Deception detection has long been a subject of interest across various disciplines, with speech being a primary medium for conveying deceit. This paper presents a thorough investigation into the acoustic characteristics of deceptive speech, aiming to elucidate the subtle cues that betray falsehood. Through a comprehensive review of existing literature and empirical studies, we examine the intricate interplay of vocal features such as pitch, intensity, rhythm, and spectral properties in deceptive communication. Additionally, advancements in computational methods for automatic deception detection are explored, shedding light on the potential of machine learning algorithms in uncovering deceitful speech patterns. Furthermore, this research delves into the psychological underpinnings of deception, exploring how emotional arousal, cognitive load, and individual differences influence acoustic cues. By synthesizing findings from linguistic, psychological, and computational perspectives, this paper offers valuable insights into the complex nature of deceptive speech and provides a foundation for future research in deception detection and forensic linguistics.

The significant surge of haze pollution in China stemmed primarily from the enforcement of policies in January 2012 that assessed air quality using weight-based methodologies, thereby permitting the essentially no-cost emission of substantial quantities of water vapor laden with ultrafine particulate matter. Focusing on the public factors in the formulation and implementation of haze prevention and control policies, this paper discusses the satisfaction of haze prevention and control policies and its influencing factors from the perspective of policy expectation and policy performance. Five representative cities with different haze pollution severity were selected to conduct a questionnaire survey to investigate the status quo of haze prevention and control policy satisfaction, policy expectation and policy performance. Variance analysis was used to investigate the differences among regions. Structural equation model was used to analyse the influence path and intensity among satisfaction, expectation and performance of haze control policy. It is found that the public has high expectations for the haze prevention and control policy, while the satisfaction evaluation and performance perception of the haze prevention and control policy are at a poor level. For regions with different haze pollution severity, there is no significant difference in either public satisfaction or performance perception of haze prevention and control policies, but there are striking differences in policy expectations. Haze prevention and control policy expectation has a clear positive impact on policy performance, so does policy performance on policy satisfaction, which is very strong.

This study examines the relationship between classroom teachers' attitudes toward their profession and level of satisfaction with leisure activities, and implicitly highlights the significance of using metacognitive strategies based on findings regarding the psychological well-being variable. The data were gathered using valid and reliable scales, and the study group consists of 298 primary school teachers. According to the results of the regression analysis, attitudes toward teaching as a profession and leisure satisfaction are partially mediated by psychological well-being. In order to improve the professional attitudes and behaviours of primary school teachers, this knowledge is thought to be instructive in pre-service and in-service training programs.

Although neural machine translation systems like ChatGPT and Gemini are widely used in education, recent studies suggest that tailored assistance is necessary based on students’ specific writing proficiency levels (Lu et al., 2024). However, effective collaboration between AI and human writing and students’ perceptions of AI-led platforms across various text types and topics remain under-researched. This paper examines which AI-assisted writing tasks are most important to students’ perceptions, focusing on correct grammar, word usage, clear logic and faithful to the requirements, and reader friendliness. It evaluates the impact of AI writing and reviewing across five text types: exploration papers, complaint letters, email requests, CVs, and personal essays, and assesses students’ perceptions of AI’s effectiveness. The study involved 137 English-language undergraduates who wrote and reviewed five practical writing tasks both individually and with AI assistance. This was followed by a series of questionnaires and interviews conducted over an eighteen-week AI-Assisted Practical Writing course during the autumn semester of 2023. Our findings revealed that undergraduates performed better on opinion-based and format-heavy tasks, such as exploration papers, formal emails, and letters, due to AI’s strengths in vocabulary, structure, logic, and style. However, AI was less helpful for tasks requiring personal touch, like CVs and personal essays, where it lacked originality and emotional depth. Students preferred human reviews over AI reviews because of AI’s instability, incomplete mistake recognition, illogical statements, generic feedback, and lack of empathy. To address these weaknesses, we propose a two-directional model of human-AI cooperation for practical writing (HAIC), based on Cardon et al.’s (2023) Capabilities of AI Literacy for Communication. This model includes four capabilities: brainstorming, integration, verification, and critique. The two pathways are: starting with an AI-generated draft, followed by human verification and post-editing (AI-human pathway), or beginning with a human-generated draft, followed by AI verification and human post-editing (human-AI pathway). Our data shows that formal email writing and formal letter writing are most effective with the AI-human pathway, while personal papers and CV writing are best suited to the human-AI pathway. Our findings are validated by the alignment between students’ perceptions of AI effectiveness and their use of AI first for format-loaded types of practical writing. Additionally, the more a task requires human elements and creativity, the less likely students are to use AI initially. This paper concludes with practical implications for translation teaching and training.

About twenty years ago, analyses were conducted in the United States concerning an international terrorism strategy known in negotiation theory as the “lose-lose” concept. In the “lose-lose” strategy, terrorists seek to destroy the enemy at any cost, even though methods that fall outside the conceptual framework of civilizational development. The aim of this study is to demonstrate the real possibility of a "lose-lose" scenario based on dangerous chemical substances. The authors propose that lone wolves, state structures, and youth groups-fascinated by violence for its own sake-may be capable of employing a "lose-lose" strategy.

The study focused on different government schools, comprising a total of 156 teachers and 3,409 students. The study concentrated on teachers in special needs education, students with disabilities (LwDs), and other individuals with special needs (OSNs). Purposive sampling was used to pick the necessary samples for a descriptive study that applied a qualitative methodology. Data was collected using open-ended questionnaires and interview guides to explore the important role of instructors in aiding learners with disabilities and other special needs in inclusive environments. Teachers, particularly those lacking sufficient training, encounter difficulties when managing big classes with students with learning disabilities and other special needs. The classroom's physical environment and resource availability affect teachers' capacity to create effective learning experiences for individuals with learning disabilities and other special needs. An inclusive classroom setting is essential for meeting a variety of needs, including those of individuals with disabilities. Enforcing education regulations for individuals with learning disabilities and other special needs has a direct impact on teachers' effectiveness in the classroom. Consistent enforcement of these policies is essential to guarantee that instructors are provided with sufficient assistance and resources.

The current study aims to understand digital citizenship, based on the assumptions of Ribble (2014) and Saunders et al. (2016), by examining factors affecting participation and involvement in the Internet virtual societies among higher education students. A quantitative approach using a survey questionnaire was implemented. The study sample consisted of the entire student population, with a total of 54,291 students, as reported in the annual statistics report of King Khalid University (KKU). The data was collected from a sample of 342 students, selected by a random sampling method, and determined using Morgan's table to establish the appropriate sample size. The descriptive statistics showed that the students generally have good levels of perceived Internet attitude, computer self-efficacy, and digital citizenship, especially in terms of respecting oneself and others online. The results of this study showed that digital access, digital communications, and digital literacy are affecting the development of digital citizenship in universities. Students with higher levels of digital experience are more involved in activities related to educating oneself and connecting with others online compared with students with less experience. Further, students with higher levels of daily average technology use tend to protect themselves and others online more compared with students with lower levels of technology use. Moreover, higher levels of students’ Internet attitude and computer self-efficacy are associated with higher levels of respect for oneself and others, of educating oneself and others, and of total digital citizenship. Based on the current study findings, appropriate recommendations are proposed in terms of policy and practice.