Ruppin Academic Center (Hebrew: הַמֶרְכָּז הַאָקָדֶמִי רוּפִּין), also known as Ruppin College, is a college in Israel, which has the status of an institutional settlement. It was established in 1949, is named after Arthur Ruppin, and is located near the moshav Kfar Monash, and within the Hefer Valley Regional Council area. In 2015, it had 4,500 students, whilst the village had a population of 307 in 2019.
This article examines the enduring impact of the 1948 Palestine-Israel War through the conceptual lenses of hauntology and intergenerational transmission. It focuses on the legacy of Netiva Ben-Yehuda, a Palmach veteran who challenged the prevailing Zionist myth of purity of arms by documenting her participation in war atrocities. While the state-sanctioned narrative celebrated the war as a semi-mythical resurrection, Ben-Yehuda’s public testimonies revealed the ghosts of massacres and executions. Drawing on Jacques Derrida’s theory of spectrality, the study explores how these suppressed truths seeped into the lives of Ben-Yehuda’s descendants. Through qualitative interviews with her daughter and grandsons, the article traces the manifestation of these ghosts across three generations. Findings suggest that for the second generation, the trauma manifested as an embodied presence of the war and the mother’s untreated PTSD, shaping a childhood lived in the shadow of a momentous event. For the third generation, these ghosts triggered historical inquiries and public political dissent, reflecting a refusal to conform to social codes of silence. The article also refers to the work of Mahmoud Darwish on Palestinian ghosts who roam the landscape and haunt the living Israelis. The encounters between the dead and the living highlight the interconnectedness of Palestinian and Jewish-Zionist narratives. By centering the voices of the perpetrator’s descendants alongside the haunting presence of the victims, the study demonstrates how the unresolved injustices of 1948 continue to demand accountability. The ghosts of 1948 bind the two societies together, challenging the national silence surrounding the Nakba.
Artificial intelligence (AI) is increasingly run on high-density computing infrastructure, yet its environmental footprint is still assessed mainly through electricity use and associated greenhouse-gas emissions. A critical, less visible dimension is water: AI infrastructure consumes freshwater through evaporative cooling, indirect water use in electricity generation, and water-intensive semiconductor manufacturing. Projections suggest AI's global water footprint could reach 4.2-6.6 billion cubic meters annually by 2027. Many data centers are located in water-stressed regions. While technologies, including cold-climate siting, natural water body cooling, waterless designs, and waste heat recovery, can reduce on-site demand, their deployment remains limited. This work introduces "digital water sobriety" as a governance framework linking evaluation of which AI applications justify freshwater consumption, water conscious siting, and mandatory facility-level water use transparency. Achieving water-sustainable AI demands not merely technological optimization but fundamental policy reform integrating water constraints into computational infrastructure planning.
This longitudinal study examines the relationship between prewar status-quo bias (SQB) and emotional measurements taken during the Isarel-Hamas War. A sample of 1,053 Israeli participants (ages 25–66; 50
Suspension feeding, in which organisms filter particles from the water for nutrition, is a widespread and ecologically critical feeding strategy in aquatic ecosystems. And yet, the principles governing particle capture remain unresolved. Mucus-based particle capture is widespread, and classical models typically describe it as a size-dependent sieve-like mesh of mucous nanofibres. Here, we show that for small particles, surface physico-chemistry, not size alone, strongly influences capture. Using polystyrene microspheres with tailored amphiphilic polymer coatings, we found that steric repulsion altered particle mobility within the mucous filter, reducing capture efficiency for submicrometre beads, while larger particles were retained regardless of coating. Cryogenic scanning electron microscopy imaging revealed that, contrary to prevailing models, the ascidian mucous filter is an approximately 5 µm thick continuous sheet rather than a nanofibre mesh. These findings call for a revision of suspension-feeding models to incorporate colloid-mucus interactions and bulk transport phenomena. Beyond advancing our understanding of plankton ecology, this work provides principles for the design of synthetic filtration systems that couple high efficiency with self-cleaning.
Accurate emotion recognition is a foundational component of social cognition, yet human biases can compromise its reliability. The emergent capabilities of multimodal large language models (MLLMs) offer a potential avenue for objective analysis, but their performance has been tested mainly with ethnically homogenous stimuli. This study provides a systematic cross-ethnic evaluation of leading MLLMs on an emotion recognition task to assess their accuracy and consistency across diverse groups. We evaluated three leading MLLMs: ChatGPT-4, ChatGPT-4o, and Claude 3 Opus. Performance was tested twice using three "Reading the Mind in the Eyes Test" (RMET) versions featuring White, Black, and Korean faces. We analyzed accuracy against chance (25%) and compared scores to established human normative data for each ethnic version. ChatGPT-4o achieved performance significantly above chance levels across all tests (p < .001), with large effect sizes indicating robust performance (Cohen's h = 1.253-1.619; RD = 0.583-0.694). The model obtained a mean accuracy of 83.3% (30/36) on the White RMET, 94.4% (34/36) on the Black RMET, and 86.1% (31/36) on the Korean RMET, placing it in the 85th, 94th, and 90th percentiles of human norms, respectively. This high accuracy remained consistent across ethnic stimuli. In contrast, ChatGPT-4 performed near the human average, while Claude 3 Opus performed near chance level. These preliminary findings highlight the rapid evolution of MLLMs, highlighting a significant performance leap between consecutive versions. This study suggests that ChatGPT-4o demonstrated performance scores exceeding average human accuracy on this specific task in recognizing complex emotions from static images of the eye region, with its performance remaining consistent across different ethnic groups. While these results are notable, the pronounced performance gaps between models and the inherent limitations of the RMET task underscore the need for continuous validation and careful, ethical consideration to fully understand the capabilities and boundaries of this technology.
