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Objective: To evaluate the impact of assistive mobility carts on companion animals and caretakers' quality of life by investigating factors pertaining to caretaker satisfaction, the ability to perform daily tasks, and complication rates. Materials and methods: A 23-question survey was distributed to caretakers of animals using carts to evaluate the animal and caretakers' quality of life, acceptance, ability to complete functional tasks, and complications. Data from canine, feline, and rabbit responses were analyzed separately. Results: Dogs and cats had improved quality of life in 62 and 57% of responses and 61 and 60% for their caretakers, respectively. There was no improvement in the quality of life of rabbits or their caretakers. Regarding the complication rate, 64% were reported to have at least one complication associated with cart use, 53% of which were wounds. Across all species, there was a reported improvement in ability to perform daily tasks and activities. Conclusions and clinical relevance: Caretakers reported that assistive mobility carts improve both companion animals' and caretakers' quality of life, despite high prevalence of complications, including wounds. Future studies exploring specific disease conditions and long-term outcomes will be useful for guiding clinical recommendations. [ABSTRACT FROM AUTHOR]

In dynamic scenarios, the status of a Radio Frequency Identification (RFID) system fluctuates with environmental changes. The key to improving system efficiency lies in the real-time monitoring and evaluation of the system status, along with adaptive adjustments to the system parameters and read algorithms. This paper focuses on the status changes in RFID systems in dynamic scenarios, aiming to enhance system robustness and reading performance, ensuring high link quality, reasonable resource scheduling, and real-time status evaluation under varying conditions. This paper comprehensively considers the system parameter settings in dynamic scenarios, integrating the interaction model between readers and tags. The system's real-time status is evaluated from both the physical layer and the Medium Access Control (MAC) layer perspectives. For the physical layer, a link quality evaluation model based on Uniform Manifold Approximation and Projection (UMAP) and K-Means clustering is proposed from the link quality. For the MAC layer, a multi-criteria decision-making evaluation model based on combined weighting and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is proposed, which comprehensively considers both subjective and objective factors, utilizing the TOPSIS algorithm for an accurate evaluation of the MAC layer system status. For the RFID system, this paper proposes a real-time status evaluation model based on the Classification and Regression Tree (CART), which synthesizes the evaluation results of the physical layer and MAC layer. Finally, engineering tests and verification were conducted on the RFID robot system in mobile scenarios. The results showed that the clustering average silhouette coefficient of the physical layer link quality evaluation model based on K-Means was 0.70184, indicating a relatively good clustering effect. The system status evaluation model of the MAC layer, based on the combined weighting-TOPSIS method, demonstrated good flexibility and generalization. The real-time status evaluation model of the RFID system, based on CART, achieved a classification accuracy of 98.3%, with an algorithm runtime of 0.003 s. Compared with other algorithms, it had a higher classification accuracy and shorter runtime, making it well suited for the real-time evaluation of the RFID robot system's status in dynamic scenarios. [ABSTRACT FROM AUTHOR]

We report on a patient with ciltacabtagene autoleucel-induced movement and neurocognitive toxicity, which was refractory to immunosuppression but responsive to combination dopaminergic therapy (carbidopa/levodopa, ropinirole, amantadine). Response was seen upon both initial treatment and rechallenge after unintended withdrawal. This is the first report of a successful symptomatic treatment of this well-described neurotoxic syndrome. [ABSTRACT FROM AUTHOR]

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness. [ABSTRACT FROM AUTHOR]

Global temperature is increasing at an alarming rate, which increases the number of heatwaves. Heatwaves have significant impacts, both directly and indirectly, on human and natural systems and can create considerable risk to public health. Predicting the occurrence of a heatwave can save lives, increase the production of crops, improve water quality, and reduce transportation restrictions. Because of its geographical location, Bangladesh is particularly vulnerable to cyclones, droughts, earthquakes, floods, and heatwaves. The Bangladesh Meteorological Department collects temperature data at multiple weather stations, and we use data from 10 weather stations in this research. Data show that most heatwaves occur in the summer months, namely, April, May, and June. In this research, we develop Classification and Regression Tree (CART) models that use daily temperature data for the months of March, April, May, and June to predict the likelihood of a heatwave within the next 7 days, the next 28 days, and on any particular day based on daily high temperatures from the previous 14 days. We also use different model parameters to evaluate the accuracy of the models. Finally, we develop treed Stepwise Logistic Regression models to predict the probability of heatwaves occurring. Even though this research uses data from Bangladesh Meteorological Department, the developed modeling approach can be used in other geographic regions. [ABSTRACT FROM AUTHOR]

Decision trees are a fundamental statistical learning tool for addressing classification and regression problems through a recursive partitioning approach that effectively accommodates numerical and categorical data [1-2]. The Classification and regression tree (CART) algorithm underlies modern Boosting methodologies such as Gradient boosting machine (GBM), Extreme gradient boosting (XGBoost), and Light gradient boosting machine (LightGBM). However, the standard CART algorithm may require improvement due to its inability to learn from unlabeled data. This study proposes several modifications to incorporate test data into the training phase. Specifically, we introduce a method based on Graph-based semi-supervised learning called "Distance-based Weighting," which calculates and removes irrelevant records from the training set to accelerate the training process and improve performance. We present Semi-supervised classification and regression tree (Semi-Cart), a new implementation of CART that constructs a decision tree using weighted training data. We evaluated its performance on thirteen datasets from various domains. Our results demonstrate that Semi-Cart outperforms standard CART methods and contributes to statistical learning. [ABSTRACT FROM AUTHOR]

Introduction: The accurate prediction of COVID-19 mortality risk, considering influencing factors, is crucial in guiding effective public policies to alleviate the strain on the healthcare system. As such, this study aimed to assess the efficacy of decision tree algorithms (CART, C5.0, and CHAID) in predicting COVID-19 mortality risk and compare their performance with that of the logistic model. Methods: This retrospective cohort study examined 5080 cases of COVID-19 in Babol, a city in northern Iran, who tested positive for the virus via PCR from March 2020 to March 2022. In order to check the validity of the findings, the data was randomly divided into an 80% training set and a 20% testing set. The prediction models, such as Logistic regression models and decision tree algorithms, were trained on the 80% training data and tested on the 20% testing data. The accuracy of these methods for the test samples was assessed using measures like ROC curve, sensitivity, specificity, and AUC. Results: The findings revealed that the mortality rate for COVID-19 patients who were admitted to hospitals was 7.7%. Through cross validation, it was determined that the CHAID algorithm outperformed other decision tree and logistic regression algorithms in specificity, and precision but not sensitivity in predicting the risk of COVID-19 mortality. The CHAID algorithm demonstrated a specificity, precision, accuracy, and F-score of 0.98, 0.70, 0.95, and 0.52 respectively. All models indicated that factors such as ICU hospitalization, intubation, age, kidney disease, BUN, CRP, WBC, NLR, O2 sat, and hemoglobin were among the factors that influenced the mortality rate of COVID-19 patients. Conclusions: The CART and C5.0 models had outperformed in sensitivity but CHAID demonstrates a better performance compared to other decision tree algorithms in specificity, precision, accuracy and shows a slight improvement over the logistic regression method in predicting the risk of COVID-19 mortality in the population under study. [ABSTRACT FROM AUTHOR]

Parts manufactured by additive manufacturing (AM) are criticized due to the high surface roughness. Roller burnishing is one of the post-processing processes which may be applied for reducing the roughness and asperities on surfaces of 3D printed parts. In this study, innovative zirconium oxide (ZrO2) reinforced acrylonitrile butadiene styrene (ABS) based composites have developed in filament form for 3D printing of roller burnishing rapid tools. The filaments of ABS-ZrO2 were extruded under the range of 230-240 °C of barrel temperature and 4-6 RPM of screw speed under heat treatment as per Taguchi L9 based experimentation. As per the mechanical properties concern of composite filament material, the combination of preheat treatment, 235 °C of barrel temperature, and 6 RPM of screw speed have been noted best setting (Young's modulus: 886.00 MPa). The filament preparation has been supported with such as x-ray diffraction (XRD), Fourier transforms infrared (FTIR), and microstructural analysis using metallurgical image analysis software (MIAS). A machine learning (ML) approach based on classification and regression trees (CART) has been utilized to predict the tensile peak and break strength. Finally, the 3D printed roller burnishing tool has significantly reduced the surface roughness after burnishing. [ABSTRACT FROM AUTHOR]

More than 80 million people worldwide have been infected with the human immunodeficiency virus (HIV). There are now approximately 39 million individuals living with HIV/acquired immunodeficiency syndrome (AIDS). Although treatments against HIV infection are available, AIDS remains a serious disease. Combination antiretroviral therapy (cART), also known as highly active antiretroviral therapy (HAART), consists of treatment with a combination of several antiretroviral drugs that block multiple stages in the virus replication cycle. However, the increasing usage of cART is inevitably associated with the emergence of HIV drug resistance. In addition, the development of persistent cellular reservoirs of latent HIV is a critical obstacle to viral eradication since viral rebound takes place once anti-retroviral therapy (ART) is interrupted. Thus, several efforts are being applied to new generations of drugs, vaccines and new types of cART. In this review, we summarize the antiviral therapies used for the treatment of HIV/AIDS, both as individual agents and as combination therapies, and highlight the role of both macrophages and HIV cellular reservoirs and the most recent clinical studies related to this disease. [ABSTRACT FROM AUTHOR]

Prostate cancer (PCa) poses a significant challenge because of the difficulty in identifying aggressive tumors, leading to overtreatment and missed personalized therapies. Although only 8% of cases progress beyond the prostate, the accurate prediction of aggressiveness remains crucial. Thus, this study focused on studying retinoblastoma phosphorylated at Serine 249 (Phospho-Rb S249), N-cadherin, β-catenin, and E-cadherin as biomarkers for identifying aggressive PCa using a logistic regression model and a classification and regression tree (CART). Using immunohistochemistry (IHC), we targeted the expression of these biomarkers in PCa tissues and correlated their expression with clinicopathological data of the tumor. The results showed a negative correlation between E-cadherin and β-catenin with aggressive tumor behavior, whereas Phospho-Rb S249 and N-cadherin positively correlated with increased tumor aggressiveness. Furthermore, patients were stratified based on Gleason scores and E-cadherin staining patterns to evaluate their capability for early identification of aggressive PCa. Our findings suggest that the classification tree is the most effective method for measuring the utility of these biomarkers in clinical practice, incorporating β-catenin, tumor grade, and Gleason grade as relevant determinants for identifying patients with Gleason scores ≥ 4 + 3. This study could potentially benefit patients with aggressive PCa by enabling early disease detection and closer monitoring. [ABSTRACT FROM AUTHOR]

Abstract Introduction The accurate prediction of COVID-19 mortality risk, considering influencing factors, is crucial in guiding effective public policies to alleviate the strain on the healthcare system. As such, this study aimed to assess the efficacy of decision tree algorithms (CART, C5.0, and CHAID) in predicting COVID-19 mortality risk and compare their performance with that of the logistic model. Methods This retrospective cohort study examined 5080 cases of COVID-19 in Babol, a city in northern Iran, who tested positive for the virus via PCR from March 2020 to March 2022. In order to check the validity of the findings, the data was randomly divided into an 80% training set and a 20% testing set. The prediction models, such as Logistic regression models and decision tree algorithms, were trained on the 80% training data and tested on the 20% testing data. The accuracy of these methods for the test samples was assessed using measures like ROC curve, sensitivity, specificity, and AUC. Results The findings revealed that the mortality rate for COVID-19 patients who were admitted to hospitals was 7.7%. Through cross validation, it was determined that the CHAID algorithm outperformed other decision tree and logistic regression algorithms in specificity, and precision but not sensitivity in predicting the risk of COVID-19 mortality. The CHAID algorithm demonstrated a specificity, precision, accuracy, and F-score of 0.98, 0.70, 0.95, and 0.52 respectively. All models indicated that factors such as ICU hospitalization, intubation, age, kidney disease, BUN, CRP, WBC, NLR, O2 sat, and hemoglobin were among the factors that influenced the mortality rate of COVID-19 patients. Conclusions The CART and C5.0 models had outperformed in sensitivity but CHAID demonstrates a better performance compared to other decision tree algorithms in specificity, precision, accuracy and shows a slight improvement over the logistic regression method in predicting the risk of COVID-19 mortality in the population under study.

The study conducts exhaustive field surveys in 67 wards in Bhubaneswar city, Odisha. 29 factors under 10 aspects have been considered for the study to assess the water requirement per household per day. SEM and CART modeling have been used to estimate the water requirement. The SEM model predicts that 4 aspects, namely, expenses, governance, possession, and resources are the major aspects that decide the water requirement of a household. Similarly, construction and repair costs, energy consumption, reinforcement practices, awareness, presence of a garden, presence of washing machine, presence of other appliances, water charges, and the type of storage majorly affect the water requirement. CART predicts energy consumption, storage, construction and repair, and washing machines to be important estimators with MAPE < 1% for the prediction of water requirement. The study reveals that with proper governance and proper use of water-intensive appliances, the required quantity of water can be decreased in any household. Secondly, by abiding by certain rules while using washing machines, like using them daily or weekly two times, etc., the inequity of water among households can be reduced.

The human immunodeficiency virus (HIV) epidemic is an ongoing global health problem affecting 38 million people worldwide with nearly 1.6 million new infections every year. Despite the advent of combined antiretroviral therapy (cART), a large percentage of people with HIV (PWH) still develop neurological deficits, grouped into the term of HIV-associated neurocognitive disorders (HAND). Investigating the neuropathology of HIV is important for understanding mechanisms associated with cognitive impairment seen in PWH. The major obstacle for studying neuroHIV is the lack of suitable in vitro human culture models that could shed light into the HIV-CNS interactions. Recent advances in induced pluripotent stem cell (iPSC) culture and 3D brain organoid systems have allowed the generation of 2D and 3D culture methods that possess a potential to serve as a model of neurotropic viral diseases, including HIV. In this study, we first generated and characterized several hiPSC lines from healthy human donor skin fibroblast cells. hiPSCs were then used for the generation of microglia-containing human cerebral organoids (hCOs). Once fully characterized, hCOs were infected with HIV-1 in the presence and absence of cART regimens and viral infection was studied by cellular, molecular/biochemical, and virological assays. Our results revealed that hCOs were productively infected with HIV-1 as evident by viral p24-ELISA in culture media, RT-qPCR and RNAscope analysis of viral RNA, as well as ddPCR analysis of proviral HIV-1 in genomic DNA samples. More interestingly, replication and gene expression of HIV-1 were also greatly suppressed by cART in hCOs as early as 7 days post-infections. Our results suggest that hCOs derived from hiPSCs support HIV-1 replication and gene expression and may serve as a unique platform to better understand neuropathology of HIV infection in the brain. [ABSTRACT FROM AUTHOR]

The huge volume of data from the Ice, Cloud and land Elevation Satellite-2 (ICESat-2), designed for mapping polar ice, sea ice, and continental vegetation, requires a highly automated data analysis and reliable terrain classification. In particular, we have developed a method to identify 4 distinct terrain categories in observed terrain, namely ocean, land, sea ice, and ice sheets. This study performed the following efforts: first, the spatial distribution characteristics for each of the 4 categories within individual ICESat-2 "major frames" along the orbit were extracted; second, these features were fed into Classification and Regression Tree (CART) and Random Forest (RF) for training; and lastly, post-processing enhancement was used to improve the classification results. Based on the 76,891 major frame samples (10,764,740 m along track) acquired via various ICESat-2 datasets, the accuracy of the two model were calculated using ten-fold cross-validation. The results indicate that the RF algorithm obtained higher classification accuracy (average accuracy [AA] = 0.9353, overall accuracy [OA] = 0.9342, and Cohen's Kappa coefficient [kappa] = 0.9122) when compared with the CART algorithm (AA = 0.9066, OA = 0.9057, and kappa = 0.8743). Overall, our approach can effectively reduce the workload of human field investigation or visual inspection of altimetry data, improve the accuracy for Earth surface classification, and add to the variety of ways to obtain global surface information from ICESat-2 data. [ABSTRACT FROM AUTHOR]

Interface bonding between asphalt layers plays a vital role in ensuring the proper functionality of pavement structures. Interlayer Shear Strength (ISS) is recognized as an indicator quantifying the interface bonding quality. Consequently, accurate evaluation and prediction of the ISS is imperative in determining the performance of asphalt pavement structures. By conducting laboratory experiments and employing machine learning (ML) techniques, this research aims to predict and assess the ISS in asphalt pavement. In this regard, the classification and regression trees (CART) model was proposed based on measured data collected from laboratory experiments. Three experimental factors of curing temperature, normal stress, and tack coat application rate were selected as variables. The findings showed that the developed CART model explained over 98% of the experimental data in a relatively short period. The curing temperature was found to have the most significant influence on the ISS, followed by normal stress and tack coat dosage. Moreover, a parametric analysis of the interaction effects of input parameters on the ISS revealed that higher curing temperature and lower normal stress reduced the ISS. In contrast, a high tack coat application rate and low normal stress corresponded to a lower ISS of the asphalt pavement. The outcomes of this study could pave the way for the realization of a reliable and efficient design of interlayer bonding between asphalt pavement layers. [ABSTRACT FROM AUTHOR]

The efforts to discover HIV therapeutics have continued since the first human immunodeficiency virus (HIV) infected patient was confirmed in the 1980s. Ten years later, the first HIV drug, zidovudine (AZT), targeting HIV reverse transcriptase, was developed. Meanwhile, scientists were enlightened to discover new drugs that target different HIV genes, like integrase, protease, and host receptors. Combination antiretroviral therapy (cART) is the most feasible medical intervention to suppress the virus in people with HIV (PWH) and control the epidemic. ART treatment has made HIV a chronic infection rather than a fatal disease, but ART does not eliminate latent reservoirs of HIV-1 from the host cells; strict and life-long adherence to ART is required for the therapy to be effective in patients. In this review, we first discussed the scientific history of conventional HIV drug discovery since scientists need to develop more and more drugs to solve drug-resistant issues and release the side effects. Then, we summarized the novel research technologies, like gene editing, applied to HIV treatment and their contributions to eliminating HIV as a complementary therapy. [ABSTRACT FROM AUTHOR]

The current study analyzed the intersecting biophysical, biochemical, and functional properties of extracellular particles (EPs) with the human immunodeficiency virus type‐1 (HIV‐1) beyond the currently accepted size range for HIV‐1. We isolated five fractions (Frac‐A through Frac‐E) from HIV‐infected cells by sequential differential ultracentrifugation (DUC). All fractions showed a heterogeneous size distribution with median particle sizes greater than 100 nm for Frac‐A through Frac‐D but not for Frac‐E, which contained small EPs with an average size well below 50 nm. Synchronized and released cultures contained large infectious EPs in Frac‐A, with markers of amphisomes and viral components. Additionally, Frac‐E uniquely contained EPs positive for CD63, HSP70, and HIV‐1 proteins. Despite its small average size, Frac‐E contained membrane‐protected viral integrase, detectable only after SDS treatment, indicating that it is enclosed in vesicles. Single particle analysis with dSTORM further supported these findings as CD63, HIV‐1 integrase, and the viral surface envelope (Env) glycoprotein (gp) colocalized on the same Frac‐E particles. Surprisingly, Frac‐E EPs were infectious, and infectivity was significantly reduced by immunodepleting Frac‐E with anti‐CD63, indicating the presence of this protein on the surface of infectious small EPs in Frac‐E. To our knowledge, this is the first time that extracellular vesicle (EV) isolation methods have identified infectious small HIV‐1 particles (smHIV‐1) that are under 50 nm. Collectively, our data indicate that the crossroads between EPs and HIV‐1 potentially extend beyond the currently accepted biophysical properties of HIV‐1, which may have further implications for viral pathogenesis. [ABSTRACT FROM AUTHOR]

Soil moisture is the most direct evaluation index for agricultural drought. It is not only directly affected by meteorological conditions such as precipitation and temperature but is also indirectly influenced by environmental factors such as climate zone, surface vegetation type, soil type, elevation, and irrigation conditions. These influencing factors have a complex, nonlinear relationship with soil moisture. It is difficult to accurately describe this non-linear relationship using a single indicator constructed from meteorological data, remote sensing data, and other data. It is also difficult to fully consider environmental factors using a single drought index on a large scale. Machine learning (ML) models provide new technology for nonlinear problems such as soil moisture retrieval. Based on the multi-source drought indexes calculated by meteorological, remote sensing, and land surface model data, and environmental factors, and using the Cubist algorithm based on a classification decision tree (CART), a comprehensive agricultural drought monitoring model at 10 cm, 20 cm, and 50 cm depth in Gansu Province is established. The influence of environmental factors and meteorological factors on the accuracy of the comprehensive model is discussed, and the accuracy of the comprehensive model is evaluated. The results show that the comprehensive model has a significant improvement in accuracy compared to the single variable model, which is a decrease of about 26% and 28% in RMSE and MAPE, respectively, compared to the best MCI model. Environmental factors such as season, DEM, and climate zone, especially the DEM, play a crucial role in improving the accuracy of the integrated model. These three environmental factors can comprehensively reduce the average RMSE of the comprehensive model by about 25%. Compared to environmental factors, meteorological factors have a slightly weaker effect on improving the accuracy of comprehensive models, which is a decrease of about 6.5% in RMSE. The fitting accuracy of the comprehensive model in humid and semi-humid areas, as well as semi-arid and semi-humid areas, is significantly higher than that in arid and semi-arid areas. These research results have important guiding significance for improving the accuracy of agricultural drought monitoring in Gansu Province. [ABSTRACT FROM AUTHOR]

Crop classification plays a pivotal role in agricultural remote sensing, offering critical insights into planting areas, growth monitoring, and yield evaluation. Leveraging the power of Google Earth Engine, this paper centers on the agricultural landscape of Krishna District as its study region. It explores the efficacy of multiple machine learning approaches, specifically Random Forest (RF), Classification and Regression Tree (CART), Naive Bayes, and Support Vector Machine (SVM), in composition of Sentinel-1 and Sentinel-2 satellite imagery for crop categorization. By meticulously assessing and contrasting the evaluations of these four classification methods, the results highlight the efficacy of RF. The overall accuracy (OA) regarding RF classification reaches 0.86, surpassing the results obtained by Naive Bayes (OA = 0.68), CART (OA = 0.63), and SVM (OA = 0.78). This scalable and straightforward classification methodology harnesses the advantages of cloud-based platforms for data handling and analysis. The timely and precise identification in crop typing holds immense importance for monitoring alterations in harvest patterns, estimating yields, and issuing crop safety alerts in the Krishna District and beyond. This paper contributes to the agricultural geospatial sensing domain by providing an innovative approach for accurate crop classification, with broad applications in precision farming and crop management. [ABSTRACT FROM AUTHOR]

Semiconductor chip is the core of the digital economy, has important strategic significance, in recent years, the global semiconductor market size is growing rapidly. At present, the domestic semiconductor industry is still in the late stage relative to the leading enterprises with first-mover advantages, and is in urgent need of rapid and stable development. The international and domestic semiconductor market has been analysed and focuses on the opportunities and challenges currently facing the domestic semiconductor industry. Based on the semiconductor component risk assessment data, the risk value of semiconductor components is modelled and analysed from a microscopic point of view using the CART regression tree method in data mining, and the model accuracy is further verified and improved by XGBoost, which analyses the microscopic influencing factors of semiconductor component risk, and is an important revelation for the construction of a complete semiconductor component risk prediction mechanism in China. [ABSTRACT FROM AUTHOR]

Present study employes a picosecond laser (532 nm) for selective P1 laser scribing on the indium tin oxide (ITO) layer and subsequent fine‐tuning of P1 scribing conditions with machine learning (ML) techniques. Initially, the scribing is performed by varying different laser parameters and further evaluate them via an optical microscope and two probe resistivity measurements. The corresponding scribing width and sheet resistance data are used as input databases for ML analysis. The classification and regression tree (CART)‐based ML analysis revealed that median pulse energy <5.7 μJ insufficient to separate the adjacent scribing regions. While pulse energy >5.7 μJ, APL > 35%, LSO > 46%, and processing speed ≥1250 mm s−1 gives ≥16 μm of scribing width. Further, the decision tree (DT) analysis showed that pulse energy of ≥8.1 μJ, and LSO ≥ 37% are required for electrically isolated lines. The feature importance score suggests that laser fluence and pulse energy determined the scribing width, whereas electrical isolation strongly depends on LSO and processing speed. Finally, the ML achieved conditions experimentally validated and reassessed via scanning electron microscope, and atomic force microscopy aligns well with optical microscope measurements. [ABSTRACT FROM AUTHOR]

This study was carried out to determine the factors affecting the wet grass yield of pea plants grown in Turkey. Wet grass yield was predicted using parameters such as genotype, crude protein, crude ash, acid detergent fiber (ADF), and neutral detergent fiber (NDF) with some data mining algorithms. These techniques provided easily interpretable data trees and precise cutoff values. This led to a comparison of the predictive abilities of data mining methods, including multivariate adaptive regression spline (MARS), Chi-square automatic interaction detection (CHAID), classification and regression tree (CART), and artificial neural network (ANN). To test the compatibility of the data mining algorithms, seven goodness-of-fit criteria were used. The predictive abilities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R2), adjusted R2, root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), Akaike information criterion (AIC), and corrected Akaike information criterion (AICc). With the greatest R2 and adjusted R2 values (0.998 and 0.986) and the lowest values of RMSE, MAPE, SD ratio, AIC, and AICc (10.499, 0.7365, 0.047, 268, and 688, respectively), the MARS method was determined to be the best model for quantifying plant fresh herbage yield. In estimating the fresh herbage production of the pea plant, the results showed that the MARS method was the most appropriate model and a good substitute for other data mining techniques.

ObjectiveTo evaluate the impact of assistive mobility carts on companion animals and caretakers’ quality of life by investigating factors pertaining to caretaker satisfaction, the ability to perform daily tasks, and complication rates.Materials and methodsA 23-question survey was distributed to caretakers of animals using carts to evaluate the animal and caretakers’ quality of life, acceptance, ability to complete functional tasks, and complications. Data from canine, feline, and rabbit responses were analyzed separately.ResultsDogs and cats had improved quality of life in 62 and 57% of responses and 61 and 60% for their caretakers, respectively. There was no improvement in the quality of life of rabbits or their caretakers. Regarding the complication rate, 64% were reported to have at least one complication associated with cart use, 53% of which were wounds. Across all species, there was a reported improvement in ability to perform daily tasks and activities.Conclusions and clinical relevanceCaretakers reported that assistive mobility carts improve both companion animals’ and caretakers’ quality of life, despite high prevalence of complications, including wounds. Future studies exploring specific disease conditions and long-term outcomes will be useful for guiding clinical recommendations.

Biological invasion poses a critical global issue, leading to substantial detrimental impacts on biodiversity, the environment, and the economy. The objective of the study is to offer a thorough understanding of how both climatic and human-induced elements impact the geographic richness of invasive plant species across the Loess Plateau. We evaluate the distribution of invasive plant species at the county level across the Loess Plateau by examining herbarium records from China. We incorporate 16 climatic and anthropogenic variables to depict the local environmental settings. Furthermore, we apply a classification and regression tree approach to investigate the correlation between the richness of invasive plant species and the identified factors. Our study demonstrates that a total of 401 invasive plant species are identified, which are spread across 249 genera and 61 families. Among these, the Asteraceae family stands out as the most prevalent, trailed by Poaceae and Fabaceae. The spatial distribution of invasive plant species richness reveals a notable trend, with the highest frequencies found in the southeastern parts of the region and the lowest in the northwestern areas. It is noteworthy that regions with higher levels of economic advancement tend to harbor a more significant abundance of invasive plant species. The richness of invasive plant species on the Loess Plateau is predominantly shaped by a combination of climatic and human variables, such as annual precipitation, gross domestic product, maximum temperature of warmest month, and minimum temperature of coldest month. To fully comprehend the ecological and biological mechanisms underlying the diversity of invasive plant species on the Loess Plateau, a pioneering conceptual framework has been established. Our study suggests that achieving a harmonious equilibrium among development, conservation, and invasion mitigation is essential for recognizing emerging risks associated with habitat alterations, climate change, and socio-economic advancements in arid regions.

Abstract Little is known about the outcome for HIV-associated Hodgkin lymphoma (HIV-HL) as this is less common than HIV-negative lymphoma. Therefore, we performed a multi-center study to analyze the clinical characteristics and outcomes of HIV-HL patients in China. Nineteen cases of HIV-HL were diagnosed and treated at three center and including the sixth people’s hospital of Zhengzhou, Peking union medical college hospital, and Chongqing university cancer hospital, between December 2013 and June 2022. Data on the clinical features, laboratory results, response, and prognosis were collected and analyzed. The median age at diagnosis was 43(22–74) years. All patients were infected with HIV through sexual transmission, with ten cases transmitted through man having sex with man (MSM) and nine cases transmitted through heterosexual transmission. Seven patients were diagnosed with lymphoma and found to be infected with HIV. Four cases were in stage III, and fifteen cases were in stage IV. After a median follow up of 46.8(4.0-112.9) months, 17 cases were alive after ABVD regimen chemotherapy combined with combination antiretroviral therapy (cART). The 5-year progression-free survival (PFS) and overall survival (OS) rate were 83.9% and 89.5%,respectively. HIV-HL exhibits an invasive process in clinical practice, and cART combined with ABVD regimen chemotherapy can achieve long-term survival for patients.

CART is one of the algorithms in data exploration techniques with decision tree techniques. Unbalanced class proportions in the classification process can cause classification results of minor data to be incorrect. One way to overcome the problem of data imbalance is to use an ensemble bagging algorithm. The bagging algorithm utilizes the resampling method to carry out classification so that it can reduce bias in imbalanced data. The data used is secondary data from Fernandes and Solimun's 2023 research report. The number of sample are 100 respondents that has been valid and reliable. The sample for this research was mothers with toddlers in Wajak village, Malang Regency. The results showed that the ensemble bagging CART method is better at overcoming the problem of imbalance in the proportion of classes with a performance value of accuracy, sensitivity, specificity, and F1-Score values of 85%, 94.1%, 66.7%, and 78%. This research is limited to the Sumberputih Village area. So, the results of this research are only representative for the Wajak District area.

Abstract Background Millions of working equids provide socio-economic support for many low-income communities worldwide. With the prevalence of harness-related wounds reported as higher than 60%, this study aims to describe the equipment used by working equids in three locations of the Ethiopian national regional state of Oromia (Fiche, Bishoftu and Shashamene), and the attitudes and practices of equid owners, users and harness makers regarding work equipment. This mixed-methods study consists of cross-sectional surveying of working equids used for taxi or transport of goods or water, as well as cart-driver questionnaires and focus groups (FG) with working equid stakeholders. Activities conducted with FG included participatory ranking of equipment attributes and equipment drawing exercises. Indicators of equipment design and assembly, as well as cart-driver attitudes and practices were described quantitatively. Associations between equipment characteristics and species, work-type and cart-driver indicators were investigated through univariable logistic regression models, whereas focus group discussions were transcribed and analysed thematically. Results In total, 368 working equid surveys and cart-driver questionnaires were completed and 87 participants took part in nine FG. Equipment composition and characteristics differed considerably from ideal animal draught and harnessing principles described in the literature, with none of the observed harnesses adhering to all principles and thus not considered fully adequate. Various harness compositions were used, with only saddles and breast collars present in all. Donkey equipment had fewer components than that of horses, such as swingle trees (OR 0.02; 95% CI 0.01–0.06; p

Abstract Human recognition and automated image validation are the most widely used approaches to validate the output of binary segmentation methods but, as the number of pixels in an image easily exceeds several million, they become highly demanding from both practical and computational standpoint. We propose a method, called PARSEG, which stands for PArtitioning, Random Selection, Estimation, and Generalization; being the basic steps within this procedure. Suggested method enables us to perform statistical validation of binary images by selecting the minimum number of pixels from the original image to be used for validation without deteriorating the effectiveness of the validation procedure. It utilizes binary classifiers to accomplish image validation and selects the optimal sample of pixels according to a specific objective function. As a result, the computational complexity of the validation experiment is substantially reduced. The procedure’s effectiveness is illustrated by considering images composed of approximately 13 million pixels from the field of seed recognition. PARSEG provides roughly the same precision of the validation process when extended to the entire image, but it utilizes only about 4% of the original number of pixels, thus reducing, by about 90%, the computing time required to validate a binary segmented image.

Familiar and unfamiliar drivers may exhibit different behaviours in response to the road environment. Overall familiarity with the road environment is a human factor believed to play a role in road crash injury severity due to its effect on a driver's decision-making process, reaction time, etc. Hence, there is a need to separately analyse familiar and unfamiliar drivers regarding the injury severity of crashes. Using a six-year database of 30,481 rural two-vehicle crashes in Guilan province, Iran, this research first defined four categories of crashes, reflecting various levels of the involved drivers’ familiarity with the environment (72% of drivers were from the same vs. 28% from a different province). Next, the injury severity of crashes in each familiarity crash category was analysed using both non-parametric (classification and regression trees) and parametric (logistic regression) methods. When both crash parties were unfamiliar, several results are different compared to when both parties were familiar or when ignoring driver familiarity. For instance, young at-fault drivers increased the injury severity of crashes if they were unfamiliar, while they decreased the crash severity if they were familiar. Also, crashes in winter tended to be more severe when one or especially both crash parties were unfamiliar, but winter crashes were less severe when both drivers were familiar or when driver familiarity was ignored. Overall, when both drivers were familiar, 63% of crashes were injury/fatal; however, when both drivers were unfamiliar, only 31% of crashes involved an injury or fatality.

Pou6f2 is a genetic connection between central corneal thickness (CCT) in the mouse and a risk factor for developing primary open-angle glaucoma. POU6F2 is also a risk factor for several conditions in humans, including glaucoma, myopia, and dyslexia. Recent findings demonstrate that POU6F2-positive retinal ganglion cells (RGCs) comprise a number of RGC subtypes in the mouse, some of which also co-stain for Cdh6 and Hoxd10. These POU6F2-positive RGCs appear to be novel of ON–OFF directionally selective ganglion cells (ooDSGCs) that do not co-stain with CART or SATB2 (typical ooDSGCs markers). These POU6F2-positive cells are sensitive to damage caused by elevated intraocular pressure. In the DBA/2J mouse glaucoma model, heavily-labeled POU6F2 RGCs decrease by 73% at 8 months of age compared to only 22% loss of total RGCs (labeled with RBPMS). Additionally, Pou6f2−/− mice suffer a significant loss of acuity and spatial contrast sensitivity along with an 11.4% loss of total RGCs. In the rhesus macaque retina, POU6F2 labels the large parasol ganglion cells that form the magnocellular (M) pathway. The association of POU6F2 with the M-pathway may reveal in part its role in human glaucoma, myopia, and dyslexia. [ABSTRACT FROM AUTHOR]

Machine learning algorithms often contain many hyperparameters whose values affect the predictive performance of the induced models in intricate ways. Due to the high number of possibilities for these hyperparameter configurations and their complex interactions, it is common to use optimization techniques to find settings that lead to high predictive performance. However, insights into efficiently exploring this vast space of configurations and dealing with the trade-off between predictive and runtime performance remain challenging. Furthermore, there are cases where the default hyperparameters fit the suitable configuration. Additionally, for many reasons, including model validation and attendance to new legislation, there is an increasing interest in interpretable models, such as those created by the decision tree (DT) induction algorithms. This paper provides a comprehensive approach for investigating the effects of hyperparameter tuning for the two DT induction algorithms most often used, CART and C4.5. DT induction algorithms present high predictive performance and interpretable classification models, though many hyperparameters need to be adjusted. Experiments were carried out with different tuning strategies to induce models and to evaluate hyperparameters' relevance using 94 classification datasets from OpenML. The experimental results point out that different hyperparameter profiles for the tuning of each algorithm provide statistically significant improvements in most of the datasets for CART, but only in one-third for C4.5. Although different algorithms may present different tuning scenarios, the tuning techniques generally required few evaluations to find accurate solutions. Furthermore, the best technique for all the algorithms was the Irace. Finally, we found out that tuning a specific small subset of hyperparameters is a good alternative for achieving optimal predictive performance. [ABSTRACT FROM AUTHOR]

Machine safety is not only a prerequisite for successful production but also the foundation for the sustainability and growth of any manufacturing organization. The latest approaches in this rapidly developing field integrate effective risk management tools and strategies into occupational health and safety (OHS) management systems. The study, through a real example from practice, describes the use of the analytic hierarchy process (AHP) method for machine safety improvement, considering the possible types of losses. Classification and Regression Tree Analysis (CART) was applied to assess the efficiency, cost-effectiveness, and, therefore, the overall sustainability level of the relevant safety measures. These were proposed risk reduction measures that typically raised uncertainty among managers regarding the estimation of cost-effectiveness. The advantage of the application decision tree approach is the possibility to identify and establish relatively homogeneous groups of undesirable events and their impact on the organization's objectives. A comprehensive model has been developed to support management decision making in manufacturing organizations towards implementing and improving safety measures in line with manufacturing sustainability goals. [ABSTRACT FROM AUTHOR]

Little is known about the outcome for HIV-associated Hodgkin lymphoma (HIV-HL) as this is less common than HIV-negative lymphoma. Therefore, we performed a multi-center study to analyze the clinical characteristics and outcomes of HIV-HL patients in China. Nineteen cases of HIV-HL were diagnosed and treated at three center and including the sixth people's hospital of Zhengzhou, Peking union medical college hospital, and Chongqing university cancer hospital, between December 2013 and June 2022. Data on the clinical features, laboratory results, response, and prognosis were collected and analyzed. The median age at diagnosis was 43(22–74) years. All patients were infected with HIV through sexual transmission, with ten cases transmitted through man having sex with man (MSM) and nine cases transmitted through heterosexual transmission. Seven patients were diagnosed with lymphoma and found to be infected with HIV. Four cases were in stage III, and fifteen cases were in stage IV. After a median follow up of 46.8(4.0-112.9) months, 17 cases were alive after ABVD regimen chemotherapy combined with combination antiretroviral therapy (cART). The 5-year progression-free survival (PFS) and overall survival (OS) rate were 83.9% and 89.5%,respectively. HIV-HL exhibits an invasive process in clinical practice, and cART combined with ABVD regimen chemotherapy can achieve long-term survival for patients. [ABSTRACT FROM AUTHOR]

Background: Millions of working equids provide socio-economic support for many low-income communities worldwide. With the prevalence of harness-related wounds reported as higher than 60%, this study aims to describe the equipment used by working equids in three locations of the Ethiopian national regional state of Oromia (Fiche, Bishoftu and Shashamene), and the attitudes and practices of equid owners, users and harness makers regarding work equipment. This mixed-methods study consists of cross-sectional surveying of working equids used for taxi or transport of goods or water, as well as cart-driver questionnaires and focus groups (FG) with working equid stakeholders. Activities conducted with FG included participatory ranking of equipment attributes and equipment drawing exercises. Indicators of equipment design and assembly, as well as cart-driver attitudes and practices were described quantitatively. Associations between equipment characteristics and species, work-type and cart-driver indicators were investigated through univariable logistic regression models, whereas focus group discussions were transcribed and analysed thematically. Results: In total, 368 working equid surveys and cart-driver questionnaires were completed and 87 participants took part in nine FG. Equipment composition and characteristics differed considerably from ideal animal draught and harnessing principles described in the literature, with none of the observed harnesses adhering to all principles and thus not considered fully adequate. Various harness compositions were used, with only saddles and breast collars present in all. Donkey equipment had fewer components than that of horses, such as swingle trees (OR 0.02; 95% CI 0.01–0.06; p < 0.001) or girths (OR 0.09; 95%CI 0.02–0.4; p = 0.002). Horse equipment was more likely to have functional elements such as breeching (OR 7.8; 95% CI 2.9–20.9; p < 0.001). Of all equipment attributes, FG participants ranked cost, design and impact on animal wellbeing as having the highest importance. Thematic analysis identified motivations and priorities regarding equipment; awareness and knowledge of design and function; barriers to using ideal equipment; and consequences of equipment design and practices as key themes. Conclusions: Various weaknesses of design, assembly and use of equid work equipment were identified. Promoting behavioural change through awareness and training could lead to a sustainable improvement of work equipment quality, access, and prevention of equipment-related problems. [ABSTRACT FROM AUTHOR]

This research was carried out to compare selected forecasting methods, such as the following: Artificial Neural Networks (ANNs), Classification and Regression Trees (CARTs), Chi-squared Automatic Interaction Detector (CHAID), Fuzzy Logic Toolbox (FUZZY), Multivariant Adaptive Regression Splines (MARSs), Regression Trees (RTs), Rough Set Theory (RST), and Support Regression Trees (SRTs), in the context of determining the temperature of brine from vertical ground heat exchangers used by a heat pump heating system. The subject of the analysis was a public building located in Poland, in a temperate continental climate zone. The results of this study indicate that the models based on Rough Set Theory (RST) and Artificial Neural Networks (ANNs) achieved the highest accuracy in predicting brine temperature, with the choice of the preferred method depending on the input variables used for modeling. Using three independent variables (mean outdoor air temperature, month of the heating season, mean solar irradiance), Rough Set Theory (RST) was one of the best models, for which the evaluation rates were as follows: CV RMSE 21.6%, MAE 0.3 °C, MAPE 14.3%, MBE 3.1%, and R2 0.96. By including an additional variable (brine flow rate), Artificial Neural Networks (ANNs) achieved the most accurate predictions. They had the following evaluation rates: CV RMSE 4.6%, MAE 0.05 °C, MAPE 1.7%, MBE 0.4%, and R2 0.99. [ABSTRACT FROM AUTHOR]

Significant changes have been observed from 2019 in the COVID period as study styles shifted from traditional to hybrid learning. This paper predicted the applicability of the hybrid learning mode of education in the programming labs for the informatics faculty. It explored novel vital features that predicted the applicability of hybrid learning in the practical labs of the informatics domain. For this, the Classification and Regression Tree (CART) identified the relevance of hybrid education with an accuracy of 71.7%. The confusion matrix of the CART model has 67.3% correctness (sensitivity) for "Yes", and 76% correctness (specificity) for "No" during validation. The false negative rate (type II error) is 32.7%, and the false positive rate (type I error) is 24%. The AUC value of the CART model is 0.68. Some obstacles were identified, such as isolation from the natural study environment, reduced quality of interaction with other faculty, and turning o ff the mic or camera during labs. Also, it was found that the university initiated and provided support to create less stress and ease of communication. Adopting the Hyrbid learning mode is unsuitable for handling group tasks or projects in the laboratory. Also, the hybrid learning mode proved a vital mode of study in practical labs as online learning with a safe environment. [ABSTRACT FROM AUTHOR]

Flood susceptibility mapping plays a crucial role in flood risk assessment and management. Accurate identification of areas prone to flooding is essential for implementing effective mitigation measures and informing decision-making processes. In this regard, the present study used high-resolution remote sensing products, i.e., synthetic aperture radar (SAR) images for flood inventory preparation and integrated four machine learning models (Random Forest: RF, Classification and Regression Trees: CART, Support Vector Machine: SVM, and Extreme Gradient Boosting: XGBoost) to predict flood susceptibility in Metlili watershed, Morocco. Initially, 12 independent variables (elevation, slope angle, aspect, plan curvature, topographic wetness index, stream power index, distance from streams, distance from roads, lithology, rainfall, land use/land cover, and normalized vegetation index) were used as conditioning factors. The flood inventory dataset was divided into 70% and 30% for training and validation purposes using a popular library, scikit-learn (i.e., train_test_split) in Python programming language. Additionally, the area under the curve (AUC) was used to evaluate the performance of the models. The accuracy assessment results showed that RF, CART, SVM, and XGBoost models predicted flood susceptibility with AUC values of 0.807, 0.780, 0.756, and 0.727, respectively. However, the RF model performed better at flood susceptibility prediction compared to the other models applied. As per this model, 22.49%, 16.02%, 12.67%, 18.10%, and 31.70% areas of the watershed are estimated as being very low, low, moderate, high, and very highly susceptible to flooding, respectively. Therefore, this study showed that the integration of machine learning models with radar data could have promising results in predicting flood susceptibility in the study area and other similar environments. [ABSTRACT FROM AUTHOR]

This article shows that decision trees constructed with Classification and Regression Trees (CART) and C4.5 methodology are consistent for regression and classification tasks, even when the number of predictor variables grows sub-exponentially with the sample size, under natural 0-norm and 1-norm sparsity constraints. The theory applies to a wide range of models, including (ordinary or logistic) additive regression models with component functions that are continuous, of bounded variation, or, more generally, Borel measurable. Consistency holds for arbitrary joint distributions of the predictor variables, thereby accommodating continuous, discrete, and/or dependent data. Finally, we show that these qualitative properties of individual trees are inherited by Breiman's random forests. A key step in the analysis is the establishment of an oracle inequality, which allows for a precise characterization of the goodness of fit and complexity tradeoff for a mis-specified model. for this article are available online. [ABSTRACT FROM AUTHOR]

Abstract The current study analyzed the intersecting biophysical, biochemical, and functional properties of extracellular particles (EPs) with the human immunodeficiency virus type‐1 (HIV‐1) beyond the currently accepted size range for HIV‐1. We isolated five fractions (Frac‐A through Frac‐E) from HIV‐infected cells by sequential differential ultracentrifugation (DUC). All fractions showed a heterogeneous size distribution with median particle sizes greater than 100 nm for Frac‐A through Frac‐D but not for Frac‐E, which contained small EPs with an average size well below 50 nm. Synchronized and released cultures contained large infectious EPs in Frac‐A, with markers of amphisomes and viral components. Additionally, Frac‐E uniquely contained EPs positive for CD63, HSP70, and HIV‐1 proteins. Despite its small average size, Frac‐E contained membrane‐protected viral integrase, detectable only after SDS treatment, indicating that it is enclosed in vesicles. Single particle analysis with dSTORM further supported these findings as CD63, HIV‐1 integrase, and the viral surface envelope (Env) glycoprotein (gp) colocalized on the same Frac‐E particles. Surprisingly, Frac‐E EPs were infectious, and infectivity was significantly reduced by immunodepleting Frac‐E with anti‐CD63, indicating the presence of this protein on the surface of infectious small EPs in Frac‐E. To our knowledge, this is the first time that extracellular vesicle (EV) isolation methods have identified infectious small HIV‐1 particles (smHIV‐1) that are under 50 nm. Collectively, our data indicate that the crossroads between EPs and HIV‐1 potentially extend beyond the currently accepted biophysical properties of HIV‐1, which may have further implications for viral pathogenesis.

Present study employes a picosecond laser (532 nm) for selective P1 laser scribing on the indium tin oxide (ITO) layer and subsequent fine‐tuning of P1 scribing conditions with machine learning (ML) techniques. Initially, the scribing is performed by varying different laser parameters and further evaluate them via an optical microscope and two probe resistivity measurements. The corresponding scribing width and sheet resistance data are used as input databases for ML analysis. The classification and regression tree (CART)‐based ML analysis revealed that median pulse energy 5.7 μJ, APL > 35%, LSO > 46%, and processing speed ≥1250 mm s−1 gives ≥16 μm of scribing width. Further, the decision tree (DT) analysis showed that pulse energy of ≥8.1 μJ, and LSO ≥ 37% are required for electrically isolated lines. The feature importance score suggests that laser fluence and pulse energy determined the scribing width, whereas electrical isolation strongly depends on LSO and processing speed. Finally, the ML achieved conditions experimentally validated and reassessed via scanning electron microscope, and atomic force microscopy aligns well with optical microscope measurements.

ObjectiveCXCR4 (X4)-tropic HIV-1 was found previously to herald CD4 + cell depletion and disease progression in individuals who were antiretroviral-naive or took combination antiretroviral therapy (cART) for less than 5 years. We updated this finding by investigating whether the deleterious effect of X4-tropic strains is mitigated by long-term cART.DesignWe examined morbidity and mortality in relation to HIV-1 tropism and cART in 529 participants followed up to 18 years in the Women's Interagency HIV Study; 91% were women of color.MethodsPlasma-derived HIV-1 tropism was determined genotypically.ResultsWe categorized participants according to the number of visits reported on cART after initiation. Group 1: three or less visits, 74% of these participants reporting no cART; group 2: at least four visits and less than 70% of visits on cART; group 3: at least 70% of visits on cART. AIDS mortality rates for participants in each group with X4 virus compared with those with R5 virus exclusively were, respectively: 62 vs. 40% ( P  = 0.0088); 23% vs. 22% [nonsignificant (NS)]; 7% vs. 14% (NS). Kaplan-Meier curves showed accelerated progression to AIDS death or AIDS-defining illness in participants with three or less cART visits and X4 viruses ( P  = 0.0028) but no difference in progression rates stratified by tropism in other groups. Logistic regression found that HIV-1 suppression for at least 10 semiannual visits (≥5 years total) mitigated X4 tropism's deleterious effect on mortality, controlling for maximal viral load, and CD4 + nadir.ConclusionLong-term cART markedly mitigated the deleterious effect of X4 viruses on AIDS morbidity and mortality. Mitigation was correlated with duration of viral suppression, supporting HIV-1 suppression as a crucial goal.

The use of cumulative incidence functions for characterizing the risk of one type of event in the presence of others has become increasingly popular over the past two decades. The problems of modeling, estimation and inference have been treated using parametric, nonparametric and semi-parametric methods. Efforts to develop suitable extensions of machine learning methods, such as regression trees and ensemble methods, have begun comparatively recently. In this paper, we propose a novel approach to estimating cumulative incidence curves in a competing risks setting using regression trees and associated ensemble estimators. The proposed methods use augmented estimators of the Brier score risk as the primary basis for building and pruning trees, and lead to methods that are easily implemented using existing R packages. Data from the Radiation Therapy Oncology Group (trial 9410) is used to illustrate these new methods.

Background: Late cART initiation (CD4 count ≤200 cells/μL or AIDS-defining opportunistic illnesses [AOIs] at cART initiation) impedes CD4 count recovery and virologic suppression after cART initiation. However, studies to evaluate trends of and modifiable factors for optimal immunological response (IR) and virological response (VR) in people living with HIV (PLWH) with late cART initiation with the current HIV treatment strategies are limited. Methods: We retrospectively identified 475 PLWH with late cART initiation in 2009–2020. Patients were grouped based on the presence of IR (CD4 count ≥200 cells/μL) or VR (plasma viral load [PVL] ≤ 50 copies/mL) within 18 months after cART initiation (403 [84.8%] IR(+) and 72 [15.2%] IR(−); 422 [88.8%] VR(+) and 53 [11.2%] VR(−)). We used Joinpoint regression to identify IR (+) and VR(+) proportion changes. Results: From 2009 to 2020, the proportion of IR(+) patients remained unchanged (75% to 90%, P = 0.102), whereas that of VR(+) patients increased significantly (75% to 95%, P = 0.007). No join point was identified for either IR(+) or VR(+), and the annual percentage change was 0.56% (nonsignificant) and 1.35% (significant) for IR(+) and VR(+), respectively. Compared to IR(−) patients, IR(+) patients were more likely to have a higher pre-cART PVL, to start with a first-line INSTI-based regimen, or to start cART within 14 days of HIV diagnosis but were less likely to have chronic kidney disease, composite AOIs, or a lower pre-cART CD4 count. Compared to VR(−) patients, VR(+) patients were more likely to start a single-tablet regimen but were less likely to have a higher pre-cART PVL. Conclusions: Our study identified several modifiable factors for optimal IR (rapid cART initiation and INSTI-based regimen initiation) and for optimal VR (STR initiation) among late initiators, which may guide early treatment modifications to reduce their AIDS-defining event incidence and mortality.

Water injection is a commonly used development method in oilfields. Water injection systems are large and complex, with horizontally and vertically interconnected pipeline networks often buried underground. Faults in pipeline networks cannot be detected and handled in time, thereby posing significant safety hazards to production. This study focuses on fault diagnosis and decision-making within a water injection system. We established a fault tree for an oilfield water injection system and proposed an optimized BP neural network with a Self-Adaptive Differential Evolution Algorithm for the first time. This method constructs a two-layer fault diagnosis model for a water injection system. The model diagnosed fault positions and types based on parameters such as the fault point flow, pressure, and pipeline flow. Compared with the traditional BP algorithm, this algorithm has better diagnostic accuracy and faster convergence speed. Simultaneously, the decision tree CART method was employed to classify decision types based on multiple parameter indicators of the fault points and generated decisions. We designed and implemented a fault diagnosis and decision platform for an oilfield water injection system. Finally we built an experimental pipeline network model with EPANET to simulate the system fault conditions. The diagnostic performance of the proposed algorithm was tested. The results showed that the proposed method achieved a 99% accuracy rate in diagnosing faults in a water injection system. This method significantly improves the scientific management of water injection systems, holding great potential for broad application and value in achieving smart oilfields.

Credit risk prediction is a critical task in the financial industry, allowing lenders to assess the likelihood of a borrower defaulting on a loan. Traditional machine learning (ML) classifiers have been widely used for this purpose, and they often struggle with imbalanced data and lack interpretability, making it challenging for financial institutions to make informed decisions. This article explores the use of ensemble classifiers and Synthetic minority over-sampling Edited nearest neighbor (SMOTE-ENN) technique in credit risk prediction, aiming to improve the classification performance. The ensemble classifiers include Random Forest, adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM). The study addresses the class imbalance issue by leveraging ensemble classifiers and the SMOTE-ENN technique while employing Shapley additive exPlanations (SHAP) for model interpretability. The experimental results showed that the proposed approach resulted in improved classification performance. Specifically, on the German credit dataset, XGBoost outperformed the other models with a Recall of 0.930 and a Specificity of 0.846, while Random Forest obtained the best performance on the Australian dataset, achieving a Recall of 0.907 and Specificity of 0.922. Additionally, the integration of SHAP enhanced the models’ transparency by providing valuable insights into the contribution of individual features, which is crucial for informed financial decision-making.

Machine learning (ML) has been instrumental in solving complex problems and significantly advancing different areas of our lives. Decision tree-based methods have gained significant popularity among the diverse range of ML algorithms due to their simplicity and interpretability. This paper presents a comprehensive overview of decision trees, including the core concepts, algorithms, applications, their early development to the recent high-performing ensemble algorithms and their mathematical and algorithmic representations, which are lacking in the literature and will be beneficial to ML researchers and industry experts. Some of the algorithms include classification and regression tree (CART), Iterative Dichotomiser 3 (ID3), C4.5, C5.0, Chi-squared Automatic Interaction Detection (CHAID), conditional inference trees, and other tree-based ensemble algorithms, such as random forest, gradient-boosted decision trees, and rotation forest. Their utilisation in recent literature is also discussed, focusing on applications in medical diagnosis and fraud detection.

Greedy search (GS) or exhaustive search plays a crucial role in decision trees and their various extensions. We introduce an alternative splitting method called smooth sigmoid surrogate (SSS) in which the indicator threshold function used in GS is approximated by a smooth sigmoid function. This approach allows for parametric smoothing or regularization of the erratic and discrete GS process, making it more effective in identifying the true cutoff point, particularly in the presence of weak signals, as well as less prone to the inherent end-cut preference problem. Additionally, SSS provides a convenient means of evaluating the best split by referencing a parametric nonlinear model. Moreover, in many variants of recursive partitioning, SSS can be reformulated as a one-dimensional smooth optimization problem, rendering it computationally more efficient than GS. Extensive simulation studies and real data examples are provided to evaluate and demonstrate its effectiveness.

More than 80 million people worldwide have been infected with the human immunodeficiency virus (HIV). There are now approximately 39 million individuals living with HIV/acquired immunodeficiency syndrome (AIDS). Although treatments against HIV infection are available, AIDS remains a serious disease. Combination antiretroviral therapy (cART), also known as highly active antiretroviral therapy (HAART), consists of treatment with a combination of several antiretroviral drugs that block multiple stages in the virus replication cycle. However, the increasing usage of cART is inevitably associated with the emergence of HIV drug resistance. In addition, the development of persistent cellular reservoirs of latent HIV is a critical obstacle to viral eradication since viral rebound takes place once anti-retroviral therapy (ART) is interrupted. Thus, several efforts are being applied to new generations of drugs, vaccines and new types of cART. In this review, we summarize the antiviral therapies used for the treatment of HIV/AIDS, both as individual agents and as combination therapies, and highlight the role of both macrophages and HIV cellular reservoirs and the most recent clinical studies related to this disease.

Crop classification plays a pivotal role in agricultural remote sensing, offering critical insights into planting areas, growth monitoring, and yield evaluation. Leveraging the power of Google Earth Engine, this paper centers on the agricultural landscape of Krishna District as its study region. It explores the efficacy of multiple machine learning approaches, specifically Random Forest (RF), Classification and Regression Tree (CART), Naive Bayes, and Support Vector Machine (SVM), in composition of Sentinel-1 and Sentinel-2 satellite imagery for crop categorization. By meticulously assessing and contrasting the evaluations of these four classification methods, the results highlight the efficacy of RF. The overall accuracy (OA) regarding RF classification reaches 0.86, surpassing the results obtained by Naive Bayes (OA = 0.68), CART (OA = 0.63), and SVM (OA = 0.78). This scalable and straightforward classification methodology harnesses the advantages of cloud-based platforms for data handling and analysis. The timely and precise identification in crop typing holds immense importance for monitoring alterations in harvest patterns, estimating yields, and issuing crop safety alerts in the Krishna District and beyond. This paper contributes to the agricultural geospatial sensing domain by providing an innovative approach for accurate crop classification, with broad applications in precision farming and crop management.