Top Model Research Articles

Sim-Boxing Fraud Detection System Using Artificial Neural Network

ABSTRACT Telecommunication fraud is when con artists use pretense to obtain discounted or complimentary services. Globally, prevalent telecommunication fraud results in significant yearly revenue losses for telecom firms. The SIM-box bypass fraud is one tactic scammers use to deceive telecom operators. Installing numerous prepaid SIM cards in a SIM box is known as SIM-box fraud. This makes it possible for fraudsters to place international calls using local phone numbers in the destination countries and receive calls over VoIP (the Internet). By starting the call using the local SIM that is inserted in the SIM box, it appears to be local. This study created a model that employed Artificial Neural Network (ANN) model to more accurately and efficiently identify fraudulent customers; diverted SIM-Box calls using Customer Detail Record (CDR) data. A framework using multi-layer perceptrons, a class of ANN, to identify patterns indicative of SIM-box operations by analysing Call Detail records (CDRs) was developed. The model was trained using 500 iterations on a dataset comprising labelled records of both legitimate and fraudulent calls, using features such as call duration, frequency of calls, timestamp patterns, and originating and terminating number characteristics. The range of prediction accuracy obtained from the developed artificial neural network models was 56.8% to 62.3%. The top model, consisting of five layers, yielded 62.3%. These layers consist of an input layer with neurons, five, nine, and eighteen hidden levels, and one output layer with a single neuron that stands for fraud. It is recommended that the dataset be expanded to include a wider range of SIM-box fraud scenarios from various geographic locations and over different periods. This would improve the model’s robustness and adaptability to changing fraud techniques.

Deep Learning-Based Classification of Rice Leaf Diseases Using Hybrid Ensemble Models

This research aimed to identify the possibility of applying deep learning to rice leaf diseases diagnostic procedures. The study applied different data preprocessing techniques that were the key to getting the data ready for the analysis process to a set of commonly used rice leaf images. The first process implemented was training different kinds of deep learning models, such as ResNet and DenseNet architectures, on the preprocessed data. To ensure the best possible performance, the top single models from each set were then combined into all the ensemble methods in a way that made them the most potent. The pre-trained models used were ResNet152V2, DenseNet121, InceptionResNetV2, MobilityNetv2, and all models were trained on the preprocessed data. The algorithms' success in classification was assessed based on the measures of accuracy, precision, recall, and F1 score. The most successful classifiers were combined into hybrid ensembles using the Ensemble method. The holistic approach to this matter brought the desired result that is a 98% accuracy in the classification of rice leaf diseases, which is truly a remarkable result that no single model can bring.

Research on Stiffness Analysis and Technology of the Heavy Spidle Top

Background: The spindle top is an important component used to withstand the shaft workpiece on machine tools so that the spindle can meet high efficiency and high precision requirements. However, the selection principles under various load conditions are not stipulated in use. In addition, material selection, manufacturing, heat treatment technology, etc., are of practical significance for the production of high hardness, high wear resistance, and high precision spindle tops. Objectives: The spindle top type, material selection principles, heat treatment, cold working, and other manufacturing processes are given. Provide a reference for highperformance and top-notch design and manufacturing. Methods: The model of the spindle top will be created in UG software, then using ANSYS finite element analysis software to analyze stiffness of spindle top whose height-to-diameter ratios are 1:4 and 1:7 types in a variety of different load cases. The design and manufacturing process of the spindle top is analyzed and expounded from the selection and performance comparison of metal materials, heat treatment of different materials, cold manufacturing technology, and other aspects. Results: The deformation laws of different types of spindle tops are obtained. According to the deformation regular, find the selection principle of height to diameter ratio of spindle top. The defects that are easy to occur in the technology are obtained and the preventive and solution measures are put forward. Conclusion: According to the deformation regular, find the selection principle of height to diameter ratio of spindle top. The material selection, heat treatment technology, and other technical research on the spindle top provide the necessary basis for the production of the spindle top.

Non-linearity and chaos in the kicked top

Classical chaos arises from the inherent non-linearity of dynamical systems. However, quantum maps are linear; therefore, the definition of chaos is not straightforward. To address this, we study a quantum system that exhibits chaotic behavior in its classical limit: the kicked top model, whose classical dynamics are governed by Hamilton’s equations on phase space, whereas its quantum dynamics are described by the Schrödinger equation in Hilbert space. We explore the critical degree of non-linearity signifying the onset of chaos in the kicked top by modifying the original Hamiltonian so that the non-linearity is parameterized by a quantity p. We find two distinct behaviors of the modified kicked top depending on the value of p. Chaos intensifies as p varies within the range of 1≤p≤2, whereas it diminishes for p>2, eventually transitioning to a purely regular oscillating system as p tends to infinity. We also comment on the complicated phase space structure for non-chaotic dynamics. Our investigation sheds light on the relationship between non-linearity and chaos in classical systems, offering insights into their dynamical behavior.

Combining past and contemporary species occurrences with ordinal species distribution modeling to investigate responses to climate change

Many organisms leave evidence of their former occurrence, such as scat, abandoned burrows, middens, ancient eDNA or fossils, which indicate areas from which a species has since disappeared. However, combining this evidence with contemporary occurrences within a single modeling framework remains challenging. Traditional binary species‐distribution modeling reduces occurrence to two temporally coarse states (present/absent), so thus cannot leverage the information inherent in temporal sequences of evidence of past occurrence. In contrast, ordinal modeling can use the natural time‐varying order of states (e.g. never occupied versus previously occupied versus currently occupied) to provide greater insights into range shifts. We demonstrate the power of ordinal modeling for identifying the major influences of biogeographic and climatic variables on current and past occupancy of the American pika Ochotona princeps, a climate‐sensitive mammal. Sampling over five years across the species' southernmost, warm‐edge range limit, we tested the effects of these variables at 570 habitat patches where occurrence was classified either as binary or ordinal. The two analyses produced different top models and predictors – ordinal modeling highlighted chronic cold as the most‐important predictor of occurrence, whereas binary modeling indicated primacy of average summer‐long temperatures. Colder wintertime temperatures were associated in ordinal models with higher likelihood of occurrence, which we hypothesize reflect longer retention of insulative and meltwater‐provisioning snowpacks. Our binary results mirrored those of other past pika investigations employing binary analysis, wherein warmer temperatures decrease likelihood of occurrence. Because both ordinal‐ and binary‐analysis top models included climatic and biogeographic factors, results constitute important considerations for climate‐adaptation planning. Cross‐time evidences of species occurrences remain underutilized for assessing responses to climate change. Compared to multi‐state occupancy modeling, which presumes all states occur in the same time period, ordinal models enable use of historical evidence of species' occurrence to identify factors driving species' distributions more finely across time.

Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies

Article Low Dose CT Image Denoising: A Comparative Study of Deep Learning Models and Training Strategies Heng Zhao 1, Like Qian 1, Yaqi Zhu 1 and Dingcheng Tian 1,2,∗ 1 Research Institute for Medical and Biological Engineering, Ningbo University, Ningbo 315211, China 2 College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110016, China ∗ Correspondence: 2310520@stu.neu.edu.cn Received: 8 August 2024; Revised: 10 October 2024; Accepted: 14 October 2024; Published: 5 November 2024 Abstract: Low-dose computed tomography (LDCT) denoising is an important topic in CT image research. Compared with normal-dose CT images, LDCT can reduce the radiation dose of X-rays, decreasing the radiation burden on the human body, which is beneficial to human health. However, quantum noise caused by low-dose rays will reduce the quality of CT images, thereby decreasing the accuracy of clinical diagnosis. In recent years, deep learning-based denoising methods have shown promising advantages in this field. Researchers have proposed some optimized models for low-dose CT image denoising. These methods have enhanced the application of low-dose CT image denoising from different aspects. From the perspective of experimental research, this paper investigates and evaluates some top deep learning models proposed in the field of low-dose image denoising in recent years, with the aim of determining the best models and training strategies for this task. We conducted experiments on seven deep learning models (REDCNN, EDCNN, QAE, OCTNet, UNet, WGAN, CTformer) on the AAPM dataset and the Piglet dataset. Our research shows that UNet has the best denoising effect among the models, obtaining PSNR = 33.06 (AAPM dataset) and PSNR = 31.21 (Piglet dataset), and good generalization capacity is also observed. However, UNet has a large number of parameters, and the time it takes to process an image is about 8 ms, while EDCNN takes about 4.8 ms to process an image, and its average PSNR is ranked second after UNet. EDCNN strikes a balance between denoising performance and processing efficiency, making it ideal for low-dose CT image denoising tasks.

Individual return patterns of spawning flannelmouth sucker (Catostomus latipinnis) to a desert river tributary

Tributaries provide temporal and spatial habitat heterogeneity in river networks that can be critical for parts of the life history of a species. Tributary fidelity can benefit individual fish undergoing spawning migrations by reducing time and energy spent exploring new areas and leveraging previous experience, but anthropogenic activities that fragment or degrade these systems can eliminate those benefits. We used multistate models based on passive integrated transponder (PIT) detection data from 2013 to 2023 to estimate the proportion of flannelmouth suckers (Catostomus latipinnis) migrating to a tributary, McElmo Creek, from the mainstem San Juan River for spawning. Survival varied among years and among states. The top model for migration probability included sex, with males slightly more likely to migrate (0.93 vs 0.90), and the next model identified interannual variation in migration probability ranging from 0.875 to 0.999 across years, indicating high site fidelity. Individuals showed consistency in relative arrival timing across years, with the highest correlation generally during years with greater spring discharge and extended tributary residence time. Successful tributary spawning may be important for the maintenance of the mainstem San Juan River flannelmouth sucker population, but site fidelity may be maladaptive where tributaries are vulnerable to human alterations.

Artificial intelligence-driven assessment of critical inputs for lead adsorption by agro-food wastes in wastewater treatment

Due to environmental concerns and economic value, the adsorption process using agricultural wastes is one of the promising methods to remove lead (Pb) from contaminated water. The relationships between agricultural waste properties, adsorption conditions, and the maximum Pb adsorption capacity of selected adsorbents have not been adequately explored. A thorough understanding of these interactions is crucial for optimizing adsorption processes and enhancing the efficiency of agricultural wastes as sustainable adsorbents. To assess Pb adsorption by agricultural wastes and identify the key influencing factors, three artificial intelligence techniques, namely Extreme Learning Machine (ELM), Adaptive Nuro-Fuzzy Inference Systems (ANFIS), and Group Method of Data Handling (GMDH) have been employed in this study. Seven input variables, namely time, ratio, initial ion concentration, type of adsorbents from agricultural wastes, pH, temperature, and agitation speed, from 771 data points were used as inputs for model development, while the quantity of Pb adsorbed was chosen as target parameter. To identify the best input combinations with one to seven variables, 127 models were defined and analyzed using ELM integrated with the cross-validation technique. The results highlighted that the initial ion concentration is the most critical factor in enhancing heavy metal adsorption, and temperature is the least important factor. The top models, utilizing one to seven input variable(s), were then modeled with ANFIS and GMDH. Subsequently, all three models were compared. The GMDH model with four input variables (initial ion concentration, type of adsorbent, time, and agitation speed) demonstrated the highest performance in terms of accuracy and simplicity.

Estimating Demographic Parameters for Bearded Seals, Erignathus barbatus, in Alaska Using Close-Kin Mark-Recapture Methods.

Reliable estimates of population abundance and demographics are essential for managing harvested species. Ice-associated phocids, "ice seals," are a vital resource for subsistence-dependent coastal Native communities in western and northern Alaska, USA. In 2012, the Beringia distinct population segment of the bearded seal, Erignathus barbatus nauticus, was listed as "threatened" under the US Endangered Species Act requiring greater scrutiny for management assessments. We sought to estimate requisite population parameters from harvested seals by using close-kin mark-recapture (CKMR) methods, the first such application for marine mammals. Samples from 1758 bearded seals harvested by Bering, Chukchi, and Beaufort Sea communities during 1998-2020 were genotyped, genetically sexed, and aged by tooth annuli. After rigorous quality control, kin relationships were established for 1484 seals including two parent-offspring pairs (POPs) and 25 potential second-order kin pairs. Most of the second-order kin were half-sibling pairs (HSPs), but four were potential grandparent-grandchild pairs (GGPs). There were no full sibling pairs, suggesting a lack of mate fidelity. Mitochondrial DNA analysis identified 17 potential HSPs as paternally related, providing substantial evidence of persistent heterogeneity in reproductive success among adult males. The statistical CKMR model incorporates probabilities associated with POPs, HSPs, and GGPs and assumes known ages and a stable population. Our top model accommodates heterogeneity in adult male breeding success and yields an abundance estimate of ~409,000 with a coefficient of variation (CV) = 0.35, which is substantially greater than the "non-heterogeneity" model estimate of ~232,000 (CV = 0.21), an important difference for managing a harvested species. Using CKMR methods with harvested species provides estimates of abundance with the added opportunity to acquire information about adult survival, fecundity, and breeding success that could be applied to other species of concern, marine and terrestrial.

Remote Sensing of Nitric Acid and Temperature via Design of Experiments, Chemometrics, and Raman Spectroscopy.

This study presents an effective method for the quantification of nitric acid (0.1-9 M) and the temperature (20-60 °C) through optimal experimental design, chemometrics, and Raman spectroscopy. Raman spectroscopy can be deployed using fiber-optic cables in hot cell environments to support processing operations in the nuclear field and industry. Chemical operations frequently use nitric acid and operate at nonambient temperatures either by design or by circumstance. Examples of Raman spectroscopy for the quantification of nitric acid with applications in the industrial field are profuse. However, the effect of temperature on quantification is often ignored and should be considered in real-world scenarios. Statistical design of experiments was used to build training sets for partial least-squares regression and support vector regression (SVR) models. The SVR model with a nonlinear kernel outperformed the top partial least-squares models with respect to temperature and resulted in percent root-mean-square error of prediction of 1.8% and 2.3% for nitric acid and temperature, respectively. The D-optimal design strategy decreased the sampling time by 75% compared to a more traditional seven-level full factorial option. The new method advances chemometric applications within and beyond the nuclear field and industry.

Seroprevalence of Erysipelothrix rhusiopathiae in Beaufort Sea Polar Bears (Ursus maritimus) is Linked to Ringed Seal (Pusa hispida) Demographics.

Polar bear (Ursus maritimus) life history is intimately associated with the distribution of sea ice and their prey in Arctic ecosystems. These ecosystems are changing in response to climate warming, resulting in the increased prevalence of pathogens in polar bears. Erysipelothrix rhusiopathiae has a long history of infection in domestic species and more recently in wildlife in the Canadian Arctic. As a result of increasing reports of E. rhusiopathiae causing morbidity and mortality in Arctic terrestrial mammals, we tested the seroprevalence of E. rhusiopathiae in Beaufort Sea polar bears sampled in 1985-87, 1992, 1994, and 2003-11. Our sample of 180 polar bears (117 females, 61 males, two unknown) with a median age of 9 yr (range 1-26 yr) had a seropositivity of 27.2% (49/180 individuals). We used binomial logistic regressions to investigate biotic and abiotic factors that may be linked to seropositivity. The resulting top model found that increased predation on adult ringed seals (Pusa [Phoca] hispida) and negative winter Arctic Oscillation Index (AOI) years were associated with a higher probability of seropositivity. Ringed seals may be a reservoir for E. rhusiopathiae via their consumption of infected prey, as the pathogen can persist in marine fish, molluscs, and crustaceans. Negative winter AOIs in our data set reflected high ice volume years, which reduced ringed seal natality, resulting in fewer seal pups available as prey. Our results suggest that exposure to E. rhusiopathiae in Beaufort Sea polar bears is modulated by a predator-prey mechanism.

COADREADx: A comprehensive algorithmic dissection of colorectal cancer unravels salient biomarkers and actionable insights into its discrete progression.

Colorectal cancer is a common condition with an uncommon burden of disease, heterogeneity in manifestation, and no definitive treatment in the advanced stages. Renewed efforts to unravel the genetic drivers of colorectal cancer progression are paramount. Early-stage detection contributes to the success of cancer therapy and increases the likelihood of a favorable prognosis. Here, we have executed a comprehensive computational workflow aimed at uncovering the discrete stagewise genomic drivers of colorectal cancer progression. Using the TCGA COADREAD expression data and clinical metadata, we constructed stage-specific linear models as well as contrast models to identify stage-salient differentially expressed genes. Stage-salient differentially expressed genes with a significant monotone trend of expression across the stages were identified as progression-significant biomarkers. The stage-salient genes were benchmarked using normals-augmented dataset, and cross-referenced with existing knowledge. The candidate biomarkers were used to construct the feature space for learning an optimal model for the digital screening of early-stage colorectal cancers. The candidate biomarkers were also examined for constructing a prognostic model based on survival analysis. Among the biomarkers identified are: CRLF1, CALB2, STAC2, UCHL1, KCNG1 (stage-I salient), KLHL34, LPHN3, GREM2, ADCY5, PLAC2, DMRT3 (stage-II salient), PIGR, HABP2, SLC26A9 (stage-III salient), GABRD, DKK1, DLX3, CST6, HOTAIR (stage-IV salient), and CDH3, KRT80, AADACL2, OTOP2, FAM135B, HSP90AB1 (top linear model genes). In particular the study yielded 31 genes that are progression-significant such as ESM1, DKK1, SPDYC, IGFBP1, BIRC7, NKD1, CXCL13, VGLL1, PLAC1, SPERT, UPK2, and interestingly three members of the LY6G6 family. Significant monotonic linear model genes included HIGD1A, ACADS, PEX26, and SPIB. A feature space of just seven biomarkers, namely ESM1, DHRS7C, OTOP3, AADACL2, LPHN3, GABRD, and LPAR1, was sufficient to optimize a RandomForest model that achieved > 98% balanced accuracy (and performant recall) of cancer vs. normal on external validation. Design of an optimal multivariate model based on survival analysis yielded a prognostic panel of three stage-IV salient genes, namely HOTAIR, GABRD, and DKK1. Based on the above sparse signatures, we have developed COADREADx, a web-server for potentially assisting colorectal cancer screening and patient risk stratification. COADREADx provides uncertainty measures for its predictions and needs clinical validation. It has been deployed for experimental non-commercial use at: https://apalanialab.shinyapps.io/coadreadx/.

Summer roost site selection of a declining bat species

AbstractSummer habitats are critical to bat population persistence as they support multiple life history stages, including maternity colonies, nursery sites, and foraging locations. The tricolored bat (Perimyotis subflavus) is a hibernating North American bat species that uses forested landscapes during summer months; however, information on the summer habitat requirements is limited. The objective of this work was to quantify the characteristics of roost sites selected by tricolored bats during summer months. We captured, tagged, and tracked 15 bats using radio-telemetry to 55 roost locations. At each roost, we recorded roost habitat characteristics and other forest characteristics within a 0.1 ha circular plot surrounding the roost tree and a 1 km buffer at the landscape scale. We repeated these measurements for three random trees per roost tree to characterize available habitat for selection. We used a suite of mixed conditional logistic regression models to test multiple factors known to influence roost-site selection for various bat species and compared using Akaike information criterion to select the best model. The top model at the roost scale demonstrated that roost selection was influenced by roost tree height, while the landscape scale was influenced by deciduous forest and distance to roads. There is a critical information gap for the ongoing recovery of tricolored bats; better understanding of summer habitat and proper forest management implications, as well as information on scale-specific habitat selection, is needed to better understand tricolored bat management needs.

Linking summer nutrition to behavior and performance of black‐tailed deer

AbstractMany large‐herbivore populations are regulated at least in part by bottom‐up forces, and thus relationships between herbivores and their habitat are of fundamental importance to wildlife managers. Variation in nutritional resources—and how herbivores respond to that variation—influences rates of nutrient intake, which directly affect nutritional condition, pregnancy rates, timing of parturition, offspring birth mass, and survival. Accordingly, nutrition‐focused research holds great potential for uncovering the mechanisms that govern population performance of large herbivores and for assessing the nature and magnitude of bottom‐up limitations. We quantified relationships between the foodscape (i.e., spatiotemporal variation in forage quality and abundance) in southwestern Oregon, USA, and black‐tailed deer (Odocoileus hemionus columbianus) behavior and performance, with a focus on the influence of maternal nutrition on fawn survival. We hypothesized that black‐tailed deer performance (i.e., fawn birth mass and survival) is influenced by the availability of high‐quality forage during spring and summer and patterns of foodscape use exhibited by individual deer. From 2016–2023 we monitored movement and survival of adult female black‐tailed deer and their offspring. We also conducted intensive vegetation sampling and used generalized additive modeling to map the foodscape available to deer in spring and summer. Suitable forage biomass (i.e., maximum biomass of forage that together exceeded quality thresholds for supporting one fawn) was highly variable across space and time, and our top foodscape model explained 70% of the variation in suitable biomass (adjusted R2 = 0.70). We observed a strong, positive relationship between use of the foodscape by maternal females prior to parturition and fawn birth mass. Although maternal foodscape use after parturition did not influence the probability of fawn survival, survival increased with increasing birth mass. These results suggest that the effects of nutrition on fawn survival in our study system are indirectly mediated by maternal behavior (i.e., use of the foodscape) and the corresponding effects on birth mass of fawns. Our study adds to a growing body of literature supporting a fundamental link between foodscape use and population performance of large herbivores. Wildlife managers can use the dynamic models we developed to assess habitat quality and to make quantitative predictions about how different management actions (e.g., forest thinning) are likely to influence habitat quality and performance of black‐tailed deer.

UTran-DSR: a novel transformer-based model using feature enhancement for dysarthric speech recognition

Over the past decade, the prevalence of neurological diseases has significantly risen due to population growth and aging. Individuals suffering from spastic paralysis, brain attack, and idiopathic Parkinson’s disease (PD), among other neurological illnesses, commonly suffer from dysarthria. Early detection and treatment of dysarthria in these patients are essential for effectively managing the progression of their disease. This paper provides UTrans-DSR, a novel encoder-decoder architecture for analyzing Mel-spectrograms (generated from audios) and classifying speech as healthy or dysarthric. Our model employs transformer encoder features based on a hybrid design, which includes the feature enhancement block (FEB) and the vision transformer (ViT) encoders. This combination effectively extracts global and local pixel information regarding localization while optimizing the mel-spectrograms feature extraction process. We keep up with the original class-token grouping sequence in the vision transformer while generating a new equivalent expanding route. More specifically, two unique growing pathways use a deep-supervision approach to increase spatial data recovery and expedite model convergence. We add consecutive residual connections to the system to reduce feature loss while increasing spatial data retrieval. Our technique is based on identifying gaps in mel-spectrograms distinguishing between normal and dysarthric speech. We conducted several experiments on UTrans-DSR using the UA speech and TORGO datasets, and it outperformed the existing top models. The model performed significantly in pixel’s localized and spatial feature extraction, effectively detecting and classifying spectral gaps. The Tran-DSR model outperforms previous research models, achieving an accuracy of 97.75%.

Predicting CDR status over 36 months with a recall-based digital cognitive biomarker.

Word-list recall tests are routinely used for cognitive assessment, and process scoring may improve their accuracy. We examined whether Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-Cog) derived, process-based digital cognitive biomarkers (DCBs) at baseline predicted Clinical Dementia Rating (CDR) longitudinally and compared them to standard metrics. Analyses were performed with Alzheimer's Disease Neuroimaging Initiative (ADNI) data from 330 participants (mean age=71.4±7.2). We conducted regression analyses predicting CDR at 36 months, controlling for demographics and genetic risk, with ADAS-Cog traditional scores and DCBs as predictors. The best predictor of CDR at 36 months was M, a DCB reflecting recall ability (area under the curve =0.84), outperforming traditional scores. Diagnostic results suggest that M may be particularly useful to identify individuals who are unlikely to decline. These results suggest that M outperforms ADAS-Cog traditional metrics and supports process scoring for word-list recall tests. More research is needed to determine further applicability with other tests and populations. Process scoring and latent modeling were more effective than traditional scoring. Latent recall ability (M) was the best predictor of Clinical Dementia Rating decline at 36 months. The top digital cognitive biomarker model had odds ≈ 90 times greater than the top Alzheimer's Disease Assessment Scale-Cognitive subscale model. Particularly high negative predictive value supports literature on cognitive testing as a useful screen. Consideration of both cognitive and pathological outcomes is needed.

Afterglow Linear Polarization Signatures from Shallow GRB Jets: Implications for Energetic GRBs

Gamma-ray bursts (GRBs) are powered by ultrarelativistic jets. The launching sites of these jets are surrounded by dense media, which the jets must cross before they can accelerate and release high-energy emission. Interaction with the medium leads to the formation of a mildly relativistic sheath around the jet, resulting in an angular structure to the jet’s asymptotic Lorentz factor and energy per solid angle, which modifies the afterglow emission. We build a semi-analytical tool to analyze the afterglow light curve and polarization signatures of jets observed from a wide range of viewing angles, and focus on ones with slowly declining energy profiles known as shallow jets. We find overall lower polarization compared to the classical top hat jet model. We provide an analytical expression for the peak polarization degree as a function of the energy profile power-law index, magnetic field configuration, and viewing angle, and show that it occurs near the light-curve break time for all viewers. When applying our tool to GRB 221009A, suspected to originate from a shallow jet, we find that the suggested jet structures for this event agree with the upper limits placed on the afterglow polarization in the optical and X-ray bands. We also find that at early times the polarization levels may be significantly higher, allowing for a potential distinction between different jet structure models and possibly constraining the magnetization in both forward and reverse shocks at that stage.

Multi-Scale Information Fusion and Decoupled Representation Learning for Robust Microbe-Disease Interaction Prediction

Research indicates that microbe activity within the human body significantly influences health by being closely linked to various diseases. Accurately predicting microbe-disease interactions (MDIs) offers critical insights for disease intervention and pharmaceutical research. Current advanced AI-based technologies automatically generate robust representations of microbes and diseases, enabling effective MDI predictions. However, these models continue to face significant challenges. A major issue is their reliance on complex feature extractors and classifiers, which substantially diminishes the models’ generalizability. To address this, we introduce a novel graph autoencoder framework that utilizes decoupled representation learning and multi-scale information fusion strategies to efficiently infer potential MDIs. Initially, we randomly mask portions of the input microbe-disease graph based on Bernoulli distribution to boost self-supervised training and minimize noise-related performance degradation. Secondly, we employ decoupled representation learning technology, compelling the graph neural network (GNN) to independently learn the weights for each feature subspace, thus enhancing its expressive power. Finally, we implement multi-scale information fusion technology to amalgamate the multi-layer outputs of GNN, reducing information loss due to occlusion. Extensive experiments on public datasets demonstrate that our model significantly surpasses existing top MDI prediction models. This indicates that our model can accurately predict unknown MDIs and is likely to aid in disease discovery and precision pharmaceutical research. Code and data are accessible at: https://github.com/shmildsj/MDI-IFDRL.

A Top Model of Urban Residents’ Subjective Well-Being in China

Past research on subjective well-being (SWB) in Western society and China has revealed various contributing factors. This study conducted a series of interviews with Chinese urban residents to systematically analyze those factors based on Bronfenbrenner’s Ecological System Theory. The results revealed that 5 layers, 23 factors, and 55 elements, many of which had distinct Chinese characteristics, contributed to Chinese urban residents’ SWB. The Top Model of SWB, visualized as a spinning top or gyroscope, showcased the interdependent layers of factors impacting Chinese urban residents’ SWB. At its vertical axis is a time dimension as residents often reflect on factors by comparing their present situation with the past and the future. Discussion regarding each layer, the primary factors that comprise each layer, links between the layers, and the impact of a time dimension was included to substantiate the proposed model. The discussions focus on the study’s contribution to SWB literature, public policy-making, and future research on urban Chinese residents’ SWB.

Effects of particle size composition and burden ratio on burden segregation in the blast furnace throat based on DEM

In blast furnace charging, burden segregation affects the distribution of gas flow and the efficiency of chemical reactions, which in turn impacts the overall efficiency and quality of ironmaking. This work developed a bell-less top charging model based on the DEM to analyze burden segregation at the furnace throat under various particle size compositions, chute inclinations, and burden ratios. The results show that the burden exhibits a ring-like distribution around the furnace throat, with smaller particles concentrated in the inner ring near the center. When particle size difference of the burden is significant, adjusting the proportion of large or small particles has a more pronounced effect on particle size segregation near the center of the blast furnace. The smaller the chute inclination, the more serious the particle size segregation of the burden. Pellet tends to accumulate near the center of the blast furnace, with a segregation index reaching 0.8 in this region. Adjusting the burden ratio has a more pronounced effect on the segregation of sinter. This work provides theoretical support for charging optimization in blast furnace operation.