Many fields of human activity benefit from the ability to create images of obscured objects placed behind walls and to map their displacement in a noninvasive way. Usually, imaging devices like Synthetic Aperture Radars (SAR) and Ground Penetrating Radars (GPR) use expensive dedicated electronics which results in prohibitive prices. This paper presents the experimental implementation and the results obtained from an imaging system capable of performing SAR imaging and interferometric displacement mapping of targets located behind walls, as well as 3D GPR imaging using a low-cost general-purpose radar sensor. The proposed solution uses for the RF section of the system a K-band microwave radar sensor module implementing Frequency Modulated Continuous Wave operation. The low-cost sensor was originally intended for simple presence detection and ranging for domestic applications. The proposed system was tested in several scenarios and proved to operate as intended for a fraction of the cost of a commercial imaging device.

Against the background of the sky, imaging and perception of aircraft are cru- cial. Among these, the location of aircrafts is not a very difficult problem in general, because that deep learning algorithms and object detection models based on convolutional neural network(CNN) are ever-evolving. In order to have a better identification of various aircrafts, or to accurately locate different types of flying targets in the sky, it becomes significant to distinguish and recognize different types of aircrafts. One question is that, distinguishing and recognizing between sub-categories of aircrafts pose great challenges. Although fine-grained recognition focuses on exploring and studying such problems, aircrafts under different sub-categories and granularities lead us to rethink the application of features. We noticed that features in swin-transformer demonstrates the under- standing and processing of images, fully showcasing the encoding and indexing of information. Through this research and proposed approach, we discovered a better understanding of features encoding and use, which are inspired by the feature encoding and computation in swin-transformer. In our paper, our ap- proach has achieved effects on features encoded graphically, manifested from the architecture design and convolutional neural network computation, and outper- forms other famous fine-graiend classification models on this issue. Not only approach we proposed has demonstrated superior performance in fine-grained aicraft classification, but also the mechanisms of the feature encoding under different sample space partitions is revealed for this issue, which provides a re- search for the representation of aircraft fineness characteristics. The relationship between representation orientation of aircraft feature information under various grained divisions, shows that the recognition of different categories according to man-made, such as aircraft, can be achieved by means of the feature represen- tations studied in this paper, for more specific defined classification tasks and various man-made targets partition criteria, which may influences the princi- ple of design for calculation and feature extraction in fine-grained classification models.

The ability to detect the presence as well as classify the activities of individuals behind visually obscuring structures is of significant benefit to police, security and emergency services in many situations. This paper presents the analysis from a series of experimental results generated using a through-the-wall (TTW) Frequency Modulated Continuous Wave (FMCW) C-Band radar system named Soprano. The objective of this analysis was to classify whether an individual was carrying an item in both hands or not using micro-Doppler information from a FMCW sensor. The radar was deployed at a standoff distance, of approximately 0.5 m, outside a residential building and used to detect multiple people walking within a room. Through the application of digital filtering, it was shown that significant suppression of the primary wall reflection is possible, significantly enhancing the target signal to clutter ratio. Singular Value Decomposition (SVD) signal processing techniques were then applied to the micro-Doppler signatures from different individuals. Features from the SVD information have been used to classify whether the person was carrying an item or walking free handed. Excellent performance of the classifier was achieved in this challenging scenario with accuracies up to 94%, suggesting that future through wall radar sensors may have the ability to reliably recognize many different types of activities in TTW scenarios using these techniques.

This study explores fault-tolerant consensus in leader-following heterogeneous multi-agent systems, focusing on actuator failures in uncrewed aerial vehicles (UAVs) and uncrewed ground vehicles (UGVs). An optimization-based fault-tolerant consensus algorithm is proposed. The algorithm utilizes the Euler-Lagrange formula to ensure system consistency under actuator failures, with the Lyapunov stability theory proving the asymptotic stability of the consistency error. The algorithm is applied to heterogeneous multi-agent systems of UAVs and UGVs to derive optimal fault-tolerant consensus control laws for each vehicle type. Simulation experiments give evidence for the feasibility of the proposed control strategy.

This paper presents a low-cost C-band frequency-modulated continuous wave (FMCW) radar system for use in indoor through-wall metal detection. Indoor remote-sensing applications, such as through-wall detection and positioning, are essential for the comprehensive realization of the internet of things or super-connected societies. The proposed system comprises a two-stage radio-frequency power amplifier, a voltage-controlled oscillator, circuits for frequency modulation and system synchronization, a mixer, a 3-dB power divider, a low-noise amplifier, and two cylindrical horn antennas (Tx/Rx antennas). The antenna yields gain values in the 6.8 ~ 7.8 range when operating in the 5.83 ~ 5.94 GHz frequency band. The backscattered Tx signal is sampled at 4.5 kHz using the Arduino UNO analog-to-digital converter. Thereafter, the sampled signal is transferred to the MATLAB platform and analyzed using a customized FMCW radar algorithm. The proposed system is built using commercial off-the-shelf components, and it can detect targets within a 56.3 m radius in indoor environments. In this study, the system could successfully detected targets through a 4-cm-thick ply board with a measurement accuracy of less than 10 cm.

The problem of simulating wakes in a stratified oceanic environment with active background turbulence is considered. Anisotropic RANS turbulence models are tested against laboratory and eddy-resolving models of the problem. An important aspect of our work is to acknowledge that the environment is not quiescent; therefore, additional sources are included in the models to provide a non-zero background turbulence. The RANS models are found to reproduce some key features from the eddy-resolving and laboratory descriptions of the problem. Tests using the freestream sources show the intuitive result that background turbulence causes more rapid wake growth and decay.

Sentinel eggs used to monitor field parasitism of stink bug pests (Hemiptera: Pentatomidae) can only be deployed for a few days to avoid releasing the pest in the monitored area. Using sterile eggs removes the risk of accidental pest introduction and extends deployment time. Freezing the eggs before deployment is one common method for sterilizing sentinel eggs. However, some egg parasitoid species have lower to no parasitism on frozen eggs. In this study, x-ray irradiation was used to sterilize Bagrada hilaris sentinel eggs intended for monitoring parasitism by Gryon aetherium (Hymenoptera: Scelionidae), the most promising biological control candidate. In this case, freezing sentinel eggs is not recommended because G. aetherium has low levels of parasitism on frozen eggs. Doses as low as 10 Gy induced 100% sterility. Irradiated eggs successfully sustained the development of G. aetherium and Ooencyrtus californicus (Hymenoptera: Encyrtidae), another egg parasitoid attacking B. hilaris, and parasitism levels were comparable to fresh eggs up to seven days. In addition, G. aetherium showed no preference for fresh non-irradiated over seven-day-old irradiated eggs. Our results indicate that x-ray irradiation is a suitable alternative to produce safe and reliable sentinel eggs to monitor egg parasitism of B. hilaris and possibly other species.

The increasing prevalence of Metabolic Syndrome (MetS), a serious condition associated with elevated risks of cardiovascular diseases, stroke, and type 2 diabetes, underscores the urgent need for effective diagnostic tools. This research carefully examines the effectiveness of 16 diverse machine learning (ML) models in predicting MetS, a multifaceted health condition linked to increased risks of heart disease and other serious health complications. Utilizing a comprehensive, unpublished dataset of imbalanced blood test results, spanning from 2017 to 2022, from the Laboratory Information System of the General Hospital of Amfissa, Greece, our study embarks on a novel approach to enhance MetS diagnosis. By harnessing the power of advanced ML techniques, we aim to predict MetS with greater accuracy using non-invasive blood test data, thereby reducing the reliance on more invasive diagnostic methods. Central to our methodology is the application of the Borda count method, an innovative technique employed to refine the dataset. This process prioritizes the most relevant variables, as determined by the performance of the leading ML models, ensuring a more focused and effective analysis. Our selection of models, encompassing a wide array of ML techniques, allows for a comprehensive comparison of their individual predictive capabilities in identifying MetS. This study not only illuminates the unique strengths of each ML model in predicting MetS but also reveals the expansive potential of these methods in the broader landscape of health diagnostics. The insights gleaned from our analysis are pivotal in shaping more efficient strategies for the management and prevention of Metabolic Syndrome, thereby addressing a significant concern in public health.

Aboriginal Australians have a fundamental human right to opportunities that lead to healthy and flourishing lives. While the impact of trauma on Aboriginal Australians is well-documented, a pervasive deficit narrative that focuses on problems and pathology persists in research and policy discourse. This narrative risks further exacerbating Aboriginal disadvantage, through a focus on ‘fixing what is wrong’ with Aboriginal Australians, and the internalising of these narratives by Aboriginal Australians. While a growing body of research adopts strength-based models, limited research has sought to explore Aboriginal flourishing. This conceptual paper seeks to contribute to a burgeoning paradigm shift in Aboriginal research, seeking to understand what can be learned from Aboriginal people who flourish, how we best determine this, and in what contexts this can be impactful. Within, we argue the case for a new approach to exploring Aboriginal wellbeing that integrates salutogenic, positive psychology concepts with complex systems theory to understand and promote Aboriginal wellbeing and flourishing, While deeper work may be required to establish the parameters of a strength-based, culturally aligned Aboriginal conceptualisation of positive psychology, we suggest the integration of Aboriginal and Western methodologies offer unique and potent means of shifting the dial on seemingly intractable problems.

The genetic heterogeneity of autism has stymied the search for causes and cures. Whole-genomic studies on large numbers of families have helped identify combinations of inherited and de novo signal. In the present work, we re-analyze DNA microarrays using a novel strategy that takes prior knowledge of genetic relationships into account and that was designed to boost signal important to our understanding of the molecular basis of autism. Our strategy was designed to identify significant genomic variation within a priori defined biological concepts and improves signal detection while lessening the severity of multiple test correction seen in standard analysis of genome-wide association data. Upon application of our approach using 3,244 biological concepts, we detected genomic variation in 68 biological concepts with significant association to autism in comparison to family-based controls. These concepts clustered naturally into a total of 19 classes, principally including cell adhesion, cancer, and immune response. The top-ranking concepts contained high percentages of genes already suspected to play roles in autism or in a related neurological disorder. In addition, many of the sets associated with autism at the DNA level also proved to be predictive of changes in gene expression within a separate population of autistic cases, suggesting that the signature of genomic variation may also be detectable in blood-based transcriptional profiles. This cross-validation with gene expression data from individuals with autism coupled with the enrichment within autism-related neurological disorders supported the possibility that the mutations play important roles in the onset of autism and should be given priority for further study. Our work provides new leads into the genetic underpinnings of autism and highlights the importance of reanalysis of genomic studies of complex disease using prior knowledge of genetic organization.

1). BACKGROUND: Cell wall deficient mycobacteria (CWDM) have seldom been isolated in bacterial culture from human subjects. particularly in clinical disease, such as Crohn’s disease. Techniques have focused on the use of polymerase chain reaction (PCR) for the detection of CWDM in association with inflammatory bowel disease. (IBD). This paper is a report of the development of methods to demonstrate the presence of CWDM using the Ziehl-Neelsen (ZN) stain. This is in addition to the detection of the growth of CWDM in fluid media. 2). METHODS: The use of the ZN stain is suitable for the demonstration of CWDM in clinical samples and cultures, when the staining methods that were common prior to the 1970’s, are adopted. Specific culture media are developed and tested in parallel. Alterations in the concentration of basic fuchsin, and changes in the decolourising solutions are necessary. Decolourising solutions such as 20% concentrated sulphuric acid in water are preferable for detection of CWDM in cultures. 3). RESULTS: CWDM can be readily demonstrated using the simple methods outlined in this report. The ability to visualise the presence of CWDM opens the door to the development of specific fluid media for the isolation of CWDM in culture. 4). CONCLUSION: The ZN stain methods optimised for the demonstration of Mycobacterium tuberculosis complex (MTBC) are not suitable for research into infectious causes of inflammatory bowel disease (IBD), as well as for the detection of CWDM in latent tuberculosis. Revisiting the techniques for the detection of CWDM may also refocus attention on inflammatory diseases considered to be idiopathic, but having hallmarks of an infectious process.

In the early stage of new vehicle development, the requirements of maximum performance of the air conditioning (AC) system are obtained through Amesim software simulation, but the calibration control of the AC system is usually carried out in the prototype-build vehicle test stage, and the test time is spanned up to 10 months after spring (autumn), summer and winter, of which up to 2 months are for environment chamber tests. Due to the long calibration cycle of AC control in the road test, which seriously affects the vehicle launch time, virtual calibration technology of air conditioning system is developed to shorten the calibration cycle of AC systems and ensure the quality of vehicles. In the project development stage, the calibration control of the vehicle AC system can be realized to carry out the functional test of the AC system in advance. In that, the grade of maturity for the AC system control can be improved, the on-road calibration test cycle can be shortened, and the development cost can be saved.

Single-phase-to-ground fault in non-effectively grounded systems poses serious public safety concern. Along with increasing deployment of low-voltage (LV) sensors, new opportunities and ways are created for solving this traditional problem in distribution systems. A new approach, identifying primary single-phase-to-ground line fault and locating it through measurement data from smart sensors deployed in the secondary side of distribution transformers, is proposed in this paper. When a fault occurs in primary distribution feeder, the transient aerial mode voltage (AMV) of each LV smart sensor is calculated. The fault line can be identified by an abrupt change value of AMV at LV side. Next, by finding the position of the maximum value of this AMV from LV sensors on the fault line, the fault location can be pinpointed to two neighboring distribution transformers. The proposed method has been proven to be efficient and reliable by simulation studies.

This study explores novel techniques to improve the detection accuracy of skarn iron deposits using advanced image processing methodologies. Leveraging the capabilities of Aster image, Band Ratio (BR) images, and Principal Component Analysis (PCA), alongside the power of 3D Convolutional Neural Networks (3D-CNN), the research aims to enhance the precision and efficiency of ore detection in complex geological environments. The proposed method employs a specific 3D-CNN architecture, accepting input as a 7×7×C image patch, where C represents the combined number of selected Aster image bands, Principal Component (PC) bands, and computed BR images. To evaluate the accuracy of the proposed method, five distinct image bands combinations, including the proposed bands combination, were tested and evaluated based on the overall accuracy (OA), average accuracy (AA), and kappa coefficient. The results demonstrated that while the incorporation of BR images alongside Aster bands initially seemed promising, it introduced significant confusion in certain classifications, leading to unexpected mis-classification rates. Surprisingly, utilizing solely Aster bands as input parameters yielded higher accuracy rates (OA= 93.13%, AA = 91.96%, Kappa = 90.91%) compared to scenarios involving the integration with band ratios (OA = 87.02%, AA = 79.15, and Kappa = 82.60%) or the integration of BR images to PC bands (OA= 87.78%, AA = 82.39%, Kappa = 83.81%). However, the amalgamation of Aster bands with selected PC bands showed slight improvements in accuracy (OA= 94.65%, AA = 92.93%, Kappa = 93.45%), although challenges in accurately classifying certain features persisted. Ultimately, the proposed combination of Aster bands, PC bands, and BR images (proposed bands combination) presented the most visually appealing and statistically accurate results (OA = 96.95%, AA= 94.87%, and Kappa = 95.93%), effectively addressing misclassifications observed in the other combinations. These findings underscore the synergistic contributions of each of the Aster bands, PC bands, and BR images, with Aster bands proving pivotal for optimal skarn classification, PC bands enhancing intrusions classification accuracy, and BR images strengthening wall rock classification accuracy. In conclusion, the proposed combination of input image bands emerges as a robust and comprehensive methodology, demonstrating unparalleled accuracy in the remote sensing detection of skarn iron minerals.

Influenced by geological, climatic, and natural weathering factors, the Great Wall heritage is sus-ceptible to deformations, posing significant challenges to the comprehensive preservation of the Great Wall sites. To investigate techniques for monitoring deformations in extensive linear cultural heritage sites, such as the Great Wall, this study utilizes the SBAS-InSAR technique for deformation monitoring and analysis. A dataset comprising 161 Sentinel-1A images spanning from March 2017 to January 2022 was chosen for SBAS-InSAR processing, yielding a deformation velocity field. To verify result credibility, a typical mountainous segment spanning approximately 896.53 km within the Great Wall landscape corridor underwent analysis. The findings suggest that around 75.8% of the landscape corridor along the Shanxi section of the Ming Great Wall maintain relative stability, displaying deformation rates varying from -10 to 10 mm/year. The remaining 24.2% of the land-scape corridor experiences notable deformations, including a maximum subsidence rate of 33.1 mm/year and a maximum subsidence of 148.6 mm. This study illustrates the potential utility of the SBAS-InSAR technique for monitoring and evaluating surface deformations in extensive linear cultural heritage sites.

The microbiome gut brain (MGB) axis involves bidirectional routes of communication and has emerged as a potential therapeutic target for multiple medical specialities including psychiatry. Significant numbers of preclinical trials have taken place with some transitioning to clinical studies in more recent years. Some positive results have been reported secondary to probiotic administration in both healthy populations and specific patient groups. This review aims to summarise the current understanding of the MGB axis and the preclinical and clinical findings relevant to psychiatry. The link between the gut microbiome and irritable bowel syndrome (IBS) is well established. Significant differences have been identified between the microbiome of patients with a diagnosis of depressive disorder and healthy controls. Similar findings have occurred in patients diagnosed with bipolar affective disorder. A probiotic containing Lactobacillus acidophilus, Lactobacillus casei, and Bifidobacterium bifidum produced clinically measurable symptom improvement in patients with depressive disorder. To date some promising results have suggested that probiotics could play a role in the treatment of stress-related psychiatric disease. However, more well-controlled clinical trials are required to determine which clinical conditions are likely to benefit most significantly from this novel approach.

This paper explores the application of machine learning (ML) in predicting functional recovery in patients with ischemic stroke. As technology advances, ML shows significant potential in the field of stroke medicine, especially in the areas of big data analytics and personalized medicine. Studies have shown that ML algorithms can improve the accuracy of stroke image analysis, subtype classification, risk assessment, treatment guidance, and prognosis prediction. However, the widespread use of ML still faces challenges such as data standardization, model validation, privacy, and bias. This paper reviews the current application status of ML in the field of stroke, discusses the challenges faced, and looks forward to the future development direction, aiming to promote the practical application of ML technology in the diagnosis and treatment of stroke to improve the prognosis and quality of life of patients.

We presented unusual case of double left ventricular false aneurysm complicating late presentation AMI successfully treated with the combination of urgent surgical drainage of tamponade, staged PCI of non-culprit lesion, preventive implantation of ICD-DR, guideline directed heart failure medical therapy and delayed but effective reconstructive operation.

Sepsis still remains one of the most difficult conditions to diagnose as it presents unspecific clinical signs and symptoms, which makes early detection crucial. Our study aimed to enhance the accuracy of sepsis diagnosis by integrating multimodal monitoring technologies with traditional diagnostic methods. The research involved 121 newborns, comprising 39 cases of late-onset sepsis, 35 cases of early-onset sepsis, and 47 control subjects. Biosignals including pulse oximetry (PO), near-infrared spectroscopy (NIRS), and skin temperature (ST) were continuously monitored and an algorithm was developed using Python for identifying early signs of sepsis. The model demonstrated the ability to detect sepsis 6 to 48 hours in advance with an accuracy rate of 87.67% ± 7.42%. Sensitivity and specificity were recorded at 76% and 90%, respectively, with NIRS and ST having the most significant impact on predictive accuracy. Despite the promising results, limitations such as sample size, data variability, and potential biases were noted. The findings highlight the critical role of non-invasive biosensing methods in conjunction with traditional biomarkers and cultures, offering a robust framework for early sepsis detection and improved neonatal care. Further studies are recommended to validate these results across diverse clinical settings.

This work attempts to answer in a clear way the following key questions regarding the optimal initialization of the Sysmon tool, towards the identification of Lateral Movement in the MS Windows ecosystem. First, from an expert’s standpoint and with reference to the relevant literature, what are the criteria of determining the possibly optimal initialization features of the Sysmon’s event monitoring tool, which are also applicable as custom rules within the config.xml configuration file? Second, based on the identified features, how can a functional configuration file, able to identify as many LM variants as possible, be generated? To answer these questions, we relied on the MITRE ATT&CK knowledge base of adversary tactics and techniques, and focused on the execution of the nine commonest LM methods. The conducted experiments, performed on a properly configured testbed, suggested a great number of interrelated networking features, that were implemented as custom rules in the Sysmon’s config.xml file. Moreover, by capitalizing on the rich corpus of the 870K Sysmon logs collected, we create and evaluate in terms of TP and FP rates an extensible Python .evtx file analyzer, dubbed PeX, which can be used towards automatizing the parsing and scrutiny of such voluminous files. Both the .evtx logs dataset and the developed PeX tool are provided publicly for further propelling future research in this interesting and rapidly evolving field.

This paper presents a new interactive visualization system to represent and compare rate of spread of COVID 19 pandemic across different countries over time. We surveyed existing visualization techniques used in various websites and media outlets and introduced use of a knee detection algorithm that divides the exponential spread in multiple linear components. A set of use cases demonstrates the utility of the system in comparing the spread across different countries for both analysis and prediction.

In the last few decades, glaucoma became the second biggest leading cause of irreversible vision loss. Because of its asymptotic growth, it is not properly diagnosed until the relatively late stage. To stop the severe damage by glaucoma it is needed to detect glaucoma in its early stages. Surprisingly diabetes also be the greatest cause of glaucoma. In the modern era, artificial intelligence makes great progress in the medical image processing field. Image analysis based on machine learning gives a huge success in diagnosis glaucoma without any misdiagnosis. The aim of this proposed paper is to create an automated process that can detect glaucoma and diabetic retinopathy. Here various Machine Learning models are used and results of these methods are presented.

Public safety and intelligent surveillance systems critically depend on anomaly behavior detection for effective monitoring. In real-world pedestrian detection scenarios, prevalent challenges such as missed detections, complex background interference, and small target sizes hinder accurate anomaly identification. To address these issues, this study introduces YOLO-ABD, a lightweight method for anomaly behavior detection that integrates small target detection and channel shuffling. This method employs YOLOv8n as the baseline model, incorporating a small target detection mechanism in the Head part and utilizing GSConv convolution in the Backbone to enhance perceptual capability. Additionally, the SimAM attention mechanism is integrated to mitigate complex background interference, thereby improving target detection performance. Evaluation on the IITB-Corridor dataset demonstrated mAP50 and mAP50-95 scores of 89.3% and 60.6%, respectively. Generalization testing on the street-view-gdogo dataset further highlighted the superiority of YOLO-ABD over advanced detection algorithms, underscoring its effectiveness and generalizability. With a relatively small parameter count, YOLO-ABD presents an excellent lightweight solution for pedestrian anomaly behavior detection.

In the field of 2-D image processing and computer vision, accurately detecting and segmenting objects in scenarios where they overlap or are obscured remains a challenge. This difficulty is worse in the analysis of shoeprints used in forensic investigations because they are embedded in noisy environments such as the ground and can be indistinct. Traditional Convolutional Neural Networks (CNNs), despite their success in various image analysis tasks, struggle with accurately delineating overlapping objects due to the complexity of segmenting intertwined textures and boundaries against a background of noise. This study introduces employs the YOLO (You Only Look Once) model enhanced by edge detection and image segmentation techniques to improve the detection of overlapping shoeprints. By focusing on the critical boundary information between shoeprint textures and the ground, our method demonstrates improvements in sensitivity and precision, achieving confidence levels above 85% for minimally overlapped images and maintaining above 70% for extensively overlapped instances. Heatmaps of convolution layers were generated to show how the network converges towards successful detection using these enhancements. This research may provide a potential methodology for addressing the broader challenge of detecting multiple overlapping objects against noisy backgrounds.

: Background: Overweight and obesity are major risk factors for many chronic diseases, and weight-loss interventions often include systematic exercising and nutritional supplements. The purpose of this study was to determine the independent/synergistic effects of Spirulina maxima supplementation (6-week, 4.5 g/day) and a systematic physical exercise program (6 weeks/ 2 times) on body composition and cardiorespiratory fitness of overweight and obese subjects. Methods: 27 overweight and 25 obese sedentary male subjects were assigned to four interventions through a randomized double-blind, crossover controlled trial: Physical exercise program with (SE) or without (Ex) Spirulina maxima or no-exercise program with (Sm) and without (C) Spirulina maxima. Body composition and cardiorespiratory fitness parameters were taken during a maximal intensity test. Results: As compared to C group, SE, Sm and Ex groups reduced (p < 0.05) their body fat percentage while improving their maximal oxygen uptake (VO₂max; r = −0.40), being obese subjects more benefited. Weight loss, time to reach fatigue and onset of blood lactate accumulation were improved in both Spirulina maxima supplemented groups, regardless the subjects’ body composition. Conclusions: Spirulina maxima supplementation synergistically improves the effects of systematic exercise in body composition and cardiorespiratory fitness parameters in overweight but mostly in obese adults.

This paper considers the problem of quantifying the quality of a model selection problem for a graphical model. The model selection problem often uses a distance measure such as the Kulback-Leibler (KL) distance to quantify the quality of the approximation between the original distribution and the model distribution. We extend this work by formulating the problem as a detection problem between the original distribution and the model distribution. In particular, we focus on the covariance selection problem by Dempster, [1], and consider the cases where the distributions are Gaussian distributions. Previous work showed that if the approximation model is a tree, that the optimal tree that minimizes the KL divergence can be found by using the Chow-Liu algorithm [2]. While the algorithm minimizes the KL divergence it does not minimize other measures such as other divergences and the area under the curve (AUC). These measures all depend on the eigenvalues of the correlation approximation measure (CAM). We find expressions for KL divergence, log-likelihood ratio, and AUC as a function of the CAM. Easily computable upper and lower bounds are also found for the AUC. The paper concludes by computing these measures for real and synthetic simulation data.

Relationships between drought and fire danger indices are examined to 1) incorporate fire risk information into the National Integrated Drought Information System California-Nevada Drought Early Warning System and 2) provide a baseline analysis for application of drought indices into a fire risk management framework. We analyzed four drought indices that incorporate precipitation and evaporative demand (E₀) and three fire indices that reflect fuel moisture and potential fire intensity. Seasonally averaged fire danger indices were most strongly correlated to multi-scalar drought indices that use E₀ (the Evaporative Demand Drought Index [EDDI] and Standardized Precipitation Evapotranspiration Index [SPEI]) at approximately annual time scales that reflect buildup of antecedent drought conditions. Results indicate that EDDI and SPEI can inform seasonal fire potential outlooks at the beginning of summer. An E₀ decomposition case study of conditions prior to the Tubbs Fire in Northern California indicate high E₀ (97th percentile) driven predominantly by low humidity signaled increased fire potential several days before the start of the fire. Initial use of EDDI by fire management groups during summer and fall 2018 highlights several value-added applications, including seasonal fire potential outlooks, funding fire severity level requests, and assessing set-up conditions prior to large, explosive fire cases.

The end of 2019 marked a bushfire crisis for Australia that affected more than 100000km2 of land and destroyed more than 2000 houses. Here, we propose a method of in-orbit bushfire detection with high efficiency to prevent a repetition of this disaster. An LEO satellite constellation is first developed through NSGA-II (Nondominated Sorting Genetic Algorithm II), optimising for coverage over Australia. Then edge computing is adopted to run a bushfire detection algorithm using several constellation satellites as edge nodes to reduce fire detection time. A geostationary satellite is used for inter-satellite communications, such that an image taken by a satellite can be distributed among several satellites for processing. The geostationary satellite also maintains a constant link to the ground, so that a bushfire detection can be reported back without any significant delay. Overall, this system is able to detect fires that span more than 5m in length, and can make detections in 1.39s per image processed. This is faster than any currently available bushfire detection method.

Vegetative desiccation tolerance has evolved within the genus Craterostigma and Lindernia. A centre of endemism and diversification for these plants appears to occur in ancient tropical montane rainforests of east Africa in Kenya and Tanzania. Lindernia subracemosa, a desiccation-sensitive relative of C. plantagineum, occurs in these rainforests and experiences adequate rainfall and thus does not require desiccation tolerance. However, sharing this inselberg habitat, another species Lindernia brevidens does retain vegetative desiccation tolerance and is also related to the resurrection plant C. plantagineum found in South Africa. Leaf material was collected from all three species at different stages of hydration: fully hydrated, half-dry (ca. 45% relative water content), and fully desiccated. Cell wall monosaccharide datasets were collected from all three species. Comprehensive microarray polymer profiling (CoMPP) was performed using ca. 27 plant cell wall specific antibody and carbohydrate binding module probes. Global datasets do not show significant differences between the selected species suggesting that cell wall modifications in response to vegetative desiccation involve subtle cell wall remodelling not reflected by compositional analysis and that the plants and their walls are constitutively protected against desiccation.

Stress has emerged as a major concern in modern society, significantly impacting human health and well-being. Statistical evidence underscores the extensive social influence of stress, especially in terms of work-related stress and associated healthcare costs. This work addresses the critical need for accurate stress detection, emphasising its far-reaching effects on health and social dynamics. Focusing on remote stress monitoring, this work proposes an efficient deep learning framework to discern stress from facial videos. In contrast to research on wearable devices, this paper investigates the application of *dl for stress detection based on *rppg. The methodology involves selecting suitable *dl models (*lstm, *gru, *1dcnn), optimising hyperparameters, and investigating augmentation techniques. 1D-CNNv1 model achieved the best performance compared to other approaches, particularly with augmentation techniques such as linear interpolation and white noise, achieving a stress classification accuracy of 95.83% while maintaining excellent computational efficiency. The experimental results demonstrate the use of *dl for stress detection based on *rppg, with the potential to make significant contributions to the international standard in the field.

For autonomous driving, perception is a primary and essential element that fundamentally deals with the insight into the ego vehicle’s environment through sensors. Perception is a challenging task suffering from dynamic objects and continuous environmental changes. The issue gets worse due to interrupting the quality of perception by adverse weather like snow, rain, fog, night light, sand storm, strong daylight, etc. In this work, we have tried to improve camera-based perception accuracy, such as autonomous driving-related object detection in adverse weather. We proposed the improvement of YOLOv8-based object detection in adverse weather through transfer learning using merged data from various harsh weather datasets. Two prosperous open-source datasets (ACDC and DAWN) and their merged dataset were used to detect primary objects on the road in harsh weather. A set of training weights were collected from training on the individual datasets, their merged version, and several subsets of those datasets according to their characteristics. A comparison between the training weights also occurred by evaluating the detection performance on the above-mentioned datasets and their subsets. The evaluation revealed that using custom datasets for training significantly improves the detection performance compared to the YOLOv8 base weights. And using more images through the feature-related data merging technique steadily increases the object detection performance.

In most studies reported in literature, a flat, constant coefficient is used to convert the amount of optical power required at the input of an optical link into the effective power consumption. This constant coefficient approach is very simple to apply but not accurate, since laser efficiencies are dependent on the optical output power in addition to the operating temperature. In this paper the relationship between laser wall plug efficiency and optical power output is investigated and the effect of this variance on optical links and on-chip laser source electric power consumption is presented

COVID-19 was a terrible worldwide pandemic that caused a global public health crisis, with numerous deaths and a severe economic depression. To suppress the spread of COVID-19 and initiate patient isolation and contact tracing to reduce its effect, early diagnosis was required, with real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test being the most widely used. However, the RT-PCR test was sometimes found to be lengthy and inaccurate and; for that reason, in many cases of severe complications of COVID-19 pneumonia, chest screening with radiography imaging was preferred. In this paper, we carried out a study for COVID-19 detection from computed tomography images, and compared the obtained results using deep learning (DL) and support vector machine (SVM). The findings showed that, despite the excellent results shown by deep learning, the accuracy in predicting COVID-19 using SVM, in the presence of small databases, was slightly higher than DL. The execution time using SVM was also shorter

The dopamine and uric acid are primary products of purine metabolism and they coexist in serum and the extracellular fluid of the central nervous system. Some diseases of the human body are due to abnormal levels of uric acid and dopamine. Thus, the clinical diagnosis of some diseases needs a sensitive and simultaneous analysis of uric acid and dopamine. In this study, a novel conductive composite film containing multi-walled carbon nanotubes (MWTs) with cooper phthalocyanine attached to acrylate polymer (MWCNTs-CuMAPC-PnBA) was fabricated and dropped on a glassy carbon electrode and used for the simultaneous determination of Uric Acid (UA) and dopamine (DA). The modified glassy carbon electrode showed satisfactory electrocatalytic activity toward dopamine and uric acid. Low detection limits were achieved at 0.174 µM of DA and 6.87 µM of UA and the linear detection ranges of DA and UA are 5-200 µM and 20-250 µM respectively. The proposed sensing system based on the modified GCE offers promising results for rapid, cost-effective and selective simultaneous detection of uric acid and dopamine. Also, the complex modified electrode showed satisfactory results to detecting simultaneously UA and DA in real human samples (human Urine) via the standard addition method.

Cotton fiber is the leading natural textile material, and fiber elongation plays an essential role in the formation of cotton yield and quality. Although a wealth of components in the molecular network controlling cotton fiber elongation have been reported, a lot of players still need to be functionally dissected to understand the regulatory mechanism of fiber elongation comprehensively. In the present study, an R2R3-MYB transcription factor gene, GhMYB201, was characterized and functionally verified via CRISPR/Cas9 mediated gene editing. GhMYB201 was homologous to Arabidopsis AtMYB60, and both coding genes (GhMYB201At and Dt) were preferentially expressed in elongating cotton fibers. Knocking-out of GhMYB201 significantly reduced the rate and duration of fiber elongation, resulting in shorter and coarser mature fibers. It was found that GhMYB201 could bind and activate the transcription of cell wall loosening genes (GhRDLs), and also β-ketoacyl-CoA synthase (GhKCSs) genes to enhance very-long-chain fatty acid (VLCFA) levels in elongating fibers. Taken together, our data demonstrated that the transcription factor GhMYB201s play essential roles in promoting fiber elongation via activating genes related to cell wall loosening and VLCFA biosynthesis.

Recently, the destructive impact of wildfires has proliferated; for instance, the August Complex wildfire in 2020 burned around 4% of California's landmass. This has caused increased economic damage and risk to human life. Additionally, climate change is anticipated to increase the severity of wildfires, making it imperative for accurate and efficient detection of wildfires. Machine Learning approaches allow for the automatic detection of wildfires, simultaneously prioritizing accuracy and efficiency, with minimal human intervention, thus decreasing the likelihood of increased economic damage and increasing firefighting responses. Convolutional Neural Networks (CNNs), while showing promise, are often limited by their inability to learn and capture deep spatial dependencies in satellite imagery tasks. In this paper, we propose FlameViT, a novel wildfire detection architecture based on Vision Transformers (ViT). Satellite images are more efficient to obtain and can cover wide areas prone to wildfires. We obtain a dataset of 40K+ satellite images from Canada's Open Government Portal, allowing FlameViT to be optimized to detect wildfires in satellite imagery. FlameViT uses Patch Extractor and Patch Embedding layers, followed by multiple Transformer Encoder layers with Multi-Head Self-Attention and feed-forward neural networks. FlameViT is hyperparameter-tuned and achieves a validation accuracy of 95%, outperforming various baselines in the wildfire detection task. FlameViT shows the power of Vision Transformers for wildfire detection tasks, and in conjunction with the use of satellite imagery, can provide an efficient and accurate way of detecting wildfires.

Mahalanobis distance is a useful multivariate statistic for determining how far apart two points are from one another. It is a very helpful statistic with excellent uses in multivariate anomaly detection, one-class classification, and classification on severely unbalanced datasets.

Recent SDN advances address traditional network management challenges through centralized control and plane separation. SDN prevents breaches using a centralized controller but introduces risks. The controller can be a single point of failure. Thus, an OpenFlow Controller's flow-based anomaly detection enhances SDN security. Our research explored two OpenFlow intrusion detection methods. The first employed machine learning, NSL-KDD dataset, and feature selection, yielding 82% accuracy with random forest. The second combined deep neural networks with GRU-LSTM, achieving 88% accuracy using ANOVA F-Test and feature elimination. Experiments highlighted deep learning as superior for OpenFlow intrusion detection.

To enhance the peak-shaving capability of the boiler, a mathematical model of hydrodynamic and wall temperature characteristics for the water-cooled wall of a 1000 MW boiler was established. The attributes pertaining to the mass flow distribution, the temperature of the effluent working fluid, pressure drop, and the thermal distribution across the wall surfaces at 30% of the boiler's maximum continuous rating (BMCR) were meticulously computed and subjected to a thorough analytical review. The findings suggest that, under the operation at 30% BMCR load, there is a substantial equilibrium in the flow distribution across the quartet of walls that constitute the water-cooled wall assembly. The maximum mass flow rate deviations in the helical and vertical sections are 1.95% and 3.47%, respectively, showing small flow deviations and reasonable distribution. The temperature deviation in the helical section is 0.3 °C, reflecting the characteristic low thermal deviation in helical tubes. While the temperature deviation at the outlet of the vertical section is higher, it remains within safe limits. The pressure loss across the water-cooled wall system amounts to 0.4 MPa. The peak wall temperature reaches 337.5°C, remaining within the material's permissible safety limits. In-depth performance analysis ensures the safe operating conditions of the water-cooled wall during low load conditions.

Living walls (LWs) are a climate change adaptation strategy for cities, as they have a cooling effect. Previous studies of the cooling effect of LWs were carried out in different climatic zones. These studies differed in their experimental design, or simulated data via models. Plants’ cooling capacity is explained by shading and transpiration, and depends on physical plant parameters, environmental factors, and system-related influences. A three-year-long trial was carried out between 2017 and 2019 at an experimental garden in Geisenheim, Germany. We chose a textile-based LW system with high water demand and plants from a wet/fresh habitat. We assumed that this would achieve high evaporative cooling. The experimental setup included four experimental walls which were exposed to the north, south, east, and west, respectively. The plant choice was divided into three plant mix variants (Cascade, Ground cover, and Meadow) and a Control with no vegetation. We measured the temperature with sensors and a thermal imaging (IR) camera in different setups. The main results were that the measured vegetation temperature (TV) depends on air temperature (TA), measurement position, plant mix variant, and plant species. We could detect the cooling effect only at a small distance from the LW (microclimatic). Our methodological approaches should be continued in further studies.

Optinalysis, as a method of symmetry detection, is a new algorithm that intrametrically (within elements or variables) or intermetrically (between elements or variables) computes and compares two or more univariate or multi-clustered or multivariate sequences as a mirror-like reflection of each other (optics-like manner), hence the name is driven. Optinalysis is based by the principles of reflection and moment about a symmetrical line which detects symmetry that reflects a similarity measurement. This proposed methodology was validated in comparison with Pearson method of skewness detection, and also with some algorithms for pairewise alignment and comparison of genomic sequences (Needle, Stretcher, Water, Matcher) on EMBL-EBI website. A results comparison shows that optinalysis is more advance, more sensitive, more inferential and simple alternative approach of skewness detection and pairewise sequence comparison.

Controlling the concentration of free radicals in polymerization systems is advantageous for preparing cationic polyacrylamide (CPAM) with high molecular weight and acceptable dissolvability. In this study, a novel ultraviolet (UV)-initiated system characterized with adjustable light intensity and redox-azo complex initiator was used to synthesize a CPAM flocculant named NP. Comparatively, another CPAM flocculant named SP with stable UV light intensity and single initiator was prepared. The chemical structure, morphology, and thermal stability were analyzed through instrumental analysis. Proton nuclear magnetic resonance indicated that NP was successfully prepared, and a small fraction of cationic homopolymer was mixed in the product. Polymerization conditions were optimized, and polymerization mechanism was determined by investigating the effects of various parameters on intrinsic viscosity, conversion, and dissolvability. Results showed that the best performance was obtained at indexes of 0.45 wt‰ redox initiator concentration, 0.2 wt‰ azo initiator concentration, 40.0 wt% of cationic monomer, first- and second-stage light intensities of 8.5 and 13 mW/cm², respectively, and 3 wt% urea. Sludge conditioning performances of NP and SP were comparatively evaluated, and the mechanism was determined by investigating the sedimentation behavior and floc size distribution. High intrinsic viscosity, porous morphology structure, and the mixed cationic homopolymer of NP resulted in better sludge conditioning performance.

Automating pruning tasks entails overcoming several challenges, encompassing not only robotic manipulation but also environment perception and detection. To achieve efficient pruning, robotic systems must accurately identify the correct cutting points. A possible method to define these points is to choose the cutting location based on the number of nodes present on the targeted cane. For this purpose, in grapevine pruning, it is required to correctly identify the nodes present on the primary canes of the grapevines. In this paper, a novel method of node detection in grapevines is proposed with four distinct state-of-the-art versions of the YOLO detection model: YOLOv7, YOLOv8, YOLOv9 and YOLOv10. These models were trained on a public dataset with images containing artificial backgrounds and afterwards validated on different cultivars of grapevines from two distinct Portuguese viticulture regions with cluttered backgrounds. This allowed to evaluate the robustness of the algorithms on the detection of nodes in diverse environments, compare the performance of the YOLO models used, as well as create a publicly available dataset of grapevines obtained in Portuguese vineyards for node detection. Overall, all used models were capable of achieving correct node detection on images of grapevines from the three distinct datasets. Considering the trade-off between accuracy and inference speed, the YOLOv7 model demonstrated to be the most robust in detecting nodes in 2D images of grapevines, achieving F1-Score values between 70 % and 86.5 % with inference times of around 89 ms for an input size of 1280×1280 px. Considering these results, this work contributes with an efficient approach for real-time node detection for further implementation on an autonomous robotic pruning system.

This study aimed on evaluating the potential diagnostic value of a novel, sonographic, B-Flow Imaging (BFI)-based sign (“flashlight sign”, FLS) for the detection of wall-adherent, floating arterial structures (WAFAS). The FLS, characterized by a fast moving, very bright, intraluminal signal was detected in 28 patients with WAFAS. We divided this cohort into three subgroups, depending on the affected vascular segments: (1) peripheral arteries (n = 10), (2) native abdominal aorta (n = 8), and (3) abdominal aorta after endovascular aortic repair (EVAR; n = 10). Clinical characteristics were analyzed and BFI-findings were compared with contrast enhanced ultrasound (CEUS) and computed tomography angiography (CTA). Seven patients (25%) suffered from arterial embolism downstream to the FLS (EVAR, n = 4; native abdominal aorta, n = 1; peripheral arteries, n = 2). WAFAS of the abdominal aorta (native or after EVAR), as indicated by the FLS, were visible by CEUS and CTA in 60% and 93.3%, respectively. Based on the so far largest cohort of patients with WAFAS, we propose a clinically useful, BFI-based sonographic sign for detection of these so far underrated arterial pathologies in the abdominal aorta and the peripheral arteries.

This review is a first attempt at bringing together various concepts from research on wall- and magnetically-bounded turbulent flows. Brief reviews of both fields are provided: The main similarities identified are coherent (turbulent) structures, flow generation and transport barriers. Examples are provided and discussed.

Natural risks encompass a range of disasters and dangers, requiring comprehensive management through assessments, anticipations, and advanced alert systems. Our specific focus is on landslides in challenging terrains. The evaluation of landslide risks employs sophisticated multicriteria models, such as the weighted sum approach, integrating qualitative parameters. Despite the challenges of the rugged terrain in Northern Algeria, it paradoxically hosts a dense population attracted by valuable hydro-agricultural resources. Our research is dedicated to studying landslide risks in these areas, particularly in the Mila region, with the aim of constructing a statistical model integrating considerations of both danger and vulnerability. This complex process identifies threats and their determining factors, including geomorphology and socio-economic conditions. The result is a valuable tool for effective risk management, strengthening safety and resilience against natural disasters. By understanding the intricate interplay of geological, environmental, and social elements, our research can contribute to the development of targeted interventions, fostering a more resilient community in the face of natural adversities.

Free-living terrestrial mites (Acari) have persisted through numerous glacial cycles in Antarctica. Very little is known, however, of their genetic diversity and distribution, particularly within the Ross Sea region. To redress this gap, we sampled for mites throughout the Ross Sea region, East Antarctica, including Victoria Land and the Queen Maud Mountains (QMM), covering a latitudinal range of 72-85oS, as well as from Lauft Island near Mt Siple (73oS) in West Antarctica and Macquarie Island (54oS) in the sub-Antarctic. We assessed genetic diversity using mitochondrial cytochrome c oxidase subunit I gene sequences (COI-5P DNA barcode region), and also morphologically identified voucher specimens. We obtained 130 sequences representing four genera: Nanorchestes (n = 30 sequences), Stereotydeus (n = 46), Coccorhagidia (n = 18) and Eupodes (n = 36). Tree-based analyses (maximum likelihood) revealed 13 genetic clusters, representing as many as 23 putative species indicated by Barcode Index Numbers (BINs) from the Barcode of Life Datasystems (BOLD) database. We found evidence for geographically-isolated cryptic species, e.g. within Stereotydeus belli and S. punctatus, as well as unique genetic groups occurring in sympatry (e.g. Nanorchestes spp. in QMM). Collectively, these data confirm high genetic divergence as a consequence of geographic isolation over evolutionary timescales. From a conservation perspective, additional targeted sampling of understudied areas in the Ross Sea region should be prioritised, as further diversity is likely to be found for these short-range endemic mites.

Background: Exclusive enteral nutrition (EEN) is the recommended first-line induction therapy for pediatric patients with active luminal Crohn’s disease (CD). The primary aim of this retrospective study was to ascertain the efficacy of EEN used as induction therapy for children with newly di-agnosed CD resident in the South Island of NZ over a ten-year period (2010 – 2021). Secondary aims were to assess the clinical impact of EEN therapy on anthropometry, biochemistry, the pro-gression to medical therapies over three time periods, and to identify positive predictors of re-sponse to EEN therapy. Methods: This retrospective clinical case review collected data from children diagnosed with CD at Christchurch Hospital, Christchurch, New Zealand, and prescribed EEN as induction therapy between the years January 1st, 2010 – December 31st, 2021. Descriptive statistics, Spearman’s correlation coefficient, multivariate logistic regression test and standard t-tests were used for analyses. Results: Data were collected for total of 161 children, 128 (80%) children were offered EEN as induction therapy and were included in this analysis. One hundred and twenty-one (94.5%) children successfully completed an eight-week course of EEN, and a de-crease in PCDAI scores ≥ 15 points from baseline to the end of week 8 of EEN treatment was seen in 119 (98.3%). L1 and L4 disease location were positive predictors of response (p

This study evaluates the efficiency of biodegradable polyurethane foams as thermal insulation materials compared to conventional materials like expanded polystyrene (EPS). Key parameters analyzed include thermal conductivity, energy savings, CO2 emission reductions, and investment payback periods. Biodegradable polyurethane foams demonstrated a thermal conductivity of 0.022 W/(m·K), significantly lower than EPS’s 0.035 W/(m·K), leading to higher energy savings. Calculations reveal an annual energy saving of 443,820 kWh for polyurethane foams, compared to 441,330 kWh for EPS. Financially, polyurethane foams save approximately €53,258.4 annually, while EPS saves €52,959.6. In terms of environmental impact, polyurethane foams reduce CO2 emissions by 88,764 kg annually, versus 88,266 kg for EPS. Despite the higher initial cost of polyurethane foams (€50/m² compared to €30/m² for EPS), the payback period is remarkably short at 0.094 years (1.13 months) compared to EPS’s 0.057 years (0.68 months). These findings highlight biodegradable polyurethane foams as a superior option for thermal insulation, offering significant energy, financial, and environmental benefits.

The organic coating of bridge steel girders is subjected to physical scratches, corrosion, and aging in natural weathering. The breakdown of the coating may cause serviceability and safety problems if left unnoticed. A hyperspectral imaging method is proposed to detect the condition of steel coatings based on coating-responsive features in reflectance spectra. A field test was conducted on the real-world bridge which shows obvious signs of degradation. The hyperspectral signature enables an assessment of the coating health and defect severity. The results indicated that the coating scratch can be effectively located in the domain of a hyperspectral image and the scratch depth can be determined by mapping a scratch depth indicator (SDI=R532nm / R641nm). Rust sources and products in steel girders can be identified by the unique spectral signatures and the rust stains (and thus stain areas) scattered on the coating can be pinpointed at pixel level by the corrosion rust indicators. The chemical integrity of a topcoat is demonstrated by the short-wave infrared spectroscopy and the topcoat degradation can be evaluated by the absorption at 8000 cm-1 and 5850 cm-1.

Recently, the availability of many omics data source has given the rise of modelling biological networks for each individual or patient. Such networks are able to represent individual-specific characteristics, providing insights into the condition of each person. Given a set of networks of individuals, a network representing a particular condition (e.g., an individual with a specific disease) may be seen as an anomaly network. Consequently, the use of Graph Anomaly Detection techniques may support such analysis. Among the others, Generative Adversarial Networks present optimal per- formances in anomaly detection. This paper presents ADIN (Anomaly Detection in Individual Networks), a framework based on Generative Adversarial Attributed Networks (GAANs) for anomaly detection in convergence/divergence patients at- tributed networks. Preliminary results on networks generated from computational biology gene expression data demonstrate the effectiveness of our approach in detecting and explaining bladder cancer patients.

Diabetes becomes a life threatening non-communicable disease in the world, according to International Diabetic Federation (IDF) in 2023, an estimated 537 million adults (20-79 years) are living with diabetes, which is equivalent to 9.3% of the global adult population. This number is predicted to rise to 643 million by 2030 and 783 million by 2045. Over 3 in 4 adults with diabetes live in low- and middle-income countries. Diabetes is a persistent metabolic condition marked by increased levels of glucose in the bloodstream. It is a significant global health concern, affecting millions of individuals worldwide. It having the symptoms of drowsiness for the whole day if not properly examined or treated well. It is targeted to spread over the younger age community and the number is growing in the exponential fashion. Even day to day many advancements have come from the researcher to diagnose, to find solutions to prevent from newer entry. Authors have taken a dataset with 57 non diabetic and 20 diabetic patients with the total 28735 micro array gene to undergone pre-processing process and reduced up to 22960 gene data using Dimensionality Reduction (DR) such as Detrend Fluctuation Analysis (DFA), Chi square probability density function (Chi2PDF), Firefly algorithm Cuckoo search were used in this research. Meta heuristic algorithms like Particle swarm Optimization (PSO) and Harmonic Search (HS) are used for feature selection. Further seven classification techniques such as Non-Linear Regression (NLR), Linear Regression (LR), Logistics Regression (LoR), Gaussian Mixture Model (GMM), Bayesian Linear Discriminant Classifier (BLDC), Softmax Discriminant Classifier (SDC), Support Vector Machine – Radial Basis Function (SVM-RBF) are using to make a decision, predictive analysis and segregate the data according to the level of blood glucose as Diabetic Patient (DP) and Non-Diabetic Patient (NDP).

Diffusion in recent decades of Cannabis sativa L. varieties with low concentrations of tetrahydrocannabinol (THC) is leading to a specialization in the whole sector, requiring innovative techniques for input optimization according to the variety and the growing environment. The continuous agricultural evolution aims at increasing the sustainability of cultivation systems, pushing toward precision technologies application for inputs management. Cannabis monitoring can benefit from Unmanned Aerial Systems applications combined with image thresholding techniques for reliable and effective near-real-time plant detection and numbering. The work compares and evaluates the potential of two threshold segmentation techniques for Cannabis plant detection and counting in two experimental fields in Italy on a multitemporal scale, bringing such techniques in competition with machine learning for object detection. The Otsu segmentation technique demonstrated more reliable performances at the early stage of cultivation with an accuracy of 0.95. The Canopy Height Model technique showed increasing performances during the growing season. Future works will compare thresholding segmentation techniques with machine learning (ML) approaches and their potential as a supporting tool for ML image annotation.

With advancements in digital technologies such as 5G communications, big data, and cloud computing, the components of network operation systems have become increasingly complex, significantly complicating system monitoring and maintenance. Correspondingly, automated log anomaly detection has become a crucial means to ensure stable network operation and protect them from malicious attacks or failures. Conventional machine learning and deep learning methods assume consistent distributions between the training and testing data, adhering to a closed-set recognition paradigm. Nevertheless, in realistic scenarios, systems may encounter new anomalies that were not present in the training data, especially in log anomaly detection. Inspired by evidential learning, we propose a novel anomaly detector called LogEDL, which supervises training of the model through an evidential loss function. Unlike traditional loss functions, the evidential loss function not only focuses on correct classification, but also quantifies the uncertainty of predictions. This enhances the robustness and accuracy of the model in handling anomaly detection tasks, while achieving functionality similar to open-set recognition. To evaluate the proposed LogEDL method, we conduct extensive experiments on three datasets, i.e., HDFS, BGL, and Thunderbird, to detect anomalous log sequences. Experimental results demonstrate that our proposed LogEDL achieves state-of-the-art performance in anomaly detection.

Background: Ice formation on power transmission lines presents significant risks, including structural damage and power outages. Traditional methods for detecting ice on these lines have limitations, particularly in challenging environmental conditions. This study aims to enhance ice detection accuracy by introducing a novel image segmentation and edge detection methodology using improved multi-scale Retinex and advanced morphological operations. Method: The proposed method integrates multi-stage image processing techniques, including image enhancement, grayscale conversion, thresholding, segmentation, object isolation, and edge detection. High-resolution binocular cameras were used to capture images of ice formations on transmission lines. The effectiveness of the method was assessed through a series of metrics, including accuracy, sensitivity, specificity, and precision, and compared with existing methodologies. Results: The proposed method demonstrated superior performance in both ice detection and thickness measurement. The method effectively identified and isolated ice formations under various conditions with an average accuracy of 98.35%, sensitivity of 91.63%, specificity of 99.42%, and precision of 96.03%. Additionally, the analysis of ice thickness measurements further highlighted the method’s accuracy, with a notably low root mean squared error (RMSE) of 1.20 mm and mean absolute error (MAE) of 1.10 mm, along with a strong R-squared value of 0.95. These statistical metrics show that the proposed method not only outperforms previous methods in consistency and alignment with manual measurements but also stands out as the most reliable option for tasks requiring precise measurements, offering significant advancements in accuracy and reliability over existing methodologies. Conclusions: The enhanced image segmentation and edge detection approach provides a more accurate and reliable method for monitoring ice formations on power transmission lines.

The growing expansion of the economy and population has resulted in an increased inclination towards the utilization of groundwater resources. Conducting a geophysical survey is a widely employed method for subsurface mapping and the detection of groundwater. A geophysical study was conducted in Nanshan township, Huarong County, located in the Hunan province of the South-Central region of China. The investigation involved the utilization of a 2D Electrical Resistivity Imaging technique employing forward and inverse pole-dipole electrode array. A total of six survey lines were established, each with an electrode distance from the nearest measuring point to exceeding 800 m. The maximum current electrode separation was utilized in this setup. The spacing between the electrical resistivity sounding points was established at regular intervals. The findings from the exploration indicate the existence of multiple faults within the surveyed region. The study additionally identified two regions of structural failure, which occurred due to the convergence of faults oriented in different directions. This convergence led to the fracturing of rocks, an increase in water content, and a decrease in resistivity. The findings from the exploration were utilized in the formulation of five verification target boreholes. The results of this study offer significant insights that can inform future investigations into groundwater exploration endeavors within the region.

Wall Shear Stress (WSS) abnormalities in carotid artery blood flow are key hemodynamic factors leading to arterial endothelial damage, atherosclerotic plaque formation, and carotid artery stenosis. Precise simulation of carotid artery blood flow WSS in vitro play an important role in understanding the underlying hemodynamic mechanism of carotid artery stenosis. This study presents a design for a microfluidic cell culture chamber with a variable cross-section and a step structure, leveraging microfluidic technology and principles of fluid dynamics. We propose an optimization strategy for microchannel dimensions based on Computational Fluid Dynamics (CFD) simulations. This strategy aims to replicate the distinct WSS waveform features found at various locations within the carotid bifurcation as observed in vivo. Our simulations indicate that with a step height of 0.09 mm, a width of 4 mm for the initial segment, and 5 mm for the subsequent wider channel, the device can effectively model the characteristics of low oscillatory WSS seen at sites of carotid sinus stenosis. Furthermore, it accurately represents the high-magnitude pulsatile WSS waveforms in the more uniform arterial sections downstream of the carotid sinus. The study also reveals that vortex formation and variations in low oscillatory WSS within the stepped section of the microchannel correlate with the step's height and width, as well as with the dimensions of the wider channel section. An increase in step width leads to a decrease in both the vortex area and the magnitude of negative WSS oscillations. When the step height is less than 0.03 mm, vortex formation is inhibited, challenging the simulation of low oscillatory WSS patterns. The variable cross-section microfluidic channel developed in this study provides a platform for simulating carotid artery WSS waveforms and facilitates research into the interplay between low oscillatory WSS at sites of carotid sinus stenosis and arterial endothelial dysfunction.

Detecting small objects in images poses significant challenges due to their limited pixel representation and the difficulty in extracting sufficient features, often leading to missed or false detections. To address these challenges and enhance detection accuracy, this paper presents an improved small object detection algorithm, CRL-YOLOv5. The proposed approach integrates the CBAM attention mechanism into the C3 module of the backbone network, which enhances the localization accuracy of small objects. Additionally, the Receptive Field Block (RFB) module is introduced to expand the model's receptive field, thereby fully leveraging contextual information. Furthermore, the network architecture is restructured to include an additional detection layer specifically for small objects, allowing for deeper feature extraction from shallow layers. When tested on the VisDrone2019 small object dataset, CRL-YOLOv5 achieved an mAP50 of 39.2%, representing a 5.4% improvement over the original YOLOv5, effectively boosting the detection precision for small objects in images.

Spacetime torsion is known to be highly suppressed at the end of inflation, called preheating. Of course this fact does not avoid that metric could affect the cosmic dynamos at preheating phase as was showed by Bassett et al, in the general relativity (GR) case. In this paper, is to show that a torsionful magnetogenesis in QED effective lagrangean, drives either a torsion damping or an amplification mechanism or dynamo effect, depending upon torsion chirality. The present extension of Basset et al theory yields a new magnetic field expression $B\sim{a^{(1\pm{\beta})}}$, obtained from dynamo equation in QED. Here a cosmic factor ${\beta}=g_{T}{\eta}k$ is present. In a second example we find Higgs inlationary fields in Einstein-Cartan gravity thick domain walls (DWs). Recently a moodified Einstein-Cartan gravity was given by M Shaposhnikov et al [PRL (2020)] to obtain higgs like inflatons as a portal to dark energy.

The focus of this work lies in the construction of monitoring system designed in the form of a patch using a long-lasting potentiometric electrolyte concentration sensor from metal electrodes, in a galvanic cell configuration Mg(s)|NaCl(aq)|Cu(s). Initially, a mathematical analysis was conducted on the expected sensor output through the Nernst equation. The experimental phase involved conducting controlled tests in the laboratory using various molar concentrations of sodium chloride (NaCl) in aqueous solution. Results showed that the electrolyte sensor allows for monitoring electrolyte levels in NaCl in the range of 0.02 to 1 M/L, with electrode lifespan exceeding 10 days. On the other hand, when patch is using human sweat during controlled exercise routines, it showed the sensor's capability for monitoring changes in the subjects' hydration levels in real time. The sensor output potential is greater than 1.7 volts when the sensor electrodes are in contact with human sweat, which is equivalent to measuring molar concentrations at normal hydration and moderately high hydration levels. Sensor results indicate that the system can function in a self-powered manner without external batteries. Additionally, the extended lifespan of its electrodes allows for continuous monitoring of imbalances in the human body, showcasing the system capability for long-term health assessment.

Mutation analysis of circulating tumor DNA (ctDNA) has applications in monitoring of colorectal cancer (CRC) patients for recurrence. Considering the low tumor fraction of ctDNA in cell-free DNA (cfDNA) isolated from blood plasma, the sensitivity of the detection method is important. Here, plasma DNA collected at diagnosis and follow-up from 25 CRC patients was analyzed using a multiplex superRCA mutation detection assay. The assay was also performed on genomic DNA (gDNA) from tumor and normal tissue from 20 of these patients. The lower limit of detection for most sequence variants was in the range of 10^-5, while when analyzing cfDNA from plasma with a typical input of 33 ng, the practical detection limit was ~10^-4 or 0.01% mutant allele frequency (MAF). In 17 of 19 patients with identified hotspot mutations in tumor gDNA, at least one hotspot mutation could be detected in plasma DNA at the time of diagnosis. The MAF increased at subsequent time points in four of the patients who experienced a clinical relapse. Multiplex superRCA analysis of the remaining six patients did not reveal any hotspot mutations. In conclusion, multiplex superRCA assays proved suitable for monitoring CRC patients by analyzing hotspot mutations in cfDNA, and dynamic changes in MAF were observed in patients with clinical relapse.

Quantitative analysis of human gait is critical for early discovery, progressive tracking, and rehabilitation of neurological and musculoskeletal disorders, such as Parkinson’s Disease, stroke, and Cerebral Palsy. Gait analysis typically involves estimating gait characteristics, such as spatio-temporal gait parameters and gait health indicators (e.g., step time, length, symmetry, and balance). Traditional methods of gait analysis involve the use of cameras, wearables, and force plates, but are limited in operational requirements when applied in daily life - such as direct line-of-sight, carrying devices, and dense deployment. This paper introduces a novel approach for gait analysis by passively sensing footstep-induced floor vibrations during walking using vibration sensors mounted on the floor surface. Our approach is non-intrusive, scalable, and perceived as more privacy-friendly, making it suitable for continuous gait health monitoring in daily life. We developed a floor vibration-based gait analysis framework to estimate various gait parameters that are used as standard metrics in medical practices, including temporal parameters (step, stride, stance, swing, double-support, single-support time) and spatial parameters (step length, width, angle, stride length), and extracts gait health indicators (cadence/walking speed, left-right symmetry, gait balance, initial contact types). The main challenge is that floor vibrations are distinct across different floor types. To address this challenge, our approach develops floor-adaptive algorithms to extract floor-specific features and is designed to be generalizable to various settings, including homes, hospitals, and eldercare facilities. We evaluate our approach through real-world walking experiments with 20 adults with 12,231 labeled gait cycles across concrete and wooden floors. Our results show 90.5% (RMSE 0.08s), 71.3% (RMSE 0.38m), and 92.3% (RMSPE 7.7%) accuracy in estimating temporal, spatial parameters, and gait health indicators, respectively.

Satellite imagery has had limited application in the analysis of pre-colonial settlement archaeology in the Caribbean; visible evidence of wooden structures perishes quickly in tropical climates. Only slight topographic modifications remain, typically associated with middens. Nonetheless, surface scatters, as well as the soil characteristics they produce, can serve as quantifiable indicators of an archaeological site, which can be detected by analysis of remote sensing imagery. A variety of data sets were investigated, with the intention to combine multispectral bands to feed a direct detection algorithm, providing a semi-automatic process to cross-correlate the datasets. Sampling was done using locations of known sites, as well as areas with no archaeological evidence. The pre-processed very diverse remote sensing data sets have gone through a process of image registration. The algorithm was applied in the northwestern Dominican Republic on areas that included different types of environments, chosen for having sufficient imagery coverage, and a representative number of known locations of indigenous sites. The resulting maps present quantifiable statistical results of locations with similar pixel value combinations as the identified sites, indicating higher probability of archaeological evidence. The results show the variable potential of this method in diverse environments.

A central hypothesis on brain functioning is that plasticity regulates the signals transfer function by modifying the efficacy of synaptic transmission. In the cerebellum, granular layer has been shown to control the gain of signals transmitted through the mossy fiber pathway. Until now, the impact of plasticity on incoming activity patterns was analyzed by combining electrophysiological recordings in acute cerebellar slices and computational modeling, unraveling a broad spectrum of different forms of synaptic plasticity in the granular layer, often along with forms of intrinsic excitability changes. Here, we attempt to provide a brief overview of the most prominent forms of plasticity at excitatory synapses formed by mossy fibers onto principal neurons (granule cells, Golgi cells and unipolar brush cells) in the granular layer. Specifically, we will highlight current understanding of the mechanisms and their functional implications of the synaptic and intrinsic plasticity, providing valuable insights into how inputs are processed and reconfigured at the cerebellar input stage.

The Brazilian livestock is predominantly extensive, with approximately 90% of the production being sustained on pasture, which occupies around 20% of the territory. In the current climate change scenario and where cropland is becoming a limited resource, there is a growing need for a more efficient land use and occupation. It is estimated that more than half of the Brazilian pastures have some level of degradation; however there is still no mapping of the quality of pastures on a national scale. In this study, we mapped and evaluated the spatio-temporal dynamics of pasture quality in Brazil, between 2010 and 2018, considering three classes of degradation: Absent (D0), Intermediate (D1), and Severe (D2). There was no variation in the total area occupied by pastures in the evaluated period, in spite of the accentuated spatial dynamics, with a retraction in the center-south and expansion to the north, over areas of ​​native vegetation. The percentage of non-degraded pastures increased ~12%, due to the recovery of degraded areas and the emergence of new pasture areas as a result of the prevailing spatial dynamics. However, about 44 Mha of the pasture area is currently severely degraded. The dynamics in pasture quality were not homogeneous in property size classes. We observed that in the approximately 2.68 million properties with livestock activity, the proportion with quality gains was twice as low in small properties compared to large ones, and the proportion with losses was three times greater, showing an increase in inequality between properties with more and less resources (large and small, respectively). The areas occupied by pastures in Brazil present an unique opportunity to increase livestock production and make available areas for agriculture, without the need for new deforestation in the coming decades.

The digital transformation in Industry 4.0 has revolutionized quality control paradigms, integrating advanced technologies such as augmented reality, deep learning, and computer vision systems into a new era called "Quality 4.0". This study systematically reviews how these technologies are shaping new practices in continuous quality supervision and improvement, adapting to increasingly automated and connected production environments. Through a comprehensive analysis of the literature, practical applications in various industrial sectors, from manufacturing and agriculture to the production of sweets and snacks, are highlighted. Additionally, the conceptual and detailed design of a quality control system using artificial intelligence tools is presented, focusing on the inspection of dimensions, color, weight, and labeling of MDF boxes. The proposed system demonstrates an effective integration of sensors, high-definition cameras, and deep learning algorithms, validated through tests with boxes of different sizes. The results suggest that the adoption of these technologies not only improves the accuracy and efficiency of inspections but is also essential to maintaining high-quality standards in a dynamic and competitive industrial environment.

All types of sports are potential application scenarios for automatic and real-time visual object and event detection. In rink hockey, the popular roller quad skate variant of hockey team sports, it is of great interest to automatically track player’s movements and positions, player’s sticks and, also, making other judgments, such as being able to locate the ball. In this work, we introduce a real-time pipeline composed by an object detection model, created specifically for rink hockey games, followed by a knowledge-based event detection module. Even in the presence of occlusions and quick motions, our deep learning object detection model effectively identifies and tracks, in real-time, important visual elements such as: ball; players; sticks; referees; crowd; goalkeeper; and goal. Using a curated dataset composed by a collection of videos of rink hockey, comprising 2525 annotated frames, we trained and evaluated the algorithm performance and compare it to state of the art object detection techniques. Our object detection model, based on YOLOv7, presents a global accuracy of 80%, and presents a good performance in terms of accuracy and speed, according to our results, making it a good choice for rink hockey applications. In our initial tests, the event detection module successfully detected one important event type in rink hockey games, the occurrence of penalties.

Aerosol-cloud interactions play a crucial role in shaping Earth’s climate and hydrological 1 cycle. Observing these interactions with high precision and accuracy is of the utmost importance 2 for improving climate models and predicting Earth’s climate. Over the past few decades, lidar 3 techniques have emerged as powerful tools for investigating aerosol-cloud interactions due to their 4 ability to provide detailed vertical profiles of aerosol particles and clouds with high spatial and 5 temporal resolutions. This review paper provides an overview of recent advancements in the study 6 of aerosol-cloud interactions using lidar techniques. The paper begins with a description of the 7 different cloud microphysical processes that are affected by the presence of the aerosol, and with 8 an outline of lidar remote sensing application in characterizing aerosol particles and clouds. The 9 subsequent sections delve into the key findings and insights gained from lidar-based studies of 10 aerosol-cloud interactions. This includes investigations into the role of aerosol particles in cloud 11 formation, evolution, and microphysical properties. Finally, the review concludes with an outlook 12 on future research. By reporting the latest findings and methodologies, this review aims to provide 13 valuable insights for researchers engaged in climate science and atmospheric research.

In patients with heart failure, the evaluation of left ventricular (LV) diastolic function is vital, offering crucial insights into hemodynamic impact and prognostic accuracy. Echocardiography stands as the primary imaging modality for diastolic function assessment, and using it effectively requires a profound understanding of the underlying pathology. This review covers four main topics: first, the fundamental driving forces behind each phase of normal diastolic dynamics, along with the physiological basis of two widely used echocardiographic assessment parameters, E/e' and mitral annulus early diastolic velocity (e'); second, the intricate functional relationship between the left atrium and LV in patients with varying degrees of LV diastolic dysfunction (LVDD); third, the role of stress echocardiography in diagnosing LVDD and the significance of parameter changes in this context; and fourth, the clinical utility of evaluating diastolic function from echocardiography images across diverse cardiovascular care areas.

Road cracks significantly affect the serviceability and safety of roadways, especially in mountainous terrain. Traditional inspection methods, such as manual detection, are excessively time-consuming, labor-intensive, and inefficient. Additionally, multi-function detection vehicles equipped with various sensors are expensive and not suitable for mountainous roads due to poor conditions. To address these challenges, this study proposes a customized Unmanned Aerial Vehicles (UAV) inspection system designed for automatic crack detection. The system focuses on enhancing autonomous capabilities in mountainous terrains by incorporating embedded algorithms for route planning, autonomous navigation, and automatic crack detection. The slide window method is proposed to enhance the autonomous navigation of UAV flights by generating path planning in mountainous roads. This method compensates for GPS/IMU positioning errors, particularly in GPS-denied or GPS-drift scenarios. Moreover, the YOLOv8 algorithm is introduced to conduct autonomous crack detection from UAV imagery in the offboard module. To validate our UAV inspection system's performance, we conducted several experiments to assess its accuracy, robustness, and efficiency. The experimental results demonstrate that our UAS inspection system accurately estimates the flight trajectory along the mountainous road for autonomous navigation. Furthermore, the experimental detection results also demonstrate that the YOLOv8 algorithm achieves higher recognition accuracy compared to the YOLOv5, and YOLOv7-tiny algorithms, exceeding 3.1%,and 6.8% of mAP, respectively. In summary, the UAV inspection system proposed in this study can provide a useful tool and technological guidance for the regular inspection of mountainous roads.

The Doppler band compensated by the keystone transform (KT) is limited. Therefore, it needs to be used in conjunction with the Doppler ambiguity compensation function to correct the range migration (RM) caused by maneuvering targets with Doppler ambiguity. However, the KT implemented by sinc interpolation suffers from significant performance loss at boundaries of compensation Doppler bands. Additionally, in a multi-target scenario, KT implementation methods occupy high complexity when the Doppler range of targets spans over two compensation Doppler bands. To address aforementioned issues, this study presents a variable Doppler starting point keystone transform (VDSPKT) method, where a new form of ambiguity compensation function is constructed, turning the Doppler starting point of the compensation band in KT variable. Firstly, the position of the compensation Doppler band is changed from fixed to adjustable as needed, enhancing the flexibility of KT. Crucially, the connection points of the compensation Doppler bands in sinc interpolation are reset, avoiding the performance loss at their boundaries. Also, the compensation band is adjusted to cover the narrow Doppler frequency range caused by targets, significantly improving the computational efficiency. Finally, the simulation results demonstrate that the proposed approach effectively addresses the performance degradation and high computational complexity of KT in the aforementioned scenarios, resulting in a computational load reduced by approximately 50% compared to traditional methods.

The extensive utilization of drones has led to numerous scenarios that encompass both advantageous and perilous outcomes. By using deep learning techniques, it is aimed to reduce the dangerous effects of drone use through early detection of drones.The state of the art of the study is evaluation of deep learning approaches such as pre trained YOLOv8 drone detection for security issues. In this study, dataset collected by Mehdi Özel for a UAV competition are sourced from GitHub. These images are labeled using Roboflow, and the model is trained on Google Colab. To enhance model performance, dataset augmentation techniques including rotation and blurring are implemented. Following these steps, a significant precision value of 0.946, a notable recall value of 0.9605 and a considerable precision-recall curve value of 0.978 are achieved, surpassing many popular models such as Mask CNN, CNN, and YOLOv5.

Pulmonary Embolism (PE) is the obstruction of blood arteries in the lungs by a blood clot. The mortality risk for PE is approximately 30%. Detecting pulmonary embolism in the segmental arteries of the lung is more challenging than in the main arteries and is very prone to being missed. A computer-based approach was created in this work to automatically identify pulmonary embolism in the segmental arteries using computed tomography images. The system's infrastructure includes an enhanced Mask R-CNN deep neural network that has been trained with images containing PE. The network accurately identifies the location of the pulmonary embolism on the computed tomography picture and successfully extracts its borders. The investigation was conducted by generating a local dataset. The study's performance was evaluated using pulmonary embolisms identified manually by the expert radiologist. The sensitivity, specificity, accuracy, dice coefficient, and Jaccard index values for 2130 pictures were 96.4%, 93.5%, 96.1%, 0.96, and 0.90, respectively. The improved Mask R-CNN model has demonstrated superior performance when compared to the traditional Mask R-CNN and U-Net models. This high-performance technology is designed to identify pulmonary embolism and will aid professionals in assessing the size of the embolism.

The envelope (façade) of a building is the first barrier between the exterior and the interior of the building and withstands the highest variation of temperature and solar radiation. Trombe walls are used to take advantage of that and contribute to the heating of interior air, helping the heating system. In this study, a new Trombe wall design is presented with the aim to contribute significantly to the ventilation of residential buildings. For this an exterior wall equipped with a proposed Trombe system was studied in a numerical simulation analysis. The proposed systems consist of two parts, the air cavities on the exterior of the wall, covered with silica glass, and another layer used for heat transfer, on the interior, comprised of a MPCLBs wall of 115mm. Two cities in Romania were considered to represent the coldest and hottest climate in Romania. Brașov representing the cold climate and Constanța representing the hot climate. In the investigation, both the presence and absence of solar radiation were taken into account. In total 4 cases were established. For all four cases monthly research was made using monthly means temperature, direct and diffuse solar radiation. The results are promising and illustrate that the system works the best during the transitional seasons of spring and autumn. The lower the outdoor temperature the higher the temperature can be increased. Overall, solar radiation accounted for an average of 2 °C increase. The temperature increase varied between 3.4 °C to 15.99 °C for Brașov and between 6.42 °C to 12.07 °C in Constanța.

With the increasing demand for security surveillance, assisted driving, and remote sensing, target detection networks with rich environmental perception capabilities and high detection accuracy have become a research hotspot. However, detection techniques based on single-modality images face limitations in environmental adaptability, as they are easily affected by lighting conditions, as well as obstacles such as smoke, rain, and vegetation, leading to information loss and failing to meet the required accuracy. To address these issues, we propose a target detection network—IV-YOLO, which integrates features from both visible and infrared images. We built a dual-branch fusion network based on YOLOv8 (You Only Look Once v8) that combines infrared and visible images for target detection. On this foundation, we designed a Bidirectional Pyramid Feature Fusion structure (Bi-Fusion) to fully integrate complementary features from different modalities, reducing detection errors caused by feature redundancy and extracting fine-grained features of small targets through dual-modal fusion. Additionally, we developed the Shuffle-SPP structure, which combines channel and spatial attention mechanisms to enhance focus on deep features and further aggregate upsampling, extracting richer features. To improve the model’s expressive power, we designed a loss function tailored for multi-scale target detection boxes, accelerating the network’s convergence during training. Experimental results show that the proposed IV-YOLO network achieves an average precision (mAP) of 74.6% on the UAV remote sensing dataset, 75.4% on the KAIST pedestrian dataset, and 84.8% on the FLIR dataset. While ensuring detection accuracy, our model significantly reduces computational load, making it well-suited to meet real-time requirements. This architecture is expected to find broad application in fields such as autonomous driving, public safety, and others.

Drowsiness is a main factor for various costly defects, even fatal accidents in areas such as construction, transportation, industry and medicine, due to the lack of monitoring vigilance in the mentioned areas. The implementation of a drowsiness detection system can greatly help to reduce the defects and accident rates by alerting individuals when they enter a drowsy state. This research proposes an Electroencephalography (EEG) based approach for detecting drowsiness. EEG signals are passed through a preprocessing chain composed of artifact removal and segmentation to ensure accurate detection followed by different feature extraction methods to extract the different features related to drowsiness. This work explores the use of various machine learning algorithms such as Support Vector Machine (SVM) the K Nearest Neighbor (KNN) the Naive Bayes (NB) the Decision Tree (DT) and the Multilayer Perceptron (MLP) to analyze EEG signals sourced from the DROZY database, carefully labeled into two distinct states of alertness (awake, and drowsy). Segmentation into 10-second intervals ensures precise detection, while a relevant feature selection layer enhances accuracy and generalizability. The proposed approach achieves high accuracy rates of 99.84% and 96.4% for intra (subject by subject) and inter (cross-subject) modes, respectively. SVM emerges as the most effective model for drowsiness detection in the intra mode, while MLP demonstrates superior accuracy in the inter mode. This research offers a promising avenue for implementing proactive drowsiness detection systems to enhance occupational safety across various industries.

The term Air Quality (AQ) pertains to the standard of air, which has a direct impact on the health and well-being of individuals. It is crucial to maintain high Air Quality (AQ), for better health and productivity. Though most of people spend more time indoors nowadays, it is not to be overlooked that the pollutant concentration in indoor air is directly linked to the outdoor air quality. Due to the changes in occupancy patterns, outdated maintenance of ventilation systems, and structural flaws in buildings, the indoor air is significantly polluted; pollutants emitted outdoors enter indoor air through open windows, ventilation systems and infiltration. In this paper, we have proposed a new technique, DTMCPM (discrete-time Markov chain (DTMC) model for the analysis and forecasting of AQ, using the power method). Specialized sensors and equipment to measure different pollutants, such as particulate matter, ozone, nitrogen dioxide, sulfur dioxide, benzene and toluene, are used to collect data by the Institute of Pakistan Air Quality Monitor in various cities; the data is converted to US EPA standard for all pollutant species. This data is used to calculate the transition matrix, steady-state values and mean return rate for the analysis of IAQ. The calculated and actual return rates have been compared and the proposed model is found to have a low average prediction error of 2.356%.

Interaction between multiple phases especially liquid-gas is one of the most common phenomena in industrial systems. Computer Vision enables modern systems to derive an automated and robust image-processing technique to address this issue. However, diverse experimental conditions still make it a challenging task to overcome. In Deep Learning, instance segmentation has created a niche in various computer vision applications as it aims to provide different instance IDs to objects belonging to the same class. In this paper, we propose a novel approach employing the YOLO (You Look Only Once) an end-to-end deep learning architecture for the detection and segmentation of bubbles in fluid flows. YOLOv5 offers significant improvements in both speed and accuracy compared to its predecessors like YOLOv3. Therefore, we train the YOLOv5 network on a dataset consisting of annotated images representing instances of bubbles across varied fluid flow environments. This paper provides both qualitative and quantitative evaluation of three YOLOv5 networks (small, medium, and extra-large) in detecting and segmenting bubbles with high accuracy and efficiency. Based on the metrics all three YOLOv5 models achieved high precision above 0.98 for bounding box detection and 0.97 for segmentation mask, and high recall value greater than 0.97 for box and 0.96 for mask quality was achieved. The achieved results indicate that the models accurately localize bubbles within the image frame, and distinguish bubble pixels from the background. Also, mean average precision (mAP50-95) consistently exceeds 0.84 across all models for the bounding box and 0.73 for the segmentation mask. The ascertained results emphasize the potential of YOLOv5 architecture as a powerful tool for automated bubble analysis in real-world applications. Our research contributes to advancing the field of deep neural networks in fluid systems and provides a fresh approach for future developments in automated bubble analysis.

Toll plaza optimization is essential for improving traffic flow and easing congestion on road networks. This study aims to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer facilitation. Different resources are used for data collection, like the National Highway Authority of Pakistan. Physical data is collected from the established Toll Plaza of Peshawar, along with the development of each line. The goal of this study is to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer happiness. The analysis of present traffic patterns, the detection of bottlenecks, and the creation of plans to ease congestion and enhance overall traffic flow are all part of the optimization process. The approaches used in this study include data analysis, simulation, and traffic modeling. PTV VISSIM is software for transportation planning, design, and analysis. The simulations consider various conditions, such as variable traffic volumes, toll collection methods, and lane configurations. When comparing the different optimization strategies, performance indicators like trip time, latency, and queue length are utilized to determine which approach is practical. Waiting time Vs. Existing each lane, etc, is explained. In the end, comparing queue lengths and throughput across different lane scenarios shows that maximizing throughput with existing lanes yields up to 45% improvement with E-tag lanes, 40% with existing E-tag lanes, 38% with E-tag plus additional toll and cash lanes, 32% with three cash lanes, and 28% with two E-tag lanes. Failure to regularly monitor and adjust the existing lane pattern scenario based on traffic patterns can lead to queues being 20% longer. Without proactive evaluation and optimization, there is a potential 63% opportunity for queue length improvement

In electromagnetic ultrasonic testing, it is difficult to recognize small-size bottom cracks by time of flight (ToF), and the lift-off fluctuation of the probe affects the accuracy and consistency of the inspection results. In order to overcome the difficulty, a novel EMAT (electromagnetic acoustic transducer)-PEC (pulse eddy current) composite sensor is designed. We use amplitude of bottom echo recorded by EMAT to identify the tiny bottom crack as well as the amplitude of PEC signals picked up by the integrated symmetric coils to measure the average lift-off of the probe in real time. Firstly, the effects of lift-off and bottom cracks on amplitude of bottom echo are distinguished combining the theoretical analysis and finite element method (FEM). And then an amplitude correction method based on the fusion of EMAT and PEC signals is proposed to reduce the impact of lift-off on the defect signal. The experimental results demonstrate that the designed composite sensor can effectively detect the bottom crack as small as 0.1mm* 0.3mm. The signal fusion method can accurately correct the amplitude of defect signal and the relative error is less than ±8%.

In order to shorten the detection time and improve the average precision on embedded devices, A lightweight and high accuracy model is proposed for passion fruit in complex environments (backlight, occlusion, overlap, sunny, cloudy, rainy). Firstly, replacing the backbone network of YOLOv5 with a lightweight GhostNet model reduces the number of parameters and computation while improving detection speed. Secondly, a new feature branch is added to the GhostNet network, and the feature fusion layer in the neck network is reconstructed to effectively combine the lower-level and higher-level features, which not only improves the accuracy of the model but also maintains its lightweight. Finally, the knowledge distillation methods are used to transfer the knowledge from the more capable teacher model to the less capable student model, which significantly improving the detection accuracy. The improved model is denoted as G-YOLO-NK. The average accuracy of the G-YOLO-NK network is 96.00%, which is 1.00% higher than the original YOLOv5s model. Furthermore, the improved model size is 7.14MB, reduced to half of the original model, and the real-time detection frame rate is 11.25 FPS on the Jetson Nano. Compared to the state-of-the-art model, the proposed model outperforms them in terms of average precision and detection performance. The present work provides an effective model for real-time detection of passion fruits in complex orchard scenes, which can provide valuable technical support for the development of orchard picking robots and greatly improve the intelligence level of orchards.

On the basis of the force and safety factor analysis of traditional retaining wall, a new analytical method of force and displacement of slope is suggested, the numerical theoretical solution of the stress distribution of the sliding body can be obtained under the conditions, which the stresses distribution are satisfied with the differential equilibrium equations, the boundary conditions, the compatibility equation and the macroscopic equilibrium equations. The interface stresses between the sliding body and the retaining wall are continuous, the theoretical solution of retaining wall stress distributions can be gotten, while the stress differential equilibrium equation, the compatibility equation, macroscopic force and moment balance equations are satisfied. The strain and displacement solutions can be obtained by using Duncan Chang and Hooke constitutive equations for the slope and the retaining wall respectively. The transfer station of landfill in the Guandukou Town of Badong County is taken as an example, the results of the sliding body and retaining wall analysis show: The stress and strain solutions of a slope and a retaining wall can be obtained by the proposed method, the forces between the retaining wall and the sliding body include shear stress and normal stress, and the stress distributions change with the deformation of the sliding body, the shape and material of retaining wall can be optimized according to the calculation results，it is feasible for the proposed analysis method of slope with retaining wall design to be run many years.

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

Detecting, tracking, and classifying unmanned aerial vehicles (UAVs) in a swarm presents significant challenges due to their small and diverse radar cross-sections, multiple flight altitudes, velocities, and close trajectories. To overcome these challenges, adjustments of the radar parameters and/or position of the radar (for airborne platforms) are often required during runtime. The runtime adjustments help to overcome the anomalies in the detection, tracking, and classification of UAVs. The runtime adjustments are performed either manually or through fixed algorithms, each of which can have its limitations for complex and dynamic scenarios. In this work, we propose the use of multi-agent reinforcement learning (RL) to carry out the runtime adjustment of the radar parameters and position of the radar platform. The radar used in our work is a multibeam multifunction phased array radar (MMPAR) placed onboard UAVs. The simulations show the cognitive adjustment of the MMPAR parameters and position of the airborne platform using RL helps to overcome anomalies in the detection, tracking, and classification of UAVs in a swarm. A comparison with other artificial intelligence (AI) algorithms shows that RL performs better due to runtime learning of the environment through rewards.

Electrical rotating machines like Induction Motors (IMs) are widely used in several industrial applications since their robust elements, provide high efficiency and give versatility in industrial applications. Nevertheless, the occurrence of faults in IMs is inherent to their operating conditions, hence, Inter-turn short-circuit (ITSC) is one of the most common failures that affect IMs and its appearance is due to electrical stresses leads to the degradation of the stator winding insulation. In this regard, this work proposes a diagnosis methodology for the assessment and detection of incipient ITSC in IMs, the proposed method is based on the processing of vibration, stator currents and magnetic stray-flux signals. Certainly, the novelty and contribution include the characterization of different physical magnitudes by estimating a set of statistical time domain features, as well as, their fusion and reduction through the Linear discriminant Analysis technique within a feature-level fusion approach. Furthermore, the fusion and reduction of information from different physical magnitudes leads to perform the automatic fault detection and identification by a simple Neural-Network (NN) structure. The proposed method is evaluated under a complete set of experimental data and the obtained results demonstrate that the fusion of information from different sources (physical magnitudes) allows to improve the accuracy during the detection of ITSC in IMs , the results make this proposal feasible to be incorporated as a part of condition-based maintenance programs in the industry.

The article focusses on detecting the unbalance of a mechanical component in the electric drive system of a two-mass servomechanism with a PMSM motor, which is connected to the load via a long, flexible shaft. In the example analysed, the degree of unbalance was determined using the reference current signal from the speed controller of the FOC control system. The authors pre-sented a two-mass model with an unbalanced mechanical system. The STFT transform was used to analyse the symptoms of unbalance and an artificial neural network MLP was used for system state inference. The effectiveness of the presented analysis, based on the reference current signal from the sensor embedded in the control system, was experimentally confirmed.

Mulberry leaves, when infected by pathogens, can suffer significant yield loss or even death if early disease detection and timely spraying are not performed. To enhance the detection performance of mulberry leaf diseases in natural environments and to precisely locate early small lesions, we propose a high-precision, high-efficiency disease detection algorithm named YOLOv8-RFMD. Based on improvements to You Only Look Once version 8 (YOLOv8), we first proposed the Multi Dimension Feature Attention (MDFA) module, which integrates important features at the pixel-level, spatial and channel dimensions. Building on this, we designed the RFMD Module, which consists of the Conv-BatchNomalization-SiLU (CBS) module, Receptive-Field Coordinated Attention (RFCA) Conv, and MDFA, replacing the Bottleneck in the model’s Residual block. We then employed Adown down sampling structure to reduce the model size and computational complexity. Finally, to improve the detection precision of small lesion features, we replaced the complete intersection over union (CIOU) loss function with the Normalized Wasserstein Distance (NWD) loss function. Results show that the YOLOv8-RFMD model achieves an mAP50 of 94.3% and an mAP50:95 of 67.8% on experimental data, representing increases of 2.9% and 4.3% respectively compared to the original model. The model size is reduced by 0.53 MB to just 5.45 MB, and the number of floating-point operations is reduced by 0.3 G to only 7.8 G. The improved model meets the deployment requirements of mobile embedded devices and can provide a theoretical reference for the automated spraying operations of mulberry leaves.

More than 90% of patients affected by mastocytosis are characterized by a somatic point mutation of c-KIT, which induces ligand-independent activation of the receptor and downstream signal triggering, ultimately leading to mast cell proliferation and survival. The most frequent mutation is c-KIT p.D816V, but other rarer mutations can also be found. These mutations often have a very low variant allele frequency (VAF), well below the sensitivity of common next-generation sequencing (NGS) methods used in routine diagnostic panels. Highly sensitive methods are developing for detecting mutations. This review summarizes the current indications on the recommended methods and on how to manage and interpret molecular data for the diagnosis and follow-up of patients with mastocytosis.

This paper is focused on typical rural settlements in the Stara Planina Mountain area in Serbia. They are distinguished by their authenticity, identity and character, and they have still not been destroyed by inappropriate development as is the case in some other mountain areas in Serbia, where the effects of urbanization, globalization and homogenization are obvious. Consequently, there is a need to prevent these effects in mountain areas where there is a threat of disbalance between development and the inherited natural and cultural values. The paper starts from several important concepts that relate contemporary approaches to cultural heritage preservation, place morphology and planning future development. The concept definitions from recent documents from related fields are used. These concepts are then applied to the area of Stara Planina Mountain and its rural settlements. The necessity for an integrative approach to planning this area with significant natural and anthropogenic potential is pointed out, including its integration into international programs of sustainable development.

ABSTRACT Purpose: Tongue image analysis for disease diagnosis is an ancient traditional non-invasive diagnosis technique widely used by traditional medicine practitioners. Deep Learning based multi-label disease detection models offer tremendous potential to clinical decision support systems, by facilitating preliminary diagnosis. Methods: In this work, we propose a multi-label disease detection pipeline, where in observation and analysis of tongue images captured and received via smartphones assist in predicting the health status of an individual. All images are voluntarily given by subjects consulting collaborating physicians. Images thus acquired are first and foremost classified either into a diseased or a normal category by a 5-fold cross-validation algorithm using a convolution neural network (MobileNetV2) model for binary classification. Once the diseased label is predicted, the image is used to diagnose multiple diseases using the prediction algorithm based on DenseNet121. Results: Average accuracy of 93 % was achieved in classifying diseased from normal healthy tongue by detection model with MobileNetV2 architecture. Multilabel disease classification produced more than 90% accurate results for the seven class labels considered. Conclusion: AI based image analysis shows promising results and an extensive dataset could provide further improvements to this approach. Rather than employing high-cost sophisticated image capturing setup, experimenting with smartphone images opens opportunity to provide preliminary health status to individuals on smartphone prior to further line of treatment and diagnosis.

Mobile inspections conducted by intelligent tunnel robots are instrumental in broadening the inspection reach, economizing on inspection expenditures, and augmenting the operational efficiency of inspections. Despite differences from fixed surveillance, mobile-captured traffic videos have complex backgrounds and device conditions that interfere with accurate traffic event identification, warranting more research. This paper proposes an improved algorithm based on YOLOv9 and DeepSORT for intelligent event detection in edge computing mobile device using the intelligent tunnel robot. The enhancements comprise the integration of the Temporal Shift Module to boost temporal feature recognition and the establishment of logical rules for identifying diverse traffic incidents in mobile video imagery. Experimental results show that our fused algorithm achieves a 93.25% accuracy rate, an inprovement of 1.75% over the baseline. The algorithm is also applicable to inspection vehicles, drones, and autonomous vehicles, effectively enhancing the detection of traffic events and improving traffic safety.

YOLOv8, as an efficient object detection method, can swiftly and precisely identify objects within images. However, traditional algorithms encounter difficulties when detecting small targets in remote sensing images, such as missing information, background noise, and interactions among multiple objects in complex scenes, which may affect performance. To tackle these challenges, we propose an enhanced algorithm optimized for detecting small objects in remote sensing images, named HP-YOLOv8. Firstly, we design the C2f-D-Mixer (C2f-DM) module as a replacement for the original C2f module. This module integrates both local and global information, significantly improving the ability to detect features of small objects. Secondly, we introduce a feature fusion technique based on attention mechanisms, named Bi-Level Routing Attention in Gated Feature Pyramid Network (BGFPN). This technique utilizes an efficient feature aggregation network and reparameterization technology to optimize information interaction between different scale feature maps, and through the Bi-level Routing Attention (BRA) mechanism, it effectively captures critical feature information of small target objects. Finally, we propose the Smooth Mean Perpendicular Distance Intersection over Union (SMPDIoU) loss function. The method comprehensively considers the shape and size of detection boxes, enhances the model's focus on the attributes of detection boxes and provides a more accurate bounding box regression loss calculation method. To demonstrate our approach's efficacy, we conduct comprehensive experiments across the RSOD, NWPU VHR-10, and VisDrone2019 datasets. The experimental results show that the HP-YOLOv8 achieves 95.11%, 93.05%, and 53.49% in the [email protected] metric, and 72.03%, 65.37%, and 38.91% in the more stringent [email protected]:0.95 metric, respectively.

Artificial neural networks are highly efficient tools for numerous kinds of tasks, which include forecasting, regression, and recognizing patterns in classification and pattern recognition. An artificial neural network is a model for processing information using essential components called artificial neurons that are inspired by the human brain. Two significant aspects of delayed neural networks dynamic activity are their stability and the synchronization phenomenon. This work presents a bidirectional associative memory (BAM) incommensurate fractional-order delay neural network model. The incommensurate fractional orders are calculated within the stability region through the corresponding eigenvalues and their points of equilibrium. The involved neuron states are synchronized with each other for the calculated incommensurate stable fractional orders. The reduction of the time delay will give more relaxation time for the state variable $w_4(t)$ to be stable as compared to the state variable $w_3(t)$ due to the induction of extra activation functions. The theoretical findings of this research have significance for providing guidance and controlling the dynamic behavior of artificial neural networks.

Traffic sign detection plays a pivotal role in autonomous driving systems. The intricacy of the detection model necessitates high-performance hardware. Real-world traffic environments exhibit considerable variability and diversity, posing challenges for effective feature extraction by the model. Therefore, it is imperative to develop a detection model that is not only highly accurate but also lightweight. In this paper, we proposed YOLO-ADual, a novel lightweight model. Our method leverages the C3Dual and Adown lightweight modules as replacements for CPS and CBL modules in YOLOv5. The Adown module effectively mitigates feature loss during downsampling while reducing computational costs. Meanwhile, C3Dual optimizes the processing power for kernel feature extraction, enhancing computation efficiency while preserving network depth and feature extraction capability. Furthermore, the inclusion of the CBAM module enables the network to focus on salient information within the image, thus augmenting its feature representation capability. Our proposed algorithm achieves a [email protected] of 70.1% while significantly reducing the number of parameters and computational requirements to 51.83% and 64.73% of the original model, respectively. Compared to various lightweight models, our approach demonstrates competitive performance in terms of both computational efficiency and accuracy.

Strip steel surface defect recognition research has important research significance in industrial production. Aiming at the problems of defect feature extraction difficulty, slow detection speed and insufficient dataset, YOLOv5 is improved on the basis of YOLOv5, and YOLO-LFPD (Lightweight fine particle detection) model is proposed. By introducing the RepVGG (Re-param VGG) module, the robustness of the model to the surface defect features of strip steel is enhanced, and the expressive ability of the model is improved. FasterNet is used to replace the backbone network, which ensures the accuracy and accelerates the inference speed, making the model more suitable for real-time monitoring. The use of pruning, GA genetic algorithm with OTA loss function method further reduces the model size while better learning the strip steel defect feature information, thus improving the generalisation ability and accuracy of the model. The experimental results show that the introduction of the RepVGG module and the use of FasterNet can well improve the model performance, with a reduction of 48% in the number of parameters, a reduction of 13% in the number of GFLOPs, an inference time of 77% of the original, and an optimal accuracy compared with the network models in recent years. The experimental results on the NEU-DET dataset show that the accuracy of YOLO-LFPD is improved by 3% to 81.2%, which is better than other models, and provides new ideas and references for the lightweight strip steel surface defect detection scenarios and application deployment.

Numerous image processing techniques (IPTs) have been employed to detect crack defects, offering an alternative to human-conducted onsite inspections. These IPTs manipulate images to extract defect features, particularly cracks in surfaces produced through Additive Manufacturing (AM). This article presents a vision-based approach that utilizes deep convolutional neural networks (CNNs) for crack detection in AM surfaces. Traditional image processing techniques face challenges with diverse real-world scenarios and varying crack types. To overcome these challenges, our proposed method leverages CNNs, eliminating the need for extensive feature extraction. Annotation for CNN training is facilitated by LabelImg without the requirement for additional IPTs. The trained CNN, enhanced by OpenCV preprocessing techniques, achieves an outstanding 99.54% accuracy on a dataset of 14,982 annotated images with resolutions of 1536 × 1103 pixels. Evaluation metrics exceeding 96% precision, 98% recall, and a 97% F1-score highlight the precision and effectiveness of the entire process.

Cascading landslides represent a dynamic and hazardous geological phenomenon, char-15 acterized by a sequential chain of slope failures triggered by various factors such as heavy rainfall, 16 seismic activity, or anthropogenic activities. These events can amplify the damage caused by the 17 initial trigger and propagate instability along a slope, often resulting in significant environmental 18 and societal impacts. The Morino-Rendinara cascading landslide, situated in the Ernici mountains 19 along the border of Abruzzo and Lazio region (Italy), serves as a notable example of the complexities 20 and devastating consequences associated with such events. In March 2021, a substantial debris flow 21 event in Morino obstructed the Liri River, marking the latest step in a series of landslide events 22 characterized by a complexity far beyond initial expectations. This study employs a multidiscipli-23 nary approach, combining conventional techniques and advanced technologies, to understand the 24 complexities of the Morino-Rendinara landslide. Field activities, UAV image acquisition, SAR inter-25 ferometry based on SENTINEL-1 images and pixel offset analysis based on high-resolution Google 26 Earth images, offer insights into the geological and hydrogeological setting of the unstable slope, 27 landslide geometry, mechanism and kinematics. To supplement the analysis a specific FEM slope 28 stability analyses is used to reconstruct the deep geometry of the system emphasizing the modula-29 tion action of groundwater flow to the slope stability

Parkinson's disease (PD) is the second most common neurodegenerative disorder, entailing several motor-related symptoms that contribute to a reduced quality of life in affected subjects. Recent advances in wearable technologies and computing resources have shown great potential for the assessment of PD-related symptoms. However, the potential applications (e.g., early diagnosis, prognosis and monitoring) and key features of digital biomarkers for motor symptoms of PD (DB-MS-PD) have not been comprehensively studied. This study aims to provide a state-of-the-art review of current digital biomarker definitions for PD, focusing on the use of wearable devices. This review systematically examines research articles from 2012 to 2024, focusing on key features and recent technologies in PD research. A total of 22 studies were included and thoroughly analyzed. Results indicate that DB-MS-PD can accurately distinguish patients with PD (PwPD) from healthy controls (HC), assess disease severity or treatment response, and detect motor symptoms. Large sample sizes, proper validation, non-invasive devices, and ecological monitoring make DB-MS-PD promising for improving PD management. Challenges include sample and method heterogeneity and lack of public datasets. Future studies can leverage evidence of the current literature to provide more effective and ready-to-use digital tools for monitoring PD.

Proteomics research has become one of the most important topics in the fields of life science and natural science. At present, research on protein–protein interaction networks (PPINs) mainly focuses on detecting protein complexes or function modules. However, existing approaches are either ineffective or incomplete. In this paper, we investigate function module detection mechanisms in PPIN, including open databases, existing detection algorithms and recent solutions. After that, we describe the proposed solution based on simplified swarm optimization (SSO) algorithm and gene ontology knowledge. The proposed solution implements SSO algorithm for clustering proteins with similar function, and imports biological gene ontology knowledge for further identifying function complexes and improving detection accuracy. Furthermore, we use four different categories of species dataset for experiment: Fruitfly, Mouse, Scere, and Human. The testing and analysis result show that the proposed solution is feasible, efficient and could achieve a higher accuracy of prediction than existing approaches.

Nowadays, inquiries are one of the most important tasks to be executed in order to get valued information. When an inquiry is done to gather information about a human behavior there are some problems inherent to it. One of the main problems is how the information about many different persons can be processed to give good information about their environment. Most of the information gathered by these inquiries is used to analyze the behavior of the respondents. It can be used to estimate the success of a certain product on the market or just to validate the satisfaction of current costumers. The veracity of this information can be critical to model environments with some acceptable level of precision. Modelling environments through Artificial Neural Networks (ANN) is highly common because ANN’s are excellent to model predictable environments using a set of data. ANN’s are good in deal with sets of data with some noise, but they are fundamentally surjective mathematical functions and they aren’t able to give different results for the same input. So if an ANN is trained using data where the same input has different images which can be the case of survey data, this can be a problem for the success of modelling the environment. Therefore is necessary to process the data in order to adjust and eliminate invalid data.