Miracle-dz/UniFAR-FTUs huggingface dataset:
Trailer Overturning during Wood Transportation: an Experimental Investigation of Effects of Trailer Joint Point and Frame Structure

Trailers may increase the risk of tractor overturn during wood transportation in dangerous conditions. In this work, tests were carried to simulate a trailer rollover using three two wheel tractors and a crawler tractor and three trailers (two single-axle and one two-axle), all of their combinations moving downhill along the path on a short dirt road. The trailers were always loaded with the same load of logs cut at a length of about 1.5 m and put transversely to the longitudinal axis of the trailer. During each test, the following parameters were measured: the lateral dragging of the rear wheels/crawler of the tractor, the ground detachment of the rear upstream wheel/crawler and both the longitudinal and transversal strains (released over the tractor hooking system) produced by the trailer overturn. The study highlighted that the biaxle trailer structure with a turntable steering had the best performances compared to the single-axle in terms of safety during trailer overturning. Independently of the trailer type considered in this work, a tied load is more dangerous than a load restrained only by steel struts, because during the overturn the load forms a single unit with the trailer mass, which increases the transversal and longitudinal strain.
Performance anxiety related to sexual functioning – the role of sexual identity and minority stress.

Aim of the study: The study aimed to inquire if sexual identity was related to performance anxiety, and if proximal minority stress processes contributed to performance anxiety in gay and bisexual men. Subject or material and methods: The present study was based on a database obtained from a larger In-ternet-based research project on the sexuality of Polish men of different sexual identities. The present sample consisted of 3,495 participants, i.e., 1,096 straight, 1,701 gay, and 698 bisexual men. The main outcome measure was performance anxiety which was measured using a single-item question: During the last 12 months has there ever been a period of several months when you felt anxious about your ability to perform sexual-ly? Explanatory variables were demographics, relational and sex life characteristics – measured using a self-designed questionnaire, and minority stress processes – measured using the Sexual Minority Stress Scale. Results: Gay and bisexual identities were related to performance anxiety in bivariate analyses however they lost their significance when other factors were controlled for. Relational status was identified as responsible for this change. Internalised homophobia, and expectations of rejection contributed to performance anxiety in minority men. Discussion: A subset of gay and bisexual men, i.e., men who do not stay in committed relationships, may be particularly prone for developing performance anxiety, which is one of major factors contributing to sexual dysfunctions. Minority stress processes should be included in planning clinical interventions targeting sexual problems in theses populations. Conclusions: Gay and bisexual men’s sexual problems need to be addressed in an individualised manner, with inclusion of minority specific processes.
Spatial and Spectral-Channel Attention Network for Denoising on Hyperspectral Remote Sensing Image

Hyperspectral images (HSIs) are frequently contaminated by different noises (Gaussian noise, stripe noise, deadline noise, impulse noise) in the acquisition process as a result of the observation environment and imaging system limitations, which makes image information lost and difficult to recover. In this paper, we adopt a 3D-based SSCA block neural network of U-Net architecture for remote sensing HSI denoising, named SSCANet (Spatial and Spectral-Channel Attention Network), which is mainly constructed by a so-called SSCA block. By fully considering the characteristics of spatial-domain and spectral-domain of remote sensing HSIs, the SSCA block consists of a spatial attention (SA) block and a spectral-channel attention (SCA) block, in which the SA block is to extract spatial information and enhance spatial representation ability, as well as the SCA block to explore the band-wise relationship within HSIs for preserving spectral information. Compared to earlier 2D convolution, 3D convolution has a powerful spectrum preservation ability, allowing for improved extraction of HSIs characteristics. Experimental results demonstrate that our method holds better-restored results than other compared approaches, both visually and quantitatively.
Both uniconazole and 5-aminolevulinic acid increase maize (Zea mays L.) yield by changing its ear morphology and increasing photosynthetic efficiency and antioxidants in saline-alkali land

thiobarbituric Saline-alkaline stress is one of the most detrimental abiotic stresses that restrict the yield and physiological activity of maize ( Zea mays L.). In the present study, maize was planted on saline-alkali land, while 25 mg L –1 uniconazole (S3307) and 40 mg L –1 5-aminolevulinic acid (ALA) were sprayed at the stage of nine expanded leaves. Our results showed that both S3307 and ALA applications significantly increased all ear width, volume, and mass in the maturity stage. Both applications also upregulated photosynthetic efficiency via increasing the chlorophyll content, net photosynthetic rate, transpiration rate, and stomatal conductance, as well as reduced the intercellular CO 2 concentration after the silking stage. In addition, both applications upregulated further the antioxidant system via enhancing the activity of antioxidants and contents of soluble protein and sugar, as well as reducing the malondialdehyde content after the silking stage. Thus, both S3307 and ALA applications can improve maize yield in saline-alkali land via enhancing ear morphology and increasing photosynthetic efficiency and antioxidants.
A Program for the Comprehensive Cognitive Training of Excess Weight (TRAINEP): The Study Protocol for A Randomized, Controlled Trial

Background: The available treatments for people with excess weight have shown small effects. Cognitive training has shown promising results, but most of the research focused on normal-weight university students and reported immediate results after a single training session. This parallel group, randomized, controlled trial aims to study the efficacy of a program for the comprehensive cognitive treatment of excess weight. Methods and Analysis: Participants will be 150 people with excess weight recruited through social media, who will be randomized into three groups: cognitive intervention, sham cognitive intervention, and treatment as usual. All assessment and intervention sessions will be online in groups of 5–6 participants. The three groups will attend a motivational interviewing session, and they will receive individualized diet and physical exercise guidelines throughout the program. The cognitive training will consist of four weekly sessions of approximately 60–90 min, each based on approach–avoidance bias training, inhibitory control training, implementation of intentions, and episodic future thinking, respectively. The main outcome measure will be a change in Body Mass Index (kg/m2). Secondary outcomes include changes in cognitive measures, eating and physical exercise behaviors, and anthropometric measures. Assessments will be conducted up to 6 months after the end of the program. In addition, data on the use of the health system will be collected to analyze the cost-effectiveness and the cost-utility of training. Linear mixed models will be used for statistical analysis. Findings of this study will expand the available evidence on cognitive interventions to reduce excess weight.
Comparison of the Physical Fitness Profile of Muay Thai and Brazilian Jiu-Jitsu Athletes with Reference to Training Experience

Background: In combat sports, successful competition and training require comprehensive motor fitness. The aim of this study was to diagnose the level of physical fitness and to determine the level of differences between athletes of combat sports characterized by stand-up fighting, such as Muay Thai; and ground fighting, such as Brazilian jiu-jitsu. Methods: The study examined and compared 30 participants divided into two equal groups: Muay Thai athletes (n = 15; age: 24.24 ± 3.24; body height: 174.91 ± 5.19; body weight: 77.56 ± 7.3), and Brazilian jiu-jitsu (BJJ) (n = 15; age: 22.82 ± 1.81; body height: 175.72 ± 7.03; body weight: 77.11 ± 8.12). Basic characteristics of the somatic build were measured. Selected manifestations of the motor potential of motor skills were also evaluated using selected tests from the EUROFIT test battery, the International Test of Physical Fitness, and computer tests of coordination skills. Relative strength and maximal anaerobic work (MAW) indices were calculated. The strength of the relationship between the effect of motor fitness and training experience was also assessed. Results: The athletes of both groups (Muay Thai and BJJ) presented similar levels of basic characteristics of the somatic build. Motor fitness in the tested groups showed significant differences between the athletes of these sports in static strength (p = 0.010), relative strength (p = 0.006), arm muscle strength in pull-ups (p = 0.035), and functional strength in bent arm hanging (p = 0.023). Higher levels of these components of motor fitness were found for the athletes in the BJJ athletes. In the Muay Thai group, significant very high strength of association was found between training experience and five strength tests. Furthermore, a significantly high strength of association was found in two tests. In the BJJ group, significant relationships with very high correlation were found between the variables in five strength tests. Conclusions: Brazilian jiu-jitsu athletes performed better in strength tests (static strength, relative strength, shoulder girdle strength, functional strength). High correlations between the training load and the level of physical fitness were found in flexibility and strength tests in BJJ athletes and most strength tests in Muay Thai athletes.
Molecule graph reconstruction from atomic force microscope images with machine learning

Abstract Despite the success of noncontact atomic force microscopy (AFM) in providing atomic-scale insight into the structure and properties of matter on surfaces, the wider applicability of the technique faces challenges in the difficulty of interpreting the measurement data. We tackle this problem by proposing a machine learning model for extracting molecule graphs of samples from AFM images. The predicted graphs contain not only atoms and their bond connections but also their coordinates within the image and elemental identification. The model is shown to be effective on simulated AFM images, but we also highlight some issues with robustness that need to be addressed before generalization to real AFM images. Impact statement Developing better techniques for imaging matter at the atomic scale is important for advancing our fundamental understanding of physics and chemistry as well as providing better tools for materials R&D of nanotechnologies. State-of-the-art high-resolution atomic force microscopy experiments are providing such atomic-resolution imaging for many systems of interest. However, greater automation of processing the measurement data is required in order to eliminate the need for subjective evaluation by human operators, which is unreliable and requires specialized expertise. The ability to convert microscope images into graphs would provide an easily understandable and precise view into the structure of the system under study. Furthermore, a graph consisting of a discrete set of objects, rather than an image that describes a continuous domain, is much more amenable to further processing and analysis using symbolic reasoning based on physically motivated rules. This type of image-to-graph conversion is also relevant to other machine learning tasks such as scene understanding. Graphical abstract
Amplifying the Influence of CSR Disclosure on Investment Inefficiency by Choosing Woman Directors: Is it Effective?

Research Aims: The article was written with the intention to inspect the amplified impact of CSR disclosure by choosing women on the boards of directors in the companies on investment inefficiency. Design/Methodology/Approach: The sample studies were non-financial companies listed on IDX during 2018-2019 that published sustainability and annual reporting.Research Finding: This research revealed that, in the latest two years, overinvestment was done by most of the sample companies (76%). Hereafter, there was a negative effect on investment inefficiency due to the increasing corporate social responsibility disclosure. Nevertheless, women on the board of directors had no effect as moderating variable.Theoretical Contribution: This study adds literature on investment inefficiency issues, especially on amplifying the influence of corporate social responsibility disclosure on investment inefficiency by choosing women on the board of directors.Research Limitation: Very limited position on the board of directors for women in companies causes it to have no effect as a moderating variable. Moreover, this research did not categorize investment efficiency in the overinvestment and underinvestment schemes.
Map-based Visual-Inertial Localization: A Numerical Study

We revisit the problem of efficiently leveraging prior map information within a visual-inertial estimation framework. The use of traditional landmark-based maps with 2D-to-3D measurements along with the recently introduced keyframe-based maps with 2D-to-2D measurements are inves-tigated. The full joint estimation of the prior map is compared within a visual-inertial simulator to the Schmidt-Kalman filter (SKF) and measurement inflation methods in terms of their computational complexity, consistency, accuracy, and memory usage. This study shows that the SKF can enable efficient and consistent estimation for small workspace scenarios and the use of 2D-to-3D landmark maps have the highest levels of accuracy. Keyframe-based 2D-to-2D maps can reduce the required state size while still enabling accuracy gains. Finally, we show that measurement inflation methods, after tuning, can be accurate and efficient for large-scale environments if the guarantee of consistency is relaxed.
Exploration of Classroom Cultures in EFL

This article documents an investigation of classroom cultures within the context of teaching English in a Russian university and aspires to shed light on the context of teaching English as a foreign language (EFL) in Russia. It provides a comprehensive understanding of what classroom cultures the teachers of English create, and how their vision of these cultures is influenced by the context in which they are situated. Echoing previous research suggesting that classroom contexts are co-constructed, this study also accentuates students’ contributions in their implicit role in the construction of classroom cultures. The findings reveal that tensions that arise in the classroom trigger processes of negotiation between teachers and their students. Owing to these negotiations, the teachers manage to acquire the students’ acceptance of their rules, and this appears to be considered as some sort of ‘validation point’ for the teachers, which, in turn, facilitates the development of the teachers and their respective classroom cultures. This finding positions the students as central to the teachers’ estimations of themselves.
Ultra-Low-Power Design and Hardware Security Using Emerging Technologies for Internet of Things

In this review article for Internet of Things (IoT) applications, important low-power design techniques for digital and mixed-signal analog–digital converter (ADC) circuits are presented. Emerging low voltage logic devices and non-volatile memories (NVMs) beyond CMOS are illustrated. In addition, energy-constrained hardware security issues are reviewed. Specifically, light-weight encryption-based correlational power analysis, successive approximation register (SAR) ADC security using tunnel field effect transistors (FETs), logic obfuscation using silicon nanowire FETs, and all-spin logic devices are highlighted. Furthermore, a novel ultra-low power design using bio-inspired neuromorphic computing and spiking neural network security are discussed.
Baseband-Function Placement With Multi-Task Traffic Prediction for 5G Radio Access Networks

The 5G Radio Access Network (RAN) virtualization aims to improve network quality and lower the operator’s costs. One of its main features is the functional split, i.e., dividing the instantiation of RAN baseband functions into different units over metro-network nodes. However, its optimal placement is non-trivial: it depends on the application requirements and on the expected traffic volume, whose daily variation highly impacts the total power consumption. Current optimization solutions fail to provide a placement solution capable of handling traffic fluctuations. In fact, the standard machine learning algorithms used in the literature for planning the network resources in advance result in an allocation that is inadequate to carry the actual traffic at all the time-slots. Hence, we must reserve an artificial buffer capacity in the nodes to ensure feasibility. Instead, our proposed method exploits a fine-grained two-step multi-task algorithm that predicts the mean and quantile traffic, making the artificial capacity no longer necessary. The subsequent placement uses mixed-integer linear programming and a heuristic. The former considers the expected traffic in the objective function (to estimate costs) and the quantile in the constraints (to enforce capacity limits). The heuristic combines the mean and quantile results to minimize the power and comply with the requirements. While using sufficiently large artificial buffers guarantees robustness with a mild power increase compared to the oracle, the fine-grained multi-task model improves the results, reducing the power consumption compared to the mean and meets all constraints. The heuristic enables significant computational time reduction.
Camera eats first: exploring food aesthetics portrayed on social media using deep learning

Purpose The purpose of this paper is to explore and examine discrepancies of food aesthetics portrayed on social media across different types of restaurants using a large-scale data set of food images. Design/methodology/approach A neural food aesthetic assessment model using computer vision and deep learning techniques is proposed, applied and evaluated on the food images data set. In addition, a set of photographic attributes drawn from food services and cognitive science research, including color, composition and figure–ground relationship attributes is implemented and compared with aesthetic scores for each food image. Findings This study finds that restaurants with different rating levels, cuisine types and chain status have different aesthetic scores. Moreover, the authors study the difference in the aesthetic scores between two groups of image posters: customers and restaurant owners, showing that the latter group tends to post more aesthetically appealing food images about the restaurant on social media than the former. Practical implications Restaurant owners may consider performing more proactive social media marketing strategies by posting high-quality food images. Likewise, social media platforms should incentivize their users to share high-quality food images. Originality/value The main contribution of this paper is to provide a novel methodological framework to assess the aesthetics of food images. Instead of relying on a multitude of standard attributes stemming from food photography, this method yields a unique one-take-all score, which is more straightforward to understand and more accessible to correlate with other target variables.
REDfly: An Integrated Knowledgebase for Insect Regulatory Genomics

Simple Summary Understanding how genes are regulated is a vital area of current biological research and a crucial adjunct to ongoing efforts to sequence entire genomes. Knowing the DNA sequences responsible for gene regulation—transcriptional cis-regulatory modules (CRMs, e.g., “enhancers”) and transcription factor binding sites (TFBSs)—is important for many areas of research including interpretation and validation of data developed by large-scale genomics projects, providing training data for machine-learning CRM-discovery methods, genome annotation, modeling gene-regulatory networks, studying the evolution of gene regulation, and numerous aspects of the basic biology of transcriptional regulation. Knowledge of insect CRMs is also an important step in developing biotechnology methods for control of insect disease vectors and for eliminating pathogen transmission. The REDfly (Regulatory Element Database for Fly) database integrates all of the available insect cis-regulatory information from multiple sources to provide a comprehensive collection of known regulatory elements. In this paper, we describe REDfly’s basic contents and data model, emphasizing recently added features, and provide illustrated walk-throughs of some common search scenarios. Abstract We provide here an updated description of the REDfly (Regulatory Element Database for Fly) database of transcriptional regulatory elements, a unique resource that provides regulatory annotation for the genome of Drosophila and other insects. The genomic sequences regulating insect gene expression—transcriptional cis-regulatory modules (CRMs, e.g., “enhancers”) and transcription factor binding sites (TFBSs)—are not currently curated by any other major database resources. However, knowledge of such sequences is important, as CRMs play critical roles with respect to disease as well as normal development, phenotypic variation, and evolution. Characterized CRMs also provide useful tools for both basic and applied research, including developing methods for insect control. REDfly, which is the most detailed existing platform for metazoan regulatory-element annotation, includes over 40,000 experimentally verified CRMs and TFBSs along with their DNA sequences, their associated genes, and the expression patterns they direct. Here, we briefly describe REDfly’s contents and data model, with an emphasis on the new features implemented since 2020. We then provide an illustrated walk-through of several common REDfly search use cases.
Effect of fluorinated N-alkylthioamides on HSV-1 multiplicity

L. Biliavska, Y. Pankivska, O. Povnitsa © 2018 L. Biliavska et al.; Published by the Institute of Molecular Biology and Genetics, NAS of Ukraine on behalf of Biopolymers and Cell. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited UDС: 578.825: 615.281. 8