Traditional Evaluation Research Articles

The Constructed Enemy - The Palestinian-Israeli Conflict as an Example

Abstract: With the continuous development of international relations theory, it has been increasingly recognized that neither traditional rationalism nor reflexivism is sufficient to fully explain many phenomena in the present world. With the criticism of realism and the development of postmodernism, constructivism has been embraced by many researchers as a new research paradigm. The realist approach to research explains international society by explaining the meaning as well as the origin of things in the objective world. Carl Schmitt's ideas are ostensibly more focused on material forces and should be more closely aligned with realism. But in reality, he used ideas to define the emergence of political affairs. Thus, at its core, Carl Schmitt is inherently similar to constructivism. However, the Palestinian-Israeli conflict has been intensifying in recent years. Therefore, using the above theory to explain the reasons for the emergence and escalation of the Palestinian-Israeli conflict has important theoretical and practical significance. This not only means combining the theories of traditional thinkers with new research paradigms, but also helps people to understand the nature of the Palestinian-Israeli conflict and find better ways to promote peace and development in the Middle East.

Ideological Uses and Management Policies of Archaeological Sites in Post-Socialist Albania: The Case of Saranda Area (Finiq and Butrint)

This article is a sample of the research I am conducting in the wider area of Saranda as part of the wider framework of critical analysis of cultural heritage, this scientific field that is placed at the meeting point of archaeology and anthropology. The two sciences have intersected in many different ways since the 1980s. Archaeology as a science that studies the archaeological remains of the past was confronted with anthropology whose purpose is the cultural analysis of social phenomena and relationships. The result of this partnership is a wide range of approaches, which are called post-process archaeologies . These approaches represent a variety of analyses, which emphasize a contextual, and thus contemporaneous, nature of archaeological evidence, the critique of archaeology as a scientific search for objective truth, its links with history and the contested nature of archaeological data and of the archaeological work. This perspective turns the interest of research from explanation to interpretation and from the positivist tradition of natural science to the tradition of interpretational philosophy, i.e. the preeminent study of meaning.

Language Model Method for Collocation Rules of Parts of Speech in Machine Translation System

With the development of the times, modern society has now entered the Internet of Things (IoT) information age and Machine Translation (MT) plays an important role in increasingly frequent cross-language communication. In recent years, China's artificial intelligence industry has been in a stage of rapid construction, and the scale of its core industries has grown explosively, and a large number of artificial intelligence companies, including issuers, have emerged. Part of speech has always been a major problem in MT. One of the reasons is that there are a large number of multi-category words in Chinese and a large number of polysemy words in English, so part of speech collocation problems account for a large proportion of MT errors, which to some extent affects the credibility and accuracy of the translation. To reduce the error problem in MT of part of speech collocation, this paper used Machine Learning (ML) methods to study the Language Model (LM) of part of speech collocation based on recurrent neural network (NN) and compared it with the traditional statistical LM. In terms of the accuracy rate of the two LMS in the automatic evaluation index of machine translation, the experimental results show that the recursive NN LM established by the ML method had an accuracy rate of 80.42% and 83.57%, respectively, for the part-of-speech matching rules of the IoT machine translation system in the dialogue between Chinese and English and the translation of articles. The accuracy of traditional statistical LM evaluation was 71.29% and 69.52%, respectively. Compared to traditional statistical LM, the accuracy of translation was higher. This showed that the recurrent NN LM reduced the number of errors in the collocation of parts of speech in MT and improved the accuracy and credibility of MT.

Exploring Digital Health Tool Adoption and the Impact of HR Analytics on Decision-making in Jalandhar’s Healthcare Organizations

This study assesses the virtual tool adoption panorama within healthcare organizations. The healthcare enterprise is undergoing a length of huge transformation, pushed by way of the ever-developing need for advanced performance, patient care, and value control. Digital fitness equipment, encompassing a wide variety of technologies from affected person portals and telemedicine platforms to wearable health trackers and digital fitness records (EHRs), are hastily reworking how healthcare is introduced. This study delves into now not most effective the extent of virtual tool adoption however additionally the influence of HR analytics on choice-making within these businesses. HR analytics, the software of statistics analysis techniques to human resource management practices, offers precious insights into personnel traits, recruitment strategies, and employee performance. By examining how healthcare organizations leverage HR statistics to shape their group of workers strategies, the have a look at explores the ability blended effect of those developments on decision-making inside the healthcare panorama. This research seeks to offer insights into the changing landscape of healthcare pushed via technology and its considerable impact on HR decision making. Understanding the elements that power virtual tool adoption within healthcare corporations is critical for optimizing using these technologies. This observe will explore the perceived benefits and demanding situations related to digital health equipment, in conjunction with the position of leadership and organizational tradition in facilitating a success implementation. Additionally, the studies will study how HR analytics are being utilized to tell staffing decisions, training programs, and overall performance control inside healthcare groups.

Pareto’s legacy in the Italian tradition: the case of mathematical economics

Pareto’s personality had a great influence on the evolution of economic theory in the last century at least in the Italian context. At the beginning of the twentieth century a small but combative circle of followers aimed to defend and spread the new Paretian approach based on the idea of general economic equilibrium. Paretian scholars were well recognizable in the Italian context for their contributions to mathematical and statistical aspects. The purpose of this contribution is to outline the essential features of the Italian Paretian school in the field of pure economics or as we would say today of mathematical economics. This favorable position of the Italian tradition in the field of mathematical economics was basically due to two circumstances. The first was represented by the legacy of Pareto and his school while the second was to be found in the fact that among the students of Pareto there was a pure mathematician of great value Luigi Amoroso. We see then that an important voice even if poorly valued was that of Bruno de Finetti who published a remarkable series of articles on mathematical economics in the second half of the 1930s. After World War II the Paretian tradition in this field weakened considerably and effectively disappeared internationally.

Freed, S. (2019). AI and Human Thought and Emotion. CRC Press.

Sam Freed’s AI and Human Thought and Emotion is a pioneering venture into the possibility of programming an anthropic model of artificial intelligence grounded on the author’s philosophical reflection on the accuracy-oriented and optimisation-driven making of AI. In critique of the rationalist tradition of AI development and expurgation of introspection from cognitive science, this book draws heavily upon phenomenology to argue for the necessity of incorporating human non-linear thinking processes into the technical design of AI. Freed’s conceptual revolution enters a substantive dialogue with Hubert Dreyfus’s What Computers Can’t Do to offer an answer to the impossibility of formalising human introspection, which is conventionally deemed unprogrammable under the ostracism of human subjectivity from the science discipline and anti-scientism that pervades the intellectual circle of the humanities.

Rasionalitas Kebijakan Desentralisasi Pendidikan di Provinsi Lampung (Kenyataan dan Harapan)

This research uses Max Weber's theory of rationality to describe the reality, hopes, and rationality of educational decentralization, which are included in the rational category or vice versa. Data was collected through interviews, and observations related to the phenomenon of reality, hopes, and rationality of educational decentralization in Lampung Province and analyzed qualitatively. The research results show that in reality, the education decentralization policy in Lampung Province is rational. Based on instrumental theory, guaranteed education costs (free school), educational infrastructure and educational scholarships can support the ability of decentralization policies. Rationality in the value at which individuals decide to continue or not to continue their education is relatively stable at the basic education level in Lampung province. In the affective rationale, the conditions between individual objects of education such as students and teachers are the spearhead of education at the lower level. Education personnel must shape the character of their students in line with freedom of learning in the world of education. However, traditional rationality, namely custom, ethnicity and religion, does not influence education decentralization policies. The hope for the future is that the decentralization of education in Lampung Province requires strengthening each region's commitment so that progressive education is implemented evenly and cannot be separated from the responsibility of the regional government.

Experiment-driven rationalism

Philosophers debate about which logical system, if any, is the One True Logic. This involves a disagreement concerning the sufficient conditions that may single out the correct logic among various candidates. This paper discusses whether there are necessary conditions for the correct logic; that is, I discuss whether there are features such that if a logic is correct, then it has those features, although having them might not be sufficient to single out the correct logic. Traditional rationalist arguments suggest that the necessary conditions of thought are necessary and sufficient conditions singling out the correct logical and mathematical theories. In the contemporary debate, Chalmers advocates a view along this line. Jago, analogously, suggests that the necessary conditions for thought—or, as he calls them, our basic epistemic expectations—single out a family of logical and mathematical theories. Warren and Williamson, on the other hand, argue that there are no necessary conditions of thought. I argue that there are necessary conditions for thought, and these are necessary but not sufficient conditions to be the correct logic; indeed, these are features that all logics—correct or incorrect—share. No view we can understand is ruled out by the necessary conditions for thought, but we cannot understand quite any view. Human linguistic and conceptual abilities are genetically constrained, and these constraints are our best guide to the boundaries of logic. Arguing for this, I tackle two dogmas of modern rationalism: namely, the view that the biological constraints of human cognition have no bearing on the boundaries of the epistemic space, and the view that the boundaries of thought coincide with the boundaries of language.

Редукция трансцендентного как основание имагинативного поворота в неопрагматизме

<span lang="EN-US">The article is devoted to the analysis of the conceptual and methodological grounds of the imaginative turn made within the framework of neopragmatist discourse. The specific works of R. Rorty, H. Putnam, and M. Johnson are used as examples to present the features of the interpretation of ethics, moral law, and rationalist tradition. The article emphasizes the process of metaphorization of key concepts of Kant's ethics by the neopragmatists. It is argued that for Putnam Kant's ethics has value first of all for its figurative component, the presence of colorful metaphors based on the core concepts of the Judeo-Christian tradition. In adding, it is suggested to critically evaluate the position of Johnson, who deals in detail with Kant's main moral law, the meaning of the categorical imperative. It is established on the basis of the fact that neopragmatists have reduced the transcendental dimension that the crucial role begins to be taken by the sensuality of the empirical subject and imagination as a moral moderator. The author refers to this phenomenon by the term "imaginative turn". Citing the works of Russian researcher E.N. Ustyugova the author makes a conclusion that for neopragmatists the human being turns out to be an empirical natural phenomenon, and aesthetics is the only fundament for moral judgments.</span>

Genetic evaluations of dairy goats with few pedigree data: different approaches to use molecular information.

One of the limitations of implementing animal breeding programs in small-scale or extensive production systems is the lack of production records and genealogical records. In this context, molecular markers could help to gain information for the breeding program. This study addresses the inclusion of molecular data into traditional genetic evaluation models as a random effect by molecular pedigree reconstruction and as a fixed effect by Bayesian clustering. The methods were tested for lactation curve traits in 14 dairy goat herds with incomplete phenotypic data and pedigree information. The results showed an increment of 37.3% of the relationships regarding the originals with MOLCOAN and clustering into five genetic groups. Data leads to estimating additive variance, error variance, and heritability with four different models, including pedigree and molecular information. Deviance Information Criterion (DIC) values demonstrate a greater fitting of the models that include molecular information either as fixed (genetic clusters) or as random (molecular matrix) effects. The molecular information of simple markers can complement genetic improvement strategies in populations with little information.

A Fast Reliability Evaluation Strategy for Power Systems under High Proportional Renewable Energy—A Hybrid Data-Driven Method

With the increasing scale of the power system, the increasing penetration of renewable energy, and the increasing uncertainty factors, traditional reliability evaluation methods based on Monte Carlo simulation have greatly reduced computational efficiency in complex power systems and cannot meet the requirements of real-time and rapid evaluation. This article proposes a hybrid data-driven strategy to achieve a rapid assessment of power grid reliability on two levels: offline training and online evaluation. Firstly, this article derives explicit analytical expressions for reliability indicators and component parameters, avoiding the computational burden of repetitive Monte Carlo simulation. Next, a large number of samples are quickly generated by parsing expressions to train convolutional neural networks (CNNs), and the system reliability index is quickly calculated under changing operating conditions through CNNs. Finally, the effectiveness and feasibility of the proposed method are verified through an improved RTS-79 testing system. The calculation results show that the method proposed in this article can achieve an online solution of second-level reliability indicators while ensuring calculation accuracy.

Regional investment risk evaluation based on compound risk correlation coefficient and migration learning approach

The high profit of regional investment is often accompanied by high risks, and the prior assessment of investment risks is conducive to avoid investment risks. However, the traditional evaluation methods usually only focus on individual risk factors, and it is difficult to evaluate and manage risks on the whole. Therefore, the study introduces deep learning algorithm, first build regional investment risk evaluation index system, then according to the characteristics of risk evaluation, design based on deep learning regional investment risk evaluation model, the final use parameter based migration learning algorithm and composite correlation coefficient to improve the evaluation model, solve the problem of insufficient training samples. The test results showed that the randomly selected 50 test samples with two different risk assessment models were 0.80 and 0.86, the deep learning algorithm tested 0.84, and the transfer learning improved model tested was 0.92, with the highest accuracy. This shows that the deep learning regional investment risk evaluation model improved by transfer learning effectively solves the problem of insufficient training data and improves the accuracy of prediction evaluation. In the field of venture capital, the model can help investors to evaluate and predict investment risks more accurately and improve the effect of investment decisions.

Application of BPNN’s intelligent ETQ model in network English teaching

Teaching evaluation is a key initiative to improve the quality of education and teaching. The research significance of this study is rooted in addressing the limitations of the traditional evaluation of teaching quality (ETQ) model, which often relies on a single evaluation index, exhibits a one-sided perspective, and suffers from pronounced subjectivity. In this context, this paper delves into the application of the backpropagation neural network (BPNN) to enhance and refine the ETQ model. The intelligent ETQ model was constructed and utilized in network English teaching to enhance the effect and quality of network English teaching. By analyzing the characteristics and needs of network English teaching, the advantages of BPNN in the ETQ were explored. The intelligent evaluation model was constructed, and its application effect in network English teaching was studied and evaluated. The total number of students satisfied with the BPNN based network English ETQ model was 151, with a total satisfaction rate of 75.5%. The ETQ model on the basis of BPNN was applied to network English teaching, which helped the average final score of Class 2 improve by 5.44 points compared to the division exam. The ETQ model based on BPNN was applied to network English teaching, which can improve the rationality of teaching evaluation and help improve students’ school English proficiency.

Proactive safety assessment of urban through-roads based on GPS data

Road safety is a worldwide issue, while urban roads account for a high share of serious road injuries, especially involving vulnerable road users, such as pedestrians or cyclists. Specifically, the safety of major roads through built-up areas (through-roads) is insufficient due to mixed traffic conditions including vulnerable road users, varying driving behaviour, and many disruptions, which are combined with excessive speed.. In this context, various traffic calming measures have been implemented to improve road safety, such as gateways or pedestrian refuge islands. However, the specific safety impacts of traffic calming combined with specific characteristics of through-roads are often unknown, since most traditional evaluations have been limited by small sample sizes of crash data, as well as wide variations in physical and road characteristics. To overcome the limitations of crash-based evaluations, we used the GPS-based data from a sample of 21 Czech and 12 Polish through-roads to develop the Speed-Safety Index, which combines speed, speed variance, and traffic volume. Our study has three novelty features: (1) To assess safety, we used speed and speed variance simultaneously. (2) To complete the missing link between specific traffic calming measures and safety, we validated the statistical relationship between the developed Speed-Safety Index and crash history. (3) To prove the usefulness of the developed index, we also showed its practical interpretation by proving the effect of spacing between traffic calming measures on safety. The index proved to be well correlated to crash frequency and it also proved the effect of spacing between traffic calming measures: the longer spacing, the smaller speed-reducing effect. The paper concludes with a discussion on the limitations, which we plan to address in further research, by moving from the current macro-perspective (Speed-Safety Index on the level of through-roads) to the micro-perspective (focusing on individual directions, locations, and traffic calming measures). We also plan to investigate the method’s applicability in different contexts. If the approach proves feasible, with reliable and valid results, it may become an alternative for a proactive network-wide road assessment, as called for by the European Road Infrastructure Safety Management Directive.

Group Validation in Recommender Systems: Framework for Multi-layer Performance Evaluation

Evaluation of recommendation systems continues evolving, especially in recent years. There have been several attempts to standardize the assessment processes and propose replacement metrics better oriented toward measuring effective personalization. However, standard evaluation tools merely possess the capacity to provide a general overview of a system’s performance; they lack consistency and effectiveness in their use, as evidenced by most recent studies on the topic. Furthermore, traditional evaluation techniques fail to detect potentially harmful data on small subsets. Moreover, they generally lack explainable features to interpret how such minor variations could affect the system’s performance. This proposal focuses on data clustering for recommender evaluation and applies a cluster assessment technique to locate such performance issues. Our new approach, named group validation , aids in spotting critical performance variability in compact subsets of the system’s data and unravels hidden weaknesses in predictions where such unfavorable variations generally go unnoticed with typical assessment methods. Group validation for recommenders is a modular evaluation layer that complements regular evaluation and includes a new unique perspective to the evaluation process. Additionally, it allows several applications to the recommender ecosystem, such as model evolution tests, fraud/attack detection, and the capacity for hosting a hybrid model setup.

Theory-driven neural network for total organic carbon content estimation from well logs

Abstract Total organic carbon content (TOC) is a crucial geochemical indicator for assessing the hydrocarbon generation potential of source rock formations. Traditional TOC evaluation methods employing well logs do not match well with measured data in complex formations. While intelligent algorithms have the potential to improve TOC estimation accuracy, they have poor petrophysical significance. In this study, we develop the theory-driven neural networks (TdNN) to extend TOC prediction accuracy via combining logging mechanism models into the input of neural network. TdNN have two ways for merging petrophysical models with intelligent model, specifically TdNN based on series connection (STdNN) and based on embedded way (ETdNN). STdNN processes log responses through logging mechanism models and subsequently inputs them to the neural network for TOC evaluation. ETdNN inputs log responses and calculation results from logging mechanism models to the neural network to estimate TOC. Two ways achieve more accurate prediction results than ordinary neural network via improving the quality of input data, analogous to applying data augmentation methods on intelligent models. This study utilizes the Schmoker method, 𝛿logR method, and multivariate regression method as logging mechanism models of TdNN. The prediction performance of the proposed models is tested using data from two shale reservoirs in the Longmaxi Formation and the Shahejie Formation. Results show that the proposed ETdNN achieves the highest prediction accuracy, meaning that the ETdNN is suitable for predicting TOC of shale formations.

A development Evaluation Index System of Public Sports Football University Course Based on OBE Concept in Guangdong Province

Background and Aim: The teaching evaluation index system is an essential part of teaching activities, which is related to the improvement of teaching quality and teaching effect, the stimulation of students 'learning motivation, and the improvement of teachers' teaching behavior. Thus, this research aimed to develop and articulate a development evaluation index system of public sports football university courses based on the OBE concept in Guangdong Province. Materials and Methods: The scope of this research covers all 66 undergraduate universities in Guangdong Province (including five independent colleges) using stratified random sampling. The first layer is the geographical distribution. The second layer is the school type, and the third is the target group, including teachers, students, and administrators. The calculated optimal sample size is 385 people. The research used a combination of interviews, questionnaires, and the specialist software SPSS. The research process included data collection and first draft indicators, index system confirmation using the Delphi method, and focus groups to examine the suitability and feasibility of the index system. Results: The results showed that a new evaluation index system based on the OBE concept was proposed. Five first-level indexes, 12 second-level evaluation indexes, and 29 three-level evaluation indexes are established. This index system focuses on students 'knowledge and skills, attitudes, values, and other non-cognitive factors and establishes an accurate and objective teaching evaluation index system that comprehensively reflects the public sports and football courses in Guangdong Province. Conclusions: Compared with the traditional evaluation index system, the original evaluation index system was reconfirmed and added the first-level indicators of "continuous teaching improvement and learning effect improvement", two second-level indicators, and six three-level indicators, which proved the uniqueness and innovation of this study. The new indicators encourage teachers to constantly reflect on and improve their teaching methods to achieve better results. They also enrich the evaluation index system of the OBE concept and provide a clear direction for the subsequent teaching evaluation index system of other sports projects.

Regional Cloud Forecast Verification Using Standard, Spatial, and Object-Oriented Methods

Abstract This study assesses the accuracy of the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) forecasts for clouds within stable and unstable environments (thereafter refers as “stable” and “unstable” clouds). This evaluation is conducted by comparing these forecasts against satellite retrievals through a combination of traditional, spatial, and object-based methods. To facilitate this assessment, the Model Evaluation Tools (MET) community tool is employed. The findings underscore the significance of fine-tuning the MET parameters to achieve a more accurate representation of the features under scrutiny. The study’s results reveal that when employing traditional pointwise statistics (e.g., frequency bias and equitable threat score), there is consistency in the results whether calculated from Method for Object-Based Diagnostic Evaluation (MODE)-based objects or derived from the complete fields. Furthermore, the object-based statistics offer valuable insights, indicating that COAMPS generally predicts cloud object locations accurately, though the spread of these predicted locations tends to increase with time. It tends to overpredict the object area for unstable clouds while underpredicting it for stable clouds over time. These results are in alignment with the traditional pointwise bias scores for the entire grid. Overall, the spatial metrics provided by the object-based verification methods emerge as crucial and practical tools for the validation of cloud forecasts. Significance Statement As the general Navy meteorological and oceanographic (METOC) community engages in collaboration with the broader scientific community, our goal is to harness community tools like MET for the systematic evaluation of weather forecasts, with a specific focus on variables crucial to the Navy. Clouds, given their significant impact on visibility, hold particular importance in our investigations. Cloud forecasts pose unique challenges, primarily attributable to the intricate physics governing cloud development and the complexity of representing these processes within numerical models. Cloud observations are also constrained by limitations, arising from both top-down satellite measurements and bottom-up ground-based measurements. This study illustrates that, with a comprehensive understanding of community tools, cloud forecasts can be consistently verified. This verification encompasses traditional evaluation methods, measuring general qualities such as bias and root-mean-squared error, as well as newer techniques like spatial and object-based methods designed to account for displacement errors.

Qingxiangxing Baijiu sensory quality grade classification by 1H NMR and GC combined with multivariate statistical analysis

The traditional uniform artificial sensory evaluation makes it difficult to standardize the classification of different sensory quality grades of Baijiu. In this study, a total of 92 authentic Qingxiangxing Baijiu samples with 3 sensory quality grades were carefully collected. Gas chromatography (GC) was used to determine 46 main flavor components and proton nuclear magnetic resonance (1H NMR) spectroscopy was employed to obtain hydrogen atom characteristic information of organic compounds. The principal component analysis (PCA), k-nearest neighbor (KNN) and linear discriminant analysis (LDA) models were conducted and fully validated by internal leave-one-out cross validation (LOOCV) and external repeated double random cross validation (RDRCV). The sensory quality grades of Qingxiangxing Baijiu were effectively classified by using GC and 1H NMR techniques coupled with PCA/KNN analysis with the averaged accuracy higher than 80%. In addition, synthetic minority oversampling technique (SMOTE) algorithm was successfully used to address the model overfitting problem caused by an unbalanced sample composition. This study demonstrated that 1H NMR and GC combined with multivariate statistical analysis were effective for sensory quality classification of Qingxiangxing Baijiu.

Risk assessment for the liquefied petroleum gas filling industry using fuzzy logic and hazard and operability

The object of this study is to integrate Hazard and Operability (HAZOP) analysis and Fuzzy Logic to improve risk assessment in the Liquefied Petroleum Gas (LPG) filling industry. the research introduces a new approach to resolve the uncertainties and imprecise information inherent in traditional risk evaluation methods. Using HAZOP, a qualitative analysis conducted by the experts of industry identifies potential process deviations and hazards, categorized by impact type such as fire, explosion, and environmental damage. Fuzzy Logic, quantifies these risks by evaluating the likelihood and consequences of accidents using linguistic variables like "high," "medium," and "low." The simulation of different parameters considers various scenarios, including the interplay of consequence severity, event frequency, and detection capability on overall risk using model fuzzy-Hazop. The results provide more precision and actionable risk assessments, showing the potential of the combination of Hazop and fuzzy logic for safety measurement in the LPG filling process. The study concludes HAZOP with fuzzy logic evaluates risks in uncertain conditions, offering an alternative for clearer, decision-making in risk-based design processes