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The Indian summer monsoon (ISM) is a complex and multiscale interacting climate system, which is responsible for major contribution in India's annual rainfall. Understanding the spatial and temporal variability of ISM rainfall is critical for managing water resources, which directly impact and regulate the functioning of India's socio-economic conditions and subsequently, sustenance of over a billion people. This study evaluates the suitability of various gridded precipitation data products with different spatiotemporal resolutions, essential requirement for hydrologic modeling, disaster mitigation, irrigation allocation and agricultural application. Hence, we evaluate the performance of seven gridded datasets in generating time-matched characteristic event occurrences and their respective magnitudes using gauge-based Indian Meteorological Department (IMD) gridded data as reference. We observe that reanalysis datasets underperform compared to satellite and hybrid products in identifying both normal and extreme precipitation events. We develop a performance measure, called 'rank score' that considers deviations from IMD data in magnitude, statistical moments, and rain event detectability for a robust assessment and identifying best-suited dataset. Results indicate that APHRODITE, MSWEP, and CHIRPS (in descending order) are the most suitable data products across India. Additionally, region-specific evaluations provide valuable insights into the applicability of these datasets in different climatic and homogeneous rainfall zones. [ABSTRACT FROM AUTHOR]

Copyright of JMD: Journal für Mathematik-Didaktik 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.)

System-on-chip designs for high-performance computing systems widely use network-on-chip (NoC) technology. The critical metrics such as latency, throughput, and total energy directly impact the performance of NoCs. However, a cycle-accurate simulator takes considerable execution time for different values of packet injection rate. This paper proposes a machine learning approach with various regression models to predict the essential NoC performance metrics. The work explores regression models, including decision trees, K-nearest neighbors, random forest, polynomial regression, and support vector regression, alongside baseline linear regression for 2D and 3D NoC-based architectures. Experiment results demonstrate high accuracy, ranging from 96.72 to 99.96% for 2D and up to 99.99% for 3D NoC-based architectures. Root-mean-squared error values vary from 0.2792 to 0.03143 for 2D NoC and 0.1857 to 0.0063 for 3D NoC architecture. The proposed framework achieves impressive speedup, reaching up to 4678.8X and 46152.8X for system sizes 8 × 8 × 4 and 10 × 10 × 20, respectively. [ABSTRACT FROM AUTHOR]

In routine Generalizability Theory (G-theory) research, the task of establishing or assessing cut-off scores for performance evaluation is consistently sought after yet simultaneously poses significant challenges. While many studies have presented diverse indices to evaluate potential cut-off scores, these indices are frequently limited by their design-specific nature, limiting adaptability across different assessment contexts. This paper reframes G-theory within the context of a Linear Mixed-Effects Model (LMM) and employs LMM-based bootstrapping techniques to generate Intervals of Predicted Total Scores (IPTS) for each individual to evaluate the suitability of potential cut-off score candidates in the assessment. We propose PredC, to quantify the proportion of individuals whose 95% IPTS encompass a given candidate value of interest, thereby gauging the appropriateness of different cut-off choices. A lower PredC is deemed preferable since it signifies that the corresponding cut-off score effectively discriminates between individuals, categorizing them into distinct groups (e.g., pass or fail). The study comprises two parts: Firstly, a Monte Carlo simulation study was carried out to compare the performance of three distinct techniques—parametric, semiparametric, and nonparametric bootstrapping—in constructing IPTS. The second part applied a genuine dataset to illustrate the practical implementation of our proposed methodology. The simulation findings revealed that the distribution of scores greatly influences the efficacy of IPTS, with semiparametric and nonparametric bootstraps being the preferred methods in situations involving ordinal data points, as was the case in the real-world dataset. When assessing cut-off scores, the PredC serves as a valuable complement to the existing suite of quantitative tools, thereby contributing to enhanced standard-setting practices. [ABSTRACT FROM AUTHOR]

To establish more efficient and equitable education systems for students, increasing importance has been attached to the efficient usage of educational resources and the evaluation of students' success in education. Until recently, studies that focus on efficiency assessment in education have generally employed conventional data envelopment analysis (DEA) models that enable each country to specify its most favorable set of factor weights, and thus may yield an impractical weighting scheme as well as poor discriminatory characteristics for evaluation purposes. This study assesses education efficiency of OECD countries through implementing common-weight DEA-based models and using the PISA 2018 database. The results obtained by conventional DEA and common-weight DEA-based models are comparatively evaluated with regard to rankings as well as dispersions of input and output weights. Although conventional DEA determines ten countries as efficient, most of the common-weight DEA-based models employed herein yield full ranking. In vast majority of these approaches, Estonia and Finland are found to be the best performing countries. Rank reversal phenomenon in these models is also addressed and a weighted aggregate ranking index incorporating the rank reversal issue is developed. [ABSTRACT FROM AUTHOR]

The systematic literature review (1) investigates whether 'serious games' provide a viable solution to the limitations posed by traditional high-stakes performance assessments and (2) aims to synthesize game design principles for the game-based performance assessment of professional competencies. In total, 56 publications were included in the final review, targeting knowledge, motor skills and cognitive skills and further narrowed down to teaching, training or assessing professional competencies. Our review demonstrates that serious games are able to provide an environment and task authentic to the target competency. Collected in-game behaviors indicate that serious games are able to elicit behavior that is related to a candidates' ability level. Progress feedback and freedom of gameplay in serious games can be implemented to provide an engaging and enjoyable environment for candidates. Few studies examined adaptivity and some examined serious games without an authentic environment or task. Overall, the review gives an overview of game design principles for game-based performance assessment. It highlights two research gaps regarding authenticity and adaptivity and concludes with three implications for practice. [ABSTRACT FROM AUTHOR]

Emerging applications such as smart city infrastructures and virtual reality landscapes are setting rigorous benchmarks for 6G mobile networks, requiring elevated levels of confidentiality, integrity, non-repudiation, authentication, and stringent access controls. Blockchain technology is heralded as a transformative enabler for meeting 6G standards, owing to its intrinsic attributes. However, a gap exists in the holistic investigation of blockchain’s applicability in 6G realms, particularly addressing the “whether”, “when”, and “how” of its deployment. Present research trails in developing robust methodologies to gauge blockchain’s efficacy within 6G use cases. Addressing this, our study introduces a novel confluence of blockchain with 6G networks, where data resides in distributed Hash tables (DHTs) while their hashes are secured in distributed ledger technology (DLT), harnessing blockchain’s core strengths-immutability, traceability, and fortified security. We delineate seven specific 6G use cases poised for enhancement through blockchain integration, and scrutinize the rationale, nature, and timing of this convergence. Furthermore, we devise a comprehensive methodology for assessing blockchain’s performance metrics and scalability in 6G environments. Our extensive experimental analyses evaluate the synergistic performance of this integration, revealing that the Quorum blockchain satisfactorily supports 80% of 6G scenarios. The findings suggest that, with appropriate configurations, consortium blockchains are well-equipped to fulfill the demanding performance and scalability requisites of 6G networks. [ABSTRACT FROM AUTHOR]

Why is voting behaviour tied to ethnicity in many new democracies? A predominant view, offered by the instrumentalist theory, argues that voters use candidates' identity -ethnic or partisan- as a signal of expected performance. However, voters will reward well-performing politicians by being more likely to vote for them and punish bad performance regardless of candidates' identity. To test these assumptions, we conduct a nationally representative survey in Kenya asking voters about their spending preference for the country's main anti-poverty programme, which is managed by MPs in collaboration with citizens. We find that voters, regardless of ethnicity or party, have an erroneous view of how their MP spent the anti-poverty funds. Then in an experiment, we share with voters the spending made by the MPs and audit results probing for corruption. Once voters are aware that their MP's spending does not match their spending priorities, their approval and likelihood of voting for the MP decreases if primed to view the politician as partisan. Voters also electorally punish MPs for corruption whether copartisan or non-copartisan. In contrast to instrumentalist expectations, if voters are primed to view their MP in ethnic terms, they are more likely to vote for their coethnic overlooking bad performance and corruption. The availability of alternative parties in local elections may explain why voters attach partisanship to performance evaluation. Still, in our case study, as in other new democracies, politicians continue mobilising support along ethnic lines, perhaps explaining why voters prioritise group loyalties if primed to think about ethnicity. [ABSTRACT FROM AUTHOR]

This manuscript aims to study the reliability of different variables related to performance and acceleration during the golf putt in players with medium-to-high handicaps and to determine the number of attempts necessary to find reliable values for these variables. Eight males and two females [55.67 (13.64) years, 78.4 (11.4) kg, 1.75 (7.95) m] participated in two experimental sessions separated by one week. In these sessions, they performed three blocks of 10 putts trying to stop the golf ball at the center of a dartboard painted 2 m away. The performance was assessed depending on the area of the dartboard where the ball stopped, and the acceleration signals were acquired using the Xsens Dot. The results showed that to evaluate performance, 18 trials were necessary to reach reliable values using the 0–10 scoring system, and 28 trials were necessary for the 0–3 scoring system. Regarding the reliability of the accelerometer-related variables, 7 attempts were necessary to obtain good-to-excellent reliability values for most of the variables. It could be concluded that putting in medium-to-high handicap golf players can be reliably measured using the abovementioned protocol. [ABSTRACT FROM AUTHOR]

With the development of information technology, such as cloud computing and 5G technologies, the construction of smart cities has been an essential part of the city's long-term development strategy for the 14th five-year plans in China. After a few years of development, the research problem of the dynamic performance assessment of smart cities has been a hot and urgent issue. This paper constructed the dimensions of smart productivity, smart livability, smart governance, and eco-community based on the connotations of smart cities. Then, an indicator system including 35 indicators was built based on the four dimensions. To enlarge the overall differences of the final assessment values, we used the vertical and horizontal deviation maximization (VH-DM) method to measure the performances of 31 smart cities in China from the year 2014–2018. Two essential findings and driving indicators were identified from the assessment results. Firstly, the development among the smart cities was unbalanced and more than half of the smart cities (accounting for 64.52%) were below the average level. The local authorities considered the urban agglomeration strategies to alleviate the problem of unbalanced urban development. Secondly, some smart cities (such as Chongqing) with large scale of population and total resources showed a low level after the total resources were allocated to per capita, which provided a warning for the smart cities to pay more attention to individual development. The local authorities have proposed the high-quality development strategies to deal with such problems. [ABSTRACT FROM AUTHOR]

Biomethane may contain trace components that can have adverse effects on gas vehicles performances and on the pipelines when injected in the gas grid. Biomethane quality assurance against specifications is therefore crucial for the integrity of the end-users' appliances. Analytical methods used to assess biomethane conformity assessment must be validated properly and possibly, new methods specifically for biomethane should be developed. This paper provides an overview of the biomethane quality assurance infrastructure and the challenges faced with focus on sampling, analysis methods, reference gas mixtures, and performance evaluation. Currently, requirements for analytical method validation and fit-for-purpose assessments do not exist for biomethane. The industry is in urgent need of a protocol to evaluate the fit-for-purpose of methods in a harmonized manner. Reference gas mixtures to check the accuracy of the instrument and to determine the traceability of the measurement are also urgently required. [ABSTRACT FROM AUTHOR]

Performance indicators are rapidly becoming popular in performance assessment of water supply systems (WSSs) for finding the weaknesses and solving them. The selection and use of the key performance indicators (KPIs) for WSSs need the consideration of the regional and local conditions. Moreover, analyzing the results of the assessment requires a clear understanding of the relationship and intensity of the interaction of the KPIs with each other and the overall performance index. In this study, fuzzy Delphi method was used for selecting the KPIs in four dimensions for urban WSSs of Iran. Then, influential network relationship map of dimensions and selected indicators were determined and constructed by using fuzzy DEMATEL method. To facilitate the draw of the maps and more clarity, K-means clustering was used. Then, influential weights of the KPIs were calculated by using fuzzy DANP method. Among the KPIs, Aging of Mains related to the organizational dimension is the most independent and causal one. Further, Complaints related to the social dimension are the most dependent and affected indicator. The organizational dimension is the most causal dimension with high influence on the environmental, economic, and social dimensions. Therefore, the organizational dimension should be prioritized for improving the other dimensions and should be well-promoted to have a positive impact on overall performance. The outcomes provide a good guide for WSSs management, and researchers can use the data and methods of this study in other similar fields. [ABSTRACT FROM AUTHOR]

The study focuses on the critical concern of designing secure and resilient bridge piers, especially regarding scour phenomena. Traditional equations for estimating scour depth are limited, often leading to inaccuracies. To address these shortcomings, modern data-driven models (DDMs) have emerged. This research conducts a comprehensive comparison involving DDMs, including support vector machine (SVM), gene expression programming (GEP), multilayer perceptron (MLP), gradient boosting trees (GBT) and multivariate adaptive regression spline (MARS) models, against two regression equations for predicting scour depth around cylindrical bridge piers. Evaluation employs statistical indices, such as root-mean-square error (RMSE), coefficient of determination (R2), mean average error (MAE) and normalized discrepancy ratio (S(DDRmax)), to assess their predictive performance. A total of 455 datasets from previous research papers are employed for assessment. Dimensionless parameters Froude number F r = U gy , Pier Froude number F r P = U g ′ D , and the ratio of scour depth to pier diameter (y D) are carefully selected as influential model inputs through dimensional analysis and the gamma test. The results highlight the superior performance of the SVM model. In the training phase, it exhibits an RMSE of 0.1009, MAE of 0.0726, R2 of 0.9401, and SDDR of 2.9237. During testing, the SVM model shows an RMSE of 0.023, MAE of 0.017, R2 of 0.984, and SDDR of 5.301. Additionally, it has an average error of − 0.065 and a total error of − 20.642 in the training set and an average error of − 0.005 and a total error of − 0.707 in the testing set. Conversely, the M5 model exhibits the lowest accuracy. The statistical metrics unequivocally establish the SVM model as significantly outperforming the experimental models, placing it in a higher echelon of predictive accuracy. [ABSTRACT FROM AUTHOR]

Data on surgical robots are not openly accessible, limiting further study of the operation trajectory of surgeons' hands. Therefore, a trajectory monitoring system should be developed to examine objective indicators reflecting the characteristic parameters of operations. 20 robotic experts and 20 first-year residents without robotic experience were included in this study. A dry-lab suture task was used to acquire relevant hand performance data. Novices completed training on the simulator and then performed the task, while the expert team completed the task after warm-up. Stitching errors were measured using a visual recognition method. Videos of operations were obtained using the camera array mounted on the robot, and the hand trajectory of the surgeons was reconstructed. The stitching accuracy, robotic control parameters, balance and dexterity parameters, and operation efficiency parameters were compared. Experts had smaller center distance (p < 0.001) and larger proximal distance between the hands (p < 0.001) compared with novices. The path and volume ratios between the left and right hands of novices were larger than those of experts (both p < 0.001) and the total volume of the operation range of experts was smaller (p < 0.001). The surgeon trajectory optical monitoring system is an effective and non-subjective method to distinguish skill differences. This demonstrates the potential of pan-platform use to evaluate task completion and help surgeons improve their robotic learning curve. [ABSTRACT FROM AUTHOR]

Performance assessment is crucial for managing the health of complex systems, including single-agent and multi-agent systems. However, research on the latter has been relatively sparse. Approaches to assessing system performance must consider both assessment result accuracy and process explainability, especially in cases with small or unbalanced samples, which limits the application of data-driven approaches. In this paper, the Belief Rule Base (BRB) grey-box expert system is adopted to assess the performance of leader-following multi-agent systems subject to switching topologies and unpredictable disturbances. By fusing historical data and expert knowledge, the precision of assessment results and the traceability of the assessment process are balanced. Two time-varying features based on the states and distributed communication characteristics of the leader-following MASs are designed as model input. The robustness of the feature matching degree is quantitatively analysed, and the obtained robustness factors can reflect their effectiveness on the final performance utility produced by the BRB model. This approach helps raise the cognitive ability of specialists and devise more rational model structures and initial parameters. In comparative studies, better prediction accuracy of the proposed BRB performance assessment model is demonstrated compared to other classical models based on small training samples, while avoiding an opaque inference process. [ABSTRACT FROM AUTHOR]

With the intensification of urbanization, the application of contemporary technology to make cities smarter is the key to their sustainable development (SD). This study proposes a comprehensive sustainable assessment framework for the SD level of smart cities. First, an assessment system is proposed, which is composed of built form, urban infrastructure, environmental, social, and economic dimensions. These dimensions can be subdivided into 25 indicators. Second, a Z-fuzzy-based multiple criteria decision-making (MCDM) approach is developed to clarify the internal influence of the indicators and to determine the SD performance of smart cities. The Z-based decision-making trial and evaluation laboratory (Z-DEMATEL) technique was used to determine the mutual influence relationship among the indicators and to obtain their influence weights. Moreover, the Z-based technique for order preference by similarity to the ideal solution with the aspiration level (Z-TOPSIS-AL) approach was applied to analyze the sustainable development performance of a smart city. In this paper, we selected Xiamen city as a case study. The results demonstrate that "quality of life," "per capita gross domestic product (GDP)," and "GDP growth rate" are the top three indicators, and their influence weights are 0.05, 0.046, and 0.046, respectively. From the perspective of dimensions, "economic" is the most influential dimension in the sustainable development of Xiamen. In addition, "GDP growth rate" has the greatest room for improvement overall. This study provides a reference for follow-up-related research, and the management findings provide a basis for managers to make decisions on the development of smart cities. [ABSTRACT FROM AUTHOR]

Low-cost Global Navigation Satellite System (GNSS) devices offer a cost-effective alternative to traditional GNSS systems, making GNSS technology accessible to a wider range of applications. Nevertheless, low-cost GNSS devices often face the challenges in effectively capturing and tracking satellite signals, which leads to losing the observations at certain frequencies. Moreover, the observation peculiarities of low-cost devices are in contradistinction to those of traditional geodetic GNSS receivers. In this contribution, a low-cost PPP-RTK model that considers the unique characteristics of different types of measurements is developed and its performance is fully evaluated with u-blox F9P receivers equipped with three distinctive antenna configurations: vertical dipole, microstrip patch, and helix antennas. Several static and kinematic experiments in different scenarios are conducted to verify the effectiveness of the proposed method. The results indicate that the mixed-frequency PPP-RTK model outperforms the traditional dual-frequency one with higher positioning accuracy and fixing percentage. Among the three low-cost antennas tested, the vertical dipole antenna demonstrates the best performance under static conditions and shows a comparable performance as geodetic antennas with a positioning accuracy of 0.02 m, 0.01 m and 0.07 m in the east, north, and up components, respectively. Under low-speed kinematic scenarios, the helix antenna outperforms the other two with a positioning accuracy of (0.07 m, 0.07 m, 0.34 m). Furthermore, the helix antenna is also proved to be the best choice for vehicle navigation with an ambiguity fixing rate of over 95% and a positioning accuracy of (0.13 m, 0.14 m, 0.36 m). [ABSTRACT FROM AUTHOR]

The performance assessment and enhancement model is the research goal for the maintenance decision-making of a steel cable structure system. Firstly, a novel simplified axial cable force calculation method is introduced. The central safety factor (CSF) and the cable forces' reliability-based safety factor (RBSF) are used for the performance assessment. The multi-level safety factor thresholds for single cable components are preset as the target resistance level, considering the fatigue and corrosion effects. Secondly, the reliability analysis expresses the relationship between each cable component's target resistance and load effect. Thirdly, three failure probability criteria are adopted to comprehensively evaluate the cable structure system's safety factors. The evaluation criterion includes the failure probability criterion of the series structure system (FPC), maximum failure probability estimation method of components (MFPC), and wide-narrow reliability bounds (WNRB). Finally, the maintenance decision-making model for the cable structure system is suggested to enhance the structural performance by two assignment methods: the equal reliability assignment method and the priority assignment method of lower reliability. A cable-stayed bridge case is selected to validate the reasonability of the above algorithms. The research results demonstrated that both the CSF and RBST are valid in evaluating the cable components' performance. The most unfavorable cable safety performance determines the cable force's performance safety. The three evaluation criteria are conducive to assessing the overall cable structure system's safety performance. The maintenance number and location of the cable components can be optimized to meet the target reliability of the cable structure system by two assignment methods. [ABSTRACT FROM AUTHOR]

Solar dryers have always been criticized for their lower performances. There are numerous ways to define the performance of a solar drying system such as thermal performance, drying kinetics, environmental aspects, economic evaluations, and quality of the dried product. Different modeling techniques have also been developed to design and analyze solar dryers and drying processes. This article presents a systematic, comprehensive, and state-of-the-art overview of various performance indicators and modeling techniques used for the evaluation and analysis of solar dryers, especially domestic and low cost solar dryers. Environmental analysis has severe global implications, and product quality is one of the biggest concerns of consumers. But the environmental impact and product quality assessments for domestic solar dryers are observed to be rarely reported in the literature. The use of modeling techniques in solar drying has changed the way of analyzing any thermal system. Here, an attempt is made to establish an overall assessment criterion for domestic solar dryers and to give a one-stop solution for researchers and users around the world. [ABSTRACT FROM AUTHOR]

Dairy Cooperative Societies (DCSs) maintain the availability and supply of clean processed milk in accordance with consumer needs, as well as the provision of essential milk products of the appropriate standard to consumers at competitive prices. In this study, numerous aspects responsible for the success of India's Dairy Cooperative Society (DCS) have been taken into account before developing a composite index. The criteria addressed include milk union support, DCS support, governance and management support, allied support, and common support factors for DCS performance evaluation. The current study is projected to empower the dairy industry by offering a tool that can evaluate the relative success of DCSs and assist milk unions and dairy organizations in tailoring their assistance to their specific requirements. We deployed the Analytical Hierarchal Process (AHP) to determine the weights of specified criteria and sub-criteria impacting DCS performance; these weights were deployed to determine the relative performance of a DCS using the composite index developed in the study. Criteria such as governance and management support given to members, and support received from milk unions have significant weightage- 0.3002, 0.2854, and 0.2715, respectively. We also deployed the suggested technique to conduct a comparative analysis of 98 DCSs identified from six Indian provinces constituted under NDP I (National Dairy Plan, Phase-I). Finally, the results of the sensitivity analysis have been presented to validate our proposed method. [ABSTRACT FROM AUTHOR]

There are contexts where communication with TCP/IP protocol is not possible due to the lack of infrastructure or a reliable and continuous data transmission. In this cases, alternatives such as Opportunistic Networks (OPPNets) are valid. Such challenging conditions are common in rural areas and are a significant obstacle for the deployment of eHealth technologies for older adults. Considering this context, the present work introduces Interest-based System for Communication in Isolated Areas (ISCA), an OPPNet architecture for remote monitoring and emergency detection in ageing people who live alone. For this, the energetic requirements are considered, providing efficient and sustainable operation. The proposal makes use of a routing algorithm based on interests which enables asynchronous communication among entities. ISCA is evaluated over a realistic scenario and compared with similar state-of-the-art solutions. Experimental results show that ISCA notably improves the delivery probability with an enhancement of 52.25% in comparison to the second best alternative and provides a suitable average latency. Moreover, it also achieves better performance in terms of overhead and hops number compared to the other studied protocols [ABSTRACT FROM AUTHOR]

Organ transplant is one of the best options for many medical conditions, and in many cases, it may be the only treatment option. Recent evidence suggests, however, that the COVID-19 pandemic might have detrimentally affected the provision of this type of healthcare services. The main purpose of this article is to use Data Envelopment Analysis and the Malmquist Index to assess the impact that the pandemic caused by the novel coronavirus SARS-CoV-2 had on the provision of solid organ transplant services. To this purpose, we use three complementary models, each focusing on specific aspects of the organ donation and transplantation process, and data from Brazil, which has one of the most extensive public organ transplant programs in the world. Using data from 17 States plus the Federal District, the results of our analysis show a significant drop in the performance of the services in terms of the organ donation and transplantation process from 2018 to 2020, but the results also indicate that not all aspects of the process and States were equally affected. Furthermore, by using different models, this research also allows us to gain a more comprehensive and informative assessment of the performance of the States in delivering this type of service and identify opportunities for reciprocal learning, expanding our knowledge on this important issue and offering opportunities for further research. [ABSTRACT FROM AUTHOR]

Copyright of Zeitschrift für Erziehungswissenschaft 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.)

The combination of measuring performance and giving feedback creates tension between formative and summative purposes of progress evaluations and can be challenging for supervisors. There are conflicting perspectives and evidence on the effects supervisor-trainee relationships have on assessing performance. The aim of this study was to learn how progress evaluations are used in postgraduate education with longitudinal supervisor-trainee relationships. Progress evaluations in a two-year community-pharmacy specialization program were studied with a mixed-method approach. An adapted version of the Canadian Medical Education Directives for Specialists (CanMEDS) framework was used. Validity of the performance evaluation scores of 342 trainees was analyzed using repeated measures ANOVA. Semi-structured interviews were held with fifteen supervisors to investigate their response processes, the utility of the progress evaluations, and the influence of supervisor-trainee relationships. Time and CanMEDS roles affected the three-monthly progress evaluation scores. Interviews revealed that supervisors varied in their response processes. They were more committed to stimulating development than to scoring actual performance. Progress evaluations were utilized to discuss and give feedback on trainee development and to add structure to the learning process. A positive supervisor-trainee relationship was seen as the foundation for feedback and supervisors preferred the roles of educator, mentor, and coach over the role of assessor. We found that progress evaluations are a good method for directing feedback in longitudinal supervisor-trainee relationships. The reliability of scoring performance was low. We recommend progress evaluations to be independent of formal assessments in order to minimize roles-conflicts of supervisors. [ABSTRACT FROM AUTHOR]

This study aimed at investigating the English as a foreign language (EFL) learners' perceptions of L1-based and L2-based elicitations in the English classroom employing an explanatory sequential mixed-method design. Ninety-seven Iranian intermediate EFL learners of English have been selected from Islamic Azad University (Science and Research Branch) in Tehran Province using a convenient sampling method. Of these, in the qualitative phase, 15 individuals were selected through a convenience sampling method as the focus group (N = 15; n = 8 for the L1 group and n = 7 for the L2 group). In the quantitative phase, 90 intermediate EFL learners were selected. The selected participants' L2 performances were assessed through L1-based and L2-based elicitation techniques. They completed two validated researcher-made questionnaires to capture their perceptions of the elicitation techniques. Accordingly, five separate exploratory factor analyses were run to investigate the underlying constructs of the five components of the L1-based and L2-based perception questionnaires, the results of which showed that the correlation matrices were not singular and there were perfect correlations among all variables of L1-based and L2-based perception questionnaires. The findings show that the majority of respondents prefer to use their L2 in speaking classes and believe that L2 should be the prior language in general speaking classes. More than two-thirds of the respondents prefer to use L2 when they want to communicate with each other inside and outside of the classroom and prefer to use L2 in doing their assignments or performing orally in class. More than half of the respondents prefer to use L2 in assessment sessions. [ABSTRACT FROM AUTHOR]

Innovations in the Internet of Things (IoT) technology have revolutionized several industrial domains for smart decision-modeling. The capacity to perceive data about ubiquitous instances has resulted in numerous innovations in sensitive sectors like national security, and police departments. In this paper, an extensive IoT-based framework is introduced for assessing the integrity of police personnel based on his/her performance. The work introduced in this research is centered around analyzing several activities of police personnel to assess his/her integral behavior. In particular, the Probabilistic Measure of Integrity (PMI) is formalized based on professional data analysis for classification based on Bayesian Model. Moreover, the 2-player game model has been presented to assess the performance of police personnel for efficient decision-making. For validation purposes, the presented framework is deployed over challenging datasets acquired from the online repository of UCI. Based on the comparative analysis with the state-of-the-art decision-making models, the presented approach has registered enhanced performance in terms of Temporal Delay, Classification, Prediction, Reliability, and Stability. [ABSTRACT FROM AUTHOR]

Various studies show that sustainability and education are closely interdependent. Design and implementation of the right performance assessment for students' skills acquisition and achievements is, therefore, critical for achieving the Sustainable Development Goals (SDGs). This article presents an in-depth analysis of the Qatar education system (K-12 level), focusing on the current assessment approaches and remaining challenges that hinder the development and implementation of proper performance-assessment methods aligned with SDGs. Based on a proposed theoretical framework influenced by the constructive alignment theory, this article examines the current performance assessment practices in Qatar and recommends potential improvement avenues with respect to SDGs and education goals (EGs). Using this framework as an analytical tool, results reveal a lack of alignment between the assessment practices, educational goals, and the SDGs. This work shows that tailored, contextually proper, and progressive assessment strategies need to be developed to accurately evaluate and guide the twenty-first-century skills of the students toward the achievement of SDGs. Further findings of this article concern presentation and discussion of the locally relevant and consistent recommendations for performance assessment methodologies that must be redesigned to be compatible, aligned, and supporting the SDGs and EGs. [ABSTRACT FROM AUTHOR]

The atmospheric cutterhead is a new type of cutterhead with high technical difficulty. Compared with the conventional cutterhead, the spatial structure of the atmospheric cutterhead is more complicated, and a mature design theory has not yet been formed. This paper presents an atmospheric cutterhead design method for slurry shield machines. The method was divided into three steps: (i) design load analysis; (ii) multiobjective topology optimization; and (iii) evaluation and selection of cutterhead structures. Based on topology optimization theory, four types of cutterhead structures with different numbers of radial arms are obtained. The performance of the cutterhead was evaluated by stress, deformation, vibration mode, opening situation and space complexity, and the five-radial arm cutterhead was determined to be the best solution. Analysing the actual tunnelling parameter data shows good engineering adaptability. This paper provides a reference for the optimal design of the atmospheric cutterhead of slurry shield machine. [ABSTRACT FROM AUTHOR]

This study presents results from a project to develop and pilot a new type of performance assessment of the skills used when teaching reading and writing. To provide context for this new approach, we begin by identifying the different types of knowledge that have historically been the focus of teacher assessment. Next we describe the newly developed performance assessment and present results from a pilot with 59 teacher candidates. The results suggest that the performance tasks can provide reliable evidence of the skills used to teach a range of reading and writing content across the K-6 grade span. We conclude by discussing the importance of future studies to further evaluate the validity argument for this type of performance assessment. We suggest potential uses such as studying teacher knowledge and learning, evaluating teacher education and professional development, and providing formative feedback as part of teacher preparation. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to develop a performance assessment modeling for managing transport enterprises and provide a framework that explains the conditions under which performance of enterprises optimized. In doing so, the multiples streams built upon the different stakeholders and shareholders as well as social actors play in the transport enterprise performance optimization. The novelty of this paper is the development of a computational modeling framework addressing the added value footprint by large transport operators providing mobility and accessibility to a regional economy. The analysis outputs promoting explanations towards operational efficiency and business productivity on one hand; and supporting decisions toward sustainable development, business intelligence and new investments on the other. The assessment methodology is based on "Fuzzy TOPSIS" modelling, where a series of key performance indicators have been introduced, providing results over time to assess business innovation and illustrate drivers of action towards sustainable development and business intelligence. Managerial implication of the paper is the development of a multi-objective assessment framework providing essential support for managing real business applications in transportation and support decisions towards resources optimization, adjustments in cooperate governance model and policies, assessment of new investments and short-term or/and long-term action plans. [ABSTRACT FROM AUTHOR]

A new approach to assessing an effectiveness of corporate management is considered. The stages in the assessment are outlined. Budgeting systems must be introduced in order to improve management. [ABSTRACT FROM AUTHOR]

Purpose: Navigational strategies create a scenario whereby percutaneous needle-based interventions of the liver can be guided using both pre-interventional 3D imaging datasets and dynamic interventional ultrasound (US). To score how such technologies impact the needle placement process, we performed kinematic analysis on different user groups. Methods: Using a custom biopsy phantom, three consecutive exercises were performed by both novices and experts (n = 26). The exercise came in three options: (1) US-guidance, (2) US-guidance with pre-interventional image-registration (US + Reg) and (3) US-guidance with pre-interventional image-registration and needle-navigation (US + Reg + Nav). The traveled paths of the needle were digitized in 3D. Using custom software algorithms, kinematic metrics were extracted and related to dexterity, decision making indices to obtain overall performance scores (PS). Results: Kinematic analysis helped quantifying the visual assessment of the needle trajectories. Compared to US-guidance, novices yielded most improvements using Reg (PSavg(US) = 0.43 vs. PSavg(US+Reg) = 0.57 vs. PSavg(US+Reg+Nav) = 0.51). Interestingly, the expert group yielded a reversed trend (PSavg(US) = 0.71 vs PSavg(US+Reg) = 0.58 vs PSavg(US+Reg+Nav) = 0.59). Conclusion: Digitizing the movement trajectory allowed us to objectively assess the impact of needle-navigation strategies on percutaneous procedures. In particular, our findings suggest that these advanced technologies have a positive impact on the kinematics derived performance of novices. [ABSTRACT FROM AUTHOR]

With the acceleration of urbanization process in China, the usage of sustainable green buildings is getting a great attention and significance in order to reduce energy consumption. However, there are still challenges for evaluating the performance of green buildings in China due to the lack of unified codes/standards. This paper proposes a method based on cloud model to evaluate the performance of green buildings. This method overcomes the randomness and subjectivity of fuzzy concepts to the maximum extent; on this basis, the system's indexes including environmental–economic balance, regional social coordination, health, comfort, and green management can be established. The analytic hierarchy process and entropy weight methods are used to calculate the index weight; the cloud model is used to realize the transformation between qualitative and quantitative characteristics; and then, a mathematical evaluation model based on the cloud model is established. Finally, an empirical study is made based on a typical case in Shenzhen city, China. The findings of this research work reveal a performance index for green building created based on expert evaluations and a standard cloud yardstick created in the comprehensive cloud model. Based on the results of this manuscript, the industry can acquire benefits by suggesting effective measures that can be implemented in all stages of green buildings construction where the measurements execution and persuasive components make appraisal models for a green building. This study provides a consistent framework for researchers and other public to evaluate green buildings, and provides a solid foundation for further research and consideration basis on sustainable development and green building operation. [ABSTRACT FROM AUTHOR]

Non-targeted analysis (NTA) using high-resolution mass spectrometry has enabled the detection and identification of unknown and unexpected compounds of interest in a wide range of sample matrices. Despite these benefits of NTA methods, standardized procedures do not yet exist for assessing performance, limiting stakeholders' abilities to suitably interpret and utilize NTA results. Herein, we first summarize existing performance assessment metrics for targeted analyses to provide context and clarify terminology that may be shared between targeted and NTA methods (e.g., terms such as accuracy, precision, sensitivity, and selectivity). We then discuss promising approaches for assessing NTA method performance, listing strengths and key caveats for each approach, and highlighting areas in need of further development. To structure the discussion, we define three types of NTA study objectives: sample classification, chemical identification, and chemical quantitation. Qualitative study performance (i.e., focusing on sample classification and/or chemical identification) can be assessed using the traditional confusion matrix, with some challenges and limitations. Quantitative study performance can be assessed using estimation procedures developed for targeted methods with consideration for additional sources of uncontrolled experimental error. This article is intended to stimulate discussion and further efforts to develop and improve procedures for assessing NTA method performance. Ultimately, improved performance assessments will enable accurate communication and effective utilization of NTA results by stakeholders. [ABSTRACT FROM AUTHOR]

In remote semi-arid area of Algerian Sahara, water supplying by PV panels for livestock and irrigation purposes is considered as an appropriate solution to developing the desert agriculture and improving the living conditions of the local population (Gov.dz. Available: https://madrp.gov.dz. Accessed: 26 May 2021). However, the operating performance of PV pumping system is affected by many dynamic factors, especially solar radiation and ambient temperature. On real well located at Sebseb—Ghardaia, Algeria (Latitude 32.26 N° and longitude 03.46E°), a PV pumping system for irrigation purposes is installed to investigate and evaluate its performance under different real meteorological conditions. Monthly and seasonal results were analyzed and discussed through the data monitoring system within one year (April 2020 to March 2021). The experimental results obtained show that the system can produce an average water volume of 17,122.22 m3/ year; only 62% of this capacity (divided as: 94.32% in summer, 52% in autumn, 68.5% in spring and 24.8% in winter) is used for watering the 70 palm trees, the rest (38%) will be employed to supply other associated type of crops (multiple crop irrigation). Also, the minimum and maximum values of performance ratio, capacity factor, reference yield and final yield are recorded (59.7% in June and 93.14% in December), (17.07% in January and 26.83% in May), (5.13 h/day in December and 7.09 h/day in June) and (4.1 h/day in January and 6.44 h/day in May), respectively. Furthermore, a sensitive analysis has shown that the best operating performances of the system are found in winter and spring after in autumn. [ABSTRACT FROM AUTHOR]

The Gurumukhi script has a complex structure for which text recognition based on an analytical approach can misinterpret the script. For error-free results in text recognition, the author has proposed a holistic approach based on classification of Gurumukhi month's name images. For this, a new convolutional neural model has been developed for automatic feature extraction from Gurumukhi text images. The proposed convolutional neural network is designed with five convolutional, three polling layers, one flatten layer and one dense layer. To validate the results of the proposed model, the dataset was self-created from 500 distinct writers. The performance of the model has been analyzed with 100 epochs, 40 batch sizes and different optimizers. The various optimizers that have been used for this experimentation are SGD, Adagrad, Adadelta, RMSprop, Adam, and Nadam. The experimental results show that the proposed CNN model performed best with Adam optimizer in terms of accuracy, computational time, F1 score, precision and recall. [ABSTRACT FROM AUTHOR]

The article considers methodological and informational problems related to calculating the dynamics of fixed assets (fixed capital) in the Russian economy over 1990–2000. Special aspects of measuring the fixed assets performance using various statistical classifiers, the effect produced by the base of comparable prices on assessing the performance and reproduction characteristics of fixed capital are analyzed. Calculated estimates of its rate of change in the context of certain types of economic activity for 2005–2019 are given; and a possible direction for their refinement is also proposed. [ABSTRACT FROM AUTHOR]

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Since 1968, the Aerial Forest Protection Service (Avialesookhrana) has taken measures to extinguish wildfires and reduce fire danger in adjacent areas by rainmaking with methods and technologies of precipitation enhancement developed at Roshydromet research institutions. The efficiency of the performed modification is analyzed in the absence of a network of Doppler weather radars, as well as in conditions of a sparse ground-based weather station network in most of Siberia and the Far East. Due to the significant spatial variability of precipitation, the floating control method was applied using data of numerical weather prediction models. The analysis of precipitation enhancement activities demonstrated a rather high efficiency of applying this method: the actual amount of precipitation was 1.4–1.5 times higher than the expected amount averaged according to the forecast models. [ABSTRACT FROM AUTHOR]

Purpose: To develop two item content-matched, precise, score-level targeted inpatient physical function (PF) short form (SF) measures: one clinician-reported, one patient-reported. Items were derived from PROMIS PF bank content; scores are reported on the PROMIS PF T-score metric. Methods: The PROMIS PF item bank was reviewed for content measuring lower-level PF status (T-scores 10–50) with high item set score-level reliability (≥ 0.90). Selected patient-reported (PR) items were also edited to function as clinician-reported (CR) items. Items were reviewed by clinicians and field tested; responses were assessed for meeting PROMIS measure development standards. New CR and PR items were calibrated using patient responses to the original PROMIS PF items as anchoring data. SFs were constructed, based on content and precision. Results: Nine PROMIS PF items were candidates for CR and PR inpatient PF assessment; three new items were written to extend content coverage. An inpatient sample (N = 515; 55.1% female; mean age = 66.2 years) completed 12 PR items and was assessed by physical therapists (using 12 CR items). Analyses indicated item sets met expected measure development standards. Twelve new CR and three new PR items were linked to the PROMIS PF metric (raw score r = 0.73 and 0.90, respectively). A 5-item CR SF measure was constructed; score-level reliabilities were ≥ 0.90 for T-scores 13–45. A 5-item PR SF measure was assembled, mirroring CR SF content. Conclusions: Two item content-matched SFs have been developed for clinician and patient reporting and are an effective, efficient means of assessing inpatient PF and offer complementary perspectives. [ABSTRACT FROM AUTHOR]

In response to the imperative to fortify supply chain (SC) resilience amidst deep uncertainty, particularly in the context of a pandemic, this study endeavors to introduce a pioneering methodology termed fuzzy network data envelopment analysis. The developed model stands out for its adaptability, being capable of accommodating an array of membership functions tailored to varying network configurations. By synthesizing complex network dynamics into a unified framework, the proposed methodology offers a robust solution for evaluating SC resilience. Notably, its integration of a single objective function addresses disparate network complexities through efficient linear programming techniques, thereby providing precise resilience assessments essential for strategic decision-making. We present an empirical study that aims to conduct a thorough assessment and enhancement of the resilience within face mask supply chains, comprising suppliers, producers, and distributors, to demonstrate the capabilities of our novel model. The findings of this study provide insights into enhancing the performance of SCs in the face of unexpected events such as COVID-19, how uncertainty can affect the performance of SCs, and how inefficiencies can be identified and improved. [ABSTRACT FROM AUTHOR]

Computational thinking (CT) skills are critical for the science, technology, engineering, and mathematics (STEM) fields, thus drawing increasing attention in STEM education. More curricula and assessments, however, are needed to cultivate and measure CT for different learning goals. Maker activities have the potential to improve student CT, but more validated assessments are needed for maker activities. We developed a set of activities for students to improve and assess essential CT skills by creating real-life applications using Arduino, a microcontroller often used in maker activities. We examined the psychometric features of CT performance assessments with rubrics and the effectiveness of the maker activities on improving CT. Two high school physics teachers implemented these Arduino activities and assessments with fifteen high school students during three days in a summer program. The participating students took an internal content-involved and an external CT tests before and after participating in the program. The students also took the performance-based CT assessment at the end of the program. The data provide reliability and validity evidence of the Arduino assessment as a tool to measure CT. The pre- and post-test comparison indicates that students significantly improved their scores on the content-involved assessment aligned with the Arduino activities, but not on the content-free CT assessment. It shows that Arduino, or some equipment similar, can be used to improve students' CT skills and the Arduino maker activities can be used as performance assessments to measure students' engineering involving CT skills. [ABSTRACT FROM AUTHOR]

Green development is the cognition of geography to human-nature nexus under the background of the new era. As China is facing various eco-environment problems, green development has become a key approach towards ecological progress, and it is ultimately an explicit means to respond to support sustainable development in China. Quantifying green development performance is essential to track efforts towards sustainability and guide policymakers. However, applying the balanced property of 'Economy-Ecology-Society' of green development to its performance assessment is rarely discussed. Here we elaborated the connotation of green development and developed a quantification model with coupling coordination degree to assess green development performance of the largest old industrial base of China, Northeast China. We found that the green development performance has been improved from a score of 0.443 in 2003 to 0.530 in 2019 but the disparities of green development performance were enlarging over time, especially for the cities in Heilongjiang. A positive spatial autocorrelation phenomenon of green development performance was confirmed, and Low-Low clusters in the northeastern Heilongjiang and High-High clusters in the central-eastern Liaoning were discovered. This study suggests the need to track the spatio-temporal dynamics of green development performance to provide references for achieving sustainable development goals in northeast China and other regions. [ABSTRACT FROM AUTHOR]

Proficiency testing providers, accreditation bodies and testing laboratories should be aware that a laboratory participating in a proficiency testing round might have reported a biased result despite a satisfactory performance indicated by an assessment using uniquely the z score. A complementary performance evaluation, based on the ζ score and the assessment of the measurement uncertainty, is therefore highly recommended. This work presents an intuitive graphical tool (the Naji2 plot) that combines z and ζ scores together with the reported measurement uncertainties. This tool allows a comprehensive assessment of the laboratory performance and enables to identify the need for corrective actions. The concerned laboratory should then perform a root cause analysis and investigate their bias and/or their measurement uncertainty evaluation. [ABSTRACT FROM AUTHOR]

Transdisciplinary research, in which academics and actors from outside the academy co-produce knowledge, is an important approach to address urgent sustainability challenges. Indeed, to meet these real-world challenges, governments, universities, development agencies, and civil society organizations have made substantial investments in transdisciplinary partnerships. Yet to date, our understanding of the performance, as well as impacts, of these partnerships for sustainability is limited. Here, we provide a guide to assess the performance and impacts of transdisciplinary partnerships for sustainability. We offer key steps to navigate and examine the partnership process for continuous improvement, and to understand how transdisciplinary partnership is contributing to sustainable futures. [ABSTRACT FROM AUTHOR]

This study assesses the performance of Turkish science pre-service teachers (PSTs) in a TPACK-Practical Course that consists of the training course and lesson-plan-micro-teaching stages. The subjects of the study were 46 PSTs (19 males and 27 females). In this study, after PSTs took a TPACK training course, they created a TPACK-based lesson plan and presented it to their classmates through micro-teaching. Data sources included lesson plans and video recordings of lesson presentations. Researchers developed and used a rubric of TPACK-based learning environments to evaluate the data sources. The rubric consisted of 12 items, and each item contained five performance levels. Researchers analyzed the data by using descriptive statistics and the Wilcoxon signed-rank test. There was a significant increase in the total score of TPACK among the PSTs after the course. There was also a significant increase in the items guiding, providing active participations of students, making assessment and evaluation, appropriateness of chosen teaching methods, and accuracy of the given information/concepts when teaching science subjects with technology. Possible reasons for this positive effect include using worksheets with technological tools such as simulation, the influence of the course lecturers as role models, the introduction of new technologies to the PSTs in the training course, and using class discussions to provide feedback. [ABSTRACT FROM AUTHOR]

Recent trends in educational technology have led to emergence of methods such as teaching analytics (TA) in understanding and management of the teaching–learning processes. Didactically, teaching analytics is one of the promising and emerging methods within the Education domain that have proved to be useful, towards scholastic ways to make use of substantial pieces of evidence drawn from educational data to improve the teaching–learning processes and quality of performance. For this purpose, this study proposed an educational process and data mining plus machine learning (EPDM + ML) model applied to contextually analyze the teachers' performances and recommendations based on data derived from students' evaluation of teaching (SET). The EPDM + ML model was designed and implemented based on amalgamation of the Text mining and Machine learning technologies that builds on the descriptive decision theory, which studies the rationality behind decisions the learners are disposed to make based on the textual data quantification and statistical analysis. To this effect, the study determines pedagogical factors that influences the students' recommendations for their teachers, what role the sentiment and emotions expressed by the students in the SET play in the way they evaluate the teachers by taking into account the gender of the teachers. This includes how to automatically predict what a student's recommendation for the teachers may be based on information about the students' gender, average sentiment, and emotional valence they have shown in the SET. Practically, we applied the Text mining technique to extract the different sentiments and emotions (intensities of the comments) expressed by the students in the SET, and then utilized the quantified data (average sentiment and emotional valence) to conduct an analysis of covariance and Kruskal Wallis Test to determine the influential factors, as well as, how the students' recommendation for the teachers differ by considering the gender constructs, respectively. While a large proportion of the comments that we analyzed (n = 85,378) was classified to be neutral and predominantly interpreted to be positive in nature considering the sentiments (76.4%), and emotional valence (88.2%) expressed by the students. The results of our analysis shows that for the students' comments which contain some kind of positive or negative sentiment (23.6%) and emotional valence (11.8%); that females students recommended the teachers taking into account the sentiments (p =.000). While the males appear to be slightly borderline in terms of emotions (p =.056) and sentiment (p =.077). Also, the EPDM + ML model showed to be a good predictor and efficient method in determining what the students' recommendation scores for the teachers would be, going by the high and acceptable values of the precision (1.00), recall (1.00), specificity (1.00), accuracy (1.00), F1-score (1.00) and zero error-rate (0.00) which we validated using the k-fold cross-validation method, with 63.6% of optimal k-values observed. In theory, we note that not only does the proposed method (EPDM + ML) proves to be useful towards effective analysis of SET and its implications within the educational domain. But can be utilized to determine prominent factors that influences the students' evaluation and recommendation of the teachers, as well as helps provide solutions to the ever-increasingly need to advance and support the teaching–learning processes and/or students' learning experiences in a rapidly changing educational environment or ecosystem. [ABSTRACT FROM AUTHOR]

Quality assessment (QA) for landslide susceptibility maps (LSMs) is essential to increase their usability. A QA approach based on the landslide activity after a triggering event can be useful for the performance evaluation of the methods used for LSM production. Landslides triggered by earthquakes can be employed for this purpose as they occur frequently throughout the active seismic regions of the world. After an earthquake occurred in Elazig, Turkey on 24 Jan 2020 (Mw 6.8), several landslides were activated in the mountainous parts. Here, the performances of two state-of-the-art machine learning methods, i.e., the random forest (RF) and the multi-layer perceptron (MLP), were investigated using the activated landslides. The landslide inventory was derived in a previous study by using pre- and post-event aerial photogrammetric datasets and classified according to their activity types and temporal observations. The classes observed in the pre-event photogrammetric datasets were inactive (L1) and active mass movements (L2). The ones observed in the post-event photogrammetric datasets were new active zones inside the existing landslide (L3) and new activity (L4). Here, only the L1 and L2 type landslides observed in a part of the study area were used for the model training and the LSMs were produced for the whole area to investigate the model transferability. The L3 and L4 type landslides were used for validation. In addition, the area under curve (AUC) values obtained from the methods and the volumetric change maps obtained from the pre- and post-event digital elevation models were also used for the performance assessment. The results demonstrated that RF exhibited higher classification accuracy (AUC = 0.93) than MLP (AUC = 0.87); and accurate LSMs could be produced by using a sub-part of the basin for training. [ABSTRACT FROM AUTHOR]

The performance of CNC spindle directly affects the machining accuracy and processing speed of the machine tool. The time-varying environment and complex working conditions increase difficulty of thoroughly assessing the spindle relying on experimental test. Therefore, this paper proposes a comprehensive framework for assessing the performance of the spindle driven by digital twin. First, a multi-domain modeling method is used to establish a spindle twin model, and then an index system for assessing spindle performance is constructed combining experimental test and simulation. A multi-index fusion assessment that combines subjective and objective weights is presented to construct the performance assessment level, which comprehensively assesses the performance of the spindle from multiple dimensions such as vibration, temperature, and stiffness. Finally, the JSC180A electric spindle is selected to verify the proposed method. The conclusion that the electro-spindle is operating in good condition is consistent with the on-site inspection, which verifies the effectiveness of the proposed method and the feasibility of its on-site application. [ABSTRACT FROM AUTHOR]

In this work, guidelines are provided for designing illumination-grade light-emitting diodes (LEDs) into luminaires. A generalized concept of deriving a multi-domain compact model of LED from data-sheet information is discussed, however, the process described can be applied to the design of any kind of LED luminaire. The most critical aspect of LED luminaire design is to decide on how many LEDs are required to meet the design goals. The rest of the design process revolves around the number of LEDs since it directly impacts the optical, electrical, and thermal aspects of a luminaire. The design process is not simple as it seems, it is not estimating the number of LEDs based on photometric characteristics listed on their datasheets and divides the target lumens from the design goals by that number. However, this approach looks too simple but not always will lead to a design that will meet the application's illumination requirements. The photometric properties of a LED are dependent on a variety of factors, including drive current and junction temperature. The accurate number of LEDs can be estimated only when the inefficiencies of the optical, thermal and electrical systems are accounted for in the design process. This work serves as a guide to estimate some of the major losses. [ABSTRACT FROM AUTHOR]