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Traditional approaches to monitoring water quality in aquaculture tanks present numerous limitations, including the inability to provide real-time data, which can lead to improper feeding practices, reduced productivity, and potential environmental risks. To address these challenges, this study aimed to create an accurate water quality monitoring system for Asian seabass fish farming in aquaculture tanks. This was achieved by enhancing the accuracy of low-cost sensors using simple linear regression and validating the IoT system data with YSI Professional Pro. The system's development and validation were conducted over three months, employing professional devices for accuracy assessment. The accuracy of low-cost sensors was significantly improved through simple linear regression. The results demonstrated impressive accuracy levels ranging from 76% to 97%. The relative error values which range from 0.27% to 4% demonstrate a smaller range compared to the values obtained from the YSI probe during the validation process, signifying the enhanced accuracy and reliability of the IoT sensor by using simple linear regression. The system's enhanced accuracy facilitates convenient and reliable real-time water quality monitoring for aquafarmers. Real-time data visualization was achieved through a microcontroller, Thingspeak, Virtuino application, and ESP 8266 Wi-Fi module, providing comprehensive insights into water quality conditions. Overall, this adaptable tool holds promise for accurate water quality management in diverse aquatic farming practices, ultimately leading to improved yields and sustainability.

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Exposure measurement error is a pervasive problem for epidemiology research projects designed to provide valid and precise statistical evidence supporting postulated exposure-disease relationships of interest. The purpose of this commentary is to highlight an important real-life example of this exposure measurement error problem and to provide a simple and useful diagnostic tool for physicians and their patients that corrects for the exposure measurement error. More specifically, prostate-specific antigen doubling time (PSADT) is a widely used measure for guiding future treatment options for patients with biochemically recurrent prostate cancer. Numerous papers have been published claiming that a low calculated PSADT value (denoted PSADT^) is predictive of metastasis and premature death from prostate cancer. Unfortunately, none of these papers have adjusted for the measurement error in PSADT^, an estimator that is typically computed using the popular Memorial Sloan Kettering website very often visited by both physicians and their patients. For this website, the estimator PSADT^ of the true (but unknown) PSADT for a patient (denoted PSADT∗) is computed as the natural log of 2 (i.e., 0.6931) divided by the estimated slope of the straight-line regression of the natural log of PSA (in ng/mL) on time. We utilize PSADT^ to derive an expression for the probability that the unknown PSADT∗ for a patient is below a specified value C (>0) of concern to both the physician and the patient. This probability is easy to interpret and takes into account the fact that PSADT^ is a statistical estimator with variability. This variability introduces measurement error, namely, the difference between a computed value PSADT^ and the true, but unknown, value PSADT∗. We have developed an Excel calculator that, once the [time, ln(PSA)] values are entered, outputs both the value of PSADT^ and the desired probability. In addition, we discuss problematic statistical issues attendant with PSADT∗ estimation typically based on at most three or four PSA values. We strongly recommend the use of this probability when physicians are discussing PSADT^ values and associated treatment options with their patients. And, we stress that future epidemiology research projects involving PSA doubling time should take into account the measurement error problem highlighted in this Commentary.

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Inaccessibility to extension services by smallholder farmers remains one of the impediments to achieving high agricultural productivity and food security. Extension services play a critical role in information dissemination that can avert food insecurity and increase smallholder dairy farmers' incomes. However, access to extension services remains a significant challenge in developing countries. This study investigated the influence of access to extension services on milk productivity among smallholder dairy farmers in Njoro Sub-County, Nakuru County, Kenya. The study's target and accessible population was 17,000 smallholder dairy farmers. The study used simple random and proportionate sampling techniques to select study farmers. Nassiuma's formula generated a sample of 120 smallholder dairy farmers. The hypothesis underwent testing using simple linear regression. The regression results found a statistically significant influence between access to extension services and milk productivity at a 5% significance level (p < 0.05). Findings show that most smallholder dairy farmers accessed extension services through television, radio, neighbours, and friends. In contrast, the top animal husbandry practices that most farmers were interested in were parasite and disease control, breed selection, and feed preparation. The Government of Kenya mainly provided vaccination services, while the other veterinary services, including deworming, pregnancy and disease diagnosis, breed selection, and treatment, were dominated by private entities. The Government of Kenya should improve smallholder dairy farmers' access to extension services. The study recommends channeling agricultural information in all possible vernacular languages and Kiswahili, the national language, via television and radio platforms to reach all smallholder dairy cow farmers. Additionally, more emphasis should be on the importance of appropriate milking techniques and record-keeping among smallholder dairy farmers to help monitor their animals' health, feeding, breeding, and milk productivity.

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The estimative of the leaf area using a nondestructive method is paramount for successive evaluations in the same plant with precision and speed, not requiring high-cost equipment. Thus, the objective of this work was to construct models to estimate leaf area using artificial neural network models (ANN) and regression and to compare which model is the most effective model for predicting leaf area in sesame culture. A total of 11,000 leaves of four sesame cultivars were collected. Then, the length (L) and leaf width (W), and the actual leaf area (LA) were quantified. For the ANN model, the parameters of the length and width of the leaf were used as input variables of the network, with hidden layers and leaf area as the desired output parameter. For the linear regression models, leaf dimensions were considered independent variables, and the actual leaf area was the dependent variable. The criteria for choosing the best models were: the lowest root of the mean squared error (RMSE), mean absolute error (MAE), and absolute mean percentage error (MAPE), and higher coefficients of determination (R2). Among the linear regression models, the equation yˆ=0.515+0.584*LW was considered the most indicated to estimate the leaf area of the sesame. In modeling with ANNs, the best results were found for model 2-3-1, with two input variables (L and W), three hidden variables, and an output variable (LA). The ANN model was more accurate than the regression models, recording the lowest errors and higher R2 in the training phase (RMSE: 0.0040; MAE: 0.0027; MAPE: 0.0587; and R2: 0.9834) and in the test phase (RMSE: 0.0106; MAE: 0.0029; MAPE: 0.0611; and R2: 0.9828). Thus, the ANN method is the most indicated and accurate for predicting the leaf area of the sesame.

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This paper tries to introduce a time-series of temperature parameters as a potential method for studying the global warming. So, we investigated the spatial-temporal variations of warm-season temperature parameters (WSTP), including start time, end time, length of season, base value, peak time, peak value, amplitude, large integrated value, right drive, and left drive, using a database of 30 years' period in different climates of Iran. We used daily temperature data from 1989 to 2018 over Iran to extract the parameters by TIMESAT software. We studied the trend analysis of WSTP through the Mann-Kendall method. Then, we considered the Pearson correlation coefficient to calculate the correlation between WSTP and time. We assessed the trends of the slope using a simple linear regression method. Then, we compared the results of the WSTP trend analysis in climatic zones. Our results accused the hyper-arid climatic zone has the longest warm season (194.89 days a year). The warm season in this region starts earlier than other regions and increases with moderate speed (left drive, 0.19 °C day-1). Then, it reaches a peak value (31.3 °C) earlier than the different climatic zones. On the other hand, the humid regions' warm season starts with the shortest length and ends later than the other climatic zones (112.1 and 297.5 days a year for start and end times, respectively). We detected that the trend of the start time parameter has decreased by 98.02% of the study area during the last 30 years. The base value, length, and large integrated value parameters have an increasing trend of 66.47%, 80.11%, and 92.95% in Iran. The highest correlation coefficient with time was for start time and large integrated value parameters. Hence, the start time and large integrated value parameters have almost the most negative (< - 0.5) and positive (> 5) trend slope, among other parameters, respectively. In general, these results demonstrate that the studied region has faced global warming impacts over time by increasing the warm season and thermal energy, especially in arid and hyper-arid. We highlight the necessity of planning the land use under the high natural vulnerability of the studied local, especially in this new age of global warming.

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In this work, a regression method is implemented on a low-cost digital temperature sensor to improve the sensor's accuracy; thus, following the EN12830 European standard. This standard defines that the maximum acceptable error regarding temperature monitoring devices should not exceed 1 °C for the refrigeration and freezer areas. The purpose of the proposed method is to improve the accuracy of a low-cost digital temperature sensor by correcting its nonlinear response using simple linear regression (SLR). In the experimental part of this study, the proposed method's outcome (in a custom created dataset containing values taken from a refrigerator) is compared against the values taken from a sensor complying with the EN12830 standard. The experimental results confirmed that the proposed method reduced the mean absolute error (MAE) by 82% for the refrigeration area and 69% for the freezer area-resulting in the accuracy improvement of the low-cost digital temperature sensor. Moreover, it managed to achieve a lower generalization error on the test set when compared to three other machine learning algorithms (SVM, B-ELM, and OS-ELM).

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In this paper, we are working on a pandemic of novel coronavirus (COVID-19). COVID-19 is an infectious disease, it creates severe damage in the lungs. COVID-19 causes illness in humans and has killed many people in the entire world. However, this virus is reported as a pandemic by the World Health Organization (WHO) and all countries are trying to control and lockdown all places. The main objective of this work is to solve the five different tasks such as I) Predicting the spread of coronavirus across regions. II) Analyzing the growth rates and the types of mitigation across countries. III) Predicting how the epidemic will end. IV) Analyzing the transmission rate of the virus. V) Correlating the coronavirus and weather conditions. The advantage of doing these tasks to minimize the virus spread by various mitigation, how well the mitigations are working, how many cases have been prevented by this mitigations, an idea about the number of patients that will recover from the infection with old medication, understand how much time will it take to for this pandemic to end, we will be able to understand and analyze how fast or slow the virus is spreading among regions and the infected patient to reduce the spread based clear understanding of the correlation between the spread and weather conditions. In this paper, we propose a novel Support Vector Regression method to analysis five different tasks related to novel coronavirus. In this work, instead of simple regression line we use the supported vectors also to get better classification accuracy. Our approach is evaluated and compared with other well-known regression models on standard available datasets. The promising results demonstrate its superiority in both efficiency and accuracy.

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This study harnessed some of the many opportunities provided by the TRMM 3B43 data in order to generate maps illustrating the spatial and temporal distribution of significant linear rates of change of annual total precipitation for the surface of earth bounded by latitudes 50° S and 50° N for the years 1998-2018 by applying pixel-based simple linear regression. These maps are valuable for many applications and should enhance our understanding of the global precipitation patterns and trigger more research in order to explain what has not been explained. It has been found that the whole study area had a mean significant linear rate of change of - 0.4 mm/year. Nearly half of its area had significant linear rates of increase with a mean of 8.5 mm/year while the other half had significant linear rates of decrease with mean of - 7.6 mm/year. Landmass alone can be divided into nearly two halves; the first had significant linear rates of increase with a mean of 5.2 mm/year while the second had significant linear rates of decrease with mean of - 7.0 mm/year. Water areas alone also can nearly be divided into two halves; the first showed significant linear rates of increase with a mean of 9.6 mm/year while the second showed significant linear rates of decrease with mean of - 7.8 mm/year. Grouping the whole study area into six climatic zones and 21 administrative land and water regions and applying pixel-based Tukey test showed that the obtained significant linear rates of change varied significantly among these climatic and administrative regions.

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PURPOSE: There is still debate whether intraoperative Schneiderian membrane (SM) perforation in the maxillary sinus lift causes an increase the risk of implants failure. The aim of this study was to assess an association between SM perforation and implants loss following the maxillary sinus lift. MATERIALS AND METHODS: A systematic review and meta-analysis of clinical studies assessing association between SM perforation and implants failure based on PRISMA was conducted. Three major databases were used to gather research dating from their respective inception up until March 2018. All clinical studies expressly reported the number of the SM perforation and implants loss that installed in the perforated and nonperforated sinuses were included. The statistical analyses used were Pearson's correlation, simple linear regression, and meta regression. The risk ratio (RR) of implant loss between perforated and nonperforated sites was estimated. RESULTS: A total of 2947 patients with 3884 maxillary sinuses augmentations who received 7358 implants, enrolled in 58 studies were included in this study. There was a significant relationship between the implants' failure and SM perforation according to simple linear regression (P < .001) and meta regression analysis (P = .06). There was a significant decrease (moderate quality evidence) in implant loss in the nonperforated sinuses compared to perforated sunrises (RR = 2.17, CI: 1.52-3.10, P = .001). There was also no significant association between implant loss in the perforated sinuses and the surgical devices used (piezosurgical or rotary), surgical approach applied (lateral or crestal sinus lift), barrier membrane used and type of bone grafting materials. CONCLUSION: The results of this study showed that an intraoperative SM perforation could increase the risk of implant failure after the sinus lift surgery.

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BACKGROUND: Internet provides tremendous educational benefits for college students and also provided better opportunities for communication, information, and social interaction for young adults; however, excessive internet use can lead to negative psychological well-being (PWB). OBJECTIVE: The present study was conducted with the objective to find out the relationship between internet addiction and PWB of college students. MATERIALS AND METHODS: A multicenter cross-sectional study was carried out in college students of Jabalpur city of Madhya Pradesh, India. A total of 461 college students, using internet for at least past 6 months were included in this study. Young's Internet addiction scale, consisting of 20-item, based on five-point Likert scale was used to calculate internet addiction scores and 42-item version of the Ryff's PWB scale based on six-point scale was used in this study. RESULTS: A total of 440 questionnaire forms were analyzed. The mean age of students was 19.11 (±1.540) years, and 62.3% were male. Internet addiction was significantly negatively correlated to PWB (r = -0.572, P < 0.01) and subdimensions of PWB. Students with higher levels of internet addiction are more likely to be low in PWB. Simple linear regression showed that internet addiction was a significant negative predictor of PWB. CONCLUSION: PWB of college students negatively affected by internet addiction. Hence, it is essential to develop strategies for prevention of internet addiction which is very important for promoting PWB of college students.

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To establish population-specific age estimation models in adults from costal cartilage for contemporary Chinese by using three-dimensional volume-rendering technique. Five hundred and twelve individuals (254 females and 258 males) with documented ages between 20 and 85 years were retrospectively included. Their clinical CT examinations (1 mm slice thickness) were used to develop the sex-specific age prediction model. A validation sample comprising 26 female and 24 male individuals was then used to test the predictive accuracy of the established models. Simple linear regression (SLR), multiple linear regression (MLR), gradient boosting regression (GBR), support vector machine (SVM), and decision tree regression (DTR) were utilized to build the age diagnosis models from calibration samples. By comparison, the decision tree regression was the relatively more accurate age prediction model for male, with mean absolute error = 5.31 years, least absolute error = 0.10 years, correct percentage within 5 years = 54%, and the correct percentage within 10 years = 88%. The stepwise multiple linear regression equations was the relatively more accurate one for female, with mean absolute error = 6.72 years, least absolute error = 0.68 years, correct percentage within 5 years = 42%, and correct percentage within 10 years = 77%. Our results indicated that the present established age estimation model can be applied as an additional guidance for age estimation in adults.

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To solve the calibration transmission problem in near-infrared ( NIR) spectroscopy, a novel model transfer method, Simple Linear Regression Direct Standard-ization ( SLRDS ) , has been presented. To investigate the validity of the proposed method, a real corn sample NIR dataset was tested and the direct standardization ( DS ) method and piecewise direct standardization ( PDS ) method were involved as a comparison. Our results indicated that SLRDS can correct compressed NIR data differences among instruments and enable the user to share corn sample PLS calibration model among three instruments, at the same time it has higher prediction accuracy, fewer parameters and simpler model than DS and PDS.

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This study examines the predictive role of meaning in life and gender-specific differences on psychological well-being of 226 Spanish undergraduates (87 men, 38.5%; 139 women, 61.5%) ranging in age from 17 to 25 years, M = 21.08, SD = 2.18. Measures included both the Spanish adaptations of the Crumbaugh and Maholic's Purpose-In -Life Test and the Ryff's Scales of Psychological Well-Being. The hypothesis stated that meaning in life would predict psychological well-being and that women would reach a higher score in several dimensions of psychological well-being. Statistical analysis included simple linear regressions, and a t-test. Results showed that: (1) meaning in life was a significant predictor variable of psychological well-being, especially of global psychological well-being, self-acceptance, purpose in life, and environmental mastery; and (2) women reached a higher score, statistically significant, in global psychological well-being, environmental mastery, personal growth and purpose in life. Findings were discussed in the light of previous researches.

Se examinaron el papel predictivo del Sentido de la Vida y las diferencias en función del género en el Bienestar Psicológico en un grupo de 226 estudiantes universitarios españoles (87 hombres, 38.5%; 139 mujeres, 61.5%), con edades entre los 17 y los 25 años, M = 21.08, DT = 2.18. Se usaron adaptaciones españolas del Purpose-In-Life Test de Crumbaugh y Maholic y de las Escalas de Bienestar Psicológico de Ryff. Las hipótesis a contrastar fueron que de manera significativa el Sentido de la Vida predeciría el Bienestar Psicológico y que las mujeres alcanzarían puntuaciones más altas en algunas dimensiones del mismo. Los análisis estadísticos incluyeron regresiones lineales simples y la prueba t para muestras independientes. Los resultados mostraron que: (1) El Sentido de la Vida predijo significativamente el Bienestar Psicológico, especialmente el Bienestar Psicológico global, la Autoaceptación, el Propósito en la Vida y el Dominio del Entorno, y (2) las mujeres alcanzaron puntuaciones significativamente superiores en Bienestar Psicológico global, Dominio del Entorno, Crecimiento Personal y Propósito en la Vida. Estos resultados fueron discutidos a la luz de la investigación precedente.

Examinou-se o papel preditivo do Sentido da Vida e as diferenças em função do gênero no Bem-estar Psicológico em um grupo de 226 estudantes universitários espanhóis (87 homens, 38.5%; 139 mulheres, 61.5%), com idade entre 17 e 25 anos, M = 21.08, DT = 2.18. Foram usadas adaptações espanholas do Purpose-In-Life Test de Crumbaugh e Maholic e das Escalas de Bem-estar Psicológico de Ryff. As hipóteses a contrastar foram que de maneira significativa o Sentido da Vida prediria o Bem-estar Psicológico e que as mulheres alcançariam pontuações mais altas em algumas dimensões do mesmo. As análises estatísticas incluíram lineais simples e o teste t para mostras independentes. Os resultados mostraram que: (1) O Sentido da Vida predisse significativamente o Bem-estar Psicológico, especialmente o Bem-estar Psicológico global, a Autoaceitação, o Propósito na Vida e o Domínio do Entorno, e (2) as mulheres alcançaram pontuações significativamente superiores em Bem-estar Psicológico global, Domínio do Entorno, Crescimento Pessoal e Propósito na Vida. Estes resultados foram discutidos à luz da pesquisa precedente.

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