Miracle-dz/UniFAR-FTUs huggingface dataset:
Ein Regulationsnetzwerk aus H-NS und den Antirepressoren BglJ und LeuO

Das Nukleoid-assoziierte Protein H-NS steuert als globaler Repressor 200 bis 300 der Gene von Escherichia coli. Die Repression eines dieser Loci, des bgl-Operons (aryl-beta,D-Glukosid Operon) ist ungewoehnlich strikt und erfolgt durch Bindung von H-NS an zwei regulatorische Regionen oberhalb und unterhalb des Promotors. Die strikte Repression ist die Ursache dafuer, dass das bgl-Operon kryptisch ist, d.h. es wird unter Labor-ueblichen Wachstumsbedingungen nicht exprimiert. Allerdings fuehrt die Ueberexpression der Transkriptionsfaktoren LeuO und BglJ zu einem Bgl+ Phaenotyp. In dieser Arbeit wurde gezeigt, dass LeuO und BglJ als Anti-Repressoren von H-NS wirken. LeuO und BglJ heben die Repression durch Bindung von H-NS oberhalb des Promotors auf. Bindestudien zeigen, dass LeuO direkt an die 5'-bgl-Promotorregion bindet und dadurch den H-NS-Repressionskomplex moduliert. Ein Screen nach Mutanten sowie Expressionsanalysen zeigten, dass die Aktivierung des bgl-Promotors durch BglJ von RcsB abhaengig ist. RcsB ist der Response-Regulator des Rcs-Phosphorelaysystems, eines der drei Membranstress-Sensing-Systeme von E.coli. BglJ und RcsB gehoeren zur selben Familie der LuxR-aehnlichen Transkriptionsfaktoren und bilden im Two-Hybrid-System Heterodimere. Daher ist anzunehmen, dass ein BglJ/RcsB-Heterodimer als Anti-Repressor im bgl-Promotorbereich bindet und den Promotor aktiviert. Beim Versuch BglJ und RcsB fur in-vitro-Interaktionsanalysen und DNA-Bindestudien zu exprimieren, zeigte sich, dass RcsB die Stabilitaet von BglJ vermindert. RcsB ist also einerseits fur die Aktivierung von bgl durch BglJ notwendig und reguliert andererseits BglJ negativ. H-NS, LeuO und BglJ bilden ein komplexes Netzwerk. Die Antirepressoren LeuO und BglJ heben die Repression von bgl durch H-NS auf. Die leuO und bglJ-Gene werden aber selbst durch H-NS reprimiert, wobei LeuO wiederum Aktivator von bglJ ist. Dieses Regulationsnetzwerk wird ergaenzt durch den Response-Regulator RcsB, der fur die bgl-Aktivierung durch BglJ notwendig ist. Die Komplexitaet der Wechselwirkungen legt nahe, dass die Aktivierung von bgl in vivo an streng definierte Bedingungen geknuepft ist, wobei ueber RcsB eine moegliche Verbindung zu Membranstress hergestellt wird und LeuO auf Aminosaeuremangel als Signal hinweist.
Ensemble Learning in Predicting Financial Distress of Indonesian Public Company

Predicting financial distress can avoid firm bankruptcy. That is an important issue in matters of company sustainability and the economic growth in general. Indonesia as a developing country needs a reliable system that is able to predict the bankruptcy of a company because it can affect the overall economic condition at different levels. The ensemble learning which is built to achieve better performance of prediction can be implemented to forecast the unhealthy company conditions. Random forest ensemble learning and AdaBoost have been proven superior to the single one. Both methods are applied to Indonesia Public Company data with 6 variables based on Altman Z-Score and one additional variable. The accuracy, precision, recall, and f1-score have an average of 91% regardless of the data imbalance. The ensemble score determines its superiority to the single machine learning.
On Testing Asymmetry in the Bounded Degree Graph Model

We consider the problem of testing asymmetry in the bounded-degree graph model, where a graph is called asymmetric if the identity permutation is its only automorphism. Seeking to determine the query complexity of this testing problem, we provide partial results. Considering the special case of n -vertex graphs with connected components of size at most s ( n ) = Ω(log n ), we show that the query complexity of (cid:15) -testing asymmetry (in this case) is at most O ( √ n · s ( n ) /(cid:15) ) and at least Ω( (cid:112) n 1 − O ( (cid:15) ) /s ( n )). In particular, the query complexity of o (1 /s ( n ))-testing asymmetry is at least Ω( (cid:112) n/s ( n )). In addition, we show that testing asymmetry in the dense graph model is almost trivial.
Spherical fuzzy extension of DEMATEL (SF‐DEMATEL)

In recent years, many versions of fuzzy sets are introduced in the literature. Spherical fuzzy sets (SFSs) concept is one of these latest developments. An SFS is a synthesis of Pythagorean fuzzy set (PFS) and neutrosophic set for providing a larger preference domain for experts and allowing them to express their hesitancy more comprehensively. The distinctive feature of SFSs is that the squared sum of membership, nonmembership, and hesitancy degrees is between 0 and 1 while each degree should be independently defined in [0,1]. The application of SFS on decision‐making field may be about describing expert evaluations more informatively and explicitly. In this study, spherical fuzzy version of DEMATEL, one of the most cited multiple criteria decision‐making approach, is introduced for considering the hesitancy degrees of experts when they evaluate the potential influences among the criteria. Another contribution made is the fuzzification of all the steps in DEMATEL until the last ones since the existing propositions in literature perform early defuzzification operations. The defuzzification is here applied as close to the end as possible for keeping the whole process fuzzy. The applicability of the proposition is shown in an illustrative example of a building contractor evaluation problem, and its robustness is shown by comparing the results with PFS‐based and neutrosophic DEMATEL versions.
J‐divergence‐based Power Allocation in Wireless Sensor Networks with Distributed Detection under Correlated Noises

In this paper, the power allocation problem in a wireless sensor network (WSN) with binary distributed detection is considered. It is assumed that the sensors independently transmit their local decisions to a fusion center (FC) through a slow fading orthogonal multiple access channel (OMAC), where, in every channel, the interferences from other devices are considered as correlated noises. In this channel, the associated power allocation optimization problem with equal power constraint is established between statistical distributions under different hypotheses by using the Jeffrey divergence (J‐divergence) as a performance criterion. It is shown that this criterion for the power allocation problem is more efficient compared to other criteria such as mean square error (MSE). Moreover, several numerical simulations and examples are presented to illustrate the effectiveness of the proposed approach.
Information transmission in trigeminal afferents from teeth

The purpose of this study was to measure the rate at which information is transmitted by periodontal mechanosensitive neurons. In addition, an attempt was made to develop a quantitative means for expressing their responsiveness, or sensitivity. The experiments were carried out on cats anaesthetized with sodium pentobarbital. The canine teeth were stimulated with computer-controlled forces, and the response of single first-order axons supplying the periodontal mechanoreceptors were recorded following microdissection of the inferior dental nerve. Thirty mechanosensitive units, selected from twelve animals, were used in the study. For fifteen of these units a stimulus uncertainty of 5 bits, a stimulus duration of 500 msec, and an inter-stimulus interval of 500 msec was employed. Under these conditions, the mean channel capacity was found to be 3.2 bits/ stimulus + S.E. = 0.09. The channel capacity for the other fifteen mechanosensitive units was measured using a 7 bit/stimulus uncertainty. This increased the channel capacity to a mean of 3.77 bits/stimulus, S.E. =+ . 14. It was found that this calculated rate of information transmission could be further significantly increased (P.> 0.0001) to 4.33 bits/stimulus, S.E. = + 0.11, by treating the data so as to prevent aver-sampling of parts of the response.When the outputs of seven neurons innervating a single tooth were pooled, the maximum rate of information transmission for the group as a whole was found to be 6.84 bits/ stimulus. Comparison of the pooled data with the results of psychophysical experiments suggests that the rates of encoding of information in primary afferents far exceeded the ability of the CNS to receive such data. A computational method for the measurement of the sensitivity of the neurons was also developed. It is proposed that the sensitivity, or responsiveness, of neurons can be measured properly only by the use of information theory, and that it depends upon the particular method of coding chosen by the experimenter.
Deep Recursive Network for Hyperspectral Image Super-Resolution

Fusion based hyperspectral image (HSI) super-resolution method, which obtains a spatially high-resolution (HR) HSI by fusing a low-resolution (LR) HSI and an HR conventional image, has been a prevalent method for HSI super-resolution. One effective fusion based method is to cast HSI super-resolution into a unified optimization problem, where handcrafted priors such as sparse prior or low rank prior are always adopted to regularize the latent HR HSI to be optimized. However, these priors show limitations in generalizing to challenging cases due to the heuristic assumption on image statistics as well as the restricted expressiveness capacity of the shallow structure. Taking advantages of the powerful expression ability of deep learning based method, a new HSI super-resolution network is proposed which implicitly incorporates a deep structure as the regularizer/prior. Specifically, we reformulate the original unified optimization problem into three sub-optimization problems, one is related with the regularizer and the others are without. Thanks to the fact that the one related with the regularizer naturally equals to a denoising problem, a recursive residual network is proposed for this sub-optimization problem. In addition, we unfold the other sub-optimization problems into network representations, with which the original unified optimization problem can be represented into a fully end-to-end network. Experimental results shows the superiority of the proposed method for HSI super-resolution on three benchmark datasets.
Outage Analysis for MISO Zero-Forcing Precoding With Outdated CSI

Zero-forcing precoding is a commonly-used multiple-user, multiple-input multiple-output beamforming technique. Applying such precoding, the signal-to-interference-plus-noise ratio (SINR) statistics with outdated channel state information, which involve various projections related to the multi-dimensional channel vectors and precoding vectors, have never been explicitly derived. In this paper, for the multiple-input single-output scenario with uniform power allocation, the distributions of these projections are derived, and then the SINR distribution and the conditional outage probability can be computed using those distributions.
Compact Microwave Half-Wave Resonator Filters with Increased Frequency Selectivity

In this paper, we study two approaches to increase the maximum frequency selectivity and to reduce the dimensions of microstrip filters with a significantly limited number of resonators. Conducted a comparative analysis of these approaches' efficiency in the design of the filters. The possibility of combining these contradictory qualities in compact structures of highly selective microstrip filters on half-wave co-directional hairpin resonators folded into the shape of a “paper clip” is shown.
A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting

Electric energy forecasting domain attracts researchers due to its key role in saving energy resources, where mainstream existing models are based on Gradient Boosting Regression (GBR), Artificial Neural Networks (ANNs), Extreme Learning Machine (ELM) and Support Vector Machine (SVM). These models encounter high-level of non-linearity between input data and output predictions and limited adoptability in real-world scenarios. Meanwhile, energy forecasting domain demands more robustness, higher prediction accuracy and generalization ability for real-world implementation. In this paper, we achieve the mentioned tasks by developing a hybrid sequential learning-based energy forecasting model that employs Convolution Neural Network (CNN) and Gated Recurrent Units (GRU) into a unified framework for accurate energy consumption prediction. The proposed framework has two major phases: (1) data refinement and (2) training, where the data refinement phase applies preprocessing strategies over raw data. In the training phase, CNN features are extracted from input dataset and fed in to GRU, that is selected as optimal and observed to have enhanced sequence learning abilities after extensive experiments. The proposed model is an effective alternative to the previous hybrid models in terms of computational complexity as well prediction accuracy, due to the representative features’ extraction potentials of CNNs and effectual gated structure of multi-layered GRU. The experimental evaluation over existing energy forecasting datasets reveal the better performance of our method in terms of preciseness and efficiency. The proposed method achieved the smallest error rate on Appliances Energy Prediction (AEP) and Individual Household Electric Power Consumption (IHEPC) datasets, when compared to other baseline models.
Performance Improvement of Existing Three Phase Synchronous Reluctance Machine: Stator Upgrading to 5-Phase With Combined Star-Pentagon Winding

This paper investigates the performance of 3-phase synchronous reluctance machines (SynRMs) when upgrading their stator to 5-phase, keeping the same rotor. The design of the 5-phase stator has been optimized to select the optimal dimensions of the slots and teeth. Moreover, a combined star-pentagon winding is employed to further improve the machine performance. Different winding configurations have been studied and compared using 2D Ansys Maxwell transient simulations. It is observed that at optimal current angle and rated current, the average torque is increased by 17.41% when changing the 3-phase stator with the proposed 5-phase one. In addition, the efficiency of the 5-phase SynRM is increased by about 0.8% compared to 3-phase SynRM. At 3 times the rated speed, the torque and efficiency are significantly increased by around 33% and 3.5% respectively. Moreover, the 5-phase SynRM shows a superior performance in the faulty case with one phase opened. It works at 98.84% of the rated torque of the healthy 3-phase machine, whereas the 3-phase machine works at only 43.35% with huge torque ripple (228%). Finally, an experimental validation using the reference 3-phase machine has been done.
Effects of Ascorbic Acid and Reduced Glutathione on the Alleviation of Salinity Stress in Olive Plants

ABSTRACT The aim of this study was to evaluate the effects of low molecular mass antioxidants and NaCl salinity on growth, ionic balance, proline, and water contents of ‘Zard’ olive trees under controlled greenhouse conditions. The experiment was carried out by spraying 2 mM of ascorbic acid (Asc) and 3 mM of reduced glutathione (GSH) on the plants that were treated with two salinity levels (0 and 100 mM NaCl) on their root medium. Plant growth parameters (leaf fresh weight, leaf dry weight, leaf number, total fresh weight, and total dry weight) were significantly improved by Asc compared with growth parameters in GSH and control plants. Higher concentrations of Na+ and Cl– were observed in salt-stressed plants, while Na+ and Cl– concentrations were decreased in the olive leaves that were sprayed with Asc. Salinity in the root zone caused a considerable decline in both K+ concentration and K/Na ratio. K+ concentration and K/Na ratio were significantly increased by application of Asc on plant leaves. Salinity caused an increase in electrolyte leakage (EL) compared with the control plants. Lowest EL and tissue water content (TWC) was obtained in Asc-sprayed plants, whereas TWC was increased in salt-stressed plants. Plants were subjected to salt stress and showed a higher relative water content (RWC) than the control plants. Salt stress induced proline accumulation in olive leaves. In conclusion, exogenous application of Asc is recommended to improve tolerance of olive plants under saline conditions.
Targetless Camera-LiDAR Calibration in Unstructured Environments

The camera-Lidar sensor fusion plays an important role in autonomous navigation research. Nowadays, the automatic calibration of these sensors remains a significant challenge in mobile robotics. In this article, we present a novel calibration method that achieves an accurate six-degree-of-freedom (6-DOF) rigid-body transformation estimation (aka extrinsic parameters) between the camera and LiDAR sensors. This method consists of a novel co-registration approach that uses local edge features in arbitrary environments to get 3D-to-2D errors between the data of both, camera and LiDAR. Once we have 3D-to-2D errors, we estimate the relative transform, i.e., the extrinsic parameters, that minimizes these errors. In order to find the best transform solution, we use the perspective-three-point (P3P) algorithm. To refine the final calibration, we use a Kalman Filter, which gives the system high stability against noise disturbances. The presented method does not require, in any case, an artificial target, or a structured environment, and therefore, it is a target-less calibration. Furthermore, the method we present in this article does not require to achieve a dense point cloud, which holds the advantage of not needing a scan accumulation. To test our approach, we use the state-of-the-art Kitti dataset, taking the calibration provided by the dataset as the ground truth. In this way, we achieve accuracy results, and we demonstrate the robustness of the system against very noisy observations.
Beyond 5G: Big Data Processing for Better Spectrum Utilization

This article emphasizes the great potential of big data processing for advanced user- and situationoriented, context-aware resource utilization in future wireless networks. In particular, we consider the application of dedicated, detailed, and rich-in-content maps and records called radio-service maps (RSMs) for unlocking the spectrum opportunities in 6G networks. Due to the characteristics of 5G, in the future, there will be a need for high convergence of various types of wireless networks, such as cellular and Internet of Things (IoT) networks, which are steadily growing and consequently considered as the studied use case in this article. We show that the 6G network significantly benefits from effective dynamic spectrum management (DSM) based on RSMs that provide rich and accurate knowledge of the radio context, a knowledge that is stored and processed within database-oriented subsystems designed to support wireless networks for improving spectral efficiency. In this article, we discuss contextaware RSM subsystem architecture and operation for DSM in convergent 6G radio and IoT networks. By providing various use cases, we demonstrate that accurate definition of and access to the rich context information leads to a significant improvement of system performance. As a consequence, we also claim that efficient big data processing algorithms will be necessary in future applications.
Determination of the dha regulon of C. werkmanii and characterization of ΔdhaD

Citrobacter sp. is a poorly investigated micro-organism with a lot of interesting properties in different areas. One property is that they can precipitate heavy metals like sulphate and copper (Qiu et al. 2009). This phenomenon may be useful in the bioremediation of acid mine drainage. Another quality is that the bacteria can grow on glycerol as the sole carbon source (Bouvet et al. 1995). The genes responsible for this characteristic are structured in the dha regulon. This regulon is already characterized for Citrobacter freundii (Daniel et al. 1998). However, when a PCR was performed on the genome of C. werkmanii with primers for different parts of the regulon based on the dha regulon of C. freundii, only one product was positive. So the dha regulon of C. werkmanii was explored through genome walking. This resulted in a regulon with a similar organization as the regulon of C. freundii. Moreover, the deduced protein sequence showed 98,8% identity compared to the regulon of C. freundii, 70,2% identity with C. koseri ATCC BAA-895 and 96,8% with C. youngae ATCC 29220. In a second phase of this study, the dhaD gene coding for the glycerol dehydrogenase was knocked out using the one-step inactivation method (Datsenko and Wanner 2000). This mutant was characterized by sequence analysis and an enzyme assay. To our knowledge, this is the first knockout mutant made in Citrobacter sp.
An agorà for collective innovation: designing a dynamic web-based platform for communities

Achieving successful, repeated organizational innovation is a never ending primary challenge for companies. But often this cannot be pursued in a standalone modality. New practices are emerging to foster innovation, by building networks for collaboration and leveraging networks of outsiders. The paper empirically investigates the state of the art of open innovation web-based platforms (OIWP) through a classification of more than 200 OIWP based on nine variables. The aim is to offer a framework to position the different platforms, investigating the trends and evolving paths. The research suggests that the evolution of OIWP is going towards a more structured collective approach to favor cross-fertilization and technology transfer among companies, where the main roots to feed an innovation ecosystem based on web platforms are even more the concepts of communities and cognitive diversity, self-organization, ecosystem and shared fate. Finally, we present the concept, design and undergoing development of an OIWP named iCommunity, based on these concepts and projected to increase the innovation landscape of European SMEs and SMEAGs.
Applications of Head-Mounted Displays for Virtual Reality in Adult Physical Rehabilitation: A Scoping Review.

IMPORTANCE Head-mounted displays for virtual reality (HMD-VR) may be used as a therapeutic medium in physical rehabilitation because of their ability to immerse patients in safe, controlled, and engaging virtual worlds. OBJECTIVE To explore how HMD-VR has been used in adult physical rehabilitation. DATA SOURCES A systematic search of MEDLINE, Embase, Cochrane Library, CINAHL, Web of Science, PsycINFO, and ERIC produced 11,453 abstracts, of which 777 underwent full-text review. STUDY SELECTION AND DATA COLLECTION This scoping review includes 21 experimental studies that reported an assessment or intervention using HMD-VR in a physical rehabilitation context and within the scope of occupational therapy practice. FINDINGS HMD-VR was used for assessment and intervention for patients with a range of disorders, including stroke, multiple sclerosis, spinal cord injury, and Parkinson's disease. CONCLUSIONS AND RELEVANCE HMD-VR is an emerging technology with many uses in adult physical rehabilitation. Higher quality clinical implementation studies are needed to examine effects on patient outcomes. WHAT THIS ARTICLE ADDS We review existing research on how immersive virtual reality (e.g., using head-mounted displays) has been used for different clinical populations in adult physical rehabilitation and highlight emerging opportunities in this field for occupational therapists.