تفکر مراقبتی در برنامه فلسفه برای کودکان با تأکید بر ارزش‌ها و عواطف، رشد اخلاقی و اجتماعی را تقویت می‌کند. از این رو، پژوهش حاضر اثربخشی داستان‌های ایرانی گروه سنی «ج» را در حلقه‌های کندوکاو فلسفی بر تفکر مراقبتی دانش‌آموزان پایه ششم ابتدایی بررسی نموده است. این پژوهش با روش نیمه‌آزمایشی و طرح پیش‌آزمون-پس‌آزمون در سال تحصیلی ۱۴۰۳-۱۴۰۴ در شهر اصفهان اجرا شد. جامعه آماری شامل دانش‌آموزان پایه ششم شهر اصفهان در سال تحصیلی ۱۴۰۳-۱۴۰۴ بود که ۶۰ دانش‌آموز پسر به روش نمونه‌گیری خوشه‌ای مرحله‌ای انتخاب و به دو گروه آزمایش و کنترل (هر گروه ۳۰ نفر) تخصیص یافتند. مداخله در ۱۰ جلسه با داستان‌های پرفروش ایرانی (۱۳۹۵-۱۴۰۳) اجرا شد. داده‌ها با استفاده از پرسشنامه هدایتی (۱۳۹۷) جمع‌آوری و با روش تحلیل کوواریانس تک‌متغیره و چندمتغیره با نرم‌افزار SPSS 26 تحلیل شدند. یافته‌ها نشان داد میانگین تفکر مراقبتی گروه آزمایش از ۹۴/۸۰ به ۱۰۵/۴۷ رسیده است. در حالی که تحلیل کوواریانس تک‌متغیره تفاوت غیرمعناداری را نشان داد و آزمون بنفرونی نیز تفاوت غیرمعناداری را تأیید کرد، کوواریانس چندمتغیره تفاوت کلی مؤلفه‌ها را تأیید نکرد، اما اثرات بین‌آزمونی بهبود معنادار مؤلفه تفکر همدلانه (p<0.05) را نشان داد. در نتیجه، داستان‌های ایرانی تفکر مراقبتی را به‌ویژه مؤلفه تفکر همدلانه ارتقا می‌دهند و پیشنهاد می‌شود تسهیلگران فبک در حلقه‌های کندوکاو از این منابع بهره گیرند و پژوهش‌های آتی اثرات بلندمدت این داستان‌ها بر تفکر مراقبتی دانش‌آموزان را بررسی کنند.
Background: In the age of artificial intelligence, AI image generation platforms have become active agents in cultural representation. This study examines how three core Iranian cultural values— belief in God, patriotism, and the idealized woman— are represented on platforms such as Gemini, ChatGPT, and Copilot. The main problem is the presence of cultural biases in training data and algorithmic architectures, which may lead to stereotypical, inaccurate, or Western-centric portrayals of non-Western cultural identities.Aims: This research aims to analyze the mechanisms through which these three fundamental values are represented in AI-generated images and to determine the extent of bias in these platforms compared to indigenous and religious models within Iranian society.Methodology: Employing a semiotic analysis based on Roland Barthes's five-code framework and Stuart Hall's theory of representation, this study qualitatively analyzed 18 images generated by three platforms (Gemini, ChatGPT, and Copilot).Findings: The findings indicate that these platforms do not neutrally reflect cultural reality but actively participate in meaning construction and ideological representation. Three key findings emerged: (1) Inaccurate and generalized representation of specific Shiite rituals, with a tendency toward generic Sunni Islamic imagery; (2) A significant shift in the representation of patriotism, centering women as primary agents of national identity, in contrast to traditional masculine and military iconography; and (3) A contradictory, algorithm-driven representation of the ideal woman, where her agency and success are defined not by traditional roles (such as motherhood) but by her ability to navigate the tension between tradition and modernity and conform to modern professional standards.Conclusion: AI systems function not as neutral tools but as powerful cultural actors. Algorithms reproduce algorithmic majoritarianism, whereby dominant cultural patterns in training data marginalize minority traditions (such as Shiism) and redefine values like womanhood based on modern, algorithmically preferred criteria. This research underscores the necessity of critically revising AI's representational politics and involving represented communities in the design of these systems to achieve fairer and more accurate cultural representations.
Generator, as a key element of a power plant, is among the most important and costly components of the power system. An unreliable protection system of a generator may cause false outages and propagate faults throughout the network, potentially leading to a blackout. Cyber attackers can cause mal-operation of generator-transformer protection systems by launching false data injection (FDI) attacks. In this paper, a new method for countering FDI attacks on generator-transformer protection systems is proposed. To achieve this, a new interlock circuit leveraging network data analysis via an LSTM deep-learning algorithm is proposed in this paper. In addition, backup signals from the relays are also considered. To enhance the performance accuracy of the proposed method, the LSTM algorithm is trained and tested on various types of generators and transformer voltage and current data. All network states are considered: nominal state, three-, two- and single-phase faults, and various forms of FDI attacks. One of the advantages of the proposed method is that it is independent of the network topology and is applicable even in large-scale networks. Simulations results shows that the proposed method is fast and effectively distinguishes real faults from FDI attacks across a wide range of conditions.
Given the widespread use of caffeine among athletes, this meta-analysis quantifies the incidence of acute side effects associated with caffeine supplementation. Despite its well-established performance benefits, evidence on caffeine’s side effects remains fragmented, as these outcomes are often reported only as secondary findings. To address this gap, we systematically reviewed and meta-analyzed evidence from randomized controlled trials on acute caffeine-related side effects in athletes. Following PRISMA guidelines, we searched five databases (MEDLINE, Scopus, Web of Science, Embase, Google Scholar) up to July 2025. Eligible studies were randomized controlled trials in athletes aged ≥ 15 years examining acute caffeine ingestion versus placebo with reported side effects. Risk of bias was assessed using PEDro and Cochrane criteria. Data on frequency and magnitude of side effects were pooled using random-effects meta-analyses, with subgroup and dose–response analyses. A total of 48 studies (940 athletes; 63
Dye-sensitized solar cells (DSSCs) represent a promising technology for sustainable energy conversion; however, their performance optimization demands considerable experimental and computational effort. This study employs density functional theory (DFT) and time-dependent DFT (TD-DFT) to investigate the optoelectronic and photovoltaic properties of a C2N monolayer quantum dot and its complexes with two organic dyes including T2-1 and para-methyl red (PMR). Six dye/C2N configurations (T1-T3 and P1-P3) were computationally analyzed for their electronic structure, intramolecular charge transfer (ICT), and interaction with TiO2 electrodes. Key photovoltaic descriptors were evaluated to predict the device performance. Results revealed that the complexation of the dyes with C2N significantly enhances the light-harvesting efficiency, charge separation, and electron injection kinetics compared to those of isolated dyes on the TiO2 electrode. The calculated gas-phase values of short-circuit current densities and open-circuit voltages for T1, T3, P2, and P3 vary in the ranges of 51 to 53 mA cm-2 and 54 to 58 V, respectively, which shows their suitable photovoltaic performance for application in DSSCs. On the other hand, the T3 complex shows the highest open-circuit voltage (0.33 V) among the complexes while the highest short-circuit current densities (87.38 mA cm-2) is for the T1 complex in solvent (acetonitrile). The findings validate C2N as a viable photoanode material, and a cosensitizer demonstrated the efficacy of theoretical screening in improving DSSC performance.
