Miracle-dz/UniFAR-FTUs huggingface dataset:
SOIL CARBON RELATIONSHIPS WITH TERRAIN ATTRIBUTES, ELECTRICAL CONDUCTIVITY, AND A SOIL SURVEY IN A COASTAL PLAIN LANDSCAPE

Soil organic carbon (SOC) estimation at the landscape level is critical for assessing impacts of management practices on C sequestration and soil quality. We determined relationships between SOC, terrain attributes, field scale soil electrical conductivity (EC), soil texture and soil survey map units in a 9 ha coastal plain field (Aquic and Typic Paleudults) historically managed by conventional means. The site was composite sampled for SOC (0-30 cm) within 18.3 × 8.5-m grids (n = 496), and two data sets were created from the original data. Ordinary kriging, co-kriging, regression kriging and multiple regression were used to develop SOC surfaces that were validated with an independent data set (n = 24) using the mean square error (MSE). The SOC was relatively low (26.13 Mg ha−1) and only moderately variable (CV = 21%), and showed high spatial dependence. Interpolation techniques produced similar SOC maps but the best predictor was ordinary kriging (MSE = 9.11 Mg2 ha−2) while regression was the worst (MSE = 20.65 Mg2 ha−2). Factor analysis indicated that the first three factors explained 57% of field variability; compound topographic index (CTI), slope, EC and soil textural fractions dominated these components. Elevation, slope, CTI, silt content and EC explained up to 50% of the SOC variability (P ≤ 0.01) suggesting that topography and historical erosion played a significant role in SOC distribution. Field subdivision into soil map units or k-mean clusters similarly decreased SOC variance (about 30%). The study suggests that terrain attributes and EC surveys can be used to differentiate zones of variable SOC content, which may be used as bench marks to evaluate field-level impact of management practices on C sequestration.
Adsorption of ammonium from aqueous solutions on environmentally friendly barbecue bamboo charcoal: Characteristics and kinetic and thermodynamic studies

Environmentally friendly barbecue bamboo charcoal (EFBBC), an inexpensive biosorbent, was successfully used to remove ammonium (NH4+) from aqueous solutions. The effects of contact time, pH, initial concentration, average particle size, biosorbent dosage, and temperature on NH4+ removal were investigated. Biosorption conditions were as follows: contact time, 80 min; pH, 6.0–10; initial concentration, 60 mg/L; optimum particle size, 0.075–0.180 mm; biosorbent dosage, 4.0 g/L; temperature, 30°C. To investigate adsorption mechanisms, pseudo‐first‐order, pseudo‐second‐order, Elovich, and intraparticle diffusion models were fit to experimental results. Results indicated that kinetic data for NH4+ adsorption were better described by the pseudo‐second‐order model. Also, the Langmuir, Freundlich, and Temkin models were employed to analyze equilibrium data. Results indicated that the Freundlich isotherm best represented the data for NH4+ adsorption. Changes in the standard free energy, enthalpy, and entropy were also evaluated: the reaction was spontaneous and endothermic. The EFBBC surface was analyzed by X‐ray diffraction, scanning electron microscopy, X‐ray energy dispersive spectrometry, and Fourier transform infrared spectrometry. From the results, it was proposed that the adsorption behavior of NH4+ on the EFBBC surface depends on the internal structure and chemical composition, and the results highlight the need to understand specific surface characteristics. © 2014 American Institute of Chemical Engineers Environ Prog, 34: 655–662, 2015
Effect of grafting on the tomato's yield, quality and main fruit components in spring forcing

Soil-born diseases often cause problems in vegetable forcing; this is because growers use greenhouses for the longest time possible. During the years several solutions have been found to displace chemical control. Grafting is a possible chemical free solution that shows several changes in plants thanks to the vigorous rootstock. In this study we examined the effect of grafting on the yield and fruit characteristics in tomato cultivar. Lemance F1was used as scion and Beaufort as rootstock. Earliness, total yield, fruit weight and number of each graft combination were recorded. Moreover, the main fruit components such as Brix°, carbohydrate, acid and the carbohydrate/acid ratio were analysed. We harvested more yield from the grafted plants. The increase of yield was mainly caused by higher average fruit weight. Brix° and carbohydrate content were lower in the fruits on grafted plants than on ungrafted ones but there was no significant difference in acid content.
Range-Visual-Inertial Odometry: Scale Observability Without Excitation

Traveling at constant velocity is the most efficient trajectory for most robotics applications. Unfortunately without accelerometer excitation, monocular Visual-Inertial Odometry (VIO) cannot observe scale and suffers severe error drift. This was the main motivation for incorporating a 1D laser range finder in the navigation system for NASA's Ingenuity Mars Helicopter. However, Ingenuity's simplified approach was limited to flat terrains. The current letter introduces a novel range measurement update model based on using facet constraints. The resulting range-VIO approach is no longer limited to flat scenes, but extends to any arbitrary structure for generic robotic applications. An important theoretical result shows that scale is no longer in the right nullspace of the observability matrix for zero or constant acceleration motion. In practical terms, this means that scale becomes observable under constant-velocity motion, which enables simple and robust autonomous operations over arbitrary terrain. Due to the small range finder footprint, range-VIO retains the minimal size, weight, and power attributes of VIO, with similar runtime. The benefits are evaluated on real flight data representative of common aerial robotics scenarios. Robustness is demonstrated using indoor stress data and full-state ground truth. We release our software framework, called xVIO, as open source.
Persuasive Technology for Mental Health: One Step Closer to (Mental Health Care) Equality?

<bold>The increasing trend</bold> of mental health issues, especially among younger people, is not a new phenomenon. World organizations, leaders, and decision-makers are recognizing its devastating effect, resulting in mental health well-being appearing in goal 3 of the 17 UN sustainable development goals (SDGs) <xref ref-type="bibr" rid="ref1">[1]</xref>. Among mental health issues, stress, anxiety, and depression (SAD) seem to be on the forefront—reaching 74% for disabling stress <xref ref-type="bibr" rid="ref2">[2]</xref>, 28% for anxiety disorder <xref ref-type="bibr" rid="ref3">[3]</xref>, and 48% for depression <xref ref-type="bibr" rid="ref4">[4]</xref>, in some groups. What is more, between 76% and 85% of people in low- and middle-income countries receive no treatment for their disorder <xref ref-type="bibr" rid="ref5">[5]</xref>, whereas the treatment coverage for, for example, depression is only 33% in high-income countries <xref ref-type="bibr" rid="ref6">[6]</xref>. Mental health issues have large, multifaceted effects on the patient, on their immediate surroundings (family or caretakers), and on the wider society <xref ref-type="bibr" rid="ref7">[7]</xref>. Individuals face decreased quality of life, poor educational outcomes, lowered productivity and potential poverty, social problems, abuse vulnerabilities, and additional health problems. Caretakers face increased emotional and physical challenges as well as decreased household income and increased financial costs. Society faces the loss of several gross domestic product (GDP) percentage points and billions of dollars annually, alongside with exacerbating public health issues and corrosion of social cohesion. All these lead to an increasingly stronger positive reinforcement loop—SAD increasingly perpetuates SAD. Too often, mental health issues directly result in the worst possible outcome and loss of human life, as many countries struggle with a high suicide rate <xref ref-type="bibr" rid="ref8">[8]</xref>. It has been recognized that the reasons for increasing SAD include a severe lack of mental health professionals and regulations <xref ref-type="bibr" rid="ref9">[9]</xref> as well as unequal access to mental health care <xref ref-type="bibr" rid="ref10">[10]</xref>. These factors make the field ripe for technological and other scientific therapy-based interventions, as individuals with mental health issues prefer therapies to medication <xref ref-type="bibr" rid="ref11">[11]</xref>.
Effects of Al Concentration on Structural and Optical Properties of Al-doped ZnO Thin Films

Aluminium (Al)-doped zinc oxide (AZO) thin films with different Al concentrations were prepared by the solgel spin-coating method. Optical parameters such as the optical band gap, absorption coefficient, refractive index, dispersion parameter, and optical conductivity were studied in order to investigate the effects of the Al concentration on the optical properties of AZO thin films. The dispersion energy, single-oscillator energy, average oscillator wavelength, average oscillator strength, and refractive index at infinite wavelength of the AZO thin films were found to be affected by Al incorporation. The optical conductivity of the AZO thin films also increases with increasing photon energy.
Any Way You Look at It: Semantic Crossview Localization and Mapping With LiDAR

Currently, GPS is by far the most popular global localization method. However, it is not always reliable or accurate in all environments. SLAM methods enable local state estimation but provide no means of registering the local map to a global one, which can be important for inter-robot collaboration or human interaction. In this work, we present a real-time method for utilizing semantics to globally localize a robot using only egocentric 3D semantically labelled LiDAR and IMU as well as top-down RGB images obtained from satellites or aerial robots. Additionally, as it runs, our method builds a globally registered, semantic map of the environment. We validate our method on KITTI as well as our own challenging datasets, and show better than 10 m accuracy, a high degree of robustness, and the ability to estimate the scale of a top-down map on the fly if it is initially unknown.
Parameter Estimation of Software Reliability Growth Models: A Comparison Between Grey Wolf Optimizer and Improved Grey Wolf Optimizer

In modern era, industries demand for innovative and reliable software solutions. To maintain the reliability level of softwares various software reliability growth models were proposed in last four decades. These models performance relies on parameter estimation approaches utilized to find the optimum values of their unknown model parameters. But, developing an approach that provides the perfect optimum parameter for software reliability growth models (SRGMs) has been the issue of concern within the research community over the decades. This paper adopted the Improved Grey Wolf Optimizer (IGWO) for parameter estimation and compares its accuracy with existing approach Grey Wolf Optimizer (GWO) in estimating the optimum parameters for software reliability growth models. GWO imitates the social leadership pyramid and the hunting methods adopted by grey wolves; IGWO was later proposed to resolve the deficiencies observed in GWO for improved performance. Seven real world failure datasets have been utilized to measure and evaluate the performance of the proposed approach against the existing approach. The results indicate that proposed approach (IGWO) outperform the existing one (GWO).
Quantum Bloom Filter and Its Applications

A quantum Bloom filter is a spatially more efficient data structure which is used to represent a set of <inline-formula><tex-math notation="LaTeX">$n$</tex-math></inline-formula> elements by using <inline-formula><tex-math notation="LaTeX">$O({{\rm{log}}nk})$</tex-math></inline-formula> qubits. In this article, we define and design a quantum Bloom filter and its corresponding algorithms. Due to the reversibility of quantum operators, it can not only add a new element to a quantum Bloom filter but also delete an existing element from the quantum Bloom filter. Furthermore, we employ the quantum Bloom filter to solve two private issues, i.e., oblivious set-member decision and multiparty private set intersection cardinality. The results show that the quantum Bloom filter has inherent advantages in privacy-preserving applications concerning set operations.
Enabling Highly Efficient Capsule Networks Processing Through Software-Hardware Co-Design

As the demand for the image processing increases, the image features become increasingly complicated. Although the Convolutional Neural Network (CNN) have been widely adopted for the imaging processing tasks, it has been found easily misled due to the massive usage of pooling operations. A novel neural network structure called Capsule Networks (CapsNet) is proposed to address the CNN challenge and essentially enhance the learning ability for the image segmentation and object detection. Since the CapsNet contains the high volume of the matrix execution, it has been generally accelerated on modern GPU platforms with the highly optimized deep-learning library. However, the routing procedure of CapsNet introduces the special program and execution features,including massive unshareable intermediate variables and intensive synchronizations, causing inefficient CapsNet execution on modern GPU. To address these challenges, we propose the software-hardware co-designed optimizations, SH-CapsNet, which includes the software-level optimizations named <italic>S-CapsNet</italic> and a hybrid computing architecture design named <italic>PIM-CapsNet</italic>. In software-level, S-CapsNet reduces the computation and memory accesses by exploiting the computational redundancy and data similarity of the routing procedure. In hardware-level, the PIM-CapsNet leverages the processing-in-memory capability of today's 3D stacked memory to conduct the off-chip in-memory acceleration solution for the routing procedure, while pipelining with the GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet. Evaluation results demonstrate that either our software or hardware optimizations can significantly improve the CapsNet execution efficiency. Together, our co-design can achieve greatly improvement on both performance (<inline-formula><tex-math notation="LaTeX">$3.41\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>3</mml:mn><mml:mo>.</mml:mo><mml:mn>41</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="zhang-ieq1-3056929.gif"/></alternatives></inline-formula>) and energy savings (68.72 percent) for CapsNet inference, with negligible accuracy loss.
Driver Activity Recognition Through Deep Learning

Distracted drivers contribute to a significant proportion of road accidents all over the world. Activities such as texting on cellphones, eating, and reaching for something at the back of the vehicle lead to drivers not paying attention to the road and may result in traffic accidents. This paper proposes the use of residual neural networks (ResNet) with spatio-temporal three-dimensional (3D) kernels to perform distracted driver behaviour recognition. Recently, convolutional neural networks (CNN) with 3D kernels have become an effective tool for action recognition. The 3D kernels extract spatio-temporal features from videos to perform tasks such as human activity recognition. ResNets are a variant of CNNs that utilise skip-connections to realise the training of very deep networks. The large number of parameters in 3D ResNets exposes the possibility of overfitting. Using a large video dataset is thus essential, as it avoids the occurrence of overfitting. This paper examines how different datasets and network depths influence the performance of 3D ResNets. The results are overwhelmingly positive. The findings present a significant positive correlation between the accuracy of a model and the network depth. Furthermore, the quality of the dataset greatly determines the model’s ability to generalise effectively.
Baseline Testing of a Filter Press and Solid-Bowl Centrifuge for Dewatering Coal Thickener Underflow Slurry

Laboratory testing was conducted to evaluate the performance of a plate-and-frame filter press and solid-bowl centrifuge for dewatering a bituminous coal refuse slurry. Filter press testing was conducted using a pilot-scale Technicas Hidraulicas (TH) filter press, which contains a single set of plates. The unit is also equipped with an air-blow system to remove additional moisture from the filter cake voids. Testing was conducted to evaluate the effects of filter time on filtrate flow and solids content, cake moisture, solids recovery, and unit capacity. Product moistures ranged from 27.3% to 29.6%. Filtrate solids contents were less than 0.6% with solids recoveries over 98%. The unit capacities ranged from 73 kg/hr/m2 to 131 kg/hr/m2, with the lower values corresponding to the lower cake moistures. Centrifuge testing was conducted using a Sharples P660 solid-bowl centrifuge. Testing was conducted at several slurry feed rates at one dam setting with another test at a second dam setting. Product moistures ranged from 24.2% to 36.4%. Centrate solids ranged from 0.5% to 11.0% with solids recoveries from 65.2% to 98.4%. The solids feed rates ranged from 89 kg/hr to 338 kg/hr. Lower product moistures were associated with higher solids feed rates but at lower solids recoveries.
A Facial Expression Recognition Method Based on a Multibranch Cross-Connection Convolutional Neural Network

This paper proposes a new method based on a multiple branch cross-connected convolutional neural network (MBCC-CNN) for facial expression recognition. Compared with traditional machine learning methods, the proposed method can extract image features more effectively. In addition, in contrast to single-structure convolutional neural networks, the MBCC-CNN model is constructed based on the residual connection, Network in Network, and tree structure approaches together. It also adds a shortcut cross connection for the summation of the convolution output layer, which makes the data flow between networks more smooth, improves the feature extraction ability of each receptive field. The proposed method can fuse the features of each branches more effectively, which solves the problem of insufficient feature extraction of each branches and increases the recognition performance. The experimental results based on the Fer2013, CK+, FER+ and RAF data sets show that the recognition rates of the proposed MBCC-CNN method are 71.52%, 98.48%, 88.10% and 87.34%, respectively. Compared with some most recently work, the proposed method can provide better facial expression recognition performance and has good robustness. The python code can be download from https://github.com/scp19801980/Facial-expression-recognition.
Touch Modality Classification Using Recurrent Neural Networks

Recurrent Neural Networks (RNNs) are mainly designed to deal with sequence prediction problems and they show their effectiveness in processing data originally represented as time series. This paper investigates the time series characteristics of RNNs to classify touch modalities represented as spatio temporal 3D tensor data. Different approaches are followed in order to propose efficient RNN models aimed at tactile data classification. The main idea is to capture long-term dependence from data that can be used to deal with long sequences represented by employing Gated Recurrent Unit (GRU) and Long Short-Term Memory (LSTM) architectures. Moreover, a case specific approach to dataset organization of the 3D tensor data is presented. The target is to provide efficient hardware-friendly touch modality classification approaches suitable for embedded applications. To this end, the proposed work achieves effective performance in terms of hardware complexity by reducing the FLOPS by 99.98% and the memory storage by 98.34%, with respect to the state-of-art solutions on the same benchmark dataset. This directly affects the time latency and energy consumption of the embedded hardware. Besides, the implemented models shows a classification accuracy higher than those state-of-art solutions. Results demonstrate that the proposed computing architecture is scalable showing acceptable complexity when the system is scaled up in terms of input matrix size and number of classes to be recognized.
Periocular recognition via effective textural descriptor

The region surrounding human eye, termed as periocular region, can act as a potential biometric trait when it comes to secure authentication of human subjects. Periocular region, being larger than the iris region and less vulnerable than the entire face region, has been an emerging biometric trait since last decade. In view of this, the present work is an attempt to develop more secure periocular recognition through the application of wavelets and a texture pattern with more robust thresholding mechanism. The wavelets are helping in the multiresolution analysis of periocular images. Whereas the texture pattern facilitates the representation of periocular region in form of histogram feature vectors. Experiments performed with a challenging publicly available database corroborate the premise of the suggested methodology. The performance measured using popular metrics like equal error rate, genuine acceptance rate and decidability index, validates the hypothesis of the suggested method. Comparison with existing approaches establishes the outperforming nature of the proposed approach.
Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows

Many IoT applications and services such as smart parking and smart traffic control contain a network of different analytical components, which are composed in the form of a workflow to make better decisions. These workflows are also known as stream workflows. The focus of existing research works is on the streaming operator graph, which differs from stream workflow application as it involves heterogeneity, multiple data sources and multiple outputs. Considering the complexity and dynamism of stream workflow, meeting real-time data analysis requirements at deployment time is not the whole story as the velocity of data changes over time. This change is the most dynamic form of stream workflow that occurs frequently during the execution of this application. In this article, we propose a new dynamic scheduling technique that manages cloud resources over time to handle data velocity changes in stream workflow while maintaining user-defined real-time data analysis requirements and minimising execution cost. The efficiency of the proposed technique is evaluated, and experimental results showed that this technique outperformed its competitors and is close to the lower bound.
Identifying Factors Affecting the Immersion and Concentration of Players in Serious Games

Serious games are a type of digital games designed for a purpose other than pure entertainment. These games are used in various fields such as defense, education, scientific exploration, health care, emergency management, city planning, engineering, and politics. Given that some of these games are story-driven, it is necessary to accurately identify the factors that affect the concentration and immersion of players. In this paper, we examine the effect of design of challenges, gameplay, aesthetic factors, the player’s previous attitude towards the game, and enjoyment on the player’s immersion. The study is based on structural analysis using data collection through a questionnaire. Through path analysis and regression, we identify important factors and then prioritize them. Our study also shows that game credibility plays an important role as a mediating variable on concentration and immersion. The results of this research have important implications for serious game designers. The test results show that our proposed model has good reliability, validity and correlation.