Miracle-dz/UniFAR-FTUs huggingface dataset:
A review of multi-factor authentication in the Internet of Healthcare Things

Objective This review paper aims to evaluate existing solutions in healthcare authentication and provides an insight into the technologies incorporated in Internet of Healthcare Things (IoHT) and multi-factor authentication (MFA) applications for next-generation authentication practices. Our review has two objectives: (a) Review MFA based on the challenges, impact and solutions discussed in the literature; and (b) define the security requirements of the IoHT as an approach to adapting MFA solutions in a healthcare context. Methods To review the existing literature, we indexed articles from the IEEE Xplore, ACM Digital Library, ScienceDirect, and SpringerLink databases. The search was refined to combinations of ‘authentication’, ‘multi-factor authentication’, ‘Internet of Things authentication’, and ‘medical authentication’ to ensure that the retrieved journal articles and conference papers were relevant to healthcare and Internet of Things-oriented authentication research. Results The concepts of MFA can be applied to healthcare where security can often be overlooked. The security requirements identified result in stronger methodologies of authentication such as hardware solutions in combination with biometric data to enhance MFA approaches. We identify the key vulnerabilities of weaker approaches to security such as password use against various cyber threats. Cyber threats and MFA solutions are categorised in this paper to facilitate readers’ understanding of them in healthcare domains. Conclusions We contribute to an understanding of up-to-date MFA approaches and how they can be improved for use in the IoHT. This is achieved by discussing the challenges, benefits, and limitations of current methodologies and recommendations to improve access to eHealth resources through additional layers of security.
Introduction to Quantum Control and Dynamics

QUANTUM MECHANICS States and Operators Observables and Measurement Dynamics of Quantum Systems MODELING OF QUANTUM CONTROL SYSTEMS: EXAMPLES Quantum Theory of Interaction of Particles and Fields Approximations and Modeling: Molecular Systems Spin Dynamics and Control Mathematical Structure of Quantum Control Systems CONTROLLABILITY Lie Algebras and Lie Groups Controllability Test: The Dynamical Lie Algebra Notions of Controllability for the State Pure State Controllability Equivalent State Controllability Equality of Orbits OBSERVABILITY AND STATE DETERMINATION Quantum State Tomography Observability Observability and Methods for State Reconstruction LIE GROUP DECOMPOSITIONS AND CONTROL Decompositions of SU(2) and Control of Two Level Systems Decomposition in Planar Rotations Cartan Decompositions Levi Decomposition Examples of Application of Decompositions to Control OPTIMAL CONTROL OF QUANTUM SYSTEMS Formulation of the Optimal Control Problem The Necessary Conditions of Optimality Example: Optimal Control of a Two Level Quantum System Time Optimal Control of Quantum Systems Numerical Methods for Optimal Control of Quantum Systems MORE TOOLS FOR QUANTUM CONTROL Selective Population Transfer via Frequency Tuning Time Dependent Perturbation Theory Adiabatic Control STIRAP Lyapunov Control of Quantum Systems ANALYSIS OF QUANTUM EVOLUTIONS: ENTANGLEMENT, ENTANGLEMENT MEASURES, AND DYNAMICS Entanglement of Quantum Systems Dynamics of Entanglement Local Equivalence of States APPLICATIONS OF QUANTUM CONTROL AND DYNAMICS Nuclear Magnetic Resonance Experiments Molecular Systems Control Atomic Systems Control: Implementations of Quantum Information Processing with Ion Traps APPENDIX A: POSITIVE AND COMPLETELY POSITIVE MAPS, QUANTUM OPERATIONS, AND GENERALIZED MEASUREMENT THEORY Positive and Completely Positive Maps Quantum Operations and Operator Sum Representation Generalized Measurement Theory APPENDIX B: LAGRANGIAN AND HAMILTONIAN FORMALISM IN CLASSICAL ELECTRODYNAMICS Lagrangian Mechanics Extension of Lagrangian Mechanics to Systems with Infinite Degrees of Freedom Lagrangian and Hamiltonian Mechanics for a System of Interacting Particles and Field APPENDIX C: CARTAN SEMISIMPLICITY CRITERION AND CALCULATION OF THE LEVI DECOMPOSITION The Adjoint Representation Cartan Semisimplicity Criterion Quotient Lie Algebras Calculation of the Levi Subalgebra in the Levi Decomposition Algorithm for the Levi Decomposition APPENDIX D: PROOF OF THE CONTROLLABILITY TEST OF THEOREM 3.2. 1 APPENDIX E: THE BAKER-CAMPBELL-HAUSDORFF FORMULA AND SOME EXPONENTIAL FORMULAS APPENDIX F: PROOF OF THEOREM 6.2. 1 REFERENCES INDEX Notes and Exercises appear at the end of every chapter.
INTEGRATING AI HARDWARE IN ACADEMIC TEACHING: EXPERIENCES AND SCOPE FROM BRANDENBURG AND BAVARIA

Abstract. The field of artificial intelligence (AI) has gained increasing importance in recent years due to its potential to sustain growth and prosperity in a disruptive way. However, the role of special hardware for AI is still underdeveloped, and dedicated AI-capable hardware is crucial for effective and efficient processing. Moreover, hardware aspects are often neglected in university teaching, which emphasizes theoretical foundations and algorithmic implementations. As a result, there is a need for courses that focus on AI hardware development and its diverse applications. In response to this need, the BB-KI Chips consortium aims to develop a series of hardware-oriented courses with real-world AI applications. This consortium includes the Technical University of Munich (TUM) and the University of Potsdam (UP), which both offer a wide range of courses that focus on AI basics, AI algorithmic development, general computer architectures, chip design, and as well applications of AI. In the BB-KI-CHIPS project, these different capacities are planned to be tightly integrated into a unified curriculum covering knowledge from chip design over AI algorithms and techniques to applications.
Inertial-Sensor-Based Monitoring of Sample Entropy and Peak Frequency Changes in Treadmill Walking during Recovery after Total Knee Arthroplasty

This study aimed to investigate whether sample entropy (SEn) and peak frequency values observed in treadmill walking could provide physical therapists valuable insights into gait rehabilitation following total knee arthroplasty (TKA). It was recognized that identifying movement strategies that during rehabilitation are initially adaptive but later start to hamper full recovery is critical to meet the clinical goals and minimize the risk of contralateral TKA. Eleven TKA patients were asked to perform clinical walking tests and a treadmill walking task at four different points in time (pre-TKA, 3, 6, and 12 months post-TKA). Eleven healthy peers served as the reference group. The movements of the legs were digitized with inertial sensors and SEn and peak frequency of the recorded rotational velocity–time functions were analyzed in the sagittal plane. SEn displayed a systematic increase during recovery in TKA patients (p < 0.001). Furthermore, lower peak frequency (p = 0.01) and sample entropy (p = 0.028) were found during recovery for the TKA leg. Movement strategies that initially are adaptive, and later hamper recovery, tend to diminish after 12 months post-TKA. It is concluded that inertial-sensor-based SEn and peak frequency analyses of treadmill walking enrich the assessment of movement rehabilitation after TKA.
Deep Learning Aided Neuroimaging and Brain Regulation

Currently, deep learning aided medical imaging is becoming the hot spot of AI frontier application and the future development trend of precision neuroscience. This review aimed to render comprehensive and informative insights into the recent progress of deep learning and its applications in medical imaging for brain monitoring and regulation. The article starts by providing an overview of the current methods for brain imaging, highlighting their limitations and introducing the potential benefits of using deep learning techniques to overcome these limitations. Then, we further delve into the details of deep learning, explaining the basic concepts and providing examples of how it can be used in medical imaging. One of the key strengths is its thorough discussion of the different types of deep learning models that can be used in medical imaging including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and generative adversarial network (GAN) assisted magnetic resonance imaging (MRI), positron emission tomography (PET)/computed tomography (CT), electroencephalography (EEG)/magnetoencephalography (MEG), optical imaging, and other imaging modalities. Overall, our review on deep learning aided medical imaging for brain monitoring and regulation provides a referrable glance for the intersection of deep learning aided neuroimaging and brain regulation.
Modified omni-scale net architecture for cattle identification on their muzzle point image pattern characteristics

Animal biometrics is a frontier field of computer vision, pattern recognition and cognitive science that plays a vital role in the registration, unique identification and verification of livestock (cattle). In this study, we propose a deep learning approach to cattle identification based on the characteristics of the muzzle point image (nose pattern) to solve the problem of missed or replaced animals and false insurance claims. Inspired on the state-of-the-art OSNet architecture, which was developed for person re-identification, we introduce significant modifications in order to improve accuracy in the cattle recognition problem. First, each convolution layer is replaced by a depth-wise separable convolution layer, and parametric rectified linear unit is used as a non-linear activation function. Next, we add two convolutional block attention module. Under the same experimental conditions, improved OSNet achieves significantly superior accuracy than the original OSNet, maintaining the same speed and compact storage.
Robust Tracking of Objects in Image Sequences Using Multiple Degrees of Freedom Mean Shift Algorithm

Current Mean Shift tracking algorithm uses a single radius parameter to describe the scale of the target,and Each target has the position and size freeness only,which is not suitable for complex movements of the objects.In this paper we provide a new algorithm in which a bandwidth matrix is employed to describe the objects with two directions determined independently.Target-angle is also introduced to accommodate the rotation of objects.Furthermore we bring forward an efficient search strategy to cope with a temporal occlusion of the objects.Experimental results show that the new algorithm is able to adapt to any kind of object's movements and therefore has better tracking precision.
Cooperative vehicular platooning: a multi-dimensional survey towards enhanced safety, security and validation

Cooperative Vehicular Platooning (Co-VP) is a paradigmatic example of a Cooperative Cyber-Physical System (Co-CPS), which holds the potential to vastly improveroad safety by partially removing humans from the driving task. However, the challenges are substantial, as the domain involves several topics, such as control theory,communications, vehicle dynamics, security, and traffic engineering, that must becoupled to describe, develop and validate these systems of systems accurately. Thiswork presents a comprehensive survey of significant and recent advances in Co-VP relevant fields. We start by overviewing the work on control strategies and underlying communication infrastructures, focusing on their interplay. We also address a fundamental concern by presenting a cyber-security overview regarding these systems. Furthermore, we present and compare the primary initiatives to test and validate those systems, including simulation tools, hardware-in-the-loop setups, and vehicular testbeds. Finally, we highlight a few open challenges in the Co-VP domain. This work aims to provide a fundamental overview of highly relevant works on Co-VP topics, particularly by exposing their inter-dependencies, facilitating a guide that will support further developments in this challenging field.
Calculations of Steady and Pulsating Impinging Jets—An Assessment of 13 Widely used Turbulence Models

Computational fluid dynamics (CFD) calculations of impinging jets have been performed with 13 widely spread Reynolds-averaged Navier-Stokes (RANS) turbulence models using commercial CFD software. It was the aim of these calculation to assess how heat transfer and flow structure can be predicted at different Reynolds numbers and different nozzle-to-plate distances. The only model able to predict correctly the laminar–turbulent transition occurring at small nozzle-to-plate distance was the SST k − ω model (“transitional flow option”). Most of the other models can only satisfactorily predict heat transfer in the turbulent wall jet region. When the SST k − ω model with transitional flow option is applied for calculation of pulsating jets, the tendencies are predicted correctly.
Study of Microwave Extraction of Essential Oil Constituents from Plant Materials

The microwave heating characteristics of extraction mixtures consisting of rosemary or peppermint leaves suspended in hexane, ethanol and hexane + ethanol mixtures were studied. The temperature rises were found to depend on the dielectric properties of the solvents and the leaves in question. The rates of extraction of several essential oil constituents increased with increasing power output between ca 200 and 640 W but not in a proportional way. Scanning electron micrographs of the leaves showed that the extraction rates were related to structural changes in the oil-containing glands.
Analysis of heavy precipitation for France using high resolution ALADIN RCM simulations

A high resolution version of ALADIN over France is analyzed here in 30-year ERA-40-driven simulations. It is demonstrated that a resolution of 12 km improves some features of the mean precipitation field with respect to the same version at 50 km resolution. This version improves also the representation of precipitation upper quantiles in summer by producing less high precipitation rates.
Additional Genetic Alterations and Clonal Evolution of MPNs with Double Mutations on the MPL Gene: Two Case Reports

Essential thrombocythemia (ET) and primary myelofibrosis (PMF) are two of the main BCR-ABL1-negative chronic myeloproliferative neoplasms (MPNs) characterized by abnormal megakaryocytic proliferation. Janus kinase 2 (JAK2) mutations are detected in 50–60% of ET and PMF, while myeloproliferative leukemia (MPL) virus oncogene mutations are present in 3–5% of cases. While Sanger sequencing is a valuable diagnostic tool to discriminate the most common MPN mutations, next-generation sequencing (NGS) is a more sensitive technology that also identifies concurrent genetic alterations. In this report, we describe two MPN patients with simultaneous double MPL mutations: a woman with ET presenting both MPLV501A-W515R and JAK2V617F mutations and a man with PMF displaying an uncommon double MPLV501A-W515L. Using colony-forming assays and NGS analyses, we define the origin and mutational landscape of these two unusual malignancies and uncover further gene alterations that may contribute to the pathogenesis of ET and PMF.
Pathological scattering by a defect in a slow-light medium

Scattering of electromagnetic fields by a defect layer embedded in a slow-light periodically layered ambient medium exhibits phenomena markedly different from typical scattering problems. In a slow-light medium, constructed by Figotin and Vitebskiy, the energy velocity of a propagating mode in one direction slows to zero, creating a "frozen mode" at a single frequency within a pass band, where the dispersion relation possesses a flat inflection point. The slow-light regime is characterized by a $3\! \times\! 3$ Jordan block of the log of the $4\! \times\! 4$ monodromy matrix for EM fields in a periodic medium at special frequency and parallel wavevector. The scattering problem breaks down as the 2D rightward and leftward mode spaces intersect in the frozen mode and therefore span only a 3D subspace $\mathring V$ of the 4D space of EM fields. Analysis of pathological scattering near the slow-light frequency and wavevector is based on the interaction between the flux-unitary transfer matrix $T$ across the defect layer and the projections to the rightward and leftward spaces, which blow up as Laurent-Puiseux series. Two distinct cases emerge: the generic, non-resonant case when $T$ does not map $\mathring V$ to itself and the quadratically growing mode is excited; and the resonant case, when $\mathring V$ is invariant under $T$ and a guided frozen mode is resonantly excited.
Optimization of the Compressive Measurement Matrix in a Massive MIMO System Exploiting LSTM Networks

Massive multiple-input multiple-output (MIMO) technology, which is characterized by the use of a large number of antennas, is a key enabler for the next-generation wireless communication and beyond. Despite its potential for high performance, implementing a massive MIMO system presents numerous technical challenges, including the high hardware complexity, cost, and power consumption that result from the large number of antennas and the associated front-end circuits. One solution to these challenges is the use of hybrid beamforming, which divides the transceiving process into both analog and digital domains. To perform hybrid beamforming efficiently, it is necessary to optimize the analog beamformer, referred to as the compressive measurement matrix (CMM) here, that allows the projection of high-dimensional signals into a low-dimensional manifold. Classical approaches to optimizing the CMM, however, are computationally intensive and time consuming, limiting their usefulness for real-time processing. In this paper, we propose a deep learning based approach to optimizing the CMM using long short-term memory (LSTM) networks. This approach offers high accuracy with low complexity, making it a promising solution for the real-time implementation of massive MIMO systems.
A case study of a heavy precipitation event along the Baiu front in northern Kyushu, 23 July 1982: Nagasaki heavy rainfall

A case study is made of an exceptionally heavy rainstorm that hit the northwestern coastal area of Kyushu, Japan, on 23 July 1982. The 5hr rainfall accumulation was as high as 412mm at the city of Nagasaki. The precipitation occurred along the warm front associated with a medium scale disturbance that developed along the Baiu front. The atmosphere in the pre-storm period was very moist through virtually the entire troposphere and its static stability was conditionally unstable with a lifted index of -1.5*, thus exhibiting characteristics more similar to the tropical atmosphere than the typical pre-storm environment over the midwestern United States in spring seasons. Prior to the development of heavy precipitation over the Nagasaki area, an intensive rainband was propagating with a speed of 40km hr-1 eastsoutheastward over northwestern Kyushu. Its structure is found to be different from the structure of typical tropical squall clusters in that the heaviest rainfalll occurred in the middle of the rainband without a well defined leading edge. A dramatic change occurred in the rainband when its southern tip reached the Nagasaki area; it stopped propagating and stayed there for the next 5hr. Its line structure changed to a blob structure at the meso-* scale (*100km). The timing of the precipitation development at Nagasaki coincided with the peak period of the incursion of very moist air associated with the southwesterly low-level jet. During the period of heavy precipitation, a new cloud cluster formed over the sea 300km west of Nagasaki. It kept developing and propagating eastward with a speed of 60km hr-1, and eventually merged with the rainstorm over Nagasaki. These observations strongly suggest that the Nagasaki rainstorm was trapped orographically. Several other heavy precipitation events are cited which also occurred in association with medium scale disturbances along the Baiu front and in which the precipitation developed over the coast areas upwind of the low-level
An evaluation of nitrogen indicators for soil health in long‐term agricultural experiments

Various soil health indicators that measure a chemically defined fraction of nitrogen (N) or a process related to N cycling have been proposed to quantify the potential to supply N to crops, a key soil function. We evaluated five N indicators (total soil N, autoclavable citrate extractable N, water-extractable organic N, potentially miner-alizable N, and N -acetyl- β - D -glucosaminidase activity) at 124 sites with long-term experiments across North America evaluating a variety of managements. We found that 59%–81% of the variation in N indicators was among sites, with indicator values decreasing with temperature and increasing with precipitation and clay content. The N indicators increased from 6%–39% in response to decreasing tillage, cover cropping, retaining residue, and applying organic sources of nutrients. Overall, increasing the quantity of organic inputs, whether from increased residue retention, cover cropping, or rotations with higher biomass, resulted in higher values of the N indicators. Although N indicators responded to management in similar ways, the analysis cost and availability of testing laboratories is highly variable. Further, given the strong relationships of the N indicators with carbon (C) indicators, measuring soil organic C along with 24-h potential C mineralization could be used as a proxy
to compare the efficacy of propofol with ketamine versus propofol with fentanyl for procedural sedation for patients undergoing endoscopic retrograde cholangiopancreatography

Objective: This study was conducted to compare the efficacy of Propofol with Ketamine versus Propofol with Fentanyl for procedural sedation for patients undergoing ERCP to evaluate Propofol consumption, recovery score, patient satisfaction and sedation related adverse events. Materials and Methods: Sixty patients aged 18-60 years, ASA Class I and II were randomly allocated to one of two groups; Propofol/Ketamine (Ketofol) group KP (n=30) and Propofol/Fentanyl group FP (n=30). The level of sedation was adjusted to achieve a Ramsay Sedation Scale (RSS) score of 5. Results: The total amount of Propofol consumed was significantly higher in FP group (109.883±11.3871 mg) compared to KP group (89.867±9.8942 mg). Time to reach acceptable recovery score was slightly longer in KP group compared to FP (Aldrete scores 9.5±0.509, 9.8±0.407 respectively at 30 min). Patient satisfaction was comparable in both the groups and sedation related side effects like hypotension, bradycardia, desaturation was more significant in FP group compared to KP group. Conclusion: Propofol-Ketamine combination provided sedation quality similar to Propofol-Fentanyl combination with better hemodynamic profile and fewer side effects. Hence Propofol-Ketamine combination can be safely used in patients undergoing ERCP.
Pankun: A New Generation of Broadband Ocean Bottom Seismograph

This paper presents a new broadband ocean bottom seismograph (OBS) developed by the SUSTech OBS lab for passive-source seafloor seismic observations. This instrument, called Pankun, has several key features that set it apart from traditional OBS instruments. In addition to the seismometer-separated scheme, these features include a unique shielding structure to minimize current-induced noise, a compact gimbal for accurate leveling, and low power consumption for extended operation on the seafloor. The design and testing of Pankun’s primary components are thoroughly described in this paper. The instrument has been successfully tested in the South China Sea, demonstrating its ability to record high-quality seismic data. The anti-current shielding structure of Pankun OBS has the potential to improve low-frequency signals, particularly on the horizontal components, in seafloor seismic data.