Stability and bifurcation analysis of a 2DOF dynamical system with piezoelectric device and feedback control.

This study aims to demonstrate the behaviors of a two degree-of-freedom (DOF) dynamical system consisting of attached mass to a nonlinear damped harmonic spring pendulum with a piezoelectric device. Such a system is influenced by a parametric excitation force on the direction of the spring's elongation and an operating moment at the supported point. A negative-velocity-feedback (NVF) controller is inserted into the main system to reduce the undesired vibrations that affect the system's efficiency, especially at the resonance state. The equations of motion (EOM) are derived by using Lagrangian equations. Through the use of the multiple-scales-strategy (MSS), approximate solutions (AS) are investigated up to the third order. The accuracy of the AS is verified by comparing them to the obtained numerical solutions (NS) through the fourth-order Runge-Kutta Method (RK-4). The study delves into resonance cases and solvability conditions to provide the modulation equations (ME). Graphical representations showing the time histories of the obtained solutions and frequency responses are presented utilizing Wolfram Mathematica 13.2 in addition to MATLAB software. Additionally, discusses the bifurcation diagrams, Poincaré maps, and Lyapunov exponent spectrums to show the various behavior patterns of the system. To convert vibrating motion into electrical power, a piezoelectric sensor is connected to the dynamical model, which is just one of the energy harvesting (EH) technologies with extensive applications in the commercial, industrial, aerospace, automotive, and medical industries. Moreover, the time histories of the obtained solutions with and without control are analyzed graphically. Finally, resonance curves are used to discuss stability analysis and steady-state solutions.

High-resolution spatio-temporal risk mapping for malaria in Namibia: a comprehensive analysis.

BACKGROUND: Namibia, a low malaria transmission country targeting elimination, has made substantial progress in reducing malaria burden through improved case management, widespread indoor residual spraying and distribution of insecticidal nets. The country's diverse landscape includes regions with varying population densities and geographical niches, with the north of the country prone to periodic outbreaks. As Namibia approaches elimination, malaria transmission has clustered into distinct foci, the identification of which is essential for deployment of targeted interventions to attain the southern Africa Elimination Eight Initiative targets by 2030. Geospatial modelling provides an effective mechanism to identify these foci, synthesizing aggregate routinely collected case counts with gridded environmental covariates to downscale case data into high-resolution risk maps. METHODS: This study introduces innovative infectious disease mapping techniques to generate high-resolution spatio-temporal risk maps for malaria in Namibia. A two-stage approach is employed to create maps using statistical Bayesian modelling to combine environmental covariates, population data, and clinical malaria case counts gathered from the routine surveillance system between 2018 and 2021. RESULTS: A fine-scale spatial endemicity surface was produced for annual average incidence, followed by a spatio-temporal modelling of seasonal fluctuations in weekly incidence and aggregated further to district level. A seasonal profile was inferred across most districts of the country, where cases rose from late December/early January to a peak around early April and then declined rapidly to a low level from July to December. There was a high degree of spatial heterogeneity in incidence, with much higher rates observed in the northern part and some local epidemic occurrence in specific districts sporadically. CONCLUSIONS: While the study acknowledges certain limitations, such as population mobility and incomplete clinical case reporting, it underscores the importance of continuously refining geostatistical techniques to provide timely and accurate support for malaria elimination efforts. The high-resolution spatial risk maps presented in this study have been instrumental in guiding the Namibian Ministry of Health and Social Services in prioritizing and targeting malaria prevention efforts. This two-stage spatio-temporal approach offers a valuable tool for identifying hotspots and monitoring malaria risk patterns, ultimately contributing to the achievement of national and sub-national elimination goals.

Latent Archetypes of the Spatial Patterns of Cancer.

The cancer atlas edited by several countries is the main resource for the analysis of the geographic variation of cancer risk. Correlating the observed spatial patterns with known or hypothesized risk factors is time-consuming work for epidemiologists who need to deal with each cancer separately, breaking down the patterns according to sex and race. The recent literature has proposed to study more than one cancer simultaneously looking for common spatial risk factors. However, this previous work has two constraints: they consider only a very small (2-4) number of cancers previously known to share risk factors. In this article, we propose an exploratory method to search for latent spatial risk factors of a large number of supposedly unrelated cancers. The method is based on the singular value decomposition and nonnegative matrix factorization, it is computationally efficient, scaling easily with the number of regions and cancers. We carried out a simulation study to evaluate the method's performance and apply it to cancer atlas from the USA, England, France, Australia, Spain, and Brazil. We conclude that with very few latent maps, which can represent a reduction of up to 90% of atlas maps, most of the spatial variability is conserved. By concentrating on the epidemiological analysis of these few latent maps a substantial amount of work is saved and, at the same time, high-level explanations affecting many cancers simultaneously can be reached.

Human and hamster sera correlate well in identifying antigenic drift among SARS-CoV-2 variants, including JN.1.

Antigenic assessments of SARS-CoV-2 variants inform decisions to update COVID-19 vaccines. Primary infection sera are often used for assessments, but such sera are rare due to population immunity from SARS-CoV-2 infections and COVID-19 vaccinations. Here, we show that neutralization titers and breadth of matched human and hamster pre-Omicron variant primary infection sera correlate well and generate similar antigenic maps. The hamster antigenic map shows modest antigenic drift among XBB sub-lineage variants, with JN.1 and BA.4/BA.5 variants within the XBB cluster, but with fivefold to sixfold antigenic differences between these variants and XBB.1.5. Compared to sera following only ancestral or bivalent COVID-19 vaccinations, or with post-vaccination infections, XBB.1.5 booster sera had the broadest neutralization against XBB sub-lineage variants, although a fivefold titer difference was still observed between JN.1 and XBB.1.5 variants. These findings suggest that antibody coverage of antigenically divergent JN.1 could be improved with a matched vaccine antigen.IMPORTANCEUpdates to COVID-19 vaccine antigens depend on assessing how much vaccine antigens differ antigenically from newer SARS-CoV-2 variants. Human sera from single variant infections are ideal for discriminating antigenic differences among variants, but such primary infection sera are now rare due to high population immunity. It remains unclear whether sera from experimentally infected animals could substitute for human sera for antigenic assessments. This report shows that neutralization titers of variant-matched human and hamster primary infection sera correlate well and recognize variants similarly, indicating that hamster sera can be a proxy for human sera for antigenic assessments. We further show that human sera following an XBB.1.5 booster vaccine broadly neutralized XBB sub-lineage variants but titers were fivefold lower against the more recent JN.1 variant. These findings support updating the current COVID-19 vaccine variant composition and developing a framework for assessing antigenic differences in future variants using hamster primary infection sera.

Exploring Journey Maps as Products From Qualitative Research: Application Through Food Insecure Veterans' Experiences.

Journey maps are graphic representations of participant, user, customer, or patient experiences or "journeys" with a particular phenomenon, product, business, or organization. Journey maps help visualize complex pathways and phases in accessible, digestible ways. They also capture emotions, reactions, and values associated with the processes participants undergo, complemented by images or quotes from participants. Here, we outline the foundations of journey maps in research and in practice settings. Our goal is to describe journey maps to researchers new to the product and emphasize the novelty and utility of journey maps as visual products from qualitative research particularly in a health setting. To explore journey maps-including their benefits, drawbacks, and relevance-we discuss examples including our own process for designing a journey map of food insecure Veterans' experiences using qualitative, in-depth interviews and supported by member checking. Our journey map depicts food insecurity as a repetitive process, a unique contribution given that many journey maps are designed with discrete starting and stopping points. We conclude by discussing the novelty of journey maps as innovative products that researchers can use to identify opportunities for process improvements and innovation using multiple data sources or methods.

Exploratory evidence maps for the WHO Classification of Tumours 5th edition for lung and thymus tumors.

The WHO Classification of Tumours (WCT) guides cancer diagnosis, treatment, and research. However, research evidence in pathology continuously changes, and new evidence emerges. Correct assessment of evidence in the WCT 5th edition (WCT-5) and identification of high level of evidence (LOE) studies based on study design are needed to improve future editions. We aimed at producing exploratory evidence maps for WCT-5 Thoracic Tumours, specifically lung and thymus tumors. We extracted citations from WCT-5, and imported and coded them in EPPI-Reviewer. The maps were plotted using EPPI-Mapper. Maps displayed tumor types (columns), descriptors (rows), and LOE (bubbles using a four-color code). We included 1434 studies addressing 51 lung, and 677 studies addressing 25 thymus tumor types from WCT-5 thoracic tumours volume. Overall, 87.7% (n = 1257) and 80.8% (n = 547) references were low, and 4.1% (n = 59) and 2.2% (n = 15) high LOE for lung and thymus tumors, respectively. Invasive non-mucinous adenocarcinoma of the lung (n = 215; 15.0%) and squamous cell carcinoma of the thymus (n = 93; 13.7%) presented the highest number of references. High LOE was observed for colloid adenocarcinoma of the lung (n = 11; 18.2%) and type AB thymoma (n = 4; 1.4%). Tumor descriptors with the highest number of citations were prognosis and prediction (n = 273; 19.0%) for lung, and epidemiology (n = 186; 28.0%) for thymus tumors. LOE was generally low for lung and thymus tumors. This study represents an initial step in the WCT Evidence Gap Map (WCT-EVI-MAP) project for mapping references in WCT-5 for all tumor types to inform future WCT editions.

Assessment of Thermal Influence on an Orthodontic System by Means of the Finite Element Method.

The development of the finite element method (FEM) combined block polynomial interpolation with the concepts of finite difference formats and the variation principle. Because of this combination, the FEM overcomes the shortcomings of traditional variation methods while maintaining the benefits of current variation methods and the flexibility of the finite difference method. As a result, the FEM is an advancement above the traditional variation methods. The purpose of this study is to experimentally highlight the thermal behavior of two stomatognathic systems, one a control and the other presenting orthodontic treatment by means of a fixed metallic orthodontic appliance, both being subjected to several thermal regimes. In order to sustain this experimental research, we examined the case of a female subject, who was diagnosed with Angle class I malocclusion. The patient underwent a bimaxillary CBCT investigation before initiating the orthodontic treatment. A three-dimensional model with fully closed surfaces was obtained by using the InVesalius and Geomagic programs. Like the tissues examined in the patient, bracket and tube-type elements as well as orthodontic wires can be included to the virtual models. Once it is finished and geometrically accurate, the model is exported to an FEM-using program, such as Ansys Workbench. The intention was to study the behavior of two stomatognathic systems (with and without a fixed metallic orthodontic appliance) subjected to very hot food (70 °C) and very cold food (-18 °C). From the analysis of the obtained data, it was concluded that, following the simulations carried out in the presence of the fixed metallic orthodontic appliance, significantly higher temperatures were generated in the dental pulp.

Dynamic patterns and potential drivers of river water quality in a coastal city: Insights from a machine-learning-based framework and water management.

River water quality continues to deteriorate under the coupled effects of climate change and human activities. Machine learning (ML) is a promising approach for analyzing water quality. Nevertheless, the spatiotemporal dynamics of river water quality and their potential mechanisms in changing environments remain incomprehensively understood through available ML-based researches. Here, we developed a ML-based framework integrating a self-organizing map (SOM) model with a random forest (RF) model. This framework was applied to simultaneously investigate the spatiotemporal patterns and potential drivers of river permanganate (CODMn), ammonia nitrogen (NH3-N), and total phosphorus (TP) dynamics across 34 sites from 2010 to 2020 in a coastal city threatened by deteriorating water environment in southeastern China. The sites were divided into two clusters in the spatial context with different water quality conditions. The year of 2015 for NH3-N and 2018 for CODMn and TP were identified as the key turning points of water quality variations. Features including sewage discharge, population dynamics, percentage of cultivated land, and fertilizer application contributed greatly to water quality deterioration. The increase in forest vegetation reflected by percentage of forest and leaf area index may improve water quality. The ML-based modeling framework demonstrated a promising way to address the spatiotemporal dynamics of river water quality, and provided insights for water management in a coastal city with intensifying human-nature interactions.

Assessment of histogram analysis in distinguishing between low-grade and high-grade brainstem glioma.

Objectives: This retrospective study was designed to evaluate the value of histogram analysis on the apparent diffusion coefficient (ADC) map in distinguishing between low-grade and high-grade brainstem glioma (BSG). In this article, we used the two VOI (volume of interest) placements of the entire tumour method and the entire solid part method, thus aiming to compare the diagnostic value between these two performances. Methods: A total of 28 patients (8 low-grade BSGs and 20 high-grade BSGs) with histological diagnosis of BSG. All victims underwent contrast-enhanced magnetic resonance imaging (MRI). We measured ADC histogram parameters (mean, median, SD, max, min, Kurtosis, Skewness, Entropy, Uniformity, and Variance) and calculated the ratios between tumour and normal brain parenchyma parameters in two methods. Independent samples test, Mann-Whitney U test, and ROC curve were used to determine each value's cut-off point, sensitivity, and specificity. Results: Among the method of VOI placing the entire tumour, the values of ADC_min, rADC_mean, rADC_median, and rADC_min are significantly different between these two neoplasms with cut-off values (sensitivity %, specificity %) of 776 x10-6 m2/s (62.5%, 90%), 2.1765 (62.5%, 95%), 2.1588 (50%, 100%), 1.0535 (100%, 50%), respectively. On the other hand, the method of VOI placing the entire solid part of the tumour showed significantly different in ADC_mean, ADC_median, ADC_min, rADC_mean, rADC_median, rADC_min at the cut-off values (sensitivity%, specificity %) of 1491 x10-6 m2/s (62.5%, 95%), 1438.9 x10-6 m2/s (62.5%, 90%), 862.5 x10-6 m2/s (75%, 100%), 2,112 (62.5%, 95%), 1.9748 (62.5%, 90%), 1.3735 (87.5%, 90%), respectively. Conclusions: The ADC histogram analysis is a promising approach to distinguishing low-grade BSG and high-grade BSG. The entire solid part VOI placement has a superior value compared to the whole tumour VOI placement. The rADC_mean showed the best performance in differentiating between these two entities, followed by ADC_min, rADC_mean, rADC_median, ADC_mean, and ADC_median.

Genetic code, the problem of coding biological cycles, and cyclic Gray codes.

This article is devoted to the problem of genetically coding of inherited cyclic structures in biological bodies, whose life activity is based on a great inherited set of mutually coordinated cyclic processes. The author puts forward and arguments the idea that the genetic coding system is capable of encoding inherited cyclic processes because it itself is a system of cyclic codes connected with Boolean algebra of logic. In other words, the physiological processes in question are cyclical because they are genetically encoded by cyclic codes. In support of this idea, the author presents a set of his results on the connection of the genetic coding system with cyclic Gray codes, which are one of many known types of cyclic codes. This opens up the possibility of using for modeling inherited cyclic biostructures those algebraic and logical theories and constructions that are associated with Gray codes and have long been used in engineering technologies: Karnaugh maps, Hilbert curve, Hadamard matrices, Walsh functions, dyadic analysis, etc. The author believes that when studying the origin, evolution and function of the genetic code, it is necessary to take into account the ability of the genetic system to encode many mutually related cyclic processes.

Geochemical baseline values determination and spatial distribution of trace elements in topsoils: An application in Sicily region (Italy).

This study proposed an approach to determine the geochemical baseline values in topsoils. The chosen study area is Sicily (Italy), a region characterized by significant lithological heterogeneity. Eighty-three topsoil samples were collected at several sites away from potential anthropogenic pollution sources. The concentrations of potentially toxic elements (As, Cd, Cr, Cu, Mo, Pb, Sb, V, and Zn) were quantified via inductively coupled plasma (ICP-MS). The elements showed median concentrations in the range 68.8-0.23 µg g-1 and the trend of abundance was: Zn > V > Cr > Cu > Pb > As>Mo > Sb > Cd. Regional geochemical baseline values for trace elements were determined using statistical methods (Me±2MAD; P95 and UTL95-95). The use of UTL95-95 was found to be the most suitable, obtaining appropriate geochemical baseline values for the entire region, regardless of lithology. The spatial distribution of the elements was determined by stochastic simulations on a convex-concave boundary with a resolution of 5 km, obtaining detailed geochemical maps that predict the distribution of concentrations of each element even in unsampled areas. The results of this study provide a methodology for a more correct assessment of the environmental contamination status of soils.

WSSS-CRAM: precise segmentation of histopathological images via class region activation mapping.

Introduction: Fast, accurate, and automatic analysis of histopathological images using digital image processing and deep learning technology is a necessary task. Conventional histopathological image analysis algorithms require the manual design of features, while deep learning methods can achieve fast prediction and accurate analysis, but rely on the drive of a large amount of labeled data. Methods: In this work, we introduce WSSS-CRAM a weakly-supervised semantic segmentation that can obtain detailed pixel-level labels from image-level annotated data. Specifically, we use a discriminative activation strategy to generate category-specific image activation maps via class labels. The category-specific activation maps are then post-processed using conditional random fields to obtain reliable regions that are directly used as ground-truth labels for the segmentation branch. Critically, the two steps of the pseudo-label acquisition and training segmentation model are integrated into an end-to-end model for joint training in this method. Results: Through quantitative evaluation and visualization results, we demonstrate that the framework can predict pixel-level labels from image-level labels, and also perform well when testing images without image-level annotations. Discussion: Future, we consider extending the algorithm to different pathological datasets and types of tissue images to validate its generalization capability.

Pressure Pain Hyperalgesia Expressed by Topographical Pressure Pain Sensitivity after Cardiac Surgery.

BACKGROUNDS: We aim to evaluate changes in pressure pain sensitivity before and after cardiac surgery using topographical sensitivity maps utilizing a pressure algometer. METHODS: Pressure pain thresholds over 17 thoracic points and 4 distant pain-free points were assessed in 70 patients (women: 29, age: 67.5 years), before and at 1, 3, and 7 postoperative days. Thoracic topographical pressure pain sensitivity maps were calculated at all follow-ups. Postoperative pain was recorded at each follow-up on a numerical pain rate scale. RESULTS: Postoperative pain intensity decreased from 6.4 (SD 1.0) on the first postoperative day to 5.5 (SD 1.9) on the third and to 4.5 (SD 1.7) on the seventh day (p < 0.001). The mixed-model ANOVA revealed that the lowest pressure pain thresholds were observed one day after surgery, increased slightly during follow-up, and were lower at the xiphoid process. Significant negative correlations between postoperative pain intensity and pressure pain thresholds were observed at each time point in thoracic measures (all, p < 0.01), but not with pressure pain thresholds from distant pain-free areas. CONCLUSIONS: Postoperative pain after cardiac surgery can be objectively quantified using algometry. Pressure pain hyperalgesia was associated with the intensity of postoperative pain.

Application of Linear Mixed-Effects Model, Principal Component Analysis, and Clustering to Direct Energy Deposition Fabricated Parts Using FEM Simulation Data.

The purpose of this study is to investigate the effects of toolpath patterns, geometry types, and layering effects on the mechanical properties of parts manufactured by direct energy deposition (DED) additive manufacturing using data analysis and machine learning methods. A total of twelve case studies were conducted, involving four distinct geometries, each paired with three different toolpath patterns based on finite element method (FEM) simulations. These simulations focused on residual stresses, strains, and maximum principal stresses at various nodes. A comprehensive analysis was performed using a linear mixed-effects (LME) model, principal component analysis (PCA), and self-organizing map (SOM) clustering. The LME model quantified the contributions of geometry, toolpath, and layer number to mechanical properties, while PCA identified key variables with high variance. SOM clustering was used to classify the data, revealing patterns related to stress and strain distributions across different geometries and toolpaths. In conclusion, LME, PCA, and SOM offer valuable insights into the final mechanical properties of DED-fabricated parts.

A Real-Time Semantic Map Production System for Indoor Robot Navigation.

Although grid maps help mobile robots navigate in indoor environments, some lack semantic information that would allow the robot to perform advanced autonomous tasks. In this paper, a semantic map production system is proposed to facilitate indoor mobile robot navigation tasks. The developed system is based on the employment of LiDAR technology and a vision-based system to obtain a semantic map with rich information, and it has been validated using the robot operating system (ROS) and you only look once (YOLO) v3 object detection model in simulation experiments conducted in indoor environments, adopting low-cost, -size, and -memory computers for increased accessibility. The obtained results are efficient in terms of object recognition accuracy, object localization error, and semantic map production precision, with an average map construction accuracy of 78.86%.

Evaluation of a Quadrivalent Shigella flexneri Serotype 2a, 3a, 6, and Shigella sonnei O-Specific Polysaccharide and IpaB MAPS Vaccine.

BACKGROUND: Shigellosis is the leading cause of diarrheal deaths worldwide and is particularly dangerous in children under 5 years of age in low- and middle-income countries. Additionally, the rise in antibiotic resistance has highlighted the need for an effective Shigella vaccine. Previously, we have used the Multiple Antigen-Presenting System (MAPS) technology to generate monovalent and quadrivalent Salmonella MAPS vaccines that induce functional antibodies against Salmonella components. METHODS: In this work, we detail the development of several monovalent vaccines using O-specific polysaccharides (OSPs) from four dominant serotypes, S. flexneri 2a, 3a, and 6, and S. sonnei. We tested several rhizavidin (rhavi) fusion proteins and selected a Shigella-specific protein IpaB. Quadrivalent MAPS were made with Rhavi-IpaB protein and tested in rabbits for immunogenicity. RESULTS: Individual MAPS vaccines generated robust, functional antibody responses against both IpaB and the individual OSP component. Antibodies to IpaB were effective across Shigella serotypes. We also demonstrate that the OSP antibodies generated are specific to each homologous Shigella O type by performing ELISA and bactericidal assays. We combined the components of each MAPS vaccine to formulate a quadrivalent MAPS vaccine which elicited similar antibody and bactericidal responses compared to their monovalent counterparts. Finally, we show that the quadrivalent MAPS immune sera are functional against several clinical isolates of the serotypes used in the vaccine. CONCLUSIONS: This quadrivalent MAPS Shigella vaccine is immunogenicity and warrants further study.

Variations of the zero degrees isotherm and environmental lapse rate recorded with ADSB and Mode S EHS messages.

This study focuses on analyzing the altitude of the zero degrees isotherm and variations in the environmental lapse rate (ELR) using the publicly available data collected from Automatic Dependent Surveillance-Broadcast (ADS-B) and Mode S Enhanced Surveillance (EHS) messages emitted by airplanes over a five-month period in 2021. The data was gathered using a professional receiver stationed in Bucharest (Romania). The aviation messages were decoded and the air temperature and pressure were determined, at the location of the airplane. The method has the advantage of the continuous messages that are emitted by the aircrafts during flight that allow instantaneous determination of the meteorological parameters at no additional costs. It can also be extended to permit almost real time maps of the ELR. When data was analyzed and a standard ELR value of 6.5 K/km is employed it was observed that the mean altitude of the 0 degrees isotherm exhibits a seasonal increase during the summer months, with an average altitude of 2874.2 m. The highest recorded altitude of the 0 degrees isotherm was found to be 5346.8 m, near Alexandria city (Romania), on 22.07.2021. Using a standard Least Mean Square algorithm alongside the International Standard Atmosphere pressure formula, the ELR values were calculated from pressure measurements data. The resulting mean ELR for the five-month period was determined to be 5.1331 K/km, slightly lower than the standard value.

Fracture Mapping in High-Energy Chest Trauma.

Background: High-energy chest trauma often results in rib fractures and associated chest injuries. This study explored fracture distribution patterns in high-energy chest trauma, using three-dimensional (3D) fracture mapping technology. Methods: This retrospective study analyzed cases of high-energy chest trauma with rib fractures treated at a Level 1 Trauma Center, from February 2012 to January 2023. Specifically, 3D computed tomography (CT) was used to reconstruct rib fractures and create fracture-frequency heat maps, analyzing the influence of other thoracic fractures on rib fracture distribution. Results: Rib fractures were frequently found in the anterior and posterior thoracic areas. On average, patients sustained 7 ± 3.87 rib fractures, with clavicle fractures in 25.5% and scapular fractures in 19.6% of cases. Scapular fractures led to more posterior rib fractures, while sternal fractures were associated with more anterior rib fractures. Clavicle fractures were linked to fractures of the first to third ribs. Conclusions: Rib fractures in high-energy chest trauma occurred most often in the anterior and posterior regions. Fractures of the scapula and sternum influence the positioning of the fracture lines. Clavicular fractures are associated with a higher incidence of upper rib fractures. These findings can help inform surgical decisions and complication management.

Robust malicious software detection and classification using global whale optimization algorithm with deep learning approach.

Software malware detection and classification leverage sophisticated procedures and methods from the cybersecurity domain for identifying and categorizing malicious software, generally called malware. This procedure analyses code behaviour, file structures, and other features to distinguish between benign and malicious programs. Machine learning (ML) and artificial intelligence (AI) are vital in this domain, allowing the progress of dynamic and adaptive systems that identify novel and developing malware attacks. By training on massive datasets of benign and malicious instances, these systems learn patterns and signatures indicative of malware. This lets them correctly categorize and respond to potential attacks in real-time. This study presents a Global Whale Optimization Algorithm with Neutrosophic Logic for Software Malware Detection and Classification (GWOANL-SMDC) technique. The GWOANL-SMDC technique secures the software via the Android malware recognition process. Primarily, the GWOANL-SMDC technique employs the Neutrosophic Cognitive Maps (NCM) model for the feature selection process. The GWOANL-SMDC technique uses a convolutional long short-term memory (ConvLSTM) model for software malware detection. At last, the GWOA-based parameter tuning is performed to improve the performance of the ConvLSTM model. The simulation values of the GWOANL-SMDC technique are examined on the malware dataset. The obtained results ensured that the GWOANL-SMDC technique improved capability in detecting software malware.

Associations of per- and polyfluoroalkyl substances with human milk metabolomic profiles in a rural North American cohort.

Background: Per- and polyfluoroalkyl substances (PFAS) are a class of persistent synthetic chemicals that are found in human milk and are associated with negative health effects. Research suggests that PFAS affect both lactation and the human metabolome. Methods: We measured perfluorooctanoate (PFOA) and perfluorooctane sulfonate (PFOS) in the milk of 425 participants from the New Hampshire Birth Cohort Study using liquid chromatography-tandem mass spectrometry (LC-MS/MS). A nontargeted metabolomics assay was performed using LC with high-resolution MS, and metabolites were identified based on in-house database matching. We observed six metabolic profiles among our milk samples using self-organizing maps, and multinomial logistic regression was used to identify sociodemographic and perinatal predictors of these profiles, including infant sex, parity, participant body mass index, participant age, education, race, smoking status, gestational weight gain, and infant age at time of milk collection. Results: Elevated PFOA was associated with profiles containing higher amounts of triglyceride fatty acids, glycerophospholipids and sphingolipids, and carnitine metabolites, as well as lower amounts of lactose and creatine phosphate. Lower concentrations of milk PFOS were associated with lower levels of fatty acids. Conclusion: Our findings suggest that elevated PFOA in human milk is related to metabolomic profiles consistent with enlarged milk fat globule membranes and altered fatty acid metabolism. Further, our study supports the theory that PFAS share mammary epithelial membrane transport mechanisms with fatty acids and associate with metabolic markers of reduced milk production.