Miracle-dz/UniFAR-FTUs huggingface dataset:
Side Channel Analysis of SPECK Based on Transfer Learning

Although side-channel attacks based on deep learning are widely used in AES encryption algorithms, there is little research on lightweight algorithms. Lightweight algorithms have fewer nonlinear operations, so it is more difficult to attack successfully. Taking SPECK, a typical lightweight encryption algorithm, as an example, directly selecting the initial key as the label can only crack the first 16-bit key. In this regard, we evaluate the leakage of SPECK’s operations (modular addition, XOR, shift), and finally select the result of XOR operation as the label, and successfully recover the last 48-bit key. Usually, the divide and conquer method often used in side-channel attacks not only needs to train multiple models, but also the different bytes of the key are regarded as unrelated individuals. Through the visualization method, we found that different key bytes overlap in the position of the complete electromagnetic leakage signal. That is, when SPECK generates a round key, there is a connection between different bytes of the key. In this regard, we propose a transfer learning method for different byte keys. This method can take advantage of the similarity of key bytes, improve the performance starting-point of the model, and reduce the convergence time of the model by 50%.
Improving the robustness of beamforming using asymmetric sensor arrays

Beamforming or phased array with an array of sensors is an advanced signal processing technique for directional signal transmission or reception. This directionality is achieved by phase shifts of received signals of each sensor for the constructive interference of wavefronts, resulting in the amplification of the signal from a particular direction. In this research, the use of an asymmetric sensor array is proposed to reduce the effects of “spatial aliasing,” which is typically encountered in the structural health monitoring practice when employing phased arrays. In this technique, a sensor array is asymmetrically and closely deployed for beamforming and for robust source localization. This sensor deployment has a great effect on reducing the spatial aliasing errors. Although many advanced signal processing algorithms have been developed in the past, an asymmetric sensor array proposed in this study is used to reduce the spatial aliasing error from the sensor deployment perspective. In order to demonstrate the proposed sensor array technique, several simulation and experimental investigations are carried out, and the performance comparison is made to demonstrate the superior robustness of the asymmetric sensor array.
Algebraic Structure and Integration in Generalized Differential Cohomology

The present thesis deals with the construction of algebraic structure, particularly products, on generalized differential cohomology from an abstract homotopy-theoretic point of view. Beginning with a multiplicative cohomology theory E, we show that the corresponding differential cohomology groups E are multiplicative in a suitable sense. Explicit integration maps and a cocycle model for the addition are also constructed.
The Role of Vascular-Immune Interactions in Modulating Chemotherapy Induced Neuropathic Pain

Chemotherapy causes sensory disturbances in cancer patients that results in neuropathies and pain. As cancer survivorships has dramatically increased over the past 10 years, pain management of these patients is becoming clinically more important. Current analgesic strategies are mainly ineffective and long-term use is associated with severe side effects. The issue being that common analgesic strategies are based on ubiquitous pain mediator pathways, so when applied to clinically diverse neuropathic pain and neurological conditions, are unsuccessful. This is principally due to the lack of understanding of the driving forces that lead to chemotherapy induced neuropathies. It is well documented that chemotherapy causes sensory neurodegeneration through axonal atrophy and intraepidermal fibre degeneration causing alterations in pain perception. Despite the neuropathological alterations associated with chemotherapy-induced neuropathic pain being extensively researched, underlying causes remain elusive. Resent evidence from patient and rodent studies have indicated a prominent inflammatory cell component in the peripheral sensory nervous system in effected areas post chemotherapeutic treatment. This is accompanied by modulation of auxiliary cells of the dorsal root ganglia sensory neurons such as activation of satellite glia and capillary dysfunction. The presence of a neuroinflammatory component was supported by transcriptomic analysis of dorsal root ganglia taken from mice treated with common chemotherapy agents. With key inflammatory mediators identified, having potent immunoregulatory effects that directly influences nociception. We aim to evaluate the current understanding of these immune-neuronal interactions across different cancer therapy drug classes. In the belief this may lead to better pain management approaches for cancer survivors.
An Interleaved Totem-Pole Bridgeless Boost PFC Converter with Soft-Switching Capability Adopting Phase-Shifting Control

This paper proposes an interleaved totem-pole bridgeless boost power factor correction (PFC) converter with soft-switching capability. In the proposed converter, an inductor is added compared with the conventional interleaved totem-pole bridgeless boost PFC converter. The added inductor is located between two PFC converter units, and by utilizing the energy of the added inductor, all switches can achieve the zero voltage switching (ZVS). In addition, by applying the phase-shifting control between two PFC converter units, the proposed converter can control the magnitude of the current flowing on the added inductor as an optimal value. Therefore, the proposed converter can achieve the ZVS operation, while minimizing the additional conduction loss and core loss of the added inductor. As a result, the proposed converter can achieve the high efficiency. The feasibility of the proposed converter is confirmed with 180–264 Vrms input and 1.6 kW (400 V/4 A) output prototype.
A novel method for identifying inertial parameters of electric vehicles based on the dual H infinity filter

ABSTRACT Accurate identification of vehicle inertial parameters is essential to the design of vehicle dynamics control systems. In this paper, a novel vehicle inertial parameter identification method based on the dual H infinity filter (DHIF) for electric vehicles (EVs) is proposed. The filter algorithm employs a nonlinear longitudinal vehicle model with three vehicle states. A hierarchical framework is engaged by the DHIF to estimate the vehicle states and inertial parameters concurrently. In order to minimise the disturbance of unknown noise, the vehicle states are estimated by using the linear H infinity filter (LHIF), while the nonlinear H infinity filter (NHIF) utilises the observed states to identify the vehicle inertial parameters. Finally, the proposed estimation method is verified and compared through the dSPACE based hardware-in-the-loop (HIL) simulation experiments. The results indicate that the DHIF-based estimation method is effective to identify the vehicle inertial parameters with high precision, remarkable robustness, and quick convergence.
Experimental Simulation of Lightning Current Discharge on Rocks

Lightning is a transient, high-current discharge occurring within a thundercloud, between clouds, or between a cloud and the ground. Cloud-to-ground (CG) lightning is the most studied because of its impact on human life. The aim of this study is to elucidate the effects of lightning in Earth materials by simulating the lightning current discharges in a laboratory setting. Technical applications of this work include the study or development of customized materials used to prevent accidents, limit damage, or reduce interruptions in electrical power system owing to lightning strikes, such as lightning arresters or high-voltage fuses. High-voltage electrical arcs were discharged through rock specimens, and power, energy, and duration of discharge were estimated to provide a better understanding of the origin of naturally occurring fulgurites (shock-impact glasses) and the lightning/rock interaction. X-ray powder diffraction showed that the samples used for the experiment represent basalt (samples A0, A1–A4) and granite (samples B1, B2). Optical microscopy provides direct evidence that materials can be physically altered due to the heat generated by an arcing event. Optical microscopy observations showed that arcs passed through the target rocks and mimicked the effect of lightning strikes hitting the surface of the rock, melting the target rock, and passing to ground. Fulgurite glass observed on basalt samples shows the impact origin lining the surface of millimeter-size craters and a slash-like coating, whereas in the granite sample, the fulgurite was not observed because the arc passed directly to the laboratory ground. Significant differences in the duration of the experimental electrical arcs that passed through dry and wet samples (A1 and A3; A2 and A4, respectively) were observed. This discrepancy can be ascribed to the variation of the electrical properties related to the distribution of the water layer on the rock sample and to the occurrence of magnetite grains, which may increase the local conductivity of the sample owing to its electromagnetic properties.
High-speed and high-efficiency shape memory alloy actuation

When standard voltage levels commonly adopted in industry are used to activate thermal shape memory alloy (SMA) wire actuators, they often result in very high electrical currents which may eventually damage or destroy the actuators. To improve performance of SMA wire actuators operating in industrial environments, in this paper we investigate a novel, fast and energy-efficient actuation strategy based on short pulses in the millisecond range. The use of higher voltages leads to a highly dynamic activation process, in contrast to commonly used quasi-static activation based on low-voltage. A test setup is designed to examine the effects of the control parameters (i.e., supply voltage, activation pulse duration, SMA wire pre-tension and wire diameter) on the measured displacement and force output of the SMA wire. It is shown that actuation times in the range of 20 ms and strokes of more than 10% of the SMA wire length can be reached. Additionally, energy savings of up to 80% with respect to conventional quasi-static actuation are achieved. Possible applications for this activation method are release mechanisms, switches or safety applications.
A Simple Optical Aerosol Sensing Method of Sauter Mean Diameter for Particulate Matter Monitoring

Mass concentration is a commonly used but insufficient metric to evaluate the particulate matter (PM) exposure hazard. Recent studies have declared that small particles have more serious impacts on human health than big particles given the same mass concentration. However, state-of-the-art PM sensors cannot provide explicit information of the particle size for further analysis. In this work, we adopt Sauter mean diameter (SMD) as a key metric to reflect the particle size besides the mass concentration. To measure SMD, an effective optical sensing method and a proof-of-concept prototype sensor are proposed by using dual wavelengths technology. In the proposed method, a non-linear conversion model is developed to improve the SMD measurement accuracy for aerosol samples of different particle size distributions and reflective indices based on multiple scattering channels. In the experiment of Di-Ethyl-Hexyl-Sebacate (DEHS) aerosols, the outputs of our prototype sensor demonstrated a good agreement with existing laboratory reference instruments with maximum SMD measurement error down to 7.04%. Furthermore, the simplicity, feasibility and low-cost features of this new method present great potential for distributed PM monitoring, to support sophisticated human exposure hazard assessment.
Characterizing Sleep Differences in Children With and Without Sensory Sensitivities

Objectives Individuals register and react to daily sensory stimuli differently, which influences participation in occupations. Sleep is a foundational nightly occupation that impacts overall health and development in children. Emerging research suggests that certain sensory processing patterns, specifically sensory sensitivities, may have a negative impact on sleep health in children. In this study, we aimed to (i) characterize sleep in children with and without sensory sensitivities and (ii) examine the relationship between sensory processing patterns (using the Sensory Profile-2) and sleep using validated parent- and child-reported questionnaires. We hypothesized that children with sensory sensitivities will exhibit more difficulties with sleep. Methods We recruited 22 children (ages 6–10) with sensory sensitivities (SS) and 33 children without sensory sensitivities (NSS) to complete validated sleep and sensory processing questionnaires: the Children’s Sleep Habits Questionnaire (CSHQ), Sleep Self-Report (SSR), and Sensory Profile-2. Results Children with SS had significantly more sleep behaviors reported by both parents (p < 0.001, g = 1.11) and children (p < 0.001, g = 1.17) compared to children with NSS. Specifically, children with SS had higher frequencies of sleep anxiety (p = 0.004, g = 0.79), bedtime resistance (p = 0.001, g = 0.83), and sleep onset delay (p = 0.003, g = 0.95). Spearman’s ρ correlations indicated significant positive correlations between parent- and child-reported sleep. Children with SS showed a larger association and greater variability between sleep and sensory processing compared to their peers. Significant positive correlations between parent-reported sleep behaviors and sensory sensitive and avoiding patterns were identified for both children with SS and NSS. Child-reported sleep behaviors were most strongly associated with sensitive and avoiding patterns for children with NSS and seeking patterns for children with SS. Conclusion We present evidence that sleep is impacted for children with SS to a greater extent than children with NSS. We also identified that a child’s sensory processing pattern may be an important contributor to sleep problems in children with and without sensory sensitivities. Sleep concerns should be addressed within routine care for children with sensory sensitivities. Future studies will inform specific sleep intervention targets most salient for children with SS and other sensory processing patterns.
Role of Phosphorus-Containing Molecules on the Formation of Nano-Sized Calcium Phosphate for Bone Therapy

Calcium phosphate (CaP) is the principal inorganic constituent of bone and teeth in vertebrates and has various applications in biomedical areas. Among various types of CaPs, amorphous calcium phosphate (ACP) is considered to have superior bioactivity and biodegradability. With regard to the instability of ACP, the phosphorus-containing molecules are usually adopted to solve this issue, but the specific roles of the molecules in the formation of nano-sized CaP have not been clearly clarified yet. Herein, alendronate, cyclophosphamide, zoledronate, and foscarnet are selected as the model molecules, and theoretical calculations were performed to elucidate the interaction between calcium ions and different model molecules. Subsequently, CaPs were prepared with the addition of the phosphorus-containing molecules. It is found that cyclophosphamide has limited influence on the generation of CaPs due to their weak interaction. During the co-precipitation process of Ca2+ and PO4 3-, the competitive relation among alendronate, zoledronate, and foscarnet plays critical roles in the produced inorganic-organic complex. Moreover, the biocompatibility of CaPs was also systematically evaluated. The DFT calculation provides a convincing strategy for predicting the structure of CaPs with various additives. This work is promising for designing CaP-based multifunctional drug delivery systems and tissue engineering materials.
CLOSED AND OPEN STRING THEORIES IN NON-CRITICAL BACKGROUNDS

This thesis is a study of closed and open string theories in low dimensional spacetimes, and the various relations between these theories. In particular, we focus on the theory of the two-dimensional black hole. We first study closed strings in the background of the Euclidean twodimensional black hole (SL2(IR)/U(1)) tensored with flat space, using the duality relating these theories to non-critical superstrings described by the supersymmetric sine-Liouville interaction on the worldsheet. We point out a subtlety in their geometric interpretation, and clarify the symmetry structure of the theories based on the understanding of these theories as near horizon limits of wrapped NS5-branes. In one such example (cigar ×IR), we use the brane description to understand the enhancement of the global symmetry in the coset theory from U(1) to SO(3) under which the sine-Liouville and cigar interactions are related. In the same example, a worldsheet description of the moduli space IR/ZZ2 is presented. We then study open strings in the topologically twisted Euclidean twodimensional black hole which is equivalent to noncritical c = 1 bosonic string theory compactified on a circle at self-dual radius. These strings live on D-branes that are extended along the Liouville direction. We present explicit expressions for the disc twoand three-point functions of boundary operators in this theory, as well as the bulk-boundary two-point function. The expressions obtained are divergent because of resonant behavior at self-dual radius. However, these can be regularised and renormalized in a precise way to get finite results. The boundary correlators are found to depend only on the differences of boundary cosmological constants, suggesting a fermionic behaviour. We initiate a study of the open-string field theory localized to the physical states, which leads to an interesting matrix model. Finally, we present evidence that the worldvolume theory of N unstable Dparticles in type IIB superstring theory in two-dimensions is represented by the supersymmetric matrix model of Marinari and Parisi. This identification suggests that the matrix model gives a holographic descriptions of superstrings in a twodimensional black hole geometry.
Euler's difference equation

It is well known that Euler's differential equation is the unique one which can be translated into difference equation with constant coefficients by substitution of argument. We tried to solve the corresponding difference equation (we called ti Euler's difference equation). It turned out that Euler's difference equation could't be reduced to difffernce equation with constant coefficients in general case. It is possible only in the case when sum of coefficients in Euler's difference equation is equal to zero. This case was investigated in detail and two linear independent particular solutions are found.
Dysbiosis in Children With Neurological Impairment and Long-Term Enteral Nutrition

Severe neurological impairment (NI) is often accompanied by the need for artificial nutritional support, normally provided enterally (enteral nutrition [EN]) to ensure growth, counteract morbidity and mortality, and improve quality of life. On the other hand, long-term EN (LTEN) may contribute to the establishment, or exacerbation, of gastrointestinal disorders that may lead to malnutrition, which in turn is associated with alterations in gut microbiota (GM) composition and functional capacities. To the best of our knowledge, we investigated, for the first time in this study, the consequences of LTEN in a pediatric population in this pathological context. Using amplicon sequencing, we compared the fecal microbiota of a pediatric population suffering from severe NI and under LTEN to that of sex- and age-matched controls. The two groups presented evident differences in GM composition and a consistent differential clustering. In general, the taxonomic picture in NI children under LTEN seemed to mirror a profound dysbiotic condition, in which anti-inflammatory taxa appear severely depleted (among others, the Clostridiales families of Lachnospiraceae and Ruminococcaceae, and, within the latter, Faecalibacterium spp. and Gemmiger spp.), while known pathobionts (Gammaproteobacteria and Klebsiella) or emerging pathogens (e.g., Synergistales, Cloacibacillus, and Fusobacterium) were significantly enriched. Our data suggest that LTEN has a significant impact on the GM taxonomic composition in NI children. Even if other factors are probably at work, such as the bidirectional interaction between gastrointestinal impairment/immaturity and the central nervous system (CNS), the assumption of drugs, and physical inactivity, these data define possible routes and targets to try to alleviate this dysbiosis, with a view to better management of these patients and an improvement in their quality of life.
Decentralized Energy Management of Networked Microgrid Based on Alternating-Direction Multiplier Method

With the ever-intensive utilization of distributed generators (DGs) and smart devices, distribution networks are evolving from a hierarchal structure to a distributed structure, which imposes significant challenges to network operators in system dispatch. A distributed energy-management method for a networked microgrid (NM) is proposed to coordinate a large number of DGs for maintaining secure and economic operations in the electricity-market environment. A second-order conic programming model is used to formulate the energy-management problem of an NM. Network decomposition was first carried out, and then a distributed solution for the established optimization model through invoking alternating-direction method of multipliers (ADMM). A modified IEEE 33-bus power system was finally utilized to demonstrate the performance of distributed energy management in an NM.
Improving Process Discovery Algorithms Using Event Concatenation

Process mining is the discipline of analyzing and improving processes which are known as an event log. The real-life event log contains noise, infrequent behaviors, and numerous concurrency, in effect the generated process model through process discovery algorithms will be inefficient and complex. Shortcomings in an event log result in current process discovery algorithms failing to pre-process data and describe real-life phenomena. Existing process mining algorithms are limited based on the algorithm’s filtering, parameters, and pre-defined features. It is critical to use a high-quality event log to generate a robust process model. However, pre-processing of the event log is mostly cumbersome and is a challenging procedure. In this paper, we propose a novel pre-processing step aimed to obtain superior quality event log from a set of raw data, consequently a better performing process model. The proposed approach concatenates events which hold concurrent relations based on a probability algorithm, producing simpler and accurate process models. This proposed pre-processing step is based on the probability of the frequency of concurrent events. The performance of the pre-processing approach is evaluated on 18 real-life benchmark datasets that are publicly available. We show that the proposed pre-processing framework significantly reduces the complexity of the process model and improves the model’s F-Measure.