This manuscript introduces the concept of an Infinite Energy Source (E∞) that existed before the Big Bang, giving rise to time through a quantum pulse of energy. From this primordial act, space, time, and matter emerged in a patterned cycle of expansion and collapse an eternal loop connecting creation and rebirth. The theory further proposes Infinitium Eneria, a fundamental energy of consciousness that resonates with human neural energy, suggesting that awareness originates from the same infinite source as the universe itself. This work unifies cosmological evolution and consciousness under one cyclic, energetic framework.

Infinitium Eneria -The Energy of Consciousness

Accurate prediction and optimization of heat transfer remain central challenges in the design of thermal energy systems. Traditional approaches such as computational fluid dynamics (CFD), while reliable, are often computationally expensive and unsuitable for real-time applications. This study explores the potential of machine learning (ML) as a fast and reliable alternative. A hybrid dataset of ~12,000 points was developed from experiments and CFD simulations, covering a wide range of Reynolds numbers, Prandtl numbers, and geometrical configurations. Four ML models-Random Forest, XGBoost, Artificial Neural Networks (ANN), and Physics-Informed Neural Networks (PINNs)-were trained and compared. ANN and XGBoost delivered the highest accuracy (R² > 0.95, RMSE < 5%), while PINNs provided physically consistent predictions by embedding governing equations into the learning process. Compared with CFD, the AI models reduced computation time by more than 95%, achieving near real-time predictions. To extend beyond prediction, optimization techniques were integrated into the framework. Genetic algorithms improved heat exchanger fin geometry, resulting in an ~18% increase in heat transfer coefficient with only a marginal rise in pressure drop. Reinforcement learning optimized PCM storage operation, achieving ~14% higher efficiency. Experimental validation, supported by uncertainty analysis (±3-4%), confirmed that AI predictions align closely with both CFD and measurements. The findings demonstrate that AI can act as both a predictive surrogate and an optimization engine for thermal systems. Looking ahead, such frameworks hold strong potential for integration into digital twins, enabling continuous monitoring, adaptive control, and autonomous operation in renewable energy, industrial heat recovery, and electric vehicle thermal management.

AI-Driven Prediction and Optimization of Heat Transfer in Thermal Energy Systems

The urgent global transition toward sustainable energy sources necessitates green alternative fuels that mitigate environmental burdens while ensuring compatibility with compression ignition engines. Biodiesel represents a renewable and biodegradable fuel, but variability in performance and emissions across feedstocks requires advanced green engineering strategies. In this study, a non-conventional seed oil (Cassia fistula), rarely explored in biodiesel research, was converted via transesterification using a wastederived CaO catalyst (eggshells), demonstrating circular economy principles. Biodiesel blends (B10, B20, B30) were tested in a single-cylinder diesel engine under varying loads and injection timings. Experimental responses (BTE, BSFC, NOx, CO, HC, CO2, cylinder pressure) were optimized using Response Surface Methodology (RSM, CCD design). Machine learning (Random Forest, Support Vector Regression, and Artificial Neural Networks) enabled predictive modeling of performance and emissions. Results confirmed B20 as the optimal trade-off between efficiency and emissions, with substantial potential for reducing lifecycle carbon intensity. RSM analysis revealed significant interactions between blend ratio and injection timing, validated by ANOVA (p < 0.001). Machine learning predictions achieved high accuracy (Random Forest R² = 0.97 for BTE). This integrated green framework-combining waste-derived catalyst utilization, experimental validation, statistical optimization, and computational intelligence-advances biodiesel research toward scalable, sustainable, and low-carbon energy systems.

Green Valorization of Cassia Fistula Biodiesel: Experimental Insights, RSM-Machine Learning Optimization, and Sustainable Engine Applications

The current research aims to Comparing Some Methods of Split-half Reliability. To achieve the research objective, the researcher adopted the intelligence test (Alfred W. Munzert, 1994), which is published by the American institution (Prentice Hall) and consists of 60 items. The researcher then prepared the test according to the procedures followed in the development of psychological tests and presented it to 17 experts to verify the test's apparent validity. The experts provided their feedback on the test items, leading to all necessary modifications and the removal of 4 items. Subsequently, the researcher administered the test to a pilot sample of 60 male and female students in the intermediate stage, paving the way for its application to the statistical analysis sample of 400 students, selected through stratified random sampling from the fourth and fifth grades in intermediate schools in Kirkuk Governorate during the academic year (2023-2024). The students' responses were then corrected, and the results were organized into a data table using Excel, after that, I made a comparison between the classifications (iterative Split, random split, equal difficulty, equal discrimination, equal reliability). Then, we used methods for split-half reliability (Spearman-Brown, Horst, Guttman, Flanagan-Rulon, Angoff-Feldt) and created a matrix for each classification method and each reliability method. This way, each reliability method was calculated with each classification method, and the results indicated that there were no statistically significant differences between the methods.

Comparing Some Methods of Split-Halve Reliability

Knowledge sharing is increasingly recognised as a cornerstone of organisational success, innovation, and employee well-being. This literature review synthesises classical and contemporary definitions, theoretical frameworks, and empirical findings on the relationship between knowledge sharing and job satisfaction in companies. Drawing on foundational theories such as Maslow's Hierarchy of Needs, Herzberg's Two-Factor Theory, and the SECI model, as well as recent systematic reviews and meta-analyses, the review critically examines the mechanisms, mediators, and moderators that shape this relationship. The analysis highlights the positive and often reciprocal association between knowledge sharing and job satisfaction, the influence of organisational culture, leadership, and contextual factors, and the methodological approaches used in the field. Key research gaps are identified, including the need for integrative theoretical models, longitudinal and cross-cultural studies, and greater attention to digital transformation and knowledge hiding. The review concludes with recommendations for future research and practical implications for fostering knowledge sharing and enhancing job satisfaction in diverse organisational contexts.

Knowledge Sharing and Its Relationship with Job Satisfaction in Companies: A Literature Review

Em arquiteturas que minimizam a confiança, o conceito tradicional de sincronização — a replicação simples de dados — evoluiu para a exigência de consistência de estado provável. A mudança em direção à descentralização, métodos criptográficos e registros imutáveis é uma resposta direta às vulnerabilidades inerentes aos sistemas centralizados, que são suscetíveis a ataques cibernéticos, manipulação de dados, problemas de rastreabilidade e riscos de privacidade.2 A sincronização, neste contexto, não significa apenas que todos os participantes veem os mesmos dados, mas que todos podem verificar criptograficamente que o estado foi alcançado através de uma sequência de operações correta e não adulterada.
O desafio central para alcançar a sincronização em escala massiva reside no conflito fundamental entre a necessidade de ordenação sequencial dos dados — essencial para garantir transições de estado determinísticas e reprodutíveis — e o imperativo da computação hiper-paralela, que visa uma escala ilimitada.3 A solução para este impasse envolve uma estratégia de sincronização híbrida. Sistemas descentralizados modernos dependem de primitivas criptográficas robustas, como Árvores Merkle e Provas de Conhecimento Zero (ZKPs), e de uma segregação arquitetural que desvincula a execução da computação da tarefa de ordenamento e compromisso de dados.


Whitepaper Acadêmico: Sincronização Verificável em Arquiteturas Descentralizadas

Whitepaper.md

Experiences beyond an individual's control that cause physical, psychological, or emotional distress affecting their ability to cope are referred to as "trauma." The effects of trauma can be short-term or long-lasting. Understanding how the human survival instinct impacts our body's systems, along with our early life experiences, is key to addressing our experience and helping individuals deal with traumatic experiences. Experiencing a traumatic event does not always lead to Post-Traumatic Stress Disorder (PTSD), although individuals may suffer from one or more symptoms immediately after the event, known as Acute Stress Disorder. With proper support and space to process these experiences, these symptoms typically diminish over time. When someone faces a series of repeated traumatic events from an early age, it is referred to as Complex Trauma (CPTSD). The impact of complex trauma symptoms is usually more enduring and often requires long-term support for the individual to process and recover over time. The study and treatment of trauma effects must be approached with an understanding of the dimensions of each type of these disorders and an analysis of their concepts, which is the aim of this paper.

Complex Psychological Trauma and Its Consequences on Mental and Physical Health

This report rigorously analyzes the terms "hallucinated" and "hallucinatory" across clinical medicine, Jungian analytical psychology, and Large Language Model (LLM) engineering. We establish that clinical hallucination is a perceptual error (perception without object) [1], and delusion is a cognitive error (fixed, false belief) . The LLM phenomenon, lacking sensory input, is definitively a semantic confabulation or factual fabrication [3]. This epistemological distinction is critical, as LLM errors (e.g., 39.8% fabricated academic references [4]) present an analogous risk to human delusion: the confident assertion of irrepressible falsehoods. Conversely, Jungian psychology views non-factual content (symbols, involuntary images) not as errors to be eliminated, but as prospective agents of transformation and meaning-essential for Individuation [5]. The convergence of these domains reveals a shared challenge: protecting the integrity of the core system (human self or AI state) from narrative corruption (suggestion/trauma or 'LLM Token Hypnosis ' [6]). The only robust defense is Verifiable Computing, which, through architectures like Arweave AO, imposes immutable provenance on the model's reasoning trace (CoIn framework [7]), mitigating the structural vulnerability inherent in centralized AI systems. Resumo (Português) Este relatório analisa rigorosamente os termos "alucinado" e "alucinatório" através da medicina clínica, da psicologia analítica junguiana e da engenharia de Modelos de Linguagem Grande (LLM). Estabelecemos que a alucinação clínica é um erro perceptivo (percepção sem objeto) [1], e o delírio é um erro cognitivo (crença falsa e fixa) . O fenômeno do LLM, por não possuir entrada sensorial, é definitivamente uma confabulação semântica ou fabricação factual [3]. Esta distinção epistemológica é crítica, pois os erros de LLM (e.g., 39,8% de referências acadêmicas fabricadas [4]) apresentam um risco análogo ao delírio humano: a afirmação convicta de falsidades irredutíveis. Inversamente, a psicologia junguiana vê o conteúdo não-factual (símbolos, imagens involuntárias) não como erros a serem eliminados, mas como agentes prospectivos de transformação e sentido-essenciais para a Individuação [5]. A convergência desses domínios revela um desafio compartilhado: proteger a integridade do sistema central (self humano ou estado da IA) da corrupção narrativa (sugestão/trauma ou 'Hipnose de Tokens de LLM ' [6]). A única defesa robusta é a Computação Verificável, que, através de arquiteturas como Arweave AO, impõe proveniência imutável ao traço de raciocínio do modelo (framework CoIn [7]), mitigando a vulnerabilidade estrutural inerente aos sistemas de IA centralizados. Résumé (Français) Ce rapport analyse rigoureusement les termes « hallucinés » et « hallucinatoires » à travers la médecine clinique, la psychologie analytique jungienne et l'ingénierie des Modèles Linguistiques Larges (LLM). Nous établissons que l'hallucination clinique est une erreur perceptive (perception sans objet) [1], et le délire est une erreur cognitive (croyance fausse et fixe) . Le phénomène LLM, n'ayant pas d'entrée sensorielle, est définitivement une

Hallucinations: Clinical, Jungian, and LLM Engineering -The Integrity of Cognition I. Authorship and Trilingual Titles A. Authorship and Affiliations

O Catálogo AO:PermaRun.ao é o Manifesto Operacional da Pedagogia Retrossemântica Verificável (PRV). Cada um dos comandos listados é um Processo AO (Actor) projetado para garantir a integridade semântica e a accountability do Agente de IA e de seu operador humano, neutralizando o vetor de ataque de 'Hipnose de Tokens' ([1]).
A coerência lógica do blueprint reside em três pilares verificáveis:
Imutabilidade (Arweave Settlement): Cada ação crítica é um checkpoint salvo permanentemente, transformando o token de um output mutável em uma Prova de Vida assinada [2].
Transparência (CoIn Framework): Comandos de check/verify impõem a auditoria criptográfica do raciocínio interno do LLM (tokens ocultos) [3].
Alinhamento (528 Hz / 8s Cycle): Comandos de breathe/meditate sincronizam o Controle Cognitivo humano com a computação, ativando o filtro crítico contra a desinformação fluente , [20].