Objective: Maintaining plasma glucose homeostasis is vital for mammalian survival, but the masticatory function, which influences glucose regulation, has, to our knowledge, been overlooked. Research methods and proced...
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Objective: Maintaining plasma glucose homeostasis is vital for mammalian survival, but the masticatory function, which influences glucose regulation, has, to our knowledge, been overlooked. Research methods and procedures: In this study, we investigated the relationship between the glycemic response curve and chewing performance in a group of 8 individuals who consumed 80 g of apple. A device called "Chewing" utilizing electromyographic (EMG) technology quantitatively assesses chewing pattern, while glycemic response is analyzed using continuous glucose monitoring. We assessed chewing pattern characterizing chewing time (tchew), number of bites (nchew), work (w), power (wr), and chewing cycles (tcyc). Moreover, we measured the principal features of the glycemic response curve, including the area under the curve (a) and the mean time to reach the glycemic peak (tmean). We used linear regression models to examine the correlations between these variables. Results: t(chew), n(chew), and wr were correlated with a (R-2 = 0.44, P < 0.05 for t(chew) and n(chew), P < 0.001 for wr), and tmean was correlated with tchew (R-2 = 0.25, P < 0.05). These findings suggest that increasing chewing time and power, while reducing the number of chews, resulted in a wider glycemic curve and an earlier attainment of the glycemic peak. Conclusions: These results emphasize the influence of proper chewing techniques on blood sugar levels. Implementing correct chewing habits could serve as an additional approach to managing the glycemic curve, particularly for individuals with diabetes. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://***/licenses/by/4.0/)
Forensic facial reconstruction is the last recourse to establish the identity of an unknown skull. The facial softtissue thickness (FSTT) is required to reconstruct various facial features on a skull. Unlike other fac...
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Forensic facial reconstruction is the last recourse to establish the identity of an unknown skull. The facial softtissue thickness (FSTT) is required to reconstruct various facial features on a skull. Unlike other facial features, the nose is made of cartilaginous tissue except for a small nasal bone. A large cavity (pyriform aperture) exists on the skull in place of the nose, which makes it a challenging job for reconstruction. The nose is a vital feature for the recognition of a face. Any change in the shape or size of the nose can alter the original aesthetic of the face. The present study proposes angles and regression functions on the bony structure to predict the various parts of the soft nose. A sample of computed tomography (CT) images of 100 males and 100 females aged between 18 and 45 years were included in the study. Apart from measuring fourteen linear parameters with three angles, simple linear regression models were derived for five pairs of parameters. Pearson's correlation coefficients for most of the parameters ranging between 0.221 and 0.872 were found to be significant at p <= 0.05 level. FSTT at three anatomical landmarks of the nose was also measured. A morphological observation study was undertaken to find the most frequent direction of the bony anterior nasal spine (ans) and its relation with the position of the pronasale (prn) on the soft nose. The devised parameters proposed in the study may also prove useful for reconstructing the nose in other populations.
As the national living standard continue to improve, the demand for goods across all categories in shopping malls is increasing, particularly for vegetable products,which are considered to be necessities of life. Afte...
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As the national living standard continue to improve, the demand for goods across all categories in shopping malls is increasing, particularly for vegetable products,which are considered to be necessities of life. After preprocessing the data in the Vegetable Industry Data In-depth Research and Analysis and Development Strategic Planning Report, this study establishes a linearregression model by Pearson correlation coefficient method. Additionally, it transforms the replenishment and pricing strategy problem into a backpack problem under dynamic programming by using time series analysis and Holt-Winter smoothing index method. It is found that the backpack problem under dynamic programming can model the automatic replenishment of vegetables, and predict the replenishment volume of each category and single product to gram units through the sales volume of each category and each single product, which is convenient for vegetable merchants to efficiently price and purchase the corresponding vegetable categories and quantities reasonably.
作者：
Th.D.PopescuResearch Department
National Institute for Research and Development in Informatics 8-10 Averescu Avenue011455 BucharestRomania
The problem of change detection and data segmentation has received considerable attention in a research context and appears to be the central issue in various application on real *** analysis of the behavior of such r...
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The problem of change detection and data segmentation has received considerable attention in a research context and appears to be the central issue in various application on real *** analysis of the behavior of such real data reveals the most of the changes that occur are either changes in the mean level,or changes in spectral *** this framework,the problem of segmentation between"homogenous"parts of the data(or detection of changes in the data) arises more or less ***,two main types of problems can be distinguished: *** of the data,the true model of which is not known,and where the model used for change or jump detection is simply a tool to locate the boundaries. *** of the data which are approximately represented by a large amount of models:the analysis is then of an artificial intelligence type,the changes may be not really abrupt. The change detection and segmentation model presented in this paper is the simplest extension of the linear regression models to data with abruptly changing *** change detection algorithm consists in two stages:residual generation and decision *** residuals represent the difference between the observed and expected system *** the stage of decision making,the residuals are processed and analyzed under certain decision rules to determine the system change *** the first part of the paper we give a general view on the main techniques used in change detection and segmentation:filtering techniques with a whiteness test and techniques based on sliding windows and distance measures.A new algorithm based on a likelihood technique, when sliding windows are used,for diagnosis of model parameter and variance changes is then *** results of some Monte-Carlo simulation for detection and diagnosis of model parameter and variance changes are presented.
Urban heat islands (UHIs) create severe human health risks. Pakistan has about 220 million inhabitants, with 287 people per Km2 on average. Over the last 100 years, the maximum extreme temperature during the previous ...
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Urban heat islands (UHIs) create severe human health risks. Pakistan has about 220 million inhabitants, with 287 people per Km2 on average. Over the last 100 years, the maximum extreme temperature during the previous summer and winter seasons has been recorded. The forecast for the coming 50 years indicates a temperature rise in Pakistan, directly affecting physical and mental health. To address this challenge, this paper analyzes the spatial variability in Heat Islands (HI) in Pakistan from 2000 to 2019, combining MODIS satellite imagery with data from fifty meteorological stations. Different statistical and geostatistical models were applied, including geographically weighted regression (GWR), simple regression, and Geostatistical interpolation models. The results reveal the variations in nocturnal thermal on a fixed temperature of 11 degrees C, variations in frequency, and the change in day and night temperatures during summer and winter. Data fusion between MODIS and data from fifty metrological stations in Pakistan shows that spatial-temporal variation in the patterns of HI must be considered in planning processes when responding to climate change. The heat island's maximum nocturnal temperatures indicated a high rate of extension and magnitude compared to the daily recorded temperature, which implies that the overall area in the metropolitan category is covered by a temperature value of greater than 20 degrees C, raising severity to 10 degrees C in June. During June, intense night-time temperature values spread their concentration, heading towards the urban center over the Islamabad, Gilgit, Peshawar, Lahore, Quetta, and Karachi station sectors. During the day, the extension difference of the heat island was in the range of 8-10 degrees C, declining steadily from summer 2006 to summer 2015 before finally covering almost the entire metropolitan area. Higher values emerged after 2015-2019, rising in magnitude and expansion until 2019.
Equine osteochondrosis (OC) is a developmental orthopedic disease that results in abnormal articular cartilage and often the formation of osteochondral bone chips (osteochondrosis dissecans). Standardbreds, a breed pr...
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Equine osteochondrosis (OC) is a developmental orthopedic disease that results in abnormal articular cartilage and often the formation of osteochondral bone chips (osteochondrosis dissecans). Standardbreds, a breed predominately used in harness racing, are prone to the development of OC. This study aimed to quantify the relationship between OC and key performance traits, fastest mile time, annual earnings, and consistency in race times over career, using a cohort of 347 North American Standardbred horses. Radiographic examination of the tarsi was previously performed to identify horses as OC-affected (n = 155) or healthy (n = 192). Performance data was acquired from the United States Trotting Association (USTA) detailing horse identification, performance, and lifetime race record. linear mixed multiple regressionmodels were constructed to evaluate the relationship between OC and performance. A total of 24 performance related variables (e.g., age, gait, track) were evaluated for inclusion in the model through subset variable selection and goodness of fit as determined by Schwarz’s Bayesian Information Criterion (SBC). Preliminary results indicate that healthy horses exhibit significantly faster mile times (P< 0.001) compared with their OC-affected counterparts. However, no significant association was detected between OC status and annual earnings (P> 0.2) or consistency in race times over career (P> 0.2). Ongoing analyses are currently being conducted to assess additional predictors and performance variables for a potential association with osteochondrosis. This study emphasizes the importance of implementing effective strategies for OC management and improving breeding selection practices when producing offspring with desired performance characteristics.
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