Miracle-dz/UniFAR-FTUs huggingface dataset:
Auditory and speech development in a 3-year-old boy with auditory neuropathy spectrum disorder

Background: Auditory neuropathy spectrum disorder (ANSD) in the auditory neural pathway can affect the auditory and speech development of children. Since the symptoms and complications of this disorder are similar in different children, hearing management and rehabilitation can help with better development of speech/ language and hearing perception in children with ANSD. The Case: In this study, the case was a one-yearold boy with ANSD and mild to moderate highfrequency sensorineural hearing loss. He first underwent various audiological examinations. Then, an aural rehabilitation program containing different auditory information and games was provided to him, his family, and caregiver at their home and in the rehabilitation center. Conclusion: Although auditory neuropathy/ dissynchrony in the auditory neural pathway has negative effect on the auditory and speech development, but the children with ANSD can use different inputs for language comprehension and acquisition if they receive effective education, especially auditory training at an earlier age.
Relationships Between the Financial and Property Markets in the Asia-Pacific Area

Abstract This paper initially reviews literature in relation to global capital flows, financial markets and real estate investment in the Asia-Pacific area. The Asian crisis is discussed in terms of the financial sector and debt financed real estate. Focus is placed upon the reorganisation of financial markets, the restructuring of bank finance and the impact of non-performing loans. Thailand in general and more specifically, the property market in Bangkok is used as a case study example to analyse these linkages. It is argued that a combination of weak regulatory framework in the financial sector and severe deficiencies in the planning/development process resulted in a highly over-supplied office market. The paper concludes that a more liquid property market needs to be created.
Social status, political priorities and unequal representation

Researchers on inequalities in representation debate about whether governments represent the preferences of the rich better than those of less afﬂuent citizens. We argue that problems of high- and low-status citizens are treated differently already at the agenda-setting stage. If afﬂuent and less afﬂuent citizens have different priorities about which issues should be tackled by government, then these divergent group priorities explain why government favours high- over low-status citizens. Due to different levels of visibility, resources and social ties, governments pay more attention to what high-status citizens consider important in their legislative agenda and pay less attention to the issues of low-status citizens. We combined three types of data for our research design. First, we extracted the policy priorities (most important issues) for all status groups from Eurobarometer data between 2002 and 2016 for 10 European countries and matched this information with data on policy outcomes from the Comparative Agendas Project. We then strengthen our results using a focused comparison of three single country studies over longer time series. We show that a priority gap exists and has representational consequences. Our analysis has important implications for the understanding of the unequal representation of status groups as it sheds light on an important, yet so far unexplored, aspect of the political process. Since the misrepresentation of political agendas occurs at the very beginning of the policy-making process, the consequences are potentially even more severe than for the unequal treatment of preferences.
Exceptionally high thermal and electrical conductivity of three-dimensional graphene-foam-based polymer composites

Graphene foams (GF) assembled with one- or few-layered ultrathin two-dimensional crystals have showed huge application potentials owing to their unique three-dimensional (3D) structure and superior properties. Here, we present a polymer-template-assisted assembly strategy for fabricating a novel class of 3D graphene architecture. A free-standing GF architecture has been built to act as thermal and electrical conduction paths in polymer composites. The obtained GF/polymer composites exhibit a high thermal conductivity (1.52 W mK−1) and high electrical conductivity (3.8 × 10−2 S cm−1) at relatively low GF loading (5.0 wt%). The GF/polymer composites are potentially useful in advanced packaging materials of high power LED and microelectronic devices.
A workflow based on Sentinel-1 SAR data and open-source algorithms for unsupervised burned area detection in Mediterranean ecosystems

ABSTRACT This paper investigates the capability of the free synthetic aperture radar (SAR) Sentinel-1 (S-1) C-band data for burned area mapping through unsupervised machine learning open-source processing solutions in the Mediterranean forest ecosystems. The study was carried out in two Mediterranean sites located in Portugal (PO) and Italy (IT). The entire processing workflow was developed in Python-based scripts. We analyzed two time-series covering about one month before and after the fire events and using both VH and VV polarizations for each study site. The speckle noise effects were reduced by performing a multitemporal filter and the backscatter time averages of pre- and post-fire datasets. The spectral contrast between changed and unchanged areas was enhanced by calculating two single-polarization radar indices: the radar burn difference (RBD) and the logarithmic radar burn ratio (LogRBR); and two temporal differences of dual-polarimetric indices: the delta modified radar vegetation index (ΔRVI) and the delta dual-polarization SAR vegetation index (ΔDPSVI), all exhibiting greater sensitivity to the backscatter changes. The scene’s contrast was enhanced by extracting the Gray Level Co-occurrence Matrix (GLCM) textures (dissimilarity, entropy, correlation, mean, and variance). A principal component analysis (PCA) was applied for reducing the number of the GLCM image layers. The burned area was delineated through unsupervised classification using the k-means clustering algorithm. A suitable number of clusters (k value) were set using a silhouette score analysis. To assess the accuracy of the resulting detected burned areas, an official burned area map based on multispectral Sentinel-2 (S-2) was used for PO, while for IT, a reference map was produced from S-2 data, based on the normalized burned ratio difference (ΔNBR) index. Recall (r), precision (p) and the F-score accuracy metrics were calculated. Our approach reached the values of 0.805 (p), 0.801 (r) and 0.803 (F-score) for PO, and 0.851 (p), 0.856 (r) and 0.853 (F-score) for IT. These results confirm the suitability of our approach, based on SAR S-1 data, for burned area mapping in heterogeneous Mediterranean ecosystems. Moreover, the implemented workflow, completely based on free and open-source software and data, offers high adaptation flexibility, repeatability, and custom improvement.
Analysis on boundary conditions of soft switching for DC electric spring with parallel topology

The concept of DC electric spring (DCES) has been introduced some years ago to solve the voltage stabilisation issue caused by the high penetration of intermittent renewable energy sources (RESs) into DC power systems. Recently, a featured DCES topology has been proposed, comprising an isolated three-port DC/DC converter and an embedded energy storage system. The three ports are connected to a DC power system at the supply side, the critical load (CL) and the non-critical load (NCL) at the user side, whilst the energy storage system is paralleled at the CL port. To improve the performance of the topology, this paper investigates the chance of making soft the switching of the DCES converters. After the DCES operation is analysed, equations describing the currents transients during switching have been worked out, from which the boundary conditions for zero-voltage switching (ZVS) to occur are formulated and the resultant ZVS zone is illustrated with the help of visual graphs. The boundary conditions are examined to evaluate how the circuit parameters inﬂuence the extension of the ZVS zone. The examination shows that a suitable selection of the parameters widens the ZVS zone notably; its optimal extension is then found for a case study. The effectiveness of the ZVS ﬁndings for the featured DCES topology is validated by both simulation and experiment results.
Effects of dietary metabolizable energy (ME) and crude protein (CP) concentrations on daily gain of suqin silky chickens aged from 0 to 12 weeks.

In this study,a 2×3 factorial arrangement was desigened,and the effects of different dietary metabolizable energy (ME) and crude protein (CP) concentrations on daily gain of Suqin Silky chicken breeds were analyzed.Three periods with particular dietary ME and CP concentrations were divided across the experiment.The results showed,the daily gains of males and females among treatment groups had significant difference (P0.005) in all the three periods.In terms of acquiring the highest daily gains,the optimal dietary ME and CP levels for Suqin Silky chickens were found out:11.72 MJ/kg ME and 20% CP for males in 0-4 weeks,11.30 MJ/kg ME and 20% CP for females in 0-4 weeks,12.55 MJ/kg ME and 19% CP for males in 5-8 weeks,11.72 MJ/kg ME and 18% CP for females in 5-8 weeks,12.69 MJ/kg ME and 17% CP for males in 9-12 weeks,12.55 MJ/kg ME and 17% CP for females in 9-12 weeks.For both males and females,CP factors had significant effects (P0.05) on daily gains in 0-4 weeks,but had no significant effects (P0.05) in 5-8 weeks and 9-12 weeks.ME factors had significant effects (P0.05) on daily gains in 5-8 weeks and 9-12 weeks,but had no significant effects (P0.05) in 0-4 weeks.
Systematic Review on Isolation Processes for Technical Lignin

Technologies for the isolation of lignin from pulping process streams are reviewed in this article. Based on published data, the WestVaco process, the LignoBoost process, the LigoForce SystemTM and the SLRP process are reviewed and discussed for the isolation of lignin from Kraft black liquor. The three new processes that have now joined the WestVaco process are compared from the perspective of product quality. Further, isolation processes of lignosulfonates from spent sulfite liquor are reviewed. The limitation for this review is that data are only available from lab scale and pilot scale experiments and not from industrial processes. Key output of this paper is a technology summary of the state of the art processes for technical lignins, showing the pros and cons of each process.
Counterintuitive active directional swimming behaviour by Atlantic salmon during seaward migration in the coastal zone

Acoustic telemetry was used to track salmon smolts during river migration and into the open marine coastal zone. We compared migration direction and speed with particle tracking simulations to test the hypothesis that marine migration pathways are defined by active swimming current following behaviour. Habitat-specific survival rates, movement speeds, depths and directions in riverine, estuarine, and coastal habitats were also quantified. Salmon post-smolts did not disperse at random as they entered the unrestricted, coastal zone of the North sea; rather they chose a common migration pathway. This was not the most direct route to marine feeding grounds (ca. 44° N); north in the direction of the prevailing currents. Particle modelling showed that the actual post-smolt migration route was best predicted by active swimming at 1.2 body length.sec. −1 at a bearing of 70° from north but not by current following behaviour. Fish migrating in larger groups and earlier in the migration period had increased migration success. We conclude that: post-smolts have preferred migration routes that are not predicted by the shortest direction to their ultimate destination; they do not simply use the current advantage to migrate; and that they actively swim, occasionally directly against the current prevailing at the time.
A DEVS-based engine for building digital quadruplets

Development of Embedded Real-Time Systems is prone to error, and developing bug-free applications is expensive and no guarantees can be provided. We introduce the concept of Digital Quadruplet which includes: a 3D virtual representation of the physical world (a Digital Twin), a Discrete-Event formal model of the system of interest (called the “Digital Triplet”), which can be used for formal analysis as well as simulation studies, and a physical model of the real system under study for experimentation (called the “Digital Quadruplet”). We focus on the definition of the idea of a Digital Quadruplet and how to make these four apparati consistent and reusable. To do so, we use the Discrete-Event formal model as a center for both simulation and execution of the real-time embedded components with timing constraints, as well as a common mechanism for interfacing with the digital counterparts, providing model continuity throughout the process. Here we focus on a principal part of the Digital Quadruplet idea: the provision of an environment to allow models to be used for simulation (in virtual time), visualization, or execution in real-time. A Discrete-EVent Systems specifications (DEVS) kernel runs on bare-metal hardware platforms, avoiding the use of an Operating RTOS in the platform, and the combination with discrete-event modeling engineering.
Fault Diagnosis via Neural Ordinary Differential Equations

Implementation of model-based fault diagnosis systems can be a difficult task due to the complex dynamics of most systems, an appealing alternative to avoiding modeling is to use machine learning-based techniques for which the implementation is more affordable nowadays. However, the latter approach often requires extensive data processing. In this paper, a hybrid approach using recent developments in neural ordinary differential equations is proposed. This approach enables us to combine a natural deep learning technique with an estimated model of the system, making the training simpler and more efficient. For evaluation of this methodology, a nonlinear benchmark system is used by simulation of faults in actuators, sensors, and process. Simulation results show that the proposed methodology requires less processing for the training in comparison with conventional machine learning approaches since the data-set is directly taken from the measurements and inputs. Furthermore, since the model used in the essay is only a structural approximation of the plant; no advanced modeling is required. This approach can also alleviate some pitfalls of training data-series, such as complicated data augmentation methodologies and the necessity for big amounts of data.
Lattice-Based mmWave Hybrid Beamforming

Conventional hybrid precoding and combining based transceivers require a large number of high-resolution radio frequency (RF) phase shifters (PSs), which impose prohibitive hardware costs and power consumption. To address the above issue, both partially connected RF PSs and low-resolution PSs have been proposed. However, the performance limits of these low-cost designs have not been investigated theoretically. Furthermore, there is room for improvement in their spectral efficiency. To fill this knowledge gap, we derive the mean square error performance discrepancy between an optimal precoder/combiner and the hybrid analog-digital precoder/combiner under the constraint of 1-bit PSs relying on lattice theory. Then, by observing that this performance gap can be reduced by deactivating parts of the PSs whilst improving both the spectral and energy efficiency, we develop an adaptive RF PS connection network. To resolve the associated hybrid precoding and combining problems, we appropriately adapt Babai’s algorithm from the lattice decoding literature. Our simulation results demonstrate the superiority of the proposed scheme both in terms of its spectral and energy efficiency.
A Two-Level Model for Traffic Signal Timing and Trajectories Planning of Multiple CAVs in a Random Environment

Connected and automated vehicles (CAVs) trajectories not only provide more real-time information by vehicles to infrastructure but also can be controlled and optimized, to further save travel time and gasoline consumption. This paper proposes a two-level model for traffic signal timing and trajectories planning of multiple connected automated vehicles considering the random arrival of vehicles. The proposed method contains two levels, i.e., CAVs’ arrival time and traffic signals optimization, and multiple CAVs trajectories planning. The former optimizes CAVs’ arrival time and traffic signals in a random environment, to minimize the average vehicle’s delay. The latter designs multiple CAVs trajectories considering average gasoline consumption. The dynamic programming (DP) and the General Pseudospectral Optimal Control Software (GPOPS) are applied to solve the two-level optimization problem. Numerical simulation is conducted to compare the proposed method with a fixed-time traffic signal. Results show that the proposed method reduces both average vehicle’s delay and gasoline consumption under different traffic demand significantly. The average reduction of vehicle’s delay and gasoline consumption are 26.91% and 10.38%, respectively, for a two-phase signalized intersection. In addition, sensitivity analysis indicates that the minimum green time and free-flow speed have a noticeable effect on the average vehicle’s delay and gasoline consumption.
Multi‐scale pedestrian detection based on self‐attention and adaptively spatial feature fusion

Pedestrian detection is a classic problem in computer vision, which has an essential impact on the safety of urban autonomous driving. Although signiﬁcant improvement has been made in pedestrian detection recently, small-scale pedestrian detection is still challenging. To effectively tackle this issue, a multi-scale pedestrian detector based on self-attention mechanism and adaptive spatial feature fusion is proposed in this paper. In order to better extract global information, the spatial attention mechanism asymmetric pyramid non-local block (APNB) module is applied. To achieve scale-invariance detection, multiple detection branches are designed, which include a high-resolution detection branch and a low-resolution detection branch. In integrating multi-scale features, the adaptively spatial feature fusion (ASFF) method is employed, which can solve the problem of feature inconsistency across different scales. Experimental results show that the proposed method obtains competitive performance on Caltech and CityPersons datasets.
Adaptive Cruise Control System Evaluation According to Human Driving Behavior Characteristics

With the rapid and wide implementation of adaptive cruise control system (ACC), the testing and evaluation method becomes an important question. Based on the human driver behavior characteristics extracted from naturalistic driving studies (NDS), this paper proposed the testing and evaluation method for ACC systems, which considers safety and human-like at the same time. Firstly, usage scenarios of ACC systems are defined and test scenarios are extracted and categorized as safety test scenarios and human-like test scenarios according to the collision likelihood. Then, the characteristic of human driving behavior is analyzed in terms of time to collision and acceleration distribution extracted from NDS. According to the dynamic parameters distribution probability, the driving behavior is divided into safe, critical, and dangerous behavior regarding safety and aggressive and normal behavior regarding human-like according to different quantiles. Then, the baselines for evaluation are designed and the weights of different scenarios are determined according to exposure frequency, resulting in a comprehensive evaluation method. Finally, an ACC system is tested in the selected test scenarios and evaluated with the proposed method. The tested vehicle finally got a safety score of 0.9496 (full score: 1) and a human-like score as fail. The results revealed the tested vehicle has a remarkably different driving pattern to human drivers, which may lead to uncomfortable ride experience and user-distrust of the system.
Does Public Housing Increase the Risk of Child Health Problems? Evidence From Linked Survey-Administrative Data

ABSTRACT Research on the effects of federal housing assistance programs on children’s outcomes has produced mixed results. Although housing assistance programs provide a rare source of affordable and stable housing for low-income families, there remains concern that living in public housing developments increases children’s risk of poor health. This paper uses a unique survey-administrative linked dataset to examine the effect of living in public housing on children’s risk of health problems, including frequent diarrhea, headaches, skin allergies, asthma, and fair/poor health status. Children living in public housing have more health problems than children who do not live in public housing. However, the analysis develops several comparison groups to demonstrate that the excess health problems reflect unobserved selection into public housing. The main selection adjustment compares children living in public housing to children who enter public housing in the near future. Results indicate that public housing does not increase the risk of child health problems, and it is important to consider selection into public housing on factors that are correlated with health. The effects of public housing may be mixed, but policymakers should not confuse the economic and health challenges of public housing residents for the effects of the program itself.
The Physico-Chemical and Mineralogical Characterization of Mg-Rich Synthetic Gypsum Produced in a Rare Earth Refining Plant

The physical, chemical and mineralogical characterization of the constituents of magnesium-rich synthetic gypsum produced in a rare earth-refining plant located in Gebeng, Pahang, Malaysia was conducted through elemental chemical analysis, scanning electron microscopy with Energy Dispersive X-ray (EDX)-analyzer, thermal analysis, X-ray fluorescence and X-ray diffraction. The crystalline nature of the by-product was studied using FTIR spectroscopy. Elemental analysis confirmed the presence of Ca and Mg, which are essential macronutrients required by plants and this Ca alongside the high pH (9.17) of MRSG may confer on the material a high acid neutralization capacity. From the result, it was observed that the studied by-product is a heterogeneous crystalline material comprising of gypsum (CaSO4.2H2O) and other major components such as calcium (magnesium) compounds (hydroxide, oxide, silicates, and carbonate) and sulfur. These aggregates may contribute to give an acid neutralization capacity to MRSG. The XRD study of MRSG indicated a high content of gypsum (45.4%), shown by the d-spacing of 7.609 Å (2-theta 11.63) in the diffractogram. The infrared absorption spectra of MRSG indicate close similarities to mined gypsum. The results of the characterization indicated that MRSG has valuable properties that can promote its use in amending soil fertility constraints on nutrient-deficient tropical acid soils.