Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient rotation-invariance remote image matching algorithm based on feature points matching

In this paper, a matching method based on feature points matching is proposed. First, a point detector is used to detect interest points of a source image and a target image. Then, genetic algorithms, which are useful in finding global optima, are used to match the detected points. A fitness function which is proven rotation-invariance is proposed to measure the similarity of two points in the paper. According to the results of the GAs, the corresponding points of the target image are found in the source image. The algorithm can overcome the rotation distortion of the images. The experiment results confirm the proposed algorithm is efficient and robust.
Inhaltsbasierte Suche in P2P-Systemen

Die Verwaltung großer Dokumentenkollektionen ohne zentrale Instanz und mit geringem Administrationsaufwand ist ein primäres Ziel von P2P-Systemen. So könnten z.B. Freiberufler Know-How-Dokumente zu ihrem Arbeitsgebiet über ein P2P-Netz gemeinsam nutzen. Auch der Aufbau verteilter Datensammlungen mit Multimediadaten z.B. für die Entwicklung von eLearning-Angeboten ist denkbar. Den meisten Nutzungsszenarien ist gemein, dass eine effiziente und effektive Suchfunktionalität erforderlich ist.
Unconstrained global optimization: A benchmark comparison of population-based algorithms

In this paper we provide a systematic comparison of the following population-based optimization techniques: Genetic Algorithm (GA), Evolution Strategy (ES), Cuckoo Search (CS), Differential Evolution (DE), and Particle Swarm Optimization (PSO). The considered techniques have been implemented and evaluated on a set of 67 multivariate functions. We carefully selected the tested optimization functions which have different features and gave exactly the same number of objective function evaluations for all of the algorithms. This study has revealed that the DE algorithm is preferable in the majority of cases of the tested functions. The results of numerical evaluations and parameter optimization are presented in this paper.
Intelligent Identification and Classification of Small UAV Remote Control Signals Based on Improved Yolov5-7.0

At present, an increasing number of small UAVs(Unmanned Aerial Vehicles) are commercialized and common, and the application of small UAVs has very good development prospects, such as UAV distribution services, UAV aerial photography services, and UAV formation performance. However, the misuse of small drones poses a significant threat. Lawbreakers use small drones equipped with various sensors to spy on personal privacy, steal corporate secrets, and threaten national conferences, which have had many adverse effects on society. In future wars, these small drones will perhaps be used on the battlefield along with high-end weapons. Therefore, it is necessary to find a solution for effectively identifying the basic information of UAV. For the existence of UAV and various small UAV types, this paper proposes a combination of RF sensing and target detection techniques with target detection algorithms to learn RF signal frequency frequency hopping features to detect UAV presence and identify the detected UAV. First, the RF signal of the UAV was obtained in real time by a software radio, and the time-frequency analysis of the short-time Fourier transform and wavelet transform is performed to generate frequency domain images with retained frequency hopping features. Then, the improved Yolov5-7.0 target detection model was employed for training, and finally, the trained model was used for identification and classification. The results showed that the method can effectively assist the detection and classification of UAVs that were obtained by identifying and classifying 5400 unlabeled images. The F1 score was 0.93, and the three assessment measures of P(Precision), R(Recall), and mAP(mean Average Precision) were 1.00, 1.00, and 0.967, respectively.
Neuronal ion-channel dynamics in silicon

We present a simple silicon circuit for modelling voltage-dependent ion channels found within neural cells, capturing both the gating particle's sigmoidal activation (or inactivation) and the bell-shaped time constant. In its simplest form, our ion-channel analog consists of two MOS transistors and a unity-gain inverter. We present equations describing its nonlinear dynamics and measurements from a chip fabricated in a 0.25 mum CMOS process. The channel analog's simplicity allows tens of thousands to be built on a single chip, facilitating the implementation of biologically realistic models of neural computation
Enhancing Blockchain Consensus With FPGA: Accelerating Implementation for Efficiency

Because of its versatility across various applications, Blockchain has emerged as a technology garnering significant interest. It has effectively addressed the challenge of transitioning from a low-trust, centralized ledger maintained by a single third-party to a high-trust, decentralized structure maintained by multiple entities, often referred to as validating nodes. Consequently, numerous blockchain systems have arisen for a multitude of purposes. Nevertheless, a considerable number of these blockchain systems are plagued by significant deficiencies concerning their performance and security. These issues have to be rectified before the realization of a widespread adoption. An essential element within any blockchain system is its foundational consensus algorithm, a crucial determinant of both its performance and security attributes. Consequently, to tackle the shortcomings observed in various blockchain systems, the hardware implementation of a series of established and innovative consensus algorithms was carried out as part of this work. This paper aims to compare and analyze the different consensus methods in blockchain, namely PoS (Proof of Stake), PoW (Proof of Work) and PoA (Proof of Authority) using VHDL (Very High-Speed Integrated Circuit Hardware Description Language). Each of these methods has unique characteristics that influence the validation of transactions and the addition of blocks to the blockchain. In this context, we aim to demonstrate the importance of optimizing consensus execution time via IPs (Intellectual Property) in VHDL. We also evaluate their impact on security, scalability and performance for IoT applications.
System of providing sustainability of tower cranes from overturn in extreme wind loads

The article is devoted to the issues of ensuring stability of tower cranes from overturn. The development stages of devices for ensuring tower cranes safety are examined and their shortcomings are revealed. The system consisting of subsystems and drives is proposed and their interaction is presented. The article deals with a subsystem based on artificial intelligence methods. The neural network models of forecasting wind parameters are developed. The quality of work of neural network models is estimated. The ways of further topic development are suggested.
Avoiding common pitfalls when clustering biological data

Properly applied, clustering methods reveal meaning in high-throughput biological data. Gloss Clustering is an unsupervised learning method, grouping data points based on similarity, with the goal of revealing the underlying structure of data. Advances in molecular biology have yielded large and complex data sets, making clustering essential to understand and visualize the data. Clustering can be a powerful technique, but it harbors potential pitfalls due to the high-dimensional nature of biological data, the failure to consider more than one clustering method for a given problem, and the difficulty in determining whether clustering has produced meaningful results. We present concrete examples of problems and solutions (clustering results) in the form of toy problems and real biological data for these pitfalls, illustrating how to avoid overinterpreting the data and missing valuable insights within high-throughput molecular measurements. The article contains six figures, four tables, and 77 references. Clustering is an unsupervised learning method, which groups data points based on similarity, and is used to reveal the underlying structure of data. This computational approach is essential to understanding and visualizing the complex data that are acquired in high-throughput multidimensional biological experiments. Clustering enables researchers to make biological inferences for further experiments. Although a powerful technique, inappropriate application can lead biological researchers to waste resources and time in experimental follow-up. We review common pitfalls identified from the published molecular biology literature and present methods to avoid them. Commonly encountered pitfalls relate to the high-dimensional nature of biological data from high-throughput experiments, the failure to consider more than one clustering method for a given problem, and the difficulty in determining whether clustering has produced meaningful results. We present concrete examples of problems and solutions (clustering results) in the form of toy problems and real biological data for these issues. We also discuss ensemble clustering as an easy-to-implement method that enables the exploration of multiple clustering solutions and improves robustness of clustering solutions. Increased awareness of common clustering pitfalls will help researchers avoid overinterpreting or misinterpreting the results and missing valuable insights when clustering biological data.
PACAP protects cerebellar granule neurons against oxidative stress‐induced apoptosis

Oxidative stress, resulting from accumulation of reactive oxygen species, plays a critical role in neuronal cell death associated with neurodegenerative diseases and stroke. In the present study, we have investigated the potential neuroprotective effect of pituitary adenylate cyclase‐activating polypeptide (PACAP) on oxidative stress‐induced apoptosis. Incubation of cerebellar granule cells with PACAP inhibited hydrogen peroxide‐evoked cell death in a concentration‐dependent manner. The effect of PACAP on granule cell survival was not mimicked by vasoactive intestinal polypeptide and was blocked by the antagonist PACAP6‐38. The protective action of PACAP upon hydrogen peroxide‐induced neuronal cell death was abolished by the MAP‐kinase kinase (MEK) inhibitor U0126 and mimicked by the caspase‐3 inhibitor Z‐DEVD‐FMK. PACAP markedly inhibited hydrogen peroxide‐evoked caspase‐3 activation and DNA fragmentation. Taken together, these data indicate that PACAP, acting through PACAP receptor type 1, exerts a potent protective effect against neuronal degeneration induced by hydrogen peroxide. The anti‐apoptotic effect of PACAP is mediated through the MAP‐kinase pathway and can be accounted for by inhibition of caspase‐3 activation resulting from oxidative stress.
Pivotal Role of Matrix Metalloproteinase 13 in Extracellular Matrix Turnover in Idiopathic Pulmonary Fibrosis

Rationale Idiopathic pulmonary fibrosis (IPF) is a fatal disease characterized by excessive deposition of extracellular matrix (ECM). Objectives We investigated the regulation of matrix metalloproteinases (MMPs) and their inhibitors (TIMPs) in lung fibrosis. Methods MMP and TIMP expression, collagenolytic activity and collagen content was assessed in IPF (n=16) versus donor (n=6) lung homogenates and accomplished by in-situ-zymography for gelatinolytic and collagenolytic activities, combined with MMP antigen detection. Role of MMP13 was assessed employing the bleomycin model of lung fibrosis in MMP-13-/- versus wild-type mice. Measurements and Main Results In IPF, MMPs-1, 2, 7, 9 and 13, but not MMP-8, were significantly upregulated, whereas none of the TIMPs (1–4) were significantly altered. Collagen content was slightly increased and collagenolytic activity was most prominent in the airways and co-localized with MMP-13. We observed an exaggerated early inflammatory response and an augmented lung fibrosis in bleomycin-challenged MMP-13-/- versus wild-type mice, with elevated lung collagen content 28d after bleomycin challenge in the MMP-13-/- mice. Conclusions Our data suggest that i) collagen deposition in IPF lungs is not primarily due to excessive TIMP production, but rather due to overwhelming ECM production in face of an overall increased, but spatially imbalanced collagenolytic activity, ii) preferential distribution of collagenolytic activity, largely MMP-13, in the airways offers an explanation for the development of honeycomb cysts and iii) despite an overall increase in inflammatory cell content the presence of MMP-13 seems to limit the overall extent of ECM deposition in lung fibrosis.
When Were Active Exoskeletons actually Born?

We are witnessing unimaginably intensive development in the field of humanoid robotics. The area of active exoskeletons is also experiencing a renaissance and is attracting increasing attention from researchers worldwide. The objective of this article is to consider the beginnings and review the history of the development of active exoskeletons, their original purpose and role in systems for rehabilitation of severely handicapped persons. The present-day development of active exoskeletons has to a large extent determined their future and applications in both the military and some specific activities that come out of the framework of rehabilitation systems and go deeply into special systems for enhancing power and expanding physical and working capabilities of humans. However, the origins of robotics, and especially of humanoid robotics, did not foreshadow such progress. We take a look back at the beginnings of the development of humanoid robots, especially bipedal locomotion systems and active exoskeletons, whose development marked the start of research in the domain of humanoid robotics. Once more we point out some of our pioneering results in the domain of active exoskeletons that were originally dedicated to supporting locomotion of handicapped persons. We show the relationships between the initial research in the field of active rehabilitation techniques and contemporary humanoid systems and try to predict the future directions of the development of humanoid robotics and active exoskeletal systems and their applications.
Object categorization via local kernels

This paper considers the problem of multi-object categorization. We present an algorithm that combines support vector machines with local features via a new class of Mercer kernels. This class of kernels allows us to perform scalar products on feature vectors consisting of local descriptors, computed around interest points (like corners); these feature vectors are generally of different lengths for different images. The resulting framework is able to recognize multi-object categories in different settings, from lab-controlled to real-world scenes. We present several experiments, on different databases, and we benchmark our results with state-of-the-art algorithms for categorization, achieving excellent results.
Description of EduCOM: A graphical modeling and programming language for teaching and learning digital communication systems

This paper presents EduCOM, a graphical language for teaching and learning digital communication systems. In EduCOM students build graphical models (i.e., block diagrams) of digital communication transmitters, channels, and receivers. Then EduCOM creates the shell of a C/C++ code realization of the graphical model, but it leaves the implementation of each block empty so that students are required to implement the functionality of the blocks in a low‐level programming language such as C/C++. Therefore, EduCOM goes beyond passive drag–drop‐connect style activities and forces students to think more deeply about each operation performed on the signals in the system, and preliminary assessment results with a small set of students bear this point out. Students indicated that the mixture of graphical modeling and low‐level programming provided by EduCOM is an improvement over pure graphical modeling or pure low‐level programming in helping them learn about communication systems. © 2009 Wiley Periodicals, Inc. Comput Appl Eng Educ 19: 697–707, 2011
Whole-Genome Sequence of Francisella noatunensis subsp. orientalis Strain FNO01 Isolated from Diseased Nile Tilapia in Brazil

ABSTRACT This paper describes the complete genome sequence of Francisella noatunensis subsp. orientalis strain FNO01, which was isolated during the first outbreak of francisellosis in cultured Nile tilapia in Brazil. The genome is composed of a circular chromosome with 1,859,830 bp and a G+C content of ~32%.
The impact of hearing loss on language performance in older adults with different stages of cognitive function

Purpose The possible relationship between audiometric hearing thresholds and cognitive performance on language tests was analyzed in a cross-sectional cohort of older adults aged ≥65 years (N=98) with different degrees of cognitive impairment. Materials and methods Participants were distributed into two groups according to Reisberg’s Global Deterioration Scale (GDS): a normal/predementia group (GDS scores 1–3) and a moderate/moderately severe dementia group (GDS scores 4 and 5). Hearing loss (pure-tone audiometry) and receptive and production-based language function (Verbal Fluency Test, Boston Naming Test, and Token Test) were assessed. Results Results showed that the dementia group achieved significantly lower scores than the predementia group in all language tests. A moderate negative correlation between hearing loss and verbal comprehension (r=−0.298; P<0.003) was observed in the predementia group (r=−0.363; P<0.007). However, no significant relationship between hearing loss and verbal fluency and naming scores was observed, regardless of cognitive impairment. Conclusion In the predementia group, reduced hearing level partially explains comprehension performance but not language production. In the dementia group, hearing loss cannot be considered as an explanatory factor of poor receptive and production-based language performance. These results are suggestive of cognitive rather than simply auditory problems to explain the language impairment in the elderly.
Critical Role of NADPH Oxidase in Neuronal Oxidative Damage and Microglia Activation following Traumatic Brain Injury

Background Oxidative stress is known to play an important role in the pathology of traumatic brain injury. Mitochondria are thought to be the major source of the damaging reactive oxygen species (ROS) following TBI. However, recent work has revealed that the membrane, via the enzyme NADPH oxidase can also generate the superoxide radical (O2 −), and thereby potentially contribute to the oxidative stress following TBI. The current study thus addressed the potential role of NADPH oxidase in TBI. Methodology/Principal Findings The results revealed that NADPH oxidase activity in the cerebral cortex and hippocampal CA1 region increases rapidly following controlled cortical impact in male mice, with an early peak at 1 h, followed by a secondary peak from 24–96 h after TBI. In situ localization using oxidized hydroethidine and the neuronal marker, NeuN, revealed that the O2 − induction occurred in neurons at 1 h after TBI. Pre- or post-treatment with the NADPH oxidase inhibitor, apocynin markedly inhibited microglial activation and oxidative stress damage. Apocynin also attenuated TBI-induction of the Alzheimer's disease proteins β-amyloid and amyloid precursor protein. Finally, both pre- and post-treatment of apocynin was also shown to induce significant neuroprotection against TBI. In addition, a NOX2-specific inhibitor, gp91ds-tat was also shown to exert neuroprotection against TBI. Conclusions/Significance As a whole, the study demonstrates that NADPH oxidase activity and superoxide production exhibit a biphasic elevation in the hippocampus and cortex following TBI, which contributes significantly to the pathology of TBI via mediation of oxidative stress damage, microglial activation, and AD protein induction in the brain following TBI.
Additive Manufacturing for Surgical Planning and Education: A Review

Additive manufacturing has been widely used in various industries, including the healthcare sector. Over the last few decades, AM has been playing an important role in the medical field in different areas, including surgical planning, implants, and educational activities. For surgical applications, AM can help surgeons practice and plan an operation until they are confident with the process. This can help to reduce operational risk and time. In addition, it can help to demonstrate the problem to other colleagues. AM has also been used to produce 3D models to teach students and doctors about human anatomy. This paper aims to comprehensively review the diverse applications of additive manufacturing within the domains of surgical planning and medical education. By focusing on the multifaceted roles played by AM in these critical areas, a contribution to the growing body of knowledge that underscores the transformative potential of this technology in shaping the future of healthcare practices is sought to be made.
Sex-related differences in hospital admissions attributed to adverse drug reactions in the Netherlands.

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT Women are more at risk for developing adverse drug reactions (ADRs) due to differences in pharmacokinetics, pharmacodynamics and drug use. ADRs regularly lead to hospital admissions. WHAT THIS STUDY ADDS There are differences between the sexes in hospital admissions attributed to ADRs. The risk of being hospitalized with an ADR varies between the sexes in the type of reaction and the causative drug. AIM Adverse drug reactions (ADRs) are a major burden in health care, regularly leading to hospital admission, morbidity or death. Women tend to have a higher risk of adverse drug reactions with a 1.5 to 1.7-fold greater risk than men. Our primary aim was to study differences in ADR-related hospitalizations between the sexes. METHODS We conducted a nationwide study of all ADR-related hospitalizations in the period between 2000 and 2005 in the Netherlands, which were selected from all 9,287,162 hospital admissions in this period. ADR-drug group combinations with at least 50 admissions in one of the sexes were selected. Relative risks and confidence intervals were calculated with respect to total admissions and total prescriptions with men as reference. RESULTS In total, 0.41% of the 4,236,368 admissions in men (95% CI 0.40, 0.42%) and 0.47% of the 5,050,794 admissions in women (95% CI 0.46, 0.48%) were attributed to an ADR by medical specialists (57% of all ADR-related admissions were in women). Differences between the sexes in risk for ADR-related hospitalization were found for antineoplastic and immunosuppressive drugs, antirheumatics, anticoagulants and salicylates, cardiovascular and neurological drugs, steroids and antibiotics. In certain drug categories, risks for hospitalization changed after taking into account total drug prescriptions. CONCLUSION In all different drug classes, significant differences exist between the sexes in ADR-related hospital admissions. Cardiovascular drugs account for the most pronounced differences between men and women. More research is needed to explain the clear sex differences in ADR-related hospital admissions.