Miracle-dz/UniFAR-FTUs huggingface dataset:
Tuning Continual Exploration in Reinforcement Learning ∗ (Draft manuscript submitted for publication)

This paper presents a model allowing to tune continual exploration in an optimal way by integrating exploration and exploitation in a common framework. It first quantifies the rate of exploration by defining the degree of exploration of a state as the probability-distribution entropy for choosing an admissible action. Then, the exploration/exploitation tradeoff is stated as a global optimization problem: find the exploration strategy that minimizes the expected cumulated cost, while maintaining fixed degrees of exploration at same nodes. In other words, “exploitation”is maximized for constant “exploration”. This formulation leads to a set of nonlinear updating rules reminiscent of the value-iteration algorithm. Convergence of these rules to a local minimum can be proved for a stationary environment. Interestingly, in the deterministic case, when there is no exploration, these equations reduce to the Bellman equations for finding the shortest path while, when it is maximum, a full “blind”exploration is performed. We also show that, if the graph of states is directed and acyclic, the nonlinear equations can easily be solved by performing a single backward pass from the destination state. Stochastic shortest path problems as well as discounted problems are also examined. The theoretical results are confirmed by simple simulations showing that this exploration strategy outperforms the naive ǫ-greedy and Boltzmann strategies.
A Distributed SLAM with UWB-VIO Fusion Framework for Enhanced Relative Localization of Multi-UAVs System

The most critical challenge of multi-UAV (unmanned aerial vehicles) systems accomplishing a common mission is to maintain relative localization among UAVs. However, it remains an open and challenging research topic in infrastructure-less environments. In this paper, we propose a novel distributed visual-inertial ultra-wideband (UWB) fusion framework for global localization that overcomes the problem of state estimation in GPS-denied areas, which reduces computational issues confronted by centralized Simultaneous Localization and Mapping (SLAM). Firstly, our solution integrates a UWB ranging front-end on each UAV for ranging and uses Visual-Inertial Odometry (VIO) to estimate the self-motion of each UAV. Using a front-end that fuses UWB measurements with VIO, we correct potential drift issues caused by VIO and perform distributed loop closure detection among multiple UAV s to achieve relative pose estimation. Additionally, we propose a consistency measurement filtering method with the adaptive threshold in three-dimensional space to improve the accuracy of state estimation under continuous non-line-of-sight (NLOS) conditions. Lastly, by using distributed outlier rejection to remove erroneous estimates and fusing measurements from both front-end and back-end pose graph optimization modules, we employ a sliding window method to recover the complete trajectory and guarantee global consistency of pose estimates. We believe our method provides a comprehensive solution for the global localization in multi-UAV systems, eliminating the environmental constraints of previous positioning methods and providing opportunities for other researchers to apply UAV cluster motion planning techniques in complex environments.
Recognizing Trained and Untrained Obstacles around a Port Transfer Crane Using an Image Segmentation Model and Coordinate Mapping between the Ground and Image

Container yard congestion can become a bottleneck in port logistics and result in accidents. Therefore, transfer cranes, which were previously operated manually, are being automated to increase their work efficiency. Moreover, LiDAR is used for recognizing obstacles. However, LiDAR cannot distinguish obstacle types; thus, cranes must move slowly in the risk area, regardless of the obstacle, which reduces their work efficiency. In this study, a novel method for recognizing the position and class of trained and untrained obstacles around a crane using cameras installed on the crane was proposed. First, a semantic segmentation model, which was trained on images of obstacles and the ground, recognizes the obstacles in the camera images. Then, an image filter extracts the obstacle boundaries from the segmented image. Finally, the coordinate mapping table converts the obstacle boundaries in the image coordinate system to the real-world coordinate system. Estimating the distance of a truck with our method resulted in 32 cm error at a distance of 5 m and in 125 cm error at a distance of 30 m. The error of the proposed method is large compared with that of LiDAR; however, it is acceptable because vehicles in ports move at low speeds, and the error decreases as obstacles move closer.
Mechanical properties and constitutive models of shape memory alloy for structural engineering: A review

Shape Memory Alloys (SMAs) are an innovative material with the unique features of superelasticity and energy dissipation capabilities under extreme loads. Due to their unique features, they have a great potential to be employed in structural engineering applications under different conditions. However, in order to effectively use SMAs in civil engineering structures and model their behaviors accurately in Finite Element (FE) packages, it is crucial for structural engineers to comprehend the mechanical properties and cyclic behavior of different SMA compositions under varying loading conditions. While previous studies have focused mainly on the cyclic behavior of SMAs under tensile loading, it is important to evaluate their fatigue behavior under cyclic tension-compression loading for seismic applications. This literature review aims to discuss the current gaps in the existing literature on the behavior of SMA rebars under low-cycle fatigue (LCF). The review provides a comprehensive overview of the primary characteristics of SMAs, summarizes the mechanical properties of SMAs presented in the literature and the parameters that affect them, and critically evaluates the effects of cyclic loading and LCF on SMAs. The review also provides a summary of the different constitutive models of SMAs and compares their advantages and limitations, which helps structural engineers to employ an appropriate constitutive model for predicting the accurate behavior of SMAs in FE software.
The Student Experience of a City-University-Community Service-Learning Classroom

The Austin City Hall Fellows Program at The University of Texas at Austin was a student civic leadership program designed to build Austin City Hall’s capacity to reach potential leaders in diverse new council districts. Undergraduate students in this program took a single-semester service-learning course that connected them with partners from the city, the community, and the university. Using Howard’s (1998) framework, our study analyzed student assessments from five cohorts of the class to understand how students experienced a service-learning class based around a multipartner community-university-city partnership. Our findings revealed that students experienced a nonlinear path to engagement in a synergistic classroom. This study offers implications for adjusting service-learning classes and projects to integrate and sustain relationships with multiple partners, even as the context of the service landscape and learning context are evolving.
Overexpression of the Purple Perilla (Perilla frutescens (L.)) FAD3a Gene Enhances Salt Tolerance in Soybean

The increasingly serious trend of soil salinization inhibits the normal growth and development of soybeans, leading to reduced yields and a serious threat to global crop production. Microsomal ω-3 fatty acid desaturase encoded by the FAD3 gene is a plant enzyme that plays a significant role in α-linolenic acid synthesis via regulating the membrane fluidity to better accommodate various abiotic stresses. In this study, PfFAD3a was isolated from perilla and overexpressed in soybeans driven by CaMV P35S, and the salt tolerance of transgenic plants was then evaluated. The results showed that overexpression of PfFAD3a increased the expression of PfFAD3a in both the leaves and seeds of transgenic soybean plants, and α-linolenic acid content also significantly increased; hence, it was shown to significantly enhance the salt tolerance of transgenic plants. Physiological and biochemical analysis showed that overexpression of PfFAD3a increased the relative chlorophyll content and PSII maximum photochemical efficiency of transgenic soybean plants under salt stress; meanwhile, a decreased accumulation of MDA, H2O2, and O2•−, increased the activities of superoxide dismutase (SOD), peroxidase (POD), catalase (CAT), and ascorbic acid peroxidase (APX), as well as the production of proline and soluble sugar. In summary, the overexpression of PfFAD3a may enhance the salt tolerance in transgenic soybean plants through enhanced membrane fluidity and through the antioxidant capacity induced by C18:3.
The Impact of a Training Program Based on Next-Generation Science Standards on Scientific Inquiry

The Next-Generation Science Standards (NGSS) are standards concerned with K-12 grades. This study aimed to identify the impact of a training program based on Next-Generation Scientific Inquiry (NGSI) through training 30 science teachers and investigating the training impact on teachers’ use of inquiry in their teaching. The study attempted to answer the following question: What is the impact of a training program based on the NGSS on the inquiry practices of primary school science teachers? The study’s data collection tools included two focus groups and nine semi-structured interviews. Data analysis utilized the thematic analysis framework. The research results showed an impact of the training program on the inquiry practices of teachers via changes in the teachers’ roles: the teacher became a facilitator and motivator through planning her/his activities efficiently. Students in the training program’s science classroom developed into researchers and scientists who autonomously acquired their knowledge.
Bacteriological Profile and Detection of β-Lactamase Producing Bacteria Isolated from Blood Samples of Neonates

The clinical impact of β-lactamase has become a public health problem around the world in terms of increased morbidity and mortality, especially in the child population. This study was aimed at determining the bacteriological profile and detection of β-lactamase producing bacteria isolated from the blood samples of neonates. For this study, a total of 1335 blood samples of neonates admitted in NICU, SCBU, and sepsis-suspected neonates visiting Paropakar Maternity and Women’s Hospital, Thapathali, Kathmandu, Nepal were collected and processed. Blood culture was performed and the identification of bacteria was done by following standard microbiological methods. Antibiotic susceptibility testing was done by using the Kirby Bauer Disk Diffusion method and confirmation of ESBL, MBL, and KPC-producing bacteria was done by Combined Disk Test. The prevalence rate of neonatal sepsis was found to be 17%. K. pneumoniae 116 (50.2%) was the predominant Gram-negative bacteria followed by K. oxytoca 31 (13.4%) whereas S. aureus 39 (16.9%) was the predominant Gram-positive bacteria causing neonatal sepsis. Among 182 Gram-negative bacterial isolates, 69 (37.9%), 22 (12.1%), and 14(31.1%) were found to be ESBL, MBL, and KPC producers respectively. K. oxytoca (54.8%), Enterobacter spp. (25%) and Citrobacter spp. (14.3%) were the predominant ESBL, MBL, and KPC producers respectively. The co-production of ESBL, MBL, and KPC was also found among the 5 Gram-negative bacteria. Colistin, Meropenem, and Imipenem seem to be the choice of the drug against Gram-negative bacteria, whereas Vancomycin and Cefoxitin seem to be the choice of the drug against Gram-positive bacteria. Therefore, to lessen the burden of antibiotic resistance, it is essential to conduct regular antimicrobial susceptibility surveillance, periodic reviews of hospital settings, and early detection of beta-lactamase-producing bacteria. Int. J. Appl. Sci. Biotechnol. Vol 11(2): 66-77.
Get Together: A digital platform for urban social participation

Citizens are the daily users of urban spaces and their facilities. It is important that they collaborate with local authorities for effective management, development, and maintenance of the cities they live in. Public participation although important is still developing in several European cities because its implementation poses difficulties. In this paper we argue that using digital technologies via a participatory digital tool, can empower citizens towards more engagement and collaboration on the city development. Despite the existence of several participation tools, we identified the lack of a tool that allows citizens to collaborate more actively in local development in a simple way. In this paper we introduce Get Together, a tool designed to improve collaboration between citizens and local authorities, that proposes a user experience that enables citizens to create design solutions, promote debate and perform a vote on the preferred solution, while having technical advice from municipal officials’ specialists.
A NEW IMPROVED THRESHOLD SEGMENTATION METHOD FOR SCANNING IMAGES OF RESERVOIR ROCKS CONSIDERING PORE FRACTAL CHARACTERISTICS

Based on the basic principle of the porosity method in image segmentation, considering the relationship between the porosity of the rocks and the fractal characteristics of the pore structures, a new improved image segmentation method was proposed, which uses the calculated porosity of the core images as a constraint to obtain the best threshold. The results of comparative analysis show that the porosity method can best segment images theoretically, but the actual segmentation effect is deviated from the real situation. Due to the existence of heterogeneity and isolated pores of cores, the porosity method that takes the experimental porosity of the whole core as the criterion cannot achieve the desired segmentation effect. On the contrary, the new improved method overcomes the shortcomings of the porosity method, and makes a more reasonable binary segmentation for the core grayscale images, which segments images based on the actual porosity of each image by calculated. Moreover, the image segmentation met.
Optimization of Parameters and Initial Values in a Marine NPZD-Type Ecosystem Model.

Parameters and initial values of a one-dimensional marine ecosystem model are optimized using a gradient-based optimization algorithm taking into account parameter bounds. Sensitivities of the optimized parameters w.r. t. errors in observations and initial values are studied numerically and found to yield parameter ranges narrow relative to the a priori parameter uncertainty reflected in upper and lower bounds on the permitted parameter range. This means, that optimal parameters can be determined accurately. We find, that optimizing for the initial values along with the parameters can greatly improve the model’s fit to the observations.
An approach for the Pasternak elastic foundation parameters estimation of beams using simulated frequencies

Abstract As a first attempt, a mix of differential quadrature (DQ) and simplex (S) method for the Pasternak elastic foundation parameters estimation of beams using simulated frequencies is presented. The method is applied for the parameters estimation of functionally graded (FG) beams. The first-order shear deformation theory is employed to derive governing equations of FG beam on the Pasternak elastic foundation. The equations are discretized by utilizing the DQ method and frequencies of the beam are calculated. Then, the simulated frequencies are obtained by applying random error to the calculated frequencies. The simulated frequencies are used as input data for estimating parameters of the problem. An objective function as a root-mean-square error between the calculated and simulated frequencies is defined. The DQ method and simplex technique as a classical optimization technique are coupled to minimize the function via finding the best foundation parameters, iteratively. Some examples are solved to show applicability, robustness and accuracy of the mixed method for elastic foundation parameters estimation of the beam. Also, it has been found that using only the first simulated frequency of the beam cannot give correct parameters of the foundation.
Regulation and Functions of α6-Integrin (CD49f) in Cancer Biology

Simple Summary Integrins play key roles in mediating cell adhesion and delivering chemical and mechanical signals to the interior of the cell. Consequently, they actively control cellular proliferation, differentiation, and apoptosis. Integrin signaling dysregulation can be a major factor in the development of tumors, and it is involved in the processes of the malignant plasticity of the epithelium, the reactivation of metastasis, and resistance to therapeutic interventions. Here, we describe the current understanding of the α6-integrin subunit (ITGA6, also known as CD49f and/or VLA6; encoded by the gene itga6) in cancer cells. The roles of ITGA6 in cell adhesion, stemness, metastasis, angiogenesis, and drug resistance, and as a diagnostic biomarker, are discussed. The importance of ITGA6 in the progression of a number of cancers, including hematological malignancies, suggests its potential usage as a novel therapeutic target. Abstract Over the past decades, our knowledge of integrins has evolved from being understood as simple cell surface adhesion molecules to receptors that have a complex range of intracellular and extracellular functions, such as delivering chemical and mechanical signals to cells. Consequently, they actively control cellular proliferation, differentiation, and apoptosis. Dysregulation of integrin signaling is a major factor in the development and progression of many tumors. Many reviews have covered the broader integrin family in molecular and cellular studies and its roles in diseases. Nevertheless, further understanding of the mechanisms specific to an individual subunit of different heterodimers is more useful. Thus, we describe the current understanding of and exploratory investigations on the α6-integrin subunit (CD49f, VLA6; encoded by the gene itga6) in normal and cancer cells. The roles of ITGA6 in cell adhesion, stemness, metastasis, angiogenesis, and drug resistance, and as a diagnosis biomarker, are discussed. The role of ITGA6 differs based on several features, such as cell background, cancer type, and post-transcriptional alterations. In addition, exosomal ITGA6 also implies metastatic organotropism. The importance of ITGA6 in the progression of a number of cancers, including hematological malignancies, suggests its potential usage as a novel prognostic or diagnostic marker and useful therapeutic target for better clinical outcomes.
Fundamental Limit of Analog Multiplication in Linear Discriminant Classifier

In this thesis analog implementation of a machine learning algorithm, Linear Discriminant Analysis, is analyzed and shown how it performs on a classification problem. Analog machine learning has emerged as a promising field that provides advantages over its digital counterpart in power consumption, circuit area and scalability. Analog computation achieves its efficiency from the physics of device or circuit operation. This allows analog computation to operate on very low signal levels. However, low signal levels make itself vulnerable to noise. Excessive noise levels can render the machine learning system unstable and prone to making wrong decisions. To ensure reasonable accuracy of the system it is essential to understand how noise behaves and propagates along the system. A key component in analog implementation of the Linear Discriminant Analysis is the analog multiplier. A noise analysis is done for the multiplier to show how noise varies with multiplication factor. This also produces a relationship between signal to noise ratio and energy consumption that gives us a limit of accuracy obtained from the multiplier for a given energy consumption. Numerical analysis is provided to show that Linear Discriminant Analysis is well suited for the classification problem. The performance of a hardware implementation of the analog classifier in commercially available 130nm silicon process is also presented. With four feature input currents and three classes to classify the classifier consumes around 4nW of power. The testing process shows
Simulation of the electromagnetic field in a cylindrical cavity of an ECR ions source

Now there are numerous sources for multicharged ions production, each being designed for certain science or technological objectives. Electron cyclotron resonance ion sources (ECRIS) are best suited for designing heavy ion accelerators of very high energies, because they can generate multicharged ion beams at relatively great intensities. In these sources, plasma heating and its confinement are effected predominantly in minimum-B magnetic traps, this type of magnetic trap consist of two current coils used for the longitudinal magnetic confinement and a hexapole system around the cavity to generate a transversal confinement of the plasma. In an ECRIS, the electron cyclotron frequency and the microwave frequency are maintained equal on a quasi-ellipsoidal surface localized in the trap volume. It is crucial to heat electrons to energies sufficient to ionize K- and L-levels of heavy atoms. In this work, we present the preliminary numerical results concerning the space distribution of TE111 microwave field in a cylindrical cavity. The 3D microwave field is calculated by solving the Maxwell equations through the Yee’s method. The magnetic field of minimum-B configuration is determined using the Biot-Savart law. The parameters of the magnetic system are that which guarantee the ECR surface location in a zone of a reasonably high microwave tension. Additionally, the accuracy of electric and magnetic fields calculations are checked.
Sequential vessel speed optimization under dynamic weather conditions

The International Maritime Organization (IMO) identifies speed optimization as a key operational measure for achieving energy efficiency through reduced emissions. Ocean Liner services have fixed port rotations and schedules. While the speed can be optimized for emissions, the service level in terms of scheduled arrival and departure need to be carefully considered not to loose market share. This already challenging problem is further complicated when dynamic weather conditions along the service route are considered. In fact, few contributions can be found that address this issue. We study the operational problem of dynamically determining a vessel’s speed, departure time and arrival time at each port of call under dynamic weather conditions. We model the minimization of cost, namely bunkering costs and early and delayed departure and arrival penalties, using the calculus of variations. The proposed algorithm leverages upon a discretization technique based on the Weierstrass–Erdmann condition. The numerical tests show the efficiency and effectiveness of this algorithm over standard techniques like IVP.