Miracle-dz/UniFAR-FTUs huggingface dataset:
Near-Field Localization Based On Beam Squint of mmWave Communications

Integrated sensing and communication (ISAC) has been regarded as a key technology of 6G wireless communications, in which large-scale multiple input and multiple output (MIMO) with higher and wider frequency bands will be adopted. However, recent studies show that the beam squint phenomenon can not be ignored in the wideband MIMO system, which generally deteriorates the communications performance. In this paper, we find that with the aid of the true-time-delay lines (TTDs), the range and trajectory of the beam squint in the near-field communications systems can be freely controlled, and hence it is possible to reversely utilize the beam squint for user localization. We derive the trajectory equation for near-field beam squint points and design a way to control such trajectory. With the proposed design, beamforming from different subcarriers would purposely point to different angles and different distances, such that users from different positions would receive the maximum power at different subcarriers. Hence, one can simply localize multiple users from the beam squint effect in frequency domain, and thus reduce the timing overhead as compared to the conventional beam sweeping approach. Simulation results demonstrate the effectiveness of the proposed scheme.
Leopard: A Pragmatic Learning-Based Multipath Congestion Control for Rapid Adaptation to New Network Conditions

Multipath TCP is a multipath transport protocol deployed on end devices, and many learning-based multipath congestion control schemes have been proposed and verified. However, these schemes cannot adapt rapidly to new network conditions because of their convergence problems and generalization issues. To rapidly adapt to new network conditions, we propose Leopard, a learning-based multi-path congestion control frame-work that uses reinforcement learning to combine offline learning with online fine-tuning. The extensive experiments in emulated network conditions and the real world demonstrate that Leopard converges quickly and maintains consistent high performance in new network conditions, which avoids long retraining when the network environment changes. Leopard improves throughput by 13% compared with DRL-CC and reduces the convergence time by 20% compared with MPCC in new network conditions.
Efficient Segment-Level Waveform Anomaly Detection for Memory Devices

With the emergence of big data and advanced technologies, memory devices play an important role. However, there is a lack of efficient approaches for waveform anomaly detection to validate the reliability of memory devices under various operations. In this paper, we propose an efficient segment-level waveform anomaly detection method for memory devices that includes the stages of waveform segmentation and anomaly analysis. Being compatible with general waveform sequences, the proposed method features lightweight computational complexity. During the waveform segmentation stage, we execute downsampled segment collection and downsampled segment pair matching to obtain downsampled segments that are associated with individual operations. During the anomaly analysis stage, we execute ensemble denoising, entrywise alignment, anomaly presence examination through excess kurtosis, and anomaly detection with density-based spatial clustering of applications with noise (DBSCAN) to predict anomaly locations. Simulation results confirm the outstanding performance of the proposed method in terms of matching accuracy, excess kurtosis measurements, and segment-level anomaly detection.
Prize-Collecting Traveling Salesman Problem: A Reinforcement Learning Approach

Prize-Collecting Traveling Salesman Problem (PC-TSP) is a new variation of TSP and is defined as follows. Given a weighted complete graph $G(V, E)$ where node $i\in V$ has an available prize of $p_{i}$, and two nodes $s, t\in V$, the goal of the traveling salesman is to find a route from $s$ to $t$ such that the sum of the prizes of all the nodes visited along the route reaches a pre-set quota while the distance along the route is minimized. In this paper, we propose a multi-agent reinforcement learning (MARL) framework for the PC-TSP. Our novel observation is that prize-collecting in PC-TSP is intrinsically related to cumulative reward maximization in reinforcement learning (RL). By integrating the prizes in PC-TSP into the reward model in RL, we design an efficient and effective MARL algorithm to solve the PC-TSP. Via extensive simulations under different network and RL parameters, we show that our learning algorithm delivers an average of 65.6% of less traveling distance compared to one existing handcrafted greedy algorithm. When changing the number of agents $m$ from 1 to 5, our algorithm reduces the prize-collecting learning time by up to 66.9%, demonstrating the effectiveness of multi-agent collaboration in reducing the prize-collecting learning time in PC-TSP.
Quantifying Greenland freshwater flux underestimates in climate models

Key processes regulating the mass balance of the Greenland Ice Sheet (GIS) are not represented in current‐generation climate models. Here using output from 19 different climate models forced with a high‐end business‐as‐usual emissions pathway, we compare modeled freshwater fluxes (FWF) to a parameterization based on midtropospheric temperature. By the mid 21st century, parameterized GIS FWF is 478 ± 215 km3 yr−1 larger than modeled—over 3 times the 1992–2011 rate of GIS mass loss. By the late 21st century, ensemble mean parameterized GIS FWF anomalies are comparable to FWF anomalies over the northern North Atlantic Ocean, equivalent to approximately 11 cm of global mean sea level rise. The magnitude and spread of these underestimates underscores the need for assessments of the coupled response of the ocean to increased FWF that recognize: (1) the widely varying freshwater budgets of each model and (2) uncertainty in the relationship between GIS FWF and atmospheric temperature.
Effect of Capping Agent on Structure, Composition, and Optical Properties of Low-Cost Chemically Deposited Zinc Oxide Thin Films and Their Antibacterial Activities

In the present report, pure zinc oxide and albumen-capped zinc oxide thin films were deposited on a glass substrate by a simple chemical method. The growth rate of the deposited film increases by means of number of dipping linearly. The films deposited were subjected to XRD, SEM, EDX, and UV-visible spectroscopy to analyze the crystal structure, morphology, composition, and optical properties. Structural feature reported that the deposited films were found to be a wurtzite structure. The degree of crystallinity depends on film thickness with deposition time per cycles. The parameters related to film structure, such as stress, strain, dislocation density, lattice constant, and bond length, were determined. The values of the fundamental absorption edge were at 3.28 and 3.06 eV for the deposited films of pure zinc oxide and albumen-capped zinc oxide, respectively. Photoluminescence measurements indicated that the peaks of emission were found to be 375 and 340 nm for zinc oxide and albumen-capped zinc oxide. The effects of an antibacterial activity against different positive and negative bacteria sources were determined.
Two‐dimensional model studies of the interannual variability of trace gases in the middle atmosphere

A parameterization of the mechanisms believed to be responsible for the quasi-biennial oscillation (QBO) in the zonal winds of the equatorial lower stratosphere has been included in an interactive dynamical-radiative-photochemical model of the atmosphere. As well as a QBO signal in the zonal wind, temperature, and ozone there are QBO signals in a range of other chemical species contained in the model. In the case of long-lived tracers the QBO signal results from the direct effect of the QBO-induced modification to the model circulation. The sign and magnitude of these QBO signals depend on the sign and strength of the vertical gradient of these long-lived tracers in the lower stratosphere. For shorter-lived species a QBO signal can result from the QBO in temperature or in trace gases controlling the species' production and loss. In many cases, much larger interannual variability is predicted for these short er-lived species. Notably, NO2 and N2O5, whose chemistry is very temperature dependent, show very strong QBO signals. In the case of ozone the transition from dynamical control in the lower stratosphere to photochemical control at higher altitudes can be seen in its QBO signal. These predictions of interannual variability of trace species should be considered when analyzing and interpreting observational data, particularly when calculating year-to-year trends.
On the Implementation of a Cross-Layer SDN Architecture for 802.11 MANETs

The adoption of the Software Defined Networking (SDN) paradigm is aggressively expanding from traditional datacenter networks to 5G and IoT deployments due to its flexibility in network management and routing. Mobile Ad-Hoc Networks (MANETs), with their unique characteristics and requirements stemming from the mobility and volatile wireless medium conditions, have not yet realized the benefits of the SDN approach. Adoption has been hampered by technical challenges and the contradiction between the centralized concept of SDN and the distributed one of traditional MANET routing schemes. In this paper, we present our implementation of a fully-fledged SDN framework, which attempts to bridge the gap by combining the benefits of both worlds. Our developed prototype integrates with 802.11 Ad-Hoc networks and offers a) fault tolerance for the SDN controller by recovering from failures with the (re)-election of a new controller, b) dynamic network topology discovery, and c) a cross-layer approach in routing based on 802.11 MAC layer statistics that more accurately characterize link quality and capacity. We validated the applicability and performance benefits of our method by evaluating our proposed SDN architecture in a proof-of-concept scenario.
Model‐based optimization of an ion exchange chromatography process for the separation of von Willebrand factor fragments and human serum albumin

Column liquid chromatography plays a vital role in the downstream processing of biopharmaceuticals, where the goal is to extract and purify a target protein from a mixture. In this work, we examine a real‐world ion exchange chromatography process where electrostatic interactions between proteins and the chromatographic medium are the predominant forces being exploited for the separation of the participating species. More specifically, we successfully separate a recombinant dimeric fragment of the von Willebrand factor, used for the half‐life prolongation of certain proteins, from human serum albumin. Compared to the originally conducted experiments, the optimized experiment achieves a higher product purity in less time and with lower consumption of buffer salts, hence being favorable both in economical and ecological terms.
Wideband Time-delay Active Reconfigurable Intelligent surface-assisted Mainlobe Interference Cancellation for Radio Astronomy

Radio frequency interference (RFI) is a potential threat to radio telescopes, especially when the RFI comes from the mainlobe of the high-directivity telescope. This paper exploits the wideband time-delay active reconfigurable intelligent surface (WBTD-ARIS) to mitigate the mainlobe wideband RFI before the RFI enters the radio frequency chains. Considering the practical radiation pattern of the radio telescope, we formulate the wideband interference mitigation model based on the refractive beamforming of large WBTD-ARIS. Furthermore, a partitioning strategy-guided deep reinforcement learning algorithm is proposed to solve the constrained high-dimensional optimization problem. Experiment results demonstrate that the developed technique outperforms the conventional RIS-based method under various incident angles and bandwidths of RFIs.
Assessing Attachment Using Projective Tests: A Scoping Review on Adolescent and Adult Studies

Background: Projective assessment has been widely used in clinical settings to reveal personal characteristics, including relationship attachment. However, more often attachment was observed in children, rather than older groups. Study aims: The present study aims to conduct scoping review on projective test that had been used to assess a parent-child attachment reported by adolescents and adults. Method: We search original articles in ScienceDirect, Scopus, EBSCOhost, Proquest and Emerald Insight. Initial search resulted 2447 articles, but after selection process was done, finally there are 11 articles to be reviewed. Results: Three projective tests had been used frequently to assess attachment on adolescents and adults were: 1) the Adult Attachment Picture System (AAP); (2) the Bird’s Nest Drawings (BND); and (3) the Draw-a-person test (DAP). These tools were predominantly developed in US and European cuntries. Similar tools validated in Asian countries is very imited. Conclusion: Future studies, particularly outside US and Eurpean countries, need to validate these measures prior to the study.
Construct and criterion validity of muscle ultrasonography for assessment of skeletal muscle in patients recovering from COVID-19

Background: The purpose was to investigate the content, construct, and criterion validity of muscle ultrasound in a mixed cohort of participants recovering from mild and critical COVID-19. Methods: A secondary analysis of a prospective cross-sectional study was conducted on data obtained from a battery of muscle and physical function assessments including a muscle biopsy and muscle ultrasonography (US). Rectus femoris (RF) muscle thickness (mT), quadricep complex (QC) mT, RF muscle cross-sectional area (CSA) using 2D freeform trace and estimated from Feret’s diameter, and RF echo intensity (EI) were assessed with US. Muscle fiber CSA, fiber type, protein content in muscle fibers, extracellular matrix content (ECM; wheat-germ agglutin), and percent area of collagen in ECM (picrosirius red) were examined from vastus lateralis muscle biopsies. Spearman rho correlations (r) were performed to assess validity of ultrasound parameters. Results: Thirty-three individuals participated including 11 patients surviving critical COVID-19, 15 individuals recovering from mild-COVID, and 7 controls. There were several significant correlations between RF mT, QC mT, RF CSA, and RF EI with age, comorbid burden, body-mass index, and measures of muscle strength, muscle power, and physical function (range r = 0.35–0.83). RF Feret’s CSA correlated to CSA of type II muscle fibers (r = 0.41, p = 0.022) and the average size of all muscle fibers (r = 0.39, p = 0.031). RF EI was correlated with collagen in muscle ECM (r = 0.53, p = 0.003) and protein content in muscle tissue (r = −0.52, p = 0.012). Conclusion: Muscle size and quality measured using US has moderate content and construct validity, and to lesser extent, fair to moderate criterion validity in a mixed cohort of individuals recovering from COVID. Muscle ultrasound quality (EI) appears to be sensitive at detecting muscle dysfunction as it is associated with strength, power, physical function, and collagen distribution in a mixed group of individuals recovering from COVID-19.
Deep Learning Based Fingerprint Positioning for Multi-Cell Massive MIMO-OFDM Systems

In this paper, we investigate user terminal (UT) fingerprint positioning for multi-cell massive multiple-input multiple-output orthogonal frequency division multiplexing (MIMO-OFDM) systems in non-line-of-sight scenario. We first introduce a refined double beam-based channel model to the positioning problem and extract the energy coupling matrix in the refined beam domain as location-related fingerprint. By taking advantage of refined spatial and frequency beams, the new fingerprint (i.e., energy coupling matrix) contains plentiful and stationary multi-path information, such as power, angle of arrival (AoA), and delay of arrival (DoA), which are favorable to positioning. We then propose a novel deep learning-based fingerprint positioning method to locate the 2-dimension (2D) position of UTs, utilizing the multi-cell fingerprint as the input. In particular, we propose two deep neural network (DNN) architectures in this paper. The first DNN consists of convolutional neural networks (CNN) and multilayer perceptron, refining the elementary features from the multi-cell fingerprints and dividing the UTs into their belonging categories. In the second DNN, a new network architecture, Transformer, is applied to the positioning problem. The Transformer is directly based solely on self-attention mechanisms to sequences of fingerprint patches, which can perform outstandingly on the 2D position coordinates regression. Simulation results show that the proposed positioning methods can outperform the existing methods in terms of positioning error.
Simulation of Discharge using Different Methods of Meteorological Data Distribution, Basin Discretization and Snow Modelling

This contribution investigates the performance of discharge modelling when proceeding from rather simple methods of snowmelt modelling to more sophisticated, distributed approaches. In all cases, the conceptual HBV runoff model is used for the calculation of discharge from the Romanche River basin (French Alps, area = 224 km2,12.5 % glacierized), feeding the Lac de Chambon reservoir used for hydroelectric power production. The results show that when using temperature-index approaches, no gain in performance is achieved by the more detailed basin discretization. However, a clear gain is observed when the precipitation and air temperature distribution of the elaborate meteorological data analysis system “SAFRAN” is used as input to the conceptual snow model. When using the original version of the physically-based “CROCUS” snow model, a strong overestimation of discharge resulted during the main snow melt season. A revised SAFRAN model version employing a new radiation routine, and the use of a realistic distribution of snow cover in each sub-unit considered by CROCUS resulted in a discharge simulation performance which is as good as the one from the calibrated conceptual models. Sensitivity analyses of the various snow models investigating the hydrological consequences of climatic change show surprisingly similar results.
Design of Adaptive PID Controllers Subject to Process Constraints

This paper presents a new method to design adaptive Proportional-Integral-Derivative (PID) controllers which optimize a performance criterion (integral absolute error or integral square error) subject to constraints such as controlled variable, manipulated variable and rate of change constraints. The design method is applicable to linear or nonlinear processes. Modeling of such processes is done using a linear ARX model. In addition, the design method is independent of the PID controller type and accounts for controllers with proportional or derivative action on the control error or the process variable. The proposed design method is not analytic in nature, but rather it is implemented using existing MATLAB optimization functions and demonstrated using simulation experiments for linear and nonlinear processes.
Energy conservation effects of continuous in-home feedback in all-electric homes

Feedback devices continuously displaying electricity consumption in cents per hour in twenty-five of 101 energy-efficient, all-electric homes in Polk's Landing, Cairboro, North Carolina, were associated with an average electricity savings of 12 per cent. Homes with monitors had lower consumption in all eleven months analyzed (September 1976-July 1977), with the greatest differences in months with moderate weather and low overall consumption. This suggests that the conservation actions taken by households with monitors primarily affected energy uses other than heating and cooling. Several recent studies have investigated the effects of daily energy consumption feedback on the use of electricity. Seligman and Darley (1977), Becker (1978), and Winett, Neale, Williams, Yokley, and Kauder (in press) all studied centrally air conditioned homes during summer months [1-3] ; Winett, Neale, and Grier (in press) studied electrically heated homes in winter [4]. Electricity savings of 10 to 20 per cent over study periods of three to eight weeks were found; savings generally were larger in periods of more extreme weather, indicating primary effects on heating and cooling energy. (For feedback studies with less frequent feedback and/or very small samples see references [5-10].) Reported here is an * This work was performed under contract EC-77-S-02-4165.A001 to the authors from the U.S. Department of Energy. This is publication number 176 of the Institute of Behavioral Science.
N-Type Coating of Single-Walled Carbon Nanotubes by Polydopamine-Mediated Nickel Metallization

Single-walled carbon nanotubes (SWCNTs) have unique thermal and electrical properties. Coating them with a thin metal layer can provide promising materials for many applications. This study presents a bio-inspired, environmentally friendly technique for CNT metallization using polydopamine (PDA) as an adhesion promoter, followed by electroless plating with nickel. To improve the dispersion in the aqueous reaction baths, part of the SWCNTs was oxidized prior to PDA coating. The SWCNTs were studied before and after PDA deposition and metallization by scanning and transmission electron microscopy, scanning force microscopy, and X-ray photoelectron spectroscopy. These methods verified the successful coating and revealed that the distribution of PDA and nickel was significantly improved by the prior oxidation step. Thermoelectric characterization showed that the PDA layer acted as a p-dopant, increasing the Seebeck coefficient S of the SWCNTs. The subsequent metallization decreased S, but no negative S-values were reached. Both coatings affected the volume conductivity and the power factor, too. Thus, electroless metallization of oxidized and PDA-coated SWCNTs is a suitable method to create a homogeneous metal layer and to adjust their conduction type, but more work is necessary to optimize the thermoelectric properties.
Distributed Motion Control for Multiple Mobile Robots Using Discrete-Event Systems and Model Predictive Control

Distributed motion control is critical in multiple mobile robot systems (MMRSs). Current research usually focuses on either discrete approaches, which aim to deal with high-level collisions and deadlocks without considering the low-level motion commands, or continuous approaches, which can optimize low-level continuous commands to mobile robots but cannot deal with deadlocks efficiently. In this article, by combining discrete and continuous methods, we design a hybrid motion control method for MMRSs where each robot should move along a predefined path. First, each robot’s motion is modeled as a discrete transition system, based on which a real-time supervisory control policy is illustrated to avoid collisions and deadlocks. Second, according to the discrete decisions, the continuous speed at each discrete state is computed using model predictive control and sequential convex programming. The proposed hybrid approach brings two advantages. First, the discrete control component guarantees collision and deadlock avoidance and reduces the scale of the optimization problems. Second, continuous control optimizes the continuous speed in real time and fulfills other performance requirements like time and energy costs. To move in a fully distributed way, each robot needs to predict the motion of its neighbors by retrieving their immediately available information through communications. The simulation and real-world experimental results show the effectiveness of our approach.