Miracle-dz/UniFAR-FTUs huggingface dataset:
Rapid Identification of Aphid Species by Headspace GC-MS and Discriminant Analysis

Simple Summary Aphids are small insects that damage plants by sucking plant phloem sap. Aphids cause severe losses to agriculture. In order to control aphids by environmentally friendly methods, it is necessary to identify them. Identification of aphids is difficult because there are ca. 5000 species which undergo different life stages and vary much in appearance (e.g., coloration). Here, we presented a novel method to determine aphid species using their chemical profiles. There are compounds (biomarkers) that are characteristic for a particular aphid species like a fingerprint. After grinding aphids, the released compounds were collected and the compound mixtures were separated by gas chromatography connected to a mass spectrometer to identify the individual compounds. The obtained compound profiles were subjected to the software CAP12.exe to reveal the biomarkers from the headspace profiles for each aphid species. It was possible to reliably differentiate aphid species using our approach. Compared to the established identification of aphids that uses characteristic DNA sequences from their genome, using the chemical biomarkers is much less laborious and faster. Moreover, it is possible with the chemical profile analysis to reveal other aspects of an aphid’s life history such as the food plant it fed on. Abstract Aphids are a ubiquitous group of pests in agriculture that cause serious losses. For sustainable aphid identification, it is necessary to develop a precise and fast aphid identification tool. A new simple chemotaxonomy approach to rapidly identify aphids was implemented. The method was calibrated in comparison to the established phylogenetic analysis. For chemotaxonomic analysis, aphids were crushed, their headspace compounds were collected through closed-loop stripping (CLS) and analysed using gas chromatography—mass spectrometry (GC-MS). GC-MS data were then subjected to a discriminant analysis using CAP12.exe software, which identified key biomarkers that distinguish aphid species. A dichotomous key taking into account the presence and absence of a set of species-specific biomarkers was derived from the discriminant analysis which enabled rapid and reliable identification of aphid species. As the method overcomes the limits of morphological identification, it works with aphids at all life stages and in both genders. Thus, our method enables entomologists to assign aphids to growth stages and identify the life history of the investigated aphids, i.e., the food plant(s) they fed on. Our experiments clearly showed that the method could be used as a software to automatically identify aphids.
CLINICAL COURSE OF PROSTATE CANCER AND FACTORS THAT DETERMINE IT

skeleton (WR=2.42; p<0.001), as well as the presence of mac - rohematuria (WR=2.85; p=0.008 ), comorbid renal clear cell carcinoma (WR=2.69; p=0.011) and breast adenocarci - noma (WR=2.50; p=0.001). Conclusions: effective markers of the tumour process, AP and APP activity parameters for the PC screening have prognostic significance and can be criteria for the effectiveness of treatment.
Farmers’ Acceptance of a Micro-irrigation System: A Focus Group Study

Despite water scarcity and lots of benefits, implementation of micro-irrigation systems on potato crops in the Bekaa Valley of Lebanon is notably low. This could be related to farmers’ acceptance to use this technique. The objective of this study is to investigate the factors that can affect or not the adoption and the investment in a new micro-irrigation system. For this aim, the unified theory of acceptance and use of technology (UTAUT) served as the conceptual framework. A qualitative approach using focus group discussion was applied. A total of 34 farmers in six focus groups were conducted in the three main districts of the Bekaa Valley. From the analysis, performance expectancy, effort expectancy and facilitating conditions emerged as the three most prominent factors to understand farmers’ acceptance and adoption of micro-irrigation systems. The focus group findings indicated that potato farmers are willing to adopt a new micro-irrigation system if they ensure its provision in gains, its reduction of time and effort correlated with the farming activities. Barriers included lack of knowledge about the system, financial capabilities and extension services. Generally speaking, participants were enthusiastic about the idea to adopt a micro-irrigation system, but they are hindered by the unstable socio-economic conditions in Lebanon and the financial abilities. It was concluded that age, experience and voluntariness of use exert an effect on the related major determinants. This study will provide recommendations that can be considered while drafting agricultural policies.
Token Economy for People With Severe Mental Illness: A Narrative Review of the Literature

Background: People with chronic mental illness have long been rejected from society and lost their self-identity and self-efficacy due to individual obstacles, such as continuous negative symptoms, lack of motivation, or social obstacles, like stigma, which reduce their self-confidence and prevent them from engaging in healthy activity patterns. One of the behavioral therapy methods used to increase the motivation of people with severe mental illness is token economy. Objectives: The purpose of this narrative review was to review and investigate the impact of the token economy on people with severe mental illness. Methods: A search for studies published from 2002 to 2022 in PubMed and Google Scholar databases was conducted using the keywords “token economy”, “token reinforcement”, “contingency management”, “severe mental disorder”, “serious mental illness”, “chronic psychiatric disorder”, and “participation”. Results: In the initial search, 92 articles were obtained, and after reviewing and rejecting duplicates, 44 articles related to severe mental illness and token economy remained. Finally, 15 articles met the inclusion criteria and were selected for review. token economy can be an effective method in improving the performance of daily life activities, reducing the symptoms of mental illnesses and avoiding drugs, reducing alcohol consumption, and quitting smoking Conclusion: token economy can be an effective method in improving the performance of daily life activities, reducing the symptoms of mental illnesses, drug abstinence, reducing alcohol use, and quitting smoking, but in reducing violence, the results of the studies were contradictory. It is recommended to carry out wider studies on the impact of the token economy method on the occupational performance and participation of chronic mental patients.
Optatives: Deriving Desirability from Scalar Alternatives

A compositional analysis of optative sentences is challenging for at least two reasons: they encode desirability without having any overt marker, and they are if -clauses with or without consequents, raising the question of whether they are actually conditionals. In this paper I argue that optatives are condition- als even when they do not have overt consequents. With respect to desirability, I argue that in optatives modality is pragmatically derived. The investigation of op- tatives sheds light on the interaction between syntax, pragmatics and discourse.
Heterogeneous Retirement Savings Strategy Selection with Reinforcement Learning

Saving and investment behaviour is crucial for all individuals to guarantee their welfare during work-life and retirement. We introduce a deep reinforcement learning model in which agents learn optimal portfolio allocation and saving strategies suitable for their heterogeneous profiles. The environment is calibrated with occupation- and age-dependent income dynamics. The research focuses on heterogeneous income trajectories dependent on agents’ profiles and incorporates the parameterisation of agents’ behaviours. The model provides a new flexible methodology to estimate lifetime consumption and investment choices for individuals with heterogeneous profiles.
Outcome of primary total hip arthroplasty through direct anterior approach

Background and Aim: Total hip arthroplasty is one of the most effective orthopedic procedures, adoption of minimally invasive approaches aims to enhance functional outcome and decrease the burden of patient’s recovery. Aim of the study: Is to explain the surgical approach and to demonstrate the results that were attained and the complications that occurred. Methods: 30 hips were included in this study; they underwent a n anterior approach for hip replacement. Complications, surgical factors, and patient characteristics were all noted and assessed. Harris hip score was employed to assess the clinical results. Results: The mean clinical score was 96 points at 1 year follow-up compared to pre-operative score of 54. One patient had a revision while other complications were clinically insignificant. With carefully chosen patients, the direct anterior interval for hip replacement is a viable procedure that produces positive results.
Numerical Simulation and Modeling of UNSA91060 for Heat Transfer in Four-Stroke ICE Cylinder Head

Heat transfer is one of a number of indispensable tools in studying of ICEs, due to its influence on decisive parameters of operation like temperature and pressure inside the cylinder. It is safe to say that modeling of the engine heat transfer is among the most complex problems for engineers. Application of numerical methods to predict the heat transfer in a cylinder of reciprocating ICEs is a process of high importance, which was recognized from the earliest stages of their development. This is done to examine performance optimization and design improvement in order to meet nowadays demands exhibited on the engines. This present study focuses on a 3-D transient state temperature distribution analysis on a gasoline engine model via formulated of models, simulating using FEM in-built in the COMSOL Multiphysics software 4.3a to determine the temperature distribution and gradient of the engine cylinder head model. The number of degrees of freedom solved for were 32685 in 383 s (12 minutes, 20 seconds) in the mesh optimization. From the result obtained it was discovered that the heat transfer in the combustion chamber of the ICE varies with time. Thus, it took the engine 10 minutes to complete a cycle vis-a-vis transfer of heat after combustion and that the heat transfer starts after 30 seconds of combustion. In addition, the temperature of the cylinder dropped from 1273.2 K to 301 K over a period of 10 minute.
A numerical model for sediment transport and bed change with river ice

ABSTRACT Sediment transport and river morphology in cold regions can be significantly influenced by the presence of ice in winter. This paper presents a two-dimensional numerical model on sediment transport coupled with a river ice model. The sediment transport and bed change model is based on recently developed sediment transport formulations under the effect of ice cover. Incorporating changing surface ice dynamics allows the interaction of ice and sediment to be fully realized. Numerical examples are presented that illustrate surface ice effects on sediment transport and bed changes in alluvial channels. These examples include the effects of surface ice runs on sediment transport, sediment transport and bed change in the vicinity of an ice cover front, effect of ice cover breakup, and the interaction of ice jam formation with bed change. Simulation results showed the importance of considering ice effects when studying sediment and channel morphological processes in alluvial rivers.
Defensive Collaborative Multi-task Training - Defending against Adversarial Attack towards Deep Neural Networks

Deep neural network (DNNs) has shown impressive performance on hard perceptual problems. However, researchers found that DNN-based systems are vulnerable to adversarial examples which contain specially crafted humans-imperceptible perturbations. Such perturbations cause DNN-based systems to misclassify the adversarial examples, with potentially disastrous consequences where safety or security is crucial. As a major security concern, state-of-the-art attacks can still bypass the existing defensive methods. In this paper, we propose a novel defensive framework based on collaborative multi-task training to address the above problem. The proposed defence first incorporates specific label pairs into adversarial training process to enhance model robustness in black-box setting. Then a novel collaborative multi-task training framework is proposed to construct a detector which identifies adversarial examples based on the pairwise relationship of the label pairs. The detector can identify and reject high confidence adversarial examples that bypass black-box defence. The model whose robustness has been enhanced work reciprocally with the detector on the false-negative adversarial examples. Importantly, the proposed collaborative architecture can prevent the adversary from finding valid adversarial examples in a nearly-white-box setting.
Cassini observations of aperiodic waves on Saturn's magnetodisc

The location and motion of Saturn's equatorial current sheet is the result of an interplay between a quasi‐static deformation that varies in radial distance and local time, impulsive perturbations that produce large‐scale displacements, quasiperiodic perturbations near the planetary rotation period, and wave‐like structures on shorter timescales. This study focuses on the latter, aperiodic wave pulses with periods from 1 to 30 min, which are unrelated to the quasiperiodic “flapping” with a period near that of Saturn's rotation. Cassini magnetometer data were surveyed for these aperiodic structures and then fitted to a simple model in order to estimate the properties of the waves. The model consists of a modified Harris current sheet model deformed by a Gaussian pulse wave function. This then allows for the extraction of wave parameters and current sheet properties. In particular, we show an increase in current sheet scale height with radial distance from Saturn, an increase in the wave amplitude with radial distance, and the resolution of propagation directions using the wave vector fitted by the model. The dominant propagation direction is found to be radially outward from Saturn.
Decentralised adaptive architectures for control of large-scale active–passive modular systems with stability and performance guarantees

ABSTRACT Decentralised control of large-scale active–passive modular systems is considered in this paper. The considered class of large-scale systems consist of physically interconnected and generally heterogeneous modules, where local control signals can only be applied to a subset of these modules (i.e. active modules) and the rest do not admit any control signals (i.e. passive modules). Specifically, based on a set-theoretic model reference adaptive control approach predicated on restricted potential functions, we design and analyse decentralised command following control laws for each active module such that they can effectively perform their tasks in the presence of unknown physical interconnections between modules and module-level system uncertainties. The key feature of our framework allows the system error trajectories of the active modules to be contained within a-priori, user-defined compact sets. Thus, they are guaranteed to achieve strict performance guarantees, where this is of paramount importance for practical applications. In addition to our theoretical findings and research contributions, the efficacy of the proposed decentralised adaptive control architecture is demonstrated in an illustrative numerical example.
Social Stars: Modeling the Interactive Lives of Stars in Dense Clusters

Author(s): MacLeod, Morgan Elowe | Advisor(s): Ramirez-Ruiz, Enrico | Abstract: This thesis uses computational modeling to study of phases of dramatic interaction that intersperse stellar lifetimes. In galactic centers stars trace dangerously wandering orbits dictated by the combined gravitational force of a central, supermassive black hole and all of the surrounding stars. In binary systems, stars' evolution -- which causes their radii to increase substantially -- can bring initially non-interacting systems into contact. Moments of strong stellar interaction transform stars, their subsequent evolution, and the stellar environments they inhabit. In tidal disruption events, a star is partially or completely destroyed as tidal forces from a supermassive black hole overwhelm the star's self gravity. A portion of the stellar debris falls back to the black hole powering a luminous flare as it accretes. This thesis studies the relative event rates and properties of tidal disruption events for stars across the stellar evolutionary spectrum. Tidal disruptions of giant stars occur with high specific frequency; these objects' extended envelopes make them vulnerable to disruption. More-compact white dwarf stars are tidally disrupted relatively rarely. Their transients are also of very different duration and luminosity. Giant star disruptions power accretion flares with timescales of tens to hundreds of years; white dwarf disruption flares take hours to days. White dwarf tidal interactions can additionally trigger thermonuclear burning and lead to transients with signatures similar to type I supernovae. In binary star systems, a phase of hydrodynamic interaction called a common envelope episode occurs when one star evolves to swallow its companion. Dragged by the surrounding gas, the companion star spirals through the envelope to tighter orbits. This thesis studies accretion and flow morphologies during this phase. Density gradients across the gravitationally-focussed material lead to a strong angular momentum barrier to accretion during common envelope. Typical accretion efficiencies are in the range of 1 percent the Hoyle-Lyttleton accretion rate. This implies that compact objects embedded in common envelopes do not grow significantly during this phase, increasing their mass by at most a few percent. This thesis models the properties of a recent stellar-merger powered transient to derive constraints on this long-uncertain phase of binary star evolution.
Depiction of the Balkans on Internet Memes from 9GAG

Contrary to all the techno-optimistic expectations regarding the liberating and equalising impacts of online communication – especially of web 2.0 and the emerging social media – stereotypes and oppressive practices are still widespread in discourses on online platforms in many online genres, including Internet memes or meme-aggregating platforms. Researchers have studied many aspects of emergence of stereotypes regarding skin colour, sexual orientation, or gender, but there is a notable research gap in analysing stereotypes towards a special region of Europe: the Balkan Peninsula and its nations. What is more, no research can be found that examines Balkan stereotypes in Internet memes, especially not in a quantitative way on a larger sample. Working with 595 meme specimens from the popular 9GAG portal, this research seeks to learn more about this phenomenon: to find signs of the asymmetric relationship between the Western centrum and the periphery or semi-periphery, as represented by the Balkan states. This study seeks to identify the critical elements of how these stereotypes are displayed, and to compare these elements and their correlations. Another dimension of this research is a review of audience reactions as gauged by “likes”, comments and relative popularity.
Public Administration Digitalization and Government Effectiveness in EU Countries

Purpose: This study continues the series of previous research carried out by the authors on the digital transformation in EU countries. It aims to identify and analyse the progress made by EU administrations in terms of digitization and effective governance in the period 2019–2021. Design/methodology/approach: Based on selected variables from Eurostat and the World Bank databases, the article provides insights into the dynamic changes that occurred in the EU in the period under consideration. The research employs Principal Component Analysis (PCA) and K-means clustering as the primary research methods to analyse the variations and correlations among 10 numerical variables selected from Eurostat and World Bank databases. Findings and Practical Implications: The results obtained through PCA show the impact of digitalization on government effectiveness. A comprehensive analysis of government effectiveness has been conducted using PCA, which reveals that the first two principal components account for 77.05% of the initial variance. A K-means clustering with four clusters reveals that the most significant and noteworthy increase in digitalization and government effectiveness in the EU is achieved by the Nordic countries, which exhibit high levels of digitalization and government efficiency. Originality: In this ranking, clusters 2 and 3 comprise well-developed countries with a positive relation between governmental digitalization and efficiency, while cluster 4 consists of emerging countries where the correlation between digitization and governmental efficiency is low or very low, which is primarily attributed to the limited degree of digitization within government institutions.
A Novel Duo-Stage driven Deep Neural Network Approach for Mitigating Electrode Shift Impact on Myoelectric Pattern Recognition Systems

A major barrier to the commercialization of pattern recognition (PR)-based myoelectric prostheses is the lack of robustness to confounding factors such as electrode shift which has been lingering for years. To overcome this challenge, a novel Duo-Stage Convolutional Neural Network (DS-CNN) is proposed. The DS-CNN is comprised of two cascaded stages in which the first stage deciphers the occurrence of a particular kind of shift upon which a requisite CNN model is triggered in the second stage for accurate decoding of individual motion intent, which is necessary for initiating robust control of the prostheses. The proposed scheme works on raw EMG signals as input which reduces the preprocessing time that would be required in conventional machine learning-based PR schemes, to effectively mitigate both transverse and longitudinal shifts using the same network architecture. This approach was validated for four distinct electrode shift conditions (with shifts in the range of 7.50mm-10.05mm) in a dataset obtained from 18 able-bodied subjects that performed 8 classes of targeted hand gestures. The experimental results show that the proposed dual-stage driven deep neural network model can adequately resolve the effects of electrode shift with classification accuracy near the No-shift scenario (< 1.70% difference between shift mitigation and No shift scenarios). These outcomes suggest that our method can provide a practical solution for adaptation to electrode shift, thus improving the robustness of the EMG pattern recognition systems in both clinical and commercial settings.