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The evaluation of dental malpractice claims database is helpful to analyze the risk and lower the chances of potential litigation. In this descriptive analysis, the Taiwan Judicial Judgment Database was used to analyze the civil court decisions of dental malpractice related to infection from January 1, 2000 to August 31, 2023. After rigor verdicts reviewed, 53 dental malpractice civil judgments related to infection were included and further analyzed. Total 15 lawsuits were required to recover the punitive damages. The overall civil conviction rate was 28.3%. Two cases occurred in the dental department at medical center and 13 cases were found in the local clinics. The top one awarded compensation is dental implant. The amount of compensation was ranged from NT$ 110,000 to NT$ 3,000,000. In conclusion, this analysis can provide the important information and reference in the amounts of compensation for dental malpractice in Taiwan. [ABSTRACT FROM AUTHOR]

The rapid development of natural language processing (NLP) technologies has enabled the emergence of legal intelligence assistance systems, with legal charge prediction (LCP) being a critical technology. The automatic LCP aims to determine the final charges based on fact descriptions of criminal cases. LCP assists human judges in managing workloads and improving efficiency, provides accessible legal guidance for individuals, and supports enterprises in litigation financing and compliance monitoring. However, distinguishing between confusing charges in real-world judicial practice remains a significant challenge. Most exist works cannot effectively capture complex relationships and discern subtle differences in fact descriptions while ignoring the legal schematic knowledge. In order to improve confusing LCP performance, we propose a novel knowledge-aware model for legal charge prediction that leverages Graph Neural Networks (GNNs) to capture complex relationships within criminal case descriptions. Specifically, the model constructs structural and semantic graphs from fact descriptions and integrates information from both through a dual-graph interaction process. A legal knowledge transformer generates key knowledge representations at schema and charge levels, while a knowledge matching network incorporates hierarchical charge knowledge into facts. Besides, we also propose two real-world datasets namely Criminal-All and Criminal-Confusing, containing 203 different charges and 86 confusing charges, respectively. To the best of our knowledge, these datasets are the first well-organized datasets for confusing LCP task. Experimental results demonstrate that the proposed model outperforms baselines and significantly improves the distinction of confusing charges, providing valuable support for intelligent legal judgment systems. • A legal schematic knowledge-aware model for confusing charges. • Dual-graph interaction to integrate information from structural and semantic graphs. • A hierarchical transformer to obtain legal knowledge representations. • A deep matching network for incorporating domain knowledge into the fact. • Our model improvement outperforms baselines and achieves a significant. [ABSTRACT FROM AUTHOR]

Emergencies are typically characterized by abruptness, time urgency, and complexity, which give rise to challenges such as incomplete information, compromised information effectiveness, and reduced efficiency. To address these issues, this study proposes a novel multi-criteria group emergency decision-making (MCGEDM) method considering knowledge granularity. Within the framework of a hierarchical criterion system (HCS), decision makers' (DMs') judgment information is extracted using belief distributions (BDs) on knowledge chunks, based on the conceptualization of knowledge granularity. This enables DMs to make judgments as effectively as possible, thereby improving efficiency in terms of time and enhancing the effectiveness of information. The generalized combination (GC) rule is applied for individual information fusion within basic nests, demonstrating internal revision and complementation of information. Automatic parameter determination methods are proposed to enhance the effectiveness of information and the efficiency of MCGEDM. Finally, the proposed method is demonstrated through a simulative case of an oil spill emergency, and the subsequent sensitivity analysis and comparisons verify its feasibility and effectiveness. • An emergency decision-making method is proposed considering knowledge granularity. • The generalized combination rule for evidence is combined with knowledge granularity. • Decision makers are allowed to only make judgment within their capacity. • The presented information fusion promotes internal revision and complementation. • The offered automatic determination of parameters benefits efficient decision-making. [ABSTRACT FROM AUTHOR]

Legal Artificial Intelligence (Legal AI) has garnered significant attention in both academic and industrial domains in recent years. However, most legal judgment prediction (LJP) methods concentrate on single-charge prediction tasks, ignoring the practical scenario of "one person with multiple charges". To mitigate this limitation, we propose a multi-charge prediction method based on the C riminal A ct- D riven L aw R etrieval A ugmentation (CADLRA), which utilizes Large Language Models (LLMs) to convert the multi-charge classification task into a dynamic multi-charge generation task, achieving enhanced prediction accuracy. To address knowledge solidification and hallucination in LLMs and align with the legal process of sentencing based on criminal acts and relevant laws, we employ contrastive learning to train a retriever to aid LLMs in charge prediction by referencing prior law articles. Finally, experiments were conducted using the public dataset from the Legal AI Challenge, demonstrating that the CADLRA method has achieved state-of-the-art results in both multi-label classification algorithms and charge prediction. [ABSTRACT FROM AUTHOR]

The environmental, social, and economic challenges associated with the massive activities of the oil and gas industry require analysis and evaluation of companies' sustainability priorities. Evaluating the environmental performance of oil companies requires the application of strict mathematical tools and models that provide rigorous results. Multi-criteria decision-making (MCDM) is a rigorous tool that evaluates the performance and practices of oil and sustainable energy companies. However, classical multi-criteria decision analysis has problems related to inconsistency because of the subjective nature of pairwise comparisons. This study responded to the literature call by utilizing the weight fuzzy judgment method (WFJM) to determine the weight coefficients of criteria with zero consistency. Such a method employed criteria values and expert judgment for decision-making to create a bridge between the mathematical approach and the human approach. This study aims to develop a solid decision support system by evaluating and benchmarking 11 companies utilizing the fuzzy judgment method based on fuzzy theory and the VIekriterijumsko KOmpromisno Rangiranje (VIKOR) method to determine sustainability priorities for oil and gas companies. The oil companies' criteria were efficiently weighted by the fuzzy judgment method, and the organizational capabilities represented the greatest weight value of 0.128, whilst the low-weight criteria were the high costs of technologies at 0.080. Besides, the benchmarking results for oil companies revealed that COM 8 was the best, whilst COM 11 was the worst. The fuzzy judgment technique is a unique approach to solving complicated decision-making problems, and its influence can be significant. Furthermore, this method can be applied in sectors other than energy, including banking, medical services, and engineering, where decision-making is not straightforward and requires a systematic approach. [Display omitted] • We developed a novel method called the weight fuzzy judgment method (WJSM). • This study arised the decision matrix based on 9 main criteria and 11 alternatives. • This study evaluated oil and gas companies' sustainability behaviors. • A comparison analysis was conducted between WJSM and BWM methods. [ABSTRACT FROM AUTHOR]

To address ambiguity arising from linguistic decision judgments, Guo and Zhao introduced a model of the well-known Multi-Criteria Decision-Making (MCDM) method, Best-Worst Method (BWM), using fuzzy sets, known as Fuzzy Best–Worst Method (FBWM). Despite its versatility, FBWM has limitations, such as criteria-weights being independent of fuzzy comparison values' shape and reliance on approximate fuzzy arithmetic operations. In response to these limitations, we introduce a novel model of Fuzzy Best–Worst Method based on α -cut intervals. In this model, the calculation of optimal weights involves formulating an optimization problem that utilizes exact fuzzy arithmetic operations defined in terms of α -cut intervals. This approach aims to optimize the entire shape of fuzzy comparison values simultaneously. However, it turns out that, although we have proven the existence of optimal solution for this problem, solving it proves to be challenging. To resolve this issue, we approximate optimal weight set(s) using triangular fuzzy weight sets and assess the Error of Approximation (EA). The approximate weight set with the desired EA is then defuzzified to obtain a crisp weight set. In situations where there are multiple approximate weight sets with the same EA, interval-weights are calculated. Following that, we establish the necessary conditions for a Fuzzy Pairwise Comparison System (FPCS) to be consistent. Using these conditions, we calculate a lower bound of the Consistency Index (CI), providing a measure of the accuracy of weights. Finally, we discuss numerical examples and a real-world scenario, ranking of risk factors in supply chain 4.0, to validate the effectiveness of the proposed model. • Introducing an α -cut interval based model of the best–worst method. • Approximation of optimal weights and estimation of error of approximation. • Addressing non-uniqueness of optimal weights by calculating interval-weights. • Introduction of a consistency ratio to assess the reliability of weights. • The proposed model outperforms existing models in a thorough comparison. [ABSTRACT FROM AUTHOR]

• Negative sequence current suppression strategy for distributed generation. • The negative sequence power direction characteristics are obvious. • Multi-terminal directional pilot protection can effectively distinguish T-line faults. After high proportion of distributed photovoltaic and energy storage is connected to the distribution network by distributed multi-point T-connection, the traditional two-terminal directional pilot protection criterion will be affected by the output characteristics of distributed generation (DG) fault current, which leads to the wrong judgment of the fault direction. In response to the asymmetric fault of typical high proportion of DG T-connected to rural distribution network under the background that DG generally adopts negative sequence current suppression strategy, the paper studies the characteristics of negative sequence power direction when the measuring points of the main line and branch lines of DG distribution network in forward fault or backward fault under the negative sequence current suppression strategy, and puts forward a multi-terminal directional pilot protection method of distribution network with high proportion of DG based on negative sequence power direction, which solves the problem that the traditional two-terminal directional pilot protection in DG distribution network is not applicable. Finally, the correctness of the protection criterion is verified in Pscad/Emtdc. © 2017 Elsevier Inc. All rights reserved. [ABSTRACT FROM AUTHOR]

The decision-maker's judgment deviates from uncertain attribute information will lead to decision risk in multiple attribute decision making (MADM), and different aggregation approaches result in different risk levels. This paper aims to study the risk levels of aggregation operators in MADM with uncertain attribute information. We use the signal detection theory to characterize the decision-maker's noisy perceptions of multiple attributes to present his/her judgment deviation. Then, we establish the aggregation models to aggregate these perceptions based on the weighted averaging (WA) and the ordered weighted averaging (OWA) operators. Furthermore, a risk measurement model of each aggregation approach is constructed to measure the risk levels of the commission risk (CR), the omission risk (OR), and the overall risk. A numerical example is used to verify the validity of the proposed model, while simulation experiments are designed to compare the risk levels of the WA and OWA operators. The results reveal that the overall risk level of the WA operator is higher than that of the OWA operator when judging the quality of the alternative with high standards; otherwise, the WA operator is lower. This finding provides a scientific reference for aggregation approach selection under uncertain information. Permanent link to reproducible Capsule: https://doi.org/10.24433/CO.0911610.v1. • We characterize a decision-maker's noisy perceptions of attributes. • We obtain the noisy perceptions of alternatives according to the characteristics of aggregation approaches. • We propose a risk measurement model of aggregation approaches for MADM. [ABSTRACT FROM AUTHOR]

The reliability of decision makers' judgments together with their degree of hesitancy are often ignored in decision models. The inclusion of judgments with only triangular belonging functions causes a great deal of information loss since non-belongingness functions are ignored in those decision models. In the literature, a method on how to integrate the intuitionistic reliability function into the decision maker's judgment has not been clearly proposed. To handle these issues, we aim at developing a novel intuitionistic Z-AHP (Analytic Hierarchy Process) & Z-TOPSIS (Technique for Order Preference by Similarity to Ideal Solutions) methodology in this paper. Z-numbers can be used to express uncertain quantities with different degrees of precision, which can be very useful in practical applications. Our methodology considers both the restriction function and its reliability function under intuitionistic fuzziness based on α-cut approach and uses non-linear intuitionistic belonging and unbelonging functions. We compare our approach with interval-valued fuzzy TOPSIS and conduct a sensitivity analysis to assess the effects of changes in criteria weights on the rankings of the alternatives. Our results show that the proposed methodology can effectively evaluate and rank the existing alternatives, considering the uncertainties and complexities of the decision environment. This research provides valuable insights for decision-makers and can take part in their decision support systems. The proposed methodology has been applied to a hydrogen storage technology selection problem. According to the results, the most favorable hydrogen storage option in the considered case is chemical storage alternative followed by liquid storage, compressed storage, carbon nanostructure storage, and metal–organic framework storage alternatives. The inference of the comparative analysis with intuitionistic Z-AHP and interval-valued fuzzy TOPSIS method is that the best two alternatives are same in both methodologies whereas the third and the fifth alternatives are replaced since a different linearization approach is applied to the non-linear restriction function multiplied by the reliability function. [ABSTRACT FROM AUTHOR]

In recent years, applying AI techniques in the legal field has attracted researchers' attention. In particular, Legal Judgment Prediction (LJP), which aims to predict accusations based on given case description texts, has attracted much attention from the natural language processing community. However, most of the existing LJP methods are data-intensive. As we know, data annotation in the legal field is expensive. Prompt learning is a recently prevalent methodology, which often achieves surprising results in few-shot or even zero-shot scenarios. We propose a novel method for Chinese LJP based on prompt learning called KnowPrompt4LJP. The method aligns the Chinese LJP task with the pre-training task of a Pre-trained Language Model (PLM) via a prompt template to stimulate the PLM's recall of learned knowledge. In addition, the well-designed prompt template can enhance the PLM's understanding of the Chinese LJP task. We also use an external knowledge base to extract keyword information from the Chinese case description texts and incorporate it into the prompt template, thus enhancing the guidance of the prompt template to the PLM. Experimental results on CAIL2018, a high-quality Chinese LJP competition dataset, show that KnowPrompt4LJP achieves far better results than the baselines in zero-shot, few-shot, and full-size training data scenarios. KnowPrompt4LJP can achieve a macro F1 value of 0.70 in the low-resource scenario, which is comparable to the baselines' results in the data-rich scenario. In the scenario of using full-size training data, KnowPrompt4LJP can achieve a macro F1 value of 0.81. [ABSTRACT FROM AUTHOR]

The civil case judgment prediction (CCJP) task involves automatically determining whether the plea of a plaintiff should be supported by analyzing the given civil case materials. However, most existing studies usually rely on inadequate legal essential elements (e.g. , fact descriptions and pleas), and are specifically designed for single-cause scenarios. Consequently, these methods struggle to generalize effectively to real courts, where civil cases involve more complicated legal elements and numerous causes. To resolve the above limitations, we present a novel T opology-aware M ulti-task L earning framework, called TML. Concretely, TML adopts the transformer-family pre-trained language models (PLMs) as the backbone to capture the fine-grained semantic interactions among various legal elements. To exploit the structural information of the case, we collocate distinct special tokens for each legal element, and then extract the features of the case from different perspectives. Furthermore, to address multiple-cause scenarios, TML incorporates a multi-task learning paradigm to simultaneously predict multiple civil judicial subtasks (e.g. , civil causes, civil law articles and final judgment of pleas). To utilize topological dependencies among subtasks, three parameter-free retrievers are integrated to establish inter-task connections. Extensive experiments are conducted on a real-world dataset, and the experimental results show the effectiveness of our proposed method. • TML adopts PTM to capture semantic interactions between various legal elements. • Assigning special tokens to elements makes TML aware of case structure information. • TML utilizes a multi-task learning paradigm to address multiple-cause situations. • Three parameter-free retrievers are devised to establish inter-task connections. [ABSTRACT FROM AUTHOR]

• The centroid distance of PFNs is proposed and its properties are discussed. • Propose a score function based on the centroid distance and discuss its properties. • Applications of Pythagorean fuzzy clustering, ranking and MCDM are developed. • Some advantages of the proposed methods are showed through comparative analysis. By using Pythagorean fuzzy set (PFS), people can quantitatively describe and process information with fuzziness, and then make reasonable and effective judgments or decisions. In this process, distance measure often serves as a useful and important tool. The purpose of this paper is to explore some information processing methods and techniques in Pythagorean fuzzy environment. Firstly, the research progress of PFS is reviewed, and the main research directions and some hot issues in relevant research fields are discussed. Secondly, by analyzing the information of hesitation degree of Pythagorean fuzzy number (PFN), definitions of hesitation factor and centroid distance measure based on the representation of centroid coordinates of the hesitation regions are proposed, and properties of hesitation factor and centroid distance measure are discussed. Finally, the novel clustering algorithm, ranking method and multi criteria decision making (MCDM) method are developed in Pythagorean fuzzy environment, which are based on the new centroid distance measure. The effectiveness of the proposed algorithm and methods are verified through numerical examples and comparative analysis. The research results indicate that the centroid distance measure proposed in this paper can effectively characterize the distance between PFNs, thus making the clustering algorithm of PFNs, the ranking method of PFNs, and the Pythagorean fuzzy MCDM method have obvious superiorities and advantages. [ABSTRACT FROM AUTHOR]

Decomposed fuzzy sets (DFSs) are one of the latest extensions of intuitionistic fuzzy sets which are introduced to express vague and imprecise information to be used in multi-criteria decision-making. DFSs represent the human thinking structure in a multidirectional way, and they enable it through functional and dysfunctional judgments. However, DFSs cannot completely represent the entire human mindset as they are incapable of capturing reliability information, as it is in the other extensions, and this inability may cause wrong decisions to be given. To handle this problem, decomposed Z-fuzzy numbers, which are the integrated DFSs with reliability information provided by Z-fuzzy numbers, are introduced to model functional and dysfunctional judgments for taking the consistency of decision makers into account. Collecting judgments with both their fuzzy restrictions and fuzzy reliabilities from decision makers based on functional and dysfunctional questions provide more consistent and reliable judgments in the practice. Subsequently, a new decomposed Z-fuzzy linguistic scale and defuzzification formula are introduced to reach a final solution. Then, decomposed Z-fuzzy TOPSIS method is developed. Finally, we analyze the effect of the reliability parameter on the given decisions and present a comparative analysis with crisp TOPSIS method by an application of transfer center location selection for a private cargo company in Marmara Region of Turkey. [ABSTRACT FROM AUTHOR]