Distinctive Features Research Articles

Performance Analysis of LSTM, SVM, CNN, and CNN-LSTM Algorithms for Malware Detection in IoT Dataset

Machine learning is an effective technique to tackle both the detection and classification tasks of malware. This is realized through learning algorithms that use various distinguishing features that characterize malware. Today's malware uses extremely sophisticated techniques, which means that various techniques to combat it are intensively developed. When malware is invisible, it can compromise many different data of a large number of users. Therefore, it is necessary to first analyze the types of malicious software and then propose appropriate countermeasures. In this regard, this work aims to analyze the performance of some well-known machine-learning techniques based on neural networks and support vector machines, originally developed as a method for the efficient training of neural networks. For the goal SVM, LSTM, CNN, and CNN-LSTM algorithms are analyzed concerning their effectiveness in the classification of malware in IoT datasets. For all the algorithms studied, their confusion matrices are presented along with receiver operating characteristic curves. The best results were obtained using the hybrid CNN-LSTM approach. Its results showed an accuracy of 97% and balanced performance across all metrics.

On the Translation of Chinese Four-Character Phrase from the Perspective of Skopos Theory: A Case Study of 2022 Government Work Report

As a unique linguistic phenomenon in Chinese, four-character phrase has its distinguishing features: being concise and comprehensive in content, being neat and orderly in form, being rhythmical in pronunciation, and being infectious in expression. All of these make it popular in political speeches and documents. Choosing Chinese version and English version of 2022 Government Work Report as corpus, this paper counts the adopted four-character phrases, analyzes the translation strategies of different types of four-character phrases, aiming to explore the employed rules of the Skopos theory. According to the statistics and analysis, this study acquires the following findings: 1) The total of four-character phrases adds up to 1458. 2) Of all free four-character phrases, subordinating phrases have the highest frequency, followed by coordinating phrases, verb-object phrases and subject-predicate phrases. 3) Four translation strategies are employed, including literal translation for the Fidelity rule, liberal translation for the Skopos rule, omission for the Coherence rule and the Skopos rule, and amplification for the Skopos rule.

Microbial Biotechnologies for Salt Tolerance in Alfalfa: Agro-Nutritional Comparison Between Local and Imported Varieties

Increasing soil salinity is threatening agricultural productivity which implies the development of new sustainable strategies to deal with this challenge. The main objective here is to assess the potential for improving the tolerance of alfalfa to salinity by combining inoculations with rhizobia and AMF. However, the distinguishing feature of this study is the comparison of two alfalfa varieties’ microbial response to salinity. The greenhouse trial was conducted on an Australian variety Siriver and an indigenous Demnate population, which were inoculated with Rhizoglomus irregulare and/or native AMF, and/or a RhOL1 rhizobial strain. The RhOL1 strain was selected from nine rhizobia tested for their plant growth promoting rhizobacteria (PGPR) activities. In addition to its ability to tolerate high salinity levels (769 mM) and solubilize insoluble phosphate as well as potassium, it can also synthesize auxins such as IAA. The application of these biofertilizers was carried out in the absence and the presence of the saline stress (0 and 120 mM NaCl). The double inoculations of native AMF and RhOL1 significantly improve the shoot and root dry biomass, plant elongation, number of formed leaves, and mineral nutrition, as well as the number of nodules and the rate of mycorrhizal root colonization. The synergistic effects between the native AMF and RhOL1 strain have been demonstrated in this study. However, the behavior of alfalfa genotypes towards microbial inoculation was significantly different. The ability to react to the double indigenous RhOL1 + AMF inoculation is more important in the Siriver than in the Demnate population. Thus, the possibility of formulating biofertilizers is based on the AMF–rhizobia–hote tripartite combination for alfalfa production in saline areas.

Increasing the Classification Achievement of Steel Surface Defects by Applying a Specific Deep Strategy and a New Image Processing Approach

Defect detection is still challenging to apply in reality because the goal of the entire classification assignment is to identify the exact type and location of every problem in an image. Since defect detection is a task that includes location and categorization, it is difficult to take both accuracy factors into account when designing related solutions. Flaw detection deployment requires a unique detection dataset that is accurately annotated. Producing steel free of flaws is crucial, particularly in large production systems. Thus, in this study, we proposed a novel deep learning-based flaw detection system with an industrial focus on automated steel surface defect identification. To create processed images from raw steel surface images, a novel method was applied. A new deep learning model called the Parallel Attention–Residual CNN (PARC) model was constructed to extract deep features concurrently by training residual structures and attention. The Iterative Neighborhood Component Analysis (INCA) technique was chosen for distinguishing features to lower the computational cost. The classification assessed the SVM method using a convincing dataset (Severstal: Steel Defect Detection). The accuracy in both the binary and multi-class classification tests was above 90%. Moreover, using the same dataset, the suggested model was contrasted with pre-existing models.

Improved immersed boundary/wall modeling method for RANS solver coupled with wall functions: application to Cartesian grid systems

A two-dimensional (2D) Reynolds-averaged Navier–Stokes (RANS) equations solver with k–ω turbulence closure is developed, employing immersed boundary (IB) technique on Cartesian grids. Generalized wall functions are introduced to enhance computational efficiency for problems with high Reynolds numbers. To address existing challenges in applying wall functions within IB methods, a novel, effective and easy-to-implement strategy is proposed. Another distinguishing feature of this turbulent-flow solver is that it employs the highly accurate immersed-boundary generalized harmonic polynomial cell (IB-GHPC) method to solve the Poisson equation for fluid pressure. The new solver is firstly validated by simulating channel flows on both hydraulically smooth and rough walls, achieving excellent agreement with benchmark experimental and numerical studies for various flow parameters including velocity, turbulent kinetic energy and shear stress. For channel flow simulations, our implementation of generalized wall functions using the proposed strategy results in a remarkable reduction of grid nodes by over 80%. Moreover, the solver is applied to simulate flow around both smooth and rough cylinders, producing promising results for drag, lift, and pressure coefficients. Our analysis demonstrates a robust performance of the developed solver in modeling turbulent flows based on Cartesian grids, offering a substantial improvement in computational efficiency for tackling problems involving large Reynolds numbers.

NPM1 mutation frequency and protein in Cutaneous Blastic Plasmacytoid Dendritic Cell Neoplasms.

NPM1 mutation frequency and protein in Cutaneous Blastic Plasmacytoid Dendritic Cell Neoplasms.

Constraint Accounts of Laws

In recent work, Adlam (2022b), Chen and Goldstein (2022), and Meacham (2023) have defended accounts of laws that take laws to be primitive global constraints. A major advantage of these accounts is that they're able to accommodate the many different kinds of laws that appear in physical theories. In this paper I'll present these three accounts, highlight their distinguishing features, and note some key differences that might lead one to favor one of these accounts over the others. I'll conclude by briefly discussing a version of a “constraint” account that I think is especially attractive.

How to improve research methodology in gender care: a non-binary choice

ABSTRACT Discussions about the evidence base for the Dutch gender care model, specifically puberty blockers, easily culminate in a binary choice between randomized controlled trials (RCTs, called ‘not ethical/feasible’ by some) and the pre-post design which compares patient outcomes after treatment with measurements before treatment within a single group of treated patients (called ‘scientifically weak’ by others). The RCT has two distinguishing features: First, an RCT compares a treated group with a control group that has received no, or another, treatment. Second, an RCT assigns patients to treatment or control by randomization to ensure that both groups are comparable before treatment. To make the discussion non-binary, this paper focuses on the design with a control group but without randomized assignment, known as a quasi-experiment in psychology. Its pros and cons are discussed, as are some improvements to it and statistical methods that partly make up for the lack of randomization.

Expanding Distribution, Taxonomy and Morphological Descriptions of the Collected Fish Species Halieutaea indica Annandale & Jenkins, 1910 (Indian Handfish) from the Jafrabad Fish Landing Center, Arabian Sea, Northwest Coast of India

This study documents the taxonomy and morphology of Halieutaea indica specimens collected from the Gujarat coast, of India. Two specimens (total lengths: 7.6 cm and 10.3 cm) were obtained from a trawler operating from the Jafrabad Fish Landing Center and identified using original species descriptions. A distinguishing feature of seven inverted rostral spines protecting the mouth was observed. Fresh specimens exhibited yellow pectoral fins with a white band near the tip, further corroborating diagnostic characters. Morphometric parameters, including body proportions, fin lengths, and meristic counts, were measured and compared with existing data from earlier studies. The results align with prior morphological descriptions of H. indica, reinforcing its identification and confirming its presence in Gujarat’s coastal waters. This study contributes to baseline data on the species distribution in the Arabian Sea and supports regional biodiversity documentation in understudied fisheries ecosystems.

Machine learning-derived cumulative health measure for assessing disease impact in dairy cattle

Dairy cattle’s susceptibility to diseases significantly impacts their health, welfare, and longevity. Disability weights reflect the relative severity or impact of important diseases and provide an extension of epidemiological frequency measures. They are central for comparing disease burden across diverse causes when summarizing health status and disease severity. Yet, they often reflect group-level health status and rely on expert judgment, which is subjective. In absence of an objective approach, this study aimed to create disability weight metrics using pathophysiological data with machine learning approach. Four binary classifiers using a generalized linear model with Lasso regularization were developed to identify distinguishing features for healthy and diseased cows affected by hypocalcemia, ketosis, metritis and mastitis. Model performance, assessed via the Area Under the Curve (AUC), reached values of 0.72, 0.66, 0.82, and 0.92 for distinguishing hypocalcemia, ketosis, metritis and mastitis in cows from healthy groups. The selected features were combined into a summary disability weight – cumulative health measure – for each disease computed through weighted sums of feature importance from classification models. Notably, the average cumulative health measure differed significantly between healthy and diseased groups (p < 0.05). The relative ranking of diseases based on the average cumulative health measure was comparable to the expert survey-based approach. Such features will offer insights into disease impact and will provide a standardized metric for comparing disease severity.

Advanced Techniques for Transit Priority at Roundabouts utilizing Signal Metering

Transit priority strategies frequently focus on conventional Transit Signal Priority (TSP) at intersections, overlooking the distinct operational characteristics of roundabouts, including signalized, metered, and yield-based control methods. This study introduces a new approach, Transit Metering Signal Priority (TMSP), which uses metering signals to provide Public Transport Vehicles (PTVs) preference at roundabouts. A distinguishing feature of TMSP is its compatibility with the existing yield or metering control strategies employed by roundabouts, allowing them to maintain these methods without the full signalization of all approaches for priority allocation to PTVs the latter involves. The efficacy of the proposed TMSP model is assessed through numerical experiments, with yield control (no priority) serving as the baseline. Comparisons are drawn between conventional TSP and TMSP scenarios under varying congestion levels. The findings suggest that the proposed TMSP logic can lead to a reduction in bus delays by 2 sec to 16.6 sec, with minimal impact on general traffic, while also decreasing travel time variability by up to 19 sec (standard deviation). In comparison to TSP, TMSP exhibits clear advantages for public transportation by reducing delays and providing more stable travel times, while minimizing disruptions to the general traffic flow. The implementation of the TMSP method enhances the performance and reliability of public transport services, contributing to the development of more resilient and sustainable urban mobility systems.

Cellulose-based delivery systems for bioactive ingredients: A review.

Cellulose-based delivery systems for bioactive ingredients: A review.

Familial Frontal Fibrosing Alopecia Occurs Early in Daughters With Affected Mothers: A Case Report and a Review of the Literature.

Frontal fibrosing alopecia (FFA) is a form of cicatricial alopecia that is being increasingly diagnosed in recent years. It predominantly affects post-menopausal women of various ethnic backgrounds, but cases have also been reported in pre-menopausal women and rarely in men. Eleven familial cases of FFA have been published in the literature, with some authors raising speculation about potential genetic predisposition or shared exposure to environmental factors as triggers for the condition. Despite this, there remains a lack of detailed characterisation of the clinical features specific to familial cases of FFA. In this report, we aim to contribute to the understanding of FFA by presenting the first Australian case of familial FFA involving a mother and her daughter, while also attempting to define the clinical distinguishing features in familial FFA and sporadic FFA.

A physics-constrained deep learning treatment of runaway electron dynamics

An adjoint formulation leveraging a physics-informed neural network (PINN) is employed to advance the density moment of a runaway electron (RE) distribution forward in time. A distinguishing feature of this approach is that once the adjoint problem is solved, its solution can be used to project the RE density forward in time for an arbitrary initial momentum space distribution of REs. Furthermore, by employing a PINN, a parametric solution to the adjoint problem can be learned. Thus, once trained, this adjoint-deep learning framework is able to efficiently project the RE density forward in time across various plasma conditions while still including a fully kinetic description of RE dynamics. As an example application, the temporal evolution of the density of primary electrons is studied, with particular emphasis on evaluating the decay of a RE population when below threshold. Predictions from the adjoint-deep learning framework are found to be in good agreement with a traditional relativistic electron Fokker–Planck solver, for several distinct initial conditions, and across an array of physics parameters. Once trained, the PINN thus provides a means of generating RE density time histories with exceptionally low online execution time.

Enhancing explainability via distinguishable feature learning based on causality in image classification

Enhancing explainability via distinguishable feature learning based on causality in image classification

Monocular depth estimation via a detail semantic collaborative network for indoor scenes

Monocular image depth estimation is crucial for indoor scene reconstruction, and it plays a significant role in optimizing building energy efficiency, indoor environment modeling, and smart space design. However, the small depth variability of indoor scenes leads to weakly distinguishable detail features. Meanwhile, there are diverse types of indoor objects, and the expression of the correlation among different objects is complicated. Additionally, the robustness of recent models still needs further improvement given these indoor environments. To address these problems, a detail‒semantic collaborative network (DSCNet) is proposed for monocular depth estimation of indoor scenes. First, the contextual features contained in the images are fully captured via the hierarchical transformer structure. Second, a detail‒semantic collaborative structure is established, which establishes a selective attention feature map to extract details and semantic information from feature maps. The extracted features are subsequently fused to improve the perception ability of the network. Finally, the complex correlation among indoor objects is addressed by aggregating semantic and detailed features at different levels, and the model accuracy is effectively improved without increasing the number of parameters. The proposed model is tested on the NYU and SUN datasets. The proposed approach produces state-of-the-art results compared with the 14 performance results of recent optimal methods. In addition, the proposed approach is fully discussed and analyzed in terms of stability, robustness, ablation experiments and availability in indoor scenes.

Differential Diagnoses of Acute Myocardial Infarction

Introduction: Acute myocardial infarction (AMI) is a leading cause of morbidity and mortality worldwide. Accurate diagnosis is critical, yet challenging due to the myriad conditions that can mimic its presentation. Distinguishing AMI from its differential diagnoses is essential for timely and appropriate management. Objective: This narrative review aims to elucidate the various conditions that can present similarly to AMI, highlighting key distinguishing features to aid clinicians in accurate diagnosis. Methods: A comprehensive literature search was conducted across databases including PubMed, Scopus, Web of Science, Cochrane Library, and Google Scholar. Recent studies published within the last five years were prioritized to ensure up-to-date information. Results and Discussion: Several conditions, such as acute pericarditis, aortic dissection, pulmonary embolism, and gastroesophageal reflux disease, can mimic the clinical presentation of AMI. Each condition possesses unique clinical, electrocardiographic, and biomarker profiles that can aid in differentiation. For instance, acute pericarditis often presents with pleuritic chest pain and diffuse ST-segment elevations, whereas aortic dissection may involve tearing chest pain radiating to the back and mediastinal widening on imaging. Conclusion: A thorough understanding of the differential diagnoses of AMI is imperative for clinicians to ensure accurate diagnosis and appropriate management, ultimately improving patient outcomes.

Analytical reconstruction of equivalent purely kinetic k-essence description for cs2(w) barotropic fluid models

Abstract A novel approach to the class of cosmic barotropic fluids in which the speed of sound squared is defined as a function of the Equation of State parameter, so called $c_s^2(w)$ models, is examined. For this class of models, a new analytical reconstruction method is introduced for finding their equivalent purely kinetic k-essence formulation. The method is explicitly demonstrated for several $c_s^2(w)$ models. A distinguished feature of the studied models is a peak in $c_s^2$ during cosmic expansion. A possible role that such model features could play in understanding of so far unexplained aspects of cosmic evolution is discussed. The application of the obtained explicit or closed form solutions in understanding dark sector unification models is outlined.

Revisiting eye blink in Parkinson’s disease

Spontaneous blinking is a rapid and unconscious type of blinking that has been linked to several cognitive processes. Blink rate has been established as a reliable measure of cognitive processing and a predictor of dopamine-related cognitive function. Patients with Parkinson’s disease (PD) have a reduced spontaneous blink rate. In this study, we propose an additional measure of blink characteristic, namely blink duration, and evaluate its alteration in a large cohort of 107 patients with PD. We also investigate the relationship between blink rate and blink duration and disease characteristics such as severity and dopaminergic neuronal loss. The results show that the blink rate is reduced, and blink duration is increased in patients compared to healthy controls. Blink rate is related to motor deficit severity and significantly correlated with dopamine depletion (dopamine transporter striatal binding ratio). Conversely, blink duration is related to non-motor aspects such as sleepiness. These findings highlight the potential of blink as a distinguishing feature of Parkinson’s disease, underscoring the importance of incorporating blink assessments into standardized oculomotor testing protocols for PD.

Systemic EBV+ T-Cell Lymphoma of Childhood with Hemophagocytic Lymphohistiocytosis in a Patient with a Highly Complex Karyotype

Background/Objective: Epstein-Barr Virus (EBV) infection can be associated with lymphocytic hematological malignancies, including systemic Epstein-Barr virus-positive T-cell lymphoma of childhood (SEBVTCL). A common complication of EBV infection, hemophagocytic lymphohistiocytosis (HLH), is a life-threatening condition of immune activation present in virtually all cases of SEBVTCL that requires urgent treatment, as this malignancy can be rapidly fatal. Abnormal karyotypes have been strongly associated with SEBVTCL as a distinguishing feature from HLH in the literature. Here, we discuss the diagnostic challenges and social complications in the case of an unaccompanied minor immigrant patient with a highly complex karyotype diagnosed with SEBVTCL with associated HLH. Methods: Laboratory testing confirmed the presence of EBV+ HLH and cytogenetic analysis was performed to investigate a neoplastic process in this patient, confirming SEBVTCL. Chromosomal microarray (CMA) was performed to try to clarify the complex findings by chromosome analysis but demonstrated normal results. Results: Chromosome analysis demonstrated a highly complex hypertriploid clone that confirmed the diagnosis of SEBVTCL. After declining treatment, the patient was discharged to his guardian against medical advice and succumbed to his disease shortly after. Conclusions: SEBVTCL can be challenging to diagnose due to the similarity in clinical and pathological presentations. In virtually all cases reported in the literature, an abnormal karyotype has been reported to be the most important prognostic factor. We propose that in cases with diagnostic ambiguity, an abnormal karyotype can help favor SEBVTCL over EBV+ HLH.