Dublin Independent School District

This paper examines computational propaganda use in creating political narratives in Pakistan via platform X. The research compares the strategies adopted in Pakistan, which are the rivalry among mainstream political parties of Pakistan viz-a-viz Pakistan Tehreek-e-Insaf (PTI), Pakistan Muslim League Nawaz (PML-N), Pakistan People Party (PPP) and Jamat-e-Islami (JI), which is against established models in Russia and the U.S. The results indicate that the digital environment of Pakistan has been transformed into a hybrid between the Russian state-sponsored model and the Macedonian commercial model. PTI uses an industrialised approach that is marked by the centralised Cyber Forces, and the use of automated bot networks and hiring of party-funded influencers that include paid teams. PML-N has an organic latency, and it is based on institutional engagement, which is reminiscent of traditional grassroots campaigning in the U.S. Jamaat-e-Islami (JI) is an extremely organised ideological model of volunteerism, whereas the Pakistan People Party (PPP) exhibits a combination of individual activists and paid groups during large-scale campaigns. The study finds that commercialisation of dissent, whereby political participation is motivated by money, as opposed to ideology, forms a digital mirage that misrepresents the opinion of the masses and weakens democratic transparency.

Abstract The anchoring effect is one of the cognitive biases studied within the phenomena observed in psychology and behavioral economics. Through the anchoring effect, human judgments and decisions can be suboptimal due to excessive focus on random information. This phenomenon plays a significant role in many aspects of social and economic life, including the real estate market, leading to incorrect decisions among both consumers and professionals. The aim of this study was to evaluate the impact of the anchoring effect on decisions regarding the value of a random apartment among Polish specialists in real estate market, based on a simplified description of the apartment. The research group included real estate valuators, real estate brokers, real estate managers, and academicians. The sample size included 170 respondents. The research showed that the effect of “anchors” is evident in every professional group, regardless of the type of services provided, educational background, or years of professional experience. The anchoring index among the surveyed group was 46 percent. The study confirms that expertise in real estate market does not guarantee unbiased assessment, and therefore, there is a need to conduct the process of real estate evaluation in a careful way to increase the chances of obtaining trustworthy results.

Abstract A new daily composite of the solar flare index (SFI) and the hemispherically‐resolved versions (hSFI) are presented for 1937 to 2024. The data set confirms that the northern hemisphere (NH) dominated solar flare activity during Solar Cycles 17 to 21, but that the southern hemisphere has dominated from Solar Cycle 22 to present. That said, the highest SFI value occurred in the NH during the recent superstorm of May 2024. In sunspot activity, the “Gnevyshev‐Ohl rule” shows that the sum of sunspot numbers during even‐numbered cycles is related to those of adjacent odd‐numbered cycles. A similar rule appears to apply to SFI. The “Gnevyshev gap” phenomenon where solar maximum activity sometimes has two peaks separated by up to 1–2 years of a gap is confirmed for SFI. Although our data set represents the longest continuous daily data set for solar flare activity to‐date, it is known that stronger solar flare events occurred before 1937. Therefore, a brief discussion of earlier solar flare events in the historical record is also provided for context. The statistics of the SFI and hSFI series are compared to other solar and geomagnetic activity indices, including the May and October 2024 solar storms. Statistical analysis of past geomagnetic storms confirms they are more frequent during active cycles and less frequent during solar minima. Strong geomagnetic storms are also more likely to occur during the positive phase of a 1.7 year's quasi‐biennial oscillation in solar activity. The likelihood of low‐magnetic latitude aurorae seems to have a 30 year periodicity component.

This study investigates how players acquire high-precision skills under sustained cognitive and tem-poral pressure by analyzing learning trajectories in the game Sekiro: Shadows Die Twice, knownfor its punishing difficulty. By focusing on the deflection mechanic, which is an exacting systemthat requires precise timing for execution, we conducted an observational analysis of first-timeYouTube playthroughs of the Genichiro Ashina boss fight, a key difficulty spike. Using behavioralcoding across seven participants, we tracked deflection success, missed inputs, recovery patterns,and rhythmic breakdowns over time. Our findings revealed a non-linear learning trajectory, char-acterized by cycles of performance Collapse and Rhythmic Re-engagement. We term this recurringbehavioral loop the ’Valley of the Almost’: a dynamic in which players oscillate between cyclesof Collapses and Re-engagement before successfully executing the skill. These patterns complicatetraditional flow-based and scaffolded learning models by showing how skill learning can stabilizethrough breakdown and recalibration over smooth progression. The results extend the theories ofcognitive load and motor learning by showing how high-stakes environments create internal timingschemas under stress. We propose the Valley of the Almost, a recurring behavioral pattern ofCollapse and Re-engagement observed in the gameplay of Sekiro, as a conceptual lens for motorlearning in high-pressure games.

ReliefAI is a modular AI-powered decision support framework designed to enhance access to critical public resources—specifically food, shelter, and clothing—for underserved populations. Integrating policy-aware logic with gradient boosting models, the system efficiently pre-screens user eligibility and recommends localized programs offered by government and nonprofit agencies. The hybrid architecture combines rule-based screening, real-time scoring, and simulation-based evaluation across multiple domains. ReliefAI emphasizes ethical AI deployment, low-resource readiness, and integration with open data pipelines. Results from structured simulations across 50 synthetic user profiles indicate significant improvements in eligibility matching and response latency compared to conventional search-based methods. This paper presents the design, system architecture, evaluation results, and future directions of the ReliefAI framework as a scalable public-interest AI system.

The Delphi research method was specifically designed as a forecasting tool for the Rand Corporation in the 1950s. However, in the last several decades, Delphi research has been more frequently used for facilitating group communication for decision making and planning. Because of the Delphi Method's increased use, more information is needed for researchers to understand how to best utilize the method to precisely complete a Delphi study with rigor. This chapter explores the Delphi Method's origin, provides an explanation of the methodology, acknowledges the types and variations in Delphi studies, discusses the advantages and limitations, and provides clear, step-by-step guidelines for employing a successful research study.