Miracle-dz/UniFAR-FTUs huggingface dataset:
An efficient skin cancer detection and classification using Improved Adaboost Aphid–Ant Mutualism model

Skin cancer is the most common deadly disease caused due to abnormal and uncontrolled growth of cells in the human body. According to a report, annually nearly one million people are affected by skin cancer worldwide. To protect human lives from such life‐threatening diseases, early identification of skin cancer is the only precautionary measure. In recent times, there already exist numerous automated techniques to detect and classify skin lesion malignancies using dermoscopic images. However, analyzing the dermoscopic images becomes an arduous task due to the presence of troublesome features such as light reflections, illumination variations, and uneven shape and dimension. To address the challenges faced during skin cancer recognition process, in this paper, we proposed an efficient intelligent automated system to detect and discriminate the dermoscopic images into malignant or benign. The proposed skin cancer detection model utilizes the HAM10000 dataset for evaluation. The dermoscopic images acquired from the HAM10000 dataset are initially preprocessed to enhance the quality of image and thus making it fit to train the classifier. Afterward, the most significant image patterns are extracted by the AlexNet architecture without any loss of detailed information. Later on, the extracted features are inputted to the proposed Improved Adaboost‐based Aphid–Ant Mutualism (IAB‐AAM) classification model to discriminate the images into malignant and benign categories. The proposed IAB‐AAM approach witnessed extensive enhancement in classification accuracy. The enhanced performance is attributed by integrating the AAM optimization concept with the IAB model. By comparing the performance of the proposed IAB‐AAM with other modern methods in terms of different evaluation indicators namely accuracy, precision, specificity, sensitivity, and f‐measure, the efficiency of the proposed IAB‐AAM technique is analyzed. From the experimental results, it is known that the proposed IAB‐AAM technique attains a greater accuracy rate of 95.7% in detecting skin cancer classes than other compared approaches.
COMPUTATIONAL STUDY OF RAREFIED FLOW INSIDE A LID DRIVEN CAVITY USING A MIXED MODAL DISCONTINUOUS GALERKIN METHOD

With the advancement of fabrication technology and miniaturization, fluid flows at microand nanoscales has received considerable attention. Flow characteristics in these systems significantly vary from those of macroscale devices, due to geometric restrictions. In such cases, Navier-Stokes-Fourier(NSF) equations with no-slip condition may not be valid for studying gas flows inside a microcavity. This article therefore investigates the cavity flows using modified NSF equations with velocity slip and temperature jump conditions. In the present case, a monatomic gas is considered for modelling gas flows. To accurately predict the flow physics, mixed modal discontinuous Galerkin(DG) method is being developed. The flow characteristics of monatomic gas is studied by varying the Reynolds number(Re) and Knudsen number(Kn), respectively. Results obtained are compared with the previous results and are found to be in good agreement with them.
Teachers’ Perspectives on Planning for School Improvement: Processes, Critical Factors, and Perceived Outcomes

Strategic planning and strategic action have been identified as valuable frameworks for school improvement. Therefore, school reforms in different parts of the world have mandated formal planning to support improvement. However, planning is only sometimes fully embraced and knowledge-based, raising questions about schools' efficacy in strategic planning and action processes. In that sense, developing a better understanding of strategic planning and action is critical. This study focuses on the school planning processes in Portuguese schools by exploring how three Portuguese schools engaged in strategic action planning processes. A qualitative approach was employed. School strategic processes, critical factors for high-quality strategic action and planning, and perceived outcomes of strategic action plans were identified. The findings indicate that schools have embraced strategic action plans, despite the difficulties encountered during the planning and implementing process. Planning was limited by deadlines, failing in teachers, and other stakeholders' participation. Limited financial support and human resources were identified as factors hindering plan implementation. Compromise to school mission was identified as the main factor facilitating planning and action. The results point to crucial challenges for policymakers and school leaders. It also has relevant implications for future research related to school improvement.
An Adaptive Convolutional Neural Network for Object Classification

Since object detection has so many practical applications in areas as diverse as image retrieval, medical diagnosis, autonomous vehicle systems, surveillance, and more, it is one of the most investigated areas of automation. As the scope of use has grown, so too have the difficulties that must be overcome. This growth is due to the current models and architectures’ incapacity to identify obscured or diminutive items inside a picture. Images with low contrast or underexposure could benefit from sharpening or contrast restoration, or the input resolution could be increased to better detect small details. Alternately, the input resolution could be boosted to enhance large-object detection. Using multiple models for object detection has the potential to boost overall performance. The suggested study of the CDNN model aims to speed up object detection in challenging environments by decreasing the amount of time spent processing data. A custom-built deep convolutional neural network with additional augmentation layers has been proposed to take the place of the previous model’s backbone. It was demonstrated that the proposed model vastly increased the precision of
Automorphism group of the moduli space of parabolic vector bundles over a curve

The main objective of this thesis is the computation of the automorphism group of the moduli space of parabolic vector bundles over a smooth complex projective curve. We will start by de ning the notion of a parabolic -module { a module over a sheaf of rings of di erential operators with a parabolic structure at certain marked points { and building their moduli space. This will provide us a common theoretical framework that allows us to work with several kinds of moduli spaces of bundles with parabolic structure such parabolic vector bundles, parabolic (L-twisted) Higgs bundles, parabolic connections or parabolic -connections. As an application, we build the parabolic Hodge moduli space and the parabolic Deligne{Hitchin moduli space. Then, we will address the computation of the automorphism group of the moduli space of parabolic bundles. Let X and X0 be irreducible smooth complex projective curves with sets of marked points D X and D0 X0 and genus g 6 and g0 6 respectively. LetM(X; r; ; ) be the moduli space of rank r stable parabolic vector bundles on (X;D) with parabolic weights and determinant . We classify the possible isomorphisms : M(X; r; ; ) 􀀀���! M(X0; r0; 0; 0). First, a Torelli type theorem is proved, implying that for to exist it is necessary that (X;D) = (X0;D0) and r = r0. Then we prove that the possible isomorphisms are generated by automorphisms of the pointed curve (X;D), tensorization with suitable line bundles, dualization of parabolic vector bundles and Hecke transformations at the parabolic points. These results are extended to birational equivalences : M(X; r; ; ) 99K M(X0; r0; 0; 0) which are de ned over \big" open subsets. The particular case of \concentrated" weights (corresponding to \small" stability parameters) is studied further. In this case Hecke transformations give rise to birational morphisms that do not extend to automorphisms of the moduli space. Moreover, an analysis of the stability chambers for the weights allows us to determine an explicit computable presentation of the group of automorphisms of the moduli space for arbitrary generic weights. Finally, the automorphism group of the moduli space of framed bundles over a smooth complex projective curve X of genus g > 2 with a framing over a point x 2 X is also described. It is shown that this group is generated by pullbacks using automorphisms of the curve X that x the marked point x, tensorization with certain line bundles over X and the action of PGLr(C) by composition with the framing.
A XGBoost-Based Downscaling-Calibration Scheme for Extreme Precipitation Events

Extreme precipitation events have caused severe societal, economic, and environmental impacts through the disasters of floods, flash floods, and landslides. The coarse-resolution of satellite-derived precipitation data, however, makes it difficult to quantitatively capture certain fine-scale heavy rainfall processes. Therefore, to improve the spatial resolution and accuracy of satellite-based precipitation extremes, a downscaling-calibration scheme based on eXtreme Gradient Boosting (XGBoost_DC) was proposed in this study, where the XGBoost algorithm was applied in both downscaling and calibration procedures. The performance of XGBoost_DC was evaluated with two other comparative methods, in which XGBoost was only used in either downscaling (XGBoost_Spline) or calibration (Spline_XGBoost) process. The results showed that: 1) XGBoost_DC achieved the best performance, as it obtained the highest accuracy and well reproduced the occurrence and the spatial distribution of precipitation during typhoon events; 2) XGBoost_DC could capture the spatial variations of the precipitation. Although Spline_XGBoost obtained results only slightly worse than the XGBoost_DC, it significantly underestimated the spatial variability; and 3) the model assessment between the XGBoost_DC and Spline_XGBoost illustrated the essential contribution of XGBoost algorithm in downscaling process, and improved our understanding of the capability of machine learning (ML) algorithm in reproducing spatial variance of precipitation. These findings imply that our proposed downscaling-calibration scheme can be applied for generating high-resolution and high-quality precipitation extremes during typhoon events, which would benefit water and flood management, as well as other various applications in hydrological and meteorological modeling.
From Grain to Genome: Investigating Arsenic Levels in Triticum turgidum ssp durum Desf. Using GWAS

Producing safe and high-quality food is increasingly important, and developing durum wheat varieties with low toxicity is crucial to meeting this demand. Durum wheat breeders can achieve this goal by developing new varieties that are either more resistant to arsenic uptake or better adapted to grow in areas with high arsenic levels. High levels of arsenic can pose serious health hazards, which makes it critical to evaluate the arsenic levels. Therefore, this study evaluated the arsenic levels in diverse durum wheat genotypes, including Turkish-released cultivars and local landraces. The results showed that all genotypes had significantly low and non-toxic levels of arsenic, with an average concentration of 5.24 μg/kg. These concentrations were much lower than the minimum reported in numerous published research studies and well below the risky international standard limits for durum wheat grain (0.1 mg/kg). The study also identified two significant marker-trait associations linked to arsenic contents located on chromosomes 4A and 7B, which explained 11-17% of the phenotypic variation. These findings provide valuable insights into the arsenic levels in durum wheat genotypes and highlight the need for ongoing monitoring to ensure safe and healthy food for consumers. By conducting collaborative genome-wide association studies and employing marker-assisted selection, durum wheat breeders can accelerate the creation of new varieties that have reduced arsenic levels by identifying alleles linked to arsenic content. This study emphasizes the importance of developing low-toxicity durum wheat varieties to ensure the safety and quality of our food supply. The findings can inform breeding programs to develop such varieties and contribute to sustainable agriculture. While the study’s methodology was robust, further research is necessary to confirm and validate the genetic factors contributing to variation in arsenic content among different durum wheat genotypes.
Parametric Characterization on the Thermal Performance of a Closed Loop Pulsating Heat Pipe

Recently closed loop pulsating heat pipes have been receiving much attention because of their potential applications in high heat flux micro-electronic systems. They work by self thermal driven oscillation without any mechanical parts. Though they are simple in structure, understanding of the heat transfer mechanism is highly complex having a strong thermohydro dynamic coupling governing their performance. In this paper, an experimental study on a closed loop PHP with a single turn has been conducted there by providing vital information regarding parameter dependence on its performance. The PHP is made of brass tube having an internal diameter of 2 mm and outer diameter of 3 mm. The parametric characterization has been done for the variation in internal diameter, fill ratio, working fluid and orientation of the device. The working fluids Acetone, Methanol, Ethanol and Propanol are considered for experimentation with volumetric filling ratios of 50%, 60%, 70% and 80%. Input heat power of 7 to 12 W is varied at the evaporator section. The CLPHP is also verified for its thermal performance at 0, 30 and 60 orientations. The transient and steady state experiments are conducted and operating temperatures are measured using Ktype thermocouples. The results highlighted that the thermal performance of a PHP is strongly influenced by change in fill ratios, orientation and heat input. 80% fill ratio yields an effective heat transfer rate for a horizontal mode of operation. Appreciable fluid movement and better heat transfer rate are observed for the 30 orientation of PHP operation. Acetone exhibits better heat transport capability compared to other working fluids in all orientations.
Detection of Palm Fruit Maturity Using Convolutional Neural Network Method

Palm oil has an important role as a source of foreign exchange in the economy in Indonesia. Oil palm is one of the vegetable oil-producing plants that has the highest economic value compared to other crops such as soybeans, olives, coconuts or sunflowers. Palm oil quality is also influenced by water content, dirt content, free fatty acid content and the level of maturity of the palm fruit. Maturity of palm fruit is a very important factor in determining the quality of crude oil produced by palm fruit. In determining the maturity of oil palm, sorting is necessary to get quality palm fruit with the appropriate level of maturity. The use of image processing technology (ImageProcessing) can facilitate the process of analyzing objects. Meanwhile, the implementation of deep learning using the Convolutional Neural Network method can help identify the maturity level of oil palm fruit with a high level of accuracy. The results showed a very good effectiveness with an accuracy reaching 99% and a precision level reaching 99.8%.
INVESTIGATING THE EFFECTS OF ESL TEACHERS’ FEEDBACK ON ESL UNDERGRADUATE STUDENTS’ LEVEL OF MOTIVATION, ACADEMIC PERFORMANCE, AND SATISFACTION: MEDIATING ROLE OF STUDENTS’ MOTIVATION

The purpose of this empirical research was to investigate the effects of ESL Teachers’ Feedback on ESL undergraduate students’ level of motivation, academic performance, and satisfaction: Mediating role of students’ motivation. It is commonly acknowledged that teacher feedback is an important factor in students' academic success and that it is an integral part of the teaching and learning process. Students are encouraged by feedback, which also offers advice on how to perform well academically. The goal of the current study was to ascertain how teachers' ideas about feedback, its significance, and its consequences on ESL students' motivation, academic performance, and satisfaction at the higher education level. Students' motivation was also evaluated as a mediating variable between comments from ESL teachers and academic performance. Additionally, no previous research has examined the relationship between the study variables and Pakistani higher education institutions, making this study extremely relevant in addressing the identified research gaps in the literature. Students at the Department of English served as the target population, and a survey questionnaire was used to gather data for this study. A representative sample size was chosen using a methodical random sampling methodology, yielding a viable sample of n = 379. Using SPSS version 26 for data screening, descriptive statistics, and the PLSSEM method to investigate the correlations or associations between the variables, the gathered data were examined. All of the hypotheses were supported by the study's findings. The findings of this study have practical ramifications for educators, learners, policymakers, curriculum designers, and upcoming scholars in Pakistani higher education contexts of English Language Teaching (ELT). The study offers a discussion of the results and admits its limitations.
DYNAMICS OF TUBULAR CYLINDRICAL STRUCTURES IN AXIAL FLOW

The dynamics of slender cylinders in axial flow subject to conventional boundary conditions is recalled first. This is followed by brief reviews and discussion of new developments in (i) the dynamics of unconventionally supported cylinders, e.g. cylinders free at the upstream end and supported at the downstream one, (ii) the dynamics of cylinders subject to both internal and external flow for applications in oil and gas drilling and production, as well as hydrocarbon storage in salt-mined caverns, and (iii) the peculiar dynamics of very long cylinders, such as acoustic streamers used in underwater oil and gas exploration.
Towards Readability-Aware Recommendations of Source Code Snippets

: Nowadays developers search online for reusable solutions to their problems in the form of source code snip-pets. As this paradigm can greatly reduce the time and effort required for software development, several systems have been proposed to automate the process of finding reusable snippets. However, contemporary systems also have certain limitations; several of them do not support queries in natural language and/or they only output API calls, thus limiting their ease of use. Moreover, the retrieved snippets are often not grouped according to the APIs/libraries used, while they are only assessed for their functionality, disregarding their readability. In this work, we design a snippet mining methodology that receives queries in natural language and retrieves snippets, which are assessed not only for their functionality but also for their readability. The snippets are grouped according to their used API calls (libraries), thus enabling the developer to determine which solution is best fitted for his/her own source code, and making sure that it will be easily integrated and maintained. Upon providing a preliminary evaluation of our methodology on a set of different programming queries, we conclude that it can be effective in providing reusable and readable source code snippets.
FORECASTING VALUE-AT-RISK WITH NOVEL WAVELET BASED GARCH-EVT MODEL

In this study, wavelet based GARCH-Extreme Value Theory (EVT) is proposed to model financial return series to forecast daily value-at-risk. Wavelets based GARCH-EVT is hybrid model combining the wavelet analysis and EVT. Proposed model contains three stages. In first stage, return series is decomposed into wavelet series and approximation series by applying the maximal overlap discrete wavelet transform. Second stage, detrended return series and approximation series are obtained by using wavelet series and scaling series. GARCH model is fitted to each obtained series to forecast daily volatility. Final stage, EVT is used to estimate quantile estimation of standardized residuals of GARCH model obtained for detrended return series and daily VaR value is forecasted by using volatility forecasts and quantile estimation. Daily VaR forecasting accuracy of proposed hybrid model is compared with the GARCH models specified under heavy-tailed distributions and GARCH-EVT model. Empirical findings show that wavelet based GARCH-EVT model is outperformed at high quantiles according to backtesting results.
Evolution of Mesoscale Convective System Organizational Structure and Convective Line Propagation

AbstractThis study examines organizational changes and periods of rapid forward propagation in an MCS on 6 July 2015 in South Dakota. The MCS case was the focus of a Plains Elevated Convection at Night (PECAN) IOP. Data from the Sioux Falls WSR-88D and a high-resolution WRF simulation are analyzed to examine two periods of rapid forward propagation (or surges) and organizational changes. During the first surge (surge A), the northern portion of the convective line propagates eastward faster than the southern portion, and the northern portion of the leading line transitions from a single convective core to a multicellular structure as it merges with convection initiation. Radar reflectivity factor Z and graupel concentrations decrease above the melting layer, while at lower altitudes Z increases. The MCS cold pool also intensifies and deepens beneath an expanded region of high rainwater content and subsaturated air. Throughout surge A, a mesoscale circulation with strong rear-to-front near-surface flow and.
Efforts and Changes in Health Aspects to Fulfilling Child Welfare in the Child Correctional Institution

Several studies have shown that physical health problems are more common among children in conflict with the law (CICL) in correction institutions than among their peers outside the institutions. The children's health issues are not just health and disease problems, but also environmental, behavioral and health service factors that directly affect their health status. The CICL health fulfillment must be completed to support behavior change in children under the guidance of the Child Correctional Institution (LPKA). The purpose of implementing the health aspect is to increase their awareness and attention to maintaining their health. The purpose of this study is to analyze the CICL welfare in LPKA Bandung, Palembang and Tangerang on health needs. This research adopts a qualitative method and type of explanatory research. This study also used purposive sampling techniques. Data collection by the researcher included in-depth interviews, observations and literature studies. The results of the study examined the health status, efforts made and access to health care of the children in conflict with the law and the presence of risk and protective factors affecting behavioral change in their health.