Miracle-dz/UniFAR-FTUs huggingface dataset:
Hydrodynamic and environmental modelling in the vicinity of Scott Reef

The proposed Browse Liquid Natural Gas (LNG) Developmentincludes three offshore gas-condensate fields in the Browse Basin located 290 km from the Kimberley coast and 425 km north of Broome. As a basis for the environmental impact assessment process for the upstream development a suite of models has been set-up based on the MIKE 3 modelling package. The basic model describes the physical oceanography (hydrodynamics), while routine and non-routine discharges are simulated in separate models. The development area includes the coral atoll Scott Reef, which is located in 500m water depth on the edge of the continental shelf. Modelling the hydrodynamics in this area is challenging not least because of the steep gradients in the bathymetry. The MIKE 3 model was calibrated and validated with water level, current and temperature measurements collected over 12 months.
RoboAuditor: Goal-Oriented Robotic System for Assessing Energy-intensive Indoor Appliance via Visual Language Models

Energy auditing is a crucial step in building retrofitting to enhance building energy efficiency. However, auditing tasks, such as profiling energy-consuming appliances in buildings, rely heavily on human inspectors, resulting in a time- and capital-intensive process. To this end, we propose an autonomous robotic system, dubbed RoboAuditor, for identifying and localizing energy-intensive appliances in buildings given text queries from humans. RoboAuditor utilizes visual language models to predict relevance scores between text queries and observed images for goal selection in robot navigation. It then automatically identifies and localizes queried appliances while self-navigating with efficient navigational strategies. For evaluation, we deploy the proposed robotic system on a wheeled robot equipped with an RGB-D camera and run auditing tests in 12 residential buildings in 3D simulation. These buildings exhibit diverse room counts, appliance quantities, and navigable areas, and they all feature energy-intensive appliances, such as air conditioners, heaters, dishwashers, and refrigerators. We conduct two groups of experiments: the first group uses the relevance score, and the second serves as a control group without the relevance score. Results demonstrate that RoboAuditor detects queried appliances and accurately localizes their positions in buildings with an average success rate of 68.05%, showing a significant margin of 6.8% higher than the control group.
The Need for MORE: Unsupervised Side-channel Analysis with Single Network Training and Multi-output Regression

Deep learning-based profiling side-channel analysis has gained widespread adoption in academia and industry due to its ability to uncover secrets protected by countermeasures. However, to exploit this capability, an adversary must have access to a clone of the targeted device to obtain profiling measurements and know secret information to label these measurements. Non-profiling attacks avoid these constraints by not relying on secret information for labeled data. Instead, they attempt all key guesses and select the most successful one. Deep learning approaches form the foundation of several non-profiling attacks, but these methods often suffer from high computational complexity and limited performance in practical applications. This work explores the performance of multi-output regression (MOR) models in side-channel analysis. We start with the recently proposed multi-output regression (MOR) approach for non-profiling side-channel analysis. Then, we significantly improve its performance by updating the 1) loss function, 2) distinguisher, and 3) employing a novel concept of validation set to reduce overfitting. We denote our approach as MORE - Multi-Output Regression Enhanced, which emphasizes significantly better attack performance than MOR. Our results demonstrate that combining the MORE methodology, ensembles, and data augmentation presents a potent strategy for enhancing non-profiling side-channel attack performance and improving the reliability of distinguishing key candidates.
Exploring the therapeutic implications of nanoparticles for liquid tumors: A comprehensive review with special emphasis on green synthesis techniques in the context of Dalton’s lymphoma

Nanoparticles (NPs) have emerged as promising tools for treating and diagnosing various diseases, including cancer, human immunodeficiency virus (HIV), and immune-related diseases. Multiple reviews have been published compiling the effects of NPs on different illnesses. However, a few reviews are available regarding the impact of NPs on liquid tumors. Therefore, this review aims to advance knowledge in this direction by drawing conclusions from research on Dalton’s lymphoma (DL), a type of T-cell lymphoma, for the therapeutic implication for liquid tumors. NPs synthesized from both metals (such as silver, gold, copper, and zinc) and nonmetals (such as silica, selenium, polymer, and lipid-based) have been tested for their anticancer efficacy against DL. These NPs reduce the viability of DL cells in a dose-dependent manner by inducing apoptosis. In mice, NPs increase the lifespan, reduce tumor growth, and restore the normal physiology and structure of organs such as the liver, spleen, and kidney. NPs synthesized from plant sources or through some chemical compounds proved effective in reducing the cytotoxicity of NPs. This review will provide basic information on DL and discuss the various types of NPs used against it, their significance for therapeutics of T-cell lymphoma or liquid tumors and conclude with some major findings that can guide future research in this field. By synthesizing the latest research on NPs and DL, this review will serve as a valuable resource for researchers and clinicians alike in their efforts to combat liquid tumors.
Pregnancy-Related Information-Seeking Behavior and Information Literacy Profiles of Women at Reproductive Age

ABSTRACT The purpose of this article is to capture pregnancy-related information seeking behavior of women and its correlation with socio-economic status and with Internet health information literacy profiles. A user survey based on Wilson’s’ macro-model of information seeking behavior was designed and carried out. The survey included 289 women at reproductive age who provided valid feedback on information needs, motives for seeking pregnancy-related information online, information resources employed and Internet information literacy. Responses were collected via an anonymous questionnaire and processed using SPSS (Statistical Package for Social Sciences) version 25.0. The results demonstrate that women have increased information needs during pregnancy, that they are primarily concerned about the health of their embryos and that they tend to turn mainly to their doctors, their family and to the Web for obtaining pregnancy-related information. Internet is most used by younger women and online information is generally deemed useful. The analysis of survey responses indicated that pregnancy-related information literacy profiles are influenced by women’s educational status and age. Official online information sources should be made available in Greece, where women could find useful and trusted information about pregnancy issues.
ASTRAGALUS GHAMISHLUENSIS (FABACEAE), A NEW SPECIES OF SECT. AMMODENDRON FROM IRAN

Arezoo Dastpak (correspondence, e mail: Dastpak@srbiau.ac.ir, Dastpak55@gmail.com.) and Taher Nejadsattari, Department of Plant Science, Faculty of Basic Sciences, Science and Research Branch, Islamic Azad University, Tehran, Iran. -Aliasghar Maassoumi, Research Institute of Forests and Rangelands, Tehran 13185-116, Iran Shahrokh Kazempour Osaloo, Department of Plant Biology, Faculty of Biological Sciences, Tarbiat Modares University, Tehran 14115-175, Iran.
From the ‘Workshop of the World’ to an emerging global city-region: Restructuring of the Pearl River Delta in the advanced services economy

For a long time, China’s economic growth and urbanization are recognized as a development outcome of its astonishing manufacturing industries. However, this conventional wisdom is increasingly challenged by the rapid growth of services, especially advanced services, in the country in recent years. The rise of advanced services is leading to a new wave of economic and urban transitions in China. This study explores the development implications of the advanced services economy for Chinese cities and regions using one of the most archetypical manufacturing city-regions in the country- the Pearl River Delta (PRD). It aims to understand how this ‘workshop of the world’ is being restructured by advanced service activities under the conditions of contemporary globalization, and how this process is shaped by the region’s special economic, political, institutional and cultural contexts. Multiple data and methods, including both quantitative and qualitative ones, are employed to explore different research questions. The study contributes to three key issues (debates) that revolve around the development implications of the advanced services economy in the urban and regional context: a) the impacts of advanced services on the spatial transformations of city-regions, b) the global and local dynamics that contribute to urban transitions in the new economy, and c) the challenges to urban policy and planning presented by such transitions.
Renal Oxidative Status in Diabetic Wistar Rats Administered Methanol Fraction of Ethanol Extract of Dialium guineense Stem Bark

days with metformin (50 mg/kg bwt) or the extract at doses of 200 and 300 mg/kg bwt, respectively. The results showed that the activities of catalase, superoxide dismutase (SOD), glutathione peroxidase (GPX), and Glutathione Reductase (GR) as well as levels of reduced glutathione (GSH) and % GSH were significantly lower in diabetic control group than in the normal control group, but they were increased by MEDG treatment (p < 0.05). On the other hand, the levels of nitric oxide (NO) and malondialdehyde (MDA) elevated by STZ were markedly reduced after treatment with MEDG (p < 0.05). It can be concluded that MEDG stem bark possesses an ameliorative potential against STZ-induced oxidative stress in rats.
Computer vision for unmanned aerial vehicles in agriculture: applications, challenges, and opportunities

Unmanned Aerial Vehicles (UAVs), commonly called drones, have gained significant attention in agriculture due to their potential to revolutionize traditional farming practices. This paper explores the integration of computer vision techniques with UAV technology to enhance agricultural processes. The fusion of these two domains has the potential to provide farmers and agronomists with valuable insights for optimized decision-making. The primary focus of this research is to showcase the various applications of computer vision for UAVs in agriculture. The paper begins by presenting an overview of the challenges faced by modern agriculture, such as resource optimization, crop monitoring, disease detection, and yield prediction. It then delves into the technological advancements in UAVs and their suitability for addressing these challenges.
Temporal dynamics of steppe plant communities

Global climate change affects the conditions of ecosystems. However, the nature of changes induced by climatic factors remains unknown due to the complex nature of climatic transformations. The global trend of temperature increase is associated with an increase in precipitation and changes in its rhythm. The dynamics of plant communities under the influence of climate occurs against the background of natural successional phenomena. The aim of the study is to develop methodological approaches in order to identify aspects of vegetation variability that are caused by global climate change and give them an ecological interpretation. Geobotanical descriptions of vegetation in steppe ecosystems were carried out over the last 20 years. Exactly in this period dramatic climatic changes were observed, which allows to compare climatic and vegetation trends. For ecological interpretation the results of ordination of plant communities were explained with the help of phytoindication scales. Plant communities exhibit dynamics that are driven by endogenous and exogenous causes. These aspects of community dynamics were differentiated using the ordination procedure as different ordination axes. The axes that reflect endogenous dynamics were described using temporal variables. The axes that reflect exogenous dynamics were described using phytoindication scales. The phytoindication scales made it possible to assess the ecological directions of plant community transformation under the influence of global climate change. The transformation of the plant community under the influence of global climate change is inconsistent with the concepts of desertification. The revealed temporal patterns of the plant community have a complex and multidirectional trajectory. The plant community studied over the twenty-year investigation period exhibited a dynamic that is a superposition of two processes that are driven by the temperature and precipitation. The temperature trend is directional and reflects the tendency of global warming. This trend is accompanied by an increase in species richness and projective cover of the plant community. At the same time, thermophilicity and continentality of the community increase against the decrease of soil trophic status, acidity increase and soil carbonation decrease. Obviously, along with the mineralization of organic matter, the soil decarbonization can also be the cause of increased carbon dioxide emission into the atmosphere as a result of global warming. The trend, which is regulated by precipitation, is oscillatory. This trend changes the regime of moisture, light and cryoclimate of the plant community.
Pulmonary disease detection and classification in patient respiratory audio files using long short-term memory neural networks

Introduction In order to improve the diagnostic accuracy of respiratory illnesses, our research introduces a novel methodology to precisely diagnose a subset of lung diseases using patient respiratory audio recordings. These lung diseases include Chronic Obstructive Pulmonary Disease (COPD), Upper Respiratory Tract Infections (URTI), Bronchiectasis, Pneumonia, and Bronchiolitis. Methods Our proposed methodology trains four deep learning algorithms on an input dataset consisting of 920 patient respiratory audio files. These audio files were recorded using digital stethoscopes and comprise the Respiratory Sound Database. The four deployed models are Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), CNN ensembled with unidirectional LSTM (CNN-LSTM), and CNN ensembled with bidirectional LSTM (CNN-BLSTM). Results The aforementioned models are evaluated using metrics such as accuracy, precision, recall, and F1-score. The best performing algorithm, LSTM, has an overall accuracy of 98.82% and F1-score of 0.97. Discussion The LSTM algorithm's extremely high predictive accuracy can be attributed to its penchant for capturing sequential patterns in time series based audio data. In summary, this algorithm is able to ingest patient audio recordings and make precise lung disease predictions in real-time.
Effects of Wisdom on Mental Health in Old Age: Exploring the Pathways Through Developmental Tasks Attainment and Self-Rated Health

Purpose A number of studies indicate a relationship between wisdom and mental health in older adults, which can be explained by different pathways. However, the role of developmental task accomplishment, and of self-rated health in the relationships between wisdom and mental health in later life remain unclear. The aim of the current study is to explore the structure of the relationships between wisdom, developmental task attainment, self-rated health, and positive and negative mental health outcomes (viz. wellbeing and depressive symptoms) in older adults. Methods The study included 381 Polish older adults aged 60–86 (M= 67.33; SD = 5.08). The respondents completed the Personal Wellbeing Index, Geriatric Depression Scale, Three-Dimensional Wisdom Scale, Developmental Tasks Attainment Questionnaire for Seniors, self-rated health scale and sociodemographic survey. Results Three-dimensional wisdom, developmental task attainment and self-rated health scores were positive predictors of personal wellbeing and negative predictors of depressive symptoms in older adults. Structural equation modelling revealed that developmental tasks attainment and self-rated health partially mediated the relations between wisdom and mental health in older adults. Conclusion It can be assumed that wise older adults implement developmental tasks more efficiently and assess their subjective health more favorably, which may strengthen their personal wellbeing and prevent depression. The findings suggest that wisdom plays an important role in healthy aging.
In Vitro Antibacterial Activity of Flower Extract of Leucas Indica

İn this study, the antimicrobial activities of methanol extracts of flowers of Leucas indica was investigated by using agar well diffusion method against selected ten bacterial species (Pseudomonas (MTCC 424), Corynebacterium (MTCC 1349), Spingomonas (MTCC 6362), Bacillus subtilis (MTCC 441), Bacillus licheniformis (MTCC 429), Bacillus coagulans (MTCC 5856), Streptococcus (MTCC 9724), Klebsiella pneumonia (MTCC 452), E. coli (MTCC 443), and Staphyloccus (MTCC 3160). Among the ten bacterial species studied, the flower extract at concentrations of 20µg and 40µg showed antimicrobial activity against Klebsiella pneumonia, E. coli, Streptococcus, Bacillus subtilis, Spingomonas, Staphyloccus and Corynebacterium respectively.
Application of the Reconfigurable Environmental Intelligence Platform for HVAC Control

Researchers of varying experience levels need to be able to quickly prototype and deploy sensor networks so that they can collect data and analyze their phenomena of interest as soon as possible. However, custom sensor deployments are subject to several constraints including computing resources available and types of data that are being acquired. We show how the use of the Reconfigurable Environmental Intelligence Platform (REIP) streamlines the process of multimodal environmental sensing. We offered the original sensors to a group of student researchers to monitor indoor occupancy alongside temperature and humidity in a modern office building. We demonstrate how the modular design of REIP made such a study feasible for young researchers in the context of a course project. The study resulted in findings leading to practical solutions on how air conditioning and ventilation systems can be operated more efficiently to minimize the building’s energy use without affecting the comfort of its residents. It also demonstrates the potential of REIP for the rapid prototyping of multimodal sensor networks that can, in turn, enable a more data-driven approach to decision-making.
Continuum semiconductor physics model compression via Data-driven Discrete Exterior Calculus.

High-fidelity partial differential equations (PDE) based continuum models of physical conservation laws are a standard tool in research and engineering. Often, it becomes necessary to produce reduced-order models of these same physical systems in order to explore large parameter spaces, couple hierarchies of models, and etc. In this talk, we will present our method for producing machine-learned reduced order models formulated using the mechanism of Datadriven Discrete Exterior Calculus (DDEC), trained on high-fidelity PDE physics data and in which the natural expression of physical conservation laws can be embedded in the machine learning architecture [4],[2]. As an example, we will focus on a semiconductor physics continuum PDE model involving the drift-diffusion equations for electron and hole transport coupled with the Poisson equation for the electric potential. We will present a spectral graph coarsening method
Etude et modélisation de la turbulence homogène stratifiée instable

Cette these est consacree a l’etude de la turbulence homogene stratifiee instable, un ecoulement idealise decrivant l’evolution de la turbulence au sein d’une zone de melange de type Rayleigh-Taylor. Cette approche se concentre sur l’evolution des quantites fluctuantes ;l’influence de l’ecoulement moyen est prise en compte au travers d’un gradient moyen de densite. Un modele spectral est utilise pour etudier cette turbulence, conjointement a des simulations numeriques directes. En comparaison avec ces simulations, l’etape de validation du modele met en lumiere le role des termes de stratification sur la dynamique du transfert d’energie. Une premiere etude montre l’etablissement, dans l’etat autosemblable, de lois d’echelles ainsi que l’influence de la distribution initiale d’energie sur l’etat asymptotique et sur l’anisotropie de l’ecoulement. Dans une seconde etude, la retroaction de la turbulence sur le gradient moyen est introduite, dans un premier temps, afin de rapprocher la dynamique autosemblable de la turbulence homogene stratifiee instable de celle observee en turbulence Rayleigh-Taylor. Dans un second temps, l’influence d’un renversement de la stratification sur la dynamique du melange est etudiee au travers d’un profil d’acceleration variable.
Age and Geochemistry of High Arctic Large Igneous Province Tholeiitic Magmatism in NW Axel Heiberg Island, Canada

The Cretaceous High Arctic Large Igneous Province (HALIP) in Canada involved extrusion of continental flood basalts (CFBs) at 130–120 Ma and 100‐95 Ma and emplacement of an extensive sill and dike network that intersected the Carboniferous to Paleogene Sverdrup Basin. In this paper, we present new 40Ar/39Ar ages, major and trace elements, and Sr‐Nd‐Pb isotope ratios for HALIP lava, dikes, and sills from Bukken Fiord, NW Axel Heiberg Island, Canadian Arctic Islands. Our best constrained 40Ar/39Ar ages yield a weighted average of 124.1 ± 1 (2σ) Ma, coincident with the first pulse of tholeiitic CFB magmatism in the Arctic‐wide HALIP as exemplified by Isachsen Formation flood basalts on Axel Heiberg Island. The Bukken Fiord samples are plagioclase and clinopyroxene‐phyric tholeiitic basalts, are relatively evolved (3.2–6.5 wt% MgO), and share similar major and trace element compositions to typical HALIP tholeiites. Initial 143Nd/144Nd ranges from 0.51260 to 0.51291 and initial 87Sr/86Sr ranges from 0.70362 to 0.70776, while measured 206Pb/204Pb, 207Pb/204Pb, and 208Pb/204Pb range from 18.614 to 19.199, 15.534 to 15.630, and 38.404 to 39.054, respectively. The most primitive sample in this study has Sr‐Nd‐Pb isotope signatures that suggest an enriched plume‐derived mantle source for HALIP tholeiites. Most samples, however, possess relatively radiogenic isotope signatures that can be explained by moderate degrees of assimilation of Sverdrup Basin sedimentary rocks. Magma‐crust interaction in the HALIP plumbing system was likely widespread and may have increased the environmental impact of the HALIP, particularly if crustal carbon was volatilized.
Real-time Face Recognition System Using Deep Learning Method

Face recognition is one of the most popular methods currently used for biometric systems. The selection of a suitable method greatly affects the reliability of the biometrics system. This research will use Deep learning to improve the reliability of the biometric system and will compare it with the SVM method. The Deep Learning method will be adopted using the Siamese Network with the YoloV5 detection method as a real-time face detector. There are two stages in this research: the registration process and the recognition process. The registration process is image acquisition using YoloV5. The image result will be saved in the storage folder, and the preprocessing and training process will use the Siamese Network. The face feature model will be stored in the database. The recognition process is the same as the registration, but the feature extraction result will be embedded and compared with the already trained models. The accuracy rate using the Siamese model was 94%.