Miracle-dz/UniFAR-FTUs huggingface dataset:
Fish as indicators of large river connectivity: the Danube and its tributaries

Within four-dimensional alluvial floodplain river systems, connectivity plays an important role for diverse fish communities. This is evident at three different temporal/ spatial levels. The zoogeographic level (1) shows that connectivity at a large temporal and spatial scale is an important regulatory mechanism in the colonization of river systems. At the population level (2), three characteristic genera of Danube fish species demonstrate -from a theoretical point of view - that connectivity plays an important role in the formation und functioning of metapopulations. The lowest level describes the significance of connectivity within various life history strategies (3) of Danube fish species. Examples presented in this paper demonstrate that fish are practical indicators of the consequences of human alterations to river connectivity conditions at various temporal/spatial levels. On the life history level, extensive and practically relevant information is now available to form a sound basis for the development of ecologically orientated river restoration concepts. At the metapopulation level the theoretical significance of connectivity for fish populations is evident; currently, however, lack of practical experience restricts its value as an indicator. Connectivity is relevant to ichthyogeography not only over geological time. but is also reflected in the present distribution and ongoing dispersal of fish species, a process which is additionally altered by human activity.
Agreement Study Using Gesture Description Analysis

Choosing adequate gestures for touchless interfaces is a challenging task that has a direct impact on human–computer interaction. Such gestures are commonly determined by the designer, ad-hoc, rule-based, or agreement-based methods. Previous approaches to assess agreement grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this article, we propose a generalized framework that inherently incorporates the gesture descriptors into the agreement analysis. In contrast to previous approaches, we represent gestures using binary description vectors and allow them to be partially similar. In this context, we introduce a new metric referred to as soft agreement rate (<inline-formula><tex-math notation="LaTeX">$\mathcal {SAR}$</tex-math></inline-formula>) to measure the level of agreement and provide a mathematical justification for this metric. Furthermore, we perform computational experiments to study the behavior of <inline-formula><tex-math notation="LaTeX">$\mathcal {SAR}$</tex-math></inline-formula> and demonstrate that existing agreement metrics are a special case of our approach. Our method is evaluated and tested through a guessability study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by <inline-formula><tex-math notation="LaTeX">$\mathcal {SAR}$</tex-math></inline-formula> is 2.64 times higher than the previous metrics. Finally, we show that our approach complements the existing agreement techniques by generating an artificial lexicon based on the most agreed properties.
Model for future thin-haul air mobility demand in Germany

Progress in the development of small electric and hybrid aircraft promises business opportunities for thin-haul air mobility services. In order to develop demand-oriented flight plan scenarios for Germany, this paper presents a model to estimate the marked volume of thin-haul air mobility. To quantify the potential demand, our model includes the steps of trip generation, trip distribution and mode choice. Trip generation and distribution takes place between 412 geographic subdivisions of Germany and is based on calibrated traffic forecast data for the year 2030. For the first time the five relevant modes of transport, namely: car, intercity train, intercity bus, commercial aircraft and thin-haul air mobility services, have been included in one model. The step of choosing the transport mode is implemented via a generalized cost approach, taking into account travel costs and travel time. Additionally, route modeling of all transport modes is enhanced by real market data using large-scale data readouts of web interfaces. As primary result we predict a market share of 6 % or 81 million trips per year for thin-haul air mobility services. The demand concentrates on a small number of airports: 30 % of the trips are estimated to be between only 20 airports. Hubs and main routes are identified to offer the potential for scheduled air services.
Deep Metric Learning Based Citrus Disease Classification With Sparse Data

Early recognition of citrus diseases is important for preventing crop losses and employing timely disease control measures in farms. Employing machine learning-based approaches, such as deep learning for accurate detection of multiple citrus diseases is challenging due to the limited availability of labeled diseased samples. Further, a lightweight architecture with low computational complexity is required to perform citrus disease classification on resource-constrained devices, such as mobile phones. This enables practical utility of the architecture to perform effective monitoring of diseases by farmers using their own mobile devices in the farms. Hence, we propose a lightweight, fast, and accurate deep metric learning-based architecture for citrus disease detection from sparse data. In particular, we propose a patch-based classification network that comprises an embedding module, a cluster prototype module, and a simple neural network classifier, to detect the citrus diseases accurately. Evaluation of our proposed approach using publicly available citrus fruits and leaves dataset reveals its efficiency in accurately detecting the various diseases from leaf images. Further, the generalization capability of our approach is demonstrated using another dataset, namely the tea leaves dataset. Comparison analysis of our approach with existing state-of-the-art algorithms demonstrate its superiority in terms of detection accuracy (95.04%), the number of parameters required for tuning (less than 2.3 M), and the time efficiency in detecting the citrus diseases (less than 10 ms) using the trained model. Moreover, the ability to learn with fewer resources and without compromising accuracy empowers the practical utility of the proposed scheme on resource-constrained devices, such as mobile phones.
Machine Learning-Based Zero-Velocity Detection for Inertial Pedestrian Navigation

Zero velocity update is a common and efficient approach to bound the accumulated error growth for foot-mounted inertial navigation system. Thus a robust zero velocity detector (ZVD) for all kinds of locomotion is needed for high accuracy pedestrian navigation systems. In this paper, we investigate two machine learning-based ZVDs: Histogram-based Gradient Boosting (HGB) and Random Forest (RF), aiming at adapting to different motion types while reducing the computation costs compared to the deep learning-based detectors. A complete data pre-processing procedure, including a feature engineering study and data augmentation techniques, is proposed. A motion classifier based on HGB is used to distinguish “single support” and “double float” motions. This concept is different from the traditional locomotion classification (walking, running, stair climbing) since it merges similar motions into the same class. The proposed ZVDs are evaluated with inertial data collected by two subjects over a 1.8 km indoor/outdoor path with different motions and speeds. The results show that without huge training dataset, these two machine learning-based ZVDs achieve better performances (55 cm positioning accuracy) and lower computational costs than the two deep learning-based Long Short-Term Memory methods (1.21 m positioning accuracy).
A Survey on Log Anomaly Detection using Deep Learning

Logs generated from the security systems, network devices, servers, and various software applications are one of the ways to record the operational happening of the equipment or software. These logs are assets for extracting meaningful information related to system behavior. Increasing usage of computer devices and the evolution of software systems can be considered as one of triggering acts for the concentration on the analysis of logs. Also, considering the massive volume of unstructured data, it raises the requirement for automatic analysis of these logs. The log analysis is helpful for understanding system behavior, malfunctioning detection, security scanning, and failure prediction. Machine learning(ML) and Deep Learning (DL) methods have been proved potent tools for data classification problems and have been applied to various fields of research. The purpose of this survey is to review recent research on log anomaly detection using Deep Neural Networks. Survey also presents the brief of log parsing approaches, types of datasets used for log analysis, and various concepts proposed for Log Anomaly detection.
Molecular Evolution and Expression Analysis of Subfamily ABCB Transporter Genes in Rice

With the accomplishment of genome sequencing projects,over 130 ABCs were identified separately from the model plants of monocots and dicots,rice and Arabidopsis,but the functions of most of the members remain elusive.Therefore,27 and 29 subfamily B of ABC genes(ABCB) in rice and Arabidopsis were systematically characterized,respectively.The exon number,length and molecular weight of proteins encoded by these genes varied greatly between the two plants,albeit the isoelectric point was less diverse.Proteins of this subfamily were divided into 4 subgroups accordingpar to phylogenetic analysis,suggesting that divergence probably occurred among them;6 and 9 pairs of paralogous genes were identified from rice and Arabidopsis,respectively,indicating that species-specific expansion contributed to the evolution of this subfamily in rice and Arabidopsis after the split of monocots and dicots.Segmental and tandem duplication contributed to the amplification of ABC family in rice.Multiple sequence alignment revealed that the Walker A,Walker B,and ABC signature motifs of nucleotide-binding domain(NBD) are highly conserved,but less the Q and H loops;no significant conservatism could be pointed out as to TMD domain within a single ABC protein and among different proteins.Expression of rice ABCB genes is highly tissue-specific,and divergence has occurred among the expression profiles of different genes.Expression analysis of rice ABCB genes under abiotic stress suggested that most of the genes are responsive to at least 1 type of stress factors.Ka/Ks calculation indicated that purifying selection is essential to the functional maintenance of genes after duplication.
A Bio-Inspired Transportation Network for Scalable Swarm Foraging

Scalability is a significant challenge for robot swarms. Generally, larger groups of cooperating robots produce more inter-robot collisions, and in swarm robot foraging, larger search arenas result in larger travel costs. This paper demonstrates a scale-invariant swarm foraging algorithm that ensures that each robot finds and delivers targets to a central collection zone at the same rate regardless of the size of the swarm or the search area. Dispersed mobile depots aggregate locally collected targets and transport them to a central place via a hierarchical branching transportation network. This approach is inspired by ubiquitous fractal branching networks such as tree branches and animal cardiovascular networks that deliver resources to cells and determine the scale and pace of life. We demonstrate that biological scaling laws predict how quickly robots forage in simulations of up to thousands of robots searching over thousands of square meters. We then use biological scaling to predict the capacity of depot robots that overcome scaling constraints to produce scale-invariant robot swarms. We verify the claims for large swarms in simulation and implement a simple depot design in hardware.
Effect of parenteral supplementation of selenium and vitamin E on selected blood biochemical parameters in H-F cows during the transition period.

The transition period between 3 weeks prepartum to 3 weeks postpartum is the most sensitive period during the reproductive cycle of dairy cows. The most commonly noted perinatal disorders in dairy cows include clinical mastitis (16.5% of animals), hoof infections (14.0%), placenta retention (7.8%), recumbency (4.9%), uterine infections (4.6%) and left displacement of the abomasum (3.5%) (34). Clinical ketosis affects 7-15% of animals, depending on the type of herd and the applied diagnostic method. Subclinical ketosis is reported in 15-43% cows. Most animals (more than 50%) are affected by more than one disorder (27), and fatty liver disease also poses a significant problem in the perinatal period. Selenium plays a very important role in higher organisms. This essential trace element inhibits glycolysis, protects the body against the toxic effects of cadmium, lead and mercury salts, ionizing radiation and nitrosamines (21). Selenium boosts humoral and cellular immunity. Cows experience metabolic changes during the transition period, including excessive production of free radicals that contributes to oxidative stress and directly impairs immune function. Research has demonstrated that selenium supplements administered to cows during the transition period significantly inhibit the production of free radicals and stimulate neutrophil activity. Selenium contributes to the maintenance of vascular homeostasis under exposure to Effect of parenteral supplementation of selenium and vitamin E on selected blood biochemical parameters in H-F cows during the transition period
Lightweight and Efficient Hypervisor-Based Dynamic Binary Instrumentation and Analysis Method

At present, various vulnerabilities and malicious programs are still constantly threatening the system security, and in-depth analysis of legitimate applications and malicious code is an important link of security defense under the current security situation. Dynamic binary analysis is the important method in the field of binary analysis, and after years of research and development, many valuable results have been achieved, and some mature tools generated have been widely used in practice. But it still faces multiple challenges in terms of analysis efficiency, deployment, and the impact on the target program. Based on the existing research, this article proposes a new lightweight hypervisor-based dynamic binary instrumentation method, which uses the virtualization features of new processors to perform transparent and efficient execution interception of the target program, so that it can instrument the target program and redirect the original execution. To take full advantage of the interception solution, we further present a compact inline dynamic taint analysis method that enables fine-grained data flow analysis of the target program with multiple processes and kernel modules. Experiments in the real environment show that the proposed method is effective and efficient in the binary instrumentation and analysis, it can achieve a good balance in analysis performance, availability and stealthiness, and can be applied to the practical analysis scenarios.
Investigation of Performance of Electric Load Power Forecasting in Multiple Time Horizons With New Architecture Realized in Multivariate Linear Regression and Feed-Forward Neural Network Techniques

A new multiple parallel input and parallel output architecture-based models are developed for forecasting electric load power consumption. Attention was paid toward the improvement of the accuracy of forecasting using this new architecture built-in by us, as compared to others in the latest literature. The models have an ability to forecast daily load profiles with a lead time of one to seven days. Both multivariate linear regression and feed-forward neural network techniques have been chosen for comparative performance study and analysis. The real-time data used for this research work were collected from Tata Power Delhi Distribution Limited, Delhi, India. Based on the performance criteria provided in the literature, each model is analyzed and the results are presented for two different lead times, i.e., day-ahead and week-ahead only.
Transposed 3 Tap FIR Filter Design Using Consolidation of Pipelining and Parallel Processing Technique

The speed of the DSP processor mainly depends on the execution time of the data path and memory elements. The main objective of the paper is to reduce the critical path of the system and trying to increase the speed. The combined pipelining and parallel processing technique is applied to the 3 tap fir filter. The proposed 3 tap transposed FIR filter with combined pipeline and parallel process gives a 31.33 % improvement in power delay product compare to conventional designs. The proposed technique is operated at different supply voltages and simulation results are verified with cadence software.
Maritime Visible Image Classification Based on Double Transfer Method

Image classification using deep transfer learning has received significant attention, benefiting from pre-trained with the large-scale annotation dataset and continuous improvement of neural network structure. In contrast to universal image classification, however, few publicly available datasets of maritime environments utilize deep transfer learning. Due to data-gathering effort and computational cost, the maritime datasets are deficient in the method of merging datasets and the benchmark of few-shot dataset classifiers. In this article, we proposed the double transfer method, consisting of the merging datasets network and the backbone network, to address the problem. The merging datasets network measuring image similarity separates classes of known and unknown samples to reorganize a dataset, and the backbone network is constructed from the model EfficientNet-b5 by network-based deep transfer learning. Using the merging datasets network, we introduce the visible maritime image dataset, which has 3,750 images and twenty-five classes, including multitudinous maritime objects. The backbone networks evaluated and analyzed the dataset based on accuracy, precision, recall, and F-measure metrics. Using the double transfer method, we can achieve an accuracy of 91.39% in the visible maritime image dataset.
Robust RGB-D Camera Tracking using Optimal Key-frame Selection

We propose a novel RGB-D camera tracking system that robustly reconstructs hand-held RGB-D camera sequences. The robustness of our system is achieved by two independent features of our method: adaptive visual odometry (VO) and integer programming-based key-frame selection. Our VO method adaptively interpolates the camera motion results of the direct VO (DVO) and the iterative closed point (ICP) to yield more optimal results than existing methods such as Elastic-Fusion. Moreover, our key-frame selection method locates globally optimum key-frames using a comprehensive objective function in a deterministic manner rather than heuristic or experience-based rules that prior methods mostly rely on. As a result, our method can complete reconstruction even if the camera fails to be tracked due to discontinuous camera motions, such as kidnap events, when conventional systems need to backtrack the scene. We validated our tracking system on 25 TUM benchmark sequences against state-of-the-art works, such as ORBSLAM2, Elastic-Fusion, and DVO SLAM, and experimentally showed that our method has smaller and more robust camera trajectory errors than these systems.
Fungicidal toxicity of certain extraneous components of Douglas fir heartwood

The heartwood o f D o u g l a s f i r ( P s e u d o t s u g a m e n z i e s i i ( M i r b . ) F r a n c o ) i s known t o be r e l a t i v e l y r e s i s t a n t t o a t t a c k by w o o d d e s t r o y i n g f u n g i . P r e v i o u s i n v e s t i g a t i o n s on o t h e r s p e c i e s has e s t a b l i s h e d v a r i o u s h e a r t w o o d e x t r a c t i v e s as the p r i m a r y d e t e r r e n t s t o d e c a y . S e v e r a l e x t r a n e o u s f r a c t i o n s from Douglas f i r were i s o l a t e d and e v a l u a t e d f o r f u n g i c i d a l a c t i v i t y i n o r d e r t o d e t e r m i n e the p r e c i s e f a c t o r i n f l u e n c i n g t h e d u r a b i l i t y o f t h i s s p e c i e s . An a c e t o n e , e t h e r and water e x t r a c t i o n o f D o u g l a s f i r heartwood meal p r o v i d e d f i v e s e p a r a t e components, n a m e l y : a d i h y d r o q u e r c e t i n , f r e e a c i d , n e u t r a l , p h l o b a t a n n i n and c a r b o h y d r a t e f r a c t i o n . A b i o a s s a y o f t h e s e m a t e r i a l s was made u s i n g Fomes annosus ( F r . ) C k e . , L e n t i n u s l e p i d e u s F r . and P o r i a i n c r a s s a t a ( B . & C . ) C u r t , as t h e t e s t f u n g i . B o t h a c e l l u l o s i c and a n o n c e l l u l o s i c s u b s t r a t e were e m p l o y e d . S m a l l wood b l o c k s f r o m w h i c h c e r t a i n e x t r a c t i v e s had been removed were used f o r the c e l l u l o s i c s u b s t r a t e s , whereas m a l t agar i m p r e g n a t e d w i t h v a r y i n g c o n c e n t r a t i o n s o f the e x t r a n e o u s m a t e r i a l s r e p r e s e n t e d t h e n o n c e l l u l o s i c m e d i a . The degree o f e f f e c t i v e n e s s o f e a c h component as a f u n g i c i d e was e x p r e s s e d n u m e r i c a l l y . D i h y d r o q u e r c e t i n was f o u n d t o be t h e most p o t e n t f u n g i c i d e , c o m p l e t e l y i n h i b i t i n g growth o f t h e most s e n s i t i v e fungus a t a c o n c e n t r a t i o n o f s l i g h t l y l e s s t h a n 0.5 p e r c e n t . This value compares favorably with experimental r e s u l t s previously reported with phenolic extractives of the genus Pinus. On the basis of these data, timber selected f o r i t s high dihydroquercetin content could be expected to have an extended service l i f e when used under conditions favoring decay. The p o s s i b i l i t y of breeding highly r e s i s t a n t genetic types i s also discussed b r i e f l y .