Miracle-dz/UniFAR-FTUs huggingface dataset:
Group-Living Spider Cyrtophora citricola as a Potential Novel Biological Control Agent of the Tomato Pest Tuta absoluta

Simple Summary The tomato leafminer, Tuta absoluta, is a devastating pest moth of commercially important crops like tomato and potato. This moth has developed resistance to insecticides; therefore, novel approaches, like using natural predators, are needed to combat infestations. We explored the use of tropical tent web spiders, Cyrtophora citricola, as biological control agents, as these spiders live in groups and are not cannibalistic, and thus, create large, predator-dense webs. Furthermore, their global range overlaps with regions of moth infestations. In lab settings, we introduced different prey types to small colonies of spiders of varying body sizes and found that spiders were equally efficient at capturing pest moths and easily-caught fruit flies (Drosophila hydei). Larger spiders built larger webs and were better at catching prey. Spiders from southern Spain were large enough to capture pest moths during the tomato growing season, but >50% of spider egg sacs were attacked by egg predatory wasps (Philolema palanichamyi). Cyrtophora citricola spiders, therefore, have the potential to be an effective biological control agent of flying insect pests, at least after growing to medium-sized juveniles, and if wasp infections are controlled, forming part of integrated pest management to defend against pest infestations in the future. Abstract Group-living spiders may be uniquely suited for controlling flying insect pests, as their high tolerance for conspecifics and low levels of cannibalism result in large, predator dense capture webs. In laboratory settings, we tested the ability of the facultatively communal spider, Cyrtophora citricola, to control the tomato leafminer, Tuta absoluta; a major pest of tomato crops worldwide. We tested whether prey capture success was affected by spider body size, and whether prey capture differed among T. absoluta, flightless fruit flies (Drosophila hydei), and larger black soldier flies (Hermetia illucens). We found that larger spiders generally caught more prey, and that prey capture success was similar for T. absoluta and easily caught fruit flies, while black soldier flies were rarely caught. We further investigated the seasonal variations in web sizes in southern Spain, and found that pest control would be most effective in the tomato planting and growing season. Finally, we show that C. citricola in Spain have >50% infection rates of an egg predatory wasp, Philolema palanichamyi, which may need controlling to maintain pest control efficacy. These results suggest that using C. citricola as a biological control agent in an integrated pest management system could potentially facilitate a reduction of pesticide reliance in the future.
Experimental observation of bending wave localization

Localization of bending waves has been observed for the first time for two‐dimensional (2D) acoustic wave propagation in an inhomogeneous composite system consisting of a steel plate decorated with two sets of randomly attached Lucite blocks. A significant experimental feature of the localized modes is an exponentially decay of the mode intensity from their peaked centers, with a decay length that increases as (f0−f)−1 when the mode frequency f approaches a quasimobility edge f0. The minimum attenuation length is of the order of a block diagonal and is about 40% of the banding wave’s wavelength. The experimental data, as well as results of finite‐element calculations, identify the source of the localization phenomenon as strong scattering of the bending wave by shear and flexural resonances of the Lucite blocks. This result supports theoretical predictions that resonant scattering enhances localization [cf. Scattering and Localization of Classical Waves in Random Media, edited by P. Sheng (World Scientifi.
Root ABA Accumulation Delays Lateral Root Emergence in Osmotically Stressed Barley Plants by Decreasing Root Primordial IAA Accumulation

Increased auxin levels in root primordia are important in controlling root branching, while their interaction with abscisic acid (ABA) likely regulates lateral root development in water-deficient plants. The role of ABA accumulation in regulating root branching was investigated using immunolocalization to detect auxin (indoleacetic acid, IAA) and ABA (abscisic acid) in root primordia of the ABA-deficient barley mutant Az34 and its parental genotype (cv. Steptoe) barley plants. Osmotic stress strongly inhibited lateral root branching in Steptoe plants, but hardly affected Az34. Root primordial cells of Steptoe plants had increased immunostaining for ABA but diminished staining for IAA. ABA did not accumulate in root primordia of the Az34, and IAA levels and distribution were unaltered. Treating Az34 plants with exogenous ABA decreased root IAA concentration, while increasing root primordial ABA accumulation and decreasing root primordial IAA concentration. Although ABA treatment of Az34 plants increased the root primordial number, it decreased the number of visible emerged lateral roots. These effects were qualitatively similar to that of osmotic stress on the number of lateral root primordia and emerged lateral roots in Steptoe. Thus ABA accumulation (and its crosstalk with auxin) in root primordia seems important in regulating lateral root branching in response to water stress.
Radiomics Approaches for the Prediction of Pathological Complete Response after Neoadjuvant Treatment in Locally Advanced Rectal Cancer: Ready for Prime Time?

Simple Summary Therapeutic management of locally advanced rectal cancer has seen profound modifications in the latest years, mainly with the advent of total neoadjuvant therapy. Adding neoadjuvant chemotherapy to neoadjuvant chemoradiotherapy leads to an improvement in locoregional disease-free survival. Organ preservation rises as an option for patients with complete response after neoadjuvant therapy without compromising overall survival. A current challenge that remains is the ever-growing need for selection tools to enable a personalized therapeutic approach for each patient. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer. Abstract In recent years, neoadjuvant therapy of locally advanced rectal cancer has seen tremendous modifications. Adding neoadjuvant chemotherapy before or after chemoradiotherapy significantly increases loco-regional disease-free survival, negative surgical margin rates, and complete response rates. The higher complete rate is particularly clinically meaningful given the possibility of organ preservation in this specific sub-population, without compromising overall survival. However, all locally advanced rectal cancer most likely does not benefit from total neoadjuvant therapy (TNT), but experiences higher toxicity rates. Diagnosis of complete response after neoadjuvant therapy is a real challenge, with a risk of false negatives and possible under-treatment. These new therapeutic approaches thus raise the need for better selection tools, enabling a personalized therapeutic approach for each patient. These tools mostly focus on the prediction of the pathological complete response given the clinical impact. In this article, we review the place of different biomarkers (clinical, biological, genomics, transcriptomics, proteomics, and radiomics) as well as their clinical implementation and discuss the most recent trends for future steps in prediction modeling in patients with locally advanced rectal cancer.
Analysis of Lidar Actuator System Influence on the Quality of Dense 3D Point Cloud Obtained with SLAM

Mobile mapping technologies, based on techniques such as simultaneous localization and mapping (SLAM) and surface-from-motion (SfM), are being vigorously developed both in the scientific community and in industry. They are crucial concepts for automated 3D surveying and autonomous vehicles. For various applications, rotating multiline scanners, manufactured, for example, by Velodyne and Ouster, are utilized as the main sensor of the mapping hardware system. However, their principle of operation has a substantial drawback, as their scanning pattern creates natural gaps between the scanning lines. In some models, the vertical lidar field of view can also be severely limited. To overcome these issues, more sensors could be employed, which would significantly increase the cost of the mapping system. Instead, some investigators have added a tilting or rotating motor to the lidar. Although the effectiveness of such a solution is usually clearly visible, its impact on the quality of the acquired 3D data has not yet been investigated. This paper presents an adjustable mapping system, which allows for switching between a stable, tilting or fully rotating lidar position. A simple experiment in a building corridor was performed, simulating the conditions of a mobile robot passing through a narrow tunnel: a common setting for applications, such as mining surveying or industrial facility inspection. A SLAM algorithm is utilized to create a coherent 3D point cloud of the mapped corridor for three settings of the sensor movement. The extent of improvement in the 3D data quality when using the tilting and rotating lidar, compared to keeping a stable position, is quantified. Different metrics are proposed to account for different aspects of the 3D data quality, such as completeness, density and geometry coherence. The ability of SLAM algorithms to faithfully represent selected objects appearing in the mapped scene is also examined. The results show that the fully rotating solution is optimal in terms of most of the metrics analyzed. However, the improvement observed from a horizontally mounted sensor to a tilting sensor was the most significant.
Gastronomic Experience and Consumer Behavior: Analyzing the Influence on Destination Image

Gastronomy experiences are becoming a fundamental factor that influences the making of a decision regarding choosing a travel destination, as well as being a crucial factor in shaping tourists’ satisfaction regarding their overall travel experience. The aim of the study is to identify and explain the simultaneous impact of the key factors that influence a gastronomic experience and their impact on tourists’ satisfaction with a trip and the destination’s brand. These issues were addressed within the context of Ukraine, as this is an overlooked area of academic research, and an online survey was conducted, targeting domestic and international tourists. Structural equation modeling was used to assess and reveal the proposed hypotheses in the model. The study contributed to the theoretical understanding of the key factors that increases the occurrence of a memorable gastronomic experience and the relationship between the experience of food and its role in the satisfaction of and the perceived brand of a destination. Moreover, the finding showed that past experience and prior knowledge have a positive influence on the gastronomy experience, while tourists’ prior knowledge effects the perceived quality of a destination’s cuisine, as well as the food activities in the destination. Linkages in the model were empirically supported by statistical analyses. Nonetheless, the various level of the tourists’ involvement with gastronomy might be used as an input to examine and improve the memorable gastronomic experience on-site. The research simultaneously highlighted the importance of gastronomy to tourist destinations for positioning on international and domestic markets. The paper not only provides theoretical but also practical implications. The hospitality and tourism businesses benefit from acknowledging the importance of local food and the local food market. The findings of this study are also deemed to assist destination marketers who observe that tourists have become more demanding in search of unique experiences offered by destinations.
A demodulating approach based on local mean decomposition and its applications in mechanical fault diagnosis

Since machinery fault vibration signals are usually multicomponent modulation signals, how to decompose complex signals into a set of mono-components whose instantaneous frequency (IF) has physical sense has become a key issue. Local mean decomposition (LMD) is a new kind of time–frequency analysis approach which can decompose a signal adaptively into a set of product function (PF) components. In this paper, a modulation feature extraction method-based LMD is proposed. The envelope of a PF is the instantaneous amplitude (IA) and the derivative of the unwrapped phase of a purely flat frequency demodulated (FM) signal is the IF. The computed IF and IA are displayed together in the form of time–frequency representation (TFR). Modulation features can be extracted from the spectrum analysis of the IA and IF. In order to make the IF have physical meaning, the phase-unwrapping algorithm and IF processing method of extrema are presented in detail along with a simulation FM signal example. Besides, the dependence of the LMD method on the signal-to-noise ratio (SNR) is also investigated by analyzing synthetic signals which are added with Gaussian noise. As a result, the recommended critical SNRs for PF decomposition and IF extraction are given according to the practical application. Successful fault diagnosis on a rolling bearing and gear of locomotive bogies shows that LMD has better identification capacity for modulation signal processing and is very suitable for failure detection in rotating machinery.
Nitrate and Ammonium Affect the Overall Maize Response to Nitrogen Availability by Triggering Specific and Common Transcriptional Signatures in Roots

Nitrogen (N) is an essential macronutrient for crops. Plants have developed several responses to N fluctuations, thus optimizing the root architecture in response to N availability. Nitrate and ammonium are the main inorganic N forms taken up by plants, and act as both nutrients and signals, affecting gene expression and plant development. In this study, RNA-sequencing was applied to gain comprehensive information on the pathways underlying the response of maize root, pre-treated in an N-deprived solution, to the provision of nitrate or ammonium. The analysis of the transcriptome shows that nitrate and ammonium regulate overlapping and distinct pathways, thus leading to different responses. Ammonium activates the response to stress, while nitrate acts as a negative regulator of transmembrane transport. Both the N-source repress genes related to the cytoskeleton and reactive oxygen species detoxification. Moreover, the presence of ammonium induces the accumulation of anthocyanins, while also reducing biomass and chlorophyll and flavonoids accumulation. Furthermore, the later physiological effects of these nutrients were evaluated through the assessment of shoot and root growth, leaf pigment content and the amino acid concentrations in root and shoot, confirming the existence of common and distinct features in response to the two nitrogen forms.
Determinan Penggunaan E-Class sebagai Learning Management System oleh Tenaga Pendidik di Masa Pandemi Covid-19: Pendekatan Model UTAUT

Penelitian ini bertujuan untuk memperoleh gambaran tentang determinan perilaku pengguna E-Class di Universitas Merdeka Malang khususnya para pendidik. E-Class merupakan Learning Management System yang dibuat oleh Universitas Merdeka Malang sebagai sarana proses belajar mengajar berbasis online. Universitas Merdeka Malang telah merespon kebijakan pemerintah terkait regulasi pelaksanaan pembelajaran formal online akibat pandemi Covid-19. Namun untuk mengukur keberhasilan penerapan sistem informasi baru berupa E-Class. Peneliti mengusulkan penelitian ini dimana penerapan model UTAUT adalah untuk mengetahui determinan perilaku pengguna E-Class. Penelitian ini merupakan penelitian eksplanatori dengan pendekatan kuantitatif. Data penelitian ini adalah data primer. Analisis yang digunakan adalah analisis regresi linier berganda dengan alat statistik SmartPLS 3.0. Populasi dalam penelitian ini adalah seluruh tenaga pendidik di Universitas Merdeka Malang. Metode sampel yang digunakan adalah purposive sampling. Hasil penelitian ini menjelaskan bahwa variabel ekspektasi usaha dan pengaruh sosial berpengaruh positif terhadap niat berperilaku. Niat perilaku memiliki efek positif pada perilaku penggunaan. Sedangkan variabel harapan kinerja dan kondisi fasilitasi tidak berpengaruh terhadap niat perilaku dan perilaku penggunaan. Variabel modifikasi berupa corona fear juga tidak berpengaruh terhadap hubungan semua variabel independen terhadap behavioral intention.
Feasibility Study of Constant Eddy-Viscosity Assumption in Gradient-Based Design Optimization

A feasibility study is carried out by investigating the effects of a usual assumption of constant turbulent eddy viscosity on the aerodynamic design using an adjoint variable method, one of the most efe cient gradient-based optimization techniques. Accurate unsteady and steady e ow analyses are followed by the aerodynamic sensitivity analysis for theNavier‐ Stokes equations coupled with two-equation turbulence models. A challengeable approach for high-lift design optimization at higher angles of attack is also proposed, which is based on unsteady sensitivity analysis using a dual time-stepping method and the chimera overset grid scheme. Through the comparison of the sensitivity gradients with respect to all of the design variables including angle of attack, it is observed that the constant turbulent eddy-viscosity assumption might provide inaccurate gradients in sensitivity analyses such as transonicairfoilwithastrongshockandhigh-liftairfoilata highangleofattackcloseto stallangle.Simultaneously, however, thee nal design resultsindicate that both approachesare acceptable in engineering applications. Both the single- and multi-element airfoil design optimizations using the constant eddy-viscosity assumption are carefully assessedintermsofdesignaccuracy,computermemoryoverheads,andtotaldesigntimeinvariousdesignexamples.
Nonlocal Mean Image Denoising Using Anisotropic Structure Tensor

We present a novel nonlocal mean (NLM) algorithm using an anisotropic structure tensor to achieve higher accuracy of imaging denoising and better preservation of fine image details. Instead of using the intensity to identify the pixel, the proposed algorithm uses the structure tensor to characterize the boundary information around the pixel more comprehensively. Meanwhile, similarity of the structure tensor is computed in a Riemannian space for more rigorous comparison, and the similarity weight of the pixel (or patch) is determined by the intensity and structure tensor simultaneously. The proposed algorithm is compared with the original NLM algorithm and a modified NLM algorithm that is based on the principle component analysis. Quantitative and qualitative comparisons of the three NLM algorithms are presented as well.
A Compact Repetitive Marx Generator for Generating Intense Electron Beams for HPM Sources

A 500 kV, 80 Hz repetition rate compact Marx generator has been designed and developed for producing high intense relativistic electron beams for high-power microwave (HPM) sources. This Marx generator comprises eight bipolar stages (16 stages of unipolar) using energy storage capacitors for achieving improved performance in repetition mode operation. The maximum charging voltage per stage is 50 kV, producing an erected voltage of 800 kV. However, the load voltage will vary according to the impedance of the load connected across the generator. Custom-designed energy storage capacitors were developed to allow voltage reversal of up to 30%–50%, enabling the system to deliver a higher current. A small pulse transformer-based triggering unit with an output voltage of 30 kV was used for triggering the generator. The system’s performance was tested in pulse repetition frequency (PRF) mode with resistive and diode loads and achieved a repetition rate of 80 Hz. An energy density of ~460 J/m3 was achieved in the developed system. A vircator device was connected and tested to benchmark the Marx generator for its repetitive and burst mode operations. The test results show the measured RF output power of 100 MW.
DeepSmile: Anomaly Detection Software for Facial Movement Assessment

Facial movements are crucial for human interaction because they provide relevant information on verbal and non-verbal communication and social interactions. From a clinical point of view, the analysis of facial movements is important for diagnosis, follow-up, drug therapy, and surgical treatment. Current methods of assessing facial palsy are either (i) objective but inaccurate, (ii) subjective and, thus, depending on the clinician’s level of experience, or (iii) based on static data. To address the aforementioned problems, we implemented a deep learning algorithm to assess facial movements during smiling. Such a model was trained on a dataset that contains healthy smiles only following an anomaly detection strategy. Generally speaking, the degree of anomaly is computed by comparing the model’s suggested healthy smile with the person’s actual smile. The experimentation showed that the model successfully computed a high degree of anomaly when assessing the patients’ smiles. Furthermore, a graphical user interface was developed to test its practical usage in a clinical routine. In conclusion, we present a deep learning model, implemented on open-source software, designed to help clinicians to assess facial movements.
Structural and Functional RNA Motifs of SARS-CoV-2 and Influenza A Virus as a Target of Viral Inhibitors

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is responsible for the COVID-19 pandemic, whereas the influenza A virus (IAV) causes seasonal epidemics and occasional pandemics. Both viruses lead to widespread infection and death. SARS-CoV-2 and the influenza virus are RNA viruses. The SARS-CoV-2 genome is an approximately 30 kb, positive sense, 5′ capped single-stranded RNA molecule. The influenza A virus genome possesses eight single-stranded negative-sense segments. The RNA secondary structure in the untranslated and coding regions is crucial in the viral replication cycle. The secondary structure within the RNA of SARS-CoV-2 and the influenza virus has been intensively studied. Because the whole of the SARS-CoV-2 and influenza virus replication cycles are dependent on RNA with no DNA intermediate, the RNA is a natural and promising target for the development of inhibitors. There are a lot of RNA-targeting strategies for regulating pathogenic RNA, such as small interfering RNA for RNA interference, antisense oligonucleotides, catalytic nucleic acids, and small molecules. In this review, we summarized the knowledge about the inhibition of SARS-CoV-2 and influenza A virus propagation by targeting their RNA secondary structure.
Axisymmetrical particle-in-cell/Monte Carlo simulation of narrow gap planar magnetron plasmas. I. Direct current-driven discharge

An axisymmetrical particle-in-cell/Monte Carlo simulation is performed for modeling direct current-driven planar magnetron discharge. The axisymmetrical structure of plasma parameters such as plasma density, electric field, and electron and ion energy is examined in detail. The effects of applied voltage and magnetic field strength on the discharge are also clarified. The model apparatus has a narrow target-anode gap of 20 mm to make the computational time manageable. This resulted in the current densities which are very low compared to actual experimental results for a wider target-anode gap. The current–voltage characteristics show a negative slope in contrast with many experimental results. However, this is understandable from Gu and Lieberman’s similarity equation. The negative slope appears to be due to the narrow gap.