Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid LSTM-based Neural Network for Satellite-less UAV Navigation

This work proposes a new algorithm to address the problem of unmanned aerial vehicle (UAV) navigation in satellite-less environments by combining machine learning with existent model-based methods. The proposed network model is trained by using the predictions of two estimators, one based on a Generalized trust region sub-problem (GTRS) framework and the other one founded on a Weighted Least Squares (WLS) principle. The solutions of these two estimators are then fed to two Long Short-Term Memories (LSTMs) to create models whose predictions are averaged to achieve the final prediction output. Our numerical results show favorable performance of the new network, obtaining improved accuracy and higher robustness to noise when compared with the individual counterparts of the network used in the training phase. Consequently, the proposed method offers safer an more reliable navigation of the UAV in satellite-less environments.
A Systematic Review of Effective Hardware and Software Factors Affecting High-Throughput Plant Phenotyping

Plant phenotyping studies the complex characteristics of plants, with the aim of evaluating and assessing their condition and finding better exemplars. Recently, a new branch emerged in the phenotyping field, namely, high-throughput phenotyping (HTP). Specifically, HTP exploits modern data sampling techniques to gather a high amount of data that can be used to improve the effectiveness of phenotyping. Hence, HTP combines the knowledge derived from the phenotyping domain with computer science, engineering, and data analysis techniques. In this scenario, machine learning (ML) and deep learning (DL) algorithms have been successfully integrated with noninvasive imaging techniques, playing a key role in automation, standardization, and quantitative data analysis. This study aims to systematically review two main areas of interest for HTP: hardware and software. For each of these areas, two influential factors were identified: for hardware, platforms and sensing equipment were analyzed; for software, the focus was on algorithms and new trends. The study was conducted following the PRISMA protocol, which allowed the refinement of the research on a wide selection of papers by extracting a meaningful dataset of 32 articles of interest. The analysis highlighted the diffusion of ground platforms, which were used in about 47% of reviewed methods, and RGB sensors, mainly due to their competitive costs, high compatibility, and versatility. Furthermore, DL-based algorithms accounted for the larger share (about 69%) of reviewed approaches, mainly due to their effectiveness and the focus posed by the scientific community over the last few years. Future research will focus on improving DL models to better handle hardware-generated data. The final aim is to create integrated, user-friendly, and scalable tools that can be directly deployed and used on the field to improve the overall crop yield.
Power and Area Efficient Hybrid Memristor-CMOS based 2’s Complement FSM for High-Performance Computing System

The fourth fundamental circuit component that is frequently employed to design nanoscale electronic devices is memristor. The compact size, non-volatility, and low power of memristor, combined with their integration suitability with conventional CMOS circuitry, has become a significant advantage in the design of the forthcoming generation of chips. 2’s complement representation is one of the signed binary number representations that every computer utilizes to perform signed arithmetic operations due to its simplicity and effectiveness. Therefore, 2’s complement circuit is a part of the high-performance arithmetic unit of a computer system. This paper presents power and area-efficient design for memristor-based 2’s complement FSM. The proposed circuit is designed in Cadence Virtuoso EDA toolset and simulations have been done using Spectre simulator on 180nm CMOS technology at 1.8V power supply. A comparative analysis of the proposed design and conventional CMOS-based design is given in the paper in terms of power consumption and transistor count. The comparison findings demonstrate 50% area savings while power savings of 18.15 %are obtained. The proposed circuit has a simple structure and requires 14 transistors and 8 memristors for its implementation.
Annals of Pediatric Cardiology: A new look and a new outlook

The new version of the website is designed to be more user-friendly, with the ability to publish and access multimedia content alongside journal articles. The online experience is enriched with article content available in a rich full-text format, along with PDF downloads and a variety of personalization options. The responsive mobile version of the website, made for current-generation smartphones, is easily optimizable based on the users’ device preferences. We are using Wolters–Kluwer’s e-Journals new platform (eJP). Using the eJP’s intuitive editorial tools, we now have the capabilities to build additional content around the articles. We plan to have custom content collections, blogs, podcasts, and standalone-video libraries around the key articles. The new site can be accessed at https://journals.lww. com/ AOPC/Pages/default.aspx. We sincerely hope that the readers like the new website along with its enriched contents.
Beyond the Cox Hazard Ratio: A Targeted Learning Approach to Survival Analysis in a Cardiovascular Outcome Trial Application

Abstract The Hazard Ratio (HR) is a well-established treatment effect measure in randomized trials involving right-censored time-to-events, and the Cardiovascular Outcome Trials (CVOTs) conducted since the FDA’s 2008 guidance have indeed largely evaluated excess risk by estimating a Cox HR. On the other hand, the limitations of the Cox model and of the HR as a causal estimand are well known, and the FDA’s updated 2020 CVOT guidance invites us to reassess this default approach to survival analyses. We highlight the shortcomings of Cox HR-based analyses and present an alternative following the causal roadmap—moving in a principled way from a counterfactual causal question to identifying a statistical estimand, and finally to targeted estimation in a large statistical model. We show in simulations the robustness of Targeted Maximum Likelihood Estimation (TMLE) to informative censoring and model misspecification and demonstrate a targeted learning analogue of the original Cox HR-based analysis of the Liraglutide Effect and Action in Diabetes: Evaluation of Cardiovascular Outcome Results (LEADER) trial. We discuss the potential reliability, interpretability, and efficiency gains to be had by updating our survival methods to incorporate the recent decades of advancements in formal causal frameworks and efficient nonparametricestimation.
A novel method for identifying damage in transverse joints of arch dams from seismic responses based on the feature of local dynamic continuity interruption

Without the difficulty of applying particular excitations on arch dams, a method using the correlation coefficients of time-scale representations (CCTSRs) of seismic responses to identify the damage in transverse joints is proposed. The identification of damage using the CCTSR method is characterized by comparing the time-scale representations (TSRs) of paired seismic accelerations measured from two neighboring points crossing the transverse joints horizontally. With the CCTSR method, the TSRs of measured seismic accelerations are produced with the continuous wavelet transform; the comparison of the TSRs is carried out by calculating the correlation coefficients. The correlation coefficients of the paired TSRs are combined with the defined damage intervals to evaluate the positions and severities of the damage. The method’s feasibility is numerically verified via the identification of the damage in transverse joints by discussing the effects of the wavelet functions, seismic excitations, and noise resistance. The method’s effectiveness is experimentally validated via the detection of the damage in transverse joints of a scaled arch dam model subjected to artificial seismic loads. The proposed CCTSR method can localize the positions of various damage scenarios and quantify the damage severities caused by progressive seismic loads.
A Convolutional Neural Network Based Potato Leaf Diseases Detection Using Sequential Model

One of Bangladesh’s primary agricultural products is the potato. In recent decades, Bangladesh has seen a surge in the popularity of potato farms. Nonetheless, farmer’s expenses in potato production are rising as a result of a number of illnesses. Nonetheless, the high cost of potato production is mostly attributable to a number of illnesses that are affecting the crop. Which is wreaking havoc on the farmer’s schedule. In order to modernize the potato industry and speed up disease diagnosis, automation has been implemented. In spite of the claims to the contrary, potato leaf disease is a serious problem that can severely reduce crop yields. The leaves of diseased potato plants will show symptoms of early blight, Septoria blight, late blight, and other diseases. If such outbreaks are discovered at the initial level and enough intervention is done, the farmer will not be at risk of incurring significant economic losses. Based on the results of this study, a new model is presented for accurately identifying and detecting illnesses in potato leaf stands using image processing. While there are several methods that may be utilized in machine learning, the Convolutional Neural Network (CNN) model is what’s being employed here to identify the disease in potato leaf photos. This work implements a CNN based sequential model to predict the disease of potato leaves. This research achieved 94.2% model accuracy on this model. The presented model was tested on both typical and disordered potato leaves in an effort to distinguish between the two. Next, the algorithm is applied to the images, and the potato tree’s leaf is classified as either healthy or unhealthy.
Effects of BLG polymorphism and dietary supplementation with carob pulp on ewe milk traits and fatty acid composition

Abstract The current study examined the effect on milk traits and composition of the replacement of barley grain with carob pulp as a supplement for grazing ewes with different genotypes at the β-lactoglobulin (BLG) locus. Forty-six Valle del Belìce lactating ewes were chosen for the feeding trial and split into control and carob groups based on the BLG p.Tyr38His polymorphism. The carob group received a supplement of 250 g/d of carob pulp, whereas the control group received 250 g/g of a barley whole grain-based concentrate. There were no milk yield or gross composition variations related to the BLG genotype. Nevertheless, milk fatty acid composition was influenced by the BLG polymorphism. Compared to the AB genotype, milk from BB sheep had higher concentrations of linoleic, linolenic acids, and total polyunsaturated fatty acids. The addition of carob pulp did not significantly modify fat and protein-corrected milk, whereas reduced protein and urea concentration, increased the fat content but worsened the milk quality in terms of fatty acid composition, increasing saturated and medium chain fatty acids. The genotype × feed interaction had no appreciable effect on milk composition or quality. HIGHLIGHTS Diet, genotype, and their interaction affect the milk traits and quality. Carob pulp reduced protein, urea and worsened fat quality. The genotype positively influenced the milk fat quality.
Pentoxifylline/Chitosan Films on Wound Healing: In Vitro/In Vivo Evaluation

This study aimed to develop films of chitosan (CSF) associated with pentoxifylline (PTX) for healing cutaneous wounds. These films were prepared at two concentrations, F1 (2.0 mg/mL) and F2 (4.0 mg/mL), and the interactions between the materials, structural characteristics, in vitro release, and morphometric aspects of skin wounds in vivo were evaluated. The formation of the CSF film with acetic acid modifies the polymeric structure, and the PTX demonstrates interaction with the CSF, in a semi-crystalline structure, for all concentrations. The release for all films was proportional to the concentration, with two phases: a fast one of ≤2 h and a slow one of >2 h, releasing 82.72 and 88.46% of the drug after 72 h, being governed by the Fickian diffusion mechanism. The wounds of the mice demonstrate a reduction of up to 60% in the area on day 2 for F2 when compared to CSF, F1, and positive control, and this characteristic of faster healing speed for F2 continues until the ninth day with wound reduction of 85%, 82%, and 90% for CSF, F1, and F2, respectively. Therefore, the combination of CSF and PTX is effective in their formation and incorporation, demonstrating that a higher concentration of PTX accelerates skin-wound reduction.
Unconstrained Facial Expression Recognition With No-Reference De-Elements Learning

Most unconstrained facial expression recognition (FER) methods take original facial images as inputs to learn discriminative features by well-designed loss functions, which cannot reflect important visual information in faces. Although existing methods have explored the visual information of constrained facial expressions, there is no explicit modeling of what visual information is important for unconstrained FER. To find out valuable information of unconstrained facial expressions, we pose a new problem of no-reference de-elements learning: we decompose any unconstrained facial image into the facial expression element and a neutral face without the reference of corresponding neutral faces. Importantly, the element provides visualization results to understand important facial expression information and improves the discriminative power of features. Moreover, we propose a simple yet effective De-Elements Network (DENet) to learn the element and introduce appropriate constraints to overcome no ground truth of corresponding neutral faces during the de-elements learning. We extensively evaluate the proposed method on in-the-wild FER datasets including RAF-DB, AffectNet, SFEW and FERPlus. The comparable results show that our method is promising to improve classification performance and achieves equivalent performance compared with state-of-the-art methods. Also, we demonstrate the strong generalization performance on realistic occlusion and pose variation datasets and the cross-dataset evaluation.
Heart Rate Measurement on Smartphone using Cardiography: A Scoping Review

Background: Smartphones nowadays are equipped with the necessary hardware that can be used to provide consumers with mobile health monitoring and healthcare management. Apps that assess Heart Rate (HR) utilizing the built-in hardware of a smartphone are among these intelligent technologies. This study sought to identify previous research on employing smartphones to estimate vital signs using cardiography procedures.Methods: For this study, a scoping review was completed. Title and abstract were used to initially screen 175 papers and articles, then the eligibility was determined based on the notion that the papers and articles dealt with cardiography and the use of smartphones to estimate pulse.Results: A total of 35 papers and articles were included after screening and eligibility checks, as well as backward and forward literature searches, of which 9 items were listed and characterized as being of interest to smartphone-implemented cardiography research.Conclusions: Although cardiography and its practice in heartbeat measurement have been extensively studied, there is surprisingly little research that looks at the same techniques applied to smartphones from a Computer Science perspective. This paper seeks to serve as a starting point for additional research on the subject of smartphone-based cardiography as well as encourage further in-depth research. Paper Classification: General review.
Spatial Autocorrelation Incorporated Machine Learning Model for Geotechnical Subsurface Modeling

Machine learning models for spatial prediction have been applied in various types of research. However, spatial relation has not been fully considered in modeling, since the Cartesian coordinates of the observed points are directly employed as the location information for machine learning features. This study presents a machine learning modeling process which incorporates spatial autocorrelation for geotechnical subsurface modeling. A new set of features called the Euclidean distance field (EDF) was generated based on the distance between the query points and the observed boreholes in order to incorporate spatial autocorrelation into the machine learning model. Principal component analysis (PCA) was performed to reduce the increasing dimensionality of the dataset caused by the EDF features. Optimized machine learning models based on several popular algorithms (Support Vector Machine, Gaussian Process Regression, Artificial Neural Network, and k-Nearest Neighbor) were employed for predicting several geotechnical information as the targets. The results showed that the optimized machine learning models constructed with the EDF modeling approach generate a slightly lower Root Mean Square Error (RMSE) score compared to the model with the direct XY coordinate approach by 0.041, 0.046, 1.302, and 1.561 for ground surface elevation, groundwater level, SPT-N value, and percent finer than 0.075 mm sieve, respectively. Both modeling approaches performed well for USCS-based soil classification with the EDF model having slightly improved classification accuracy by 0.72%. Furthermore, the model can perform balance multiclass classification as indicated by the >95% precision, recall, f1-score, and balanced accuracy score. These results indicate that spatial autocorrelation has a noticeable effect. Hence, it needs to be considered to improve the overall performance of spatial machine learning modeling. Comparison of geotechnical subsurface predictions generated based on different machine learning algorithms showed that the selection of the best-performing model based only on the lowest prediction error is not appropriate for spatial prediction modeling. Therefore, thorough analysis of the predicted data by visualization is necessary in the selection process for spatial prediction modeling.
A new high-pressure high-temperature deformation apparatus to study the brittle to ductile transition in rocks.

Understanding the micro-mechanisms underlying the localized-ductile transition (LDT) as well as the brittle-plastic transition (BPT) has become crucial for our wider understanding of crustal processes and seismicity. Given how difficult in situ observations of these transitions are to perform, laboratory experiments might be our only way to investigate the processes active under these conditions (high T and high P). Here, we present Triaxial AppaRatus for GEoThermal energy, a new gas-based triaxial apparatus located at EPFL in Switzerland that was specifically designed to operate under conditions where both the LDT and BPT can occur in geomaterials. We show that the machine is capable of deforming rock samples at confining pressures of up to 400 MPa, temperatures of up to 800 °C, and pore pressures (liquid or gas) of up to 300 MPa while keeping the temperature gradient along samples of 40 mm in length and 20 mm in diameter minimal (less than 30 at 700 °C). Most importantly, the maximum load is 1000 kN (stresses as high as 2.2 GPa on 24 mm samples and 3 GPa on 20 mm samples), allowing for the deformation of very competent rock samples. Moreover, during deformation, the pair of syringe pore pressure pumps allow for continuous permeability or dilatancy recording. We benchmarked our machine against existing data in the literature and show that it accurately and precisely records stress, strain, permeability, pressure, and temperature.
Fire in the Earth System: Introduction to the Special Collection

Fire has always been an important component of many ecosystems, but anthropogenic global climate change is now altering fire regimes over much of Earth's land surface, spurring a more urgent need to understand the physical, biological, and chemical processes associated with fire as well as its effects on human societies. In 2020, AGU launched a Special Collection that spanned 10 journals, soliciting papers under the theme “Fire in the Earth System” to encourage state‐of‐the‐art publications in fire‐related science. The completed Special Collection comprises more than 100 papers. Here, we summarize the articles published in this collection, considering them to be grouped into seven themes: paleofire and its ties to climate; evolution of fire patterns in the recent past and the future, including the effects of ongoing climate change; physical (atmospheric) and chemical processes associated with fire; ecosystem effects, including on biogeochemical cycles; physical landscape change after fire and its associated hazards; fire effects on water quality, air quality, and human health; and new methods and technologies applied to fire research.
Spin-on-carbon hard masks utilising fullerene derivatives

We have developed a range of fullerene containing materials for use as organic hard masks. Recent advances in material development are reported together with some results from external evaluations of the original HM100 series. Initial results for the new HM340-383-010 formulation show it to have a high thermal stability (~5.5 % mass loss at 400°C) and a very high carbon content (at 95.3%), offering high etch durability.
Microstructural heterogeneity perspective on the yield strength of metallic glasses

We report an intrinsic universal correlation between yielding and dynamic activation of flow units in various metallic glasses, analogous to yielding in crystalline materials accompanied by activation of structural defects of dislocation. A transition point of yield strength when test temperature reaches a critical value is observed, and the evolution of the yield strength corresponds well to the evolution of activation energy and fraction of flow units with temperature, indicating the correlation between yield strength and intrinsic structural heterogeneity in glasses. We propose a model based on the flow units to understand the structural origin of yielding phenomenon and the nature of the yield strength.
Fundus Image Based DR Detection Using Image Processing

Almost any field of application that aims to address societal issues now relies heavily on image processing, thanks to recent breakthroughs in the field. Medical image processing is among the most widely appealing and vital disciplines of research in recent times, owing to its enormous potential as well as the vast volume of underlying information that needs to be recovered using systematic image processing techniques. However, this research work, has taken processing of retinal images as its prime issue of concern to predict and aid early detection of retinal disorders. This arises from the motivation that certain symptoms appearing in a particular part of the human body may indicate onset of certain diseases in which diabetes could be quoted as the best example. Retina of the eye is an essential component from an image processing aspect, as it reveals several disorders associated with it at an earlier stage, if properly processed and analysed. Some well-known retinal disorders include diabetic retinopathy, retinal tear and detachment, cataract, retinitis pigmentosa, age related macular disorders (AMD) etc. if these conditions are left undetected, they may result in increasing disorders affecting other parts of the human body. In view of the above issues and prospects of retinal image analysis, this thesis has taken retina as the prime subject of study for early detection of retinal disorders.