Miracle-dz/UniFAR-FTUs huggingface dataset:
Adversarial Examples with Specular Highlights

We introduce specular highlight as a natural adversary and examine how deep neural network classifiers can get affected by them, resulting in a reduction in their prediction performance. We also curate two separate datasets, ImageNet-AH with artificially generated Gaussian specular highlights and ImageNet-PT by flashing natural specular highlights on printed images, both demonstrating significant degradations in the performance of the classifiers. We note around 20% drop in the model prediction accuracy with artificial specular highlights and around 35% accuracy drop in torch-highlighted printed images. These drops indeed question the robustness and reliability of modern-day image classifiers. We also find that finetuning these classifiers with specular images does not improve the prediction performance enough. To understand the reason, we finally do an activation mapping analysis and examine the network attention areas in images with and without highlights. We find that specular highlights shift the attention of models which makes fine-tuning ineffective, eventually broadly leading to performance drops.
SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network

Simple Summary While melanoma accounts for 4% of skin cancer cases, it causes 75% of skin-cancer-related deaths. The survival rate for melanoma is higher for early-identified cases, so improved access to diagnosis and screening programs is essential for addressing skin cancer deaths. Computer-aided diagnosis utilizing machine learning can be used to differentiate malignant and benign skin lesions. There is significant research into the use of convolutional neural networks to classify skin lesions from dermoscopic images. However, to provide cost-effective and accessible options for early detection of malignant melanoma, smartphone applications capable of accurately classifying skin lesions from images taken on a smartphone would be beneficial. This research investigates a previously underexplored dataset of smartphone images and develops a novel multi-layer deep convolutional neural network model, named SkinLesNet, to classify three types of skin lesions, including melanoma. Further studies to validate the model should be conducted as other image datasets become available. Abstract Skin cancer is a widespread disease that typically develops on the skin due to frequent exposure to sunlight. Although cancer can appear on any part of the human body, skin cancer accounts for a significant proportion of all new cancer diagnoses worldwide. There are substantial obstacles to the precise diagnosis and classification of skin lesions because of morphological variety and indistinguishable characteristics across skin malignancies. Recently, deep learning models have been used in the field of image-based skin-lesion diagnosis and have demonstrated diagnostic efficiency on par with that of dermatologists. To increase classification efficiency and accuracy for skin lesions, a cutting-edge multi-layer deep convolutional neural network termed SkinLesNet was built in this study. The dataset used in this study was extracted from the PAD-UFES-20 dataset and was augmented. The PAD-UFES-20-Modified dataset includes three common forms of skin lesions: seborrheic keratosis, nevus, and melanoma. To comprehensively assess SkinLesNet’s performance, its evaluation was expanded beyond the PAD-UFES-20-Modified dataset. Two additional datasets, HAM10000 and ISIC2017, were included, and SkinLesNet was compared to the widely used ResNet50 and VGG16 models. This broader evaluation confirmed SkinLesNet’s effectiveness, as it consistently outperformed both benchmarks across all datasets.
Analysis of Canal Blocking Distribution to Reduce Sabangau Tropical Peat Swamp Forest Fires

The vast tropical peat swamp forest in Sabangau, Central Kalimantan, Indonesia, makes it difficult for officials to fight forest fires. Forest and tropical peatland fires are recurring events caused by anthropogenic activities around forests. This study aims to provide patterns of forest fires that have occurred over the past 11 years and produce predictive patterns for mitigation planning in 2023 and 2027. The method used is overlay, an analysis method carried out on several maps with mapping software. The results of this study show four factors cause forest fires, namely: The number of canals that penetrate the Sabangau Peat Forest Nature Laboratory of Palangka Raya University, the number of anthropogenic human activities, rainfall that is a small part of El Nino, and the limited ability of Peat Forest Nature Laboratory of Palangka Raya University area managers to maintain groundwater levels above (- 0.33 m). The overlap method stitches several theme maps together to create a new map. It is then geographically processed to describe various events and predictions of each causal factor.
Research on PID Parameter Self-Tuning Speed Control System Based on Grasshopper Optimazation Algorithm-Optimized BP Neural Network

In response to the limitations associated with conventional PID control strategies, which rely heavily on predetermined design parameters and struggle to provide precise, stable motor speed and current control, particularly in the presence of disturbances, we have developed a novel double closed-loop DC speed regulation system. This system incorporates a self-tuning PID mechanism based on the Grasshopper algorithm optimization neural network. To achieve this, we employed a Back-Propagation (BP) neural network to autonomously fine-tune the parameters of the PID control strategy. Subsequently, during the backpropagation phase of the neural network training, we harnessed the Grasshopper algorithm to optimize these parameters based on a fitness function, leading to the calculation of updated weighting coefficients. Following this iterative process, the output voltage was computed using the incremental PID formula, enabling precise motor control. Remarkably, this approach reduced overshoot by 30%, all without the need for preemptive control parameter adjustments. Moreover, the rise time experienced only a marginal increase of approximately 0.06 seconds. These advancements effectively address the limitations associated with traditional PID control strategies, expanding their utility beyond narrow operational ranges and enhancing performance in complex working conditions. The outcome is a speed regulation system characterized by significantly improved accuracy and robustness, ideally suited to meet the demanding requirements of engineering applications.
Prerequisite Structure Discovery in Intelligent Tutoring Systems

This paper addresses the importance of Knowledge Structure (KS) and Knowledge Tracing (KT) in improving the recommendation of educational content in intelligent tutoring systems. The KS represents the relations between different Knowledge Components (KCs), while KT predicts a learner's success based on her past history. The contribution of this research includes proposing a KT model that incorporates the KS as a learnable parameter, enabling the discovery of the underlying KS from learner trajectories. The quality of the uncovered KS is assessed by using it to recommend content and evaluating the recommendation algorithm with simulated students.
DESIGNING HUMAN-MACHINE SYSTEMS: TRANSFORMATION OF A DESIGNER’S THINKING

Industry 4.0 became a powerful impetus for the development of man-machine systems, which, on the one hand, increased their efficiency and reliability and, on the other hand, made them super-complex. As a result, this led to an increase in the complexity of human-machine system management and the manifestation of the human factor. The consequence is that, currently, the main cause of accidents and disasters is the human factor. Thus, the safety of human-machine systems largely depends on the person's psychological and physiological characteristics. The article shows that the study of a person's characteristics is the basis for the development of a human factor management system. The essential element of the human factor management system is the systemic ergonomics thinking of the human-machine systems designer. This type of thinking is formed as a result of training and the gradual acquisition of eco-ergonomic design skills. To explain the importance of the formation of eco-ergonomic thinking in human-machine systems design, eco-ergonomic thinking evolution is presented in the work. Through the analysis of the sequence of the development of ergonomic thought, it is shown how the approach to ensuring the safety of human-machine systems has changed from the middle of the 20th century to the present. The next stage of the work was the analysis of eco-ergonomic, human-oriented, risk-oriented, and strategic thinking. Based on the analysis results, a hierarchy of thinking types is built, and their role in the formation of systemic ergonomics thinking in human-machine systems designing is shown. The main feature of the designer’s systemic ergonomics thinking is the priority of finding a solution to the problem in the system operation through research and analysis of human-machine interaction. Based on the obtained results, the importance of the formation and development of systemic ergonomics thinking is substantiated. It is shown how to implement the formation of a designer's systemic ergonomics thinking of human-machine systems through the training system.
Non-Genetic-Induced Zebrafish Model for Type 2 Diabetes with Emphasis on Tools in Model Validation

The unrelenting increase in the incidence of type 2 diabetes (T2D) necessitates the urgent need for effective animal models to mimic its pathophysiology. Zebrafish possess human-like metabolic traits and share significant genetic similarities, making them valuable candidates for studying metabolic disorders, including T2D. This review emphasizes the critical role of animal models in diabetes research, especially focusing on zebrafish as an alternative model organism. Different approaches to a non-genetic model of T2D in zebrafish, such as the glucose solution, diet-induced, chemical-induced, and combined diet-induced and glucose solution methods, with an emphasis on model validation using indicators of T2D, were highlighted. However, a significant drawback lies in the validation of these models. Some of these models have not extensively demonstrated persistent hyperglycemia or response to insulin resistance and glucose tolerance tests, depicted the morphology of the pancreatic β-cell, or showed their response to antidiabetic drugs. These tools are crucial in T2D pathology. Future research on non-genetic models of T2D in zebrafish must extensively focus on validating the metabolic deficits existing in the model with the same metabolic defects in humans and improve on the existing models for a better understanding of the molecular mechanisms underlying T2D and exploring potential therapeutic interventions.
Effect of Gas Composition on Surfactant Injectivity in a Surfactant-Alternating-Gas Foam Process

Aqueous foam is a dispersion of gas in liquid, where the liquid acts as the continuous phase and the gas is separated by thin liquid films stabilized by a surfactant. Foam injection is a widely used technique in various applications, including CO2 sequestration, enhanced oil recovery, soil remediation, etc. Surfactant-alternating-gas (SAG) is a preferred approach for foam injection, and injectivity plays a vital role in determining the efficiency of the SAG process. Different gases can be applied depending on the process requirements and availability. However, the underlying mechanisms by which gas composition impacts injectivity are not yet fully understood. In this work, the effect of gas composition on fluid behavior and injectivity in a SAG process was investigated using three gases: N2, CO2, and Kr. Our observations revealed that gas solubility in liquid was key for the formation and evolution of liquid fingers, and therefore was very important for liquid injectivity. A lower gas solubility in liquid led to a slower increase in surfactant solution injectivity. In addition, the development of surfactant solution injectivity took significantly longer when the surfactant solution was partially pre-saturated compared to when it was unsaturated. Additionally, the propagation of the collapsed-foam bank during gas injection was accelerated when the gas had a greater solubility in water.
Optimal Linear Phase FIR Filter Design using Bare Bones Particle Swarm Optimization (BBPSO) with Ripple Constraint

The utilization of optimization techniques for designing digital filters has become widespread over the recent years. FIR filter optimization is a multi-model problem. The objective of the optimization is to derive an optimum set of filter coefficients for the given specifications. The optimization problem is phrased as least squared error (LSE) between the specified and designed filter response in the frequency domain. This paper presents an alternative approach for the designing of linear phase Finite impulse response (FIR) high pass filter by using Bare Bone particle swarm optimization (BBPSO) technique incorporated with ripple constraint in order to achieve flatter frequency response. It is expedient to use BBPSO for optimization because it do not require any parameter tuning as in the case of other PSO variants. The results justify that proposed BBPSO based technique vanquish the other PSO variants in term of accuracy, robustness, convergence speed and stability. Keywords—Particle swarm optimization, Bare bones Particle swarm optimization
Information and understanding: analysis of remotely sensed data

A review is given of the development of the field of image understanding in remote sensing, with an emphasis on the contributions of David Landgrebe and his group at the Laboratory for Applications of Remote Sensing, Purdue University. The differences in approach required for multispectral, hyperspectral and radar image data are emphasised, in which the seminal contributions to the field by Landgrebe as well as others are summarised. The treatment concludes by examining the current problem of thematic mapping from mixed spatial data types, such as would be found in a geographical information system. Rather than seeking techniques that "fuse" available data types as a means for deriving joint inferences, it is proposed instead that the most practical means is to have each individual data source analysed separately by the most appropriate techniques and the fuse at the label level using the facilities of an expert consultant.
Hybrid Geometric/Shortest-path Routing in Proliferated Low-Earth-Orbit Satellite Networks

Routing data in satellite networks using predictable orbital geometry characteristics of a satellite constellation has been proposed in a number of studies. Orbital geometry may be used as a primary mechanism for determining routes or as an adjunct or a fallback to other routing techniques such as link-state routing. In cases where geometric information is used as an adjunct or fallback for a primary routing technique, the interactions between the primary and the adjunct/fallback routing mechanisms are of key importance in determining the routing performance of the combined routing mechanisms.This paper presents a study of a hybrid routing mechanism where some satellite routing nodes use shortest-path routing and other satellite routing nodes use a completely geometric approach to routing, with no exchange of routing table data or link state between different satellites using the two different routing mechanisms. A simple distributed procedure is presented for determining whether a given satellite uses shortest-path or geometric routing, and the routing performance in the overall constellation is calculated using random mixes of satellites using shortest-path vs geometric routing. Routing performance is characterized by path failure rate, latency, and transmission efficiency, and is calculated in an example medium-inclination 720-satellite Walker Delta satellite constellation. Performance is calculated in fully intact constellations and in the presence of satellite failures up to 8% of the satellites in the constellation. 1
COMPARATIVE MORPHOMETRIC, MERISTIC AND OSTEOLOGICAL STUDIES ON ADULT AND YOUNG HILSA (TENUALOSA ILISHA)

Tenualosa ilisha (matured/adult) and jatka (immature/young), locally known as Ilish, is designated as the national fish of Bangladesh. Both the stages were collected from different fish markets from Savar, Bangladesh during the exploration. This study was carried out on comparative morphometric, meristic and osteology between the Tenualosa ilisha (matured) and jatka (immature/young). The morphometric characteristics were higher in matured Tenualosa ilisha than immature jatka and among them head length, pre-dorsal length, postorbital length, the height of anal fin, eye diameter length, and abdominal region length were found significantly different. The proportion of standard length and snout length, head length and snout length, abdominal region length and standard length were significantly different between the two stages. Among the meristic characteristics; number of dorsal fin rays, anal fin rays and scales on the lateral line were found with little differences between matured hilsa and jatka. There are very small differences in the number of scales, scutes and fin rays of different fins between the two stages of fishes. The osteological comparison reveals more or less identical structure. Most of the bones of the skull were similar in structure but differed in size, shape and thickness.
Effect of Achievement Motivation and Self-Efficacy on General Well-Being among Students at Normal Universities in Ningxia: The Mediating Role of Time Management

General well-being is a positive evaluation of one’s mental health, which is an important topic in mental health. General well-being is fundamental to the positive development of young people. A thorough understanding of the factors that influence happiness have not yet been developed for students at normal universities in Ningxia Province in China. This study examined the mediation effects of time management on the relationship between achievement motivation, self-efficacy, and general well-being among students at normal universities in Ningxia Province in China. Using a random sampling, 163 participants (68 males and 95 females) completed the Achievement Motivation Scale (AMS), General Self-efficacy Scale (GSES), Time Management Questionnaire (TMQ) and General Well-being Scale (GWBS). Achievement motivation, self-efficacy, and time management were positively correlated with general well-being. The mediation effects of time management on the relationships between achievement motivation, self-efficacy, and general wellbeing (0.169 and 0.447) were demonstrated. These results add to the nuanced relationship between self-efficacy, achievement motivation, and general well-being. According to the Achievement Motivation Theory and the Self-efficacy Theory, this study reveals the role of achievement motivation, self-efficacy on general well-being and the mediating effect of time management in the relationship between achievement motivation, self-efficacy, and general well-being. According to the findings, the school can carry out a series of lectures and tutoring activities to enhance students’ achievement motivation, self-efficacy, time management, and general well-being.
Growth Performance, Metabolomics, and Microbiome Responses of Weaned Pigs Fed Diets Containing Growth-Promoting Antibiotics and Various Feed Additives

Simple Summary Understanding the biological mechanisms associated with growth performance improvements that may occur when adding various feed additives to diets of weaned pigs fed is essential for their strategic and optimal use. The objective of this study was to evaluate the growth performance, metabolic profiles, and intestinal microbiome composition of nursery pigs fed various feed additives and to determine potential biological mechanisms associated with growth promotion. The growth performance responses of high-health weaned pigs (20 days of age) were determined in three separate 42 day experiments when fed antibiotics (chlortetracycline and sulfamethazine; PC), herbal blends, turmeric, garlic, bitter orange extract, sweet orange extract, volatile and semi-volatile milk-derived substances, yeast nucleotide, and cell wall products, compared with feeding a non-supplemented diet (NC). None of the feed additives except antibiotics significantly improved growth performance, compared with feeding NC. Furthermore, none of the dietary treatments affected the metabolome and microbiome profiles or alpha and beta microbiome diversity in the ileum and cecum. However, some additives, such as herbal blends and garlic, increased the relative abundance of certain bacteria genera in the ileal and cecal microbiome that may provide some protection for weaned pigs when experiencing disease challenge. Abstract The objective of this study was to determine the potential biological mechanisms of improved growth performance associated with potential changes in the metabolic profiles and intestinal microbiome composition of weaned pigs fed various feed additives. Three separate 42 day experiments were conducted to evaluate the following dietary treatments: chlortetracycline and sulfamethazine (PC), herbal blends, turmeric, garlic, bitter orange extract, sweet orange extract, volatile and semi-volatile milk-derived substances, yeast nucleotide, and cell wall products, compared with feeding a non-supplemented diet (NC). In all three experiments, only pigs fed PC had improved (p < 0.05) ADG and ADFI compared with pigs fed NC. No differences in metabolome and microbiome responses were observed between feed additive treatments and NC. None of the feed additives affected alpha or beta microbiome diversity in the ileum and cecum, but the abundance of specific bacterial taxa was affected by some dietary treatments. Except for feeding antibiotics, none of the other feed additives were effective in improving growth performance or significantly altering the metabolomic profiles, but some additives (e.g., herbal blends and garlic) increased (p < 0.05) the relative abundance of potentially protective bacterial genera that may be beneficial during disease challenge in weaned pigs.
How much groundwater did California's Central Valley lose during the 2012–2016 drought?

We estimate net groundwater storage change in the Central Valley from April 2002 to September 2016 as the difference between inflows and outflows, precipitation, evapotranspiration, and changes in soil moisture and surface water storage. We also estimate total water storage change attributable to groundwater change using Gravity Recovery and Climate Experiment (GRACE) satellite data, which should be equivalent to our water balance estimates. Over two drought periods within our 14‐1/2 years study period (January 2007 to December 2009 and October 2012 to September 2016), we estimate from our water balance that a total of 16.5 km3 and 40.0 km3 of groundwater was lost, respectively. Our water balance‐based estimate of the overall groundwater loss over the 14‐1/2 years is −20.7 km3, which includes substantial recovery during nondrought periods The estimated rate of groundwater loss is greater during the recent drought (10.0 ± 0.2 versus 5.5 ± 0.3 km3/yr) than in the 2007–2009 drought, due to lower net inflows, a transition from row crops to trees, and higher crop water use, notwithstanding a reduction in irrigated area. The GRACE estimates of groundwater loss (−5.0 km3/yr for both water balance and GRACE during 2007–2009, and −11.2 km3/yr for GRACE versus −10 km3/yr for water balance during 2012–2016) are quite consistent for the two methods. However, over the entire study period, the GRACE‐based groundwater loss estimate is almost triple that from the water balance, mostly because GRACE does not indicate the between‐drought groundwater recovery that is inferred from our water balance.