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In the context of global energy transformation, the construction of smart grids is becoming a novel vogue in the evolution of power systems. As the core node of the smart grid, the efficient operation of the intelligent substation relay protection system is essential to the safety and stability of the power system. However, with the expansion of power grid-scale and complexity, traditional relay protection systems need help with fault diagnosis accuracy and response speed. This study proposes a fault diagnosis scheme of an intelligent substation relay protection system based on Transformer architecture and migration training model, aiming at improving the intelligent level of fault diagnosis. By introducing the Transformer architecture, the model can efficiently process high-dimensional and nonlinear complex data of substations, significantly improving the accuracy of fault pattern recognition from 82% of the original model to 96%, and the response speed is also increased by 30%. At the same time, using transfer learning technology, the adaptability and generalization capabilities of the model in new scenarios have been significantly enhanced, reducing the dependence on a large amount of new data and accelerating the deployment of the model among different substations. The experimental results show that this scheme can quickly and accurately identify various fault types and effectively locate fault points. This study not only promotes the development of intelligent technology for power systems but also lays a solid foundation for the safe and stable operation of smart grids and the sustainable development of the power industry. [ABSTRACT FROM AUTHOR]

Achieving increased fault tolerance in an electric motor requires decisions to be made about the type and specifications of the motor machine and its appropriate design. Depending on the type of motor, there are generally three ways to achieve an increased resistance of the drive system to tolerate resulting faults. The simplest way is to select the right motor and design it appropriately. Switched reluctance motors (SRMs) have a high tolerance for internal faults (in the motor windings). Failure tolerance can be improved by using parallel paths. The SRM 24/16 solution has been proposed, which allows for operation with four parallel paths. In this paper, a mathematical model designed to analyse the problem under consideration is provided. Based on numerical calculations, the influence of typical faults (open and partial short circuit in one of the paths) on the electromagnetic torque generated as well as its ripple and (source and phase) currents were determined. The higher harmonics of the source current (diagnostic signal) were determined. Laboratory tests were performed to verify the various configurations for the symmetric and emergency operating states. The feasibility of SRM correct operation monitoring was determined from an FFT analysis of the source current. [ABSTRACT FROM AUTHOR]

Abstract In the context of global energy transformation, the construction of smart grids is becoming a novel vogue in the evolution of power systems. As the core node of the smart grid, the efficient operation of the intelligent substation relay protection system is essential to the safety and stability of the power system. However, with the expansion of power grid-scale and complexity, traditional relay protection systems need help with fault diagnosis accuracy and response speed. This study proposes a fault diagnosis scheme of an intelligent substation relay protection system based on Transformer architecture and migration training model, aiming at improving the intelligent level of fault diagnosis. By introducing the Transformer architecture, the model can efficiently process high-dimensional and nonlinear complex data of substations, significantly improving the accuracy of fault pattern recognition from 82% of the original model to 96%, and the response speed is also increased by 30%. At the same time, using transfer learning technology, the adaptability and generalization capabilities of the model in new scenarios have been significantly enhanced, reducing the dependence on a large amount of new data and accelerating the deployment of the model among different substations. The experimental results show that this scheme can quickly and accurately identify various fault types and effectively locate fault points. This study not only promotes the development of intelligent technology for power systems but also lays a solid foundation for the safe and stable operation of smart grids and the sustainable development of the power industry.

With the increasing uncertainty of the external environment, the capabilities of companies become the driving force behind their operations and development, making the management of these capabilities an important task for management. The aim of the research is to develop a toolkit for measuring a company’s capabilities, assessing their condition, and evaluating their impact on productivity. The author’s concept is presented, according to which the final results of the company’s activity are formed through the mechanism of interaction of the company’s organizational capability with acquired resources, the formation of resource capabilities (production, financial, labor, and market) and their transformation into tangible and intangible products. Based on this model, methodological guidelines have been developed for measuring the capabilities of organizations and assessing their impact on the final results, a value-functional approach to selecting appropriate indicators, and analytical tools. The following methods have been substantiated: modeling, the indicator method, data convolution, scaling, and statistical methods. To measure capabilities, it is proposed to use indicators aggregated into composite indices through a developed algorithm. The use of sales growth rate, market capitalization, profit before taxes, and net cash flow as the final results of the company (productivity indicators) has been justified. To solve analytical tasks at different levels of management, combinations of methods, tools and indicators called “research formats” (detailed, diagnostic, express diagnostic analysis) were developed. The results of the empirical testing of the author’s research are presented: through detailed analysis, the validity of the proposed approach and tools has been established, and through diagnostic analysis, the stability of the selected indicators and constructed mathematical models beyond the observation period was identified. The result is a toolkit for researching crucial company’s capabilities, enabling management to analyze and monitor these capabilities, assess their productivity, make forecasts of productivity indicators, and develop a system for managing the organization’s capabilities.

From 31 January to 5 February, 2024, Henan Province experienced a rare freezing rain and snow process characterized by a long duration,substantial cumulative precipitation,significant snow depth,low temperatures,and complex precipitation phases.Based on the national meteorological observation data,dual-polarization radar data,and hourly reanalysis data of ERA5,a diagnostic analysis of the event's extremity,phase evolution,and causes of the freezing rain and snow was conducted.The results are as follows.During the process,5 national meteorological stations reached or exceeded the maximum snow depth of the historical records in February.At many stations,the minimum temperature has reached or set a new historical record in early February.Precipitation was observed from at least 68.3% of national meteorological observation stations in a single day,with the longest continuous precipitation at a single station being nearly 29 hours.The stable maintenance of a large-scale circulation system was one of the main reasons for this process.The persistent intrusion of the strong cold air in the lower troposphere provided both dynamic uplift and sustained cooling conditions for the freezing rain and snow process.Exceptionally strong southerly airflow provided ample moisture and momentum,enhancing unstable stratification.For precipitation phase identification,relying solely on a single-layer temperature is insufficient.Instead,the entire atmospheric layer temperature should be considered to predict possible precipitation phases.When the entire atmospheric layer temperature is below 0℃,precipitation is pure snow.If there is a melting layer or the cloud consists of supercooled water droplets,mixed precipitation will occur.Dual-polarization radar's correlation coefficient and differential reflectivity products are useful for distinguishing between sleet and pure snow.A correlation coefficient close to 1 and differential reflectivity between-1 and 0 dB indicates pure snow,while a correlation coefficient less than 1 and differential reflectivity greater than 1 dB indicate sleet.

Abstract The compositional nutrient diagnosis—CND method is a standard tool for evaluating plant nutritional status. Adjustments are crucial to elevate accuracy. The effectiveness of such methodological refinements should be rigorously assessed through accuracy tests that are benchmarked against the prescient diagnostic analysis—PDA methodology. The objective of this investigation was to refine the CND technique for a more precise evaluation of N, P, and B nutrient status in cotton. The study’s database encompasses 144 data points pertaining to crop yield and foliar nutrient concentrations from cotton plantations in the Cerrado biome of Brazil. Subsequently, the CND norms were established through rigorous calibration. Three separate nutrient-dose trials, each featuring four levels of N, P and B, were carried out to assess plant true nutritional status. Adjustments were made to the nutrient responsiveness range—NRr (0.5 and 1.0), while yield response—YR were scrutinized at threshold levels (5% and 10%). The prerequisites for achieving high diagnostic accuracy were nutrient specific. For N, maximal accuracy was linked only to the YR parameter (YR = 10%). For P, the most precise outcomes were attained with a NRr = 0.5 and YI = 5%. For B, highest diagnostic accuracy when the NRr = 1.0 and YI = 10%. These insights highlight the need to fine-tune the CND method for reliable nutritional evaluations and cotton crop productivity optimization.

The compositional nutrient diagnosis—CND method is a standard tool for evaluating plant nutritional status. Adjustments are crucial to elevate accuracy. The effectiveness of such methodological refinements should be rigorously assessed through accuracy tests that are benchmarked against the prescient diagnostic analysis—PDA methodology. The objective of this investigation was to refine the CND technique for a more precise evaluation of N, P, and B nutrient status in cotton. The study’s database encompasses 144 data points pertaining to crop yield and foliar nutrient concentrations from cotton plantations in the Cerrado biome of Brazil. Subsequently, the CND norms were established through rigorous calibration. Three separate nutrient-dose trials, each featuring four levels of N, P and B, were carried out to assess plant true nutritional status. Adjustments were made to the nutrient responsiveness range—NRr (0.5 and 1.0), while yield response—YR were scrutinized at threshold levels (5% and 10%). The prerequisites for achieving high diagnostic accuracy were nutrient specific. For N, maximal accuracy was linked only to the YR parameter (YR = 10%). For P, the most precise outcomes were attained with a NRr = 0.5 and YI = 5%. For B, highest diagnostic accuracy when the NRr = 1.0 and YI = 10%. These insights highlight the need to fine-tune the CND method for reliable nutritional evaluations and cotton crop productivity optimization. [ABSTRACT FROM AUTHOR]

This paper introduces a new family of quantile regression models whose response variable follows a reparameterized Marshall-Olkin distribution indexed by quantile, scale, and asymmetry parameters. The family has arisen by applying the Marshall-Olkin approach to distributions belonging to the location-scale family. Models of higher flexibility and whose structure is similar to generalized linear models were generated by quantile reparameterization. The maximum likelihood (ML) method is presented for the estimation of the model parameters, and simulation studies evaluated the performance of the ML estimators. The advantages of the family are illustrated through an application to a set of nutritional data, whose results indicate it is a good alternative for modeling slightly asymmetric response variables with support on the real line. [ABSTRACT FROM AUTHOR]

Pakistan is situated in an earthquake-prone area, and in the past, many destructive earthquakes have occurred including the most destructive 1935 Quetta, and 2005 Kashmir, earthquakes. Therefore, it is of vital importance to investigate the statistical properties of the earthquake temporal data of Pakistan. The present study brings novelty through the vast extension of the linear and nonlinear autoregressive conditional duration models in the field of seismology. A simple duration model capable of describing and forecasting the earthquake elapsed times data has been developed. Different specifications of the duration models were considered to completely extract the time-dependent structure in earthquake data. This study aims to identify the most suitable in-sample fitted and out-of-sample forecasting models for the earthquake elapsed times data of Pakistan. A variety of autoregressive conditional duration models were applied to the complete and updated earthquake catalog of Pakistan. The most suitable model was chosen through statistical model evaluation techniques. The method of maximum likelihood was used to estimate the model parameters. The adequacy of fitted models is assessed through residual analysis. The obtained results suggest that the Logarithmic Autoregressive Conditional Duration model of type 2 (LACD2) appeared as the most suitable in-sample fitted model for describing the earthquake temporal data of the different zones of Pakistan. Further, the simple autoregressive conditional duration (ACD) model outperforms the remaining considered models regarding 1-through-4 steps-ahead out-of-sample forecasting performance for both India–Eurasia collision (IEC) and Makran subduction (MS) zones. Autoregressive models with Burr and exponential distributions as assumed error distributions have appeared as the most suitable fitted models for IEC and MS zones, respectively. The residuals analysis results show that the most suitable fitted models are correctly identified. The results show that the earthquake short-term (h = 1) forecasting with duration models is more accurate in comparison with earthquake long-term (h = 1 , 2 , 3) forecasting. The forecasted elapsed times for IEC and MS zones are 0.60 and 2.94 years, respectively. The obtained results show that the autoregressive conditional models are a more useful tool for forecasting the earthquake elapsed times for short term in comparison with the long-term forecasting. Hence, autoregressive conditional models are capable of modeling and forecasting the earthquake temporal data of the different regions of Pakistan. Among the competing models, the best fit model can serve the purpose of data description, missing value estimation in earthquake catalogs and uncertainty quantification in the earthquake occurrence process. The most suitable forecasted model yields the future earthquake occurrence trend in the study region. [ABSTRACT FROM AUTHOR]

Purpose: Immune checkpoint inhibitors-related myocarditis (ICI-M) is one of the immune-related adverse events (irAEs), which is rare and highly lethal. This study aimed to establish nomograms based on ratio biomarkers to predict the severity and prognosis of ICI-M. Methods: We retrospectively examined patients with advanced cancers who were also diagnosed with ICI-M at the Fourth Hospital of Hebei Medical University. The patients of ICI-M were divided into mild and severe groups and a 40-day following up was carried out. The major adverse cardiovascular events(MACEs) were regarded as the endpoint. Nomogram-based models were established and validated. Results: Seventy-seven patients were involved, including 31 severe cases(40.3%). Lactate dehydrogenase-to-albumin ratio(LAR) combined with the change rate from baseline to onset of LAR(▵ LAR) which performed best to diagnose the severe ICI-M was identified to establish the nomogram-based model. The bootstrap-corrected concordance index [0.752 95% confidence interval (CI): 0.635 - 0.866] and calibration plot with good degree of fitting confirmed this diagnostic model. Neutrophil-to-high-density lipoprotein cholesterol ratio(NHR) and LAR were also screened into the nomogram-based model for 40-day MACEs after ICI-M, which performed well by validating for concordance index(0.779 95% CI: 0.677 - 0.865)and calibration plots after being bootstrap-corrected. Moreover, a ≥ 101% increase in LAR significantly separated patients in MACE-free survival. Conclusion: Ratio indexes at onset and their change rates from baseline showed good diagnostic value for the severity of ICI-M and prognostic value for subsequent MACEs, particularly LAR, NHR and their change rates. The nomogram-based models of ratio indexes could provide a potential choice for early detection and monitor of the severe ICI-M and subsequent MACEs. [ABSTRACT FROM AUTHOR]

Based on the observation data of meteorological stations, Doppler radar observation data of Ulanqab City, and ERA-5 reanalysis data, a snowstorm process in Ulanqab City from March 17 to 18, 2022 was analyzed. The results show that this was a type II snowstorm process generated under the joint influence of upper trough and ground low inverted trough and frontal cyclone. The main period of snowfall can be divided into two time stages, and the total snowfall was more in the south and less in the north, which was consistent with that of average specific humidity field. Water vapor conditions provided by strong water vapor transport and convergence, strong upward movement shown by large vertical velocity field, and the suction action of highand low-layer divergence and convergence were the reasons for the hourly heavy snowfall on the 18"'. During the process, radar echoes were mainly sheetshaped, and composite reflectivity was 15 -25 dBZ in most areas. The zero speed line in the first period was positively "S" -shaped, and there was warm advection and southwest wind. On the morning of the 18"', after the cold front transited the city, Ulanqab City was gradually controlled by northwest wind, and the snow tended to end. [ABSTRACT FROM AUTHOR]

In this paper, we introduce a relatively simple data-driven method for the representation of the uncertainty in daily discharge records. The proposed method relies only on hourly discharge data and takes advantage of a nonparametric difference-based estimator in the characterization of random errors in discharge time series. We illustrate with corrupted streamflow data that the nonparametric estimator provides an accurate characterization of the nature (homoscedastic or heteroscedastic) and magnitude of these errors. In addition, we demonstrate the practical usefulness of the estimator using discharge time series of 500+ watersheds of the Catchment Attributes and MEteorology for Large-sample Studies data set. This analysis reveals that the magnitude of errors of aleatory nature in the investigated discharge records is rather small (less than 3% for 80% of the records). We then combine the effect of random errors and measurement frequency into a daily variance estimate, which serves as input to a streamflow generation approach. This procedure produces replicates of the discharge record which portray accurately the assigned streamflow uncertainty, preserve key statistical properties of the discharge record and are hydrologically realistic. The proposed method facilitates Bayesian analysis and supports tasks such as model diagnostics, data assimilation, uncertainty quantification and regionalization.

Achieving increased fault tolerance in an electric motor requires decisions to be made about the type and specifications of the motor machine and its appropriate design. Depending on the type of motor, there are generally three ways to achieve an increased resistance of the drive system to tolerate resulting faults. The simplest way is to select the right motor and design it appropriately. Switched reluctance motors (SRMs) have a high tolerance for internal faults (in the motor windings). Failure tolerance can be improved by using parallel paths. The SRM 24/16 solution has been proposed, which allows for operation with four parallel paths. In this paper, a mathematical model designed to analyse the problem under consideration is provided. Based on numerical calculations, the influence of typical faults (open and partial short circuit in one of the paths) on the electromagnetic torque generated as well as its ripple and (source and phase) currents were determined. The higher harmonics of the source current (diagnostic signal) were determined. Laboratory tests were performed to verify the various configurations for the symmetric and emergency operating states. The feasibility of SRM correct operation monitoring was determined from an FFT analysis of the source current.

act The clothing industry has recently experienced a difficult phase in Romania as a result of the COVID 19 crisis, the invasion of Russia in Ukraine, in a general sense, and the liberalization of the clothing trade, the reduction of lohn contracts and the transition of periods of economic crisis, in particular meaning. This paper aims to present the methods by which the expenses from the production activity of the companies in the Romanian clothing industry can be analyzed and reduced. We consider this scientific research necessary because the overall analysis of expenses allows evaluating the credible and complete financial performance of the targeted activity sector. The empirical study presents a factorial analysis of expenses in the clothing industry at the microeconomic level - the chosen commercial company is representative in Romania, and the research study is conducted between 2016-2022. Following the establishment of the necessary corrections to ensure healthy principles of carrying out clothing manufacturing activities at the analyzed company, the performance analysis results through operating expenses, financial expenses and those related to income showed a low efficiency from an economic perspective in most periods

The purpose of this article is, through a comparative analysis between the development of the call-center industry in the four macroregions of Romania, to identify ways of attracting investors to areas with low attractiveness for this field of activity. Knowledge of the activity sector is a condition for identifying ways to develop call-center services in areas not yet exploited. In chapter 4.1, an overview of the call-center industry in Romania was made. The comparative study between the development of the call-center industry in Romania's macroregions was carried out by comparing the business environment of call-center companies and the evolution of these companies. The study is presented in chapter 4.2. The conclusions of the study include the causes of the gap between macroregions, detected within the analyzes carried out and ways to increase the investment attractiveness of disadvantaged areas.

The purpose of this study is to carry out a diagnostic analysis of the quality management system (QMS) in the Romanian call center industry In order to achieve this goal, the method of selective research was used, carrying out an investigation in the relevant companies. The sample was made up in such a way that the condition of representativeness in relation to the general community of the call-center industry is fulfilled. The questionnaire contained questions regarding the elements (processes) from which is compose quality management system, because the diagnosis must be made by analyzing the elements that from which the QMS is compose. In the second chapter, the QMS diagnosis was presented, analyzing the four elements that, according to the specialized literature, make the quality management system The third chapter analyzes the current status of quality management system implementation in the call center industry. Through the diagnostic analysis, the QMS dysfunctions most frequently faced by the call center industry were identified. Knowing the causes that determine nonquality, remedial and quality improvement solutions presented in the conclusions section were formulated.

The convection initiation (CI) mechanisms of severe storms have received increasing attention because severe storms have been occurring more frequently around the globe in recent years. In this work, the CI mechanisms of severe convective weather associated with a gust front (GF) which occurred on 9 July 2016, near the Korla at the northern edge of the Tarim Basin, Xinjiang, is investigated using observational data including Doppler weather radar data and automatic weather stations data, and high-resolution numerical simulation data. The results showed that, during the eastward movement of the GF, a number of convective cells were successively triggered in the vicinity of the GF, which developed rapidly and continuously merged with the convective system from behind, resulting in the further development and maintenance of this convective system. According to the diagnostic analysis of vertical acceleration which can be decomposed into dynamic acceleration ( a d ) and buoyant acceleration ( a b ), it was found that both a d (up to ~4 × 10−3 m s−2) and a b (up to ~7 × 10−3 m s−2) made positive contributions to the CI. Further analyses based on the decompositions of the a d and a b revealed that the extension term was the main contributor for the a d , while the warming of the dry air due to the release of latent heat from the precipitation condensate made the major contribution to the a b . The extension term indicates the elevated convergence jointly induced by the airflow of mid-level horizontal convective rolls (MHCRs) and updraft flow near the leading edge of the GF. The jointly induced elevated convergent updraft can be considered to be an important contributor for the CI mechanisms. [ABSTRACT FROM AUTHOR]

Using the ERA5 reanalysis data, observational data from automated weather stations, and local radar data, this paper presents a comparative study to analyze the rainfall, weather patterns, physical parameters, and radar echo characteristics during two extreme torrential rainfall events on May 20--22, 2020 (Process 1) and on June 6--8, 2020 (Process 2) in the Shenzhen-Shanwei Special Cooperation Zone (SSCZ). The results show that both of the two events occurred under the background of favorable largescale circulation, and low-level jet (LLJ) streams provided sufficient water vapor. Meanwhile, the transport of low-level warm and humid airflow kept the low level of the rainstorm area under the control of high energy and high humidity for a long time, and the strong convergent upward motion provided favorable dynamic conditions. A surface convergence line was formed under the influence of a warm and moist airflow from the ocean and a southward cold airflow along the Lianhua Mountain. The difference was that the low-level wind speed in the rainfall area was stronger and the strengthening was more obvious, and the intensity of the mesoscale kinetic energy front was stronger in Process 1. In Process 2, the large value of water vapor flux was more northerly, and the unstable atmospheric stratification in the rainfall area was maintained for a longer period of time. The height range of the large value area of vertical motion was wider. The radar echo of Process 1 was a strong mono echo moving northward to the surface convergence line of the SSCZ. Subsequently, it developed rapidly and moved less, and was accompanied by a meso-γ-scale vortex. In Process 2, the radar echo was a linear strong echo that was triggered continuously near the long-maintained surface convergence line and thus the "train effect" was developed. [ABSTRACT FROM AUTHOR]

This paper presents the results of non-invasive diagnostic investigations performed on the canvas oil painting depicting the Marian iconography “Mater Boni Consilii”. The painting, whose author and origin are unknown, was found in an old shop in Florence following the overflowing of the Arno River in 1966. In order to define the importance of the artwork, a multianalytical analysis was performed on the painting, using multispectral imaging, X-ray fluorescence (XRF), and Fourier Transform Infrared Spectroscopy (FTIR-ATR) for the definition of materials, with a particular focus on the identification of pigments. The results allowed for the drawing up of a color palette, composed mainly of ochre and earth pigments, cinnabar, lithopone, lead white, and ultramarine pigments. After cross-referencing the acquired information with other findings, it was possible to place the painting in the period between the end of the XIXth and the beginning of the XXth centuries.

In the present work, the vibration response of the porous functionally graded piezoelectric plates with electro-thermal loading using finite element formulations has been studied. A sigmoid law is used for the material property deviation along the thickness direction. The first-order shear deformation theory (FSDT) with von Karman nonlinear strains and Hamilton's principle is used to obtain the governing equations. The governing equations are solved by finite element methods with 9-noded iso-parametric rectangular elements with 7 degrees of freedom (DOFs) per node. The accuracy of the results is evaluated by comparing them with the results available in the literature. The analysis shows that the uneven type porosity distribution gives a larger nondimensional frequency than the even type porosity distribution FGP plate. The non-dimensional frequency decreases with increases in the a / h ratio. Among all the conditions, CCCC boundary conditions have a larger frequency than SSSS, SFSF, SFCF, and CFFF. The obtained frequency under applied electric loading depends on the magnitude and the applied voltage sign. As the temperature change (Δ T) increases with the porosity exponent (α) of the FGP plate, the nondimensional frequency is also increasing. The obtained results can design a porous FGP material-based smart composite structure under a thermo-electric environment. [ABSTRACT FROM AUTHOR]

WE investigated the intra-seasonal variation of the winter Polar/Eurasia (PEU) pattern, the dominant circulation pattern over the Eurasian continent, during the period 1979/80–2021/22. The PEU events have centers of action over western Europe, the Urals and the Siberian–Mongolian region. The development of PEU events is associated with a Rossby wave train triggered by anomalous horizontal divergent winds over the North Atlantic and the European continent. Diagnostic analysis based on the quasi-geostrophic geopotential height tendency equation suggests that the cross-frequency dynamics play an important role in the formation and decay of the western Europe center, as well as the formation of the Siberian–Mongolian center. The sole effect of the low-frequency forcing primarily contributes to the life cycle of the Urals center and the decay of the Siberian–Mongolian center. The effect of the cross-frequency forcing and the low-frequency forcing on the PEU centers of action is predominantly due to their advection component. The cross-frequency forcing in Europe involves an interaction between the extratropical storm activity and the low-frequency flow, where the development and decay of PEU events are associated with contrasting storm track anomalies extending from the North Atlantic to the European continent. Overall, the evolution of the intra-seasonal PEU pattern is mainly due to the internal dynamic processes over the extratropics. [ABSTRACT FROM AUTHOR]

The purpose of this study is to carry out a diagnostic analysis of the quality management system (QMS) in the Romanian call center industry In order to achieve this goal, the method of selective research was used, carrying out an investigation in the relevant companies. The sample was made up in such a way that the condition of representativeness in relation to the general community of the call-center industry is fulfilled. The questionnaire contained questions regarding the elements (processes) from which is compose quality management system, because the diagnosis must be made by analyzing the elements that from which the QMS is compose. In the second chapter, the QMS diagnosis was presented, analyzing the four elements that, according to the specialized literature, make the quality management system The third chapter analyzes the current status of quality management system implementation in the call center industry. Through the diagnostic analysis, the QMS dysfunctions most frequently faced by the call center industry were identified. Knowing the causes that determine non-quality, remedial and quality improvement solutions presented in the conclusions section were formulated. [ABSTRACT FROM AUTHOR]

Highlights: A hidden portrait of Werner Tübke's father was revealed. The state of the painting, the texture of the author's brushstroke, the technique of applying the paint layer, the artist's work on the composition, and the types of pigments and binders used were identified. The unique artistic language of Werner Tübke at an early stage of the artist's work was examined. The grounds have been laid for the further comparison of Tübke's early paintings with his later works, as well as his contemporaries' and predecessors'. This article presents the results of technical studies of the oil painting by the artist of German origin Werner Tübke "Hiroshima I" (1958). The creative heritage of this author has not been studied enough and represents scattered data on the technology of painting and artistic techniques. The aim of this work was to determine the art materials and painting technology described in his diaries, using the example of his only painting represented in Russia: "Hiroshima I". For this purpose, an in situ approach was implemented using some simple museum instrumentations—UV-induced visible luminescence, infrared reflectography (IRR), radiography, portable X-ray fluorescence (XRF), Fourier transform Infrared spectroscopy (FT-IR), and polarizing microscopy using microprobes. As a result, the pigment composition of the painting layers could be determined, the painting technology refined, and a previously unknown hidden portrait of Werner Tübke's father revealed. [ABSTRACT FROM AUTHOR]

This paper presents the results of non-invasive diagnostic investigations performed on the canvas oil painting depicting the Marian iconography "Mater Boni Consilii". The painting, whose author and origin are unknown, was found in an old shop in Florence following the overflowing of the Arno River in 1966. In order to define the importance of the artwork, a multianalytical analysis was performed on the painting, using multispectral imaging, X-ray fluorescence (XRF), and Fourier Transform Infrared Spectroscopy (FTIR-ATR) for the definition of materials, with a particular focus on the identification of pigments. The results allowed for the drawing up of a color palette, composed mainly of ochre and earth pigments, cinnabar, lithopone, lead white, and ultramarine pigments. After cross-referencing the acquired information with other findings, it was possible to place the painting in the period between the end of the XIXth and the beginning of the XXth centuries. [ABSTRACT FROM AUTHOR]

By using the Tropical Rainfall Measuring Mission (TRMM) 3B42 Version 7 and the second Modern-Era Retrospective analysis for Research and Applications (MERRA-2) datasets, the spatial distribution and temporal evolution of the precipitation over the Tibetan Plateau (TP) in boreal summer are investigated. The results show that, the climatological precipitation tends to increase towards the southeast, while it increases in the morning, reaches a peak around 21 Beijing Time Coordinate (BTC), and tends to decay after midnight in the diurnal time scale. To understand the roles of atmospheric dynamic and thermodynamic processes in causing the diurnal variation of summer precipitation over the southeastern TP, the atmospheric precipitation and moisture budget are conducted. The budget result shows that, there is a good corresponding relationship between the precipitation and moisture tendency in the night, and the anomalous vertical motion has a key effect on the diurnal variation of the precipitation. In the developing phase of the anomalous ascending motion (17–20 BTC), the anomalous linear vorticity (temperature) advections over the southeastern TP contribute to the anomalous ascending motion, and the major contribution arises from the anomalous vorticity (temperature) field over the western TP. Due to the negative zonal vorticity (temperature) gradient, the mean westerly winds induce positive advection that support the ascending motion. [ABSTRACT FROM AUTHOR]

Parameter estimation using observables is a fundamental concept in the experimental sciences. Mathematical models that represent the physical processes can enable reconstructions of the experimental observables and greatly assist in parameter estimation by turning it into an optimization problem which can be solved by gradient-free or gradient-based methods. In this work, the recent rise in flexible frameworks for developing differentiable scientific computing programs is leveraged in order to dramatically accelerate data analysis of a common experimental diagnostic relevant to laser–plasma and inertial fusion experiments, Thomson scattering. A differentiable Thomson-scattering data analysis tool is developed that uses reverse-mode automatic differentiation (AD) to calculate gradients. By switching from finite differencing to reverse-mode AD, three distinct outcomes are achieved. First, gradient descent is accelerated dramatically to the extent that it enables near real-time usage in laser–plasma experiments. Second, qualitatively novel quantities which require $\mathcal{O}(10^3)$ parameters can now be included in the analysis of data which enables unprecedented measurements of small-scale laser–plasma phenomena. Third, uncertainty estimation approaches that leverage the value of the Hessian become accurate and efficient because reverse-mode AD can be used for calculating the Hessian.

The characteristics and causes of a drop in temperature during a cold wave process in the early winter of 2020/2021 were analyzed. The results show that the air temperature at 700 -600 hPa over China was firstly and mostly influenced by the cold wave process, and then the cold air gradually extended to the lower layer, causing the most severe cooling in North China and its nearby areas. During the cold wave, the longitude of the upper-level jet over the Chinese mainland was larger; the Ural blocking high and the East Asian trough were stronger, so that the geopotential height gradient between the two was also significantly larger; the meridional air flow was abnormally strong, which was conducive to the southward transport of cold air from the middle and high latitudes. Results of the diagnostic analysis further show that the outbreak of the cold wave and the negative temperature tendency anomaly in the key area were mainly caused by the meridional temperature horizontal advection anomaly, while the temperature rise accompanied by abnormal air subsidence compensated for the abnormal decrease in temperature, which was conducive to the gradual rise of temperature in the key area. [ABSTRACT FROM AUTHOR]

"The Alchemist" and "Brida" by Paulo Coelho are two novels that explore themes of personal growth and self- discovery through the use of descriptive and diagnostic analysis models. These models are used to create rich and engaging narratives that draw readers in and allow them to connect with the characters and their journeys. Descriptive analysis is used in both novels to create vivid and detailed descriptions of the settings and characters. In "The Alchemist," the Andalusian countryside and the desert are described in detail, using sensory details to bring them to life. Similarly, in "Brida," the world of witchcraft is brought to life through the use of descriptive analysis, with Coelho using sensory details to create a mystical and magical atmosphere. The characters in both novels are also given detailed descriptions, allowing readers to form emotional connections with them and understand their motivations. Diagnostic analysis is used in both novels to explore deeper themes and ideas related to personal growth and self-discovery. In "The Alchemist”, the protagonist Santiago goes on a journey of self-discovery and learns valuable lessons about himself and the world around him. Through his experiences, he engages in self-reflection and grows as a person. Similarly, in "Brida", the protagonist engages in various spiritual practices and reflects on her past experiences to discover her true path in life. Both models work together to create narratives that engage readers on both an emotional and intellectual level. The descriptive analysis draws the reader into the world of the story, allowing them to visualize the settings and characters. The diagnostic analysis allows readers to explore deeper themes and ideas related to personal growth and self- discovery, creating a more meaningful and impactful reading experience. "The Alchemist" and "Brida" use descriptive and diagnostic analysis models to create engaging and thought-provoking narratives. Through their use of these models, Coelho explores themes of personal growth and self-discovery, creating stories that resonate with readers and leave a lasting impact. [ABSTRACT FROM AUTHOR]

The purpose of this article is, through a comparative analysis between the development of the call-center industry in the four macroregions of Romania, to identify ways of attracting investors to areas with low attractiveness for this field of activity. Knowledge of the activity sector is a condition for identifying ways to develop call-center services in areas not yet exploited. In chapter 4.1, an overview of the call-center industry in Romania was made. The comparative study between the development of the call-center industry in Romania's macroregions was carried out by comparing the business environment of call-center companies and the evolution of these companies. The study is presented in chapter 4.2. The conclusions of the study include the causes of the gap between macroregions, detected within the analyzes carried out and ways to increase the investment attractiveness of disadvantaged areas. [ABSTRACT FROM AUTHOR]

Precipitation around the Qilian Mountains has close relationship with developing of large-scale circulation system and orographic cloud forced by complex topography in this region.The present work compares synoptic pattern and relevant physical parameters as well as development mechanism of two type precipitation processes around the Qilian Mountains such as CASE1 which occurred during July 19 -20， 2018 and CASE2 which occurred on August 28， 2018 with observed data and high resolution data from WRF model version 3.8.1.Synoptic analyses show that CASE1 precipitation process is the result of interaction between favorable large-scale and local topography， CASE2 is mainly a precipitation process induced by local topography and no significant large-scale circulation system in this process.Results of diagnosis analyses indicate that CASE1 has characteristics such as sufficient water vapor supply， obviously unstable of the stratification， high effective convective energy （1064.84 J·kg-1）， strong vertical shear of the horizontal wind （27.6 m·s-1）， Richardson number （Ri） less than 0 and strong vertical ascending motion et al.before precipitation， which caused the formation of larger area and persistent precipitation process.However， the stratification in CASE2 is more stable than that in CASE1， and the maximum of MCAPE （546.15 J·kg-1） is about half of that in CASE1， the maximum horizontal wind vertical shear （17.37 m·s-1） is far less than that of CASE1， and Richardson number is greater than 0 in the precipitation stage， which means it is a weak convective precipitation process and precipitation rate is relatively smaller as well as and relatively short duration.

The convection initiation (CI) mechanisms of severe storms have received increasing attention because severe storms have been occurring more frequently around the globe in recent years. In this work, the CI mechanisms of severe convective weather associated with a gust front (GF) which occurred on 9 July 2016, near the Korla at the northern edge of the Tarim Basin, Xinjiang, is investigated using observational data including Doppler weather radar data and automatic weather stations data, and high-resolution numerical simulation data. The results showed that, during the eastward movement of the GF, a number of convective cells were successively triggered in the vicinity of the GF, which developed rapidly and continuously merged with the convective system from behind, resulting in the further development and maintenance of this convective system. According to the diagnostic analysis of vertical acceleration which can be decomposed into dynamic acceleration (ad) and buoyant acceleration (ab), it was found that both ad (up to ~4 × 10−3 m s−2) and ab (up to ~7 × 10−3 m s−2) made positive contributions to the CI. Further analyses based on the decompositions of the ad and ab revealed that the extension term was the main contributor for the ad, while the warming of the dry air due to the release of latent heat from the precipitation condensate made the major contribution to the ab. The extension term indicates the elevated convergence jointly induced by the airflow of mid-level horizontal convective rolls (MHCRs) and updraft flow near the leading edge of the GF. The jointly induced elevated convergent updraft can be considered to be an important contributor for the CI mechanisms.

Copyright of Arid Zone Research / Ganhanqu Yanjiu is the property of Arid Zone Research 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.)

In this paper, we introduce a relatively simple data‐driven method for the representation of the uncertainty in daily discharge records. The proposed method relies only on hourly discharge data and takes advantage of a nonparametric difference‐based estimator in the characterization of random errors in discharge time series. We illustrate with corrupted streamflow data that the nonparametric estimator provides an accurate characterization of the nature (homoscedastic or heteroscedastic) and magnitude of these errors. In addition, we demonstrate the practical usefulness of the estimator using discharge time series of 500+ watersheds of the Catchment Attributes and MEteorology for Large‐sample Studies data set. This analysis reveals that the magnitude of errors of aleatory nature in the investigated discharge records is rather small (less than 3% for 80% of the records). We then combine the effect of random errors and measurement frequency into a daily variance estimate, which serves as input to a streamflow generation approach. This procedure produces replicates of the discharge record which portray accurately the assigned streamflow uncertainty, preserve key statistical properties of the discharge record and are hydrologically realistic. The proposed method facilitates Bayesian analysis and supports tasks such as model diagnostics, data assimilation, uncertainty quantification and regionalization. Key Points: We present a model‐agnostic method for the generation of replicates of discharge recordsThe nonparametric estimator provides an accurate estimate of random errors, which for the investigated discharge records are rather smallThe replicates portray accurately the assigned streamflow uncertainty and preserve statistical and hydrologic properties [ABSTRACT FROM AUTHOR]

We propose a new semiparametric regression model with exponentiated power exponential errors using the B-spline basis for nonlinear effects. We adopt the framework of the generalized additive models for location, scale, and shape to fit this regression model. We obtain the maximum penalized likelihood estimates of the model parameters by considering nonlinear effects. Some global-influence measurements and quantile residuals are also investigated. Various Monte Carlo simulations are performed for inference purposes under different parameter settings, systematic components and sample sizes. The proposals are illustrated by two applications to real data. [ABSTRACT FROM AUTHOR]

In this paper, a risk assessment methodology is presented to identify, analyze, and evaluate the risk associated with different consequences of a liquefied natural gas (LNG) process facility from west Algeria. Bow‐tie analysis (BT) is used to identify the possible causes of hazardous events and the dangerous outcomes and consequences resulting from the failure of the safety barriers. Moreover, the bow‐tie is mapped into Bayesian networks (BNs) to overcome some limitation of dependencies and updating in the events. Finally, the risk‐reducing measures implemented in the process are examined using BNs. The performances of the safety barriers are investigated considering the occurrence of fire and explosion in the facility. BN uses diagnostic (posterior) and predictive (prior) analysis to calculate the probabilities of the components and to predict the occurrence frequency of the consequences and the effectiveness of safety barriers. The results from the BN model indicate that pool fire is the most likely scenario that potentially causes damage to the LNG process. [ABSTRACT FROM AUTHOR]

In the extant literature, hierarchical models typically assume a flexible distribution for the random-effects. The random-effects approach has been used in the inferential procedure of the generalized linear mixed models. In this paper, we propose a random intercept gamma mixed model to fit correlated asymmetric positive continuous outcomes. The generalized log-gamma (GLG) distribution is assumed as an alternative to the normality assumption for the random intercept. Numerical results demonstrate the impact on the maximum likelihood (ML) estimator when the random-effect distribution is misspecified. The extended inverted Dirichlet (EID) distribution is derived from the random intercept gamma-GLG model that leads to the EID regression model by supposing a particular parameter setting of the hierarchical model. Monte Carlo simulation studies are performed to evaluate the asymptotic behavior of the ML estimators from the proposed models. Analysis of diagnostic methods based on quantile residual and COVARATIO statistic are used to assess departures from the EID regression model and identify atypical subjects. Two applications with real data are presented to illustrate the proposed methodology. [ABSTRACT FROM AUTHOR]

The plateau vortex is one of the dominant weather systems that modulate summer rainfall in northwestern China, a typical arid/semi-arid area worldwide. Two heavy rainfall cases in the summer of 2012 and 2013, accompanying the northeastward movement of plateau vortexes, are selected here. The role of plateau vortexes in these two cases is explored by diagnosing their moist potential vorticity, helicity, and convective clouds characteristics, using numerous data (ERA-Interim reanalysis, multi-sourced precipitation data, FY-2E satellite images). The plateau vortexes enhanced convective instability in the rainfall area by entraining upper-level dry and cold air, manifested as salient vertical gradients of moist potential vorticity and steep pseudo-equivalent potential temperature isolines. The rainfall occurred eastward relative to the vortex movement track and intensified on the left of cloud black body temperature (TBB) with larger gradients at the middle to late stages of the vortex life span. The generation of moisture helicity is dictated by the water vapor transportation, with the negative value concentrated in the water vapor-rich areas. Notably, the two cases differ in tracks, life spans, water vapor sources, and precipitation amounts, which may be attributed to large-scale circulation background, vertical vortex structure, and moisture condition.

Copyright of Plateau Meteorology is the property of Plateau Meteorology 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.)

Objective: Objective: The purpose of this study is to enumerate a set of factors that affect students' participation in scientific olympiads. Turning to the above goal, the theoretical framework of the problem was explored at the micro and messo levels and their interaction with each other was examined. Methods: In this research, survey method and questionnaire were used. The statistical technique used in this method was diagnostic analysis. In order to calculate the validity of the questionnaire tool, face validity was used and to calculate its reliability, Cronbach's alpha was used. The sample selection method was purposeful and in order to compare between the two sets of cases (participants and non-participants) the homogeneity statistical population was selected and 384 samples were studied based on the scientific method of sampling. Results: This study, which sought to obtain a function to determine the importance of each independent variable in distinguishing groups from each other, was able to identify significant differences between the two groups and, based on the distinction made in the independent variables, predict that Participants in the Olympiad are different from non-participants in independent variables.The results showed that the variables of school educational quality, parents' economic and social status, school olympiad atmosphere, student evaluation system, his ability and ability as well as parents' encouragement were able to predict students' membership in two groups of participants / non-participants Conclusion: According to the obtained results, it can be said that the experimental findings are a kind of indicative of the theoretical framework of the research. In other words, several factors affect the participation / non-participation of students in this scientific competition at the micro and intermediate levels. Factors that start with the quality of the actors and include school and family support at the institutional level [ABSTRACT FROM AUTHOR]

We analyse the European Center for Medium‐Range Weather Forecasts reanalysis temperature time series over China and find that daily mean temperature at 850 hPa pressure level warms gradually and cools rapidly, which is known as the asymmetry property. Previous studies pointed out that front events contribute to this asymmetry property, but only presented indirect evidences. Here, we confirm this conjecture with more convincing and direct evidences. The time series of front events are obtained over China by an improved objective front detection algorithm. The high Pearson correlation between time series of monthly temperature asymmetry measure and time series of monthly front events over some specific regions indicates they are closely related to each other and front events indeed contribute to temperature asymmetry. We discover that the North China Plain is a representative region where front events contribute to temperature asymmetry significantly. Further diagnostic analysis by temperature tendency equation shows that the asymmetry of daily mean temperature series is due to the asymmetry between the frequency and intensity (mainly the intensity of meridional nonlinear advection term) of cold and warm fronts in North China Plain. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to examine Bangladesh's performance as an outsourcing destination from the perspectives of buyers and suppliers involved in a representative US apparel company which has outsourced from Bangladesh. This study employed a sequential mixed method research design which consisted of quantitative evaluation and qualitative interviews. The sample was comprised of three buyers and three suppliers in the Phillip's Van-Heusen (PVH). From the results of quantitative evaluation, it was found that Bangladesh has a greater advantage in the cost and quality clusters than Vietnam. The interviews revealed that the US buyers have some concerns in geographic location, trade restrictions, country's political situation, warranty policies, and currency exchange situations. To strengthen Bangladeshi capability, Bangladeshi government's investment to improve facilities, nomination of supplier's backward linkage, improvement of manufacturing capacity, running improvement programs, negotiation to reduce tariff, and building mutual trust between buyers and suppliers were suggested. These diagnosis results provide the US buyers and the Bangladeshi suppliers with not only valuable information on the current status of Bangladesh's performance but guidance to successful strategies for improvement of outsourcing performance. [ABSTRACT FROM AUTHOR]

In order to explore the relationship between heavy precipitation and the distribution characteristics of wet potential vorticity field, based on the NCEP lo × lo 6-hour reanalysis data, the wet potential vortex field in a heavy precipitation weather process in the central-southern part of Inner Mongolia was analyzed through the analysis of weather situation and calculation of vertical component (MPV1) and horizontal component (MPV2) of wet potential vortex (MPV). The results show that this heavy precipitation was a heavy precipitation weather process under the combined action of the cold air behind the trough and the southwest warm and humid airflow. The diagnostic analysis of 0ie shows that the south side of the area where the cold and warm air met before the precipitation was a high-temperature and high-humidity area, where a large amount of unstable energy was gathered, and the heavy precipitation occurred in the area with the most dense 0se lines. This heavy precipitation process was dominated by the release of convective unstable energy in the lower troposphere, the positive pressure term of wet potential vortex MPV1 was smaller than 0 in the lower troposphere and larger than 0 in the middle and upper layers; the posit ive-value wet potential vortex was transported to the lower layer, which was conducive to the release of unstable energy, so that precipitation increased. The negative-value center of wet potential vortex in the middle and low layers corresponded well to the heavy precipitation area, and the precipitation intensity and duration were consistent with the increase and enhancement time of the negative-value area of MPV1. The positive-value area of MPV1 was 6 - 12 h earlier than the heavy precipitation area, which has good indication significance for the prediction of the heavy precipitation area. In the lower troposphere, the configuration of MPV1 <0 and MPV2 >0 can be used as the basis for judging the location of precipitation center. [ABSTRACT FROM AUTHOR]

Irrigation projects are of paramount importance in meeting the water demand of agriculture sector. Therefore, evaluating the performance of irrigation systems makes it possible to improve agricultural water management and consequently enhances irrigation efficiency. In this perspective, the present study focuses on diagnostic analysis of the Baroda branch canal of the Som Kamla Amba irrigation project, Rajasthan, India. This study attempts to identify constraints attributed to poor irrigation efficiency and remedial strategies to improve irrigation management. Results revealed that poor maintenance and lacking immediate action of damaged water distribution network were culprits resulting in silting and vegetation infestation. Poor structures viz., tempered pipe outlets and gates, blockage of minors, unauthorized pumping, and poor operations of water distribution systems resulted in poor performance of Baroda branch canal. The performance of Baroda branch canal system can be improved by adoption of appropriate strategies. [ABSTRACT FROM AUTHOR]

PURPOSE: Dual-energy x-ray absorptiometry (DXA) is the “golden standard” for diagnosing osteoporosis. Its analyzing algorithm (software) makes it possible to distinguish the bone from the soft tissue. Until now there are only attempts to process and acquire images using automatic segmentation with convolutional neural networks (CNN). Machine reconstruction and precise specific models of anatomic structures from medical images could be accomplished using computer vision. The objective of the current work is to introduce the potential of the two computer methods and their application in the diagnostic DXA analysis. METHODS: DXA generates a report in the DICOM format which includes patient data (age, gender, height, weight, bone mineral density, T-score and Z-score) and an image of the scanned spine as well as the region of interest (ROI). The CNN methods are based mainly on intermediate analysis. The learning of the segmentation of CNN by generating segmentation labels using simple heuristic is done using computer vision. The functions of the loss and the architecture of the CNN is then determined. In that manner the right analysis of the existing medical image is made possible. RESULTS: The computer library OpenCV is the way to realize a model for the assessment of a DXA analysis. The library is available for Python programming language. The library has functions for the extraction of colour objects, image smoothing, Canny’s edge detector, Hough transform and methods for work with contours. CONCLUSIONS: The detection and extraction of images is fundamental for the analysis of DXA which is a step forward in the precision of the in-vivo diagnostic of the bone.