Miracle-dz/UniFAR-FTUs huggingface dataset:
Time-Varying Channel Estimation Scheme for Uplink MU-MIMO in 6G Systems

Channel estimation is a challenging issue for millimeter wave (mmWave) and massive multiple-input multiple-output (MIMO) in the future sixth generation (6G) wireless systems, where the conventional estimation schemes may fail to track the fast varying channels, especially in high-speed mobile scenarios. In this paper, a novel tensor-based uplink channel estimation scheme is proposed for multi-user MIMO (MU-MIMO) systems over time-varying channels. In the proposed scheme, a low-overhead pilot transmission scheme is designed to track the varying channel. The received uplink signal at the base station (BS) is formulated as a third-order tensor which admits a CANDECOMP/PARAFAC (CP) model. The CP decomposition issue is then solved using blind matrix decomposition, in which the special structures of signals in the time dimension and the matrix subspace are utilized. By exploiting low-rank structure of the signal tensor, the channel parameters (angles of arrival/departure, path gains, and Doppler shifts) are estimated from the factor matrices. Moreover, the proposed scheme is theoretically analyzed and it is guaranteed with low pilot overhead. Simulation results verify that the proposed scheme can outperform the compressed sensing (CS) based scheme and the iteration-based scheme in terms of accuracy and stability. The uniqueness of the proposed scheme is also verified in our simulation.
Foreign Direct Investment and the Profit Rate: Empirical Evidence from the European Union

This article aims to explain recent trends of foreign direct investment (FDI) in the developed world, and specifically in the case of the European Union (EU). It includes an empirical analysis of FDI in two distinct EU country groups and finds that the profit rate plays a significant role in determining FDI patterns. It is shown that relatively less developed Eastern EU members have a higher profit rate and thus have established themselves as an attractive FDI destination within the EU. Additional factors that influence FDI are market size, long-term FDI attractiveness, and trade protectionism. Finally, a sectoral analysis of FDI and trade shows that increased FDI in less developed Eastern EU members is mainly of an export-oriented type. JEL Classification: F14, F15, F21, P16
Human–Robot Interaction: A Review and Analysis on Variable Admittance Control, Safety, and Perspectives

Human–robot interaction (HRI) is a broad research topic, which is defined as understanding, designing, developing, and evaluating the robotic system to be used with or by humans. This paper presents a survey on the control, safety, and perspectives for HRI systems. The first part of this paper reviews the variable admittance (VA) control for human–robot co-manipulation tasks, where the virtual damping, inertia, or both are adjusted. An overview of the published research for the VA control approaches, their methods, the accomplished collaborative co-manipulation tasks and applications, and the criteria for evaluating them are presented and compared. Then, the performance of various VA controllers is compared and investigated. In the second part, the safety of HRI systems is discussed. The various methods for detection of human–robot collisions (model-based and data-based) are investigated and compared. Furthermore, the criteria, the main aspects, and the requirements for the determination of the collision and their thresholds are discussed. The performance measure and the effectiveness of each method are analyzed and compared. The third and final part of the paper discusses the perspectives, necessity, influences, and expectations of the HRI for future robotic systems.
Entropy as a Transitional In-Game Variable

The aim of this research is to discover the bond of entropy and the experience of video game immersion, using an Interpretative Phenomenological Analysis (IPA) to interpret the immersive experiences of players and how this bond of entropy and immersion could be transferred on other immersive technologies. The experiment was conducted on a selection of low-entropy scenes in three video games belonging to the genre of interactive drama. Six players were selected as the sample group for this research, based on their playthrough experiences of the games Heavy Rain (2010), Until Dawn (2015) and Dark Pictures Anthology: Man of Medan (2019) on the PlayStation platform. By monitoring the levels of entropy and immersion during their playthroughs, this research explores the potential of transferring immersion through the use of entropy from digital games to other immersive technologies. According to the research highlights and through data interpretation, entropy is found to be immensely influential upon achieving and maintaining narrative, physical and emotional immersion, and its effect could be further applied to other immersive technologies sharing a common ground with digital games, which features are further examined in finer detail in the current research.
Communicating bad news in the practice of nursing: an integrative review

ABSTRACT Objective To analyze current scientific knowledge about communication of bad news by nurses. Methods This is an integrative literature review carried out by searching articles published in national and international journals indexed at SciELO, MEDLINE® (PubMed®), Scopus, Bireme and CINAHL, from 2010 to 2020, by crossing the controlled descriptors “communication”, “revelation of the truth”, and “nursing”, and the uncontrolled descriptor “bad news”. Results Ten articles with qualitative and cross-sectional design, as well as case reports were included. The analysis indicated the evidence available in the literature showed the nurses’ lack of ability to communicate bad news, although they are professionals who have close contact with patients and families and who establish a strong bond with them, and often face challenging situations for communicating bad news. Conclusion There is an evident need to invest in training of nurses on skills to communicate bad news and establish a nurse-patient bond when dialoguing with the family. There are few studies in the literature addressing this issue; therefore, it is recommended to perform research that can contribute to improvements in the clinical practice and developing protocols to promote such care.
Lean-Agile FM-BIM: a demonstrated approach

Purpose This paper aims to respond to the high cost of facility management-enabled building information model (FM-BIM) creation and maintenance, a significant and under-researched barrier to adoption for existing buildings. The resultant approach focuses on only value-adding content (“Lean”) developed flexibly and iteratively in collaboration with end-users (“Agile”). Design/methodology/approach Five case studies were developed for university and hospital buildings in collaboration with end-users, guided by the process presented. These informed the refinement of a robust and flexible approach to increase BIM functionality with minimal geometry, focusing instead on the development of specific parameters to map semantic information necessary for each desired FM use. Findings The resulting BIM provided a breadth of model functionality with minimal modeling effort: 15 hours average implementation time per supported FM use. This low level of effort was achieved by limiting geometry to where it is necessary for the FM use implementation. Instead, the model incorporated the majority of geometry by reference and focused on semantic and topological parameters to house FM information. Research limitations/implications This study provides the basis for a new ontology structure focused on defining the rules for hosting asset management data (host entity, parameter type and characteristics) to reduce the reliance on complex geometric model development. Practical implications By prioritizing highly beneficial applications, early investment is minimized, providing quick returns at low risk, demonstrating the value of FM-BIM to end-users. Originality/value The Lean-Agile approach addresses the known research gap of low-effort, flexible approaches to FM-BIM model creation and maintenance and its effectiveness is analyzed through five case studies.
Glaciogenic seeding of cold-season orographic clouds to enhance precipitation: status and prospects

This essay is intended to provide stakeholders and news outlets with a plain-language summary of orographic cloud seeding research, new capabilities, and prospects. Specifically, we address the question of whether a widely-practiced type of weather modification, glaciogenic seeding of orographic clouds throughout the cold season, can produce an economically-useful increase in precipitation over a catchment-scale area. Our objective is to clarify current scientific understanding of how cloud seeding may affect precipitation, in terms that are more accessible than in the peer-reviewed literature. Public confidence that cloud seeding “works” is generally high in regions with operational seeding, notwithstanding decades of scientific reports indicating that the changes in precipitation are uncertain. Randomized seeding experiments have a solid statistical foundation and focus on the outcome, but, in light of the small seeding signal and the naturally noisy nature of precipitation, they generally require too many cases to be affordable, and therefore are discouraged. A complementary method, physical evaluation, examines changes in cloud and precipitation processes when seeding material is injected, and yields insights into the most suitable ambient conditions. Recent physical evaluations have established a robust, well-documented scientific basis for glaciogenic seeding of cold-season orographic clouds to enhance precipitation. The challenge of seeding impact assessment remains, but evidence is provided that, thanks to recent significant progress in observational and computational capabilities, the research community is finally on track to be able to provide stakeholders with guidance on the likely quantitative precipitation impact of cloud seeding in their region. We recommend further process-level evaluations combined with highly-resolved, well-constrained numerical simulations of seasonal cloud seeding.
Performance of mixed exponential and exponential distribution representing rain cell intensity in neyman-scott rectangular pulse (NSRP) model

Sub-daily timescale data such as hourly data are needed for modeling urban systems. However such series are not readily available as compared to daily rainfall series. Stochastic rainfall models are useful in estimating input for design work. One of the models that applies the clustered point process theory is the Neyman-Scott Rectangular Pulses (NSRP) model. The model uses a flexible model fitting procedure which involves matching approximately a chosen set of historical statistics which exceeds in number of set of parameters to be estimated. An optimization technique called Shuffle Complex Evolution (SCE-UA) was used to estimate the parameters. The performance of NSRP model was evaluated using 10 years hourly data taken from a station in Wilayah Persekutuan. Two distributions, namely exponential (EXP) and mixed exponential (MEXP) were used to model the cell intensities in the model. The models were evaluated on a monthly basis regarding their ability to preserve the statistical properties as well as the physical properties of the rainfall time-series over timescales of 1 h, 6 h and 24 h. The performance of the models with the two different distributions was evaluated and compared. The model with the mixed exponential (MEXP) distribution perform better in preserving most of the statistical and physical properties of the observed data.
A Composite DC–DC Converter Based on the Versatile Buck–Boost Topology for Electric Vehicle Applications

The composite converter allows integrating the high-efficiency converter modules to achieve superior efficiency performance, becoming a prominent solution for electric transport power conversion. In this work, the versatile buck–boost dc–dc converter is proposed to be integrated into an electric vehicle composite architecture that requires a wide voltage range in the dc link to improve the electric motor efficiency. The inductor core of this versatile buck–boost converter has been redesigned for high voltage applications. The versatile buck–boost converter module of the composite architecture is in charge of the control stage. It provides a dc bus voltage regulation at a wide voltage operation range, which requires step-up (boost) and step-down (buck) operating modes. The PLECS thermal simulation of the composite architecture shows a superior power conversion efficiency of the proposed topology over the well-known classical noninverting buck–boost converter under the same operating conditions. The obtained results have been validated via experimental efficiency measures and experimental transient responses of the versatile buck–boost converter. Finally, a hardware-in-the-loop (HIL) real-time simulation system of a 4.4 kW powertrain is presented using a PLECS RT Box 1 device. The HIL simulation results verified the accuracy of the theoretical analysis and the effectiveness of the proposed architecture.
A generic evolutionary computation approach based upon genetic algorithms and evolution strategies

Many problems that are treated by genetic algorithms belong to the class of NP-complete problems. The vantage of genetic algorithms when being applied to such kind of problems lies in the ability to search through the solution space in a broader sense than other heuristic methods that are based upon neighborhood search methods. Nevertheless, also genetic algorithms are frequently faced with a problem which, at least in its impact, is quite similar to the problem of stagnating in a local but not global solution what typically occurs when applying neighborhood based searches to hard problems with multimodal solution spaces. This drawback, called premature convergence in the terminology of genetic algorithms, occurs when the population of a genetic algorithm reaches such a suboptimal state that the genetic operators can no longer produce offspring that outperform their parents. During the last decades plenty of work has been investigated to introduce new coding standards and operators in order to overcome this essential handicap of genetic algorithms. As these coding standards and the belonging operators are rather problem specific in general we try to take a different approach and look upon the concepts of genetic algorithms as an artificial self organizing process in a bionically inspired generic way in order to improve the global convergence behaviour of genetic algorithms independently of the actually employed implementation. In doing so we have introduced an advanced selection model for genetic algorithms that allows adaptive selective pressure handling in a way that is quite similar to evolution strategies. This enhanced genetic algorithm-model allows further extensions like the introduction of a concept to handle multiple crossover operators in parallel or the introduction of a concept of segregation and reunification of smaller subpopulations. Both extensions rely on a variable selective pressure because the general conditions may change during the evolutionary process. The experimental part of the paper discusses the new algorithms for the traveling salesman problem (TSP) as a well documented instance of a multimodal combinatorial optimization problem achieving results which significantly outperform the results obtained with a contrastable genetic algorithm.
Anamorphic beam shaping for efficient laser homogenization: methods and high power applications

The homogenization of light is widely applied in various industrial sectors. The uniform high power processing of large areas requires a high degree of homogeneity. Sophisticated beam transformation techniques are used to optimize the illumination of standard optical diffusers such as microlens arrays and decrease the contrast of interference. Novel design techniques take advantage of a multimodal approach which is especially adapted to the characteristic properties of the laser light source. We show how anamorphic beam shaping is employed to transform the high power light source in order to meet the required level of homogeneity suited for the respective application.
Exploring diversity: a meta-analysis of wetland conservation and creation

: The rationale for conservation and creation of wetlands is bounded by the recognition of both their ecological and economic values. This paper examines the welfare impacts of goods and services provided by wetlands. We collected 353 observations on the economic value of natural and man-made wetlands from 155 studies worldwide. The resulting database is less biased towards North America than previous reviews of the literature. The relative importance of characteristics of the valuation study, of the wetland site and of the socio-economic and geographical context is estimated by means of a meta-regression of wetland values. Water quality improvement, provision of amenity and biodiversity enhancement are the most highly valued wetland services. The relevance of the socio-economic and geographical context clearly emerges from the analysis and, in particular, the proximity to other wetland sites is negatively correlated with values. An analysis of the effect of environmental stress on wetland value shows that the latter increases with stress from human development activities and uses.
Deflection distribution estimation of tied‐arch bridges using long‐gauge strain measurements

Deflection, as a critical indicator for structural performance evaluation, is difficult to be measured accurately for long‐span bridges, though various deformation sensors and devices have been developed. In this paper, a new scheme using long‐gauge fiber optic sensors for estimating the deflection distribution of tied‐arch bridges is proposed. First, the complex strain state of the tied‐arch bridge is investigated, in which a cubic function describing the axial strain distribution of the tie beam is obtained. Second, the bending strain is separated from the measured long‐gauge strain which is a combination of axial and bending components by a sensor layout scheme. Finally, the separated bending strain is utilized to estimate the deflection distribution of the tied‐arch bridge through an improved conjugate beam method. Numerical and experimental examples are studied to illustrate the effectiveness of the proposed method for deflection estimation of tied‐arch bridges in static and dynamic loading cases.
Research Online

Abstract Authentication refers to the process of confirming the identity of the authenticating entity. The use of passwords for user authentication has become ubiquitous in our everyday lives. Despite its wide-spread usage, password-based authentication has numerous deficiencies. For instance, password theft is becoming a common occurrence due to a variety of security problems associated with passwords.As such, many organizations are moving towards adopting alternative solutions like one-time passwords, which are only valid for a single session. Nevertheless, various one-time password schemes also suffer from a number of drawbacks in terms of their method of generation or delivery. In this chapter, we present a challenge-response visual one-time password authentication scheme that is to be used in conjunction with the camera on a mobile device. The main feature of the proposed scheme is to allow the server to send a challenge over a public channel for a user to obtain a session key while safeguarding the user's long-term secret key. We present the authentication protocol, its security analysis, the various design considerations and the advantages provided by our system.
Edinburgh Explorer Multi-site study of additive genetic effects on fractional anisotropy of cerebral white matter

words) Combining datasets across independent studies can boost statistical power by increasing the numbers of observations and can achieve more accurate estimates of effect sizes. This is especially important for genetic studies where a large number of observations are required to obtain sufficient power to detect and replicate genetic effects. There is a need to develop and evaluate methods for joint-analytical analyses of rich datasets collected in imaging genetics studies. The ENIGMA-DTI consortium is developing and evaluating approaches for obtaining pooled estimates of heritability through meta-and mega-genetic analytical approaches, to estimate the general additive genetic contributions to the intersubject variance in fractional anisotropy (FA) measured from diffusion tensor imaging (DTI). We used the ENIGMA-DTI data harmonization protocol for uniform processing of DTI data from multiple sites. We evaluated this protocol in five family-based cohorts providing data from a total of 2248 children and adults (ages: 9-85) collected with various imaging protocols. We used the imaging genetics analysis tool, SOLAR-Eclipse, to combine twin and family data from Dutch, Australian and Mexican-American cohorts into one large “mega-family”. We showed that heritability estimates may vary from one cohort to another. We used two meta-analytical (the sample-size and standard-error weighted) approaches and a mega-genetic analysis to calculate heritability estimates across-population. We performed leave-one-out analysis of the joint estimates of heritability, removing a different cohort each time to understand the estimate variability. Overall, meta- and mega-genetic analyses of heritability produced robust estimates of heritability. proportional to the sample size (r=~0.9), supporting the basic assumption of N-weighted meta-analysis. Further, we compared the estimates from N-weighted approach to these derived using the standard-error weighted meta and mega-genetic analyses. The estimates derived from the N-weighted meta-analysis overlapped with the confidence intervals of the other approaches, suggesting that it homed in on appropriate heritability estimates.
Appendices A . Video-to-Text Retrieval Results

Our experiments use 12 sampled frames by default following recent text-video retrieval literature [7], and we run additional experiments on the MSR-VTT-9K dataset by varying the number of sampled frames for both training and inference as shown in Figure B1. We observe worse performance for 6 frames likely due to important information being missing at this scale. As we increase the number of frames1, we observe performance saturation which is consistent with findings in [7]. However, we note that the optimal number of sampled frames remains a dataset specific hyperparameter.