is evidenced by their nearly equal N\'eel and Kondo temperatures. Our aim is to show how magnons such as measured in the antiferromagnetic phase of these Ce compounds, can be described with a microscopic Heisenberg-Kondo model introduced by J.R. Iglesias, C.Lacroix and B.Coqblin, used before for studies of the non-magnetic phase. The model includes the correlated Ce-$4 f$ electrons hybridized with the conduction band, where we also allow for correlations, and we consider competing RKKY (Heisenberg-like $J_{H} $) and Kondo ($J_{K}$) antiferromagnetic couplings. Carrying on a series of unitary transformations, we perturbatively derive a second-order effective Hamiltonian which, projected onto the antiferromagnetic electron ground state, describes the spin wave excitations, renormalized by their interaction with correlated itinerant electrons. We numerically study how the different parameters of the model influence the renormalization of the magnons, yielding useful information for the analysis of inelastic neutron scattering experiments in antiferromagnetic heavy fermion compounds. We also compare our results with the available experimental data, finding good agreement with the spin wave measurements in cubic CeIn$_3$.
Hybrid predictive control strategy for a low-cost converter-fed IM drive

The converter consisting of a single-phase half-bridge rectifier and four-switch three-phase inverter is a low-cost power converter with complicated operating constraints. It is difficult to control by the conventional strategies. This study proposes a hybrid predictive control strategy with dual loops for this converter. In the outer loop, a proportional–integral controller is designed to regulate the dc-link voltage, capacitor voltage balancing, and the speed and flux of induction motors (IM). Also, in the inner loop, the finite set model predictive control is employed to control the ac input current and stator currents of the IM. The major advantages of the control strategy include: (i) easy to deal with complicated constraints and manage multiple control targets; (ii) without the need of modulators; (iii) good dynamic response. The simulation and experimental results indicate that the proposed method can guarantee the stable operation and good performance.
Using System Dynamics modelling to frame environmental voluntary commitment programs: the French experience

This study stems from a research project aimed at investigating the factors facilitating and hampering the success of the EVE (Engagements Volontaires pour l’Environnement) program recently launched in France. Similarly to other countries, in which successfully environmental voluntary commitment programs are currently in place (SmartWay in the USA), EVE program aspires to improve transport operators efficiency and to reduce CO2 emissions in the environment. However, the multiple relationships between public and private actors involved in the program (ranging from the program coordinator to transport organisations), the complexity of transport and logistics sector (including the differences among the transport operators sub-groups) may prevent the achievement of the desired project outcomes. With the aim to support the EVE program coordinator to design and assess effective policies to pursue the expected goals, a System Dynamics (SD) model will be used. As the research project is in the early stage, the paper outlines the research path, main feedback loops and a preliminary stock-and-flow structure to be used in group-model building sessions with project’ participants.
Continuous Appropriately-Oriented Collision Detection Algorithm for Physics-Based Simulations

- The presented algorithm enables the continuous detection of the appropriate collision surface for a point penetrating into a convex bounding area in two-dimensional space. The appropriate collision surface is resolved through the transient evaluation of the separating axis theorem which tracks the last bounding area surface to lose separation from the point, thus collision. This ensures faithful application of collision restitution forces. The algorithm is presented in two forms: looping conditional statements, and binary matrix operations. The initial implementation of the algorithm is also presented and discussed.
THREE ESSAYS ON STOCK MARKET VOLATILITY

Investors will have the information in the idiosyncratic volatility spread when using two different models to estimate idiosyncratic volatility. In a theoretical framework, we show that idiosyncratic volatility spread is related to the change in beta and the new betas from the extra factors between two different factor models. Empirically, we find that idiosyncratic volatility spread predicts the cross section of stock returns. The negative spread-return relation is independent from the relation between idiosyncratic volatility and stock returns. The result is driven by the change in beta component and the new beta component of the spread. The spread-relation is also robust when investors estimate the spread using a conditional model or EGARCH method. JEL Classifications: G3, G31, C51.
Boundary Slip and Surface Interaction： A Lattice Boltzmann Simulation

影响的因素在 Couette 几何学流动滑动长度借助于包括非理想的液体液体和液体墙相互作用的一个二阶段的 mesoscopic 格子 Boltzmann 模型被分析。 影响边界的主要因素滑倒是在液体液体和液体墙粒子之间的相互作用的力量。 另外的因素体积压力例如液体粘性，可以也改变 slip 长度。 我们发现边界仅仅滑倒发生在某个密度(体积压力) 下面。 如果密度足够大， slip 长度将趋于到零。 在我们的模拟，墙附近的低密度层不需要被要求先验但是从内在的非理想的 mesoscopic 动力学自然地出现。 是低密度层导致边界滑倒。 结果可能是有用的在微流动的滑动上理解最近的试验性的观察。
Sustainable System Design at Aalborg University, Denmark

In this paper, we discuss to what extent STS knowledge is needed and contributes to complex problem sol­ving in design engineering. We discuss this based on experiences with the program of Sustainable Design Engineering at Aalborg University in Denmark. We claim that STS contributes in three meaningful ways: by supporting a critical view on existing knowledge; by providing tools to take into account controversies and delegation of agency; and by reasserting design as a social process.
Comparison analysis of leaf and flower extraction of clove which have the potential as larvacida

Efforts to control Dengue Fever both biologically, chemically and behaviourally continue to be pursued. One of the effects of chemical control is resistance to mosquitoes and environmental pollution. Alternative efforts by using larvicide from natural ingredients, one of which is derived from the clove plant. Cloves contain eugenol, saponins, flavonoids and tannins that can damage the nervous system of insects, interfere with food absorption and reduce digestive enzymes in insects. This study was to analyze the yield of clove flower and leaf extracts as an alternative to Ae. aegypti larvicides. This research is a laboratory experiment using a variety of methods and solvent variations. The results obtained were the yield of leaf extracts and flowers containing flavonoids, saponins, tannins which were the most larvicidal compounds found in leaves with a reflux process with Ethyl acetate solvent, which was 20.36%. The highest levels of flavonoids were found in the leaves through a reflux process with 96% Ethanol solvent levels of 29.53%. The highest levels of saponins were found in the leaves through the Soxhlet process with 96% ethanol as solvent, 9.92%. Clove flowers and leaves containing flavonoids and saponins can be used as an alternative as larvicide.
Cost-Based Design and Selection of Point Absorber Devices for the Mediterranean Sea

Sea wave energy is one of the most promising renewable sources, even if relevant technology is not mature enough for the global energy market and is not yet competitive if compared with solar, wind and tidal current devices. Particularly, among the variety of wave energy converters developed in the last decade, heaving point absorbers represent one of the most feasible and studied technologies, as shown by the small-scale testing and full-scale prototypes, deployed in the last years throughout the world. Nevertheless, the need for further reduction of the energy production costs requires a specialized design of wave energy converters, accounting for the restraints provided by the power take-off unit and the device operational profile. Hence, actual analysis focuses on a new cost-based design procedure for heaving point absorbers. The device is equipped with a floating buoy with an optional fully submerged mass connected, by means of a tensioned line, to the power take-off unit. It consists of a permanent magnet linear generator, lying on the seabed and equipped with a gravity-based foundation. The proposed procedure is applied to several candidate deployment sites located in the Mediterranean Sea; the incidence of the power take-off restraint and the converter operational profile is fully investigated and some recommendations for preliminary design of wave energy converter devices are provided. Current results show that there is wide scope to make the wave energy sector more competitive on the international market, by properly selecting the main design parameters of point absorbers, on the basis of met-ocean conditions at the deployment site.
A Novel Hybrid Method of Forecasting Crude Oil Prices Based on Multi-Layer Symbolic Pattern Network

: Faced with the high-dimensional characteristics of time series, symbolization is used to reduce the dimensionality and effectively eliminate redundancy and irrelevant features. Several existing network prediction methods, which based on the topology of complex networks, only consider the target sequence itself, which ignored the inﬂuence of related factors. In this paper, we improve the single-layer network and construct a Multi-layer Symbolic Pattern Network prediction model by considering the interaction be-tween different variables with time lag. The basic idea is to link the directed weighted networks based on causation entropy, then extract the multi-layer network topology to jointly predict the target sequence. The 2018/1-2018/12 ﬂuctuation trend and of crude oil futures prices are predicted, with auxiliary variables S&P 500 Index. The results show that the multi-layer network improves the directional prediction accuracy of oil price over the single-layer network. By optimizing the selection process of the neural network training set, the improved LSTM-based time series prediction model proposed in this paper has higher prediction accuracy and smaller errors than the single LSTM neural network and ARIMA model.
CARDIOPULMONARY EXERCISE TESTING FOR VO2MAX DETERMINING IN SUBJECTS OF DIFFERENT PHYSICAL ACTIVITY

Cardiopulmonary exercise testing (CPET) provides assessment of the integrative exercise responses involving the pulmonary, cardiovascular, haematopoietic, neuropsychological, and skeletal muscle systems, which are not adequately reflected through the measurement of individual organ system function. This non-invasive, dynamic physiological overview permits the evaluation of both submaximal and peak exercise responses, providing the doctor with relevant information for clinical decision making. CPET is increasingly being used in a wide spectrum of clinical applications for the evaluation of undiagnosed exercise intolerance and for the objective determination of functional capacity and impairment. Its use in patient management is increasing with the understanding that resting pulmonary and cardiac function testing cannot reliably predict exercise performance and functional capacity and that overall health status correlates better with exercise tolerance than with resting measurements. CPET involves measurements of respiratory oxygen uptake, carbon dioxide expenditure and pulmonary ventilation during a step-vice increased physical workload up to the maximum (or symptom-limited level in patients) on ergometer. In this paper the principle of CPET is described and results for VO2max and VO2max/kg of almost 3000 measurements in subjects of different physical activity are presented. These values characterizing cardiorespiratory capacity of the subjects were compared to the values of maximal performance achieved during stress test on bicycle ergometer (Wmax and Wmax/kg) and regression equations for VO2max – Wmax and VO2max/kg – Wmax/kg were calculated.