Miracle-dz/UniFAR-FTUs huggingface dataset:
One cabin equipment location method based on the visibility human-factor potential field

Ships, aircraft, armored vehicle, etc. usually consist of cabin structure, and there are a lot of cabin equipment requires manual inspection, monitoring, maintenance and operation [10]. In the cabin design process, visibility is one very important design factor to ensure the monitored equipment within the visual scope [9]. All the equipment to be maintained need reasonable operation space, and the determination of the space distribution of the equipment is an important part of the product design. There are a large number of studies analyzing the visibility of product. However, in most cases the process is based on the graphics method and needs manual participation. Yin adopts visual cone method to assess the visibility of manufacturing process [12]. Bidault combines the method of clipping plane and visual cone [3]. In order to increase the design and analysis efficiency of the space layout design, the model based on visibility optimization design method is indispensable, but there are very few relative studies at present. This paper mainly studies the cabin equipment layout method from the field point of view. To describe the relationship between the human body, the obstacle and the object, the conception of the humanfactor potential field is put forward in the paper, and the mathematical model of visibility potential field is established. Then the optimization design method of space layout based on the visibility target is studied so as to improve the spatial layout of visual design efficiency and design quality. The layout optimization design model is established and solved by the particle swarm optimization algorithm.
STUDY OF NOVEL CHANNEL MATERIALS USING III-V COMPOUNDS WITH VARIOUS GATE DIELECTRICS

The exponential rise in the density of silicon CMOS transistors has now reached a limit and threatening to end the microelectronics revolution. To tackle this difficulty, group III–V compound semiconductors due to their outstanding electron transport properties and high mobility are very actively being researched as channel materials for future highly scaled CMOS devices. In this paper, we have studied a ballistic nanoscale MOSFET using simulation approach by replacing silicon in the channel by III-V compounds. The channel materials considered are silicon (Si), Gallium arsenide (GaAs), Indium arsenide (InAs), Indium Phosphide (InP) and Indium Antimonide (InSb). The device metrics considered at the nanometer scale are subthreshold swing, Drain induced barrier lowering, on and off current, carrier injection velocity and switching speed. These channel materials have been studied using various dielectric constants. It has been observed that Indium Antimonide (InSb) has higher on current, higher transconductance, idealistic subthreshold swing, higher output conductance, higher carrier injection velocity and comparable voltage gain compared to Silicon, thus, making InSb as a possible candidate to be used as channel material in future nanoscale devices.
Supply-Demand-aware Deep Reinforcement Learning for Dynamic Fleet Management

Online ride-hailing platforms have reduced significantly the amounts of the time that taxis are idle and that passengers spend on waiting. As a key component of these platforms, the fleet management problem can be naturally modeled as a Markov Decision Process, which enables us to use the deep reinforcement learning. However, existing studies are proposed based on simplified problem settings that fail to model the complicated supply-dynamics and restrict the performance in the real traffic environment. In this article, we propose a supply-demand-aware deep reinforcement learning algorithm for taxi dispatching, where we use a deep Q-network with action sampling policy, called AS-DQN, to learn an optimal dispatching policy. Furthermore, we utilize a dueling network architecture, called AS-DDQN, to improve the performance of AS-DQN. Extensive experiments on real-world datasets offer insight into the performance of our model and show that it is capable of outperforming the baseline approaches.
Optimal design of photon-sieve holograms for fine-line image generation

A photon-sieve hologram (PSH) is a computer-generated hologram that can generate fine-line images. As a high-resolution image can be projected directly without using a reducing optical system, it can also be used for short-wavelength radiation, such as x-rays. We propose an optimum design method for the PSH so that a high-quality line image can be projected. We optimised straight-line and L-shaped bent-line images using numerical simulation based on vector diffraction theory and conjugate-gradient methods. This optimisation helped homogenise the intensity of the image and remove unwanted side lobes and spots around the image. We also experimentally examined the bent-line image before and after the optimisation to confirm its effect.
System Neural Network: Evolution and Change Based Structure Learning

System evolution analytics with artificial neural networks is a challenging and path-breaking direction, which could ease intelligent processes for systems that evolve over time. In this article, we contribute an approach to do Evolution and Change Learning (ECL), which uses an evolution representor and forms a System Neural Network (SysNN). We proposed an algorithm System Structure Learning, which is divided in two steps. First step uses the evolution representor as an Evolving Design Structure Matrix (EDSM) for intelligent design learning. Second step uses a Deep Evolution Learner that learns from evolution and changes patterns of an EDSM to generate Deep SysNN. The result demonstrates application of the proposed approach to analyze four real-world system domains: software, natural-language, retail market, and movie genre. We achieved significant learning over highly imbalanced datasets. The learning from previous states formed SysNN as a feed-forward neural network, and then memorized information as an output matrix to recommend entity-connections.
Searching for equity-centered change agents: An investigation of family and community engagement in teacher evaluation frameworks

Federal legislation in the United States granted states freedom to alter formerly test-based teacher evaluation systems and situate family engagement as a key component in school improvement efforts. Concurrently, theorizing on family engagement has moved away from deficit characterizations and school-based involvement to an asset-based approach focused on equitable stakeholder collaborations committed to driving systemic change. Seeking to understand how states are characterizing exemplary teaching in terms of family and community engagement, the current study uses directed qualitative content analysis to examine 15 teacher evaluation rubrics or standards. The findings indicate that many evaluation systems were narrowly focused on the procedural aspects of teaching and teacher engagement with families was too often viewed merely as a mechanism to yield academic gains and meet legal requirements. The rubrics that explicitly stated that teachers should be culturally responsive provided a semblance of hope for pushing evaluative practices through the engagement barrier and into a space of collaboration that departs from dominant school-based approaches. These results have implications for the design of teacher evaluation policy and the furthering of equity-centered partnerships with families and communities.
The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges

The primary objective of implementing Electronic Health Records (EHRs) is to improve the management of patients’ health-related information. However, these records have also been extensively used for the secondary purpose of clinical research and to improve healthcare practice. EHRs provide a rich set of information that includes demographics, medical history, medications, laboratory test results, and diagnosis. Data mining and analytics techniques have extensively exploited EHR information to study patient cohorts for various clinical and research applications, such as phenotype extraction, precision medicine, intervention evaluation, disease prediction, detection, and progression. But the presence of diverse data types and associated characteristics poses many challenges to the use of EHR data. In this article, we provide an overview of information found in EHR systems and their characteristics that could be utilized for secondary applications. We first discuss the different types of data stored in EHRs, followed by the data transformations necessary for data analysis and mining. Later, we discuss the data quality issues and characteristics of the EHRs along with the relevant methods used to address them. Moreover, this survey also highlights the usage of various data types for different applications. Hence, this article can serve as a primer for researchers to understand the use of EHRs for data mining and analytics purposes.
Directional Selection Property of 2-Dimensional Complex Discrete Wavelet Transform and Its Application on Defect Inspection of Semiconductor Wafer Circuits

Previously, Discrete Wavelet Transform(DWT) is widely used as the image processing method. Some of the DWT, Complex Discrete Wavelet Transform(CDWT) and Complex Wavelet Packet Transform(CWPT) have directional selection that can detect the directional edges from the high frequency components. This property is expected to be used for shape and texture analysis such like a circuit pattern images. However, the principle of directional selection is still unclear. Therefore, fundamental properties such as the detect direction and detection angular range(resolution) are also still unclear. Thus, it is difficult to apply the defect detection that has the directional shape and textures. In this research, we firstly consider the principle of directional selection and we proposed the evaluation method of directional selection based on the consideration result. Proposed evaluation method can make clear the detect direction and resolution quantitatively. Finally we apply the directional selection to defect recognition of semiconductor wafer circuit in combination with Kmeans method. As the result, directional selection of 2D-CDWT and 2D-CWPT achieved highest recognition rate and lowest miss recognition rate compared with previous DWT and FFT.
Data-driven extraction of human kinase-substrate relationships from omics datasets

Phosphorylation forms an important part of the signalling system that cells use for decision making and regulation of processes such as celll division and differentiation. To date, a large portion of identified phosphosites are not known to be targeted by any kinase. At the same time around 30% of kinases have no known target. This knowledge gap stresses the need to make large scale, data-driven computational predictions. In this paper, we have created a machine learning-based model to derive a probabilistic kinase-substrate network from omics datasets. We show that our methodology displays improved performance compared to other state of the art kinase-substrate predictions, and provides predictions for more kinases than most of them. Importantly, it better captures new experimentally-identified kinase-substrate relationships. It can therefore allow the improved prioritisation of kinase-substrate pairs for illuminating the dark human cell signalling space.
Sudden stratospheric warmings during El Niño and La Niña: sensitivity to atmospheric model biases

Abstract. The number of sudden stratospheric warmings (SSWs) per year is affected by the phase of the El Niño–Southern Oscillation (ENSO), yet there are discrepancies between the observed and modelled relationship. We investigate how systematic model biases in atmospheric winds and temperatures may affect the ENSO–SSW connection. A two-step bias correction process is applied to the troposphere, stratosphere, or full atmosphere of an atmospheric general circulation model. ENSO-type sensitivity experiments are then performed by adding El Niño and La Niña sea surface temperature (SST) anomalies to the model's prescribed SSTs, to reveal the impact of differing climatologies on the ENSO–SSW teleconnection. The number of SSWs per year is overestimated in the control run, and this statistic is improved when biases are reduced in both the stratosphere and troposphere. The seasonal cycle of SSWs is also improved by the bias corrections. The composite SSW responses in the stratospheric zonal wind, geopotential height, and surface response are well represented in both the control and bias-corrected runs. The model response of SSWs to ENSO phase is more linear than in observations, in line with previous modelling studies, and this is not changed by the reduced biases. However, the ratio of wave 1 events to wave 2 events as well as the tendency to have more wave 1 events during El Niño years than La Niña years is improved in the bias-corrected runs.
IEEE802.11 Access Point's Service Set Identifier (SSID) for Localization and Tracking

: IEEE802.11, known as WiFi has proliferated in the last decade. It can be found in smartphones, laptops, smart TVs and surveillance cameras. This popularity has revealed many issues in health, data privacy and security. In this work, a WiFi measurement study has been conducted in Amman, the capital city of Jordan. An Android App has been written to harvest WiFi information of the transmitted frames of any surrounding Access points (APs). More than 240,000 APs information has been harvested in this work. The harvested data have been analyzed to find statistics of WiFi devices in this city. Moreover, three power distribution models have been derived from the data for three different areas, closed, open and hybrid areas. In addition, the collected data revealed that the SSID can be leveraged as a landmark for the access points (APs). To this end, SSIDtrack algorithm is proposed to track shoppers/walkers in closed areas, such as malls to find their walking route utilizing only the SSID information collected from the surrounding area. The algorithm has been tested in two different malls that consist of four different floors. The accuracy recorded for the algorithm acceded 95%.
Variations in Arbuscular Mycorrhizal Colonization Associated with Root Diameter and Hypodermis Passages Cells across Temperate and Tropical Woody Species

Root hypodermis passage cells (PCs) lack suberin and lignin deposition, responsible for nutrient absorption and arbuscular mycorrhizal colonization, which are crucial for root resource acquisition. Nevertheless, their quantitative variability across diverse woody species and their relationships with root morphology and anatomy, as well as arbuscular mycorrhizal colonization, are still not well understood. Herein, the number and proportion of PCs in the root cross-section, root morphology, anatomy, and arbuscular mycorrhizal colonization rate were quantified across 10 temperate and 12 tropical woody species. The objectives of this study were to determine how PCs vary across contrasting environmental conditions and to explore their relationships with arbuscular mycorrhizal colonization rate and other root functional traits. The results showed that tropical species possessed 56% more PCs than temperate species; by contrast, they had similar proportions of PCs. In both biomes, the number of PCs had a tightly positive correlation with arbuscular mycorrhizal colonization rate (R2 = 0.35–0.87), root diameter (R2 = 0.84–0.93), and cortex thickness (R2 = 0.87–0.89), but the proportion of PCs was mostly independent of root morphological and anatomical traits. Our results suggest that variation in passage cells could well explain the tight linkage between arbuscular mycorrhizal colonization and root diameter across species and biomes, which provides insight into the collaboration gradient between plant roots and mycorrhizal fungi that dominates the root economics space.
The Association between Mothers’ Smartphone Dependency and Preschoolers’ Problem Behavior and Emotional Intelligence

This study aimed to identify the level of mothers’ smartphone dependency and determine its correlation with preschoolers’ problem behavior and emotional intelligence. From 1 November to 30 December 2020, 141 mothers of preschool children (aged three to six years) were recruited to complete questionnaires that assessed their smartphone dependency and their child’s problem behavior and emotional intelligence. The result revealed that the younger the mother and the higher the perception of boredom in daily living, the higher was the level of her smartphone dependency. Maternal smartphone dependency was also significantly correlated with the aggression, oppositional, and emotional instability subscales of the tool assessing children’s problem behavior. To prevent problem behaviors among preschoolers, strategies to reduce mothers’ smartphone dependency are needed.
Modeling the actuation of curved ionic polymer metal composites

An ionic polymer metal composites (IPMC) is a soft actuator that consists of an ionomer membrane, neutralized by mobile counterions and plated by metal electrodes. Despite their early promise in robotics, medical devices, and microsystem technologies, widespread application of IPMC actuators is far from being reached. Recent advancements in additive manufacturing technologies have the potential to expand the reach of IPMCs by affording the realization of complex, design-specific geometries that were impossible to attain with standard manufacturing techniques. For this potential to be attained, it is critical to establish physically-based models that could inform 3D printing, beyond the flat, thin, non-tapered geometries that have been the object of investigation for almost three decades. Here, we bridge this gap by presenting an analytical framework to study actuation of a double-clamped IPMC arch under an applied voltage. We adopt a thermodynamically the consistent continuum model to describe the coupled electrochemo-mechanical phenomena taking place within the IPMC. We establish an analytical solution for the electrochemistry using the method of matched asymptotic expansions, which is, in turn, utilized to compute osmotic pressure and Maxwell stress. The mechanical response of the IPMC arch is modeled as a plane strain problem with an induced state of eigenstress, which is solved with the use of a smooth Airy function. The accuracy of our analytical solution is validated through finite element simulations. Through a parametric analysis, we investigate the effect of curvature on the deformation and the reaction forces exerted by the clamps. The proposed analytical framework offers new insight into the response of curved IPMCs, in which progress on 3D printing should be grounded.
Inhibition of SGLT2 Preserves Function and Promotes Proliferation of Human Islets Cells In Vivo in Diabetic Mice

Dapagliflozin is a sodium-glucose co-transporter 2 (SGLT2) inhibitor used for the treatment of diabetes. This study examines the effects of dapagliflozin on human islets, focusing on alpha and beta cell composition in relation to function in vivo, following treatment of xeno-transplanted diabetic mice. Mouse beta cells were ablated by alloxan, and dapagliflozin was provided in the drinking water while controls received tap water. Body weight, food and water intake, plasma glucose, and human C-peptide levels were monitored, and intravenous arginine/glucose tolerance tests (IVarg GTT) were performed to evaluate islet function. The grafted human islets were isolated at termination and stained for insulin, glucagon, Ki67, caspase 3, and PDX-1 immunoreactivity in dual and triple combinations. In addition, human islets were treated in vitro with dapagliflozin at different glucose concentrations, followed by insulin and glucagon secretion measurements. SGLT2 inhibition increased the animal survival rate and reduced plasma glucose, accompanied by sustained human C-peptide levels and improved islet response to glucose/arginine. SGLT2 inhibition increased both alpha and beta cell proliferation (Ki67+glucagon+ and Ki67+insulin+) while apoptosis was reduced (caspase3+glucagon+ and caspase3+insulin+). Alpha cells were fewer following inhibition of SGLT2 with increased glucagon/PDX-1 double-positive cells, a marker of alpha to beta cell transdifferentiation. In vitro treatment of human islets with dapagliflozin had no apparent impact on islet function. In summary, SGLT2 inhibition supported human islet function in vivo in the hyperglycemic milieu and potentially promoted alpha to beta cell transdifferentiation, most likely through an indirect mechanism.
The residual circulation profile of the Bons Sinais Estuary in central Mozambique - potential implications for larval dispersal and fisheries

The residual circulation in estuaries determines the net exchange of water, heat, salt, fine sediments and drifting biological organisms between estuarine and nearshore marine waters. The Bons Sinais Estuary in central Mozambique is ~ 30 km long with the city of Quelimane and an industrial port on the northern bank of its upper reaches. To investigate residual circulation in the estuary, seasonal (wet, dry and transition season) CTD profiling data were collected at 11 fixed stations between the upper estuary and mouth, and vertical current profiles were measured over a full tidal cycle at a fixed mid-estuary station. Strong longitudinal gradients in salinity and density indicated that the estuary was river-dominated during the wet season and tide-dominated during the dry season, but the water column remained partially mixed. Tidally averaged vertical profiles from the mid-estuary station revealed: uniform vertical temperatures, warmest during the wet season; depth-stratified salinity during the wet season, but uniform profiles during the dry and transition seasons with highest salinity during the dry season when the density was also highest. The density was uniform and somewhat lower in the transition season, and in the wet season the density was even lower, but stratified. The vertical velocity profile showed a classical two-layer circulation model, with downstream flow intensifying at the surface, and upstream flow at the bottom, during the wet and transitory seasons, when freshwater discharges into the estuary. The flow velocities obtained from a calibrated Hansen and Rattray model fitted the observed data well, confirming that a simplified modelling approach is adequate to describe the residual flow of the Bons Sinais Estuary. The residual circulation model provides insights useful for fisheries research and predicting the spread of water-borne pollutants in the estuary.