Random effect generalized linear model-based predictive modelling of traffic noise.

Noise pollution is one of the negative consequences of growth and development in cities. Traffic noise pollution due to traffic growth is the main aspect that worsens city quality of life. Therefore, research around the world is being conducted to manage and reduce traffic noise. A number of traffic noise prediction models have been proposed employing fixed effect modelling approach considering each observation as independent; however, observations may have spatial and temporal correlations and unobserved heterogeneity. Random effect models overcome these problems. This study attempts to develop a random effect generalized linear model (REGLM) along with a machine learning random forest (RF) model to validate the results, concerning the parameters related to road, traffic and environmental conditions. Models were developed based on the experimental quantities in Delhi in year 2022-2023. Both the models performed comparably well in terms of coefficient of determination. Random forest models with R2= 0.75, whereas random effect generalized linear model had an R2= 0.70. REGLM model has the ability to quantify the effects of explanatory variables over traffic noise pollution and will be more helpful in prioritizing of resources and chalking out control strategies.

The "Organic" Descriptor and Its Association With Commercial Cigarette Health Risk Expectancies, Subjective Effects, and Smoking Topography: A Pilot Human Laboratory Study.

INTRODUCTION: The purpose of this mixed-method pilot study was to: (1) examine whether the "organic" descriptor affects smokers' health risk expectancies, subjective ratings of smoking, and topography, and (2) describe how smokers interpret the "organic" descriptor and relate it to their subjective smoking experience. METHODS: Twenty-two daily smokers (45.5% men, 81.8% non-Hispanic White, M (SD) age = 47.3 [12.7], M (SD) cigarettes/day = 14.5 [5.1]) completed a within-person laboratory study. Following a baseline session, smokers attended 2 experimental sessions where they smoked a study-provided cigarette (identical across conditions) paired with either an "organic" or conventional (e.g., no "organic") descriptor condition and completed subjective and behavioral measures. Participants completed a semi-structured interview at the last visit. RESULTS: Relative to the conventional cigarette, more participants rated the "organic" cigarette as healthier, having fewer chemicals, and having a more favorable burn rate (P's < 0.05). There were no differences in total puff volume by condition (P = 0.42). Stratifying by gender, men inhaled 225 ml (SE = 82.7) more in the conventional condition (P = 0.02); women inhaled 408 ml (SE = 233.3) more in the organic condition (P = 0.11). A common understanding of "organic" was that the product was "…less processed... like less chemicals and it's more natural." Some believed that "organic" cigarettes contained fewer chemicals, which in turn produced a "much cleaner and healthier smoking cigarette" and that they could "taste the difference." CONCLUSIONS: Findings support that smokers associate the "organic" descriptor with health and reduced harm. This descriptor may differentially impact puffing behavior by gender. IMPLICATIONS: This study provides qualitative and quantitative data regarding how the "organic" descriptor influences adult daily smokers' perceptions and use of cigarettes. After smoking two identical cigarettes described as "organic" and conventional (e.g., no "organic"), smokers expressed more problematic health expectancies about the "organic" cigarette condition, providing further empirical support that the "organic" descriptor is associated with expectancies of reduced harm. The source of reduced harm was understood to be fewer chemicals in the organic cigarette. Though preliminary, findings suggest that "organic" may differentially affect puffing behavior by gender.

Traffic noise monitoring and modelling - an overview.

Noise has emerged as a leading environmental problem and is an underestimated threat. The most significant source of noise pollution is road traffic. Road traffic noise problem has reached alarming levels. This proves the severity and necessity of mitigating the traffic noise from every delicate corner possible. Noise monitoring is required to check the noise levels and effectiveness of control methods implemented. Road traffic noise control can be exercised with the help of prediction models. This paper presents the traffic noise status of developing countries and a quantitative review and comparison of traffic noise prediction models developed by researchers for various cities. Findings suggest that most of the researchers have used regression modelling and use of evolutionary computing methods like genetic algorithm, fuzzy systems, and neural networks to develop traffic noise prediction model is lacking. The effect of many important variables affecting traffic noise like pavement type, vegetation along roads, road surface roughness, and gradient still needs to be studied. Further, studies are required to measure in vehicle noise levels on same roads to compare the noise levels tolerated by residents, road users, and the commuters; this will help in formulating traffic noise regulations.

Multiresolution-Based Singular Value Decomposition Approach for Breast Cancer Image Classification.

Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.

Impact of water depth on thermal efficiency, exergy efficiency, and exergy losses of finned acrylic solar still: an experimental study.

This paper presents a complete exergy analysis and exergy destruction of a finned acrylic solar still (SS) at 1, 2, and 3 cm salt water depth (Wd). The coefficients of heat transfer of salt water-glass have been computed for evaporative, convective, and radiant heat transfer. Also, thermal efficiency, exergy loss of basin, saltwater, and glass was determined. The maximum hourly output of a finned acrylic SS at 1, 2, and 3 cm Wd was1.23, 0.93, and 0.81 kg, respectively. The daily yield of 5.67, 5.16, and 4.41 kg was collected from the finned acrylic SS at 1, 2, and 3 cm salt Wd, respectively. For the finned acrylic SS at 1 cm Wd, the maximal exergy loss of the basin, saltwater, and glass was 604.3, 92.8, and 141.8 W/m2, respectively. The thermal and exergy efficiency of the finned acrylic SS at 1 cm Wd is 42.54 and 3.83%, respectively, while at 2 cm salt Wd, it is 37.92 and 3.22% and for 3 cm Wd is 31.2 and 2.7%. According to the findings, the exergy loss of saltwater in finned acrylic SS at 1 cm Wd is minimal when compared to the exergy loss of saltwater in finned acrylic SS at 2 and 3 cm Wd.

Implementing Critical Machine Learning (ML) Approaches for Generating Robust Discriminative Neuroimaging Representations Using Structural Equation Model (SEM).

Critical ML or CML is a critical approach development of the standard ML (SML) procedure. Conventional ML (ML) is being used in radiology departments where complex neuroimages are discriminated using ML technology. Radiologists and researchers found that sole decision by the ML algorithms is not accurate enough to implement the treatment procedure. Thus, an intelligent decision is required further by the radiologists after evaluating the ML outcomes. The current research is based on the critical ML, where radiologists' critical thinking ability, IQ (intelligence quotient), and experience in radiology have been examined to understand how these factors affect the accuracy of neuroimaging discrimination. A primary quantitative survey has been carried out, and the data were analysed in IBM SPSS. The results showed that experience in works has a positive impact on neuroimaging discrimination accuracy. IQ and trained ML are also responsible for improving the accuracy as well. Thus, radiologists with more experience in that field are able to improve the discriminative and diagnostic capability of CML.