Miracle-dz/UniFAR-FTUs huggingface dataset:
Identification of winter canola based on dual-polarized SAR datasets in hilly mountainous areas of southwest China

ABSTRACT To improve the accuracy of canola identification in the southwest cloudy and foggy mountains of China, this letter puts forward a group of canola recognition features with the multi-temporal Sentinel-1 images. The Jeffries-Matusita (J-M) distance of sample types and the accuracy of canola extraction of support vector machine were compared. The results showed that the J-M values of canola crop with forest land and other green vegetation were improved obviously, among which the J-M value of forest land category increased to 1.93, and that of other green vegetation 1.82. The producer’s accuracy (PA) of canola was improved to 70.93%, and the user’s accuracy (UA) was increased to 75.86% with F1-measure 73.31%. The canola crop area recognition accuracy was 82.58%. The feature combination can enhance the distinguishability among sample categories, improve the extraction accuracy of canola crops, but the results still have a gap with the reliable results proposed by previous scholars due to the complex crops planting structure, the irregularity of the plot and the resolution of Synthetic Aperture Radar (SAR) images. This research can provide a reference for the distribution extraction of canola in mountain areas.
Bayes classification of terrain cover using normalized polarimetric data

The normalized polarimetric classifier which uses only the relative magnitudes and phases of the polarimetric data is proposed for discrimination of terrain elements. For polarimetric data with arbitrary probability density function, the distance measures of the normalized polarimetric classifier based on a general class of normalization functions are shown to be equivalent to one another. When the system absolute calibration factors are common to all polarimetric channels, the normalized polarimetric classifier derived is shown to be optimal. Further assuming a complex Gaussian distribution of the unnormalized data, the distance measure of the normalized polarimetric classifier is given explicitly and is shown to be independent of the number of scatterers illuminated. The usefulness of the normalized polarimetric classifier is demonstrated by the classification of grass and tree regions in experimental data obtained from the Massachusetts Institute of Technology Lincoln Laboratory. The classification errors generated using the optimal normalized polarimetric classifier are shown to be smaller than those generated using other types of classifiers which employ only magnitude ratios or phase differences to classify radar images.
An empirical investigation of macroeconomic determinants of public debt in Ghana

Abstract Ghana has a lengthy history of accumulating public debt, primarily driven by the expansion of fiscal deficits. This has resulted in a persistent increase in the public debt ratio, as the country borrowed both externally and internally to stimulate economic growth and augment its capital stock. However, this trajectory has led to concerns about debt sustainability. The study examines the macroeconomic determinants of public debt accumulation in Ghana. It aims to analyse these determinants’ short- and long-term effects using the autoregressive distributed lag (ARDL) model. The study finds a positive association between merchandise trade and public debt, indicating that an increase in trade due to heavy reliance on external trade to meet domestic consumption needs leads to a rise in public debt. Furthermore, it identifies a positive relationship between gross fixed capital formation (growth) and public debt, indicating that as the economy grows and more capital is invested in fixed assets, public debt also increases. The study also reveals that higher interest payments contribute to a greater accumulation of public debt. It further demonstrates that government spending plays a crucial role in shaping the trajectory of public debt, as an increase in spending leads to an increase in public debt. The findings suggest that fiscal policies, external borrowing, trade, interest payments, and budget deficits substantially influence public debt levels in Ghana. As a result, the study highlights the need for policies that promote fiscal discipline, debt sustainability, and transparency to manage public debt effectively.
Congruence of Leader-follower Evaluations and the Effect of Leadership Styles on Work Engagement

The shift into relationship-oriented, positive leadership theories in recent decades brought with it a deeper investigation of leader-follower interactions. Transformational and engaging leadership styles are widely assumed to be proven determinants of work engagement, although these conclusions are based mostly on followers' evaluations. Therefore, we analysed followers' and leaders' evaluations simultaneously and used congruent results to test which leadership dimensions positively impact followers' work engagement. Our results show that follower-rated passive and charismatic leadership negatively affect work engagement. In contrast, follower-rated inspirational communication and passive leadership rated by leaders positively affect above-average work engagement. From them, inspirational communication seemed to be the most influential factor. These results draw attention to the importance of leaders' communication and emphasise its role in leader-follower interactions.
Design Optimization of Permanent Magnet Eddy Current Coupler Based on an Intelligence Algorithm

The permanent magnet eddy current coupler (PMEC) solves the problem of flexible connection and speed regulation between the motor and the load and is widely used in electrical transmission systems. It provides torque to the load and generates heat and losses, reducing its energy transfer efficiency. This issue has become an obstacle for PMEC to develop toward a higher power. This paper aims to improve the overall performance of PMEC through multi-objective optimization methods. Firstly, a PMEC modeling method based on the Levenberg-Marquardt back propagation (LMBP) neural network is proposed, aiming at the characteristics of the complex input-output relationship and the strong nonlinearity of PMEC. Then, a novel competition mechanism-based multi-objective particle swarm optimization algorithm (NCMOPSO) is proposed to find the optimal structural parameters of PMEC. Chaotic search and mutation strategies are used to improve the original algorithm, which improves the shortcomings of multi-objective particle swarm optimization (MOPSO), which is too fast to converge into a global optimum, and balances the convergence and diversity of the algorithm. In order to verify the superiority and applicability of the proposed algorithm, it is compared with several popular multi-objective optimization algorithms. Applying them to the optimization model of PMEC, the results show that the proposed algorithm has better comprehensive performance. Finally, a finite element simulation model is established using the optimal structural parameters obtained by the proposed algorithm to verify the optimization results. Compared with the prototype, the optimized PMEC has reduced eddy current losses by 1.7812 kW, increased output torque by 658.5 N · m, and decreased costs by 13%
A Short Literature on Linear Programming Problem

Researchers and scientists have developed various approaches and methodologies over time to model and analyze different types of linear programming problems, such as assignment problems and parametric programming problems. This paper provides a critical review and classification of existing modelling approaches and solution methods related to linear programming problems. Moreover, the simplex method is discussed in detail through a comprehensive literature review. The paper concludes by presenting an integrated research framework that is directly applicable to the present context, along with suggestions for future research directions.
Cervids in a dynamic northern landscape: Holocene changes in the relative abundance of moose and red deer at the limits of their distributions

It is predicted that future climate change will have a significant impact on the distribution of large ungulates on a continental scale. At the same time, changes in human land use on a more local scale may affect their distribution and dispersal abilities, possibly confounding the effects of climate. We analyze changes in the Holocene distribution and relative abundance of Alces alces (moose) and Cervus elaphus (red deer) skeletal remains along an overlapping range boundary of these species in western Norway. As moose and red deer are adapted to different climatic conditions we would expect the distribution of finds to reflect large-scale changes in climate. In accordance with this prediction our results indicate that red deer became the predominant ungulate in this area during the mid-Holocene warm period, c. 8000–4000 cal. BP. Contrary to this, remains of moose became even less abundant in the subsequent colder period to the present. This decrease seems tied to the spread of agriculture and deforestation, indicating the importance of considering changes in land use when predicting future changes in ungulate distribution.
A multi-agent architecture for learning paths-based personalized e-learning systems

The application of artificial intelligence and semantic models for increasing learning and tutoring quality by personalization is an emerging research area. Мulti-agent frameworks facilitate the communication between the different components and ontological models can be used as knowledge sources for intelligent agents. In this research we analyse knowledge models and software architectures, outline trends in the personalized learning area and propose an agent-based architecture for e-learning systems that can conduct learning by generating and recommending personalized learning paths. Initial evaluation of prototype system is proposed and learning path generation scenarios are discussed.
Predicting Bankruptcy on Oil Companies Before and After the Pandemic Using Two Altman's Z-Score Models. Industrial and Emerging Markets. Evidence from Greece

This paper is prepared with the aim of establishing whether there is a possible bankruptcy of the Greek oil companies Listed on the Athens Stock Exchange in the last six years 2016-2021. This period under consideration also includes the two years of the 2019-2020 pandemic. Design/methodology/approach -The effort of this study is carried out using the Altman Z-Score model bankruptcy model for Emerging Markets. This paper focuses on bankruptcy, a situation which is a consequence of financial failure. Bankruptcy can happen to any organization or government body. The consequences of bankruptcy, as is known, are many and important. The main ones are the possible increase in unemployment, the decrease in the standard of living and in general the creation of a climate of economic discomfort. Findings -The results of this study indicate that the exit of the Greek economy from the Memorandum of the IMF and Eurozone lenders had a positive effect on the Greek oil companies listed on the Athens Stock Exchange, which is known as an emerging market. We must also note that the two years of the pandemic did not have a significant negative effects on bankruptcy issues for the same companies. Practical implications -The use of the two Altman's Z-Score model in this study, helps the stockholders and investors to have a conclude financial icon for stocks and bonds and make the right decisions. Originality/value -In order to avoid bankruptcy and therefore the negative consequences that accompany it, many economic scientists have made efforts to predict it. Its prediction can be lifesaving and ultimately prevent it. Among the many important models that have been made, for predicting bankruptcy is the Altman Z-Score bankruptcy model that has stood out because of its very good results in bankruptcy prediction. This particular model is implemented in Greece which is a country with such a strong economic crisis, which has plagued the Greek Economy since 2010, but also during the pandemic period where almost all corporate firms are struggling daily with bankruptcy. Specifically, the Z-Score Altman model will be applied comparatively for the years 2016-2021 to the companies ELIN S.A. , MOTOR OIL and REVOIL S.A. E.P., ELPE S.A. , which are listed on the Athens Stock Exchange, in order to establish whether there has been or if there is the possibility of bankruptcy of these companies during the time period of our study.
Biodisintegration of human mucin protein by protease produced from Escherichia coli AJ55 isolated from Urinary Tract Infection of Iraqi pa-tients

Proteases have various applications in the food, pharmaceutical, medicine, pathogenicity of some pathogenic bacteria, and detergent sectors as well as meeting the needs of approximately 60% of the global enzyme industry, whereas they catalyze the breakdown of protein molecules into peptides and amino acids. Production and purification of protease enzyme by the isolate Escherichia coli AJ55 was scrutinized in the present study. Cultivation optimum conditions, were various complex medium, carbon source, nitrogen source, temperature, pH of the medium, and time of incubation were optimized to enhance the total protease production in shake flask culture of E.coli AJ55. The nutrient broth supplemented with 2% glucose and 2% yeast extract, with a pH of 7.0 and incubated at 37 °C for 24 hours, better conditioned for producing the maximum production of protease. Escherichia coli AJ55 proteolytic enzyme was separated and purified using ion-exchange chromatography on a DEAE-Cellulose column and Sephadex G-150 gel after being precipitated with 0-70% saturated ammonium sulfate. Protease that had been partially purified had a yield of 34%, a purification fold of 13.4, an activity of 12.16 U/ml, a protein concentration of 0.005 mg/ml, and a specific activity of 2432 U/mg. By using gel filtration chromatography on a Sephadex G-150 gel, partially purified protease was examined for its ability to cleave the mucin protein. The findings of mucin biodegradation showed that the five fractions of the small peptides were produced after treatment of mucin with partially purified protease.
An improved mass budget for the Greenland ice sheet

Extensive ice thickness surveys by NASA's Operation IceBridge enable over a decade of ice discharge measurements at high precision for the majority of Greenland's marine‐terminating outlet glaciers, prompting a reassessment of the temporal and spatial distribution of glacier change. Annual measurements for 178 outlet glaciers reveal that, despite widespread acceleration, only 15 glaciers accounted for 77% of the 739 ± 29 Gt of ice lost due to acceleration since 2000 and four accounted for ~50%. Among the top sources of loss are several glaciers that have received little scientific attention. The relative contribution of ice discharge to total loss decreased from 58% before 2005 to 32% between 2009 and 2012. As such, 84% of the increase in mass loss after 2009 was due to increased surface runoff. These observations support recent model projections that surface mass balance, rather than ice dynamics, will dominate the ice sheet's contribution to 21st century sea level rise.
Anxiety, Depression and Stress Symptoms among Physicians Working with COVID 19 Patients

: BACKGROUND : Since the end of December 2019, the world has been fighting one of the most serious pandemics in human history due to coronavirus disease 2019 (COVID-19). A pandemic that makes health care workers susceptible of developing depressive, anxiety symptoms now or in the future. OBJECTIVE: To identify symptoms of depression, anxiety and stress among physicians managing COVID 19 patients in Iraq and to ascertain association with socio-demographic and clinical variables. SUBJECTS AND METHODS: A cross-sectional study (descriptive and analytical) was done from the 1 st of September to the 1 st of November 2020. Physicians working in different Iraqi health care institutions (general hospitals, primary health care centers and COVID 19 quarantine centers) were asked to submit an online survey. RESULTS: Out of the 656 physicians who were included in this study, 61.9% had stress symptoms, 27.0% had anxiety symptoms and 27.1% had depressive symptoms. Stress related symptoms were more prevalent in younger age, females, single physicians, having less years of experience and lower academic degree, working in COVID 19 isolation wards, avoiding contact with the family and being infected with COVID 19. These factors were positively associated with developing stress symptoms. Anxiety symptoms were positively associated with married physicians and working in COVID 19 isolation wards. Depression was not significantly associated with any of the studied variables. CONCLUSION: COVID 19 pandemic negatively impacts the psychological status of physicians as depression, anxiety and stress symptoms were found in different levels.
DID TODAY'S OASES SHRINK GREATLY IN CONTRAST TO ANCIENT OASES──SOME NEW INSIGHT INTO OASIS DESETIFICATION PROCESS DURING HISTORICAL TIMES

Research on Dunhuang oases in Hexi Corridor and tho se in lowel reaches of Shiyanghe during historical times showed that desertifica tion process does not mean the shrinkage of the total area of an oasis but rathe r a kind of oasis migration to a great extent,and consequence of unreasonal anth ropogenic activities.Oases migration process resulted from shifting of water res ources and their redistribution in oases is by no means of constant shrinkage and vanishing of oa ses. However, along with this kind of migration process, original oases were aba ndoned and new ones occurred. Desertification might occur here but new oases mig ht form in other places as oasis is a product of actions of water resources. So far as there is micro climatic circulation regime and no great changes in draina ge total water amount, there would be no great changes in the total area of oase s. So the view that today's oases have been greatly reduced in comparison with a ncient oases lacks both theoretical and historical evidences.
Stress evolution and seismicity in the central-eastern United States: Insights from geodynamic modeling

Although the central and eastern United States is in the interior of the presumably stable North American plate, seismicity there is widespread, and its causes remain uncertain. Here, we explore the evolution of stress and strain energy in intraplate seismic zones and contrast it with that in interplate seismic zones using simple viscoelastic fi niteelement models. We fi nd that large intraplate earthquakes can signifi cantly increase Coulomb stress and strain energy in the surrounding crust. The inherited strain energy may dominate the local strain energy budget for thousands of years following main shocks, in contrast to interplate seismic zones, where strain energy is dominated by tectonic loading. We show that strain energy buildup from the 1811–1812 large events in the New Madrid seismic zone may explain some of the moderate-sized earthquakes in this region since 1812 and that the inherited strain energy is capable of producing some damaging earthquakes (M >6) today in southern Illinois and eastern Arkansas, even in the absence of local loading. Without local loading, however, the New Madrid seismic zone would have remained in a stress shadow where stress has not been fully restored from the 1811–1812 events. We also derived a Pn velocity map of the central and eastern United States using available seismic data, the results do not support the NMSZ being a zone of thermal weakening. We simulated the long-term Coulomb stress in the central and eastern United States. The predicted high Coulomb stress concentrates near the margins of the North American tectosphere, correlating spatially with most seismicity in the central and eastern United States.
Scalar Control of Single-Phase Induction Motor Using PV Fed Modified Seven Level H-Bridge Inverter with Improved Sinusoidal PWM Technique

Scalar Control of Single-Phase Induction Motor Using PV-Fed Modified Seven-Level H-Bridge Inverter with Improved Sinusoidal PWM Technique" proposes an advanced modulation technique for controlling a modified H-Bridge solar photovoltaic (PV) fed seven-level inverter, designed to drive a single-phase 230V, 1/2 HP capacitor start-run induction motor (IM). This paper introduces an innovative H-bridge seven-level inverter topology achieved by optimizing the number of switches necessary to generate seven distinct voltage levels. The induction motor's speed is regulated using a scalar control approach. Pulse width modulation (PWM) signals, crucial for controlling the switches in the seven-level inverter, are generated using a combination of three identical sine wave signals acting as reference signals. These signals have an offset equal to the amplitude of the triangular carrier signal. The proposed modulation technique is validated through simulation in the MATLAB/Simulink® environment. Results demonstrate that the seven-level inverter efficiently suppresses harmonics by over 50% compared to three and five-level inverters without the need for an external LC filter. Additionally, the proposed technique significantly reduces the voltage stress across switches by 35%, even with a reduced number of switches utilized.
Engineering Inhalable Therapeutic Particles: Conventional and Emerging Approaches

Respirable particles are integral to effective inhalable therapeutic ingredient delivery, demanding precise engineering for optimal lung deposition and therapeutic efficacy. This review describes different physicochemical properties and their role in determining the aerodynamic performance and therapeutic efficacy of dry powder formulations. Furthermore, advances in top-down and bottom-up techniques in particle preparation, highlighting their roles in tailoring particle properties and optimizing therapeutic outcomes, are also presented. Practices adopted for particle engineering during the past 100 years indicate a significant transition in research and commercial interest in the strategies used, with several innovative concepts coming into play in the past decade. Accordingly, this article highlights futuristic particle engineering approaches such as electrospraying, inkjet printing, thin film freeze drying, and supercritical processes, including their prospects and associated challenges. With such technologies, it is possible to reshape inhaled therapeutic ingredient delivery, optimizing therapeutic benefits and improving the quality of life for patients with respiratory diseases and beyond.