Criminal Law Characterization of Property Infringement Involving Third-Party Payment Platforms
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- Keywords: Theft; Fraud; Third-party payment; Alipay
- Abstract: This paper aims to discuss the criminal law evaluation of a series of financial abuses in the background of the rise of third-party payment platforms. In the first part of this paper, the concept of the third-party payment platform and the financial abuse related to the third-party payment platform and its characteristics are introduced. Then, the second part discusses the disputes concerning the criminal law evaluation of financial abuse involving the third-party payment platform, including the causes of the qualitative disputes and the premise of the settlement of the qualitative disputes. The causes of the qualitative dispute mainly include the identification of the legal status of the third-party payment platform, the identification of the legal nature of the funds in the account, and the dispute over theft and fraud. The premise of resolving qualitative disputes includes the identification of the legal status of the third-party payment platform, the legal nature of the funds in the account, and the boundary between theft and fraud. Finally, this paper analyzes the qualitative problems of financial abuse related to thirdparty payment platforms, proposes that the qualitative problems should be based on behavioral means and property attributes, and pays attention to the problems of one crime and several crimes.
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Analysis of ChatGPT’s Place Name Translation Errors: A Case Study of Thai Translations of Place Names in Nanjing
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Authors:
Yesuo Gu, Jianlong Zhang, Ran Mo, Junlan Zeng / Advances in Linguistics Research / 2025,7(1): 50-60 / 2025-04-09
- Keywords: ChatGPT; Place Names; Thai Translation; Human-AI Collaboration
- Abstract: This study investigates the accuracy of ChatGPT in translating Nanjing’s place names from Chinese into Thai, identifying key challenges that impact translation quality. The most frequent error type is phonetic transcription errors, which account for 33.80% of all errors. This highlights the significant difficulty in accurately converting Chinese phonetics into Thai. Translation deviations follow at 14.08%, representing cases where the translation partially captures the intended meaning but fails to fully convey it. Cultural background omissions, at 8.45%, reveal the system’s struggle to incorporate cultural and historical context into translations. Additionally, grammatical structure errors (4.23%) and lack of referential function (2.82%) indicate limitations in handling syntactic differences and spatial references between the two languages. Despite 28.17% of translations being correct, the high prevalence of phonetic and contextual errors underscores the need for improvements in phonetic accuracy, cultural awareness, and syntactic precision in ChatGPT’s translation capabilities. To address these shortcomings, this study proposes a collaborative human-machine model that integrates cultural knowledge databases, phonetic optimization techniques, grammar enhancement tools, specialized translation modules, and post-editing mechanisms. This approach ensures historical and contextual accuracy, refines phonetic transcription through pinyin-to-phoneme mapping, and strengthens syntactic alignment. Furthermore, the incorporation of Named Entity Recognition (NER) and Translation Memory (TM) technologies enhances consistency and linguistic precision, significantly improving the system’s ability to handle complex place name translations.
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Employment Analysis and Countermeasure Research of Higher Vocational Graduates in Sichuan and Chongqing Region —Take a Vocational and Technical College in Chongqing as an Example
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- Keywords: vocational education; High-quality employment; Employment guidance; Investigation and analysis
- Abstract: Human is the most active factor in the productive forces, but also the most decisive force. The development of new quality productive forces ultimately depends on the strength of human resources. Science and technology is the first productive force, talent is the first resource, innovation is the first driving force, and colleges and universities have undertaken the important mission of providing talent support for the development of new quality productivity. With the further implementation of the newly revised Vocational Education Law of the People’s Republic of China and the expansion of higher vocational enrollment, in the face of the new situation of diversified student distribution and diversified employment demand, the employment situation of Chongqing vocational college graduates is investigated, the existing problems are analyzed, and the appropriate employment strategy is found. Based on the survey data of graduates from a vocational and technical college in Chongqing as the empirical results, this paper proposes to construct a high-quality employment ecosystem in higher vocational colleges from four aspects, combining industrial chain, education chain and talent chain organically, so as to provide decision-making reference for talent training and highquality full employment in higher vocational colleges in Sichuan and Chongqing region.
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Research on the Regulatory Mechanism of Twist on Epithelial-Mesenchymal Transition and Vasculogenic Mimicry in Human Gastric Cancer Tissues
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- Keywords: Epithelial-Mesenchymal Transition (EMT); Twist; Invasion; Correlation
- Abstract: Objective: To explore the promoting effect of Twist on the formation of vasculogenic mimicry (VM), so as to enrich and improve the theory of vasculogenic mimicry. Methods: For gastric cancer tissues and normal gastric tissues, observe the expression status of Twist, epithelial-mesenchymal transition (EMT) marker proteins (including E-cadherin and Vimentin), and VM marker CD34 - PAS. Analyze the correlations between Twist, EMT marker proteins and the clinicopathological features of gastric cancer. Conduct in-depth research on the associations between VM and EMT marker proteins as well as between Twist and EMT marker proteins, and then verify the situation that Twist promotes the formation of VM by promoting EMT in gastric cancer. Results: There are statistical differences between EMT marker proteins and VM, and Twist has a certain correlation with EMT marker proteins. Conclusion: Twist may induce the occurrence of epithelial-mesenchymal transition (EMT) in gastric cancer cells and promote the formation of vasculogenic mimicry (VM) in gastric cancer.
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法医精神病学和刑事司法中的神经预测和人工智能：神经法视角
Neuroprediction and A.I. in Forensic Psychiatry and Criminal Justice: A Neurolaw Perspective
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- Keywords: Neuroprediction; Artificiaintelligence; Recidivism; Forensicpsychiatry; Riskassessment; Neurolaw神经预测; 人工智能; 累犯; 法医精神病学; 风险评估; 神经法
- Abstract: Advances in the use of neuroimaging in combination with A.I., and specifically the use of machine learning techniques, have led to the development of brain-reading technologies which, in the nearby future, could have many applications, such as lie detection, neuromarketing or brain-computer interfaces. Some of these could, in principle, also be used in forensic psychiatry. The application of these methods in forensic psychiatry could, for instance, be helpful to increase the accuracy of risk assessment and to identify possible interventions. This technique could be referred to as “A.I. neuroprediction” and involves identifying potential neurocognitive markers for the prediction of recidivism. However, the future implications of this technique and the role of neuroscience and A.I. in violence risk assessment remain to be established. In this paper, we review and analyze the literature concerning the use of brain-reading A.I. for neuroprediction of violence and rearrest to identify possibilities and challenges in the future use of these techniques in the fields of forensic psychiatry and criminal justice, considering legal implications and ethical issues. The analysis suggests that additional research is required on A.I. neuroprediction techniques, and there is still a great need to understand how they can be implemented in risk assessment in the field of forensic psychiatry. Besides the alluring potential of A.I. neuroprediction, we argue that its use in criminal justice and forensic psychiatry should be subjected to thorough harms/benefits analyses not only when these technologies will be fully available, but also while they are being researched and developed.神经影像学与人工智能结合使用的进步，特别是机器学习技术的使用，促进了大脑阅读技术的发展。这些技术在不久的将来可能被广泛应用，例如测谎、神经营销或大脑计算机接口。其中一些原则上也可用于法医精神病学。这些方法在法医精神病学中的应用可能有助于提高风险评估的准确性并确定可能的干预措施。这种技术可以称为“人工智能神经预测”，它涉及识别潜在的神经认知标志物来预测累犯。然而，这项技术的未来意义以及神经科学和人工智能在暴力风险评估中的作用仍有待确定。本文回顾和分析了有关使用大脑阅读人工智能对暴力和再逮捕进行神经预测的文献，以确定未来在法医精神病学和刑事司法领域使用这些技术的可能性和挑战，同时考虑了法律影响和伦理问题。分析表明，需要对人工智能神经预测技术进行更多研究，并且非常有必要了解如何在法医精神病学领域的风险评估中实施这些技术。除了人工智能神经预测的诱人潜力之外，本文认为，在这些技术完全可用时以及在其研究和开发过程中都应该对其在刑事司法和法医精神病学中的使用进行彻底的危害或者益处分析。
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