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The estimation methods of reproduction numbers and serial intervals are important in the early stages of infectious diseases. During the COVID pandemic, China implemented a dynamic zero-COVID policy on the Chinese mainland until the end of 2022. This study compares three estimation methods of basic reproduction numbers on small-scale, short-duration COVID-19 epidemics in Fujian Province. Basic reproduction numbers were investigated using a varying-strain model via a next-generation matrix method. Serial intervals were derived using the infector–infectee pairs of two epidemics from the Fujian Provincial Center for Disease Control and Prevention. Basic reproduction numbers were estimated using the maximum likelihood estimation method and the exponential growth method. The curves of the effective reproduction numbers of the three epidemics were plotted by utilizing daily cases and the EpiEstim R package. The spatial heterogeneity of infection cases was described using the Gini coefficient. This study provides significant insights on the estimation methods of reproduction numbers for policymakers in the local government. The results reveal that social contacts between infectors and susceptible individuals should be reduced to avoid an increase in deaths and to fight against the spread of infectious diseases.

In view of the fact that there is no suitable measurement method for tire driving resistance during the operation of a transporter on soil pavement, this paper proposes a simple measurement and estimation method for driving resistance on a soil runway based on the Bekker settlement model. In this paper, using the driving resistance equation based on the Bekker model, a simplified equation of driving resistance related to mass is proposed. Using the principle of kinematics, a simple test device for driving resistance was developed, and the test data of the test device were used to determine the parameters of the driving resistance estimation equation of the road surface. The driving resistance of the wheel running state was tested by the simulated aircraft load loading vehicle through the mechanical sensor, and the consistency between the theoretical value and the actual value of the driving resistance value under 8T, 10T, and 12T loads was verified. This research shows that the test method and estimation method have low cost and good accuracy and are suitable for wide promotion. The results of this paper provide a new idea and method for estimating the driving resistance of a wheel when a transporter runs on a soil road and also provide a reference for designing the length of a soil runway. [ABSTRACT FROM AUTHOR]

To calculate the losses of distribution feeders, this paper uses an iterative method that is limited to restricted measurements. The approach presented in this paper uses bill data in addition to output information from a very small number of real-time measurements located on the secondary side of distribution transformers. This method attempts to estimate the load of distribution transformers injected into LV feeders. Energy losses for LV feeders are evaluated by first estimating the power and periodic energy injected to each of the LV feeders and then subtracting the total consumption bills from these estimated values. By using this method, the amount of energy loss is estimated. In this article, a new method called iterative power factor adjustment method is considered as a potential method for estimating losses. The power factor can be increased by repeatedly using evolutionary algorithms and including capacitors in the system. In order to reduce system losses and increase network effectiveness. In this paper, a new method for examining and evaluating Non-Technical Losses (NTL) is proposed. This method considers load estimation and limited measurement to place high priority feeders. [ABSTRACT FROM AUTHOR]

Climate warming and air pollution are atmospheric environmental problems that have aroused broad concern worldwide. Greenhouse gas emissions are the main cause of global warming. In addition to reducing carbon emissions, increasing carbon sink capacity and improving environmental quality are essential for building green and low-carbon enterprises under carbon peak and carbon neutrality goals. Currently, the research on the methods and application of carbon sink capacity assessment in coal mining enterprises is limited. Given this, this study estimated the carbon absorption, carbon storage, and net ecosystem productivity of a typical coal mining area in Taiyuan City, China, and compared the characteristics and applicability of the three methods. The results showed the following: (1) The total carbon absorption (carbon sink) of the mining area in 2021 was 117.39 t, the primary source of which is forest land. (2) The total carbon storage in the mining area in 2021 was 29,561.96 t. From different land use types, the carbon storage in the mining area mainly came from forest land (27,867.73 t); from the perspective of carbon pool, soil carbon storage (21,970.96 t) had the most significant contribution to the carbon storage of mining areas. (3) The net ecosystem productivity of the mining area in 2021 was 781.97 g/(m2·a), indicating that the ecosystem of the mining area was a carbon sink. (4) The three estimation methods differed in the current case. The estimation method for carbon absorption is the simplest, and the results are the most intuitive. The estimation method for net ecosystem productivity is the most complex. The carbon sink estimation via carbon storage needs to collect two years of data. Enterprises should assess the carbon sink capacity of mining areas based on existing conditions and data. This study proposes methods for estimating carbon sink capacity in mining areas, which have positive practical significance for the low-carbon green development of coal mine enterprises. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Anatolian Environmental & Animal Science is the property of Bulent Verep and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The datasets in semi-arid and arid regions of developing countries are known for their long period of missing data. To approximate any missing data interpolation or estimation techniques are frequently used. The current study focuses on applying the concept of data uncertainty by setting out different percentages of missing data from the known dataset at all target stations calibrated and validated against the ensemble simulation interpolation and reconstitution approach by contrasting findings with voluntarily excluded data that will be re-estimated using the various approaches selected. While testing the nearby stations (6 to 50 km) for filling the missing data were used. The tests on the selected stations shows that the better estimates of precipitation and streamflow are the ID and the NR methods. Thus, the methods that revealed a better estimation were used for filling in missing data of the respective stations to undergo a water resource study. [ABSTRACT FROM AUTHOR]

Background: Based on the legal framework laid down in section 130b (9) of Book V of the German Social Code, various criteria are relevant for the negotiated price for new patented drugs in Germany. European reference prices (ERPs) are one criterion. The ERP is based on the ex-factory prices (EFPs) of the countries included in the European country basket. However, in some of these countries, the EFP is not published due to confidential wholesale margins. Wholesale margins must therefore be estimated and deducted from purchase prices. In this context literature-based estimates to date do not assume regressive margins with higher pharmaceutical prices. This assumption is questionable and can lead to systematically underestimated country prices, especially for high-priced drugs. Percentage wholesale margins in the majority of European countries develop to a comparable extent regressively with increasing prices. It should therefore be examined (1) whether statistical models can predict the margins of individual countries, in principle and especially for countries where margins are unknown and regressive trends are likely, and (2) to what extent the estimation of margins improves when regressive statistical models are used to estimate margins instead of cross-price averages published in the literature. Methods: Qualitative preliminary research explores the basic wholesale pricing mechanisms in countries with confidential wholesale margins. Wholesale margins for reimbursable drugs were then modeled for regulated European countries. Estimation quality and impact of the model was compared to estimations based on average margins. Results: In both regulated countries and in countries with confidential wholesale margins, percentage margins of wholesalers develop regressively as drug prices rise. Regressive courses of margins can be resiliently modeled for the regulated countries using a power distribution with significantly lower mean squared errors in a linear mixed model in comparison to literature-based estimations with country-specific cross-price averages. Conclusion: If there is reason to believe that margins are regressive, confidential wholesale margins are expected to be better estimated by the power function based on margins of regulated countries than by the published country-specific average margins, reducing significantly inaccurate effects on margin estimations of high-price drugs. [ABSTRACT FROM AUTHOR]

Copyright of Die Kardiologie is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This article investigates the parameters estimation and reliability systems of the stress-strength model, when x and y are independent and follow three parameters Weibull-exponential Rayleigh distribution (WED). Single, parallel, and series systems are derived for WED. The maximum likelihood estimator (MLE), Exact method of moments estimator (EMME), weighted least squares estimator (LSE), and shrinkage function (Shf) estimation methods are examined for its parameters. Different estimators are evaluated using three criteria: bias, mean squared error (MSE), and mean absolute percentage error (MAPE). The best four estimating strategies were compared with a simulation study. [ABSTRACT FROM AUTHOR]

Contingency is a critical component in the cost estimation process for any construction project. The contingency reserve considers potential costs related to risks and uncertainties associated with construction projects. It is usually assumed to damp any resulting uncertain monetary impact and to prevent project cost overrun. Many contingency calculation methods for construction projects proposed in literature ranged from simple percentage to complex mathematical methods. Deciding the optimum contingency method for a given project at a given phase represents the main challenge in cost estimation process. This study presents a comprehensive compilation of all contingency calculation methods and divided them into three main groups: deterministic, probabilistic, and modern mathematical methods which have been discussed in details. Appropriate method for estimating contingency amount depends on many criteria such as project peculiarity, complexity, ease of method used, and the accuracy level of the estimates. This study proposed a practical guidance approach for construction agencies to choose their appropriate cost contingency method.This research is expected to help agencies/owners in the budget development stage to allocate a contingency budget for their construction projects.

The importance of calorimetric data in elaboration of estimation methods of thermodynamic parameters is outlined. Four criteria for calorimeter classification are considered: (i) the parameter controlled by the instrument during the measurement, (ii) the way of taking over the heat accompanying the process, (iii) the method used in order the evaluate the value of the thermal effect and (iv) the procedure by which some reactants get in contact with the measuring part of the instrument. The progresses in the accuracy of the measurements and the extension of the applications of calorimetry during time have been evidenced.

Based on the hourly solar radiation and ambient temperature, the hourly power estimation work is carried out using the conventional photovoltaic output power (PVOP) estimation model which is used in conjunction with the conventional photovoltaic module temperature (PVMT) estimation model. These hourly data must be processed further before they can be applied to the daily power estimation work. This estimation work is carried out using conventional estimation methods, which are the multiple estimation processes that are complex, time-consuming, and error prone. Therefore, to avoid these shortcomings, one estimation process is designed and used for daily power estimation work. However, this process produces an incorrect daily output power value due to an invalid module temperature value. Thus, a new PVMT estimation model is developed to solve the problem of the invalid value based on a simple linear regression analysis. The performance of the new model has been validated, giving a Normalized Root Mean Squared Error (NRMSE) value of 0.0215 and a Coefficient of Determination (R2) value of 0.9862. The correct daily output power value is produced with a valid module temperature value, giving a NRMSE value of 0.0034 and a R2 value of 0.9999. These results demonstrate the new model's applicability and makes the one estimation process accurate, easy, user-friendly, instantaneous, and direct in daily power estimation work. ABSTRAK: Berdasarkan sinaran matahari dan suhu persekitaran per jam, kerja-kerja anggaran kuasa setiap jam dijalankan menggunakan model anggaran kuasa dari dapatan fotovolta konvensional (PVOP) yang digunakan bersempena dengan model anggaran suhu modul fotovolta konvensional (PVMT). Data per jam ini perlu diproses dengan lebih lanjut sebelum ia boleh digunakan pada kerja anggaran kuasa harian. Kerja-kerja penganggaran ini dijalankan menggunakan kaedah penganggaran konvensional, iaitu proses penganggaran berganda yang kompleks, memakan masa dan mudah ralat. Oleh itu, bagi mengelakkan kekurangan ini, satu proses anggaran direka bentuk dan diguna bagi kerja anggaran kuasa harian. Namun, proses ini menghasilkan nilai dapatan kuasa harian yang salah disebabkan oleh nilai suhu modul tidak sah. Oleh itu, model anggaran PVMT baharu telah dibina bagi menyelesaikan masalah nilai tidak sah berdasarkan analisis mudah regresi linear. Prestasi model baharu telah disahkan, memberi nilai Ralat Punca Min Kuasa Dua Ternormal (NRMSE) sebanyak 0.0215 dan nilai Pekali Penentuan (R2) sebanyak 0.9862. Nilai dapatan kuasa harian yang betul dihasilkan dengan nilai suhu modul yang sah, iaitu nilai NRMSE 0.0034 dan R2 0.9999. Dapatan ini menunjukkan bahawa kebolehgunaan model baharu menjadikan proses anggaran lebih tepat, mudah, mesra pengguna, serta-merta dan terus dalam kerja anggaran kuasa harian.

Copyright of Umm Al-Qura University Journal of Educational & Psychological Sciences is the property of Association of Arab Universities and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Remote Sensing for Natural Resources is the property of Remote Sensing for Natural Resources Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As one of the essential components in ecosystems, lakes play a major role in the global phosphorus (P) cycle. It is helpful for further understanding of the inside lake P geochemical cycle to research P pollution and storage in lakes, which is of positive significance for lake eutrophication restoration. In this study, we investigated the total phosphorus concentrations (TPC) of water and sediments from 37 lakes in the Eastern Plain Lake Zone (EPL) of China, evaluated the P pollution degree of lakes, and estimated P storage in lake sediments with quantitative data of lake area and number. The results indicate that the total phosphorus concentrations of water (TPCW) and total phosphorus concentrations of the surface sediments (0–1 cm, TPCSS) in EPL were high, the mean values were 0.11 mg·L−1 and 869.85 mg·kg−1 respectively, with obvious differences between urban and rural areas, as well as between different river basins. Over half (56.76% and 70.27% respectively) of the lakes reached severe pollution levels in water and surface sediments. There were 16224 lakes (> 0.01 km2) with a total area of 21662.37 km2 in the EPL, and the P storage in the lake sediments (0–30 cm) was about 4.87 ± 2.08 Tg (1 Tg = 1 × 1012 g), accounting for about 2.74% of the basin soil. TPCW and TPCSS of lakes in the EPL were significantly positively correlated, may suggest a close nutrient cycling relationship between the lake water and the sediment. During periods of high winds and waves, the stored P in the top sediments in the EPL may continue to participate in the internal P geochemical cycle and migrate to the overlying water, posing a potential pollution hazard. Therefore, it is crucial to take into account the sediment P pools when formulating effective lake phosphorus management strategies. [ABSTRACT FROM AUTHOR]

Penetration detection is an important method for in-situ scientific exploration of planetary surfaces, and is used to indirectly detect the mechanical properties of planetary subsurface soil. In this study, four ogive-nosed penetrators with different radii were simulated to penetrate planetary soil of varying compactness under different impact velocities using a nonlinear finite element (FE) method based on HyperMesh/LS-DYNA. A nonparametric estimation method was proposed through analysis of the corresponding results. Three main characteristic parameters in the penetration process were adopted in the method: maximum deceleration a m , penetration depth z , and trajectory deviation x p. Twenty parameters for identifying planetary soil properties were derived based on these three characteristic parameters. The terrain consisted of simulant soil classified non-parametrically and artificially defined as three states (low, medium, and high compactness) based on different harnesses, and five estimation criteria (slack, ideal, partially strict, relatively strict, and strict) were proposed. Three identification parameters were derived through the analysis of the evaluation indices (identification rate η i , accurate rate η a , and conservative rate η c) under different estimation criteria. a m 2 x p 2 was selected as the optimal identification parameter and the superior estimation criterion is the ideal criterion. The verification results of simulation tests show that the accurate success rate and conservative success rate of the final evaluation are 60% and 80%, respectively, indicating that the nonparametric estimation method in this study can be used to estimate the mechanical properties of planetary soil effectively. [ABSTRACT FROM AUTHOR]

In most multi-frame image super-resolution (SR) studies, the process that models the construction of the low-resolution (LR) images from high resolution (HR) one includes geometric transformation, blurring, downsampling, and adding noise. A simple linear approximation of this model is used in most super-resolution frameworks. The approximation error in this popular model appears as an additive noise that is generally a non-Gaussian noise. Most state-of-the-art estimators, such as the Kalman filter as a linear estimator used in super-resolution problems, suffer from this non-Gaussian noise caused by approximation error. We present a novel multi-frame super-resolution using the particle filter method that performs well in a non-Gaussian nonlinear dynamic system to produce the high-resolution output. One key innovation is that the approximation error in the super-resolution model can be handled well by using a particle filter. In this study, the optimal importance function is used to improve particle filter efficiency determined with the Taylor expansion of observation and measurement function. Experiments with simulated and real-image sequences yield that our proposed super-resolution method has good results in suppressing noise and reconstructing details in high-resolution images. [ABSTRACT FROM AUTHOR]

Climate warming and air pollution are atmospheric environmental problems that have aroused broad concern worldwide. Greenhouse gas emissions are the main cause of global warming. In addition to reducing carbon emissions, increasing carbon sink capacity and improving environmental quality are essential for building green and low-carbon enterprises under carbon peak and carbon neutrality goals. Currently, the research on the methods and application of carbon sink capacity assessment in coal mining enterprises is limited. Given this, this study estimated the carbon absorption, carbon storage, and net ecosystem productivity of a typical coal mining area in Taiyuan City, China, and compared the characteristics and applicability of the three methods. The results showed the following: (1) The total carbon absorption (carbon sink) of the mining area in 2021 was 117.39 t, the primary source of which is forest land. (2) The total carbon storage in the mining area in 2021 was 29,561.96 t. From different land use types, the carbon storage in the mining area mainly came from forest land (27,867.73 t); from the perspective of carbon pool, soil carbon storage (21,970.96 t) had the most significant contribution to the carbon storage of mining areas. (3) The net ecosystem productivity of the mining area in 2021 was 781.97 g/(m2·a), indicating that the ecosystem of the mining area was a carbon sink. (4) The three estimation methods differed in the current case. The estimation method for carbon absorption is the simplest, and the results are the most intuitive. The estimation method for net ecosystem productivity is the most complex. The carbon sink estimation via carbon storage needs to collect two years of data. Enterprises should assess the carbon sink capacity of mining areas based on existing conditions and data. This study proposes methods for estimating carbon sink capacity in mining areas, which have positive practical significance for the low-carbon green development of coal mine enterprises.

The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite, GNSS receivers are susceptible to large-ranging errors, which are particularly prevalent in urban areas where precise positioning is indispensable. The deployment of the high-spatial-density Fifth-Generation (5G) network makes it possible to provide a broad area with high-precision positioning service. Obviously, it promoting the deep integration of the GNSS system and the 5G mobile communication network and establishing a Highly Dependable Spatio-temporal Network (HDSN) have become an inevitable trend. The existing algorithm for the fusion of multiple signals has difficulty settling problems such as the fast fluctuation of available signal sources and the poor stability of multi-scale multi-type signal estimation in GNSS-5G hybrid networks. Here, we propose a Square Root Unscented Stable Filter (SRUSF) for GNSS and 5G joint positioning with a compact coupled filter group architecture in a highly dependable spatio-temporal network. A stabilized coefficient is added to guarantee positive covariance of the estimation error. The possibility of divergence of filtering results due to the variation in signal sources and the incomplete agreement between the system model and the actual situation are reduced. The simulation results show that the proposed SRUSF method substantially enhances the positioning accuracy and reliability compared with the other five joint estimation methods for multiple signals. This work will enable the terminal of mass users to provide timing and positioning services with unprecedented accuracy and dependability under the GNSS and 5 G-based spatio-temporal network's architecture. [ABSTRACT FROM AUTHOR]

Contingency is a critical component in the cost estimation process for any construction project. The contingency reserve considers potential costs related to risks and uncertainties associated with construction projects. It is usually assumed to damp any resulting uncertain monetary impact and to prevent project cost overrun. Many contingency calculation methods for construction projects proposed in literature ranged from simple percentage to complex mathematical methods. Deciding the optimum contingency method for a given project at a given phase represents the main challenge in cost estimation process. This study presents a comprehensive compilation of all contingency calculation methods and divided them into three main groups: deterministic, probabilistic, and modern mathematical methods which have been discussed in details. Appropriate method for estimating contingency amount depends on many criteria such as project peculiarity, complexity, ease of method used, and the accuracy level of the estimates. This study proposed a practical guidance approach for construction agencies to choose their appropriate cost contingency method. This research is expected to help agencies/owners in the budget development stage to allocate a contingency budget for their construction projects. [ABSTRACT FROM AUTHOR]

Cement-based treatment is a well-established technology for converting unwanted marine dredged clay (MDC) to qualified filling materials. However, a basic understanding of the strength development of cement-treated MDC is still lacking. In this study, we attempt to characterize the strength variation of cement-treated MDC under a wide range of water-to-cement ratios (w/c). The dominant influencing factors such as initial water content, cement content, curing time and pore salinity are examined through a series of laboratory tests. The results indicate that the strength of cement-treated MDC decreases exponentially with the initial water content. The strength approaches zero when the initial water content increases to 3.5–4.0 times the liquid limit, even with a high content of cement. The effect of pore salinity depends significantly on the cement content and curing time. Owing to the wide range of w/c, the extensively used Abrams' law fails to characterize the strength variation of cement-treated MDC precisely. To this end, a new strength prediction model is proposed based on the concept of minimum cement content. The volume ratio is found to be inferior to the mass ratio in defining the minimum cement content, which remains approximately constant even for a wide range of w/c. [ABSTRACT FROM AUTHOR]

The determination of the recharge area of karst water systems is difficult in karst hydrogeological investigations and research. Based on the principle of water balance and hydrological impulse function, this work introduces the calculation method to estimate the recharge area and discusses the physical meaning of the model parameters. Three typical karst water systems in South China were tested, namely, the Yaji experimental site in Guangxi, the Wulongdong underground river in Xingshan, Hubei, and the water inrush point of the Xiakou Tunnel near the Hu-Rong highways, which are well verified with the recharge area comprehensively determined by other methods. The estimation method of the recharge area based on karst hydrograph simulation proposed in this paper is a new supplement to the methods for calculating the recharge area of karst water systems and has a good application prospects in small and medium scale karst water systems in South China.

Abstract Research question This paper examines the overarching question of to what extent different analytic choices may influence the inference about country-specific cross-sectional and trend estimates in international large-scale assessments. We take data from the assessment of PISA mathematics proficiency from the four rounds from 2003 to 2012 as a case study. Methods In particular, four key methodological factors are considered as analytical choices in the rescaling and analysis of the data: (1) The selection of country sub-samples for item calibration differing at three factor levels. (2) The item sample refering to two sets of mathematics items used within PISA. (3) The estimation method used for item calibration: marginal maximum likelihood estimation method as implemented in R package TAM or an pairwise row averaging approach as implemented in the R package pairwise. (4) The type of linking method: concurrent calibration or separate calibration with successive chain linking. Findings It turned out that analytical decisions for scaling did affect the PISA outcomes. The factors of choosing different calibration samples, estimation method and linking method tend to show only small effects on the country-specific cross-sectional and trend estimates. However, the selection of different link items seems to have a decisive influence on country ranking and development trends between and within countries.

This paper assesses the performance of regularized generalized least squares (RGLS) and reweighted least squares (RLS) methodologies in a confirmatory factor analysis model. Normal theory maximum likelihood (ML) and generalized least squares (GLS) statistics are based on large sample statistical theory. However, ML and GLS goodness-of-fit tests often make incorrect decisions on the true model, when sample size is small. The novel methods RGLS and RLS aim to correct the over-rejection by ML and under-rejection by GLS. Both methods outperform ML and GLS when samples are small, yet no studies have compared their relative performance. A Monte Carlo simulation study was carried out to examine the statistical performance of these two methods. We find that RLS and RGLS have equivalent performance when N ≥ 70; whereas when N < 70, RLS outperforms RGLS. Both methods clearly outperform ML and GLS with N ≤ 400. Nonetheless, adopting mean and variance adjusted test for non-normal data, RGLS slightly outperforms RLS. Power analyses found that RLS generally showed small loss in power compared to ML and performed better than RGLS. [ABSTRACT FROM AUTHOR]

In the safety risk prediction process of driving behaviors, the influence of correlation among evaluation indexes is ignored, resulting in low prediction accuracy. Therefore, a safety risk prediction method of road traffic driving behaviors based on the information entropy algorithm is proposed. Firstly, according to the types of driving behaviors, the evaluation indexes representing driving behaviors are selected, and an evaluation index system is constructed. Then, the internal correlation among indexes is determined by the information entropy algorithm, and the comprehensive weight of indexes is calculated based on the index membership matrix. Then the prediction model is established. Finally, the safety risk level is determined to realize the safety risk prediction of driving behaviors. The simulation results show that the safety entropy value of the designed prediction method is 0.18, and the actual entropy value is 0.17, with a difference of 0.01 between them. Additionally, the deigned prediction method has high prediction accuracy, with prediction accuracy up to 0.94. [ABSTRACT FROM AUTHOR]

The urban heat island (UHI) effect, a phenomenon of local warming over urban areas, is the most well-known impact of urbanization on climate. Globally consistent estimates of the UHI intensity (UHII) are crucial for examining this phenomenon across time and space. However, publicly available UHII datasets are limited and have several constraints: (1) they are for clear-sky surface UHII, not all-sky surface UHII and canopy (air temperature) UHII; (2) the estimation methods often neglect anthropogenic disturbance, introducing uncertainties in the estimated UHII. To address these issues, this study proposes a new dynamic equal-area (DEA) method that can minimize the influence of various confounding factors on UHII estimates through a dynamic cyclic process. Utilizing the DEA method and leveraging various gridded temperature data, we develop a global-scale (>10,000 cities), long-term (over 20 years by month), and multi-faceted (clear-sky surface, all-sky surface, and canopy) UHII dataset. Based on these estimates, we provide a comprehensive analysis of the UHII and its trends in global cities. The UHII is found to be greater than zero in >80% of cities, with global annual average magnitudes around 1.0 °C (day) and 0.8 °C (night) for surface UHII, and close to 0.5 °C for canopy UHII. Furthermore, an interannual upward trend in UHII is observed in >60% of cities, with global annual average trends exceeding 0.1 °C/decade (day) and over 0.06 °C/decade (night) for surface UHII, and slightly surpassing 0.03 °C/decade for canopy UHII. Notably, there exists a positive correlation between the magnitude and trend of UHII, suggesting that cities with stronger UHII tend to experience faster growth in UHII. Additionally, discrepancies in UHII are found between different temperature data, stemming not only from distinctions in data types (surface or air temperature) but also from differences in data acquisition times (Terra or Aqua), weather conditions (clear-sky or all-sky), and processing methodologies (with or without gap filling). Overall, our proposed method, dataset, and analysis results have the potential to provide valuable insights for future urban climate studies. The UHII dataset is publicly available at https://doi.org/10.6084/m9.figshare.24821538. • A new dynamic equal-area (DEA) method is proposed to estimate UHII. • Our UHII dataset spans over 20 years and covers 10,000+ global cities. • Our UHII dataset includes surface (clear-sky and all-sky) and canopy UHIIs. • The trend of UHII is found to be positively correlated with its magnitude. [ABSTRACT FROM AUTHOR]

Abstract Suburbs have the potential for future wind energy development because of their open terrain, good wind conditions, closeness to the city, wind power that can be absorbed in place, and other advantages. Therefore, accurately evaluating the wind energy resources in the suburbs is particularly important. The three‐parameter Weibull distribution can accurately describe the wind speed in the suburbs, but research on the estimation of wind power density (WPD) with it is limited. On the basis of the mathematical models of wind speed probability distribution as Rayleigh, two‐parameter Weibull, and three‐parameter Weibull, a new WPD estimation formula with three‐parameter Weibull distribution is derived using the partial integration method and by introducing incomplete gamma function to solve the transcendental integration. A field experiment platform is built, and the accuracy of the formula is verified by the measured data. The analysis of the measured data collected in recent 3 years in the suburbs of Hohhot, Inner Mongolia, concludes that the statistical characteristics of the measured wind speed and the distribution of wind energy resources are more conducive to capture the scattered and changing wind energy in the suburbs by taking the month as the time scale. Moreover, the three‐parameter Weibull distribution estimated by the least square method can be used as the local wind speed model. The new method of WPD estimation can also reduce the calculation error by approximately 21.06%. This study provides an important reference for the subsequent analysis of wind power fluctuation characteristics and wind energy development planning.

Abstract Background Periodontal disease is the most common dental disease in dogs. Although the systemic effects of periodontal disease have not been clarified in veterinary science, it is necessary to evaluate the effects of periodontal disease in clinical trials in the future. There have been a few clinical attempts made, however, to assess the severity of periodontal inflammation and its impact on the systemic health of dogs. Meanwhile, in the field of dentistry for humans, the periodontal inflamed surface area (PISA) and periodontal epithelial surface area (PESA) have been used to quantitatively assess the degree of periodontal disease affecting a single tooth as well as the overall extent of periodontitis. Recent studies have also suggested the use of these assessments to examine the relationship between periodontal inflammation and systemic health. Results The estimation formula for a dog’s periodontal pocket surface area (PPSA), an alternative to PISA and PESA in humans, was established using body weight and periodontal pocket depth. Actual values were measured using extracted teeth from various dog breeds and sizes (2.3–25.0 kg of body weight) to obtain universal regression equations for PPSA. Altogether, 625 teeth from 73 dogs of 16 breeds were extracted and subsequently analyzed for morphological information. PPSA was measured in 61 dogs of 10 breeds with periodontal disease using the established estimation formulas, and the correlation between PPSA and preoperative blood chemistry data was analyzed accordingly. A strong correlation was found between PPSA and serum globulin (r = 0.71) while moderate correlations were found for C-reactive protein (r = 0.54) and serum albumin (r = -0.51). Conclusions Estimation formulas using body weight and the 6-point probing depth were established for determining PPSA. Direct correlations between PPSA and several blood test results were observed in the study sample. Taken together, these results suggest that PPSA could be useful for evaluating the effects of periodontitis on systemic conditions in dogs.

This study adopts a practical approach to establish the estimation and adjustment methods of the interface stiffness for the machine-tool through the finite element analysis (FEA). First, numerical and experimental modal analysis (FMA and EMA) for each single subsystem of the machine-tool structure are performed. Then, the parameters obtained from EMA are used as the objective criterion function, and the FMA is conducted iteratively to solve the material Young's modulus and Poisson's ratio for each single subsystem structure, in which the geometrical model is simplified and FE mesh convergence is performed to ensure the quality and efficiency of the numerical analysis. Next, by considering that the machine-tool is assembled by subsystem stacking and that loading on each contact surface is deduced, the FE method is used to calculate the deformation of each contact interface after the single subsystem is stacked successively. The initial value of the interface stiffness estimated by the deformation formula in the mechanics of material is utilized as the initial condition for the iterative calculation in FMA. The changes of the modal parameters are observed in the analysis and the interface, which have a significant impact on the natural frequency variations of the whole machine-tool is selected as the main adjustment object. Then, the adjustment method proposed in this study is applied repeatedly to modify this interface stiffness. The results show that footing interface stiffnesses play the most important role that intensively affects the numerical analysis results of modal parameter. After the repeated adjustments of the interface stiffnesses of the footing, the error of the natural frequency of the whole machine-tool is less than 5%, which is calculated from the comparison between EMA and FMA results. It indicates that the proposed adjustment method in this study for footing interface stiffness determination has a valuable reference in practical use. [ABSTRACT FROM AUTHOR]

The Mekong giant catfish (MGC), Pangasianodon gigas, is one of the world's largest catfish species. Endemic to the Mekong River system, the MGC is critically endangered. Six MGC were transported from Thailand to the Gifu World Freshwater Aquarium (GWFA) in Japan in May 2004. The MGC have not been measured since they were introduced to the GWFA due to the challenges associated with their large size. Traditional methods, such as anesthesia and net‐capture, could harm the fish, potentially reducing the population further. However, understanding the ecology of the species is essential to conservation efforts. Seasonal feeding rhythms and long‐term fasting were previously observed in captive MGCs. To investigate the effect of long‐term fasting on MGC growth, total length measurements are necessary. In this study, we applied a noncontact method to estimate the total length via image analysis. We shot a stereo‐video of free‐swimming MGC in the aquarium tank using two digital video cameras and analyzed the 3D images using the direct linear transformation method. We successfully estimated the total length of each MGC individual without contacting the fish. This accurate estimation method is versatile, simple, and useful in aquarium breeding and is also recommended from the viewpoint of animal welfare because it is a noninvasive method of measurement. Research Highlights: •Total length of Mekong giant catfish (MGC) was estimated in an aquarium tank, using stereo‐video recording and direct linear transformation (DLT) method. MGC can be measured safely without contact, preventing injury and stress. This is a useful method for all fish in terms of improving animal welfare. [ABSTRACT FROM AUTHOR]

In order to promote the use of biomass, we derived an equation for estimating the calorific value of biomass solid fuel with varying water content, taking into account the presence or absence of ash and its amount. The usefulness of the derived estimation equation of calorific value was verified using sugi sawdust and fermented poultry manure samples. As a result, the estimated gross calorific value obtained from the estimation equation of calorific value derived using the measurement results of sawdust and poultry manure in the initial condition agrees very well with the gross calorific value of the samples with and without ash, whose water content was changed by drying sawdust or pelletizing poultry manure. From the above results, the usefulness of the derived estimation equation of calorific value was demonstrated, and using this equation, it was clarified that the gross calorific value of the biomass solid fuel at any water content could be estimated. In addition, by using the derived estimation equation of calorific value for the gross calorific value of the sawdust sample on a dry/ash-free basis and the ash content ratio of the poultry manure sample on a dry basis, the gross calorific value of the fermented poultry manure at any water content could be estimated to some extent. [ABSTRACT FROM AUTHOR]

The Global Navigation Satellite System (GNSS) has been widely used in every area of our daily life to provide accurate Positioning, Navigation, and Timing (PNT) services. However, due to the multipatch effect and an obstructed view of the satellite, GNSS receivers are susceptible to large-ranging errors, which are particularly prevalent in urban areas where precise positioning is indispensable. The deployment of the high-spatial-density Fifth-Generation (5G) network makes it possible to provide a broad area with high-precision positioning service. Obviously, it promoting the deep integration of the GNSS system and the 5G mobile communication network and establishing a Highly Dependable Spatio-temporal Network (HDSN) have become an inevitable trend. The existing algorithm for the fusion of multiple signals has difficulty settling problems such as the fast fluctuation of available signal sources and the poor stability of multi-scale multi-type signal estimation in GNSS-5G hybrid networks. Here, we propose a Square Root Unscented Stable Filter (SRUSF) for GNSS and 5G joint positioning with a compact coupled filter group architecture in a highly dependable spatio-temporal network. A stabilized coefficient is added to guarantee positive covariance of the estimation error. The possibility of divergence of filtering results due to the variation in signal sources and the incomplete agreement between the system model and the actual situation are reduced. The simulation results show that the proposed SRUSF method substantially enhances the positioning accuracy and reliability compared with the other five joint estimation methods for multiple signals. This work will enable the terminal of mass users to provide timing and positioning services with unprecedented accuracy and dependability under the GNSS and 5 G-based spatio-temporal network’s architecture.

Research question: This paper examines the overarching question of to what extent different analytic choices may influence the inference about country-specific cross-sectional and trend estimates in international large-scale assessments. We take data from the assessment of PISA mathematics proficiency from the four rounds from 2003 to 2012 as a case study. Methods: In particular, four key methodological factors are considered as analytical choices in the rescaling and analysis of the data: (1) The selection of country sub-samples for item calibration differing at three factor levels. (2) The item sample refering to two sets of mathematics items used within PISA. (3) The estimation method used for item calibration: marginal maximum likelihood estimation method as implemented in R package TAM or an pairwise row averaging approach as implemented in the R package pairwise. (4) The type of linking method: concurrent calibration or separate calibration with successive chain linking. Findings: It turned out that analytical decisions for scaling did affect the PISA outcomes. The factors of choosing different calibration samples, estimation method and linking method tend to show only small effects on the country-specific cross-sectional and trend estimates. However, the selection of different link items seems to have a decisive influence on country ranking and development trends between and within countries. [ABSTRACT FROM AUTHOR]

BackgroundInfluenced by individual differences, the depth of gastric tube placement is often different. Clinically, it is necessary to seek a simple and accurate gastric tube insertion scheme to improve the clinical efficacy of indwelling gastric tube.Materials and MethodsA total of 100 adult patients undergoing transesophageal manometry via nose were included in the study. The in vivo length (NCL) of apex-cardia was measured. At the same time, we entered our institutional database, summarized the clinical data of 100 patients, and analyzed the risk factors affecting NCL using stepwise regression analysis.ResultsThe NCL length scores of patients with different gender, age, marital status, height, weight, BMI, sitting height, sternum length, hairline-xiphoid process, nose tip-earlobe-xiphoid process and earlobe-xiphoid process were statistically significant (P

Potential evapotranspiration is an essential parameter for regional water balance analysis. To realize the accurate calculation of potential evapotranspiration when data are lacking, scientific evaluation of the applicability of the simplified potential evapotranspiration algorithm in the headwater area of the Yellow River is carried out. Based on daily observation data of temperature, precipitation, relative humidity, wind speed, and sunshine hours from 11 meteorological stations in the headwater area of the Yellow River from 1970 to 2018, the calculation accuracy of six simplified methods for potential evapotranspiration calculation are analyzed by using the FAO Penman-Monteith (PM) method as a reference method from the aspects of the year, month and spatial scales, including the Priestly-Taylor (PT) method, Doorenbos-Pruitt (DP) method, Hargreaves-Samani (HS) method, Rohwer (RO) method, Thornthwaite (TH) method and Blaney-Criddle (BC) method. The results show that the mean deviation between the HS method and the PM method, only 3.487 mm/mon, is the lowest in the headwater area of the Yellow River and the accuracy is the highest. However, the HS method does not consider the influence of the average relative humidity on potential evapotranspiration estimation, resulting in low accuracy in the humid climate of Hongyuan County, Henan County, Ruoergai County, Maqu County and Jiuzhi County in the southeast of the headwater area of the Yellow River. Therefore, this article introduces the average relative humidity factor to modify the HS method and evaluates the application effect of the improved HS method. It is found that after the HS method is revised, the mean deviation of the overall annual potential evapotranspiration in the headwater area of the Yellow River decreases from −22.008 mm/a to 6.174 mm/a, and the mean deviation of the monthly potential evapotranspiration decreases from 3.487 mm/mon to 1.031 mm/mon, respectively. On the spatial scale, the mean deviation of the above 5 counties decreases significantly, with an average drop of 5.33 mm/mon, indicating that the improved HS method can effectively solve the problem of low accuracy in the southeast of the headwater area of the Yellow River, and can provide a reference for the simplified calculation of potential evapotranspiration in the headwater area of the Yellow River.

A new class of statistical distributions called the Type II half-Logistic odd Fréchet-G class is proposed. The new class is a continuation of the unusual Fréchet class. This class is analytically feasible and could be used to evaluate real-world data effectively. The new suggested class of distributions has many new symmetrical and asymmetrical sub-models. We propose new four sub-models from the new class of distributions which are called Type II half-Logistic odd Fréchet exponential distribution, Type II half-Logistic odd Fréchet Rayleigh distribution, Type II half-Logistic odd Fréchet Weibull distribution, and Type II half-Logistic odd Fréchet Lindley distribution. Some statistical features of Type II half-Logistic odd Fréchet-G class such as ordinary moments (ORMs), incomplete moments (INMs), moment generating function (MGEF), residual life (REL), and reversed residual life (RREL) functions, and Rényi entropy (RéE) are derived. Six methods of estimation such as maximum likelihood, least-square, a maximum product of spacing, weighted least square, Cramér-von Mises, and Anderson–Darling are produced to estimate the parameters. To test the six estimation methods' performance, a simulation study is conducted. Four real-world data sets are utilized to highlight the importance and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

State of health (SOH) is the ratio of the currently available maximum capacity of the battery to the rated capacity. It is an important index to describe the degradation state of a pure electric vehicle battery and has an important reference value in evaluating the health level of the retired battery and estimating the driving range. In this study, the random forest algorithm is first used to find the most important health factors to lithium-ion batteries based on the dataset released by National Aeronautics and Space Administration (NASA). Then the support vector regression (SVR) algorithm is developed to predict the SOH of a lithium-ion battery. The genetic algorithm-particle swarm optimization (GA-PSO) algorithm is brought forward to optimize the parameter values of the SVR, which could improve the estimation accuracy and convergence speed. The proposed SOH estimation method is applied to four batteries and gets a root mean square error (RMSE) of 0.40% and an average absolute percentage error (MAPE) of 0.56%. In addition, the method is also compared with genetic algorithm-support vector regression (GA-SVR) and particle swarm optimization-support vector regression (PSO-SVR), respectively. The results show that (i) compared with the PSO-SVR method, the proposed method can decrease the average RMSE by 0.10%, and the average MAPE by 0.17%; (ii) compared with the GA-PSO method, number of iterations under the proposed method can be reduced by 7 generations. [ABSTRACT FROM AUTHOR]

The variable-weather-parameter (VWP) methods have the fastest maximum power point tracking (MPPT) speed because their control signals are directly calculated by the measured weather data (including irradiance and temperature (I&T) data). However, they are suffering from the high hardware cost of the I&T sensors. To solve this problem, an estimation method to estimate the real-time I&T values is proposed. In this method, an equation set and two empirical equations are established to match the changeless and varying weather conditions, respectively. Based on them, a VWP optimization strategy (VWPOS) is proposed. It is without using I&T sensors (or external I&T data) that the advantage of the MPPT rapidity is inherited from the VWP methods. Finally, some simulation experiments involving the VWPOS are conducted, and the results show that the estimation method is accurate and workable regardless of the changeless or varying weather conditions. Meanwhile, simulation results also show that the tracking speed of an existing MPPT method can be greatly optimized by the VWPOS even if the I&T sensors or external I&T data are not used. In addition, simulation results still show that, when the conventional P&O method is optimized by the VWPOS, the average error of its control signal at the MPP can be decreased from 0.025% to 0.005%, and the settling time of its output power can be decreased to at least one-third of the original value. With this work, for the existing VWP methods, the trouble arising from the hardware cost of the I&T sensors can be prevented, which is beneficial to their widespread use. [ABSTRACT FROM AUTHOR]

For the past decades, computational methods have been developed to predict various interactions in biological problems. Usually these methods treated the predicting problems as semi-supervised problem or positive-unlabeled(PU) learning problem. Researchers focused on the prediction of unlabeled samples and hoped to find novel interactions in the datasets they collected. However, most of the computational methods could only predict a small proportion of undiscovered interactions and the total number was unknown. In this paper, we developed an estimation method with deep learning to calculate the number of undiscovered interactions in the unlabeled samples, derived its asymptotic interval estimation, and applied it to the compound synergism dataset, drug-target interaction(DTI) dataset and MicroRNA-disease interaction dataset successfully. Moreover, this method could reveal which dataset contained more undiscovered interactions and would be a guidance for the experimental validation. Furthermore, we compared our method with some mixture proportion estimators and demonstarted the efficacy of our method. Finally, we proved that AUC and AUPR were related with the number of undiscovered interactions, which was regarded as another evaluation indicator for the computational methods. [ABSTRACT FROM AUTHOR]

Sanitary sewage network is relatively rarely considered as the cause of urban floods. Its hydraulic overload can result not only in flooding, but also sanitary contamination of subcatchments. Stormwater is the main reason for this overload. In contrast to the stormwater or combined sewer system, these waters infiltrate into the network in an uncontrolled way, through ventilation holes of covers or structural faults and lack of tightness of manholes. Part of stormwater infiltrates into the soil, where it leaks into pipelines. This greatly hinders assessing the quantity of stormwater influent into the sanitary sewer system. Standard methods of finding correlation between rainfall and the intensity of stormwater flow are ineffective. This is confirmed, i.a. by the studies performed in an existing network, presented in this paper. Only when residuals analysis was performed using the ARIMA and ARIMAX methods, the authors were able to develop a mathematical model enabling to assess the influence of rainfall depth on the stormwater effluent from the sewage network. Owing to the possibility of using the rainfall depth forecasts, the developed mathematical model enables to prepare the local water and sewerage companies for the occurrence of urban floods as well as hydraulic overload of wastewater treatment plants. [ABSTRACT FROM AUTHOR]