Variations In Characteristics Research Articles

Urban Texture Identification and Characteristic Analysis Based on Percolation Theory—A Case Study of the Second Ring Road Area in Wuhan City

The urban texture is the physical manifestation of the urban form’s evolution. In the rapid process of urbanization, protecting and reshaping the urban texture has become an essential means to sustain the overall form and vitality of cities. Previous studies in this field have primarily relied on image analysis or typological methods, lacking a quantitative approach to identify and analyze the urban texture on a large scale. Moreover, the hierarchical structure and networked organization of the urban texture are gradually being elucidated and emphasized. This study takes a complex network perspective and applies percolation theory to identify and analyze the urban texture. The hierarchical evolutionary characteristics of the urban fabric and the underlying mechanisms driving the scale-dependent differences are analyzed, and the toughness of the urban texture is evaluated based on hierarchical connections. The research findings reveal the key scale in the cross-scale evolution of urban textures, with variations in scale dependence and hierarchical evolution characteristics among different types of urban texture. The traditional urban texture displays sensitivity to scale changes, maintaining its integrity and internal connectivity at small scales. On the other hand, the texture characteristics of modern and contemporary urban areas are only discernible at larger scales. The emergence of large-scale texture clusters at specific scale levels can be explained using multiple index systems. This study has reference significance for the preservation and renewal of the urban fabric in urban renewal processes.

Deep Learning Based Tomato Leaf Disease Detection for Smart Farming

Tomato cultivation has a major role in global agriculture, contributing to both food security and economic stability. Still, tomato plants are susceptive to various diseases, which can specially reduce yield and quality. Plant disease early detection and treatment depend more and more on automatic disease identification and categorization technologies. In this paper, we present a Deep Learning based approach for tomato leaf diseases classification using VGG16 Convolutional Neural Network architecture. The VGG16 model is well-known for its superior picture categorization ability, A data set is used to train the VGG16 that includes healthy tomato leaves and leaves that are contaminated with common diseases like Early Blight, Late Blight, Leaf Mold, and Bacterial Spot. The dataset used in training and assessment comprises a varied assortment of high-resolution images acquired from various sources, guaranteeing the resilience and adaptability of the suggested model. Our utilization of data augmentation methods aims to improve the model's capacity to address variations in leaf characteristics, such as alterations in lighting, background complexity, and leaf positioning. Through experimental findings, we have validated the efficacy of our approach in precisely categorizing tomato leaf diseases with remarkable recall rates and precision. Comparative evaluations against leading techniques highlight the superior classification performance and computational efficiency of the VGG16-based model.

Municipal Wastewater Characterization and Natural Treatment Using Reed Beds

Domestic wastewater, if not correctly disposed of, is a cause of pollution for water-based bodies. The reed bed system is a sort of phytoremediation method used to remove toxins from the environment. This system has a high potential for application in the treatment of household wastewater (WW) because it has good performance as a whole, low operating and maintenance costs, and is not harmful to the environment. This research aimed to study characteristics variation of Grdarasha area WW (located in the left side of Erbil-Kirkuk main road) before and after reed beds. The reed bed system was proven to have a good efficiency in removing Chemical Oxygen Demand (COD) (15. 91-71. 07) %, Total Solid (TS) (8.33-50) %, Total volatile Solid (TVS) (18.8-50) %, and chloride (2.5 – 82.98%). In furtherance of transferring oxygen from the environment, the interplay between pollutant-plants-medium-microbes is critical in pollutant removal utilizing the reed bed technique.

Critical linkages among floodplain hydrology, geomorphology and ecology along a lowland meandering river, Illinois, USA

AbstractThe ecology of forested floodplains is intricately linked to river hydrology through the frequency, magnitude, timing and duration of floodplain inundation. Spatial variability in inundation characteristics is influenced by the geomorphic template of a floodplain, both in terms of the topography of floodplain features and connectivity of these features to the main river channel. Spatial variability in inundation, in turn, has the potential to produce spatial variability in forest ecological characteristics. This study examines the influence of floodplain geomorphic features on spatial variability in inundation frequency as well as the relationship between these geomorphic features and the ecological characteristics of a floodplain forest. The frequencies of floods of different magnitudes are determined from flow‐duration analysis of over 100 years of discharge data for a lowland meandering river in Illinois, USA. Data on discharge, stage, and topography are then used to calibrate a two‐dimensional hydraulic model of flow across the floodplain at different levels of inundation. Integrating the frequency and inundation data yields mapping of average annual inundation frequency for different parts of the floodplain. Significant differences in inundation frequency correspond to three distinct floodplain geomorphic features: secondary channels (frequency = 12%), closed depressions (frequency = 4%) and the floodplain surface (frequency = %). Tree density is similar among the three types of geomorphic features, but tree species composition and canopy density differ significantly between secondary channels and the floodplain surface. The results provide insight into linkages among hydrology, geomorphology and tree characteristics of forested floodplains of lowland meandering rivers.

Ethnic‐racial socialization in Chinese American immigrant families: Associations with middle childhood ethnic identity

AbstractCompared to studies of ethnic identity development in adolescence, fewer investigations have examined the development of ethnic identity during middle childhood, a developmental period of dynamic social and cognitive changes that have direct implications for social identity and group membership. Moreover, research on the developmental processes of Asian American youth has also primarily utilized cross‐group comparisons, while neglecting consideration of variations in acculturation, socioeconomic status, and other sociodemographic characteristics within Asian American ethnic groups. Using a socioeconomically‐ and acculturatively‐diverse sample of Chinese immigrant families in the United States (N = 169; 49.1% girls; M = 9.16 years; SD = 1.05), the present study examined how parents’ ERS practices varied by parents’ socioeconomic status, education, and cultural orientations, then tested associations between parents’ ERS and children's self‐reported ethnic centrality and ethnic regard. More highly‐educated Chinese American immigrant parents reported more frequent cultural socialization and more acculturated parents reported more preparation of their children for racial and ethnic bias. Consistent with hypotheses, parents’ cultural socialization was positively associated with children's self‐reported ethnic centrality and positive regard. Results highlight the roles of social status and acculturation in Chinese immigrant parents’ ERS, and point to middle childhood as a key developmental window for ethnic identity socialization and development in immigrant families.

Machine-learning classifier models for predicting sarcopenia in the elderly based on physical factors.

As the size of the elderly population gradually increases, musculoskeletal disorders, such as sarcopenia, are increasing. Diagnostic techniques such as X-rays, computed tomography, and magnetic resonance imaging are used to predict and diagnose sarcopenia, and methods using machine learning are gradually increasing. This study aimed to create a model that can predict sarcopenia using physical characteristics and activity-related variables without medical diagnostic equipment, such as imaging equipment, for the elderly aged 60 years or older. A sarcopenia prediction model was constructed using public data obtained from the Korea National Health and Nutrition Examination Survey. Models were built using Logistic Regression, Support Vector Machine (SVM), XGBoost, LightGBM, RandomForest, and Multi-layer Perceptron Neural Network (MLP) algorithms, and the feature importance of the models trained with the algorithms, except for SVM and MLP, was analyzed. The sarcopenia prediction model built with the LightGBM algorithm achieved the highest test accuracy, of 0.848. In constructing the LightGBM model, physical characteristic variables such as body mass index, weight, and waist circumference showed high importance, and activity-related variables were also used in constructing the model. The sarcopenia prediction model, which consisted of only physical characteristics and activity-related factors, showed excellent performance. This model has the potential to assist in the early detection of sarcopenia in the elderly, especially in communities with limited access to medical resources or facilities. Geriatr Gerontol Int 2024; ••: ••-••.

Analyzing Variations in Characteristics between Jurisdictions That Regulate Small, Affordable Housing Solutions in the United States

This study analyzes demographic, socioeconomic, housing, and regional characteristics of jurisdictions that permit versus do not permit accessory dwelling units (ADUs), tiny homes, and mobile homes. I use descriptive statistics and t-tests and find large differences between jurisdictions that permit versus do not permit ADUs, modest differences between jurisdictions that permit versus do not permit tiny homes, and somewhat large differences between jurisdictions that permit versus do not permit mobile homes. Jurisdictions that permit ADUs have high proportions of Asians and Hispanics/Latinos and jurisdictions that permit mobile homes have high proportions of Hispanics/Latinos, showing racial and ethnic bifurcation.

Comparative genomic analyses provide insight into the pathogenicity of three Pseudomonas syringae pv. actinidiae strains from Anhui Province, China

BackgroundPseudomonas syringae pv. actinidiae (Psa) is an important bacterial plant pathogen that causes severe damage to the kiwifruit industry worldwide. Three Psa strains were recently obtained from different kiwifruit orchards in Anhui Province, China. The present study mainly focused on the variations in virulence and genome characteristics of these strains based on the pathogenicity assays and comparative genomic analyses.ResultsThree strains were identified as biovar 3 (Psa3), along with strain QSY6 showing higher virulence than JZY2 and YXH1 in pathogenicity assays. The whole genome assembly revealed that each of the three strains had a circular chromosome and a complete plasmid. The chromosome sizes ranged from 6.5 to 6.6 Mb with a GC content of approximately 58.39 to 58.46%, and a predicted number of protein-coding sequences ranging from 5,884 to 6,019. The three strains clustered tightly with 8 Psa3 reference strains in terms of average nucleotide identity (ANI), whole-genome-based phylogenetic analysis, and pangenome analysis, while they were evolutionarily distinct from other biovars (Psa1 and Psa5). Variations were observed in the repertoire of effectors of the type III secretion system among all 15 strains. Moreover, synteny analysis of the three sequenced strains revealed eight genomic regions containing 308 genes exclusively present in the highly virulent strain QSY6. Further investigation of these genes showed that 16 virulence-related genes highlight several key factors, such as effector delivery systems (type III secretion systems) and adherence (type IV pilus), which might be crucial for the virulence of QSY6.ConclusionThree Psa strains were identified and showed variant virulence in kiwifruit plant. Complete genome sequences and comparative genomic analyses further provided a theoretical basis for the potential pathogenic factors responsible for kiwifruit bacterial canker.

Autopsy-based all-cause unnatural mortality during pre-pandemic and pandemic of COVID-19 in Varanasi, India: a retrospective analysis.

The COVID-19 pandemic has led to significant disruptions in healthcare systems worldwide, with Varanasi, India, experiencing profound challenges in managing mortality rates. In order to inform public health initiatives, it is important to comprehend how the pandemic has affected all-cause unnatural death in comparison to pre-pandemic patterns. This retrospective study intended to investigate patterns of all-cause unnatural mortality employing autopsy records of cases from Varanasi's Institute of Medical Sciences, Banaras Hindu University during the pre-pandemic and pandemic period (First and Second wave) of COVID-19. The analysis included 2694 cases of unnatural mortality, such as road traffic accident (RTA), poisoning, hanging and other causes. Demographic, clinical, and circumstantial data were collected and compared between the two time periods, that revealed significant as well as non-significant shifts in all-cause unnatural mortality rates. Whilst certain types of unnatural deaths, such as RTAs, witnessed a non-significant 2.03% (p = 0.34722) decrease, others like hanging exhibited an unexpected significant 3.17% (p = 0.01732) rise, burning and poisoning witnessed a significant 4.18% (p = 0.00026) and 2.37% (p = 0.0271) decline respectively. RTA was the leading cause of mortality both during and before pandemic. Male deaths (79.18%) outweighed female deaths (20.82%) by a more substantial amount throughout research periods. Additionally, variations in demographic characteristics, circumstances surrounding deaths, and healthcare utilization were observed during the pandemic period. The majority of unnatural fatalities occur in the age group of 21-30years old in both pre-pandemic (22.62%) and pandemic conditions (26.65%). This study provides important insights into the secondary effects of the pandemic on unnatural mortality and emphasizes the need for individualized public health. Furthermore, research is warranted to explore the long-term implications and address the associated challenges for healthcare systems and public health initiatives.

Phase change of the Ordovician hydrocarbon in the Tarim Basin: A case study from the Halahatang–Shunbei area

Abstract To clarify the genetic mechanism for phase change of the hydrocarbon in the ultra-deep reservoirs, a case study from the Ordovician hydrocarbon in the Halahatang–Shunbei area (HSA), Tarim Basin, NW China, was conducted. The results show that the Ordovician reservoirs in the HSA are characterized as multi-phase reservoirs with a lateral co-existence of condensates, volatile-oil reservoirs, normal oil reservoirs, and heavy oil reservoirs. From north to south, there are regular variations in the geochemical characteristics of the Ordovician hydrocarbon in different blocks of the HSA, showing an increasing trend in GOR, dryness coefficients, methane contents, methane carbon isotope values, and ethane carbon isotope values, while a decreasing trend in oil densities and wax contents. Because the same Cambrian–Lower Ordovician source for the Ordovician hydrocarbon is observed and the kerogen-cracking gas is dominated in the HSA, the regular variations of the hydrocarbon phases and geochemical characteristics can be interpreted as records of biodegradation and multistage oil–gas filling rather than controlled by the source rock organofacies, oil cracking, and gas invasion. The formation mechanism of the Ordovician multi-phase reservoirs in the HSA suggests that the deep strata of the Tarim Basin hold potential for the exploration of natural gas resources.

Soybean Saponin Content Detection Based on Spectral and Image Information Combination

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination (R2) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests.

The incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial. This study aimed to construct a model to predict cancer risk in patients with type 2 diabetes. This study collected clinical data from a total of 5198 patients. A cancer risk prediction model was established by analyzing 261 items from routine laboratory tests. We screened 107 risk factors from 261 clinical tests based on the importance of the characteristic variables, significance of differences between groups (P< 0.05), and minimum description length algorithm. Compared with 16 machine learning classifiers, five classifiers based on the decision tree algorithm (CatBoost, light gradient boosting, random forest, XGBoost, and gradient boosting) had an area under the receiver operating characteristic curve (AUC) of > 0.80. The AUC for CatBoost was 0.852 (sensitivity: 79.6%; specificity: 83.2%). The constructed model can predict the risk of cancer in patients with type 2 diabetes based on tumor biomarkers and routine tests using machine learning algorithms. This is helpful for early cancer risk screening and prevention to improve patient outcomes.

Precise prediction of CO2 separation performance of metal–organic framework mixed matrix membranes based on feature selection and machine learning

Nowadays, the price of fossil fuels keeps setting new records, escalating continuing concerns about global warming from CO2 production from fuel combustion. As a promising membrane separation technique dealing with carbon capture, metal–organic framework (MOF) mixed matrix membranes (MMMs) have been extensively studied. Herein, a genetic algorithm (GA) optimized artificial neural network (ANN) was developed to form prediction model of MOF MMMs performances towards CO2/N2 separation. The MOF properties, polymer properties, and the operating conditions were used as the characteristic variables. To overcome the limitation, molecular descriptors were incorporated to reflect the physicochemical properties of polymers and target encoding was applied to digitalize the MOF and polymer types. In addition, recursive feature elimination algorithm was used to filter the optimal feature subset and Shapley additive explanations was utilized to analyze the feature importance. The results demonstrated that the model has a dramatically improved prediction performance than other machine learning methods.

Measuring protective efficacy and quantifying the impact of drug resistance: A novel malaria chemoprevention trial design and methodology.

Recently revised WHO guidelines on malaria chemoprevention have opened the door to more tailored implementation. Countries face choices on whether to replace old drugs, target additional age groups, and adapt delivery schedules according to local drug resistance levels and malaria transmission patterns. Regular routine assessment of protective efficacy of chemoprevention is key. Here, we apply a novel modelling approach to aid the design and analysis of chemoprevention trials and generate measures of protection that can be applied across a range of transmission settings. We developed a model of genotype-specific drug protection, which accounts for underlying risk of infection and circulating genotypes. Using a Bayesian framework, we fitted the model to multiple simulated scenarios to explore variations in study design, setting, and participant characteristics. We find that a placebo or control group with no drug protection is valuable but not always feasible. An alternative approach is a single-arm trial with an extended follow-up (>42 days), which allows measurement of the underlying infection risk after drug protection wanes, as long as transmission is relatively constant. We show that the currently recommended 28-day follow-up in a single-arm trial results in low precision of estimated 30-day chemoprevention efficacy and low power in determining genotype differences of 12 days in the duration of protection (power = 1.4%). Extending follow-up to 42 days increased precision and power (71.5%) in settings with constant transmission over this time period. However, in settings of unstable transmission, protective efficacy in a single-arm trial was overestimated by 24.3% if recruitment occurred during increasing transmission and underestimated by 15.8% when recruitment occurred during declining transmission. Protective efficacy was estimated with greater precision in high transmission settings, and power to detect differences by resistance genotype was lower in scenarios where the resistant genotype was either rare or too common. These findings have important implications for the current guidelines on chemoprevention efficacy studies and will be valuable for informing where these studies should be optimally placed. The results underscore the need for a comparator group in seasonal settings and provide evidence that the extension of follow-up in single-arm trials improves the accuracy of measures of protective efficacy in settings with more stable transmission. Extension of follow-up may pose logistical challenges to trial feasibility and associated costs. However, these studies may not need to be repeated multiple times, as the estimates of drug protection against different genotypes can be applied to different settings by adjusting for transmission intensity and frequency of resistance.

Implementation of a New Smart-Lubricant Model Using Generalized Newtonian Approach in Soft Elastohydrodynamic Lubrication

Abstract This is an inceptive attempt to replace the classical Bingham fluid model with a continuous double Newtonian power-law-based constitutive equation for smart lubricants like magneto-rheological, electro-rheological, and ferro-fluids. The implementation of Bingham model in hydrodynamic (HD) and elastohydrodynamic (EHD) lubrication analyses is highly challenging and inconvenient due to its inherent discontinuity. Therefore, the present work demonstrates the use of an already existing rheological model with an appropriate set of parameters to describe the flow behavior of smart lubricants in a soft-EHD lubrication algorithm based on the generalized Newtonian approach. The formation of both floating and adherent cores validates the proposed model. An extensive parametric study is also performed to explore the effects of operating speed, load, and slide-to-roll ratio on the soft-EHL characteristics. The results are very promising, showing that it's possible to customize smart lubricants to match specific operating conditions. This is achieved by adjusting the yield stress value accordingly, allowing for the desired variation in lubrication characteristics.

Building benchmarking and energy performance: Analysis of social and affordable housing in British Columbia, Canada

This research assesses the energy performance of social and affordable housing by conducting quantitative analysis of 23 community housing buildings in British Columbia (BC) using ENERGY STAR Portfolio Manager. The study sampled community housing buildings, tracked utility bills from 2019 to 2022, and analyzed weather-normalized energy consumption. We find that high-rise buildings in our dataset performed worse compared to other buildings in the dataset and to market housing buildings in BC. We attribute these differences to variability in building characteristics, construction methods, and mechanical systems. Results also show that average energy consumption per dwelling unit in community housing buildings is lower than the BC average, indicating that residents in social and affordable housing use less energy. This could be linked to the lack of amenities in community housing like air conditioning as well as possible efforts taken by residents to reduce utility bills by using less energy. These results demonstrate that it is important to retrofit these buildings for the sake of building and environmental performance but also, and perhaps more critically, to improve the living conditions of those who live in social and affordable housing. Limited understanding of the influence of occupant behaviour on energy performance is a study limitation warranting further study.

Practical Comparison of Two- and Three-Phase Bearingless Permanent Magnet Slice Motors for Blood Pumps

The majority of bearingless permanent magnet slice motors (BPMSMs) used in commercially available rotary blood pumps use a two-phase configuration, but it is unclear as to whether or not a comparable three-phase configuration would offer a better performance. This study compares the performance of two-phase and three-phase BPMSM configurations. Initially, two nominal designs were manufactured and empirically tested for their performance characteristics, namely, the axial stiffness, radial stiffness, and current force. Subsequently, finite element analysis (FEA) models were developed based on these nominal devices and validated against the empirical results. Simulations were then employed to assess the sensitivity of performance characteristics to variations in seven different geometric features of the models for both configurations. Our findings indicate that the nominal three-phase design had a higher axial stiffness and radial stiffness, but resulted in a lower axial-to-radial-stiffness ratio when compared to the nominal two-phase design. Additionally, while the nominal two-phase design shows a higher current force, the nominal three-phase design proves to be slightly superior when the force generated is considered relative to the power usage. Notably, the three-phase configuration demonstrates a greater sensitivity to dimensional changes in the geometric features. We observed that alterations in the air gap and rotor length lead to the most significant variations in performance characteristics. Although most changes in specific geometric features entail equal tradeoffs, increasing the head protrusion positively influences the overall performance. Moreover, we illustrated the interdependent nature of the head height and rotor height on the performance characteristics. Overall, this study delineates the strengths and weaknesses of each configuration, while also providing general insights into the relationship between specific geometric features and performance characteristics of BPMSMs.

A Novel Hybrid Deep Learning Model for Complex Systems: A Case of Train Delay Prediction

Predicting the status of train delays, a complex and dynamic problem, is crucial for railway enterprises and passengers. This paper proposes a novel hybrid deep learning model composed of convolutional neural networks (CNN) and temporal convolutional networks (TCN), named the CNN + TCN model, for predicting train delays in railway systems. First, we construct 3D data containing the spatiotemporal characteristics of real-world train data. Then, the CNN + TCN model employs a 3D CNN component, which is fed into the constructed 3D data to mine the spatiotemporal characteristics, and a TCN component that captures the temporal characteristics in railway operation data. Furthermore, the characteristic variables corresponding to the two components are selected. Finally, the model is evaluated by leveraging data from two railway lines in the United Kingdom. Numerical results show that the CNN + TCN model has greater accuracy and convergence performance in train delay prediction.

Association between age, gender, and oral traumatic ulcerative lesions: a retrospective study

BackgroundOral traumatic ulcerative lesions (OTUL) are commonly encountered in clinical practice, yet there is limited research on their clinical characteristics and traumatic etiological factors. This retrospective study aimed to analyze the age, gender, clinical characteristics, and traumatic etiological factors in a large cohort of patients with OTUL and provide valuable insights for dental clinicians to optimize patient care and prevention strategies.MethodsA total of 1543 patients with OTUL were enrolled in this study. Age, gender, medical history, clinical characteristics and traumatic etiological factors were collected and analyzed. Logistic regression analysis was performed to determine the significance of age and gender as factors related to OTUL.ResultsThe study revealed significant variations in clinical characteristics and traumatic etiological factors among different age groups and between genders. Logistic regression analysis demonstrated that both age and gender were significant factors related to OTUL.ConclusionThe clinical characteristics of OTUL and traumatic etiological factors appear to be significantly different according to age and gender. More targeted prevention strategies should be implemented for all age and gender groups.

A novel approach for location selection in logistics: Ensemble SVM and improved genetic algorithm

SummaryGathering logistics operations in an individual location offers various benefits at the macro level namely reducing environmental and community issues, replacing overflow of traffic, minimizing air pollution, and more. But the complexity in the selection of location is maximized and the logistic operation chooses the wrong location due to frequent variations in characteristics. Also, it is suitable for capturing long‐term dependencies. To overcome these difficulties, we propose ESVM‐IGS; an Ensemble support vector machine, and an Improved Genetic algorithm, with an initial search strategy to improve the efficiency. The Ensemble SVM is applied to produce a better outcome. To identify optimal configurations in the complex optimization problem an initial search‐based improved genetic algorithm is implemented. To conduct our experiments, the ESVM‐IGS is rigorously evaluated on the GIS real‐time Dataset and the efficiency of the model is validated with various performance measures. From the analysis, the proposed method results that it solved the complexity burden and improved the selection ability of long‐term dependency. The experimental results depict the better efficiency of the ESVM‐IGS method for the location selection strategy of logistics.