Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Feature Selection Techniques for Image Retrieval based on Metaheuristic Optimization

Summary Content-Based Image Retrieval (CBIR) system plays a vital role to retrieve the relevant images as per the user perception from the huge database is a challenging task. Images are represented is to employ a combination of low-level features as per their visual content to form a feature vector. To reduce the search time of a large database while retrieving images, a novel image retrieval technique based on feature dimensionality reduction is being proposed with the exploit of metaheuristic optimization techniques based on Genetic Algorithm (GA), Extended Binary Cuckoo Search (EBCS) and Whale Optimization Algorithm (WOA). Each image in the database is indexed using a feature vector comprising of fuzzified based color histogram descriptor for color and Median binary pattern were derived in the color space from HSI for texture feature variants respectively. Finally, results are being compared in terms of Precision, Recall, F-measure, Accuracy, and error rate with benchmark classification algorithms (Linear discriminant analysis, CatBoost, Extra Trees, Random Forest, Naïve Bayes, light gradient boosting, Extreme gradient boosting, k-NN, and Ridge) to validate the efficiency of the proposed approach. Finally, a ranking of the techniques using TOPSIS has been considered choosing the best feature selection technique based on different model parameters.
A Review on Effects of Cannabis Use on Sleep in Adolescents

The aim is to examine the effects of cannabis use on pain, trauma, and obstructive sleep apnea in adolescents regarding sleep patterns. With the recent legalization of cannabis as well as my role as a clinical sleep therapist for nearly a decade, I strongly believe there is a connection between both topics, and I strive to showcase the positive influence that cannabis can have on sleep patterns. This literature review strives to highlight the benefits on sleep that may be provided to those with pain, trauma, OSA as well as adolescent use. I have selected a wide variety of current literature to share the potential benefits of cannabis in relation to sleep.
A Robotic Assistant for Logistics and Disinfection in Health Centers

—The COVID-19 pandemic has pushed robotic re- searchers to develop various robotic solutions applied to healthcare, to contribute to face the worldwide emergency. In this paper the UCBM-COVID robotic platform, which relies on the service robot TIAGo, is described, and its 2-month use inside the UCBM-COVID treatment centre is discussed. The platform was used to perform logistics and disinfection tasks by exploiting the autonomous navigation and interaction capabilities of the robot.
Studying primate cognition: from the wild to captivity and back

How do nonhuman primates process information about their social and physical world, and how do they make decisions? To address this question, research in the tradition of ethology and behavioral ecology aims to uncover how animals behave under ecological and evolutionarily valid conditions. By combining detailed observations of behavior in the wild with field experimentation, such studies have shed light on what primates know about predators, their home ranges and food sources, and their social environment. A second research stream takes non-naturalistic experiments into freely moving groups of wild or captive primates to test social learning, problem-solving skills, and motivation. Finally, captivity-based studies address a wide range of questions, using an array of different methodological approaches, often with the aim to explore the limits of the animals’ abilities. This chapter compares the strengths and the limitations of the different research streams and proposes an integrative approach to developing a profound understanding of primate cognition.
Multiple Functions of MYB Transcription Factors in Abiotic Stress Responses

Plants face a more volatile environment than other organisms because of their immobility, and they have developed highly efficient mechanisms to adapt to stress conditions. Transcription factors, as an important part of the adaptation process, are activated by different signals and are responsible for the expression of stress-responsive genes. MYB transcription factors, as one of the most widespread transcription factor families in plants, participate in plant development and responses to stresses by combining with MYB cis-elements in promoters of target genes. MYB transcription factors have been extensively studied and have proven to be critical in the biosynthesis of secondary metabolites in plants, including anthocyanins, flavonols, and lignin. Multiple studies have now shown that MYB proteins play diverse roles in the responses to abiotic stresses, such as drought, salt, and cold stresses. However, the regulatory mechanism of MYB proteins in abiotic stresses is still not well understood. In this review, we will focus mainly on the function of Arabidopsis MYB transcription factors in abiotic stresses, especially how MYB proteins participate in these stress responses. We also pay attention to how the MYB proteins are regulated in these processes at both the transcript and protein levels.
Mixed-Integer Reformulations of Resource-Constrained Two-Stage Assignment Problems

The running time for solving a mixed-integer linear optimization problem (MIP) strongly depends on the number of its integral variables. Bader et al. [Math. Progr. 169 (2018) 565–584] equivalently reformulate an integer program into an MIP that contains a reduced number of integrality constraints, when compared to the original model. Generalizing the concept of totally unimodular (TU) matrices, the reformulation is determined via a so-called affine TU decomposition of the underlying constraint matrix. In this work, we develop affine TU decompositions for two-stage resource-constrained b-matching problems that have challenging applications in runway utilization of aircraft. Mathematically, the task consists in determining an optimum two-stage bipartite b-matching in a graph where a node v can be assigned up to bv ∈ N many edges. The two stages are linked by resource restrictions modeled by specific knapsack constraints. Determining an affine TU decomposition for this problem is reduced to the question of how the incidence matrix of a bipartite graph can be enlarged by appending a submatrix such that the resulting matrix again is TU. Sufficient conditions are derived for the structure of such a submatrix. We apply our findings to two-stage b-matching problem with one resource constraint. For several resource constraints, the TU requirements are not met in general. Nevertheless, in case bv = 1 for all nodes v ∈ V , we reformulate the problem with one resource constraint per node such that the underlying polytope is integral. This again leads to a reduced number of integrality constraints, when compared to the original formulation. The computational study focuses on the aircraft application. Using state-of-the-art MIP solvers, it is shown that, especially for the case with few resource constraints, the reformulated models usually lead to considerably shorter running times, when compared to solving the original formulation.
Collapse, reorganization, and regime identity: breaking down past management paradigms in a forest-grassland ecotone

The identity of an ecological regime is central to modern resilience theory and our understanding of how systems collapse and reorganize following disturbance. However, resilience-based models used in ecosystem management have been criticized for their failure to integrate disturbance outcomes into regime identity. Assessments are needed to understand how well these classifications represent ecosystem responses that occur over management relevant time scales. We tracked post-wildfire forest and grassland dynamics 27 years after wildfire in eastern ponderosa pine savanna. We tested for differences between the assigned identity of a site (forest or grassland) versus classifications based on the site's disturbance history (burned/unburned and fire severity). Under current ecosystem models used to manage these forest-grassland ecotones, forests that experience high severity fire are expected to resemble an unburned grassland following fire, while forests and grasslands that experience low severity fire are expected to resemble unburned forests and grasslands, respectively. Twenty-seven years after wildfire, burned forests and grasslands displayed a high degree of departure from their expected regime identity. Plant and bird communities deviated significantly on sites that experienced low severity fire from undisturbed sites classified under the same ecological regime (grassland or forest). Forest sites that experienced high severity fire were the most unique of all disturbance history classes. Our results demonstrate that structures and communities predicted under resiliencebased models used for eastern ponderosa pine management do not emerge over management relevant time scales following disturbance. Over 20% of variation in ecological structures and communities was explained by a single, 27-year-old disturbance. Integrating disturbance legacies will help improve applied models of ecosystem dynamics.
Towards a theory of Leader Identity Formation and its application in
executive coaching

The article presents a Leader Identity Formation Theory (LIFT) which illuminates the dynamic identity formation process of individuals across identity stages and life domains. The theory underscores the importance of leader identity in organizational leadership development and leadership effectiveness. The application of the principles of LIFT in executive coaching are suggested, including incorporating the influence of enabling and derailing factors in individual leader identity formation processes. Specific coaching questions are included to illustrate and support leader identity development within the coaching process. A recommendation for further research is also discussed.
Influence of EDTA Application on the Growth and Lead Accumulation of Mustard Seedlings Planted in Pb-contaminated Soils.

The pot experiment was conducted to investigate the influence of EDTA application on the growth and Pb accumulation of mustard seedlings planted in Pb-contaminated soils.The results indicated that different Pb concentration did not significantly change the biomass of mustard seedlings,however,shoot water content decreased remarkably under 2000mg kg-1Pb treatment.Pb contents in both shoots and roots of mustard seedlings increased with Pb concentration in soils,and Pb contents in roots was higher than that of in shoots.Application of 3mmol kg-1 EDTA to the Pb contaminated soils enhanced the available Pb concention,affected the seedling growth and decreased the shoot biomass.Pb conents in the shoots and roots of mustard seedlings were increased by treatment with EDTA,and shoot total Pb accumulation in mustard seedlings by EDTA treatment were 13.1-80 times higher than the controls without EDTA application.At the same time,the ratio of Pb contents in shoots and roots increased by EDTA treatment,which indicated that EDTA treatment improved the Pb transportation to the shoots.
Scheduling 7-day Follow-up Appointments to Help Prevent Readmissions in Patients with Acute Exacerbation Chronic Obstructive Pulmonary Disease

Objectives: Prior studies have shown that patients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) have a high hospital readmission rate and that close outpatient follow-up may reduce the need for hospital readmission. The objective of this study is to analyze the effect of scheduled 7-day post-hospital follow-up appointments on 30-day readmission in patients hospitalized with AECOPD. Methods: A quasi-experimental, multicenter, prospective cohort design with retrospective observation as part of the Integrated Michigan Patient- Centered Alliance in Care Transitions (I-MPACT) was used. Each of the four participating hospitals in Michigan selected AECOPD as their target intervention and were incentivized to increase overall rates of scheduled 7-day post-hospital follow-up for all patients discharged in this target population using a pay-for-performance system. A random sample of patients discharged with AECOPD was included and patients were excluded if discharged or transferred to another hospital, left against medical advice, died during initial hospitalization, were known to have died within 30 days of discharge without readmission, or were discharged to a skilled nursing facility. Analyses were done using Pearson chi-square and multivariate logistic regression modeling. Results: Of the 686 patients with a diagnosis of AECOPD, 29.5% (N=202) received a scheduled follow-up while 70.65% (N=484) did not. The sample was 57% female, 73% White, and 13% on Medicaid with a mean age of 66 years. Based on a risk-adjusted model, patients who received a 7-day post-hospital follow-up appointment had a 43% lower likelihood of readmission compared to patients who did not receive an appointment (OR=0.57; p=0.03). Additionally, patients who received a scheduled follow-up with a Primary Care Provider had a 53% lower likelihood of readmission compared to patients who did not receive a scheduled appointment (OR=0.47; p=0.01). Conclusion: For patients hospitalized for AECOPD, scheduling outpatient follow-up appointments within 7 days of hospital discharge was associated with a reduction in 30-day hospital readmissions. Scheduling early post-hospital follow-up appointments for patients with AECOPD may reduce readmission rates.
Interpreting Noun-Noun Compounds with Rare Modifiers

Previous studies in conceptual combination (Gagné & Shoben, 1997; Storms & Wisniewski, in press) have shown that a noun-noun combination is easier to interpret when it instantiates a thematic relation that is frequently associated with the modifier and more difficult to interpret when it instantiates a relation that is less typically associated with the modifier. In this experiment we investigated the extent to which the effects observed in these studies can be attributed to prior experience of the modifier as opposed to other sources of information that might influence interpretation. This was achieved by examining the influence of relatively rare modifiers which, because of their rarity, were unlikely to have a substantial combinational history. Gagné and Shoben’s (1997) CARIN model suggests that the influence of the modifier is due to statistical knowledge that people store about its typical usage. As a result, the model implies that the relation type frequencies of rare modifiers should exert a lesser influence due to speakers’ lack of statistical knowledge about them. However, our results showed that rare modifiers behave in a similar fashion despite the fact that they might never have been encountered in a modifying capacity. We consider possible explanations for this finding including Gagné’s (2002) proposal of a secondary process as well as the possibility of naturally arising differences in ease of interpretation.
UnityFlexML : Training Reinforcement Learning Agents in a Simulated Surgical Environment*

—Sim-to-real Deep Reinforcement Learning (DRL) has shown promising in subtasks automation for surgical robotic systems, since it allows to safely perform all the trial and error attempts needed to learn the optimal control policy. However, a realistic simulation environment is essential to guarantee direct transfer of the learnt policy from the simulated to the real system. In this work, we introduce UnityFlexML , an open-source framework providing support for soft bodies simulation and state-of-the-art DRL methods. We demonstrate that a DRL agent can be successfully trained within UnityFlexML to manipulate deformable fat tissues for tumor exposure during a nephrectomy procedure. Furthermore, we show that the learned policy can be directly deployed on the da Vinci Research Kit, which is able to execute the trajectories generated by the DRL agent. The proposed framework represents an essential component for the development of autonomous robotic systems, where the interaction with the deformable anatomical environment is involved.
Phishing URL Detection using Information-rich Domain and Path Features

Malicious or phishing detection has been drawing a serious concern since the early 21 st century because of tremendous electronic transfer accesses such as financial transitions and identity theft via online services. Amongst various detection schemes, including content-based approach, uniform resource locator (URL)-based detection is widely used not only for its comparable performance w.r. t accuracy but also for its adaptability to any other forms (for example, embedding URLs in spam messages or emails). In phishing URL detection, feature engineering is a crucial yet challenging way to improve performance. Manually-generated features are risky and highly dependent on datasets. Thus, recently, researchers tend to focus on information-based features, which extracts features based on the URL’s texts. To put it simply, researchers adapt the neural network to extract characters/words which are rich in indicating valuable information in the URLs. Our research focuses on information-based features applying a neural network-based model in which we consider both domain-based and path-based features. Then, we analyze and compare our results with previous papers and summarize opinions for a better detection system.
EFL Students Affective Attitudes towards Distance E-Learning Based on Moodle Platform during the Covid-19the Pandemic: Perspectives from Dr. MoulayTahar University of Saida, Algeria

The practice of the online learning system in Algeria is novel. However, with the spread of Covid 19, Algerian universities have been compelled to undertake remote and digital teaching. The University of Dr. MoulayTahar in Saida has arbitrarily hastened in implementing E-learning as an alternative teaching measure to maintain both positive educational outcomes and keep students safe and healthy. This research purports to examine how the University of Dr. MoulayTahar in Saida has used Moodle platform to facilitate the teaching process during the Covid 19. This paper, therefore, stresses the issues of distance e-learning that challenge both teachers and students alike. The researchers attempt to answer the following question: What are the affective attitudes of EFL students towards distance learning assisted with Moodle Platform during the Coronavirus? To supply an adequate answer to the raised question, the researchers have selected a sample of 157 participants. The researchers collected data through a questionnaire and delivered copies to the participants. The findings revealed that both the teachers and the learners were not well prepared for an online learning experience. That there are more disadvantages than advantages. The results of the research conveyed that the technical skills, the teachers’ lack of experience as well as the social situation of the students have made E. learning a complex process. The analysis also revealed that Master students have negative attitudes towards e-learning, and they would prefer face-to-face interaction in the classroom and handouts more than virtual learning.
ENTREPRENEURIAL ORIENTATION IN PERCEPTION OF EMPLOYEES FROM SELECTED COUNTRIES – CASE STUDY

The entrepreneurial orientation is a very important construct assuring the proper functioning of an organization and is perceived as one of the most important elements having a significant impact on the position of enterprises in their business environment. The goal of this article is to identify the importance of the entrepreneurial orientation for individuals from different countries in Europe and in Asia and also to check out if there are any differences in perception of this five-dimensional construct by employees working in different countries. In order to collect the necessary data, a qualitative study has been carried out, based on partially structured individual in-depth interviews with employees from Poland, France and China. Two representatives were selected from each country. Each interview consisted of two parts: the first part explained the purpose of the interview, while the second part was the substantive part of the interview. Each interview lasted about 60 minutes. The collected information helped us formulate the initial conclusions about the importance of the entrepreneurial orientation for employees from selected countries and also whether there are any differences in perception of this important construct depending on the country.
Interpretable Machine Learning

Machine learning is becoming much more profound in making decisions critical to life, death and personal wellness. However, machine learning models are black boxes that find patterns in data without being able to explain their methodology. There is a lack of sufficient techniques to explain and interpret machine learning decisions. Machine learning utilization can be problematic in areas where decisions of models must be explainable due to laws or regulations or where accountability is required. The need to trust machine learning is paramount. We define interpretability as the ability to explain or to present in understandable terms to a human [7]. Broadly, interpretability focuses on the how. Before proceeding with a formal description, we discuss why do we need interpretable machine learning:
PREPARATION, CHARACTERIZATION, THERMAL BEHAVIOUR AND DC CONDUCTIVITY OF NANO- POLYANILINE AND POLYANILINE-MULTI WALLED CARBON NANOTUBE NANOCOMPOSITES

Conducting polyaniline (PANI) in nano dimension was prepared in presence of aqueous hydrochloric acid (HCl) or toluene sulfonic acid (TSA) as doping agents and ammonium persulfate (APS) as oxidizing agent. Composites of the PANI and multi walled carbon nanotubes (MWCNT) were prepared by in situ polymerization technique at room temperature. The structural composition, morphology, thermal decomposition behavior and conductivity of PANI and the composites were investigated. Studies include Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), transmission electron microscopy (TEM), x-ray diffraction (XRD) pattern, uvvisible spectroscopy and thermogravimetric analysis (TGA). The electrical conductivity of the PANI-MWCNT composites as well as the pure PANI was measured by conventional four-probe method. The electrical conductivities of the two PANIs show that nano PANI-TSA has a higher conductivity (0.824 S/cm -1 ) compared to PANI-HCl (0.478 S/cm -1 ). PANI-MWCNT composites displayed much higher electrical conductivity (4.17 S/cm-1) compared to neat PANI.