Miracle-dz/UniFAR-FTUs huggingface dataset:
Redundant MEMS-Based Inertial Navigation Using Nonlinear Observers

We present two alternative methods for fault detection and isolation (FDI) with redundant MEMS inertial measurement units (IMUs) in inertial navigation systems (INS) based on nonlinear observers. The first alternative is based on the parity space method, while the second is expanded with quaternion-based averaging and FDI. Both alternatives are implemented and validated using data gathered in a full-scale experiment on an offshore vessel. Data from three identical MEMS IMUs and the vessel’s own industrial sensors is used to verify the methods’ FDI capabilities. The results reveal that when it comes to FDI of the IMUs’ angular rate sensors, there are differences between the two methods. The navigation algorithm based on quaternion weighting is essentially unaffected by the failure of an angular rate sensor, while the parity-space-method-based alternative experiences a perturbation. Nomenclature {b} BODY coordinate frame {t} Earth fixed tangent frame {e} Earth Centered Earth Fixed (ECEF) coordinate frame {i} Earth Centered Inertial (ECI) coordinate frame Rb Rotation matrix from frame {b} to frame {t} qb Unit quaternion representation of rotation from {b} to {t} ωib Angular velocity of {b} relative {i}, decomposed in {b} f ib Specific force of {b} decomposed in {t} ptb Position of {b} relative {t} decomposed in {t} vtb Velocity of {b} relative {t} decomposed in {t} φ,θ,ψ Euler angles: Roll, pitch, yaw μ Latitude λ Longitude S(·) Skew symmetric matrix such that S(z1)z2 = z1× z2 ‖ · ‖2 Euclidean vector norm In n×n identity matrix ⊗ Hamiltonian quaternion product TMO Translational motion observer NLO Nonlinear observer FDI Fault detection and isolation MEMS Microelectromechanical system IMU Inertial measurement unit INS Inertial navigation system
Estimation of Several Turbulent Fluctuation Quantities Using an Approximate Pulsatile Flow Model

Turbulent fluctuation behavior is approximately modeled using a pulsatile flow model analogy. This model follows as an extension to the turbulent laminar sublayer model developed by Sternberg (1962) to be valid for a fully turbulent flow domain. Here unsteady turbulent behavior is modeled via a sinusoidal pulsatile approach. While the individual modes of the turbulent flow fluctuation behavior are rather crudely modeled, approximate temporal integration yields plausible estimates for Root Mean Square (RMS) velocity fluctuations. RMS pressure fluctuations and spectra are of particular interest and are estimated via the pressure Poisson expression. Both RMS and Power Spectral Density (PSD), i.e. spectra are developed. Comparison with available measurements suggests reasonable agreement. An additional fluctuating quantity, i.e. RMS wall shear fluctuation is also estimated, yielding reasonable agreement with measurement.
New Competition in Telecommunications Markets: Regulatory Pricing Principles

Launching and stimulating competition in telecommunications markets is an important policy goal. It contains two elements: to encourage entry and to make competition effective such that consumers benefit. The first one requires that entrants can make profits after investing in infrastructure so that they have an incentive to invest. The second one requires prices to be sufficiently low so that consumers enjoy higher net utilities. At a first glance, these two elements seem difficult to achieve at the same time. In this paper, we consider price regulation in the retail and wholesale market and answer to what extent such regulatory policy can stimulate competition. Our main finding is that, in the short run, asymmetric access price regulation is an effective instrument to make the entrant and consumers better off.
Anxiety-like Behavior in Female Sprague Dawley Rats Associated with Cecal Clostridiales

The relationship between the microbiota profile and exposure to stress is not well understood. Therefore, we used a rat model of unpredictable chronic mild stress (UCMS) to investigate this relationship. Depressive-like behaviors were measured in Female Sprague Dawley rats using the sucrose preference test and the Porsolt swim test. Anxiety-like behaviors were measured with the light–dark box test. Fecal corticosterone, cecal microbiota (composition and organic acids), plasma gut permeability (lipopolysaccharide-binding protein, LBP) and plasma inflammation (12 cytokines) markers were measured. Atypical behaviors were observed in female rats following UCMS, but no depressive-like behaviors were observed. Circulating concentrations of cytokines granulocyte-macrophage colony-stimulating factor and cytokine-induced neutrophil chemoattractant 1 were higher in UCMS-exposed female rats; plasma LBP and cecal organic acid levels remained unchanged. Our results reflect a resilient and adaptive phenotype for female SD rats. The relative abundance of taxa from the Clostridiales order and Desulfovibrionaceae family did, however, correlate both positively and negatively with anxiety-like behaviors and plasma cytokine concentrations, regardless of UCMS exposure, supporting the brain-to-gut influence of mild anxiety with a microbiota profile that may involve inflammatory pathways.
Optimizing garbage collection vehicle routing problem with alternative fuel-powered vehicles

Abstract This paper considers the garbage collection problem in which vehicles with multiple compartments are used to collect the garbage. The vehicles are considered to be Alternative Fuel-powered Vehicles (AFVs). Compared with the traditional fossil fuel powered vehicles, the AFVs have limited fuel tank capacity. In addition, AFVs are allowed to refuel only at the depot. We provide a mathematical formulation and develop two solution approaches to solve the problem. The first approach is based on the saving algorithm, while the second is based on the ant colony system (ACS) metaheuristic. New problem instances have been generated to evaluate the performance of the proposed algorithms.
Deep Neural Network-Based Ensemble Model for Eye Diseases Detection and Classification

Fundus images are the principal tool for observing and recognizing a wide range of ophthalmological abnormalities. The automatic and robust methods based on color fundus images are urgently needed since few symptoms are observable in the early stages of the disease. Experts must manually evaluate images to detect diseases for screening procedures to be effective. Due to the complexity of the screening procedure and the shortage of experienced personnel, developing successful screening-based treatments is costly. Although existing automated approaches strive to address these issues, they cannot handle a wide range of diseases and real-world circumstances. We design an automated deep learning-based ensemble method to detect and classify eye diseases from fundus images to address the abovementioned problems. A deep CNN-based model is proposed in the ensemble method that incorporates a mix of 20 layers, including the activation, optimization, and loss functions. The contrast-limited adaptive histogram equalization (CLAHE) and Gaussian filter are utilized in the pre-processing step to get more explicit images and eliminate noise. To avoid overfitting in the training phase, augmentation techniques are applied. Three pre-trained CNN models, including VGG16, DenseNet201, and ResNet50, are employed to compare and assess the efficiency of the proposed CNN model. Experimental results demonstrate that the ensemble approach outperforms recent approaches, which is comparatively state-of-art in the ODIR publicly available dataset.
RIS-aided Multi-hop Backhauling for 5G/6G UAV-assisted Access Points

Drones are considered to be an important part of future 6G telecommunication systems. Thanks to their quick deployment potential, they provide additional connectivity options in the form of a ﬂying hotspot. However, in such use cases, they typically require a wireless backhaul link to facilitate their proper operation, which might be a challenging task in dense urban environments. One of the potential methods that may be relied upon to connect such nodes is the integrated access and backhaul (IAB) approach, where part of the spectrum allocated to users accessing the base station is used for wireless backhauling. Thus, in this work, we consider the problem of establishing a multi-hop wireless backhaul link following the IAB concept, with the aid of drone relay stations (DRSs) and reconﬁgurable intelligent surfaces (RISs). We formulate the problem of coverage improvement with a ﬁxed number of relays, assuming certain throughput requirements for the backhaul link. The simulations show that the use of RISs oﬀers a coverage improvement in such a scenario or a reduction in the number of nodes involved in ensuring the required backhaul performance.
Patterns in composition and occurrence of the fish fauna in shallow areas of the São Francisco River mouth

Abstract The construction of dams causes changes in river variables, as a result of direct changes in their hydrological and biogeochemical cycles. One of the most notable changes is the flow regulation, which reduces seasonal events and the hydrostatic pressure exerted by freshwater, increasing the saltwater wedge intrusion into the system. Changing the salinity regime causes modifications in downstream ecosystems as well as in the distribution and composition of the fish fauna. In Brazil, the São Francisco River stands out, which has a system of cascading dams, built between the 70’s and 90’s. Because of these changes caused in the natural course of the river, this study aimed to analyze the patterns of composition and occurrence of the ichthyofauna at the mouth of the São Francisco River and relate them to the physical and chemical variables of the region. In order to evaluate the patterns of composition and occurrence of the fish fauna at the mouth of the São Francisco River, monthly trawls were conducted along the bank and physical and chemical variables were analyzed in the river channel over a period of one year. The relationship between abundance and species richness with environmental variables was verified using Generalized Linear Models. A total of 101,958 fish belonging to 87 taxa were caught, with emphasis on marine fish, both in number of individuals (99.92%) and in biomass (99.31%). A spatial gradient was detected, in which sites 1 and 2 were under marine influence, sites 3 and 4 represented the transition between the environments and site 5 was under the influence of brackish and freshwater. In general, the effect of the São Francisco River dams on the fish fauna was observed, with a predominance of fauna with more estuarine and less freshwater characteristics.
Accuracy improvement of cooperative localization using UAV and UGV

In a simultaneous localization and mapping (SLAM) system, in general, distribution estimation based on a particle filter is used for localization of Unmanned Ground Vehicle (UGV) with a laser rangefinder, and point estimation based on optimization is used for Unmanned Aerial Vehicle (UAV) with a monocular camera. In order for such robots to perform cooperative localization to improve accuracy, it is necessary to convert the localization results of point estimation and distribution estimation into a format that can be introduced to each other. In this paper, we propose an integration method of estimation results for cooperative localization by UAV and UGV. The UAV point estimation result is converted to a normal distribution by giving a fixed covariance matrix in order to introduce it as the observation likelihood of the UGV. On the other hand, the particle pose with maximum weight obtained from the UGV distribution estimation result is added to the current pose constraint in UAV optimization. As a result, this allows stable localization of both by correcting with the estimation result of the other even in an environment where either one is disadvantageous for localization. GRAPHICAL ABSTRACT
The Mixing method: low-rank coordinate descent for semidefinite programming with diagonal constraints

In this paper, we propose a low-rank coordinate descent approach to structured semidefinite programming with diagonal constraints. The approach, which we call the Mixing method, is extremely simple to implement, has no free parameters, and typically attains an order of magnitude or better improvement in optimization performance over the current state of the art. We show that the method is strictly decreasing, converges to a critical point, and further that for sufficient rank all non-optimal critical points are unstable. Moreover, we prove that with a step size, the Mixing method converges to the global optimum of the semidefinite program almost surely in a locally linear rate under random initialization. This is the first low-rank semidefinite programming method that has been shown to achieve a global optimum on the spherical manifold without assumption. We apply our algorithm to two related domains: solving the maximum cut semidefinite relaxation, and solving a maximum satisfiability relaxation (we also briefly consider additional applications such as learning word embeddings). In all settings, we demonstrate substantial improvement over the existing state of the art along various dimensions, and in total, this work expands the scope and scale of problems that can be solved using semidefinite programming methods.
Evaluation of Linkers’ Influence on Peptide-Based Piezoelectric Biosensors’ Sensitivity to Aldehydes in the Gas Phase

Recent findings qualified aldehydes as potential biomarkers for disease diagnosis. One of the possibilities is to use electrochemical biosensors in point-of-care (PoC), but these need further development to overcome some limitations. Currently, the primary goal is to enhance their metrological parameters in terms of sensitivity and selectivity. Previous findings indicate that peptide OBPP4 (KLLFDSLTDLKKKMSEC-NH2) is a promising candidate for further development of aldehyde-sensitive biosensors. To increase the affinity of a receptor layer to long-chain aldehydes, a structure stabilization of the peptide active site via the incorporation of different linkers was studied. Indeed, the incorporation of linkers improved sensitivity to and binding of aldehydes in comparison to that of the original peptide-based biosensor. The tendency to adopt disordered structures was diminished owing to the implementation of suitable linkers. Therefore, to improve the metrological characteristics of peptide-based piezoelectric biosensors, linkers were added at the C-terminus of OBPP4 peptide (KLLFDSLTDLKKKMSE-linker-C-NH2). Those linkers consist of proteinogenic amino acids from group one: glycine, L-proline, L-serine, and non proteinogenic amino acids from group two: β-alanine, 4-aminobutyric acid, and 6-aminohexanoic acid. Linkers were evaluated with in silico studies, followed by experimental verification. All studied linkers enhanced the detection of aldehydes in the gas phase. The highest difference in frequency (60 Hz, nonanal) was observed between original peptide-based biosensors and ones based on peptides modified with the GSGSGS linker. It allowed evaluation of the limit of detection for nonanal at the level of 2 ppm, which is nine times lower than that of the original peptide. The highest sensitivity values were also obtained for the GSGSGS linker: 0.3312, 0.4281, and 0.4676 Hz/ppm for pentanal, octanal, and nonanal, respectively. An order of magnitude increase in sensitivity was observed for the six linkers used. Generally, the linker’s rigidity and the number of amino acid residues are much more essential for biosensors’ metrological characteristics than the amino acid sequence itself. It was found that the longer the linkers, the better the effect on docking efficiency.
Improved 1D convolutional neural network for near-infrared spectroscopy for fast measurement of water quality COD values

Chemical oxygen demand value is a key indicator for water quality detection and an important link in water environmental protection. Near infrared spectroscopy is a fast, non-destructive, and green identification method applied to chemical oxygen demand detection. Near infrared spectroscopy has the characteristics of high data dimensions, severe band stacking, and difficult feature extraction. In this paper, an improved one-dimensional convolutional neural network is proposed to analyze the near infrared spectroscopy of water samples to predict their chemical oxygen demand values. Firstly, one-dimensional convolution is used to extract deep spectral features. Secondly, SoftPool is used to reduce the dimension of spectral features to improve the problem of losing some features in traditional pooling methods. Finally, the prediction results are output through full connectivity layer integration features. The proposed method is validated on a near infrared spectral dataset of chemical oxygen demand. The experimental results show that this method can accurately and quickly analyze the chemical oxygen demand value in water.
Analysis of the Influence of Information Flow Topology in Platoon Applications

In a vehicle platoon, the Information Flow Topology consists of the structure and organization of the communication network that interconnects the vehicles. When there is a high Frame Error Rate in the communication network, the vehicle platoon can become unstable and put drivers and passengers at risk. This work investigates the influence of the Information Flow Topology on a platoon in scenarios with different speeds, acceleration and deceleration amplitude, and levels of packet loss. Simulation results demonstrated that the Predecessor Follower topology can maintain safety and a more accurate intervehicular distance even in nonideal vehicular communication scenarios.
Company2Vec - German Company Embeddings based on Corporate Websites

With Company2Vec, the paper proposes a novel application in representation learning. The model analyzes business activities from unstructured company website data using Word2Vec and dimensionality reduction. Company2Vec maintains semantic language structures and thus creates efficient company embeddings in fine-granular industries. These semantic embeddings can be used for various applications in banking. Direct relations between companies and words allow semantic business analytics (e.g. top-n words for a company). Furthermore, industry prediction is presented as a supervised learning application and evaluation method. The vectorized structure of the embeddings allows measuring companies similarities with the cosine distance. Company2Vec hence offers a more fine-grained comparison of companies than the standard industry labels (NACE). This property is relevant for unsupervised learning tasks, such as clustering. An alternative industry segmentation is shown with k-means clustering on the company embeddings. Finally, this paper proposes three algorithms for (1) firm-centric, (2) industry-centric and (3) portfolio-centric peer-firm identification.
MapSeg: Segmentation guided structured model for online HD map construction

The development of online high-definition maps is significant since they provide real-time, accurate, and updatable geographic information for location-based applications, such as autonomous driving and intelligent transportation, thus improving the performance and reliability of these applications. Previous works, such as VectorMapNet and MapTR, show that direct model generation of vectorized HD maps is a promising solution. However, these methods did not take into account the usage of global semantic information to improve map construction accuracy. To address this limitation, we propose a segmentation-guided structured model (MapSeg) for online HD map construction. Specifically, we added a UV segmentation module (USM) and a BEV segmentation module (BSM) based on the MapTR structure, enabling the model to better capture the semantic information.
Transparency of Shariah Supervisory Board Information in Islamic Banks of Indonesia and Malaysia: The Effect of Islamic Corporate Governance

Purpose: This study aims to determine the level of transparency of Islamic banks in Indonesia and Malaysia by looking at the level of transparency of information related to the Sharia Supervisory Board (SSB) in Islamic banks in both countries and the influence of various characteristics of SSB on the disclosure.Method: The study covers the period 2012-2019 and the research data was obtained by conducting content analysis on the annual reports of Islamic banks.Findings: The results of the regression test show that the size and expertise of SSB have no effect on the disclosure of SSB by Islamic banks in Indonesia and Malaysia. SSB cross-membership has a positive effect on SSB-related disclosures in both countries. This study shows that the experience and knowledge gained by SSB members from their positions as SSB at various other Islamic financial institutions has a positive influence on the ability of SSB to increase the transparency of information about SSBs in Islamic banks.Novelty: This study contributes to providing empirical evidence and literature on the importance of the role of SSB in determining the level of transparency carried out by Islamic banks in the majority and most populous Islamic country in the world.
Inclusion of Egyptian clover improves the value of sorghum-based cropping systems

ABSTRACT Context. In developing countries there is a preference to grow food crops rather than forage. However, suitable food–fodder based systems ensure year-round green fodder availability for profitable livestock production. Aims. This study aimed to determine whether including single cut Egyptian clover (SEC) in the existing cropping systems could improve system productivity, forage availability and economic returns. Method. Nine SEC based cropping systems were tested in a randomised complete block design with three replications. Key results. Sorghum–SEC–wheat (late sown) cropping system produced 18% greater wheat equivalent yield compared with a popularly adopted sorghum–wheat system (8.9 Mg ha−1). The water productivity (1.80 kg m−3) of this system was also improved in the sorghum–multi-cut Egyptian clover system (1.74 kg m−3). There was a marginal increase in dry matter yield and crude protein content of the succeeding sorghum crop when it was rotated with Egyptian clover. The inclusion of Egyptian clover also improved the soil organic carbon (23%) and available nitrogen (15%) compared with cereal–cereal systems. The findings identify a sustainable intensification option in the sorghum-based cropping system with short duration Egyptian clover (single cut) as an economically feasible and ecologically effective approach. Conclusion. Integration of single cut Egyptian clover in sorghum–wheat rotation as a catch crop recorded greater system productivity, water-use efficiency and economic returns to achieve the sustainable crop intensification. Implication. The SEC can be an effective option for diversification and intensification of cropping systems to improve fodder availability, yield, boost income and improve soil health.
Microscopic Origin of Surface-enhanced Circular Dichroism

Circular dichroism (CD), the difference in absorption of two opposite circularly polarized light sources by chiral molecules, can be significantly enhanced when molecules are adsorbed on the surface of nanostructures. We present a theory based on Poynting’s theorem adapted for chiral media to analyze the surface-enhanced CD of a chiral molecule/nanostructure coupled system. Our theory clarifies the microscopic origin of surface-enhanced CD signals by showing that the enhanced CD has two forms, inherent and induced. The inherent CD is the direct molecular CD that becomes enhanced due to the strongly localized optical helicity density near the nanostructure. The induced CD, previously ignored, derives from asymmetric excitation and absorption of electromagnetic fields inside the nanostructures surrounded by chiral molecules upon the injection of two oppositely circularly polarized light sources. Moreover, it is demonstrated that the induced CD can contribute significantly to the CD signals measured by surfa.