Miracle-dz/UniFAR-FTUs huggingface dataset:
Leveraging a powerful allogeneic dendritic cell line towards neoantigen-based cancer vaccines

In recent years, immunotherapy has finally found its place in the anti-cancer therapeutic arsenal, even becoming standard of care as first line treatment for metastatic forms. The clinical benefit provided by checkpoint blockers such as anti-PD-1/PD-L1 in many cancers revolutionized the field. However, too many patients remain refractory to these treatments due to weak baseline anti-cancer immunity. There is therefore a need to boost the frequency and function of patients’ cytotoxic CD8+ cellular effectors by targeting immunogenic and tumor-restricted antigens, such as neoantigens using an efficient vaccination platform. Dendritic cells (DC) are the most powerful immune cell subset for triggering cellular immune response. However, autologous DC-based vaccines display several limitations, such as the lack of reproducibility and the limited number of cells that can be manufactured. Here we discuss the advantages of a new therapeutic vaccine based on an allogeneic Plasmacytoid DC cell line, which is easy to produce and represents a powerful platform for priming and expanding anti-neoantigen cytotoxic CD8+ T-cells.
A Data-Driven Forecasting and Solution Approach for the Dial-A-Ride Problem with Time Windows

The Dial-A-Ride Problem (DARP) consists of de-signing pick-up and delivery routes for a set of customers with special needs. Particularly, it arises in door-to-door transportation services provided to elderly and impaired people. DARP's main objective is to accommodate as many customers' constraints as possible with minimum operation costs. DARP involves realistic precedence and transit time constraints on the pairing of vehicles and customers. This paper proposes a neural network forecasting approach for DARP with time windows (DARPTW). It develops and compares the results of two-layer and a three-layer artificial neural networks (ANN) which forecast demands, service and travel times based on real-life data provided by a transportation company. Experimental results show that three-layer ANN with hyperbolic tangent (tanh) and sigmoid linear unit (selu) activation functions, coupled with a stochastic gradient descent (SGD) optimizer provide the best forecasting results. This paper also develops a data-driven hybrid adaptive large neighborhood search (DD-HALNS). DD-HALNS selects the local search operators according to their updated success' rates, which are, in turn, guided by a learning mechanism from previous successful moves and cost savings. It applies four hybridization features: simulated annealing, tabu lists, genetic crossovers, and restarts. Experimental results on DARPTW benchmark instances highlight DD-HALNS’ ability to improve best known routing solutions, while its application on real life instances, from the Canadian city/region of Vancouver, confirms its implementability.
A real-time orientation feature descriptor for portable devices

A real-time orientation feature descriptor for portable devices is introduced. The descriptor requires very low computational resources and has 16 dimensions shorter than all existing methods. The patch of a candidate feature is firstly segmented into polar arranged sub-regions, which enables us to achieve rotation invariance rapidly. Furthermore, the principal orientation is used to describe each sub-region. The computations can be considerably accelerated by using integral image. The descriptor is used for object tracking and achieves 25 fps frame rate on mobile phone. Experimental results demonstrate that the proposed method offers sufficient matching performance.
GRL-PS: Graph Embedding-Based DRL Approach for Adaptive Path Selection

Forwarding path selection for data traffic is one of the most fundamental operations in computer networks, whose performance drastically impacts both transmission efficiency and reliability in network domains. Although deep reinforcement learning (DRL) has attracted considerable attention for path selection instead of hand-tuned heuristics, few works have considered how to exploit graph-structured information in networks to improve routing and forwarding efficiency. In fact, generating routes is essentially a process for finding a subgraph in a graph-structured network. To this end, this paper proposes an effective and novel graph embedding-based DRL framework for adaptive path selection (termed GRL-PS), aiming at reducing end-to-end (E2E) latency and promoting network throughput while maintaining stability in dynamically changing environments. Specifically, graph representation learning (GRL) is deployed as an effective enabler for the DRL agent to learn the relational knowledge of interacting entities for route decisions in networks. However, training such an agent in a dynamically changing environment encounters a knowledge acquisition bottleneck, since the DRL agent is always forced to learn every task from scratch. To improve the adaptation of behaviors and acquire skills beyond what the source policy can teach, we introduce potential-based reward shaping as a means of knowledge transfer to guide the agent in unfamiliar conditions with sparse rewards. Experimental results show that compared with baseline methods, our solution can achieve nearly-optimal performance with both latency and throughput, especially in large-scale dynamic networks.
Oxidation Kinetics of Ti-6Al-4V Alloys by Conventional and Electron Beam Additive Manufacturing

New manufacturing processes for metal parts such as additive manufacturing (AM) provide a technological development for the aeronautical and aerospace industries, since these AM processes are a means to reduce the weight of the parts, which generate cost savings. AM techniques such as Laser Powder Bed Fusions (LPBF) and Electron Beam Fusion (EBM), provided an improvement in mechanical properties, corrosion resistance, and thermal stability at temperatures below 400 °C, in comparison to conventional methods. This research aimed to study the oxidation kinetics of Ti-6Al-4V alloys by conventional and Electron Beam Additive Manufacturing. The thermogravimetric analysis was performed at temperatures of 600 °C, 800 °C, and 900 °C, having a heating rate of 25 °C/min and oxidation time of 24 h. The microstructural analysis was carried out by thermogravimetric analysis. Thickness and morphology of oxide layers were analyzed by field emission scanning electron microscope, phase identification (before and after the oxidation process) was realized by X-ray diffraction at room temperature and hardness measurements were made in cross section. Results indicated that the oxidation kinetics of Ti-6Al-4V alloys fabricated by EBM was similar to conventional processing and obeyed a parabolic or quasi-parabolic kinetics. The samples oxidized at 600 °C for 24 h presented the lowest hardness values (from 350 to 470 HV). At oxidation temperatures of 800 and 900 °C, however, highest hardness values (from 870 close to the alpha-case interface up to 300 HV in base metal) were found on the surface and gradually decreased towards the center of the base alloy. This may be explained by different microstructures presented in the manufacturing processes.
Stability of multi-channel harmonic sound control systems with uncertainties

Sufficient conditions for the stability of fully coupled, multi-channel harmonic sound systems, are presented. These conditions are based on generalizations of the Gershgorin theorem and yield less conservative results than those based directly on the Gershgorin theorem. More important, the conditions are applicable to systems where uncertainties in the physical parameters, and/or the frequency of the primary source within a given bandwidth, are assumed. Some simulation results are provided.
Coarse-graining Landau–Lifshitz damping

High speed switching in magnetic materials is usually studied with the Landau–Lifshitz (LL) equation, which describes damping through a phenomenological coefficient. The results of micromagnetic calculations based on the LL equation have been observed to depend strongly on the cell size. We take a coarse-graining or renormalization-group approach to this cell size dependence: from a simulation using cell size L, we look at the dynamics of a cell of size 2L and determine an effective damping coefficient that describes the larger-scale dynamics. This can be thought of as a Green–Kubo calculation of the effective damping coefficient. In principle, this makes it possible to coarse grain from the atomic scale to determine the micromagnetic damping coefficient.
Hierarchical Policy Learning With Demonstration Learning for Robotic Multiple Peg-in-Hole Assembly Tasks

The force-based control algorithm of robotic multiple peg-in-hole assembly is a challenge. For the difficulty of low adaptability of model-based control algorithms and low learning efficiency of model-free control algorithms, a goal-based hierarchical policy learning (HPL) algorithm that combines conventional control algorithm and demonstration learning (DL) algorithm is proposed to learn the assembly skill. First, the goal-based HPL algorithm adds goal as a new variable to the action value function. Multiple states reached in each episode are randomly selected as subgoals to improve the distribution of positive rewards. Second, an initial policy that combines conventional control algorithm and DL algorithm is designed. The combined coefficient of these two algorithms is learned by HPL algorithm. Finally, a conical surface is used to compute the forces and moments of simplified assembly simulation model. Our algorithm is well implemented in both simulation and real-world environments. The experimental results verify the effectiveness of the proposed method.
Fugitives from the Eos Family: First Spectroscopic Confirmation☆

Abstract The fact that some asteroids presently located in the 9/4 mean-motion resonance with Jupiter could be “Eos fugitives”, that is, original members of this asteroid family injected into the resonance and experiencing a gradual eccentricity increase, was first suggested a few years ago (Morbidelli, A., V. Zappala, M. Moons, A. Cellino, and R. Gonczi 1995. Icarus 118, 132–154). We present here the results of a campaign of spectroscopic observations of candidate Eos fugitives, and we confirm that some of these objects should come from this family, according to their observed spectral characteristics. We find also indications that these objects are probably relatively young. We can conclude that Eos fugitives are a first example of family objects observed during the dynamical process of leaving the asteroid main belt. The results confirm current ideas about the dynamics governing bodies injected into the 9/4 resonance. This can be important also from the point of view of a possible relationship between the Eos family and CV/CO chondrites, as suggested by spectral analogies.
A 1.5-mA 2.5-GHz LNA With Reusing On-Chip Matching Network in 55nm CMOS Technology

A 2.5-GHz 1.2-V 1.5-mA low-noise amplifier (LNA) with a minimum of 3-dB noise figure (NF) at 2.5 GHz, −15-dBm IIP3, with on-chip matching network is demonstrated. The LNA is designed to realize low noise in the premise of low power consumption. The 55nm CMOS LNA is simulated with embedded electrostatic discharge (ESD) protection diodes that add 165- and 185-fF loads at the RF input ports. The on-chip matching network offers a single-ended $50 \Omega$ RF terminal that is shared by both TX- and RX-chains through switch MOS. At 2.5 GHz, the LNA has a simulated input return loss (S11) of −13.6 dB and the transmission gain (S21) of 16.4 dB. The Noise Figure (NF) is 3 dB and the input third-order intercept point (IIP3) is −15-dBm. The LNA (excluding the buffer) occupies an area of 0.21 mm2.
Lamb quasi–modes in curved plates

Wave propagation in slowly varying elastic waveguides is analysed in terms of mutually uncoupled quasi–modes. These are a generalization of the Lamb modes that exist in a uniform guide to a weakly non–uniform guide. Quasi–modal propagation is dependent upon the wavelength and two geometrical length–scales, that of the longitudinal variations and the guide thickness. By changing these length–scales one enters different asymptotic regimes. In this paper the emphasis is on the mid–frequency regime, where only a few propagating modes can exist. Our aim is to present an asymptotic theory for quasi–modal propagation in a canonical geometry, an arbitrarily curved two–dimensional plate of constant thickness. We derive practically useful asymptotic expressions of the quasi–modes of a weakly curved plate; these are particularly important since an adiabatic approximation for this problem coincides with the expression for the Lamb modes of a flat plate of the same thickness.
Crowdfunding using Blockchain Technology: A Review

Crowdfunding is a method of obtaining money for a project by asking a lot of people for modest donations, usually online. Nowadays, the biggest issue with crowdfunding is the trust component. People are reluctant to donate because of the increasing number of frauds that are occurring today. However, if donors could be assured that their money would be put to good use, donations may rise significantly. The need to employ blockchain in crowdfunding arises here. With the blockchain system, transactions are tracked over a network of computers that are connected to one another. This essay discusses the shortcomings of the present crowdfunding systems in use and makes recommendations for how to make things better.
An Explainable and Robust Method for Fault Classification and Location on Transmission Lines

Machine learning-based approaches for fault diagnosis on transmission lines have attracted increasing attention in recent years, yet concerns over their robustness and explainability hamper their practical applications. Moreover, separate algorithms are normally used to solve the fault classification and location tasks. In this work, we focus on these tasks and propose an integrated model based on the convolutional neural network, whose improved performance over separate models is demonstrated by numerical results. Besides, explainability of the proposed model is demonstrated and analyzed. Specifically, the class activation maps and attention maps illustrate that the proposed structure can reduce the degradation of the model performance due to data pollution, enhancing the credibility of the proposed method in practical applications. Moreover, the proposed model outperforms existing machine learning-based models in terms of accuracy and robustness. Such a structure has been proven effective on field data and can be applied to many other tasks of fault diagnosis.
The Effect of Drop‐Out on the Efficiency of Longitudinal Experiments

It is shown that drop‐out often reduces the efficiency of longitudinal experiments considerably. In the framework of linear mixed models, a general, computationally simple method is provided, for designing longitudinal studies when drop‐out is to be expected, such that there is little risk of large losses of efficiency due to the missing data. All the results are extensively illustrated using data from a randomized experiment with rats.
Riemannian methods for optimization in a shape space of triangular meshes

In this paper, the applicability of two optimization algorithms in the context of Riemannian manifolds is investigated. More precisely, the steepest descent method and the nonlinear conjugate gradient method are used to solve the Shape-From-Shading problem in a shape space of triangular meshes. For this reason, appropriate Riemannian metrics are introduced in this shape space. Instead of taking steps along straight lines, propagation along geodesics with respect to the Riemannian metric is employed. This requires to formulate the geodesic equation and the equation of parallel translation in the shape space of triangular meshes. In general, the proposed geodesic optimization algorithms outperform the standard steepest descent method concerning various quality measures and the visual impression of the reconstructed surface. In addition, it is shown that an appropriately chosen Riemannian metric can guide the optimization process towards a desired class of surfaces.
A Novel Leaky Wave Antenna with Stable Radiation Efficiency and Gain

In this paper, a novel leaky wave antenna with stable radiation efficiency and gain for millimeter wave is designed. The leaky wave antenna unit includes an x-shaped slot and a pair of electric dipoles. The double resonance characteristics generated by the slot and the electric dipole make the leaky wave antenna work in the non-resonant region, which solves the shortcomings of the leaky wave antenna that the efficiency and gain fluctuate greatly in the working frequency band. The open-stopband(OSB) is suppressed by introducing metal vias. Finally, a leaky-wave antenna with 15 antenna elements cascaded to achieve a relative bandwidth of 19.1% through simulation, and to achieve continuous scanning. The simulation results also verify that the leaky wave antenna has stable radiation efficiency and gain.
Silk-Based Biomaterials for Designing Bioinspired Microarchitecture for Various Biomedical Applications

Biomaterial research has led to revolutionary healthcare advances. Natural biological macromolecules can impact high-performance, multipurpose materials. This has prompted the quest for affordable healthcare solutions, with a focus on renewable biomaterials with a wide variety of applications and ecologically friendly techniques. Imitating their chemical compositions and hierarchical structures, bioinspired based materials have elevated rapidly over the past few decades. Bio-inspired strategies entail extracting fundamental components and reassembling them into programmable biomaterials. This method may improve its processability and modifiability, allowing it to meet the biological application criteria. Silk is a desirable biosourced raw material due to its high mechanical properties, flexibility, bioactive component sequestration, controlled biodegradability, remarkable biocompatibility, and inexpensiveness. Silk regulates temporo-spatial, biochemical and biophysical reactions. Extracellular biophysical factors regulate cellular destiny dynamically. This review examines the bioinspired structural and functional properties of silk material based scaffolds. We explored silk types, chemical composition, architecture, mechanical properties, topography, and 3D geometry to unlock the body’s innate regenerative potential, keeping in mind the novel biophysical properties of silk in film, fiber, and other potential forms, coupled with facile chemical changes, and its ability to match functional requirements for specific tissues.
Are Colleges Concerned with Intellectual Development

The assumption is often made by college professors that incoming freshman students think logically. Using tests designed by the Swiss psychologist Jean Piaget to evaluate logical thought processes, the authors found that 66 of 131 freshmen exhibited characteristics of the concrete operational thinker, while another 32 did not meet the criteria for formal operations. Professors further compound the problem by failing to recognize the kinds of experiences incoming freshmen students must have to move toward more logical thought. McKinnon, using a newly developed inquiry-oriented science course based upon Piagetian criteria, found a highly significant difference between those students who were exposed to the course and like students who were not. The authors concluded that secondary and elementary teachers do not take advantage of inquiry-oriented techniques so necessary to the development of logical thought because college professors do not provide examples of inquiry-oriented teaching.
Cold Tolerance Mechanisms in Mungbean (Vigna radiata L.) Genotypes during Germination

Mungbean or greengram (Vigna radiata) is an important legume crop well known for its high protein with nitrogen-fixing abilities. However, the severe yield loss in mungbean occurs due to susceptibility to low temperatures at all stages of plant growth including germination and is a serious concern for its cultivation and productivity. To select cold-tolerant genotypes, a germination-based screening at 10 °C was performed in a total of 204 germplasms. The study showed that cold stress of the initial 8-days during seedling establishment imposed a negative impact throughout the life of mungbean genotypes, which were reflected in the vegetative and reproductive phase (plant height, days to 50% flowering and pods/plant, seeds/pod, yield/plant, and 100-seed weight). The biplot analysis showed that parameters such as germination rate index, Timson’s index, mean germination time, and coefficient of the velocity of germination are the key influential germination parameters for identifying cold tolerance in the seedling stage. Identified cold-tolerant genotype (PAU911) retained higher rootlet number, leaf area, and increased chlorophyll, carotenoid, and malondialdehyde (MDA) content at 10 °C. Based on the confocal microscopic study, it is noticed that the stomatal density, open pore percentage, and trichome density were significant differences in seedlings exposed to cold stress as compared to non-stress. On the basis of matrix-assisted laser desorption ionization-time-of-flight mass spectrometry (MALDI-TOF MS) analysis, it is observed that a new protein identified as TETRATRICOPEPTIDE-REPEAT THIOREDOXIN-LIKE1 (TTL1) (UNIPROT Identifier: LOC106762419) which highly correlated with the cold stress response of in the cold-tolerant genotype. Our study identifies a noble member, TTL1, whose expression has a positive role in cold tolerance response at the protein level in V. radiata. This study will help breeding programs with regard to the sustainable growth of mungbean.
Association of Psychotic Experiences and Incident Depression in a Longitudinal Population‐Based Community Survey

Objective The present study aims to determine if psychotic experiences in a general population sample are a risk factor for depressive disorders at a 15‐year follow‐up visit. Method A longitudinal population cohort of adults over age 18 from East Baltimore were followed from 1981 to 1996 with 1409 participants included in analyses. Delusions and hallucinations and depressive disorders were assessed using DSM‐III criteria. Odds ratios were obtained using logistic regression with psychotic experiences modeled both dichotomously and as count variables as predictors of major and minor depressive disorders at wave three. Age, race, and sex were included as covariates in the model. Results Both delusions and hallucinations were associated with an increased odds of incident depressive disorders. Delusions, but not hallucinations, were associated with increased odds of major depressive disorder (adjusted odds ratio, 3.04 [95% CI = 1.29–7.13]) and both delusions and hallucinations were associated with increased odds of minor depressive disorder (adjusted odds ratios, 4.6 [95% CI = 2.11–10.04] and 3.93 [95% CI = 2.11–7.32]). There was a dose‐response relationship in number of psychotic experiences reported and odds of depressive disorders. Conclusions Lifetime psychotic experiences, particularly delusions, in the absence of mental disorders, are associated with later depressive disorders. Results persist in a dose‐response manner. Future research should determine whether transitory versus persistent psychotic experiences have a differential effect on later depression.