Miracle-dz/UniFAR-FTUs huggingface dataset:
Quasi-Experimental Determination of Turbulent Dispersion Parameters for Different Stability Conditions from a Tall Micrometeorological Tower

Turbulent wind data measured by sonic anemometers installed at various heights on a 140-m-tall micrometeorological tower located at a coastal site are used to obtain vertical profiles of the velocity standard deviations σi, Lagrangian decorrelation local time scales TLi, and eddy diffusivities Kα for distinct stability conditions. The novelty of the study lies in the use of turbulent data directly measured over the extension of the atmospheric surface layer at a coastal site for that purpose. Furthermore, the approach employs the Hilbert–Huang transform to determine the wind energy spectral peak frequencies. These are applied to the asymptotic spectral equation from Taylor statistical diffusion theory to obtain the turbulent dispersion parameters, which are shown to generally agree well with those provided by a classical autocorrelation approach. For neutral and stable situations the vertical profiles of momentum eddy diffusivities agree well with those derived from the spectral and autocorrelation method. Additionally, the turbulent integral time scales and eddy diffusivities determined by the method at a coastal location are found to overestimate those predicted from analytical expressions based on continental field observations. The turbulence parameters found are suitable to be employed in air pollution dispersion models.
Effects of Vegetation Structure on Ant Diversity in Different Seasonal Periods in Impacted Fragments of Atlantic Forest

The destruction of forest habitats generates extremely fragmented areas and changes in vegetation structure. That changes the characteristics of microhabitats and the availability of resources and affects the diversity of animal species such as ants, which are a large group with an important ecological role. As ants forage on trees and shrubs, the vegetation structure can affect the presence of these organisms. Abiotic factors such as seasonality can also influence the diversity of the ant community. Based on that, this study aimed to investigate the distribution of the diversity of ant species that forage on plants in different seasonal periods. The study was carried out in eight secondary forest fragments of the Atlantic Forest. We used additive partitioning of diversity, partitioning of β diversity and canonical correspondence analysis to measure the effect of seasonality and vegetation structure on the distribution of ant species diversity. The rainy season positively influences the diversity of ants that forage on plants. Composition was affected by shrub leaf density in the rainy season and by tree circumference in the dry. Each fragment preserved a different pool of ant species, and the vegetation structure positively influenced ant diversity. These results highlight the importance of conserving the majority of forest fragments to better preserve the pool of ant species regionally.
Successful Treatment of Apathy with Electroconvulsive Therapy

Apathy is a debilitating syndrome associated with many neurological and psychiatric disorders. Although the underlying mechanisms of apathy remain unclear, the impact of apathy on quality of life is substantial. The management of apathy and apathy-like behaviors remains challenging in daily clinical routine. There is a lack of consensus with respect to treatment, with approaches typically focusing on the management of the underlying primary disorder. We report on a case of a 56-year-old female patient who presented with prominent apathy and treated with a course of right unilateral ultra-brief electroconvulsive therapy (RUL-UB ECT). Although ECT has potential to worsen cognition, in particular short-term memory, which may manifest as a symptom of apathy, in our case the patient exhibited significant improvement in apathy after treatment. This case highlights the challenges of treating patients presenting with apathy features and that ECT should be considered in complex presentations due to its ability to resolve symptoms that are not addressed by other treatment modalities.
Enhanced Recognition system for Diabetic Retinopathy using Machine Learning with Deep Learning Approach

Diabetic is the primary main reasons for Diabetic Retinopathy (DR). If Diabetic Retinopathy is untreated for long term, then it leads to total eye blindness. Now days, prevention of DR is a major challenging task, and moreover it reduces the overall risk of eye blindness. Machine learning and Deep learning is valuable methods to identifying and aiding DR diagnosis. In this paper, new method is proposed by Machine learning and Deep learning technique. In this proposed system, Kaggle dataset is used for training and testing. Totally 3662 images, in which the 2744 images is used to train and remaining 546 images are used to test the model. This system involves classifying using Convolution Neural Network (CNN), Support Vector Machines (SVM). The simulated results are obtained for the classifiers and its outputs are shown in the paper. From the result, it is found that, the CNN Classifier performs well in term of accuracy to detect diabetic retinopathy, compared with the SVM classifier.
A systematic review of the local field potential adaptations during conditioned place preference task in preclinical studies

Addiction is a global concern with a high relapse rate and without effective therapeutic options. Developing new effective therapeutic strategies is impossible without discovering the disease's neurobiological basis. The present systematic review aimed to comprehensively recognize and discuss the role of local field potentials from brain areas essential in forming and storing context‐drug/food associations following the conditioned place preference (CPP) paradigm as a popular animal model of reward and addiction. Qualified studies were incorporated by a broad search of four databases, including Web of Science, Medline/PubMed, Embase, and ScienceDirect, in July 2022, and they were evaluated via appropriate methodological quality assessment tools. The current study found that drug‐seeking behavior in different stages of the CPP paradigm is accompanied by alterations in neural oscillatory activity and adaptations in connectivity among various areas such as the hippocampus, nucleus accumbens, basolateral amygdala, and prelimbic area, intensely engaged in reward‐related behaviors. These findings need to be extended by more future advanced studies to finally recognize the altered oscillatory activity patterns of large groups of cells in regions involved in reward‐context associations to improve clinical strategies such as neuromodulation approaches to modify the abnormal electrical activity of these critical brain regions and their connections for treating addiction and preventing drug/food relapse in abstinent patients.
Rationality and Scientific Thinking as Foundations for Leadership in the World of Work

The exercise of leadership in the contemporary world is intricately linked to understanding the complexity of the current times, with an emphasis on the importance of rationality, metacognition, and scientific thinking. Among other elements, it is necessary to consider the competition between humans and machines – especially with learning machines, which are characterized by replacing rule-based programming, based on logical inference, with an approach based on pattern recognition associated with the adoption of neural networks. In this context, exploring metacognition, which transcends cognition, means prioritizing reflection on reflection – that is, focusing on learning how to learn throughout life. Therefore, the proper training of contemporary professionals implies, especially, cultivating rationality, metacognition, logic, and analytical reasoning.
A Fluid-Structure-Interaction Simulation tool for application in rotating machinery

A tool for Fluid-Structure-Interaction simulation was developed on the basis of the in-house CFD code SPARC, the open-source CSD solver CalculiX and the specially designed coupling manager FSiM. The new development exploits the advantages of block-structured meshes related to the multigrid acceleration technique, the transfinite mesh adaption method, a block-wise mesh adaption control and an efficient volume to surface mapping.
Student Performance Analysis using Bayesian Optimized Random Forest Classifier and KNN

- The importance of Educational Data Mining (EDM), a new interdisciplinary study field that builds on various other disciplines, is rising. It is directly connected to data mining (DM), which is crucial to finding knowledge in databases (KDD). This data is expanding exponentially and may include valuable hidden information for users (both teachers and students). Such knowledge can easily be recognized as models, patterns, or any other type of representational scheme that enables improved system exploitation. It is discovered that data mining can be used to make similar discoveries, giving rise to EDM. To get the best outcomes in this complicated setting, many approaches and learning algorithms are typically applied. In recent times, educational systems have witnessed a surge in using artificial intelligence (AI) systems, especially for extracting relevant information. One such AI system is EDM, which combines various techniques to support the capture, processing, and analysis of these record sets. The primary method used in EDM is machine learning, which has been applied more frequently since the emergence of big data to extract useful information from a vast amount of data. Machine learning has been used for decades in data processing in various contexts. Educational data mining tools and algorithms can be used to assess student academic achievement. This study offers a fresh approach to forecasting student success in middle school Portuguese and mathematics subjects. Hyperparameter tuning of classifiers is essential to overcome the misclassification of conventional classifiers. In order to predict student performance in the UCI dataset, this work proposes a Bayesian-optimized KNN and a random forest classifier. For random forest and KNN, the attained accuracy is 87% and 73%, respectively.
Study on the Ecology, Biology and Ethology of the Invasive Species Corythucha arcuata Say, 1832 (Heteroptera: Tingidae), a Danger to Quercus spp. in the Climatic Conditions of the City of Sibiu, Romania

This scientific paper examines the impact of climate change on the spread and survival of the invasive species Corythucha arcuata Say, 1832 (oak lace bug, or OLB) in Romania. The OLB is native to the Eastern USA and Canada, and in its native habitat, it coexists with minimal impact to the preferred host plant species Quercus. However, in Europe, the OLB in high density causes early defoliation, slowing tree growth. As Quercus spp. is a keystone species, the impact of the OLB on European forests is significant. This paper aims to address several gaps in knowledge about the OLB by presenting findings from a study conducted by the Lucian Blaga University of Sibiu (ULBS) in Romania. The study identified the species’ chronology of spread, ecology, ethology and biology of the species, in addition to foliage classification to determine the extent of attack. The study found a direct link between the temperature and the speed of the generational lifecycle. The findings support the thesis that climate change is enabling the healthy, rapid density growth of OLB, which is a danger to the Quercus spp. Further research is needed in the area of the thermal tolerance of the OLB, and the research conducted by the ULBS represents the first documented research into the thermal constant of the insect. The paper concludes that further research must be in the direction to understand how and where these insects survive during winter to find future management measures and identify chemical or biological methods as solutions to eradicate and stop the expansion of the invasion of the OLB, and the potential consequences for the Quercus sp.
Academic institutions and the changing entrepreneurial finance landscape

ABSTRACT Universities have emerged as central players in the fast-changing entrepreneurial finance landscape. This editorial introduces and describes the recent changes in academia and the entrepreneurial finance landscape. It then investigates the particular role of universities in supporting start-ups, as investors or facilitators of investments. Following this, it discusses the role of universities in democratising venture capital investments, the different archetypes of university investment vehicles, the importance of institutional context and entrepreneurship learning and finally the patterns of internationalisation and funding in academic spin-offs. The editorial also provides some practical recommendations for practitioners and gives food for thought for future research.
On the Optimal Delay Growth Rate of Multi-Hop Line Networks: Asymptotically Delay-Optimal Designs and the Corresponding Error Exponents

Multi-hop line networks have emerged as an important abstract model for modern and increasingly dense communication networks. In addition, the growth of real-time and mission-critical services has created high demand for and increased research interest in low-latency communications. The combination of these facts motivates a new investigation of data transmission schemes for <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>-hop line networks from a delay-vs-throughput perspective. To this end, this work defines a metric called the delay amplification factor for a target throughput <inline-formula> <tex-math notation="LaTeX">$R$ </tex-math></inline-formula>, denoted by <inline-formula> <tex-math notation="LaTeX">${\mathsf {DAF}}(R)$ </tex-math></inline-formula>, which characterizes the growth rate of the (asymptotic) delay with respect to the number of hops. We show that all existing relay schemes, e.g., Decode-&-Forward (DF), have <inline-formula> <tex-math notation="LaTeX">$\lim _{R\nearrow C} {\mathsf {DAF}}(R)=\Omega (L)$ </tex-math></inline-formula>, which is consistent with the decades-old perception that delay grows linearly with respect to <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>. We then design a scheme satisfying <inline-formula> <tex-math notation="LaTeX">$\lim _{R\nearrow C} {\mathsf {DAF}}(R)=1$ </tex-math></inline-formula>, if the bottleneck hop is the last hop, i.e., its asymptotic delay does not grow with respect to <inline-formula> <tex-math notation="LaTeX">$L$ </tex-math></inline-formula>. The results imply that this linearly growing delay is an artifact of the existing DF designs, and it is possible to surpass it and attain the true fundamental limit with a new delay-centric solution. In the second half of this work, we further show that if variable-length coding and one-bit stop-feedback are allowed, we can relax the condition bottleneck being the last hop and attain <inline-formula> <tex-math notation="LaTeX">$\lim _{R\nearrow C} {\mathsf {DAF}}(R)= 1$ </tex-math></inline-formula> for any arbitrary line networks.
A study on audit service quality: Reliability of local independent auditing firms

The main objective of this study was to identify, evaluate, and measure the attributes of audit service quality: reliability in local independent auditing firms in Hanoi through qualitative and quantitative research methods. The study was based on a field survey using a semi-structured questionnaire on a sample of 250 general accountants and chief accountants. The period was from October 2021 to March 2022. By using statistical analytical tools, i.e., Cronbach’s alpha analysis, independent T-test, and ANOVA, the study has identified and measured five attributes of audit service quality: reliability in local independent auditing firms in Hanoi. There is not, statistically, a significant difference in the level of audit service quality in local independent auditing firms in Hanoi between these different types of work. There is not, statistically, a significant difference in the level of audit service quality in local independent auditing firms in Hanoi between these different job seniorities. Based on the findings, some recommendations are given for local independent auditing firms to improve the quality of their audit services.
Diagnosing of a complex technical object in four-valued logic

This paper presents the essence of an investigation of a complex technical object with the use of four-valued logic. To this end, an intelligent diagnostic system (DIAG 2) is described. A special feature of this system was its capability of inferring k at {k = 4, 3, 2}, in which case the logic {k = 4} is applied. An important part of this work was to present the theoretical foundations describing the essence of inference in the four-valued logic contemplated. It was also pointed out that the basis for classification of states in the multiple-valued logic of the diagnostic system (DIAG 2) was the permissible interval of changes in the values of diagnostic signal features. Four-valued logic testing was applied to a system of wind turbine equipment.
Psychotropic Drugs Used in Psychiatry Outpatient Department: A Cross-Sectional Drug Utilization Research

Mental and behavioral disorders are common around the world. Pharmacotherapy, psychotherapy, and psychosocial rehabilitation are three treatment components for these disorders. Drug therapy is an essential part of the comprehensive treatment of these diseases. Their use pattern in psychiatric practice has changed dramatically in recent years. Mental disorders require long-term treatment. Therefore, it is crucial to study the prescribing practices of these groups of drugs. This study aims to assess drug use patterns using the WHO/INRUD Core Prescribing Indicators. Patients of both genders and ages with mental illness and prescription psychiatric drugs were evaluated from the outpatient department of the Psychiatry Unit (OPD). The prescription data were collected and analyzed using the indicators recommended by the WHO/INRUD. The data were analyzed using Microsoft Excel-2007. The percentage and average values of the variables were compared. A total of 655 prescriptions were analyzed. Male patients (54.96%) were more than women (45.04%). The morbidity profile included schizophrenia (37.86%), alcohol use disorder (18.78%), major depressive disorder (11.30%), anxiety disorder (10.23%), bipolar disorder (4.88%), and others (16.95%). The newer antipsychotic were the most commonly prescribed drugs (particularly olanzapine), along with a central anticholinergic (trihexyphenidyl) and a benzodiazepine (lorazepam). The percentage of drugs prescribed with generic names was 39.80%, and that of drugs prescribed by NLEM was 48.49%. The average number of drugs prescribed per prescription was 2.58. This study's Index of Rational Prescribing (IRDP) was 3.87, whereas the optimal score was 5. Prescriptions were complete, and principles of rational prescribing were followed, except for a low generic prescribing rate.
The Expanded Child Tax Credit and Educational Attainment in Rural, Majority-Minority Communities

Despite the status of the United States as one of the world’s economic superpowers, the country is plagued by child poverty. The issue of child poverty is most prominent in rural, majority-minority communities, where years of limited opportunity, often created by systematic oppression, have created a vicious cycle of economic despair. There are a number of policies that have been explored which could limit the effects of child poverty, most notably the Expanded Child Tax Credit (ECTC), which was introduced as part of the American Rescue Plan Act (ARPA) of 2021. This paper uses a combination of qualitative and quantitative methods to thoroughly examine this policy (ECTC) and its potential to both cut child poverty in the short term and increase educational attainment and economic mobility in the long term for recipients. The ECTC program ended in 2022 and only lasted in a short period. By examining the impacts of a similar and long-standing but less accessible and generous program, the Earned Income Tax Credit (EITC), on educational attainment in specific communities we can theorize and determine the significance the Expanded CTC would have. We use EITC program as a proxy of ECTC and reach inference of the long-term impact of ECTC program on children’s education attainment. The result of this research indicates a number of benefits of the Expanded Child Tax Credit, including its potential to cut child poverty and increase educational attainment, its impact on future economic prosperity in rural, majority-minority communities, and the policy’s ability to decrease economic disparities between non-metro and metropolitan areas.
Link importance incorporated failure probability measuring solution for multicast light-trees in elastic optical networks

Abstract. The light-tree-based optical multicasting (LT-OM) scheme provides a spectrum- and energy-efficient method to accommodate emerging multicast services. Some studies focus on the survivability technologies for LTs against a fixed number of link failures, such as single-link failure. However, a few studies involve failure probability constraints when building LTs. It is worth noting that each link of an LT plays different important roles under failure scenarios. When calculating the failure probability of an LT, the importance of its every link should be considered. We design a link importance incorporated failure probability measuring solution (LIFPMS) for multicast LTs under independent failure model and shared risk link group failure model. Based on the LIFPMS, we put forward the minimum failure probability (MFP) problem for the LT-OM scheme. Heuristic approaches are developed to address the MFP problem in elastic optical networks. Numerical results show that the LIFPMS provides an accurate metric for calculating the failure probability of multicast LTs and enhances the reliability of the LT-OM scheme while accommodating multicast services.
Microwave Nondestructive Imaging of Buried Objects Using Improved Scattered‐Field Calibration Technique

A novel microwave subsurface imaging scheme based on an improved electric field calibration technique is proposed in this paper. The proposed imaging scheme requires RF measurement system comprising of a uniform linear transmitter‐receiver antenna array wherein the region under examination is illuminated sequentially to measure the scattering parameters of the test. An appropriate imaging frequency is selected so as to minimize the errors introduced due to coupling among the coresident antennas. The electric field required for the imaging algorithm is obtained from the measured scattering parameters using the proposed calibration methodology in two subsequent steps. The first step of calibration removes the systematic errors inherent in the measured scattering parameters by using known standards such as a metal plate and absorber foam. The second calibration step transforms the calibrated scattering parameters of the test object into electric field values so as to use them in the required inverse reconstruction procedure. The transformation is based on comparison of the measured scattering parameters of a known specimen and test object under the same test environment. The imaging algorithm based on the electric field formulation uses the standard iterative distorted Born procedure. For determining the location of the buried object, a novel data interpretation scheme based on analysis of variance (ANOVA) applied to time domain reflection coefficients obtained from various antenna elements is also proposed here. The applicability including performance evaluation of the proposed imaging scheme is tested by retrieving microwave image of various standard objects buried inside sand.